A novel enhanced performance-based differential search algorithm for the optimization of multiple renewable energy sources-based hybrid power system

Integrating renewable energy sources (RESs) with traditional thermal power systems has become an essential economic and environmental imperative. The optimal power flow (OPF) problem ensures optimal power system performance while meeting various constraints. The static OPF problem focuses on the minimization of the objective functions for a single hour. However, the multi-period OPF (MOPF) problem involves optimizing the power system for the different time intervals for the 24 hour time horizon. Optimizing a RESs-integrated power system involves several uncertain and complex operating states that can be handled by a robust evolutionary optimization algorithm (EOA). This article focuses on the development of an enhanced performance-based differential search algorithm for obtaining a high-quality, accurate, and stable solution for the hybrid static OPF as well as MOPF model, including conventional thermal generators as well as multiple intermittent RESs: wind energy, photovoltaic energy, tidal energy, small hydro system, plug-in electric vehicle, and battery energy storage system. A probabilistic approach based on an interpretable mathematical technique is used to model the uncertainties of the RESs. Three sophisticated alteration techniques are dynamic population reduction schemes for efficient exploration of the search space in the earlier iteration steps while exploiting the available solution sets in the latter iteration steps in an effective manner, quasi-opposition-based learning for an effective search space exploration, a best artificial-organism-guided stopoversite discovering technique, to accelerate the local searchability by improving the exploitation capability. Simulation experiments are performed on modified IEEE 30 and 118-bus systems to evaluate the proposed method's performance, and the results are compared to the eight sophisticated EOAs. The result analysis demonstrates that our proposed EOA outperforms the considered EOAs in terms of solution accuracy, quality, and convergence ability for addressing the hybrid static OPF and MOPF problems.