Optimal Allocation and Sizing of Battery Energy Storage System in Distribution Network Using Mountain Gazelle Optimization Algorithm
This paper addresses the problem of finding the optimal position and sizing of battery energy storage (BES) devices using a two-stage optimization technique. The primary stage uses mixed integer linear programming (MILP) to find the optimal positions along with their sizes. In the secondary stage, a...
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2025-01-01
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| author | Umme Mumtahina Sanath Alahakoon Peter Wolfs |
| author_facet | Umme Mumtahina Sanath Alahakoon Peter Wolfs |
| author_sort | Umme Mumtahina |
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| description | This paper addresses the problem of finding the optimal position and sizing of battery energy storage (BES) devices using a two-stage optimization technique. The primary stage uses mixed integer linear programming (MILP) to find the optimal positions along with their sizes. In the secondary stage, a relatively new algorithm called mountain gazelle optimizer (MGO) is implemented to find the technical feasibility of the solution, such as voltage regulation, energy loss reduction, etc., provided by the primary stage. The main objective of the proposed bi-level optimization technique is to improve the voltage profile and minimize the power loss. During the daily operation of the distribution grid, the charging and discharging behaviour is controlled by minimizing the voltage at each bus. The energy storage dispatch curve along with the locations and sizes are given as inputs to MGO to improve the voltage profile and reduce the line loss. Simulations are carried out in the MATLAB programming environment using an Australian radial distribution feeder, with results showing a reduction in system losses by 8.473%, which outperforms Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Cuckoo Search Algorithm (CSA) by 1.059%, 1.144%, and 1.056%, respectively. During the peak solar generation period, MGO manages to contain the voltages within the upper boundary, effectively reducing reverse power flow and enhancing voltage regulation. The voltage profile is also improved, with MGO achieving a 0.348% improvement in voltage during peak load periods, compared to improvements of 0.221%, 0.105%, and 0.253% by GWO, WOA, and CSA, respectively. Furthermore, MGO’s optimization achieves a reduction in the fitness value to 47.260 after 47 iterations, demonstrating faster and more consistent convergence compared to GWO (47.302 after 60 iterations), WOA (47.322 after 20 iterations), and CSA (47.352 after 79 iterations). This comparative analysis highlights the effectiveness of the proposed two-stage optimization approach in enhancing voltage stability, reducing power loss, and ensuring better performance over existing methods. |
| format | Article |
| id | doaj-art-6623fe8e2c524880a97c60901b0e50cc |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Energies |
| spelling | doaj-art-6623fe8e2c524880a97c60901b0e50cc2025-01-24T13:31:16ZengMDPI AGEnergies1996-10732025-01-0118237910.3390/en18020379Optimal Allocation and Sizing of Battery Energy Storage System in Distribution Network Using Mountain Gazelle Optimization AlgorithmUmme Mumtahina0Sanath Alahakoon1Peter Wolfs2School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4701, AustraliaSchool of Engineering and Technology, Central Queensland University, Gladstone, QLD 4680, AustraliaSchool of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4701, AustraliaThis paper addresses the problem of finding the optimal position and sizing of battery energy storage (BES) devices using a two-stage optimization technique. The primary stage uses mixed integer linear programming (MILP) to find the optimal positions along with their sizes. In the secondary stage, a relatively new algorithm called mountain gazelle optimizer (MGO) is implemented to find the technical feasibility of the solution, such as voltage regulation, energy loss reduction, etc., provided by the primary stage. The main objective of the proposed bi-level optimization technique is to improve the voltage profile and minimize the power loss. During the daily operation of the distribution grid, the charging and discharging behaviour is controlled by minimizing the voltage at each bus. The energy storage dispatch curve along with the locations and sizes are given as inputs to MGO to improve the voltage profile and reduce the line loss. Simulations are carried out in the MATLAB programming environment using an Australian radial distribution feeder, with results showing a reduction in system losses by 8.473%, which outperforms Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Cuckoo Search Algorithm (CSA) by 1.059%, 1.144%, and 1.056%, respectively. During the peak solar generation period, MGO manages to contain the voltages within the upper boundary, effectively reducing reverse power flow and enhancing voltage regulation. The voltage profile is also improved, with MGO achieving a 0.348% improvement in voltage during peak load periods, compared to improvements of 0.221%, 0.105%, and 0.253% by GWO, WOA, and CSA, respectively. Furthermore, MGO’s optimization achieves a reduction in the fitness value to 47.260 after 47 iterations, demonstrating faster and more consistent convergence compared to GWO (47.302 after 60 iterations), WOA (47.322 after 20 iterations), and CSA (47.352 after 79 iterations). This comparative analysis highlights the effectiveness of the proposed two-stage optimization approach in enhancing voltage stability, reducing power loss, and ensuring better performance over existing methods.https://www.mdpi.com/1996-1073/18/2/379battery energy storage systemline loss reductionmixed integer linear programmingmountain gazelle optimization algorithmradial distribution networkvoltage profile improvement |
| spellingShingle | Umme Mumtahina Sanath Alahakoon Peter Wolfs Optimal Allocation and Sizing of Battery Energy Storage System in Distribution Network Using Mountain Gazelle Optimization Algorithm Energies battery energy storage system line loss reduction mixed integer linear programming mountain gazelle optimization algorithm radial distribution network voltage profile improvement |
| title | Optimal Allocation and Sizing of Battery Energy Storage System in Distribution Network Using Mountain Gazelle Optimization Algorithm |
| title_full | Optimal Allocation and Sizing of Battery Energy Storage System in Distribution Network Using Mountain Gazelle Optimization Algorithm |
| title_fullStr | Optimal Allocation and Sizing of Battery Energy Storage System in Distribution Network Using Mountain Gazelle Optimization Algorithm |
| title_full_unstemmed | Optimal Allocation and Sizing of Battery Energy Storage System in Distribution Network Using Mountain Gazelle Optimization Algorithm |
| title_short | Optimal Allocation and Sizing of Battery Energy Storage System in Distribution Network Using Mountain Gazelle Optimization Algorithm |
| title_sort | optimal allocation and sizing of battery energy storage system in distribution network using mountain gazelle optimization algorithm |
| topic | battery energy storage system line loss reduction mixed integer linear programming mountain gazelle optimization algorithm radial distribution network voltage profile improvement |
| url | https://www.mdpi.com/1996-1073/18/2/379 |
| work_keys_str_mv | AT ummemumtahina optimalallocationandsizingofbatteryenergystoragesystemindistributionnetworkusingmountaingazelleoptimizationalgorithm AT sanathalahakoon optimalallocationandsizingofbatteryenergystoragesystemindistributionnetworkusingmountaingazelleoptimizationalgorithm AT peterwolfs optimalallocationandsizingofbatteryenergystoragesystemindistributionnetworkusingmountaingazelleoptimizationalgorithm |