A stochastic programming approach for optimal microgrid economic operation under uncertainty using 2m + 1 point estimate method

Abstract

By increasing concern over climate change and the security of energy supplies, wind power and photovoltaic are emerging as important sources of electrical energy throughout the world. The wind speed at a given location is continuously varying. There are changes in the annual mean wind speed from year to year (annual), changes with season (seasonal), with passing weather systems (synoptic), on a daily basis (diurnal), and from second to second (turbulence). Common sense tells us that irradiation varies regionally, with the changing seasons, and hourly with the daily variation of the sun's evaluation. This paper proposes a new method for optimal management of MicroGrids under uncertain environments. In this study, the 2m + 1 point estimate method is used to model the uncertainty in the load demands, the market prices, and the electric power generation of the wind farms and the photovoltaic systems. The Weibull, Beta, and normal distributions are used to handle the uncertain input variables in this study. Moreover, a Self-Adaptive Bee Swarm Optimization algorithm is presented to achieve an optimal-operational planning with regard to the cost minimization. In the proposed framework, four different moving patterns are suggested in order to make an adaptive and robust optimization algorithm for different problems with different fitness landscape. In the devised method, each bee self-adaptively chooses one of the proposed moving patterns according to a probability model to update its position. The proposed probability model is based on the ability of each strategy to generate more optimal solutions in the past generations. The efficiency of the method is validated on a typical microgrid.