Optimal operation strategy of power system based on stochastic risk avoidance

Abstract

In order to avoid random risks in the power system and rationally allocate power to improve power consumption efficiency, this study designed a power system optimization model based on value-at-risk method, replaced the independent variables of particle swarm optimization algorithm with discrete quantities, and used discrete binary particle swarm optimization algorithm to solve the problem. The experimental results show that compared with other algorithms, DSO has the fastest downward trend, the smallest fluctuation and the best convergence. In the case of distribution power supply and optimization, the node voltage of power network operation is 0.97, the power loss is 0.54, and the system risk value is low. It is indicated that the addition of wind power generation and solar power generation, and the optimization of distribution network can effectively improve the operation of power system. It can be seen that the power system optimization model can effectively predict the risk value, and improve the accuracy of prediction and evaluation, which has certain practical significance and economic value in the field of power grid.

Keywords

• BPSO algorithm
• Risk avoidance
• Power system optimization
• CVaR
• VaR