Optimal Allocation of Battery Energy Storage Systems to Enhance System Performance and Reliability in Unbalanced Distribution Networks

Dong Zhang (), Shafiullah Gm (), Choton Kanti Das and Kok Wai Wong
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Dong Zhang: School of Engineering and Energy, College of Science, Technology, Engineering and Mathematics, Murdoch University, Perth, WA 6150, Australia
Shafiullah Gm: School of Engineering and Energy, College of Science, Technology, Engineering and Mathematics, Murdoch University, Perth, WA 6150, Australia
Choton Kanti Das: School of Engineering, Edith Cowan University, Perth, WA 6027, Australia
Kok Wai Wong: School of Information Technology, College of Science, Technology, Engineering and Mathematics, Murdoch University, Perth, WA 6150, Australia

Energies, 2023, vol. 16, issue 20, 1-35

Abstract: The continuously increasing renewable distributed generation (DG) penetration rate significantly reduces environmental pollution and power generation cost and satisfies society’s rapid growth in electricity demand. Nevertheless, high penetration of renewable DGs, such as wind power and photovoltaics (PV), might deteriorate the system’s efficiency and reliability due to its intermittent and stochastic natures. Introducing battery energy storage systems (BESSs) to the distribution system provides a practical method to compensate for the above deficiency since it can deliver and absorb power when needed. Hence, it is important to determine the optimal allocation of BESS to achieve maximum assistance in the grid. This study proposes an optimal BESS allocation methodology to improve reliability and economics in unbalanced distribution systems. The optimal BESS allocation problem is solved by simultaneously minimizing the cost of energy interruption, expected energy not supplied, power loss, line loading, voltage deviation, and BESS cost. The proposed technique is implemented and analyzed on a high renewable DG penetrated unbalanced IEEE-33 bus network using DIgSILENT PowerFactory software (version 2020 SP2A). An enhanced grey wolf optimization (EGWO) algorithm is developed to optimize BESS location and size according to the selected objective function. The simulation results show that the proposed optimal BESS optimization technique significantly improves the economics and reliability in unbalanced distribution systems and the EGWO outperforms the gray wolf optimization (GWO) and particle swarm optimization (PSO) algorithms.

Keywords: optimal allocation of BESS; reliability enhancement; DIgSILENT PowerFactory; line loading; voltage deviation; power loss minimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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