| Summary: | This paper presents a new approach to solve the profit based multi area unit commitment problem (PBMAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to maximize the profit of generation companies (GENCOs) with considering system social benefit. The proposed method helps GENCOs to make a decision, how much power and reserve should be sold in markets, and how to schedule generators in order to receive the maximum profit. Joint operation of generation resources can
result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. The tie line transfer limits were considered as a set of
constraints during optimization process to ensure the system security and reliability. The overall algorithm can be implemented on an IBM PC, which can process a fairly large system in a reasonable period of time. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the profit of evolutionary programming-based particle swarm optimization method (EPPSO)
with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results shows that the application of this evolutionary programming based particle swarm optimization
method have the potential to solve profit based multi area unit commitment problem with lesser computation time.
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