Incorporation of probabilistic hybrid renewable energy sources into an optimal power flow model using meta–heuristic algorithms

Renewable energy sources (RES) are playing an increasingly important role in the dynamic modern electrical system. Optimal power flow (OPF) is non-convex and significantly non-linear. This complex problem is made worse by the addition of RES, which are usually irregular. In this article, thermal power generators (TPGs), solar photovoltaic generators (SPGs), and wind power generators (WPGs) are considered in solving the OPF problem. To predict the unpredictable power from SPGs and WPGs, respectively, probability distribution functions (PDFs) such as Weibull and Lognormal are utilised. The underestimation and overestimation of RES production power are considered as reserve and penalty costs, respectively, in the objective function. The proposed Invasive Weed Optimisation (IWO) is implemented for minimising the generation cost with and without valve point loading, active power loss and Emission as the objective functions for the modified IEEE 30 bus system. The results obtained with the IWO algorithm are compared with Particle Swarm Optimization (PSO) technique and other existing methods in previous research. The generation cost obtained with RES is reduced to 777.104$/h from 801.768$/h, i.e. 24.66$/h of cost reduction. The proposed algorithm reduces the cost and enhances the sustainable operation of the grid. Furthermore, statistical analysis is performed to assess the robustness of IWO.