Interruptible load scheduling model based on an improved chicken swarm optimization algorithm

• Published in: CSEE Journal of Power and Energy Systems Vol. 7; no. 2; pp. 232 - 240
• Main Authors: Jinsong Wang, Fan Zhang, Huanan Liu, Jianyong Ding, Ciwei Gao
• Format: Journal Article
• Language: English
• Published: Beijing Chinese Society for Electrical Engineering Journal of Power and Energy Systems 01.03.2021; China electric power research institute
• Subjects: Algorithms; Constraint modelling; Electrical loads; Energy storage; Load; Load distribution; Operating costs; Optimization; Optimization algorithms; Peak demand; Peak load; Power consumption; Scheduling; Storage facilities
• ISSN: 2096-0042; 2096-0042
• DOI: 10.17775/CSEEJPES.2020.01150

With the continuous growth of the tertiary industry and residential loads, balancing the power supply and consumption during peak demand time has become a critical issue. Some studies try to alleviate peak load by increasing power generation on the supply side. Due to the short duration of peak load, this may cause redundant installation capacity. Alternatively, others attempt to shave peak demand by installing energy storage facilities. However, the aforementioned research did not consider interruptible load regulation when optimizing system operations. In fact, regulating interruptible load has great potential for reducing system peak load. In this paper, an interruptible load scheduling model considering the user subsidy rate is first proposed to reduce system peak load and operational costs. This model has fully addressed the constraints of minimum daily load reduction and user interruption load time. After that, by taking a community in Shanghai as an example, the improved chicken swarm optimization algorithm is applied to solve the interruptible load scheduling scheme. Finally, the simulation results validate the efficacy of the proposed optimization algorithm and indicate the significant advantages of the proposed model in alleviating the peak load and reducing operational costs.