Open Access

ARTICLE

Research on the Optimal Scheduling Model of Energy Storage Plant Based on Edge Computing and Improved Whale Optimization Algorithm

Zhaoyu Zeng¹, Fuyin Ni^1,2,*

1 School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, China
2 Jiangsu Key Laboratory of Power Transmission & Distribution Equipment Technology, Jiangsu University of Technology, Changzhou, 213001, China

* Corresponding Author: Fuyin Ni. Email:

(This article belongs to the Special Issue: Machine Learning in Energy Optimization for New Energy Solutions)

Energy Engineering 2025, 122(3), 1153-1174. https://doi.org/10.32604/ee.2025.059568

Received 11 October 2024; Accepted 31 January 2025; Issue published 07 March 2025

Abstract

Energy storage power plants are critical in balancing power supply and demand. However, the scheduling of these plants faces significant challenges, including high network transmission costs and inefficient inter-device energy utilization. To tackle these challenges, this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm (IWOA). The proposed model designs an edge computing framework, transferring a large share of data processing and storage tasks to the network edge. This architecture effectively reduces transmission costs by minimizing data travel time. In addition, the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation cost. The IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy, preventing the algorithm from converging to suboptimal solutions. Experimental results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant, facilitating efficient charging and discharging. It successfully achieves peak shaving and valley filling for both electrical and heat loads, promoting the effective utilization of renewable energy sources. The edge-computing framework significantly reduces transmission delays between energy devices. Furthermore, IWOA outperforms traditional algorithms in optimizing the objective function.

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