A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods

Abstract

Optimizing the schedule of thermal generators is probably the most important task when the operation of power systems is managed. This issue is known as the unit commitment problem in operational research. It has been profoundly studied in the literature, where several techniques have been proposed to address a computationally tractable solution. In turn, the ongoing changes of paradigms in energy markets focus the attention on the unit commitment problem as a powerful tool to handle new trends, such as the high renewable energy sources penetration or widespread use of non-conventional energy-storage technologies. A review on the unit commitment problem is propo- sed in this paper. The easy understanding of the diverse techniques applied in the literature for new researchers is the main goal of this state-of-art as well as identifying the research gaps that could be susceptible to further developments. Moreover, an overview of the evolution of the Mixed Integer Linear Programming formulation regarding the improvements of commercial solvers is presented, according to its prevailing hegemony when the unit commitment problem is addressed. Finally, an accurate analysis of modeling detail, power system representation, and computational performance of the case studies is presented. This characterization entails a significant development against the conventional reviews, which only offer a broad vision of the modeling scope of their citations at most.

Generation Unit Commitment Mixed Integer Linear Model for Simultaneous Heat and Electric Daily Load Covering

“…There are two equilibrium equations (constraints) are needed to provide the electric and heat energy production-consumption balance for each time step. The first equation is for electric energy (4), and the second one is for heat energy (5).…”

“…The unit commitment problem nowadays is widely used in the electric power sector [1,4,5]. The problem was first time formulated in the 1940-s but the first algorithm developed to solve the unit commitment problem was proposed in 1959 [1].…”

“…Total production costs = (Fuel cost + Start-up cost + Shut-down cost+ Maintenance cost) -> min. (1) This formulation was published in 2018, but in 2022 the general formulation became more comprehensive [5]: Min (Production cost + Start-up cost + Shut-down cost + Emission cost + Maintenance cost). ( 2) Equation (2) includes "Production cost" instead of "Fuel cost" in (1), and this is more correct as during the operation of the power unit not only fuel but also other resources are consumed, e.g., pure water, chemicals etc.…”

“…The unit commitment problem "behavior" is mainly described in the constraints section. The typical constraints are [5,6,7]: Demand constraint -exact equilibrium between production (supply) and consumption for each period; Power generation limits -each unit should not exceed nominal installed capacity and should not be lower than technical generating limit; Power output ramping limits -period-toperiod increase/decrease of power output should be within the technical possibility of the unit; Operation/idle time constraints -describes allowed operational modes behavior of each unit; Emission constraintsimposed to not exceed allowed emission volumes, e.g., from coal-fired units; Fuel constraints -limits available fuel on stock for each or for the group of fuel burning units for each time step. The list of the constraints and their formulation could vary depending on the goal of the study/calculations.…”

Solving wind‐integrated unit commitment problem by a modified African vultures optimization algorithm

“…It helps find adequate scheduling for the power plants. This is done by minimizing the objective function of the total generation cost while maintaining the balance between generation and load demand as a constraint, along with other equality and inequality technical constraints [1,2]. This aforesaid cost includes five parts.…”