Zhaoyu Zeng¹, Fuyin Ni^1,2,*

Energy Engineering, Vol.122, No.3, pp. 1153-1174, 2025, DOI:10.32604/ee.2025.059568 - 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… More >

Zhikang Wang^#, Chengxuan Wang^#, Wendi Wu, Cheng Sun, Zhengtian Wu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 845-863, 2024, DOI:10.32604/cmes.2023.029442 - 22 September 2023

Abstract As the current global environment is deteriorating, distributed renewable energy is gradually becoming an important member of the energy internet. Blockchain, as a decentralized distributed ledger with decentralization, traceability and tamper-proof features, is an important way to achieve efficient consumption and multi-party supply of new energy. In this article, we establish a blockchain-based mathematical model of multiple microgrids and microgrid aggregators’ revenue, consider the degree of microgrid users’ preference for electricity thus increasing users’ reliance on the blockchain market, and apply the one-master-multiple-slave Stackelberg game theory to solve the energy dispatching strategy when each market More >

Abdulaziz Alorf^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 273-286, 2023, DOI:10.32604/csse.2023.035437 - 20 January 2023

Abstract Nowadays, smart electricity grids are managed through advanced tools and techniques. The advent of Artificial Intelligence (AI) and network technology helps to control the energy demand. These advanced technologies can resolve common issues such as blackouts, optimal energy generation costs, and peak-hours congestion. In this paper, the residential energy demand has been investigated and optimized to enhance the Quality of Service (QoS) to consumers. The energy consumption is distributed throughout the day to fulfill the demand in peak hours. Therefore, an Edge-Cloud computing-based model is proposed to schedule the energy demand with reward-based energy consumption. More >