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Journal article
Optimal Allocation of Battery Energy Storage Systems to Enhance System Performance and Reliability in Unbalanced Distribution Networks
Energies (Basel), Vol.16(20), 7127
2023
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.
Details
- Title
- Optimal Allocation of Battery Energy Storage Systems to Enhance System Performance and Reliability in Unbalanced Distribution Networks
- Authors/Creators
- Dong Zhang - Murdoch UniversityG M Shafiullah - Murdoch University, Centre for Water, Energy and WasteChoton Kanti Das - Edith Cowan UniversityKok Wai Wong - Murdoch University
- Publication Details
- Energies (Basel), Vol.16(20), 7127
- Publisher
- MDPI
- Identifiers
- 991005609660307891
- Copyright
- © 2023 by the authors.
- Murdoch Affiliation
- School of Information Technology; Centre for Water, Energy and Waste
- Language
- English
- Resource Type
- Journal article
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