Abstract
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.
Highlights
This paper proposes a hybrid Discrete Particle Swarm Optimization (DPSO)-Sequential Quadratic Programming (SQP) by combining Discrete Particle Swarm
Many study cases were conducted for the thermal units of Taipower Company (TPC) and Independent Power Producers (IPPs), including
The minimum operating time of a coal-fired unit is set as 8 h, the minimum operating time of a gas-fired unit is set as 4 h, and the minimum operating time of an oil-fired unit is set as 6 h
Summary
The major purpose of unit commitment is to determine how to commit various units to satisfy the 24 h load demands under related constraints [1]. Ancillary services have become indispensable in terms of operation [2,3]. If ancillary services are absent, there may be a series of problems related to safety, reliability, and power quality. In the power transaction market, the load forecast of energy supply and the ancillary service capacity need to be simultaneously evaluated. Unit commitment in ancillary services must consider the fact that online units can rapidly adjust to satisfy the ancillary services’ capacity to safely conform to the operational standards of a power system. As there are more variables and more confined constraints, optimal power dispatch becomes very tedious and difficult to achieve
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