A voltage regulation strategy with state of charge management using battery energy storage optimized by a self-learning particle swarm optimization. (July 2022)

• Record Type: Journal Article
• Authors: Jamroen, Chaowanan; Sirisukprasert, Siriroj
• Is Part Of: Computers & electrical engineering. Volume 101(2022)
• Publication Date: 2022-07
• Subjects: Battery energy storage (BES) -- Distribution network -- Photovoltaic -- Voltage regulation -- State of charge (SoC); Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108103
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: With the proliferation of photovoltaic penetration, present distribution networks are vulnerable to voltage deviations. Therefore, this study presents a voltage regulation strategy using battery energy storage (BES) with state of charge (SoC) management. The voltage regulation strategy was designed based on an adaptive droop characteristic to alleviate the voltage deviations considering the BES SoC constraints. The SoC management was designed to compensate the BES power for SoC restoration regarding its required restoring power and restriction coefficient characteristics. In addition, a self-learning particle swarm optimization (SLPSO) was utilized to determine optimal BES operation. A simulation study was conducted to verify the proposed strategy's performance compared with that of a system without BES, a droop-based voltage regulation without SoC management, a droop-based voltage regulation with battery SoC holder, and a droop-based voltage regulation with constant SoC management. The simulation results indicate that the proposed strategy achieved superior performance: up to 12.09% compared with the existing strategies. Graphical abstract: Highlights: An adaptive voltage regulation strategy considering state of charge (SoC) constraints and adaptive SoC management is proposed. Achieving optimal voltage regulation with SoC management using a self-learning particle swarm optimization (SLPSO) algorithm. Maintaining regulation performance in terms of voltage deviations. Minimizing SoC deviations from the nominal value to avoid a loss of voltage regulation service.