Design of structured control policy for shared energy storage in residential community: A stochastic optimization approach

Walker, A.; Kwon, Soongeol

doi:10.1016/j.apenergy.2021.117182

Cited by 41 publications

(14 citation statements)

References 22 publications

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“…Tis approach results in a notable reduction in the lifespan of the ESS, as well as a decrease in the system's life cycle income and annual average income. However, when compared to the algorithm presented in this paper, the average annual return from literature [18] is only slightly lower. Tis comparison highlights that the proposed algorithm can efectively address the charging and discharging decision problem of an…”

Section: Endmentioning

confidence: 67%

“…Literature [24] addresses the ESS charging and discharging approach under theoretical conditions, assuming all the factual information. In contrast, literature [18] utilizes "actual data +17% deviation data within a normal distribution" during the training phase, introducing variations between the data and the factual information. In order to verify the robustness of the calculation, the method considers many uncertain factors such as comprehensive energy, electrical equipment charging, and electricity price.…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

“…where rand(24, 1) represents the random integer generated in the interval [1,18]. n 2 is the initial charging time of the electrical device.…”

Section: Load Analysis Of Electrical Equipmentmentioning

confidence: 99%

“…In comparison to literature [24], literature [18] uses more data than the actual data. Specifcally, it adds extra 17% deviation data based on the actual data, with these data following a normal distribution.…”

Section: Endmentioning

confidence: 99%

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Optimizing Energy Storage System Operations and Configuration through a Whale Optimization Algorithm Enhanced with Chaotic Mapping and IoT Data: Enhancing Efficiency and Longevity of Energy Storage Stations

Gao

2023

Wireless Power Transfer

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To enhance the charging and discharging strategy of the energy storage system (ESS) and optimize its economic efficiency, this paper proposes a novel approach based on the enhanced whale algorithm. Recognizing that the standard whale algorithm can sometimes suffer from local optima in high-dimensional multiobjective optimization, this study introduces chaotic mapping and individual information exchange mechanisms to address this challenge. The proposed algorithm explores optimal configurations for different energy device placements and capacities through encircling and bubble searches, evaluating various multiobjective functions for optimization. In addition, the algorithm refines both the system operation model and the ESS configuration model, with the objective function being the analysis of the average annual revenue of the ESS. Model testing results demonstrate that this algorithm yields more moderate energy storage (ES) capacity decay, extending operational time to 3,124 days and achieving a full-life cycle benefit of the ESS as high as 1,821,623.68 yuan. Also, our algorithm demonstrates high efficiency, with minimal test time (68.36 seconds) and quick optimization (0.031 seconds per cycle), regardless of problem complexity.

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Section: Endmentioning

confidence: 67%

Section: Results Analysis and Discussionmentioning

confidence: 99%

“…where rand(24, 1) represents the random integer generated in the interval [1,18]. n 2 is the initial charging time of the electrical device.…”

Section: Load Analysis Of Electrical Equipmentmentioning

confidence: 99%

Section: Endmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing Energy Storage System Operations and Configuration through a Whale Optimization Algorithm Enhanced with Chaotic Mapping and IoT Data: Enhancing Efficiency and Longevity of Energy Storage Stations

Gao

2023

Wireless Power Transfer

View full text Add to dashboard Cite

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“…In [34] , an optimisation model for planning and designing a neighbourhood-scale distributed energy system is proposed. Specifically focusing on energy storage sharing in residential communities, [35] develop an optimisation model that finds the optimal control policy. Focusing on energy storage sharing as well, [36] introduce an energy-credit-based optimisation strategy that aims to increase energy cost savings for consumers in a dynamic pricing environment.…”

Section: State-of-the-artmentioning

confidence: 99%