To address the issues of high energy costs and inadequate system response speed in complex electricity markets, we propose an electricity price optimization model. This model combines an improved Particle Swarm Optimization algorithm, Quantum-behaved Particle Swarm Optimization, and the Shuffle Frog Leaping Algorithm. The work was based on multi-regional peak and valley data, and we selected Lanzhou, Guiyang, Beijing, Guangzhou, Shanghai, and Nanjing as typical representatives for systematic validation and analysis. Our findings were as follows: (1) The model demonstrated excellent convergence and stability during the electricity price optimization process, particularly under flat-rate price conditions. This model effectively avoided local optima traps and enhanced global search capability, achieving the dual goals of rapid convergence and high stability, and showed exceptional optimization efficiency and adaptability; (2) building upon its optimization performance, the model further improved the uniformity and diversity of the solution distribution, ensuring robustness and flexibility in global search ability. Moreover, by dynamically adjusting the price function and multi-level evaluation system, the model significantly optimized price elasticity, time-of-use pricing regulation efficiency, energy consumption paths, and the operational stability of the distribution network. The model exhibited high resilience and fine-grained control capabilities in the complex electricity market; (3) finally, based on the optimized electricity price strategy derived from training, the model reduced electricity costs and price volatility. Moreover, its superior performance in economic benefits and market adaptability was comprehensively validated through high-precision power consumption forecasting. We aimed to optimize energy costs, improve system response speed, and reduce price volatility, thereby achieving more efficient energy utilization and economic benefits.
Citation: Yan Li, Yaheng Su, Qixin Zhao, Bala Wuda, Kaibo Qu, Lei Tang. An electricity price optimization model considering time-of-use and active distribution network efficiency improvements[J]. AIMS Energy, 2025, 13(1): 13-34. doi: 10.3934/energy.2025002
To address the issues of high energy costs and inadequate system response speed in complex electricity markets, we propose an electricity price optimization model. This model combines an improved Particle Swarm Optimization algorithm, Quantum-behaved Particle Swarm Optimization, and the Shuffle Frog Leaping Algorithm. The work was based on multi-regional peak and valley data, and we selected Lanzhou, Guiyang, Beijing, Guangzhou, Shanghai, and Nanjing as typical representatives for systematic validation and analysis. Our findings were as follows: (1) The model demonstrated excellent convergence and stability during the electricity price optimization process, particularly under flat-rate price conditions. This model effectively avoided local optima traps and enhanced global search capability, achieving the dual goals of rapid convergence and high stability, and showed exceptional optimization efficiency and adaptability; (2) building upon its optimization performance, the model further improved the uniformity and diversity of the solution distribution, ensuring robustness and flexibility in global search ability. Moreover, by dynamically adjusting the price function and multi-level evaluation system, the model significantly optimized price elasticity, time-of-use pricing regulation efficiency, energy consumption paths, and the operational stability of the distribution network. The model exhibited high resilience and fine-grained control capabilities in the complex electricity market; (3) finally, based on the optimized electricity price strategy derived from training, the model reduced electricity costs and price volatility. Moreover, its superior performance in economic benefits and market adaptability was comprehensively validated through high-precision power consumption forecasting. We aimed to optimize energy costs, improve system response speed, and reduce price volatility, thereby achieving more efficient energy utilization and economic benefits.
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Yan Li, Yaheng Su, Qixin Zhao, Bala Wuda, Kaibo Qu, Lei Tang. An electricity price optimization model considering time-of-use and active distribution network efficiency improvements[J]. AIMS Energy, 2025, 13(1): 13-34. doi: 10.3934/energy.2025002
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