Unit commitment problem

Authors: Fah Kumdokrub, Malcolm Hegeman, Kapil Khanal (SYSEN 6800 Fall 2021)

Introduction

Unit commitment(UC) is fundamentally NP-hard scheduling and mixed-integer. Nonlinear,Non-convex programming optimization problem[2]. The problem involves integer decision variables- to turn generators ON or OFF. It is used for optimizing the power generators schedule such that their operating cost is kept low over some time units(planning horizon) and within operating requirements[1]. A very simple problem involving UC is to try to minimize the operating cost for two power generators with different max/min power output , startup costs and operating costs for one hour. How will the problem change in n hours with n generators?

Recently, higher generation from renewable energy sources (RES) and more price responsive demand participation have made the UC problem a hard challenge, mainly due to the unpredictability and the high variability of RES[2]

Theory, methodology, and/or algorithmic discussions

Mixed Integer Non-Linear Programming

The Unit Commitment Problem (UC) is a large-scale mixed-integer nonlinear program for finding a the low-cost operating schedule for power generators. These problems typically have quadratic objective functions and nonlinear, non-convex transmission Constraints. Typically both of these are linearized1.

Objective function = $\min \sum _{g\in G}\sum _{t\in T}c_{g}(t)$

such that $\sum _{g\in {G}}A_{g}(p_{g},{\bar {p}}_{g},u_{g})+N(s)=L$

$(p_{g},{\bar {p}}_{g},u_{g})\in \prod _{g}$

Here $c_{g}$ is the cost vector associated with $p_{g},{\bar {p}}_{g},u_{g}$, such that the objective function (1a) is to minimize system operation cost. The vectors $p_{g},{\bar {p}}_{g},u_{g}$ represent the feasible generation schedule, maximum power available, and the on/off status for generator g, respectively. The matrix $A_{g}(p_{g},{\bar {p}}_{g},u_{g}$ determines how the generator interacts with the system requirements, which are written in matrix form as equation [2]

General technical constraints presented in [1] are

• Convex productions costs
• Minimum and maximum output levels
• Ramping constraints
• Minimum up and downtime

Dynamic Programming Based Approach

Dynamic programming is an optimization approach that transforms a complex problem into a sequence of simpler problems; its essential characteristic is the multistage nature of the optimization procedure[4]. It takes some creativity in coming up with the formulations of dynamic programming for a problem. Also, various heuristics are applied to decrease the search space and computation time.

$obj=F_{cost}(T,R)=\min _{P}{P_{cost}(T,R)+S_{cost}(T-1,P:T,R)+F_{cost}(T-1,P)}$

where,

    $F_{cost}(T,R)={\text{Minimum cost price at state}}(T,R)$,

    $P_{cost}(T,R)={\text{The production cost at state}}(T,R)$,

    $S_{cost}(T-1,P:T,R)={\text{The transition cost price from state}}(T-1,P)to(T,R)$,

    $T={\text{Number of hours}}$

    $R={\text{Stage of DP execution}}$

• 

  Flowchart provided on [3]

Apart from these two methods, there are other several methods outlined on [3]

• Priority List Method
• Lagrangian Relaxation method
• Branch and Bound Method
• Genetic algorithm based approach

Applications

Unit commitment problems can be further adjusted for components that reflect the real-world problem. A simple categorization can be divided into two major groups. One is related to the commitment of power generation/production/manufacturing process, and the other group involves output allocation to the committed units, often known as unit commitment and economic dispatch .

Unit commitment: Single period

This type of problem usually optimizes the number of power generators for each facility/plant to meet the demand in a specific period. Although this might not be the case study in real-world scenarios, starting off with this type of unit commitment optimization problem might help check the correctness of other constraints before adding the complexity of time and other power generating units’ components.

Unit commitment: Multi-period

Many times, the production of power should be planned in advance. This planned period could be for months, weeks, or even overnight to avoid under or over-power generation and minimize the total number of generators needed. And these problems would require a multi-period unit commitment optimization.

Unit commitment: Additional constraints

There are many more criteria that can be added to the unit commitment problem to truly reflect the system. Some scenarios may be required reserve constraints to ensure sufficient supply in response to a spike in demand . Ramping constraints can also be added since the generators take time to start and stop the process which both affect the cost and amount of power output . Types of power affect the optimization whether they come from a single source or multiple sources .

Unit commitment and Economic dispatch

With any attributes to the unit commitment problem, the economic aspects are always involved meaning that the allocation of power generation (energy output) for each committing unit must be economical with all the costs and revenues. Power generator plants not only need to meet the demand, but they also need to operate in the most economical ways, at the lowest possible cost or the highest possible profit. There are many costs involved in generating power, for example, the cost per unit of power generations, the cost of shutting down or starting up the generator, the cost of over generating power since it may cause damage to the plant, and there are many other costs and benefits that could be considered to the problem.

Commercialized unit commitment software is also available for use. For example, Power Optimisation company developed a software named POWEROP which is more generalized to wide ranges of users or power companies, customized software for Northern Ireland Electricity (NIE), and software developed specifically for the British Electricity Trading and Transmission Arrangements (BETTA) . Software for NIE considers power from multiple sources, including gas, coal, and oil-fired steam . Additionally, software for BETTA can generate electricity prices for both the general market and individuals by contract . The software was developed from a multi-stage mixed-integer linear programming (MILP) and adapted the constraints to serve customers’ specific requirements .