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```{sectnum}
---
start: 2
depth: 2
suffix: .
---
```
Data Format
===========
Input Data Format
-----------------
Instances are specified by JSON files containing the following main sections:
* Parameters
* Buses
* Generators
* Price-sensitive loads
* Transmission lines
* Reserves
* Contingencies
Each section is described in detail below. See [case118/2017-01-01.json.gz][example] for a complete example.
[example]: https://axavier.org/UnitCommitment.jl/0.3/instances/matpower/case118/2017-01-01.json.gz
### Parameters
This section describes system-wide parameters, such as power balance penalty, and optimization parameters, such as the length of the planning horizon and the time.
| Key | Description | Default | Time series?
| :----------------------------- | :------------------------------------------------ | :------: | :------------:
| `Version` | Version of UnitCommitment.jl this file was written for. Required to ensure that the file remains readable in future versions of the package. If you are following this page to construct the file, this field should equal `0.3`. | Required | N
| `Time horizon (h)` | Length of the planning horizon (in hours). | Required | N
| `Time step (min)` | Length of each time step (in minutes). Must be a divisor of 60 (e.g. 60, 30, 20, 15, etc). | `60` | N
| `Power balance penalty ($/MW)` | Penalty for system-wide shortage or surplus in production (in $/MW). This is charged per time step. For example, if there is a shortage of 1 MW for three time steps, three times this amount will be charged. | `1000.0` | Y
#### Example
```json
{
"Parameters": {
"Version": "0.3",
"Time horizon (h)": 4,
"Power balance penalty ($/MW)": 1000.0,
}
}
```
### Buses
This section describes the characteristics of each bus in the system.
| Key | Description | Default | Time series?
| :----------------- | :------------------------------------------------------------ | ------- | :-------------:
| `Load (MW)` | Fixed load connected to the bus (in MW). | Required | Y
#### Example
```json
{
"Buses": {
"b1": {
"Load (MW)": 0.0
},
"b2": {
"Load (MW)": [
26.01527,
24.46212,
23.29725,
22.90897
]
}
}
}
```
### Generators
This section describes all generators in the system, including thermal units, renewable units and virtual units.
| Key | Description | Default | Time series?
| :------------------------ | :------------------------------------------------| ------- | :-----------:
| `Bus` | Identifier of the bus where this generator is located (string). | Required | N
| `Production cost curve (MW)` and `Production cost curve ($)` | Parameters describing the piecewise-linear production costs. See below for more details. | Required | Y
| `Startup costs ($)` and `Startup delays (h)` | Parameters describing how much it costs to start the generator after it has been shut down for a certain amount of time. If `Startup costs ($)` and `Startup delays (h)` are set to `[300.0, 400.0]` and `[1, 4]`, for example, and the generator is shut down at time `00:00` (h:min), then it costs \$300 to start up the generator at any time between `01:00` and `03:59`, and \$400 to start the generator at time `04:00` or any time after that. The number of startup cost points is unlimited, and may be different for each generator. Startup delays must be strictly increasing and the first entry must equal `Minimum downtime (h)`. | `[0.0]` and `[1]` | N
| `Minimum uptime (h)` | Minimum amount of time the generator must stay operational after starting up (in hours). For example, if the generator starts up at time `00:00` (h:min) and `Minimum uptime (h)` is set to 4, then the generator can only shut down at time `04:00`. | `1` | N
| `Minimum downtime (h)` | Minimum amount of time the generator must stay offline after shutting down (in hours). For example, if the generator shuts down at time `00:00` (h:min) and `Minimum downtime (h)` is set to 4, then the generator can only start producing power again at time `04:00`. | `1` | N
| `Ramp up limit (MW)` | Maximum increase in production from one time step to the next (in MW). For example, if the generator is producing 100 MW at time step 1 and if this parameter is set to 40 MW, then the generator will produce at most 140 MW at time step 2. | `+inf` | N
| `Ramp down limit (MW)` | Maximum decrease in production from one time step to the next (in MW). For example, if the generator is producing 100 MW at time step 1 and this parameter is set to 40 MW, then the generator will produce at least 60 MW at time step 2. | `+inf` | N
| `Startup limit (MW)` | Maximum amount of power a generator can produce immediately after starting up (in MW). For example, if `Startup limit (MW)` is set to 100 MW and the unit is off at time step 1, then it may produce at most 100 MW at time step 2.| `+inf` | N
| `Shutdown limit (MW)` | Maximum amount of power a generator can produce immediately before shutting down (in MW). Specifically, the generator can only shut down at time step `t+1` if its production at time step `t` is below this limit. | `+inf` | N
| `Initial status (h)` | If set to a positive number, indicates the amount of time (in hours) the generator has been on at the beginning of the simulation, and if set to a negative number, the amount of time the generator has been off. For example, if `Initial status (h)` is `-2`, this means that the generator was off since `-02:00` (h:min). The simulation starts at time `00:00`. If `Initial status (h)` is `3`, this means that the generator was on since `-03:00`. A value of zero is not acceptable. | Required | N
| `Initial power (MW)` | Amount of power the generator at time step `-1`, immediately before the planning horizon starts. | Required | N
| `Must run?` | If `true`, the generator should be committed, even if that is not economical (Boolean). | `false` | Y
| `Reserve eligibility` | List of reserve products this generator is eligibe to provide. By default, the generator is not eligible to provide any reserves. | `[]` | N
#### Production costs and limits
Production costs are represented as piecewise-linear curves. Figure 1 shows an example cost curve with three segments, where it costs \$1400, \$1600, \$2200 and \$2400 to generate, respectively, 100, 110, 130 and 135 MW of power. To model this generator, `Production cost curve (MW)` should be set to `[100, 110, 130, 135]`, and `Production cost curve ($)` should be set to `[1400, 1600, 2200, 2400]`.
Note that this curve also specifies the production limits. Specifically, the first point identifies the minimum power output when the unit is operational, while the last point identifies the maximum power output.
<center>
<img src="../_static/cost_curve.png" style="max-width: 500px"/>
<div><b>Figure 1.</b> Piecewise-linear production cost curve.</div>
<br/>
</center>
#### Additional remarks:
* For time-dependent production limits or time-dependent production costs, the usage of nested arrays is allowed. For example, if `Production cost curve (MW)` is set to `[5.0, [10.0, 12.0, 15.0, 20.0]]`, then the unit may generate at most 10, 12, 15 and 20 MW of power during time steps 1, 2, 3 and 4, respectively. The minimum output for all time periods is fixed to at 5 MW.
* There is no limit to the number of piecewise-linear segments, and different generators may have a different number of segments.
* If `Production cost curve (MW)` and `Production cost curve ($)` both contain a single element, then the generator must produce exactly that amount of power when operational. To specify that the generator may produce any amount of power up to a certain limit `P`, the parameter `Production cost curve (MW)` should be set to `[0, P]`.
* Production cost curves must be convex.
#### Example
```json
{
"Generators": {
"gen1": {
"Bus": "b1",
"Production cost curve (MW)": [100.0, 110.0, 130.0, 135.0],
"Production cost curve ($)": [1400.0, 1600.0, 2200.0, 2400.0],
"Startup costs ($)": [300.0, 400.0],
"Startup delays (h)": [1, 4],
"Ramp up limit (MW)": 232.68,
"Ramp down limit (MW)": 232.68,
"Startup limit (MW)": 232.68,
"Shutdown limit (MW)": 232.68,
"Minimum downtime (h)": 4,
"Minimum uptime (h)": 4,
"Initial status (h)": 12,
"Must run?": false,
"Reserve eligibility": ["r1"],
},
"gen2": {
"Bus": "b5",
"Production cost curve (MW)": [0.0, [10.0, 8.0, 0.0, 3.0]],
"Production cost curve ($)": [0.0, 0.0],
"Reserve eligibility": ["r1", "r2"],
}
}
}
```
### Price-sensitive loads
This section describes components in the system which may increase or reduce their energy consumption according to the energy prices. Fixed loads (as described in the `buses` section) are always served, regardless of the price, unless there is significant congestion in the system or insufficient production capacity. Price-sensitive loads, on the other hand, are only served if it is economical to do so.
| Key | Description | Default | Time series?
| :---------------- | :------------------------------------------------ | :------: | :------------:
| `Bus` | Bus where the load is located. Multiple price-sensitive loads may be placed at the same bus. | Required | N
| `Revenue ($/MW)` | Revenue obtained for serving each MW of power to this load. | Required | Y
| `Demand (MW)` | Maximum amount of power required by this load. Any amount lower than this may be served. | Required | Y
#### Example
```json
{
"Price-sensitive loads": {
"p1": {
"Bus": "b3",
"Revenue ($/MW)": 23.0,
"Demand (MW)": 50.0
}
}
}
```
### Transmission Lines
This section describes the characteristics of transmission system, such as its topology and the susceptance of each transmission line.
| Key | Description | Default | Time series?
| :--------------------- | :----------------------------------------------- | ------- | :------------:
| `Source bus` | Identifier of the bus where the transmission line originates. | Required | N
| `Target bus` | Identifier of the bus where the transmission line reaches. | Required | N
| `Reactance (ohms)` | Reactance of the transmission line (in ohms). | Required | N
| `Susceptance (S)` | Susceptance of the transmission line (in siemens). | Required | N
| `Normal flow limit (MW)` | Maximum amount of power (in MW) allowed to flow through the line when the system is in its regular, fully-operational state. | `+inf` | Y
| `Emergency flow limit (MW)` | Maximum amount of power (in MW) allowed to flow through the line when the system is in degraded state (for example, after the failure of another transmission line). | `+inf` | Y
| `Flow limit penalty ($/MW)` | Penalty for violating the flow limits of the transmission line (in $/MW). This is charged per time step. For example, if there is a thermal violation of 1 MW for three time steps, then three times this amount will be charged. | `5000.0` | Y
#### Example
```json
{
"Transmission lines": {
"l1": {
"Source bus": "b1",
"Target bus": "b2",
"Reactance (ohms)": 0.05917,
"Susceptance (S)": 29.49686,
"Normal flow limit (MW)": 15000.0,
"Emergency flow limit (MW)": 20000.0,
"Flow limit penalty ($/MW)": 5000.0
}
}
}
```
### Reserves
This section describes the hourly amount of reserves required.
| Key | Description | Default | Time series?
| :-------------------- | :------------------------------------------------- | --------- | :----:
| `Type` | Type of reserve product. Must be either "spinning" or "flexiramp". | Required | N
| `Amount (MW)` | Amount of reserves required. | Required | Y
| `Shortfall penalty ($/MW)` | Penalty for shortage in meeting the reserve requirements (in $/MW). This is charged per time step. Negative value implies reserve constraints must always be satisfied. | `-1` | Y
#### Example 1
```json
{
"Reserves": {
"r1": {
"Type": "spinning",
"Amount (MW)": [
57.30552,
53.88429,
51.31838,
50.46307
],
"Shortfall penalty ($/MW)": 5.0
},
"r2": {
"Type": "flexiramp",
"Amount (MW)": [
20.31042,
23.65273,
27.41784,
25.34057
],
}
}
}
```
### Contingencies
This section describes credible contingency scenarios in the optimization, such as the loss of a transmission line or generator.
| Key | Description | Default
| :-------------------- | :----------------------------------------------- | ----------
| `Affected generators` | List of generators affected by this contingency. May be omitted if no generators are affected. | `[]`
| `Affected lines` | List of transmission lines affected by this contingency. May be omitted if no lines are affected. | `[]`
#### Example
```json
{
"Contingencies": {
"c1": {
"Affected lines": ["l1", "l2", "l3"],
"Affected generators": ["g1"]
},
"c2": {
"Affected lines": ["l4"]
},
}
}
```
### Additional remarks
#### Time series parameters
Many numerical properties in the JSON file can be specified either as a single floating point number if they are time-independent, or as an array containing exactly `T` elements, if they are time-dependent, where `T` is the number of time steps in the planning horizon. For example, both formats below are valid when `T=3`:
```json
{
"Load (MW)": 800.0,
"Load (MW)": [800.0, 850.0, 730.0]
}
```
The value `T` depends on both `Time horizon (h)` and `Time step (min)`, as the table below illustrates.
Time horizon (h) | Time step (min) | T
:---------------:|:---------------:|:----:
24 | 60 | 24
24 | 15 | 96
24 | 5 | 288
36 | 60 | 36
36 | 15 | 144
36 | 5 | 432
Output Data Format
------------------
The output data format is also JSON-based, but it is not currently documented since we expect it to change significantly in a future version of the package.
Current limitations
-------------------
* Network topology remains the same for all time periods
* Only N-1 transmission contingencies are supported. Generator contingencies are not currently supported.
* Time-varying minimum production amounts are not currently compatible with ramp/startup/shutdown limits.
* Flexible ramping products can only be acquired under the `WanHob2016` formulation, which does not support spinning reserves.

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# UnitCommitment.jl
**UnitCommitment.jl** (UC.jl) is a Julia/JuMP optimization package for the Security-Constrained Unit Commitment Problem (SCUC), a fundamental optimization problem in power systems used, for example, to clear the day-ahead electricity markets. The package provides benchmark instances for the problem and Julia/JuMP implementations of state-of-the-art mixed-integer programming formulations.
## Package Components
* **Data Format:** The package proposes an extensible and fully-documented JSON-based data specification format for SCUC, developed in collaboration with Independent System Operators (ISOs), which describes the most important aspects of the problem. The format supports all the most common generator characteristics (including ramping, piecewise-linear production cost curves and time-dependent startup costs), as well as operating reserves, price-sensitive loads, transmission networks and contingencies.
* **Benchmark Instances:** The package provides a diverse collection of large-scale benchmark instances collected from the literature, converted into a common data format, and extended using data-driven methods to make them more challenging and realistic.
* **Model Implementation**: The package provides a Julia/JuMP implementations of state-of-the-art formulations and solution methods for SCUC, including multiple ramping formulations ([ArrCon2000][ArrCon2000], [MorLatRam2013][MorLatRam2013], [DamKucRajAta2016][DamKucRajAta2016], [PanGua2016][PanGua2016]), multiple piecewise-linear costs formulations ([Gar1962][Gar1962], [CarArr2006][CarArr2006], [KnuOstWat2018][KnuOstWat2018]) and contingency screening methods ([XavQiuWanThi2019][XavQiuWanThi2019]). Our goal is to keep these implementations up-to-date as new methods are proposed in the literature.
* **Benchmark Tools:** The package provides automated benchmark scripts to accurately evaluate the performance impact of proposed code changes.
[ArrCon2000]: https://doi.org/10.1109/59.871739
[CarArr2006]: https://doi.org/10.1109/TPWRS.2006.876672
[DamKucRajAta2016]: https://doi.org/10.1007/s10107-015-0919-9
[Gar1962]: https://doi.org/10.1109/AIEEPAS.1962.4501405
[KnuOstWat2018]: https://doi.org/10.1109/TPWRS.2017.2783850
[MorLatRam2013]: https://doi.org/10.1109/TPWRS.2013.2251373
[PanGua2016]: https://doi.org/10.1287/opre.2016.1520
[XavQiuWanThi2019]: https://doi.org/10.1109/TPWRS.2019.2892620
### Authors
* **Alinson S. Xavier** (Argonne National Laboratory)
* **Aleksandr M. Kazachkov** (University of Florida)
* **Ogün Yurdakul** (Technische Universität Berlin)
* **Feng Qiu** (Argonne National Laboratory)
### Acknowledgments
* We would like to thank **Yonghong Chen** (Midcontinent Independent System Operator), **Feng Pan** (Pacific Northwest National Laboratory) for valuable feedback on early versions of this package.
* Based upon work supported by **Laboratory Directed Research and Development** (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357
* Based upon work supported by the **U.S. Department of Energy Advanced Grid Modeling Program** under Grant DE-OE0000875.
### Citing
If you use UnitCommitment.jl in your research (instances, models or algorithms), we kindly request that you cite the package as follows:
* **Alinson S. Xavier, Aleksandr M. Kazachkov, Ogün Yurdakul, Feng Qiu**, "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment". Zenodo (2020). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874).
If you use the instances, we additionally request that you cite the original sources, as described in the [instances page](instances.md).
### License
```text
UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment
Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted
provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of
conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of
conditions and the following disclaimer in the documentation and/or other materials provided
with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to
endorse or promote products derived from this software without specific prior written
permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY
AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
```
## Site contents
```{toctree}
---
maxdepth: 2
---
usage.md
format.md
instances.md
model.md
```

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```{sectnum}
---
start: 3
depth: 2
suffix: .
---
```
Instances
=========
UnitCommitment.jl provides a large collection of benchmark instances collected from the literature and converted to a [common data format](format.md). In some cases, as indicated below, the original instances have been extended, with realistic parameters, using data-driven methods. If you use these instances in your research, we request that you cite UnitCommitment.jl, as well as the original sources, as listed below. Benchmark instances can be loaded with `UnitCommitment.read_benchmark(name)`, as explained in the [usage section](usage.md). Instance files can also be [directly downloaded from our website](https://axavier.org/UnitCommitment.jl/0.3/instances/).
```{warning}
The instances included in UC.jl are still under development and may change in the future. If you use these instances in your research, for reproducibility, you should specify what version of UC.jl they came from.
```
MATPOWER
--------
[MATPOWER](https://github.com/MATPOWER/matpower) is an open-source package for solving power flow problems in MATLAB and Octave. It contains a number of power flow test cases, which have been widely used in the power systems literature.
Because most MATPOWER test cases were originally designed for power flow studies, they lack a number of important unit commitment parameters, such as time-varying loads, production cost curves, ramp limits, reserves and initial conditions. The test cases included in UnitCommitment.jl are extended versions of the original MATPOWER test cases, modified as following:
* **Production cost** curves were generated using a data-driven approach, based on publicly available data. More specifically, machine learning models were trained to predict typical production cost curves, for each day of the year, based on a generator's maximum and minimum power output.
* **Load profiles** were generated using a similar data-driven approach.
* **Ramp-up, ramp-down, startup and shutdown rates** were set to a fixed proportion of the generator's maximum output.
* **Minimum reserves** were set to a fixed proportion of the total demand.
* **Contingencies** were set to include all N-1 transmission line contingencies that do not generate islands or isolated buses. More specifically, there is one contingency for each transmission line, as long as that transmission line is not a bridge in the network graph.
For each MATPOWER test case, UC.jl provides 364 variations (`2017-01-01` to `2017-12-30`) corresponding different days of the year.
### MATPOWER/UW-PSTCA
A variety of smaller IEEE test cases, [compiled by University of Washington](http://labs.ece.uw.edu/pstca/), corresponding mostly to small portions of the American Electric Power System in the 1960s.
| Name | Buses | Generators | Lines | Contingencies | References |
|------|-------|------------|-------|---------------|--------|
| `matpower/case14/2017-01-01` | 14 | 5 | 20 | 19 | [MTPWR, PSTCA]
| `matpower/case30/2017-01-01` | 30 | 6 | 41 | 38 | [MTPWR, PSTCA]
| `matpower/case57/2017-01-01` | 57 | 7 | 80 | 79 | [MTPWR, PSTCA]
| `matpower/case118/2017-01-01` | 118 | 54 | 186 | 177 | [MTPWR, PSTCA]
| `matpower/case300/2017-01-01` | 300 | 69 | 411 | 320 | [MTPWR, PSTCA]
### MATPOWER/Polish
Test cases based on the Polish 400, 220 and 110 kV networks, originally provided by **Roman Korab** (Politechnika Śląska) and corrected by the MATPOWER team.
| Name | Buses | Generators | Lines | Contingencies | References |
|------|-------|------------|-------|---------------|--------|
| `matpower/case2383wp/2017-01-01` | 2383 | 323 | 2896 | 2240 | [MTPWR]
| `matpower/case2736sp/2017-01-01` | 2736 | 289 | 3504 | 3159 | [MTPWR]
| `matpower/case2737sop/2017-01-01` | 2737 | 267 | 3506 | 3161 | [MTPWR]
| `matpower/case2746wop/2017-01-01` | 2746 | 443 | 3514 | 3155 | [MTPWR]
| `matpower/case2746wp/2017-01-01` | 2746 | 457 | 3514 | 3156 | [MTPWR]
| `matpower/case3012wp/2017-01-01` | 3012 | 496 | 3572 | 2854 | [MTPWR]
| `matpower/case3120sp/2017-01-01` | 3120 | 483 | 3693 | 2950 | [MTPWR]
| `matpower/case3375wp/2017-01-01` | 3374 | 590 | 4161 | 3245 | [MTPWR]
### MATPOWER/PEGASE
Test cases from the [Pan European Grid Advanced Simulation and State Estimation (PEGASE) project](https://cordis.europa.eu/project/id/211407), describing part of the European high voltage transmission network.
| Name | Buses | Generators | Lines | Contingencies | References |
|------|-------|------------|-------|---------------|--------|
| `matpower/case89pegase/2017-01-01` | 89 | 12 | 210 | 192 | [JoFlMa16, FlPaCa13, MTPWR]
| `matpower/case1354pegase/2017-01-01` | 1354 | 260 | 1991 | 1288 | [JoFlMa16, FlPaCa13, MTPWR]
| `matpower/case2869pegase/2017-01-01` | 2869 | 510 | 4582 | 3579 | [JoFlMa16, FlPaCa13, MTPWR]
| `matpower/case9241pegase/2017-01-01` | 9241 | 1445 | 16049 | 13932 | [JoFlMa16, FlPaCa13, MTPWR]
| `matpower/case13659pegase/2017-01-01` | 13659 | 4092 | 20467 | 13932 | [JoFlMa16, FlPaCa13, MTPWR]
### MATPOWER/RTE
Test cases from the R&D Division at [Reseau de Transport d'Electricite](https://www.rte-france.com) representing the size and complexity of the French very high voltage transmission network.
| Name | Buses | Generators | Lines | Contingencies | References |
|------|-------|------------|-------|---------------|--------|
| `matpower/case1888rte/2017-01-01` | 1888 | 296 | 2531 | 1484 | [MTPWR, JoFlMa16]
| `matpower/case1951rte/2017-01-01` | 1951 | 390 | 2596 | 1497 | [MTPWR, JoFlMa16]
| `matpower/case2848rte/2017-01-01` | 2848 | 544 | 3776 | 2242 | [MTPWR, JoFlMa16]
| `matpower/case2868rte/2017-01-01` | 2868 | 596 | 3808 | 2260 | [MTPWR, JoFlMa16]
| `matpower/case6468rte/2017-01-01` | 6468 | 1262 | 9000 | 6094 | [MTPWR, JoFlMa16]
| `matpower/case6470rte/2017-01-01` | 6470 | 1306 | 9005 | 6085 | [MTPWR, JoFlMa16]
| `matpower/case6495rte/2017-01-01` | 6495 | 1352 | 9019 | 6060 | [MTPWR, JoFlMa16]
| `matpower/case6515rte/2017-01-01` | 6515 | 1368 | 9037 | 6063 | [MTPWR, JoFlMa16]
PGLIB-UC Instances
------------------
[PGLIB-UC](https://github.com/power-grid-lib/pglib-uc) is a benchmark library curated and maintained by the [IEEE PES Task Force on Benchmarks for Validation of Emerging Power System Algorithms](https://power-grid-lib.github.io/). These test cases have been used in [KnOsWa20].
### PGLIB-UC/California
Test cases based on publicly available data from the California ISO. For more details, see [PGLIB-UC case file overview](https://github.com/power-grid-lib/pglib-uc).
| Name | Buses | Generators | Lines | Contingencies | References |
|------|-------|------------|-------|---------------|--------|
| `pglib-uc/ca/2014-09-01_reserves_0` | 1 | 610 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/2014-09-01_reserves_1` | 1 | 610 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/2014-09-01_reserves_3` | 1 | 610 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/2014-09-01_reserves_5` | 1 | 610 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/2014-12-01_reserves_0` | 1 | 610 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/2014-12-01_reserves_1` | 1 | 610 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/2014-12-01_reserves_3` | 1 | 610 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/2014-12-01_reserves_5` | 1 | 610 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/2015-03-01_reserves_0` | 1 | 610 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/2015-03-01_reserves_1` | 1 | 610 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/2015-03-01_reserves_3` | 1 | 610 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/2015-03-01_reserves_5` | 1 | 610 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/2015-06-01_reserves_0` | 1 | 610 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/2015-06-01_reserves_1` | 1 | 610 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/2015-06-01_reserves_3` | 1 | 610 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/2015-06-01_reserves_5` | 1 | 610 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/Scenario400_reserves_0` | 1 | 611 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/Scenario400_reserves_1` | 1 | 611 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/Scenario400_reserves_3` | 1 | 611 | 0 | 0 | [KnOsWa20]
| `pglib-uc/ca/Scenario400_reserves_5` | 1 | 611 | 0 | 0 | [KnOsWa20]
### PGLIB-UC/FERC
Test cases based on a publicly available [unit commitment test case produced by the Federal Energy Regulatory Commission](https://www.ferc.gov/industries-data/electric/power-sales-and-markets/increasing-efficiency-through-improved-software-1). For more details, see [PGLIB-UC case file overview](https://github.com/power-grid-lib/pglib-uc).
| Name | Buses | Generators | Lines | Contingencies | References |
|------|-------|------------|-------|---------------|--------|
| `pglib-uc/ferc/2015-01-01_hw` | 1 | 935 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-01-01_lw` | 1 | 935 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-02-01_hw` | 1 | 935 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-02-01_lw` | 1 | 935 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-03-01_hw` | 1 | 935 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-03-01_lw` | 1 | 935 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-04-01_hw` | 1 | 979 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-04-01_lw` | 1 | 979 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-05-01_hw` | 1 | 979 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-05-01_lw` | 1 | 979 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-06-01_hw` | 1 | 979 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-06-01_lw` | 1 | 979 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-07-01_hw` | 1 | 979 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-07-01_lw` | 1 | 979 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-08-01_hw` | 1 | 979 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-08-01_lw` | 1 | 979 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-09-01_hw` | 1 | 979 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-09-01_lw` | 1 | 979 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-10-01_hw` | 1 | 935 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-10-01_lw` | 1 | 935 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-11-02_hw` | 1 | 935 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-11-02_lw` | 1 | 935 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-12-01_hw` | 1 | 935 | 0 | 0 | [KnOsWa20, KrHiOn12]
| `pglib-uc/ferc/2015-12-01_lw` | 1 | 935 | 0 | 0 | [KnOsWa20, KrHiOn12]
### PGLIB-UC/RTS-GMLC
[RTS-GMLC](https://github.com/GridMod/RTS-GMLC) is an updated version of the RTS-96 test system produced by the United States Department of Energy's [Grid Modernization Laboratory Consortium](https://gmlc.doe.gov/). The PGLIB-UC/RTS-GMLC instances are modified versions of the original RTS-GMLC instances, with modified ramp-rates and without a transmission network. For more details, see [PGLIB-UC case file overview](https://github.com/power-grid-lib/pglib-uc).
| Name | Buses | Generators | Lines | Contingencies | References |
|------|-------|------------|-------|---------------|--------|
| `pglib-uc/rts_gmlc/2020-01-27` | 1 | 154 | 0 | 0 | [BaBlEh19]
| `pglib-uc/rts_gmlc/2020-02-09` | 1 | 154 | 0 | 0 | [BaBlEh19]
| `pglib-uc/rts_gmlc/2020-03-05` | 1 | 154 | 0 | 0 | [BaBlEh19]
| `pglib-uc/rts_gmlc/2020-04-03` | 1 | 154 | 0 | 0 | [BaBlEh19]
| `pglib-uc/rts_gmlc/2020-05-05` | 1 | 154 | 0 | 0 | [BaBlEh19]
| `pglib-uc/rts_gmlc/2020-06-09` | 1 | 154 | 0 | 0 | [BaBlEh19]
| `pglib-uc/rts_gmlc/2020-07-06` | 1 | 154 | 0 | 0 | [BaBlEh19]
| `pglib-uc/rts_gmlc/2020-08-12` | 1 | 154 | 0 | 0 | [BaBlEh19]
| `pglib-uc/rts_gmlc/2020-09-20` | 1 | 154 | 0 | 0 | [BaBlEh19]
| `pglib-uc/rts_gmlc/2020-10-27` | 1 | 154 | 0 | 0 | [BaBlEh19]
| `pglib-uc/rts_gmlc/2020-11-25` | 1 | 154 | 0 | 0 | [BaBlEh19]
| `pglib-uc/rts_gmlc/2020-12-23` | 1 | 154 | 0 | 0 | [BaBlEh19]
OR-LIB/UC
---------
[OR-LIB](http://people.brunel.ac.uk/~mastjjb/jeb/info.html) is a collection of test data sets for a variety of operations research problems, including unit commitment. The UC instances in OR-LIB are synthetic instances generated by a [random problem generator](http://groups.di.unipi.it/optimize/Data/UC.html) developed by the [Operations Research Group at University of Pisa](http://groups.di.unipi.it/optimize/). These test cases have been used in [FrGe06] and many other publications.
| Name | Hours | Buses | Generators | Lines | Contingencies | References |
|------|-------|-------|------------|-------|---------------|------------|
| `or-lib/10_0_1_w` | 24 | 1 | 10 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/10_0_2_w` | 24 | 1 | 10 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/10_0_3_w` | 24 | 1 | 10 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/10_0_4_w` | 24 | 1 | 10 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/10_0_5_w` | 24 | 1 | 10 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/20_0_1_w` | 24 | 1 | 20 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/20_0_2_w` | 24 | 1 | 20 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/20_0_3_w` | 24 | 1 | 20 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/20_0_4_w` | 24 | 1 | 20 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/20_0_5_w` | 24 | 1 | 20 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/50_0_1_w` | 24 | 1 | 50 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/50_0_2_w` | 24 | 1 | 50 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/50_0_3_w` | 24 | 1 | 50 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/50_0_4_w` | 24 | 1 | 50 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/50_0_5_w` | 24 | 1 | 50 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/75_0_1_w` | 24 | 1 | 75 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/75_0_2_w` | 24 | 1 | 75 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/75_0_3_w` | 24 | 1 | 75 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/75_0_4_w` | 24 | 1 | 75 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/75_0_5_w` | 24 | 1 | 75 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/100_0_1_w` | 24 | 1 | 100 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/100_0_2_w` | 24 | 1 | 100 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/100_0_3_w` | 24 | 1 | 100 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/100_0_4_w` | 24 | 1 | 100 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/100_0_5_w` | 24 | 1 | 100 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/150_0_1_w` | 24 | 1 | 150 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/150_0_2_w` | 24 | 1 | 150 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/150_0_3_w` | 24 | 1 | 150 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/150_0_4_w` | 24 | 1 | 150 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/150_0_5_w` | 24 | 1 | 150 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/200_0_10_w` | 24 | 1 | 200 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/200_0_11_w` | 24 | 1 | 200 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/200_0_12_w` | 24 | 1 | 200 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/200_0_1_w` | 24 | 1 | 200 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/200_0_2_w` | 24 | 1 | 200 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/200_0_3_w` | 24 | 1 | 200 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/200_0_4_w` | 24 | 1 | 200 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/200_0_5_w` | 24 | 1 | 200 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/200_0_6_w` | 24 | 1 | 200 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/200_0_7_w` | 24 | 1 | 200 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/200_0_8_w` | 24 | 1 | 200 | 0 | 0 | [ORLIB, FrGe06]
| `or-lib/200_0_9_w` | 24 | 1 | 200 | 0 | 0 | [ORLIB, FrGe06]
Tejada19
--------
Test cases used in [TeLuSa19]. These instances are similar to OR-LIB/UC, in the sense that they use the same random problem generator, but are much larger.
| Name | Hours | Buses | Generators | Lines | Contingencies | References |
|------|-------|-------|------------|-------|---------------|------------|
| `tejada19/UC_24h_214g` | 24 | 1 | 214 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_250g` | 24 | 1 | 250 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_290g` | 24 | 1 | 290 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_480g` | 24 | 1 | 480 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_505g` | 24 | 1 | 505 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_623g` | 24 | 1 | 623 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_647g` | 24 | 1 | 647 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_836g` | 24 | 1 | 836 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_850g` | 24 | 1 | 850 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_918g` | 24 | 1 | 918 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_931g` | 24 | 1 | 931 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_940g` | 24 | 1 | 940 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_957g` | 24 | 1 | 957 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_959g` | 24 | 1 | 959 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_1069g` | 24 | 1 | 1069 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_1130g` | 24 | 1 | 1130 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_1376g` | 24 | 1 | 1376 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_1393g` | 24 | 1 | 1393 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_1577g` | 24 | 1 | 1577 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_1615g` | 24 | 1 | 1615 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_1632g` | 24 | 1 | 1632 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_1768g` | 24 | 1 | 1768 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_1804g` | 24 | 1 | 1804 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_1820g` | 24 | 1 | 1820 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_1823g` | 24 | 1 | 1823 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_24h_1888g` | 24 | 1 | 1888 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_36g` | 168 | 1 | 36 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_38g` | 168 | 1 | 38 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_40g` | 168 | 1 | 40 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_53g` | 168 | 1 | 53 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_58g` | 168 | 1 | 58 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_59g` | 168 | 1 | 59 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_72g` | 168 | 1 | 72 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_84g` | 168 | 1 | 84 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_86g` | 168 | 1 | 86 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_88g` | 168 | 1 | 88 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_93g` | 168 | 1 | 93 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_105g` | 168 | 1 | 105 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_110g` | 168 | 1 | 110 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_125g` | 168 | 1 | 125 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_130g` | 168 | 1 | 130 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_131g` | 168 | 1 | 131 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_140g` | 168 | 1 | 140 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_165g` | 168 | 1 | 165 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_175g` | 168 | 1 | 175 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_179g` | 168 | 1 | 179 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_188g` | 168 | 1 | 188 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_192g` | 168 | 1 | 192 | 0 | 0 | [TeLuSa19]
| `tejada19/UC_168h_199g` | 168 | 1 | 199 | 0 | 0 | [TeLuSa19]
References
----------
* [UCJL] **Alinson S. Xavier, Aleksandr M. Kazachkov, Ogün Yurdakul, Feng Qiu.** "UnitCommitment.jl: A Julia/JuMP Optimization Package for Security-Constrained Unit Commitment". Zenodo (2020). [DOI: 10.5281/zenodo.4269874](https://doi.org/10.5281/zenodo.4269874)
* [KnOsWa20] **Bernard Knueven, James Ostrowski and Jean-Paul Watson.** "On Mixed-Integer Programming Formulations for the Unit Commitment Problem". INFORMS Journal on Computing (2020). [DOI: 10.1287/ijoc.2019.0944](https://doi.org/10.1287/ijoc.2019.0944)
* [KrHiOn12] **Eric Krall, Michael Higgins and Richard P. ONeill.** "RTO unit commitment test system." Federal Energy Regulatory Commission. Available at: <https://www.ferc.gov/industries-data/electric/power-sales-and-markets/increasing-efficiency-through-improved-software-1> (Accessed: Nov 14, 2020)
* [BaBlEh19] **Clayton Barrows, Aaron Bloom, Ali Ehlen, Jussi Ikaheimo, Jennie Jorgenson, Dheepak Krishnamurthy, Jessica Lau et al.** "The IEEE Reliability Test System: A Proposed 2019 Update." IEEE Transactions on Power Systems (2019). [DOI: 10.1109/TPWRS.2019.2925557](https://doi.org/10.1109/TPWRS.2019.2925557)
* [JoFlMa16] **C. Josz, S. Fliscounakis, J. Maeght, and P. Panciatici.** "AC Power Flow
Data in MATPOWER and QCQP Format: iTesla, RTE Snapshots, and PEGASE". [ArXiv (2016)](https://arxiv.org/abs/1603.01533).
* [FlPaCa13] **S. Fliscounakis, P. Panciatici, F. Capitanescu, and L. Wehenkel.**
"Contingency ranking with respect to overloads in very large power
systems taking into account uncertainty, preventive and corrective
actions", Power Systems, IEEE Trans. on, (28)4:4909-4917, 2013.
[DOI: 10.1109/TPWRS.2013.2251015](https://doi.org/10.1109/TPWRS.2013.2251015)
* [MTPWR] **D. Zimmerman, C. E. Murillo-Sandnchez and R. J. Thomas.** "Matpower: Steady-state operations, planning, and analysis tools forpower systems research and education", IEEE Transactions on PowerSystems, vol. 26, no. 1, pp. 12 19, Feb. 2011. [DOI: 10.1109/TPWRS.2010.2051168](https://doi.org/10.1109/TPWRS.2010.2051168)
* [PSTCA] **University of Washington, Dept. of Electrical Engineering.** "Power Systems Test Case Archive". Available at: <http://www.ee.washington.edu/research/pstca/> (Accessed: Nov 14, 2020)
* [ORLIB] **J.E.Beasley.** "OR-Library: distributing test problems by electronic mail", Journal of the Operational Research Society 41(11) (1990). [DOI: 10.2307/2582903](https://doi.org/10.2307/2582903)
* [FrGe06] **A. Frangioni, C. Gentile.** "Solving nonlinear single-unit commitment problems with ramping constraints" Operations Research 54(4), p. 767 - 775, 2006. [DOI: 10.1287/opre.1060.0309](https://doi.org/10.1287/opre.1060.0309)
* [TeLuSa19] **D. A. Tejada-Arango, S. Lumbreras, P. Sanchez-Martin and A. Ramos.** "Which Unit-Commitment Formulation is Best? A Systematic Comparison," in IEEE Transactions on Power Systems. [DOI: 10.1109/TPWRS.2019.2962024](https://ieeexplore.ieee.org/document/8941313/).

@ -1,244 +0,0 @@
```{sectnum}
---
start: 4
depth: 2
suffix: .
---
```
JuMP Model
==========
In this page, we describe the JuMP optimization model produced by the function `UnitCommitment.build_model`. A detailed understanding of this model is not necessary if you are just interested in using the package to solve some standard unit commitment cases, but it may be useful, for example, if you need to solve a slightly different problem, with additional variables and constraints. The notation in this page generally follows [KnOsWa20].
Decision variables
------------------
### Generators
Name | Symbol | Description | Unit
-----|:--------:|-------------|:------:
`is_on[g,t]` | $u_{g}(t)$ | True if generator `g` is on at time `t`. | Binary
`switch_on[g,t]` | $v_{g}(t)$ | True is generator `g` switches on at time `t`. | Binary
`switch_off[g,t]` | $w_{g}(t)$ | True if generator `g` switches off at time `t`. | Binary
`prod_above[g,t]` |$p'_{g}(t)$ | Amount of power produced by generator `g` above its minimum power output at time `t`. For example, if the minimum power of generator `g` is 100 MW and `g` is producing 115 MW of power at time `t`, then `prod_above[g,t]` equals `15.0`. | MW
`segprod[g,t,k]` | $p^k_g(t)$ | Amount of power from piecewise linear segment `k` produced by generator `g` at time `t`. For example, if cost curve for generator `g` is defined by the points `(100, 1400)`, `(110, 1600)`, `(130, 2200)` and `(135, 2400)`, and if the generator is producing 115 MW of power at time `t`, then `segprod[g,t,:]` equals `[10.0, 5.0, 0.0]`.| MW
`reserve[r,g,t]` | $r_g(t)$ | Amount of reserve `r` provided by unit `g` at time `t`. | MW
`startup[g,t,s]` | $\delta^s_g(t)$ | True if generator `g` switches on at time `t` incurring start-up costs from start-up category `s`. | Binary
### Buses
Name | Symbol | Description | Unit
-----|:------:|-------------|:------:
`net_injection[b,t]` | $n_b(t)$ | Net injection at bus `b` at time `t`. | MW
`curtail[b,t]` | $s^+_b(t)$ | Amount of load curtailed at bus `b` at time `t` | MW
### Price-sensitive loads
Name | Symbol | Description | Unit
-----|:------:|-------------|:------:
`loads[s,t]` | $d_{s}(t)$ | Amount of power served to price-sensitive load `s` at time `t`. | MW
### Transmission lines
Name | Symbol | Description | Unit
-----|:------:|-------------|:------:
`flow[l,t]` | $f_l(t)$ | Power flow on line `l` at time `t`. | MW
`overflow[l,t]` | $f^+_l(t)$ | Amount of flow above the limit for line `l` at time `t`. | MW
```{warning}
Since transmission and N-1 security constraints are enforced in a lazy way, most of the `flow[l,t]` variables are never added to the model. Accessing `model[:flow][l,t]` without first checking that the variable exists will likely generate an error.
```
Objective function
------------------
$$
\begin{align}
\text{minimize} \;\; &
\sum_{t \in \mathcal{T}}
\sum_{g \in \mathcal{G}}
C^\text{min}_g(t) u_g(t) \\
&
+ \sum_{t \in \mathcal{T}}
\sum_{g \in \mathcal{G}}
\sum_{g \in \mathcal{K}_g}
C^k_g(t) p^k_g(t) \\
&
+ \sum_{t \in \mathcal{T}}
\sum_{g \in \mathcal{G}}
\sum_{s \in \mathcal{S}_g}
C^s_{g}(t) \delta^s_g(t) \\
&
+ \sum_{t \in \mathcal{T}}
\sum_{l \in \mathcal{L}}
C^\text{overflow}_{l}(t) f^+_l(t) \\
&
+ \sum_{t \in \mathcal{T}}
\sum_{b \in \mathcal{B}}
C^\text{curtail}(t) s^+_b(t) \\
&
- \sum_{t \in \mathcal{T}}
\sum_{s \in \mathcal{PS}}
R_{s}(t) d_{s}(t) \\
\end{align}
$$
where
- $\mathcal{B}$ is the set of buses
- $\mathcal{G}$ is the set of generators
- $\mathcal{L}$ is the set of transmission lines
- $\mathcal{PS}$ is the set of price-sensitive loads
- $\mathcal{S}_g$ is the set of start-up categories for generator $g$
- $\mathcal{T}$ is the set of time steps
- $C^\text{curtail}(t)$ is the curtailment penalty (in \$/MW)
- $C^\text{min}_g(t)$ is the cost of keeping generator $g$ on and producing at minimum power during time $t$ (in \$)
- $C^\text{overflow}_{l}(t)$ is the flow limit penalty for line $l$ at time $t$ (in \$/MW)
- $C^k_g(t)$ is the cost for generator $g$ to produce 1 MW of power at time $t$ under piecewise linear segment $k$
- $C^s_{g}(t)$ is the cost of starting up generator $g$ at time $t$ under start-up category $s$ (in \$)
- $R_{s}(t)$ is the revenue obtained from serving price-sensitive load $s$ at time $t$ (in \$/MW)
Constraints
-----------
TODO
Inspecting and modifying the model
----------------------------------
### Accessing decision variables
After building a model using `UnitCommitment.build_model`, it is possible to obtain a reference to the decision variables by calling `model[:varname][index]`. For example, `model[:is_on]["g1",1]` returns a direct reference to the JuMP variable indicating whether generator named "g1" is on at time 1. The script below illustrates how to build a model, solve it and display the solution without using the function `UnitCommitment.solution`.
```julia
using Cbc
using Printf
using JuMP
using UnitCommitment
# Load benchmark instance
instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
# Build JuMP model
model = UnitCommitment.build_model(
instance=instance,
optimizer=Cbc.Optimizer,
)
# Solve the model
UnitCommitment.optimize!(model)
# Display commitment status
for g in instance.units
for t in 1:instance.time
@printf(
"%-10s %5d %5.1f %5.1f %5.1f\n",
g.name,
t,
value(model[:is_on][g.name, t]),
value(model[:switch_on][g.name, t]),
value(model[:switch_off][g.name, t]),
)
end
end
```
### Fixing variables, modifying objective function and adding constraints
Since we now have a direct reference to the JuMP decision variables, it is possible to fix variables, change the coefficients in the objective function, or even add new constraints to the model before solving it. The script below shows how can this be accomplished. For more information on modifying an existing model, [see the JuMP documentation](https://jump.dev/JuMP.jl/stable/manual/variables/).
```julia
using Cbc
using JuMP
using UnitCommitment
# Load benchmark instance
instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
# Construct JuMP model
model = UnitCommitment.build_model(
instance=instance,
optimizer=Cbc.Optimizer,
)
# Fix a decision variable to 1.0
JuMP.fix(
model[:is_on]["g1",1],
1.0,
force=true,
)
# Change the objective function
JuMP.set_objective_coefficient(
model,
model[:switch_on]["g2",1],
1000.0,
)
# Create a new constraint
@constraint(
model,
model[:is_on]["g3",1] + model[:is_on]["g4",1] <= 1,
)
# Solve the model
UnitCommitment.optimize!(model)
```
### Adding new component to a bus
The following snippet shows how to add a new grid component to a particular bus. For each time step, we create decision variables for the new grid component, add these variables to the objective function, then attach the component to a particular bus by modifying some existing model constraints.
```julia
using Cbc
using JuMP
using UnitCommitment
# Load instance and build base model
instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
model = UnitCommitment.build_model(
instance=instance,
optimizer=Cbc.Optimizer,
)
# Get the number of time steps in the original instance
T = instance.time
# Create decision variables for the new grid component.
# In this example, we assume that the new component can
# inject up to 10 MW of power at each time step, so we
# create new continuous variables 0 ≤ x[t] ≤ 10.
@variable(model, x[1:T], lower_bound=0.0, upper_bound=10.0)
# For each time step
for t in 1:T
# Add production costs to the objective function.
# In this example, we assume a cost of $5/MW.
set_objective_coefficient(model, x[t], 5.0)
# Attach the new component to bus b1, by modifying the
# constraint `eq_net_injection`.
set_normalized_coefficient(
model[:eq_net_injection]["b1", t],
x[t],
1.0,
)
end
# Solve the model
UnitCommitment.optimize!(model)
# Show optimal values for the x variables
@show value.(x)
```
References
----------
* [KnOsWa20] **Bernard Knueven, James Ostrowski and Jean-Paul Watson.** "On Mixed-Integer Programming Formulations for the Unit Commitment Problem". INFORMS Journal on Computing (2020). [DOI: 10.1287/ijoc.2019.0944](https://doi.org/10.1287/ijoc.2019.0944)

@ -1,149 +0,0 @@
```{sectnum}
---
start: 1
depth: 2
suffix: .
---
```
Usage
=====
Installation
------------
UnitCommitment.jl was tested and developed with [Julia 1.7](https://julialang.org/). To install Julia, please follow the [installation guide on the official Julia website](https://julialang.org/downloads/). To install UnitCommitment.jl, run the Julia interpreter, type `]` to open the package manager, then type:
```text
pkg> add UnitCommitment@0.3
```
To test that the package has been correctly installed, run:
```text
pkg> test UnitCommitment
```
If all tests pass, the package should now be ready to be used by any Julia script on the machine.
To solve the optimization models, a mixed-integer linear programming (MILP) solver is also required. Please see the [JuMP installation guide](https://jump.dev/JuMP.jl/stable/installation/) for more instructions on installing a solver. Typical open-source choices are [Cbc](https://github.com/JuliaOpt/Cbc.jl) and [GLPK](https://github.com/JuliaOpt/GLPK.jl). In the instructions below, Cbc will be used, but any other MILP solver listed in JuMP installation guide should also be compatible.
Typical Usage
-------------
### Solving user-provided instances
The first step to use UC.jl is to construct a JSON file describing your unit commitment instance. See [Data Format](format.md) for a complete description of the data format UC.jl expects. The next steps, as shown below, are to: (1) read the instance from file; (2) construct the optimization model; (3) run the optimization; and (4) extract the optimal solution.
```julia
using Cbc
using JSON
using UnitCommitment
# 1. Read instance
instance = UnitCommitment.read("/path/to/input.json")
# 2. Construct optimization model
model = UnitCommitment.build_model(
instance=instance,
optimizer=Cbc.Optimizer,
)
# 3. Solve model
UnitCommitment.optimize!(model)
# 4. Write solution to a file
solution = UnitCommitment.solution(model)
UnitCommitment.write("/path/to/output.json", solution)
```
### Solving benchmark instances
UnitCommitment.jl contains a large number of benchmark instances collected from the literature and converted into a common data format. To solve one of these instances individually, instead of constructing your own, the function `read_benchmark` can be used, as shown below. See [Instances](instances.md) for the complete list of available instances.
```julia
using UnitCommitment
instance = UnitCommitment.read_benchmark("matpower/case3375wp/2017-02-01")
```
Advanced usage
--------------
### Customizing the formulation
By default, `build_model` uses a formulation that combines modeling components from different publications, and that has been carefully tested, using our own benchmark scripts, to provide good performance across a wide variety of instances. This default formulation is expected to change over time, as new methods are proposed in the literature. You can, however, construct your own formulation, based on the modeling components that you choose, as shown in the next example.
```julia
using Cbc
using UnitCommitment
import UnitCommitment:
Formulation,
KnuOstWat2018,
MorLatRam2013,
ShiftFactorsFormulation
instance = UnitCommitment.read_benchmark(
"matpower/case118/2017-02-01",
)
model = UnitCommitment.build_model(
instance = instance,
optimizer = Cbc.Optimizer,
formulation = Formulation(
pwl_costs = KnuOstWat2018.PwlCosts(),
ramping = MorLatRam2013.Ramping(),
startup_costs = MorLatRam2013.StartupCosts(),
transmission = ShiftFactorsFormulation(
isf_cutoff = 0.005,
lodf_cutoff = 0.001,
),
),
)
```
### Generating initial conditions
When creating random unit commitment instances for benchmark purposes, it is often hard to compute, in advance, sensible initial conditions for all generators. Setting initial conditions naively (for example, making all generators initially off and producing no power) can easily cause the instance to become infeasible due to excessive ramping. Initial conditions can also make it hard to modify existing instances. For example, increasing the system load without carefully modifying the initial conditions may make the problem infeasible or unrealistically challenging to solve.
To help with this issue, UC.jl provides a utility function which can generate feasible initial conditions by solving a single-period optimization problem, as shown below:
```julia
using Cbc
using UnitCommitment
# Read original instance
instance = UnitCommitment.read("instance.json")
# Generate initial conditions (in-place)
UnitCommitment.generate_initial_conditions!(instance, Cbc.Optimizer)
# Construct and solve optimization model
model = UnitCommitment.build_model(
instance=instance,
optimizer=Cbc.Optimizer,
)
UnitCommitment.optimize!(model)
```
```{warning}
The function `generate_initial_conditions!` may return different initial conditions after each call, even if the same instance and the same optimizer is provided. The particular algorithm may also change in a future version of UC.jl. For these reasons, it is recommended that you generate initial conditions exactly once for each instance and store them for later use.
```
### Verifying solutions
When developing new formulations, it is very easy to introduce subtle errors in the model that result in incorrect solutions. To help with this, UC.jl includes a utility function that verifies if a given solution is feasible, and, if not, prints all the validation errors it found. The implementation of this function is completely independent from the implementation of the optimization model, and therefore can be used to validate it. The function can also be used to verify solutions produced by other optimization packages, as long as they follow the [UC.jl data format](format.md).
```julia
using JSON
using UnitCommitment
# Read instance
instance = UnitCommitment.read("instance.json")
# Read solution (potentially produced by other packages)
solution = JSON.parsefile("solution.json")
# Validate solution and print validation errors
UnitCommitment.validate(instance, solution)
```

@ -1,861 +0,0 @@
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vertical-align: middle;
}
div.body div.math p {
text-align: center;
}
span.eqno {
float: right;
}
span.eqno a.headerlink {
position: absolute;
z-index: 1;
}
div.math:hover a.headerlink {
visibility: visible;
}
/* -- printout stylesheet --------------------------------------------------- */
@media print {
div.document,
div.documentwrapper,
div.bodywrapper {
margin: 0 !important;
width: 100%;
}
div.sphinxsidebar,
div.related,
div.footer,
#top-link {
display: none;
}
}

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:root {
/*****************************************************************************
* Theme config
**/
--pst-header-height: 60px;
/*****************************************************************************
* Font size
**/
--pst-font-size-base: 15px; /* base font size - applied at body / html level */
/* heading font sizes */
--pst-font-size-h1: 36px;
--pst-font-size-h2: 32px;
--pst-font-size-h3: 26px;
--pst-font-size-h4: 21px;
--pst-font-size-h5: 18px;
--pst-font-size-h6: 16px;
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--pst-font-size-milli: 12px;
--pst-sidebar-font-size: .9em;
--pst-sidebar-caption-font-size: .9em;
/*****************************************************************************
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/* These are adapted from https://systemfontstack.com/ */
--pst-font-family-base-system: -apple-system, BlinkMacSystemFont, Segoe UI, "Helvetica Neue",
Arial, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol;
--pst-font-family-monospace-system: "SFMono-Regular", Menlo, Consolas, Monaco,
Liberation Mono, Lucida Console, monospace;
--pst-font-family-base: var(--pst-font-family-base-system);
--pst-font-family-heading: var(--pst-font-family-base);
--pst-font-family-monospace: var(--pst-font-family-monospace-system);
/*****************************************************************************
* Color
*
* Colors are defined in rgb string way, "red, green, blue"
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--pst-color-primary: 19, 6, 84;
--pst-color-success: 40, 167, 69;
--pst-color-info: 0, 123, 255; /*23, 162, 184;*/
--pst-color-warning: 255, 193, 7;
--pst-color-danger: 220, 53, 69;
--pst-color-text-base: 51, 51, 51;
--pst-color-h1: var(--pst-color-primary);
--pst-color-h2: var(--pst-color-primary);
--pst-color-h3: var(--pst-color-text-base);
--pst-color-h4: var(--pst-color-text-base);
--pst-color-h5: var(--pst-color-text-base);
--pst-color-h6: var(--pst-color-text-base);
--pst-color-paragraph: var(--pst-color-text-base);
--pst-color-link: 0, 91, 129;
--pst-color-link-hover: 227, 46, 0;
--pst-color-headerlink: 198, 15, 15;
--pst-color-headerlink-hover: 255, 255, 255;
--pst-color-preformatted-text: 34, 34, 34;
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--pst-color-active-navigation: 19, 6, 84;
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* Icon
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--pst-icon-info-circle: '\f05a';
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--pst-icon-lightbulb: '\f0eb';
/*****************************************************************************
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**/
--pst-color-admonition-default: var(--pst-color-info);
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--pst-color-admonition-attention: var(--pst-color-warning);
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--pst-color-admonition-warning: var(--pst-color-warning);
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--pst-color-admonition-error: var(--pst-color-danger);
--pst-color-admonition-hint: var(--pst-color-success);
--pst-color-admonition-tip: var(--pst-color-success);
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--pst-icon-admonition-default: var(--pst-icon-info-circle);
--pst-icon-admonition-note: var(--pst-icon-info-circle);
--pst-icon-admonition-attention: var(--pst-icon-exclamation-circle);
--pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle);
--pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle);
--pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle);
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h1.site-logo {
font-size: 30px !important;
}
h1.site-logo small {
font-size: 20px !important;
}
h1.site-logo {
font-size: 30px !important;
}
h1.site-logo small {
font-size: 20px !important;
}
tbody, thead, pre {
border: 1px solid rgba(0, 0, 0, 0.25);
}
table td, th {
padding: 8px;
}
table p {
margin-bottom: 0;
}
table td code {
white-space: nowrap;
}
table tr,
table th {
border-bottom: 1px solid rgba(0, 0, 0, 0.1);
}
table tr:last-child {
border-bottom: 0;
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pre {
box-shadow: inherit !important;
background-color: #fff;
}
.text-align\:center {
text-align: center;
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@ -1,321 +0,0 @@
/*
* doctools.js
* ~~~~~~~~~~~
*
* Sphinx JavaScript utilities for all documentation.
*
* :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
/**
* select a different prefix for underscore
*/
$u = _.noConflict();
/**
* make the code below compatible with browsers without
* an installed firebug like debugger
if (!window.console || !console.firebug) {
var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
"dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
"profile", "profileEnd"];
window.console = {};
for (var i = 0; i < names.length; ++i)
window.console[names[i]] = function() {};
}
*/
/**
* small helper function to urldecode strings
*
* See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
*/
jQuery.urldecode = function(x) {
if (!x) {
return x
}
return decodeURIComponent(x.replace(/\+/g, ' '));
};
/**
* small helper function to urlencode strings
*/
jQuery.urlencode = encodeURIComponent;
/**
* This function returns the parsed url parameters of the
* current request. Multiple values per key are supported,
* it will always return arrays of strings for the value parts.
*/
jQuery.getQueryParameters = function(s) {
if (typeof s === 'undefined')
s = document.location.search;
var parts = s.substr(s.indexOf('?') + 1).split('&');
var result = {};
for (var i = 0; i < parts.length; i++) {
var tmp = parts[i].split('=', 2);
var key = jQuery.urldecode(tmp[0]);
var value = jQuery.urldecode(tmp[1]);
if (key in result)
result[key].push(value);
else
result[key] = [value];
}
return result;
};
/**
* highlight a given string on a jquery object by wrapping it in
* span elements with the given class name.
*/
jQuery.fn.highlightText = function(text, className) {
function highlight(node, addItems) {
if (node.nodeType === 3) {
var val = node.nodeValue;
var pos = val.toLowerCase().indexOf(text);
if (pos >= 0 &&
!jQuery(node.parentNode).hasClass(className) &&
!jQuery(node.parentNode).hasClass("nohighlight")) {
var span;
var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
if (isInSVG) {
span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
} else {
span = document.createElement("span");
span.className = className;
}
span.appendChild(document.createTextNode(val.substr(pos, text.length)));
node.parentNode.insertBefore(span, node.parentNode.insertBefore(
document.createTextNode(val.substr(pos + text.length)),
node.nextSibling));
node.nodeValue = val.substr(0, pos);
if (isInSVG) {
var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
var bbox = node.parentElement.getBBox();
rect.x.baseVal.value = bbox.x;
rect.y.baseVal.value = bbox.y;
rect.width.baseVal.value = bbox.width;
rect.height.baseVal.value = bbox.height;
rect.setAttribute('class', className);
addItems.push({
"parent": node.parentNode,
"target": rect});
}
}
}
else if (!jQuery(node).is("button, select, textarea")) {
jQuery.each(node.childNodes, function() {
highlight(this, addItems);
});
}
}
var addItems = [];
var result = this.each(function() {
highlight(this, addItems);
});
for (var i = 0; i < addItems.length; ++i) {
jQuery(addItems[i].parent).before(addItems[i].target);
}
return result;
};
/*
* backward compatibility for jQuery.browser
* This will be supported until firefox bug is fixed.
*/
if (!jQuery.browser) {
jQuery.uaMatch = function(ua) {
ua = ua.toLowerCase();
var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
/(webkit)[ \/]([\w.]+)/.exec(ua) ||
/(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
/(msie) ([\w.]+)/.exec(ua) ||
ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
[];
return {
browser: match[ 1 ] || "",
version: match[ 2 ] || "0"
};
};
jQuery.browser = {};
jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
}
/**
* Small JavaScript module for the documentation.
*/
var Documentation = {
init : function() {
this.fixFirefoxAnchorBug();
this.highlightSearchWords();
this.initIndexTable();
if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
this.initOnKeyListeners();
}
},
/**
* i18n support
*/
TRANSLATIONS : {},
PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
LOCALE : 'unknown',
// gettext and ngettext don't access this so that the functions
// can safely bound to a different name (_ = Documentation.gettext)
gettext : function(string) {
var translated = Documentation.TRANSLATIONS[string];
if (typeof translated === 'undefined')
return string;
return (typeof translated === 'string') ? translated : translated[0];
},
ngettext : function(singular, plural, n) {
var translated = Documentation.TRANSLATIONS[singular];
if (typeof translated === 'undefined')
return (n == 1) ? singular : plural;
return translated[Documentation.PLURALEXPR(n)];
},
addTranslations : function(catalog) {
for (var key in catalog.messages)
this.TRANSLATIONS[key] = catalog.messages[key];
this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
this.LOCALE = catalog.locale;
},
/**
* add context elements like header anchor links
*/
addContextElements : function() {
$('div[id] > :header:first').each(function() {
$('<a class="headerlink">\u00B6</a>').
attr('href', '#' + this.id).
attr('title', _('Permalink to this headline')).
appendTo(this);
});
$('dt[id]').each(function() {
$('<a class="headerlink">\u00B6</a>').
attr('href', '#' + this.id).
attr('title', _('Permalink to this definition')).
appendTo(this);
});
},
/**
* workaround a firefox stupidity
* see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
*/
fixFirefoxAnchorBug : function() {
if (document.location.hash && $.browser.mozilla)
window.setTimeout(function() {
document.location.href += '';
}, 10);
},
/**
* highlight the search words provided in the url in the text
*/
highlightSearchWords : function() {
var params = $.getQueryParameters();
var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
if (terms.length) {
var body = $('div.body');
if (!body.length) {
body = $('body');
}
window.setTimeout(function() {
$.each(terms, function() {
body.highlightText(this.toLowerCase(), 'highlighted');
});
}, 10);
$('<p class="highlight-link"><a href="javascript:Documentation.' +
'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
.appendTo($('#searchbox'));
}
},
/**
* init the domain index toggle buttons
*/
initIndexTable : function() {
var togglers = $('img.toggler').click(function() {
var src = $(this).attr('src');
var idnum = $(this).attr('id').substr(7);
$('tr.cg-' + idnum).toggle();
if (src.substr(-9) === 'minus.png')
$(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
else
$(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
}).css('display', '');
if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
togglers.click();
}
},
/**
* helper function to hide the search marks again
*/
hideSearchWords : function() {
$('#searchbox .highlight-link').fadeOut(300);
$('span.highlighted').removeClass('highlighted');
},
/**
* make the url absolute
*/
makeURL : function(relativeURL) {
return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
},
/**
* get the current relative url
*/
getCurrentURL : function() {
var path = document.location.pathname;
var parts = path.split(/\//);
$.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
if (this === '..')
parts.pop();
});
var url = parts.join('/');
return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
},
initOnKeyListeners: function() {
$(document).keydown(function(event) {
var activeElementType = document.activeElement.tagName;
// don't navigate when in search box, textarea, dropdown or button
if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
&& activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
&& !event.shiftKey) {
switch (event.keyCode) {
case 37: // left
var prevHref = $('link[rel="prev"]').prop('href');
if (prevHref) {
window.location.href = prevHref;
return false;
}
case 39: // right
var nextHref = $('link[rel="next"]').prop('href');
if (nextHref) {
window.location.href = nextHref;
return false;
}
}
}
});
}
};
// quick alias for translations
_ = Documentation.gettext;
$(document).ready(function() {
Documentation.init();
});

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var DOCUMENTATION_OPTIONS = {
URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
VERSION: '0.3',
LANGUAGE: 'None',
COLLAPSE_INDEX: false,
BUILDER: 'dirhtml',
FILE_SUFFIX: '.html',
LINK_SUFFIX: '.html',
HAS_SOURCE: true,
SOURCELINK_SUFFIX: '.txt',
NAVIGATION_WITH_KEYS: true
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/*
* language_data.js
* ~~~~~~~~~~~~~~~~
*
* This script contains the language-specific data used by searchtools.js,
* namely the list of stopwords, stemmer, scorer and splitter.
*
* :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
/* Non-minified version is copied as a separate JS file, is available */
/**
* Porter Stemmer
*/
var Stemmer = function() {
var step2list = {
ational: 'ate',
tional: 'tion',
enci: 'ence',
anci: 'ance',
izer: 'ize',
bli: 'ble',
alli: 'al',
entli: 'ent',
eli: 'e',
ousli: 'ous',
ization: 'ize',
ation: 'ate',
ator: 'ate',
alism: 'al',
iveness: 'ive',
fulness: 'ful',
ousness: 'ous',
aliti: 'al',
iviti: 'ive',
biliti: 'ble',
logi: 'log'
};
var step3list = {
icate: 'ic',
ative: '',
alize: 'al',
iciti: 'ic',
ical: 'ic',
ful: '',
ness: ''
};
var c = "[^aeiou]"; // consonant
var v = "[aeiouy]"; // vowel
var C = c + "[^aeiouy]*"; // consonant sequence
var V = v + "[aeiou]*"; // vowel sequence
var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
var s_v = "^(" + C + ")?" + v; // vowel in stem
this.stemWord = function (w) {
var stem;
var suffix;
var firstch;
var origword = w;
if (w.length < 3)
return w;
var re;
var re2;
var re3;
var re4;
firstch = w.substr(0,1);
if (firstch == "y")
w = firstch.toUpperCase() + w.substr(1);
// Step 1a
re = /^(.+?)(ss|i)es$/;
re2 = /^(.+?)([^s])s$/;
if (re.test(w))
w = w.replace(re,"$1$2");
else if (re2.test(w))
w = w.replace(re2,"$1$2");
// Step 1b
re = /^(.+?)eed$/;
re2 = /^(.+?)(ed|ing)$/;
if (re.test(w)) {
var fp = re.exec(w);
re = new RegExp(mgr0);
if (re.test(fp[1])) {
re = /.$/;
w = w.replace(re,"");
}
}
else if (re2.test(w)) {
var fp = re2.exec(w);
stem = fp[1];
re2 = new RegExp(s_v);
if (re2.test(stem)) {
w = stem;
re2 = /(at|bl|iz)$/;
re3 = new RegExp("([^aeiouylsz])\\1$");
re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
if (re2.test(w))
w = w + "e";
else if (re3.test(w)) {
re = /.$/;
w = w.replace(re,"");
}
else if (re4.test(w))
w = w + "e";
}
}
// Step 1c
re = /^(.+?)y$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
re = new RegExp(s_v);
if (re.test(stem))
w = stem + "i";
}
// Step 2
re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
suffix = fp[2];
re = new RegExp(mgr0);
if (re.test(stem))
w = stem + step2list[suffix];
}
// Step 3
re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
suffix = fp[2];
re = new RegExp(mgr0);
if (re.test(stem))
w = stem + step3list[suffix];
}
// Step 4
re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
re2 = /^(.+?)(s|t)(ion)$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
re = new RegExp(mgr1);
if (re.test(stem))
w = stem;
}
else if (re2.test(w)) {
var fp = re2.exec(w);
stem = fp[1] + fp[2];
re2 = new RegExp(mgr1);
if (re2.test(stem))
w = stem;
}
// Step 5
re = /^(.+?)e$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
re = new RegExp(mgr1);
re2 = new RegExp(meq1);
re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
w = stem;
}
re = /ll$/;
re2 = new RegExp(mgr1);
if (re.test(w) && re2.test(w)) {
re = /.$/;
w = w.replace(re,"");
}
// and turn initial Y back to y
if (firstch == "y")
w = firstch.toLowerCase() + w.substr(1);
return w;
}
}
var splitChars = (function() {
var result = {};
var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
var i, j, start, end;
for (i = 0; i < singles.length; i++) {
result[singles[i]] = true;
}
var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
[722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
[1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
[1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
[1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
[2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
[2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
[2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
[2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
[2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
[2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
[2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
[3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
[3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
[3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
[3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
[3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
[3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
[4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
[4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
[4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
[4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
[5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
[6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
[6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
[6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
[6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
[7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
[7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
[8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
[8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
[8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
[10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
[11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
[12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
[12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
[12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
[19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
[42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
[42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
[43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
[43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
[43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
[43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
[44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
[57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
[64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
[65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
[65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
for (i = 0; i < ranges.length; i++) {
start = ranges[i][0];
end = ranges[i][1];
for (j = start; j <= end; j++) {
result[j] = true;
}
}
return result;
})();
function splitQuery(query) {
var result = [];
var start = -1;
for (var i = 0; i < query.length; i++) {
if (splitChars[query.charCodeAt(i)]) {
if (start !== -1) {
result.push(query.slice(start, i));
start = -1;
}
} else if (start === -1) {
start = i;
}
}
if (start !== -1) {
result.push(query.slice(start));
}
return result;
}

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@ -1,82 +0,0 @@
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
.highlight .hll { background-color: #ffffcc }
.highlight { background: #f8f8f8; }
.highlight .c { color: #8f5902; font-style: italic } /* Comment */
.highlight .err { color: #a40000; border: 1px solid #ef2929 } /* Error */
.highlight .g { color: #000000 } /* Generic */
.highlight .k { color: #204a87; font-weight: bold } /* Keyword */
.highlight .l { color: #000000 } /* Literal */
.highlight .n { color: #000000 } /* Name */
.highlight .o { color: #ce5c00; font-weight: bold } /* Operator */
.highlight .x { color: #000000 } /* Other */
.highlight .p { color: #000000; font-weight: bold } /* Punctuation */
.highlight .ch { color: #8f5902; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #8f5902; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #8f5902; font-style: italic } /* Comment.Preproc */
.highlight .cpf { color: #8f5902; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #8f5902; font-style: italic } /* Comment.Single */
.highlight .cs { color: #8f5902; font-style: italic } /* Comment.Special */
.highlight .gd { color: #a40000 } /* Generic.Deleted */
.highlight .ge { color: #000000; font-style: italic } /* Generic.Emph */
.highlight .gr { color: #ef2929 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #000000; font-style: italic } /* Generic.Output */
.highlight .gp { color: #8f5902 } /* Generic.Prompt */
.highlight .gs { color: #000000; font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #a40000; font-weight: bold } /* Generic.Traceback */
.highlight .kc { color: #204a87; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #204a87; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #204a87; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #204a87; font-weight: bold } /* Keyword.Pseudo */
.highlight .kr { color: #204a87; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #204a87; font-weight: bold } /* Keyword.Type */
.highlight .ld { color: #000000 } /* Literal.Date */
.highlight .m { color: #0000cf; font-weight: bold } /* Literal.Number */
.highlight .s { color: #4e9a06 } /* Literal.String */
.highlight .na { color: #c4a000 } /* Name.Attribute */
.highlight .nb { color: #204a87 } /* Name.Builtin */
.highlight .nc { color: #000000 } /* Name.Class */
.highlight .no { color: #000000 } /* Name.Constant */
.highlight .nd { color: #5c35cc; font-weight: bold } /* Name.Decorator */
.highlight .ni { color: #ce5c00 } /* Name.Entity */
.highlight .ne { color: #cc0000; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #000000 } /* Name.Function */
.highlight .nl { color: #f57900 } /* Name.Label */
.highlight .nn { color: #000000 } /* Name.Namespace */
.highlight .nx { color: #000000 } /* Name.Other */
.highlight .py { color: #000000 } /* Name.Property */
.highlight .nt { color: #204a87; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #000000 } /* Name.Variable */
.highlight .ow { color: #204a87; font-weight: bold } /* Operator.Word */
.highlight .w { color: #f8f8f8 } /* Text.Whitespace */
.highlight .mb { color: #0000cf; font-weight: bold } /* Literal.Number.Bin */
.highlight .mf { color: #0000cf; font-weight: bold } /* Literal.Number.Float */
.highlight .mh { color: #0000cf; font-weight: bold } /* Literal.Number.Hex */
.highlight .mi { color: #0000cf; font-weight: bold } /* Literal.Number.Integer */
.highlight .mo { color: #0000cf; font-weight: bold } /* Literal.Number.Oct */
.highlight .sa { color: #4e9a06 } /* Literal.String.Affix */
.highlight .sb { color: #4e9a06 } /* Literal.String.Backtick */
.highlight .sc { color: #4e9a06 } /* Literal.String.Char */
.highlight .dl { color: #4e9a06 } /* Literal.String.Delimiter */
.highlight .sd { color: #8f5902; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #4e9a06 } /* Literal.String.Double */
.highlight .se { color: #4e9a06 } /* Literal.String.Escape */
.highlight .sh { color: #4e9a06 } /* Literal.String.Heredoc */
.highlight .si { color: #4e9a06 } /* Literal.String.Interpol */
.highlight .sx { color: #4e9a06 } /* Literal.String.Other */
.highlight .sr { color: #4e9a06 } /* Literal.String.Regex */
.highlight .s1 { color: #4e9a06 } /* Literal.String.Single */
.highlight .ss { color: #4e9a06 } /* Literal.String.Symbol */
.highlight .bp { color: #3465a4 } /* Name.Builtin.Pseudo */
.highlight .fm { color: #000000 } /* Name.Function.Magic */
.highlight .vc { color: #000000 } /* Name.Variable.Class */
.highlight .vg { color: #000000 } /* Name.Variable.Global */
.highlight .vi { color: #000000 } /* Name.Variable.Instance */
.highlight .vm { color: #000000 } /* Name.Variable.Magic */
.highlight .il { color: #0000cf; font-weight: bold } /* Literal.Number.Integer.Long */

@ -1,522 +0,0 @@
/*
* searchtools.js
* ~~~~~~~~~~~~~~~~
*
* Sphinx JavaScript utilities for the full-text search.
*
* :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
if (!Scorer) {
/**
* Simple result scoring code.
*/
var Scorer = {
// Implement the following function to further tweak the score for each result
// The function takes a result array [filename, title, anchor, descr, score]
// and returns the new score.
/*
score: function(result) {
return result[4];
},
*/
// query matches the full name of an object
objNameMatch: 11,
// or matches in the last dotted part of the object name
objPartialMatch: 6,
// Additive scores depending on the priority of the object
objPrio: {0: 15, // used to be importantResults
1: 5, // used to be objectResults
2: -5}, // used to be unimportantResults
// Used when the priority is not in the mapping.
objPrioDefault: 0,
// query found in title
title: 15,
partialTitle: 7,
// query found in terms
term: 5,
partialTerm: 2
};
}
if (!splitQuery) {
function splitQuery(query) {
return query.split(/\s+/);
}
}
/**
* Search Module
*/
var Search = {
_index : null,
_queued_query : null,
_pulse_status : -1,
htmlToText : function(htmlString) {
var virtualDocument = document.implementation.createHTMLDocument('virtual');
var htmlElement = $(htmlString, virtualDocument);
htmlElement.find('.headerlink').remove();
docContent = htmlElement.find('[role=main]')[0];
if(docContent === undefined) {
console.warn("Content block not found. Sphinx search tries to obtain it " +
"via '[role=main]'. Could you check your theme or template.");
return "";
}
return docContent.textContent || docContent.innerText;
},
init : function() {
var params = $.getQueryParameters();
if (params.q) {
var query = params.q[0];
$('input[name="q"]')[0].value = query;
this.performSearch(query);
}
},
loadIndex : function(url) {
$.ajax({type: "GET", url: url, data: null,
dataType: "script", cache: true,
complete: function(jqxhr, textstatus) {
if (textstatus != "success") {
document.getElementById("searchindexloader").src = url;
}
}});
},
setIndex : function(index) {
var q;
this._index = index;
if ((q = this._queued_query) !== null) {
this._queued_query = null;
Search.query(q);
}
},
hasIndex : function() {
return this._index !== null;
},
deferQuery : function(query) {
this._queued_query = query;
},
stopPulse : function() {
this._pulse_status = 0;
},
startPulse : function() {
if (this._pulse_status >= 0)
return;
function pulse() {
var i;
Search._pulse_status = (Search._pulse_status + 1) % 4;
var dotString = '';
for (i = 0; i < Search._pulse_status; i++)
dotString += '.';
Search.dots.text(dotString);
if (Search._pulse_status > -1)
window.setTimeout(pulse, 500);
}
pulse();
},
/**
* perform a search for something (or wait until index is loaded)
*/
performSearch : function(query) {
// create the required interface elements
this.out = $('#search-results');
this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
this.dots = $('<span></span>').appendTo(this.title);
this.status = $('<p class="search-summary">&nbsp;</p>').appendTo(this.out);
this.output = $('<ul class="search"/>').appendTo(this.out);
$('#search-progress').text(_('Preparing search...'));
this.startPulse();
// index already loaded, the browser was quick!
if (this.hasIndex())
this.query(query);
else
this.deferQuery(query);
},
/**
* execute search (requires search index to be loaded)
*/
query : function(query) {
var i;
// stem the searchterms and add them to the correct list
var stemmer = new Stemmer();
var searchterms = [];
var excluded = [];
var hlterms = [];
var tmp = splitQuery(query);
var objectterms = [];
for (i = 0; i < tmp.length; i++) {
if (tmp[i] !== "") {
objectterms.push(tmp[i].toLowerCase());
}
if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i] === "") {
// skip this "word"
continue;
}
// stem the word
var word = stemmer.stemWord(tmp[i].toLowerCase());
// prevent stemmer from cutting word smaller than two chars
if(word.length < 3 && tmp[i].length >= 3) {
word = tmp[i];
}
var toAppend;
// select the correct list
if (word[0] == '-') {
toAppend = excluded;
word = word.substr(1);
}
else {
toAppend = searchterms;
hlterms.push(tmp[i].toLowerCase());
}
// only add if not already in the list
if (!$u.contains(toAppend, word))
toAppend.push(word);
}
var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
// console.debug('SEARCH: searching for:');
// console.info('required: ', searchterms);
// console.info('excluded: ', excluded);
// prepare search
var terms = this._index.terms;
var titleterms = this._index.titleterms;
// array of [filename, title, anchor, descr, score]
var results = [];
$('#search-progress').empty();
// lookup as object
for (i = 0; i < objectterms.length; i++) {
var others = [].concat(objectterms.slice(0, i),
objectterms.slice(i+1, objectterms.length));
results = results.concat(this.performObjectSearch(objectterms[i], others));
}
// lookup as search terms in fulltext
results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
// let the scorer override scores with a custom scoring function
if (Scorer.score) {
for (i = 0; i < results.length; i++)
results[i][4] = Scorer.score(results[i]);
}
// now sort the results by score (in opposite order of appearance, since the
// display function below uses pop() to retrieve items) and then
// alphabetically
results.sort(function(a, b) {
var left = a[4];
var right = b[4];
if (left > right) {
return 1;
} else if (left < right) {
return -1;
} else {
// same score: sort alphabetically
left = a[1].toLowerCase();
right = b[1].toLowerCase();
return (left > right) ? -1 : ((left < right) ? 1 : 0);
}
});
// for debugging
//Search.lastresults = results.slice(); // a copy
//console.info('search results:', Search.lastresults);
// print the results
var resultCount = results.length;
function displayNextItem() {
// results left, load the summary and display it
if (results.length) {
var item = results.pop();
var listItem = $('<li></li>');
var requestUrl = "";
var linkUrl = "";
if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
// dirhtml builder
var dirname = item[0] + '/';
if (dirname.match(/\/index\/$/)) {
dirname = dirname.substring(0, dirname.length-6);
} else if (dirname == 'index/') {
dirname = '';
}
requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname;
linkUrl = requestUrl;
} else {
// normal html builders
requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX;
linkUrl = item[0] + DOCUMENTATION_OPTIONS.LINK_SUFFIX;
}
listItem.append($('<a/>').attr('href',
linkUrl +
highlightstring + item[2]).html(item[1]));
if (item[3]) {
listItem.append($('<span> (' + item[3] + ')</span>'));
Search.output.append(listItem);
setTimeout(function() {
displayNextItem();
}, 5);
} else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
$.ajax({url: requestUrl,
dataType: "text",
complete: function(jqxhr, textstatus) {
var data = jqxhr.responseText;
if (data !== '' && data !== undefined) {
listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
}
Search.output.append(listItem);
setTimeout(function() {
displayNextItem();
}, 5);
}});
} else {
// no source available, just display title
Search.output.append(listItem);
setTimeout(function() {
displayNextItem();
}, 5);
}
}
// search finished, update title and status message
else {
Search.stopPulse();
Search.title.text(_('Search Results'));
if (!resultCount)
Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
else
Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
Search.status.fadeIn(500);
}
}
displayNextItem();
},
/**
* search for object names
*/
performObjectSearch : function(object, otherterms) {
var filenames = this._index.filenames;
var docnames = this._index.docnames;
var objects = this._index.objects;
var objnames = this._index.objnames;
var titles = this._index.titles;
var i;
var results = [];
for (var prefix in objects) {
for (var name in objects[prefix]) {
var fullname = (prefix ? prefix + '.' : '') + name;
var fullnameLower = fullname.toLowerCase()
if (fullnameLower.indexOf(object) > -1) {
var score = 0;
var parts = fullnameLower.split('.');
// check for different match types: exact matches of full name or
// "last name" (i.e. last dotted part)
if (fullnameLower == object || parts[parts.length - 1] == object) {
score += Scorer.objNameMatch;
// matches in last name
} else if (parts[parts.length - 1].indexOf(object) > -1) {
score += Scorer.objPartialMatch;
}
var match = objects[prefix][name];
var objname = objnames[match[1]][2];
var title = titles[match[0]];
// If more than one term searched for, we require other words to be
// found in the name/title/description
if (otherterms.length > 0) {
var haystack = (prefix + ' ' + name + ' ' +
objname + ' ' + title).toLowerCase();
var allfound = true;
for (i = 0; i < otherterms.length; i++) {
if (haystack.indexOf(otherterms[i]) == -1) {
allfound = false;
break;
}
}
if (!allfound) {
continue;
}
}
var descr = objname + _(', in ') + title;
var anchor = match[3];
if (anchor === '')
anchor = fullname;
else if (anchor == '-')
anchor = objnames[match[1]][1] + '-' + fullname;
// add custom score for some objects according to scorer
if (Scorer.objPrio.hasOwnProperty(match[2])) {
score += Scorer.objPrio[match[2]];
} else {
score += Scorer.objPrioDefault;
}
results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
}
}
}
return results;
},
/**
* See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions
*/
escapeRegExp : function(string) {
return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
},
/**
* search for full-text terms in the index
*/
performTermsSearch : function(searchterms, excluded, terms, titleterms) {
var docnames = this._index.docnames;
var filenames = this._index.filenames;
var titles = this._index.titles;
var i, j, file;
var fileMap = {};
var scoreMap = {};
var results = [];
// perform the search on the required terms
for (i = 0; i < searchterms.length; i++) {
var word = searchterms[i];
var files = [];
var _o = [
{files: terms[word], score: Scorer.term},
{files: titleterms[word], score: Scorer.title}
];
// add support for partial matches
if (word.length > 2) {
var word_regex = this.escapeRegExp(word);
for (var w in terms) {
if (w.match(word_regex) && !terms[word]) {
_o.push({files: terms[w], score: Scorer.partialTerm})
}
}
for (var w in titleterms) {
if (w.match(word_regex) && !titleterms[word]) {
_o.push({files: titleterms[w], score: Scorer.partialTitle})
}
}
}
// no match but word was a required one
if ($u.every(_o, function(o){return o.files === undefined;})) {
break;
}
// found search word in contents
$u.each(_o, function(o) {
var _files = o.files;
if (_files === undefined)
return
if (_files.length === undefined)
_files = [_files];
files = files.concat(_files);
// set score for the word in each file to Scorer.term
for (j = 0; j < _files.length; j++) {
file = _files[j];
if (!(file in scoreMap))
scoreMap[file] = {};
scoreMap[file][word] = o.score;
}
});
// create the mapping
for (j = 0; j < files.length; j++) {
file = files[j];
if (file in fileMap && fileMap[file].indexOf(word) === -1)
fileMap[file].push(word);
else
fileMap[file] = [word];
}
}
// now check if the files don't contain excluded terms
for (file in fileMap) {
var valid = true;
// check if all requirements are matched
var filteredTermCount = // as search terms with length < 3 are discarded: ignore
searchterms.filter(function(term){return term.length > 2}).length
if (
fileMap[file].length != searchterms.length &&
fileMap[file].length != filteredTermCount
) continue;
// ensure that none of the excluded terms is in the search result
for (i = 0; i < excluded.length; i++) {
if (terms[excluded[i]] == file ||
titleterms[excluded[i]] == file ||
$u.contains(terms[excluded[i]] || [], file) ||
$u.contains(titleterms[excluded[i]] || [], file)) {
valid = false;
break;
}
}
// if we have still a valid result we can add it to the result list
if (valid) {
// select one (max) score for the file.
// for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
}
}
return results;
},
/**
* helper function to return a node containing the
* search summary for a given text. keywords is a list
* of stemmed words, hlwords is the list of normal, unstemmed
* words. the first one is used to find the occurrence, the
* latter for highlighting it.
*/
makeSearchSummary : function(htmlText, keywords, hlwords) {
var text = Search.htmlToText(htmlText);
var textLower = text.toLowerCase();
var start = 0;
$.each(keywords, function() {
var i = textLower.indexOf(this.toLowerCase());
if (i > -1)
start = i;
});
start = Math.max(start - 120, 0);
var excerpt = ((start > 0) ? '...' : '') +
$.trim(text.substr(start, 240)) +
((start + 240 - text.length) ? '...' : '');
var rv = $('<div class="context"></div>').text(excerpt);
$.each(hlwords, function() {
rv = rv.highlightText(this, 'highlighted');
});
return rv;
}
};
$(document).ready(function() {
Search.init();
});

@ -1,18 +0,0 @@
var initTriggerNavBar=()=>{if($(window).width()<768){$("#navbar-toggler").trigger("click")}}
var scrollToActive=()=>{var navbar=document.getElementById('site-navigation')
var active_pages=navbar.querySelectorAll(".active")
var active_page=active_pages[active_pages.length-1]
if(active_page!==undefined&&active_page.offsetTop>($(window).height()*.5)){navbar.scrollTop=active_page.offsetTop-($(window).height()*.2)}}
var sbRunWhenDOMLoaded=cb=>{if(document.readyState!='loading'){cb()}else if(document.addEventListener){document.addEventListener('DOMContentLoaded',cb)}else{document.attachEvent('onreadystatechange',function(){if(document.readyState=='complete')cb()})}}
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var initTooltips=()=>{$(document).ready(function(){$('[data-toggle="tooltip"]').tooltip();});}
var initTocHide=()=>{var scrollTimeout;var throttle=200;var tocHeight=$("#bd-toc-nav").outerHeight(true)+$(".bd-toc").outerHeight(true);var hideTocAfter=tocHeight+200;var checkTocScroll=function(){var margin_content=$(".margin, .tag_margin, .full-width, .full_width, .tag_full-width, .tag_full_width, .sidebar, .tag_sidebar, .popout, .tag_popout");margin_content.each((index,item)=>{var topOffset=$(item).offset().top-$(window).scrollTop();var bottomOffset=topOffset+$(item).outerHeight(true);var topOverlaps=((topOffset>=0)&&(topOffset<hideTocAfter));var bottomOverlaps=((bottomOffset>=0)&&(bottomOffset<hideTocAfter));var removeToc=(topOverlaps||bottomOverlaps);if(removeToc&&window.pageYOffset>20){$("div.bd-toc").removeClass("show")
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var initThebeSBT=()=>{var title=$("div.section h1")[0]
if(!$(title).next().hasClass("thebe-launch-button")){$("<button class='thebe-launch-button'></button>").insertAfter($(title))}
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sbRunWhenDOMLoaded(initTooltips)
sbRunWhenDOMLoaded(initTriggerNavBar)
sbRunWhenDOMLoaded(scrollToActive)
sbRunWhenDOMLoaded(initTocHide)

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@ -1,34 +0,0 @@
Font Awesome Free License
-------------------------
Font Awesome Free is free, open source, and GPL friendly. You can use it for
commercial projects, open source projects, or really almost whatever you want.
Full Font Awesome Free license: https://fontawesome.com/license/free.
# Icons: CC BY 4.0 License (https://creativecommons.org/licenses/by/4.0/)
In the Font Awesome Free download, the CC BY 4.0 license applies to all icons
packaged as SVG and JS file types.
# Fonts: SIL OFL 1.1 License (https://scripts.sil.org/OFL)
In the Font Awesome Free download, the SIL OFL license applies to all icons
packaged as web and desktop font files.
# Code: MIT License (https://opensource.org/licenses/MIT)
In the Font Awesome Free download, the MIT license applies to all non-font and
non-icon files.
# Attribution
Attribution is required by MIT, SIL OFL, and CC BY licenses. Downloaded Font
Awesome Free files already contain embedded comments with sufficient
attribution, so you shouldn't need to do anything additional when using these
files normally.
We've kept attribution comments terse, so we ask that you do not actively work
to remove them from files, especially code. They're a great way for folks to
learn about Font Awesome.
# Brand Icons
All brand icons are trademarks of their respective owners. The use of these
trademarks does not indicate endorsement of the trademark holder by Font
Awesome, nor vice versa. **Please do not use brand logos for any purpose except
to represent the company, product, or service to which they refer.**

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<?xml version="1.0" standalone="no"?>
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Font Awesome Free 5.13.0 by @fontawesome - https://fontawesome.com
License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
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Created by FontForge 20190801 at Mon Mar 23 10:45:51 2020
By Robert Madole
Copyright (c) Font Awesome
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<font id="FontAwesome5Free-Regular" horiz-adv-x="512" >
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<!DOCTYPE html>
<html lang="en"><head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><title>API Reference · UnitCommitment.jl</title><script data-outdated-warner src="../assets/warner.js"></script><link href="https://cdnjs.cloudflare.com/ajax/libs/lato-font/3.0.0/css/lato-font.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/juliamono/0.045/juliamono.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.4/css/fontawesome.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.4/css/solid.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.4/css/brands.min.css" rel="stylesheet" type="text/css"/><link href="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.13.24/katex.min.css" rel="stylesheet" type="text/css"/><script>documenterBaseURL=".."</script><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js" data-main="../assets/documenter.js"></script><script src="../siteinfo.js"></script><script src="../../versions.js"></script><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../assets/themes/documenter-dark.css" data-theme-name="documenter-dark" data-theme-primary-dark/><link class="docs-theme-link" rel="stylesheet" type="text/css" href="../assets/themes/documenter-light.css" data-theme-name="documenter-light" data-theme-primary/><script src="../assets/themeswap.js"></script><link href="../assets/custom.css" rel="stylesheet" type="text/css"/></head><body><div id="documenter"><nav class="docs-sidebar"><div class="docs-package-name"><span class="docs-autofit"><a href="../">UnitCommitment.jl</a></span></div><form class="docs-search" action="../search/"><input class="docs-search-query" id="documenter-search-query" name="q" type="text" placeholder="Search docs"/></form><ul class="docs-menu"><li><a class="tocitem" href="../">Home</a></li><li><a class="tocitem" href="../usage/">Usage</a></li><li><a class="tocitem" href="../format/">Data Format</a></li><li><a class="tocitem" href="../instances/">Instances</a></li><li><a class="tocitem" href="../model/">JuMP Model</a></li><li class="is-active"><a class="tocitem" href>API Reference</a><ul class="internal"><li><a class="tocitem" href="#Read-data,-build-model-and-optimize"><span>Read data, build model &amp; optimize</span></a></li><li><a class="tocitem" href="#Modify-instance"><span>Modify instance</span></a></li><li><a class="tocitem" href="#Formulations"><span>Formulations</span></a></li><li><a class="tocitem" href="#Solution-Methods"><span>Solution Methods</span></a></li><li><a class="tocitem" href="#Randomization-Methods"><span>Randomization Methods</span></a></li></ul></li></ul><div class="docs-version-selector field has-addons"><div class="control"><span class="docs-label button is-static is-size-7">Version</span></div><div class="docs-selector control is-expanded"><div class="select is-fullwidth is-size-7"><select id="documenter-version-selector"></select></div></div></div></nav><div class="docs-main"><header class="docs-navbar"><nav class="breadcrumb"><ul class="is-hidden-mobile"><li class="is-active"><a href>API Reference</a></li></ul><ul class="is-hidden-tablet"><li class="is-active"><a href>API Reference</a></li></ul></nav><div class="docs-right"><a class="docs-edit-link" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/dev/docs/src/api.md" title="Edit on GitHub"><span class="docs-icon fab"></span><span class="docs-label is-hidden-touch">Edit on GitHub</span></a><a class="docs-settings-button fas fa-cog" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-sidebar-button fa fa-bars is-hidden-desktop" id="documenter-sidebar-button" href="#"></a></div></header><article class="content" id="documenter-page"><h1 id="API-Reference"><a class="docs-heading-anchor" href="#API-Reference">API Reference</a><a id="API-Reference-1"></a><a class="docs-heading-anchor-permalink" href="#API-Reference" title="Permalink"></a></h1><h2 id="Read-data,-build-model-and-optimize"><a class="docs-heading-anchor" href="#Read-data,-build-model-and-optimize">Read data, build model &amp; optimize</a><a id="Read-data,-build-model-and-optimize-1"></a><a class="docs-heading-anchor-permalink" href="#Read-data,-build-model-and-optimize" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.read" href="#UnitCommitment.read"><code>UnitCommitment.read</code></a><span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia hljs">read(path::AbstractString)::UnitCommitmentInstance</code></pre><p>Read a unit commitment instance from a file. The file may be gzipped.</p><p><strong>Example</strong></p><pre><code class="nohighlight hljs">import UnitCommitment
instance = UnitCommitment.read(&quot;/path/to/input.json.gz&quot;)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/instance/read.jl#L48-L58">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.read_benchmark" href="#UnitCommitment.read_benchmark"><code>UnitCommitment.read_benchmark</code></a><span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia hljs">read_benchmark(name::AbstractString)::UnitCommitmentInstance</code></pre><p>Read one of the benchmark unit commitment instances included in the package. See &quot;Instances&quot; section of the documentation for the entire list of benchmark instances available.</p><p><strong>Example</strong></p><pre><code class="nohighlight hljs">import UnitCommitment
instance = UnitCommitment.read_benchmark(&quot;matpower/case3375wp/2017-02-01&quot;)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/instance/read.jl#L13-L25">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.build_model" href="#UnitCommitment.build_model"><code>UnitCommitment.build_model</code></a><span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia hljs">function build_model(;
instance::UnitCommitmentInstance,
optimizer = nothing,
variable_names::Bool = false,
)::JuMP.Model</code></pre><p>Build the JuMP model corresponding to the given unit commitment instance.</p><p><strong>Arguments</strong></p><ul><li><code>instance</code>: the instance.</li><li><code>optimizer</code>: the optimizer factory that should be attached to this model (e.g. Cbc.Optimizer). If not provided, no optimizer will be attached.</li><li><code>variable_names</code>: If true, set variable and constraint names. Important if the model is going to be exported to an MPS file. For large models, this can take significant time, so it&#39;s disabled by default.</li></ul></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/model/build.jl#L8-L28">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.optimize!" href="#UnitCommitment.optimize!"><code>UnitCommitment.optimize!</code></a><span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia hljs">function optimize!(model::JuMP.Model)::Nothing</code></pre><p>Solve the given unit commitment model. Unlike JuMP.optimize!, this uses more advanced methods to accelerate the solution process and to enforce transmission and N-1 security constraints.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/solution/optimize.jl#L5-L11">source</a></section></article><div class="admonition is-warning"><header class="admonition-header">Missing docstring.</header><div class="admonition-body"><p>Missing docstring for <code>UnitCommitment.solution</code>. Check Documenter&#39;s build log for details.</p></div></div><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.validate" href="#UnitCommitment.validate"><code>UnitCommitment.validate</code></a><span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia hljs">validate(instance, solution)::Bool</code></pre><p>Verifies that the given solution is feasible for the problem. If feasible, silently returns true. In infeasible, returns false and prints the validation errors to the screen.</p><p>This function is implemented independently from the optimization model in <code>model.jl</code>, and therefore can be used to verify that the model is indeed producing valid solutions. It can also be used to verify the solutions produced by other optimization packages.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/validation/validate.jl#L15-L26">source</a></section></article><div class="admonition is-warning"><header class="admonition-header">Missing docstring.</header><div class="admonition-body"><p>Missing docstring for <code>UnitCommitment.write</code>. Check Documenter&#39;s build log for details.</p></div></div><h2 id="Modify-instance"><a class="docs-heading-anchor" href="#Modify-instance">Modify instance</a><a id="Modify-instance-1"></a><a class="docs-heading-anchor-permalink" href="#Modify-instance" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.slice" href="#UnitCommitment.slice"><code>UnitCommitment.slice</code></a><span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia hljs">slice(instance, range)</code></pre><p>Creates a new instance, with only a subset of the time periods. This function does not modify the provided instance. The initial conditions are also not modified.</p><p><strong>Example</strong></p><pre><code class="nohighlight hljs"># Build a 2-hour UC instance
instance = UnitCommitment.read_benchmark(&quot;test/case14&quot;)
modified = UnitCommitment.slice(instance, 1:2)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/transform/slice.jl#L5-L19">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.randomize!" href="#UnitCommitment.randomize!"><code>UnitCommitment.randomize!</code></a><span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia hljs">function randomize!(
instance::UnitCommitment.UnitCommitmentInstance,
method::XavQiuAhm2021.Randomization,
rng = MersenneTwister(),
)::Nothing</code></pre><p>Randomize costs and loads based on the method described in XavQiuAhm2021.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/transform/randomize/XavQiuAhm2021.jl#L189-L197">source</a></section></article><div class="admonition is-warning"><header class="admonition-header">Missing docstring.</header><div class="admonition-body"><p>Missing docstring for <code>generate_initial_conditions!</code>. Check Documenter&#39;s build log for details.</p></div></div><h2 id="Formulations"><a class="docs-heading-anchor" href="#Formulations">Formulations</a><a id="Formulations-1"></a><a class="docs-heading-anchor-permalink" href="#Formulations" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.ArrCon2000" href="#UnitCommitment.ArrCon2000"><code>UnitCommitment.ArrCon2000</code></a><span class="docstring-category">Module</span></header><section><div><p>Formulation described in:</p><pre><code class="nohighlight hljs">Arroyo, J. M., &amp; Conejo, A. J. (2000). Optimal response of a thermal unit
to an electricity spot market. IEEE Transactions on power systems, 15(3),
1098-1104. DOI: https://doi.org/10.1109/59.871739</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/model/formulations/ArrCon2000/structs.jl#L5-L11">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.CarArr2006" href="#UnitCommitment.CarArr2006"><code>UnitCommitment.CarArr2006</code></a><span class="docstring-category">Module</span></header><section><div><p>Formulation described in:</p><pre><code class="nohighlight hljs">Carrión, M., &amp; Arroyo, J. M. (2006). A computationally efficient
mixed-integer linear formulation for the thermal unit commitment problem.
IEEE Transactions on power systems, 21(3), 1371-1378.
DOI: https://doi.org/10.1109/TPWRS.2006.876672</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/model/formulations/CarArr2006/structs.jl#L5-L12">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.DamKucRajAta2016" href="#UnitCommitment.DamKucRajAta2016"><code>UnitCommitment.DamKucRajAta2016</code></a><span class="docstring-category">Module</span></header><section><div><p>Formulation described in:</p><pre><code class="nohighlight hljs">Damcı-Kurt, P., Küçükyavuz, S., Rajan, D., &amp; Atamtürk, A. (2016). A polyhedral
study of production ramping. Mathematical Programming, 158(1), 175-205.
DOI: https://doi.org/10.1007/s10107-015-0919-9</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/model/formulations/DamKucRajAta2016/structs.jl#L5-L11">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.Gar1962" href="#UnitCommitment.Gar1962"><code>UnitCommitment.Gar1962</code></a><span class="docstring-category">Module</span></header><section><div><p>Formulation described in:</p><pre><code class="nohighlight hljs">Garver, L. L. (1962). Power generation scheduling by integer
programming-development of theory. Transactions of the American Institute
of Electrical Engineers. Part III: Power Apparatus and Systems, 81(3), 730-734.
DOI: https://doi.org/10.1109/AIEEPAS.1962.4501405</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/model/formulations/Gar1962/structs.jl#L5-L13">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.KnuOstWat2018" href="#UnitCommitment.KnuOstWat2018"><code>UnitCommitment.KnuOstWat2018</code></a><span class="docstring-category">Module</span></header><section><div><p>Formulation described in:</p><pre><code class="nohighlight hljs">Knueven, B., Ostrowski, J., &amp; Watson, J. P. (2018). Exploiting identical
generators in unit commitment. IEEE Transactions on Power Systems, 33(4),
4496-4507. DOI: https://doi.org/10.1109/TPWRS.2017.2783850</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/model/formulations/KnuOstWat2018/structs.jl#L5-L11">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.MorLatRam2013" href="#UnitCommitment.MorLatRam2013"><code>UnitCommitment.MorLatRam2013</code></a><span class="docstring-category">Module</span></header><section><div><p>Formulation described in:</p><pre><code class="nohighlight hljs">Morales-España, G., Latorre, J. M., &amp; Ramos, A. (2013). Tight and compact
MILP formulation for the thermal unit commitment problem. IEEE Transactions
on Power Systems, 28(4), 4897-4908. DOI: https://doi.org/10.1109/TPWRS.2013.2251373</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/model/formulations/MorLatRam2013/structs.jl#L5-L11">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.PanGua2016" href="#UnitCommitment.PanGua2016"><code>UnitCommitment.PanGua2016</code></a><span class="docstring-category">Module</span></header><section><div><p>Formulation described in:</p><pre><code class="nohighlight hljs">Pan, K., &amp; Guan, Y. (2016). Strong formulations for multistage stochastic
self-scheduling unit commitment. Operations Research, 64(6), 1482-1498.
DOI: https://doi.org/10.1287/opre.2016.1520</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/model/formulations/PanGua2016/structs.jl#L5-L11">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.WanHob2016" href="#UnitCommitment.WanHob2016"><code>UnitCommitment.WanHob2016</code></a><span class="docstring-category">Module</span></header><section><div><p>Formulation described in:</p><pre><code class="nohighlight hljs">B. Wang and B. F. Hobbs, &quot;Real-Time Markets for Flexiramp: A Stochastic
Unit Commitment-Based Analysis,&quot; in IEEE Transactions on Power Systems,
vol. 31, no. 2, pp. 846-860, March 2016, doi: 10.1109/TPWRS.2015.2411268.</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/model/formulations/WanHob2016/structs.jl#L5-L11">source</a></section></article><h2 id="Solution-Methods"><a class="docs-heading-anchor" href="#Solution-Methods">Solution Methods</a><a id="Solution-Methods-1"></a><a class="docs-heading-anchor-permalink" href="#Solution-Methods" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.XavQiuWanThi2019" href="#UnitCommitment.XavQiuWanThi2019"><code>UnitCommitment.XavQiuWanThi2019</code></a><span class="docstring-category">Module</span></header><section><div><p>Lazy constraint solution method described in:</p><pre><code class="nohighlight hljs">Xavier, A. S., Qiu, F., Wang, F., &amp; Thimmapuram, P. R. (2019). Transmission
constraint filtering in large-scale security-constrained unit commitment.
IEEE Transactions on Power Systems, 34(3), 2457-2460.
DOI: https://doi.org/10.1109/TPWRS.2019.2892620</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/solution/methods/XavQiuWanThi2019/structs.jl#L5-L12">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.XavQiuWanThi2019.Method" href="#UnitCommitment.XavQiuWanThi2019.Method"><code>UnitCommitment.XavQiuWanThi2019.Method</code></a><span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia hljs">mutable struct Method
time_limit::Float64
gap_limit::Float64
two_phase_gap::Bool
max_violations_per_line::Int
max_violations_per_period::Int
end</code></pre><p><strong>Fields</strong></p><ul><li><code>time_limit</code>: the time limit over the entire optimization procedure.</li><li><code>gap_limit</code>: the desired relative optimality gap. Only used when <code>two_phase_gap=true</code>.</li><li><code>two_phase_gap</code>: if true, solve the problem with large gap tolerance first, then reduce the gap tolerance when no further violated constraints are found.</li><li><code>max_violations_per_line</code>: maximum number of violated transmission constraints to add to the formulation per transmission line.</li><li><code>max_violations_per_period</code>: maximum number of violated transmission constraints to add to the formulation per time period.</li></ul></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/solution/methods/XavQiuWanThi2019/structs.jl#L15-L41">source</a></section></article><h2 id="Randomization-Methods"><a class="docs-heading-anchor" href="#Randomization-Methods">Randomization Methods</a><a id="Randomization-Methods-1"></a><a class="docs-heading-anchor-permalink" href="#Randomization-Methods" title="Permalink"></a></h2><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.XavQiuAhm2021" href="#UnitCommitment.XavQiuAhm2021"><code>UnitCommitment.XavQiuAhm2021</code></a><span class="docstring-category">Module</span></header><section><div><p>Methods described in:</p><pre><code class="nohighlight hljs">Xavier, Álinson S., Feng Qiu, and Shabbir Ahmed. &quot;Learning to solve
large-scale security-constrained unit commitment problems.&quot; INFORMS
Journal on Computing 33.2 (2021): 739-756. DOI: 10.1287/ijoc.2020.0976</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/transform/randomize/XavQiuAhm2021.jl#L5-L11">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="UnitCommitment.XavQiuAhm2021.Randomization" href="#UnitCommitment.XavQiuAhm2021.Randomization"><code>UnitCommitment.XavQiuAhm2021.Randomization</code></a><span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia hljs">struct Randomization
cost = Uniform(0.95, 1.05)
load_profile_mu = [...]
load_profile_sigma = [...]
load_share = Uniform(0.90, 1.10)
peak_load = Uniform(0.6 * 0.925, 0.6 * 1.075)
randomize_costs = true
randomize_load_profile = true
randomize_load_share = true
end</code></pre><p>Randomization method that changes: (1) production and startup costs, (2) share of load coming from each bus, (3) peak system load, and (4) temporal load profile, as follows:</p><ol><li><p><strong>Production and startup costs:</strong> For each unit <code>u</code>, the vectors <code>u.min_power_cost</code> and <code>u.cost_segments</code> are multiplied by a constant <code>α[u]</code> sampled from the provided <code>cost</code> distribution. If <code>randomize_costs</code> is false, skips this step.</p></li><li><p><strong>Load share:</strong> For each bus <code>b</code> and time <code>t</code>, the value <code>b.load[t]</code> is multiplied by <code>(β[b] * b.load[t]) / sum(β[b2] * b2.load[t] for b2 in buses)</code>, where <code>β[b]</code> is sampled from the provided <code>load_share</code> distribution. If <code>randomize_load_share</code> is false, skips this step.</p></li><li><p><strong>Peak system load and temporal load profile:</strong> Sets the peak load to <code>ρ * C</code>, where <code>ρ</code> is sampled from <code>peak_load</code> and <code>C</code> is the maximum system capacity, at any time. Also scales the loads of all buses, so that <code>system_load[t+1]</code> becomes equal to <code>system_load[t] * γ[t]</code>, where <code>γ[t]</code> is sampled from <code>Normal(load_profile_mu[t], load_profile_sigma[t])</code>.</p><p>The system load for the first time period is set so that the peak load matches <code>ρ * C</code>. If <code>load_profile_sigma</code> and <code>load_profile_mu</code> have fewer elements than <code>instance.time</code>, wraps around. If <code>randomize_load_profile</code> is false, skips this step.</p></li></ol><p>The default parameters were obtained based on an analysis of publicly available bid and hourly data from PJM, corresponding to the month of January, 2017. For more details, see Section 4.2 of the paper.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/blob/b68b4ff9e46f67a4711596883d93d0275266dc36/src/transform/randomize/XavQiuAhm2021.jl#L17-L58">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../model/">« JuMP Model</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 0.27.21 on <span class="colophon-date" title="Sunday 17 July 2022 14:42">Sunday 17 July 2022</span>. Using Julia version 1.7.3.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

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