Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm

Wong, Ling Ai; Ramachandaramurthy, Vigna K.; Walker, Sara; Taylor, Phil; Sanjari, M. J.

doi:10.1016/j.est.2019.100892

Cited by 113 publications

(61 citation statements)

References 16 publications

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“…8 represents the generic distribution system that was employed in this study. The nominal voltage of the distribution system was 11 kV with the load and branch data adapted from [18]. In total, there were 48 buses in the system with a total active and reactive load of 3.83 MW and 1.35 MVAr, correspondingly.…”

Section: Resultsmentioning

confidence: 99%

“…The studies demonstrated that the BESS should be located close to the photovoltaic distributed generation (PVDG) to best mitigate voltage fluctuation. On the other hand, optimal placement and sizing of the BESS were determined using whale optimization algorithm (WOA) to minimise total system losses [18]. Based on case studies with different numbers of photovoltaic (PV) and BESS in the distribution system, it was concluded that the BESS should be placed close to a heavy load for effective total losses reduction.…”

Section: B Literature Reviewmentioning

confidence: 99%

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Optimal Placement and Sizing of Battery Energy Storage System Considering the Duck Curve Phenomenon

et al. 2020

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This paper suggests a method to place and size the battery energy storage system (BESS) optimally to minimise total system losses in a distribution system. Subsequently, the duck curve phenomenon is taken into consideration while determining the location and sizing. The locations and sizing of BESS were optimised using a metaheuristic algorithm with high exploration and exploitation ability which is known as the Whale Optimisation Algorithm (WOA). Meanwhile, the performance of WOA was validated using other algorithms, i.e., Particle Swarm Optimisation and Firefly Algorithm. The results demonstrated the capability of WOA to determine the optimal BESS location and sizing for all cases, with and without considering the duck curve issue for loss reduction. Besides that, the duck curve issue can be mitigated by appropriately optimising the energy storage system (ESS) to reduce the steep ramp of the duck neck and ducktail and to lift the duck belly. In conclusion, although less loss reduction was achieved as a tradeoff to fulfil the constraint on net load ramp limit, the required BESS sizing was much smaller than the case without those constraints and charging operation, which makes this solution economically viable.

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Section: Resultsmentioning

confidence: 99%

Section: B Literature Reviewmentioning

confidence: 99%

Optimal Placement and Sizing of Battery Energy Storage System Considering the Duck Curve Phenomenon

et al. 2020

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“…The WOA was compared to other meta-heuristic algorithms, such as PSO and BFOA, and was tested on the IEEE 34-and 85-bus system. Wong et al [99] investigated the impacts of optimal integration of BESS and PV-DG units in various scenarios. The WOA was applied to a 25-bus meshed network to minimize total real power loss and was compared to PSO and FA.…”

Section: B Swarm-based Algorithmsmentioning

confidence: 99%

A Review of Metaheuristic Techniques for Optimal Integration of Electrical Units in Distribution Networks

et al. 2021

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“…Research works [12]- [14] have studied one or more of these objectives in different variations but not all of the objectives. • A novel approach for optimal distribution network planning is introduced, where PV-DG and BESS units are integrated simultaneously by injecting real power from the PV modules and the BESS units.…”

Section: Introductionmentioning

confidence: 99%

“…• A novel approach for optimal distribution network planning is introduced, where PV-DG and BESS units are integrated simultaneously by injecting real power from the PV modules and the BESS units. The proposed approach enables a seamless interactive mechanism between DG allocation and BESS allocation, unlike in [12], [13] where PV is either fixed or initially integrated based on physical observation before the integrating the BESS units, the approach assigns bus locations to PV-DG units at every second round of iterations. • In contrast to developing a hybrid metaheuristic algorithm that requires the whole mechanism of each algorithm to solve the optimal integration problem (as in [15]), this paper splits the problem into subproblems and assigns each algorithm according to their strengths.…”

Section: Introductionmentioning

confidence: 99%

Category-Based Multiobjective Approach for Optimal Integration of Distributed Generation and Energy Storage Systems in Distribution Networks

et al. 2021

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Distributed generation (DG) units are power generating plants that are very important to the architecture of present power system networks. The primary benefits of the addition of these units are to increase the power supply and improve the power quality of a power grid while considering the investment cost and carbon emission cost. Most studies have simultaneously optimized these objectives in a direct way where the objectives are directly infused into the multiobjective framework to produce final values. However, this method may have an unintentional bias towards a particular objective; hence this paper implements a multi-stage framework to handle multiple objectives in a categorical manner to simultaneously integrate DG units and Battery Energy Storage System (BESS) in a distribution network. A new hybrid metaheuristic technique is developed and combined with the Technique Order for Preference by Similarity to Ideal Solution (TOPSIS) approach and the crowding distance technique to produce Pareto optimal solutions from the multiple collective objectives, namely technical, economic, and environmental. Compared to the conventional direct way approach in multiobjective handling, the proposed categorical approach reduces bias towards a set of objective(s) and efficiently handles more objectives. Results also show that the Whale Optimization Algorithm and Genetic Algorithm (WOAGA) produces the smallest power loss of 101.6 kW compared to Whale Optimization Algorithm (WOA) and Genetic Algorithm (GA), which produces 105.1 kW and 105.8 kW respectively. The algorithm, although does not have a faster convergence than the WOA, has a better computational time than the WOA and GA. The multiobjective WOAGA also performs better than the Non-dominating Sorted Genetic Algorithm (NSGA-II) and the multiobjective WOA in terms of the quality of Pareto optimal solutions.

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Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm

Cited by 113 publications

References 16 publications

Optimal Placement and Sizing of Battery Energy Storage System Considering the Duck Curve Phenomenon

Optimal Placement and Sizing of Battery Energy Storage System Considering the Duck Curve Phenomenon

A Review of Metaheuristic Techniques for Optimal Integration of Electrical Units in Distribution Networks

Category-Based Multiobjective Approach for Optimal Integration of Distributed Generation and Energy Storage Systems in Distribution Networks

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