# Unit Commitment with Ancillary Services in a Day-Ahead Power Market
###### 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. Particle Swarm Optimization; Sequential Quadratic Programming; ancillary services; unit commitment 28 April 2021 8 June  
whereby special attention is given to Automatic Generation Control (AGC), Real-time Spinning Reserve (RSR), and Supplemental Reserve (SR) in TPC [4]. AGC, including up-regulation and downregulation, is the first level of regulation in reserve for the system operator. RSRs are units that can respond quickly to accidents or load increases. SR plays an important role in maintaining system security, as this reserve is more time-consuming, and is used when RSR cannot satisfy the reserve needs. Ancillary services, as required to ease the previously scheduled generating capacity, are based on online generating plants. Therefore, the operations of the energy market should consider ancillary services in unit commitment schedules. Unit commitment for the power market is an urgent problem for the TPC. The scheduling strategy contains a sequential schedule that determines the unit on/off state in each time interval, and assigns the power scheduling and ancillary services' scheduling results. There are many constraints for unit commitment in the power market, such as unit generation limits, on/off states, operating time, and ramping rates. Many local minima could be expected with mixed integers, which increase the complexity of the problem. It is difficult to find the optimal solution using a traditional mathematical tool. Ref. [5] concluded that unit commitment, as addressed before 2003, may face problems of dimensionality, excessive computational time, and local optimality. Recently, artificial intelligence algorithms are being used in unit commitment optimization [6; 7; 8; 9; 10]; while part of the artificial intelligence algorithms can overcome various constraints, convergent rates are poor if there are numerous variables, and a long solving time can be expected. Among references regarding ancillary services, [11] used mixed-integer programming and an artificial neural network to analyze and simulate the condition of a power market in order to solve the competitive bidding strategy of ancillary services. Ref. [12] discussed the ancillary services of different reserve unit capacities, and combined the settlement price with an optimum allocation in order to properly implement the strategy of demand response in the system. Ref. [13] proposed a payment mechanism, and properly implemented energy costs and demand charges by considering energy dispatch in ancillary services. Ref. [14] used dynamic optimum power flow to analyze the dispatch of energy in relation to ancillary services.