Optimal Power Flow Under Electricity Market And Its Improved Genetic Algorithm

Optimal Power How (OPF), which its purpose is to pursue maximum of economic interest based on system security, is always an important task for static analysis of power system. Nowadays, with the establishment of power market step by step, OPF plays an more important role in striving for an attractive combination of economical efficiency and security.This paper probes into the characteristics of generation sector electricity market to improve the traditional OPF model. The improved model embeds bidding transactions in calculation of the optimal power flow, considers the network losses expenses, makes the active power market be operated with reactive power market in parallel to increase economic benefit. This improved model, which's object function is the minimum of the summation of active and reactive purchasing cost including network losses expenses and reactive power compensation device investment cost, can find optimization and generate electricity purchasing and operation plan.To solve this complex model, this paper uses improved genetic algorithm to calculate OPF. It uses niche technology and learning strategy to keep population's diversity and enhance local search ability. The calibration of fitness function and self-adaptively crossover rate and mutation rate also been used. Aiming at startup and shutdown of generator and discrete control variables, startup and shutdown operators and integral operators have been designed.On matlab software platform, the improved model and improved genetic algorithm have been applied to the IEEE 14,30,57,118 bus system and the practical example of northwest 330kV transmission network.The results prove that the improved model can achieve economy optimization and the improved genetic algorithm is feasible and efficient for OPF model's calculation and optimization.