Load Frequency Control in Microgrids Based on a Stochastic Noninteger Controller

Abstract

In this paper, an adaptive multiobjective fractional-order fuzzy proportional–integral–derivative controller is proposed for the load frequency control (LFC) of islanded microgrids (MGs), while benefiting from the assets of electric vehicles (EVs) in this respect. Although the use of battery energy storage systems (BESS) can solve the unbalance effects between the load and supply of an isolated MG, their high cost and tendency toward degradation are restrictive factors, which call for the use of alternative power balancing options. In recent years, the concept of utilizing the BESSs of EVs, also known as vehicle-to-grid (V2G) concept, for frequency support of MGs has attracted a lot of attention. In order to allow the V2G controller operate optimally under a wide range of operation conditions caused by the intermittent behavior of renewable energy resources, a new multiobjective fractional-order control strategy for the EVs in V2G scenarios is proposed in this paper. Moreover, since the performance of the controller depends on its parameters, optimization of these parameters can play a significant role in promoting the output performance of the LFC control; hence, a modified black hole optimization algorithm is utilized for the adaptive tuning of the noninteger fuzzy PID controller coefficients. The performance of the proposed LFC is evaluated by using real world wind and solar radiation data. Finally, the extensive studies and hardware-in-the-loop simulations are presented to prove that the proposed controller tracks frequency with lower deviation and fluctuation and is more robust in comparison with the prior-art controllers used in all the case studies.