Comparison Between the Dynamic Programming and Particle Swarm Optimization for Solving Unit Commitment Problems

2019 ◽  
Author(s):  
Venkata Silpa Borra ◽  
K. Debnath
Keyword(s):  
Dynamic Programming ◽  
Particle Swarm Optimization ◽  
Unit Commitment ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Commitment Problems

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.

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