Sizing of Battery Energy Storage System: A Multi-Objective Optimization Approach in DIgSILENT PowerFactory

Wei Hown Tee, Khaldon Ahmed Qaid, Chin Kim Gan, Joe Siang Keek, Junainah Sardi

Abstract


In the paradigm of the increasing trend to prevent global warming, renewable energy sources applications integrated with battery energy storage system (BESS) are gaining attention for reducing the usage of fossil fuels in electrical power generation. In this regard, a multi-objective optimization script in DIgSILENT Programming Language (DPL) which links with software modelling and scripting simulation is developed in this study. Formulation for multiple objectives for optimization of BESS sizing with particle swarm optimization (MOPSO) and loadflow simulation are applied in the DPL script. The considered objective functions aim to improve the network performance by reducing power loss, voltage deviations and system costs. Pseudo code of BESS optimal sizing with multi-objective algorithm is presented in this research. The BESS with optimal sizing was discovered for improving the network performance in the tested reference network. The optimal BESS size obtained is 2.94 MW with a system cost of MYR 2404.76. The total energy losses can be reduced by approximately 16% from the base case energy losses with the optimal BESS size. The findings of the research reveal that the BESS sizing with MOPSO is applicable in DPL operations alone to solve power system problems.


Keywords


Particle swarm optimization; multi-objective optimization; battery energy storage system; optimal sizing; DIgSILENT PowerFactory

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References


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DOI (PDF): https://doi.org/10.20508/ijrer.v13i4.14232.g8838

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