Evolutionary Algorithm-Based Adaptive Robust Optimization for AC Security Constrained Unit Commitment Considering Renewable Energy Sources and Shunt FACTS Devices
Aliasghar Baziar, Rui Bo, Misagh Dehghani Ghotbabadi, Mehdi Veisi, Waqas ur Rehman
Abstract
An AC security constrained unit commitment (AC-SCUC) in the presence of the renewable energy sources (RESs) and parallel flexible AC transmission system (FACTS) devices is conventionally modeled as a deterministic optimization problem to minimize the operation cost of conventional generation units (CGUs) subject to AC optimal power flow (AC-OPF) equations, operation constraints of RESs, parallel FACTS devices, and CGUs. To cope with the uncertainties of load and RES generation, robust and stochastic optimization and linearized formulation have been used to achieve a sub-optimal solution. To achieve the optimal solution, this paper proposed an evolutionary algorithm-based adaptive robust optimization (EA-ARO) approach to solve the non-linear and non-convex optimization problem. A hybrid solver of grey wolf optimization (GWO) and teaching learning-based optimization (TLBO) obtained the robust and reliable optimal solution for the proposed AC-SCUC in the worst-case scenario. Finally, the proposed method was simulated on standard IEEE test systems to demonstrate its capabilities, and the results showed the proposed hybrid solver obtained robust optimal solutions with reduced computation time and standard deviation. Moreover, the numerical results proved the proposed strategy’s capabilities of improving the economics of generation units, such as lower operational cost, and technical performance of the transmission networks, such as improved voltage profile and reduced energy losses.