Long-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetration

作     者：Alahmad, Ahmad K. Verayiah, Renuga Shareef, Hussain 

作者机构：Univ Tenaga Nasl UNITEN Inst Power Engn Putrajaya CampusJalan IKRAM UNITEN Kajang 43000 Selangor Malaysia United Arab Emirates Univ Coll Engn Dept Elect Engn POB 15551 Al Ain U Arab Emirates 

出 版 物：《JOURNAL OF ENERGY STORAGE》 (J. Energy Storage)

年 卷 期：2024年第90卷

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：UNITEN through Bold Grant [J510050002/2022003] Dato'Low Tuck Kwong International Energy Transition Grant [ETG202205001] BOLDREFRESH2025-Centre of Excellence [J510050002-IC- 6] 

主　　题：Long term planning Renewable energy sources Battery energy storage systems Uncertainties Hybrid optimization algorithm Charging and discharging control strategy 

摘      要：In this paper, we formulate a stochastic long-term optimization planning problem that addresses the cooperative optimal location and sizing of renewable energy sources (RESs), specifically wind and photovoltaic (PV) sources and battery energy storage systems (BESSs) for a project life span of 10-years. The aim is to enhance the integrated capacity of green energy in the electric power distribution system (DS) while adhering to topological, technical, and economic constraints and considering the annual load growth. Moreover, to account for uncertainties related to various input random variables such as wind speed, solar irradiation, load power, and energy prices, Monte Carlo Simulation (MCS) is employed to generate multiple scenarios. The backward reduction method (BRM) is then applied to streamline the number of generated scenarios, reducing computational efforts. To solve the optimization planning model, a hybrid optimization algorithm is proposed, combining the non-dominating sorting genetic algorithm (NSGAII) and multi-objective particle swarm optimization (MOPSO). This hybrid approach aims to simultaneously minimize three long term objective functions from the economic, environmental, and technical point of view: total expected investment, operational, and carbon emission cost, power loss, and voltage deviation. The effectiveness of the planning model and the performance of the solver method are validated using the 69-bus benchmark test system. The adopted system is configured into three cases, including basic DS, DS with RESs, and DS with a combination of RESs and BESSs. Simulation results demonstrate the capability of the proposed planning model in achieving the following improvements: RESs without ESS achieved 3.35 MVA penetration while reducing DS dependency by 31.44 %. Moreover, the technical objectives improved: power loss by 39.14 % and voltage deviation by 45.45 %. Post-BESS deployment, green energy capacity reached 3.65 MVA, enhancing technical objectiv