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Optimal Scheduling of Distributed Energy System for Home Energy Management System Based on Dynamic Coyote Search Algorithm
Renewable and distributed power generation have been acknowledged as options for the safe, secure, sustainable, and cost-effective production, delivery, and consumption of energy in future low-carbon cities. This research introduces the Dynamic Coyote Search Algorithm (DCSA)-based optimal scheduling of distributed energy systems for home energy management systems. According to the heat storage properties of the building, a smart building energy model is established and introduced into the optimal scheduling of the distributed energy system in order to optimize the adjustment of the room temperature within the user’s acceptable room temperature range. The DCSA algorithm used is to minimize the daily comprehensive operating cost, including environmental factors. According to the simulation results, the impact of smart energy storage on scheduling is analyzed, and the results show that the optimal scheduling of building smart energy storage participating in the system reduces the total cost by about 3.8%. In addition, the DCSA has a significantly faster convergence speed than the original coyote algorithm.
Optimal Scheduling of Distributed Energy System for Home Energy Management System Based on Dynamic Coyote Search Algorithm
Renewable and distributed power generation have been acknowledged as options for the safe, secure, sustainable, and cost-effective production, delivery, and consumption of energy in future low-carbon cities. This research introduces the Dynamic Coyote Search Algorithm (DCSA)-based optimal scheduling of distributed energy systems for home energy management systems. According to the heat storage properties of the building, a smart building energy model is established and introduced into the optimal scheduling of the distributed energy system in order to optimize the adjustment of the room temperature within the user’s acceptable room temperature range. The DCSA algorithm used is to minimize the daily comprehensive operating cost, including environmental factors. According to the simulation results, the impact of smart energy storage on scheduling is analyzed, and the results show that the optimal scheduling of building smart energy storage participating in the system reduces the total cost by about 3.8%. In addition, the DCSA has a significantly faster convergence speed than the original coyote algorithm.
Optimal Scheduling of Distributed Energy System for Home Energy Management System Based on Dynamic Coyote Search Algorithm
Chunbo Li (author) / Yuwei Dong (author) / Xuelong Fu (author) / Yalan Zhang (author) / Juan Du (author)
2022
Article (Journal)
Electronic Resource
Unknown
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