Water Cycle Algorithm for Probabilistic Planning of Renewable Energy Resource, Considering Different Load Models

Abstract

This work introduces multi-objective water cycle algorithm (MOWCA) to find the accurate location and size of distributed energy resource (DERs) considering different load models for two seasons (winter, and summer). The impact of uncertainties produced from load and renewable energy resource (RES) such as wind turbine (WT) and photovoltaic (PV) on the performance of the radial distribution system (RDS) are covered as this is closer to the real operation condition. The point estimate method (PEM) is applied for modeling the RES uncertainties. An optimization technique is implemented to find the multi-objective optimal allocation of RESs in RDSs considering uncertainty effect. The main objectives of the work are to maximize the technical, economic and environmental benefits by minimizing different objective functions such as the dissipated power, the voltage deviation, DG cost and total emissions. The proposed multi-objective model is solved by using multi-objective water cycle algorithm (MOWCA), considering the Pareto criterion with nonlinear sorting based on fuzzy mechanism. The proposed algorithm is carried out on different IEEE power systems with various cases.