Hybrid Stochastic/Information Gap Decision Theory Model for Optimal Energy Management of Grid-Connected Microgrids with Uncertainties in Renewable Energy Generation and Demand

Abstract

Microgrids (MGs) are considered a reliable solution for the integration of a high level of intermittent distributed energy resources. However, renewable energy generation has added complexity to the optimal energy management of MGs (OEMMs) due to its high degree of uncertainty. As a result, the development of efficient models for handling these uncertainties is essential. As a result, a hybrid stochastic/information gap decision theory (IGDT) based model is proposed for the OEMMs. For that purpose, firstly, a two-stage stochastic mixed-integer second-order conic programming model is presented by producing scenarios for the power generated by wind turbine and photovoltaic units. Then, the proposed model has become robust against active and reactive power demand uncertainties by the deployment of IGDT. Both stochastic and hybrid Stochastic/IGDT models are implemented in AMPL and they are solved by using the commercial solver CPLEX. Moreover, the power flow equations are included to guarantee the validity of the proposed models for real-world applications. A modified IEEE 33-bus test system with a high level of renewable energy integration is utilized as a test system. The results show that the hybrid stochastic/IGDT model can efficiently cope with the uncertainties associated with renewable energy generation and electric demand.