Profit-based unit commitment for GenCos in a deregulated power market

2004 ◽  
Vol 14 (5) ◽  
pp. 321-330 ◽  
Author(s):  
H. Y. Yamin
Keyword(s):  
Unit Commitment ◽  
Power Market

scholarly journals Profit Based Unit Commitment of Thermal Units with Renewable Energy and Electric Vehicles in Power Market

2020 ◽  
Vol 16 (1) ◽  
pp. 115-129
Author(s):  
S. F. Syed Vasiyullah ◽  
S. G. Bharathidasan
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Electric Vehicles ◽  
Unit Commitment ◽  
Power Market ◽  
Scheduling Problem ◽  
Unit Commitment Problem ◽  
Market Clearing ◽  
Maximum Profit ◽  
Commitment Problem

AbstractIn restructured power system, Generation Companies (GENCOs) has an opportunity to sell power and reserve in power market to earn profit by market clearing process. Defining unit commitment problem in a competitive environment to maximize the profit of GENCOs while satisfying all the network constraints is called Profit Based Unit Commitment problem (PBUC). The main contribution of this paper is modeling and inclusion of Market Clearing Price (MCP) in PBUC problem. In Day market, MCP is determined by market operator which provides maximum social welfare for both GENCOs and Consumers.On other hand this paper proposes a novel combination of solution methodology: Improved Pre-prepared power demand (IPPD) table and Analytical Hierarchy method (AHP) for solving the optimal day ahead scheduling problem as an another contribution. In this method, the status of unit commitment is obtained by IPPD table and AHP provides an optimal solution to PBUC problem. Minimizing total operating cost of thermal units to provide maximum profit to GENCOs is called an optimal day ahead scheduling problem. Also it will be more realistic to redefine this problem to include multiple distributed resources and Electric vehicles with energy storage. Because of any uncertainties or fluctuation of renewable energy resources (RESs), Electric vehicles (EV) can be used as load, energy sources and energy storage. This would reduce cost, emission and to improve system power quality and reliability. So output power of solar (PS), wind output power (PW) and Electric Vehicles power (PEV) are modeled and included into day ahead scheduling problem.The proposed methodology is tested on a standard thermal unit system with or without RESs and EVs. Cost and emission reduction in a smart grid by maximum utilization of EVs and RESs are presented in this literature. It is indicated that the proposed method provides maximum profit to GENCOs when compared to other methodologies such as Memory Management Algorithm, Improved Particle Swarm Optimization (PSO), Muller method, Gravitational search algorithm etc.

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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