Optimal Scheduling of Wind-Solar-Gas-Storage Microgrids Based on Genetic Algorithm

Abstract

With the global energy prices and demand generally rising, traditional microgrids overly rely on empiricism and manual intervention, only using linear programming to make choices within a limited number of preset schemes. When sudden extreme weather occurs, causing a sharp drop in photovoltaic output, the system can only rely on manual intervention. However, the response of electronic equipment is at the millisecond level, and the time difference may result in huge economic losses. This paper takes the microgrid that includes wind power, photovoltaic power, gas turbines and energy storage devices as the research object, aiming to minimize the total operating cost of the system through genetic algorithms. This algorithm encodes the operation strategies of photovoltaic, wind turbines, gas turbines and energy storage as genes. Through hundreds of generations of selection, crossover and mutation, the optimal scheduling scheme is ultimately evolved. The simulation results for the Xinjiang microgrid show that the proposed model and algorithm can effectively coordinate the output of each distributed power source, significantly reduce the operating cost of the microgrid, and thereby improve the economy of the system.