Optimal Allocation and Energy Management of Units in Distribution Networks with Multiple Renewable Energy Sources and Battery Storage Based on Computational Intelligence

• Published in: Energies (Basel) Vol. 16; no. 22; p. 7567
• Main Authors: Barukčić, Marinko, Kurtović, Goran, Benšić, Tin, Jerković Štil, Vedrana
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-11-2023
• Subjects: Alternative energy sources; Batteries; battery energy storage system; computational intelligence; cosimulation; Electricity distribution; Energy industry; energy losses reduction; Energy management; Energy management systems; Energy prices; Energy resources; Energy storage; Energy trading; Genetic algorithms; Green technology; Methods; Neural networks; Objectives; Optimization algorithms; Optimization techniques; Pollutants; power distribution network; Renewable resources; Simulation methods
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16227567

The paper deals with an optimization problem in an electricity distribution network with different types of distributed generation and a battery storage system in terms of a smart grid concept. The optimization problem considers two objectives, namely, the annual energy losses and the exchange of energy with the higher-level power grid. The decision variables of the problem are the allocation of the different distributed generation units and the battery storage system, the annual power profiles of the controllable distributed generation and the battery storage system, and the power factor profiles of the controllable and noncontrollable distributed generation. All decision variables are solved simultaneously in a single optimization problem. The variable load shapes of the grid consumers and the profiles of the photovoltaic and wind power systems are considered in the study. All data are observed at the annual level with hourly resolution. The problem solving method uses computational intelligence techniques, namely, metaheuristic optimization methods and artificial neural networks. The study proposes a framework for optimizing the decision variables in the planning phase of distributed generation and battery storage, and for controlling the variable power and power factor profiles based on an artificial neural network in the implementation phase. The optimization problem is solved with a power system simulation program and a metaheuristic optimizer in cosimulation synergy. The three cases of distributed generation and battery storage are considered simultaneously. The proposed method is applied to the test grid operator IEEE with 37 buses, and reductions in annual energy losses and energy exchange are obtained in the ranges 34–86% and 41–99%, respectively.