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

Abstract

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.