scholarly journals Unit Commitment with Ancillary Services in a Day-Ahead Power Market

2021 ◽  
Vol 11 (12) ◽  
pp. 5454
Author(s):  
Whei-Min Lin ◽  
Chung-Yuen Yang ◽  
Ming-Tang Tsai ◽  
Yun-Hai Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Unit Commitment ◽  
Operating Cost ◽  
Particle Swarm ◽  
Power Market ◽  
Automatic Generation ◽  
Ancillary Services ◽  
Spinning Reserve ◽  
Swarm Optimization ◽  
Commitment Problems

This paper integrates Discrete Particle Swarm Optimization (DPSO) and Sequential Quadratic Programming (SQP) to propose a DPSO-SQP method for solving unit commitment problems for ancillary services. Through analysis of ancillary services, including Automatic Generation Control (AGC), Real Spinning Reserve (RSR), and Supplemental Reserve (SR), the cost model of unit commitment was developed. With the requirements of energy balance, ancillary services, and operating constraints considered, DPSO-PSO was used to calculate the energy supply of each source, including the associated AGC, RSR, and SR, and the operating cost of a day-ahead power market was calculated. A study case using the real data from thermal units of Taipower Company (TPC) and Independent Power Producers (IPPs) demonstrated effective results for the “summer” and “non-summer” seasons, as classified by TPC for the two charging rates. According to the test cases in this research, costs without ancillary services in non-summer and summer seasons are higher than those with ancillary services. The simulation results are also compared with the Genetic Algorithm (GA), Evolutionary Programming (EP), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). DPSO-PSO shows effectiveness in solving unit commitment problems with enhanced sorting efficiency, and a higher probability of reaching the global optimum.

scholarly journals The State of Art in Particle Swarm Optimization Based Unit Commitment: A Review

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 733 ◽  
Author(s):  
Gad Shaari ◽  
Neyre Tekbiyik-Ersoy ◽  
Mustafa Dagbasi
Keyword(s):  
Particle Swarm Optimization ◽  
Unit Commitment ◽  
State Of The Art ◽  
Cost Minimization ◽  
Particle Swarm ◽  
Environmental Constraints ◽  
Thermal Unit ◽  
Spinning Reserve ◽  
Swarm Optimization ◽  
Modified Pso

Unit Commitment (UC) requires the optimization of the operation of generation units with varying loads, at every hour, under different technical and environmental constraints. Many solution techniques were developed for the UC problem, and the researchers are still working on improving the efficiency of these techniques. Particle swarm optimization (PSO) is an effective and efficient technique used for solving the UC problems, and it has gotten a considerable amount of attention in recent years. This study provides a state-of-the-art literature review on UC studies utilizing PSO or PSO-variant algorithms, by focusing on research articles published in the last decade. In this study, these algorithms/methods, objectives, constraints are reviewed, with focus on the UC problems that include at least one of the wind and solar technologies, along with thermal unit(s). Although, conventional PSO is one of the most effective techniques used in solving UC problem, other methods were also developed in literature to improve the convergence. In this study, these methods are grouped as extended PSO, modified PSO, and PSO with other techniques. This study shows that PSO with other techniques are utilized more than any other methods. In terms of constraints, it was observed that there are only few studies that considered Transmission Line (TL), Fuel (F), Emission (E), Storage (St) and Crew (Cr) constraints, while Power Balance (PB), Generation limit (GL), Unit minimum Up or Down Time (U/DT), Ramp Up or Ramp Down Time (R-U/DT) and system Spinning Reserve (SR) were the most utilized constraints in UC problems considering wind/solar as a renewable source. In addition, most of the studies are based on a single objective function (cost minimization) and, few of them are multi-objective (cost and emission minimization) based studies.

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