Wind Power Reserve and Day-ahead Market Joint Clearing Optimization based on Improved Gray Wolf Optimization Algorithm

In order to improve the electricity market clearing mechanism, this paper establishes a day-ahead market clearing model. This model combines wind power reserve power purchase costs and thermal power units purchase costs by using t-location-scale distribution to describe wind power errors. The intelligent algorithms are used to solve the nonlinear and high-dimensional mixed integer programming model. Aiming at the problem of the traditional gray wolf algorithm’s convergence speed decline and the local optimal solution, the improved gray wolf optimization algorithm is introduced to optimize the units output. And the improved convergence weight factor and position update coefficient are proposed. Considering the power grid security constraints, the IEEE-30 bus system is analyzed by Matlab and the clearing result is compared with other intelligent algorithms. The results of the calculation example show that the improved gray wolf optimization algorithm can obtain a better power purchase costs when solving the clearing optimization problem, which is more meet the actual demand of the power system.