@ -8,42 +8,59 @@ The **Security-Constrained Unit Commitment Problem** (SCUC) is a two-stage stoch
In addition to the basic components above, modern versions of SCUC also include a wide variety of additional components, such as _energy storage devices_, _reserves_, _price-sensitive loads_ and _network interfaces_, to name a few. On this page, we present a complete definition of the problem as it is formulated in UC.jl. Please note that various souces in the literature may have different definitions.
In addition to the basic components above, modern versions of SCUC also include a wide variety of additional components, such as _energy storage devices_, _reserves_, _price-sensitive loads_ and _network interfaces_, to name a few. On this page, we present a complete definition of the problem as it is formulated in UC.jl. Please note that various souces in the literature may have different definitions.
## 1. Thermal Generators
## General modeling assumptions
A _thermal generator_ is a power generation unit that converts thermal energy, typically from the combustion of coal, natural gas or oil, into electrical energy. Scheduling thermal generators is particularly complex due to their operational characteristics, including minimum up and down times, ramping rates, and start-up and shutdown limits. Production costs for thermal generators follow a (linearized) convex production cost curve. Additionally, startup costs depend on how long has the unit been offline (e.g. cold, hot).
SCUC is a multi-period problem, with decisions typically covering a 24-hour or 36-hour time window. UC.jl assumes that this time window is discretized into time steps of fixed length. The number of time steps, as well as the duration of each time step, are configurable. In the equations below, the set of time steps is denoted by $T=\{1,2,\ldots,|T|\}$.
SCUC is also a two-stage stochastic problem. In the first stage, we must decide the _commitment status_ of all thermal generators. In the second stage, we determine the remaining decision variables, such power output of all generators, the operation of energy storage devices and load shedding. Stochasticity is modeled through a discrete number of scenarios $s \in S$, each with given probability $p(S)$. The goal is to minimize the minimum expected cost. The deterministic version of SCUC can be modeled by assuming a single scenario with probability 1.
## Thermal Generators
A _thermal generator_ is a power generation unit that converts thermal energy, typically from the combustion of coal, natural gas or oil, into electrical energy. Scheduling thermal generators is particularly complex due to their operational characteristics, including minimum up and down times, ramping rates, and start-up and shutdown limits.
### Concepts
- **Commitment, power output and startup costs:** Thermal generators can either be operational (on) or offline (off). When a thermal generator is on, it can produce between a minimum and a maximum amount of power; when it is off, it cannot produce any power. Switching a generator on incurs a startup cost, which depends on how long the unit has been offline. More precisely, each thermal generator $g$ has a number $K^{start}_g$ of startup categories (e.g., cold, warm and hot). Each category $k$ has a corresponding startup cost $Z^{\text{start}}_{gk}$, and is available only if the unit has spent at most $M^{\text{delay}}_{gk}$ time steps offline.
- **Piecewise-linear production cost curve:** Besides startup costs, thermal generators also incur production costs based on their power output. The relationship between production cost and power output is not a linear, but a convex curve, which is simplified using a piecewise-linear approximation. For this purpose, each thermal generator $g$ has a number $K^{\text{cost}}_g$ of piecewise-linear segments and its power output $y^{\text{prod-above}}_{gts}$ are broken down into $\sum_{k=1}^{K^{\text{cost}}_g} y^{\text{seg-prod}}_{gtks}$, so that production costs can be more easily calculated.
- **Ramping, minimum up/down:** Due to physical and operational limits, such as thermal inertia and mechanical stress, thermal generators cannot vary their power output too dramatically from one time period to the next. Similarly, thermal generators cannot switch on and off too frequently; after switching on or off, units must remain at that state for a minimum specified number of time steps.
- **Initial status:** The optimization process finds optimal commitment status and power output level for all thermal generators starting at time period 1. Many constraints, however, require knowledge of previous time periods (0, -1, -2, ...) which are not part of the optimization model. For this reason, part of the input data is the initial power output $M^{\text{init-power}}_{g}$ of unit $g$ (that is, the output at time 0) and the initial status $M^{\text{init-status}}_{g}$ of unit g (how many time steps has it been online/offline at time time 0). If $M^{\text{init-status}}_{g}$ is positive, its magnitude indicates how many time periods has the unit been online; and if negative, how has it been offline.
- **Must-run:** Due to various factors, including reliability considerations, some units must remain operational regardless of whether it is economical for them to do so. Must-run constraints are used to enforce such requirements.