Scheduling optimization of shared energy storage station in industrial park based on reputation factor

Cao, Zhixiang; Zhang, Minghao; Zhai, Chao; Wang, Yi

doi:10.1016/j.enbuild.2023.113596

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…Wu et al studied an individualized electricity and thermal coupled pricing strategy for consumers with different demand profiles in an industrial park and presents a dynamic pricing mechanism for the industrial park with demand response programs [13,14]. Cao et al proposed a reputation factor pricing strategy for a shared energy storage station (SESS) for industrial parks that enables the SESS to allocate energy fairly and efficiently under limited power constraints [15]. Yang et al constructed an industrial park microgrid integrated energy system model to improve the energy efficiency of an industrial park [16].…”

Section: Literature Reviewmentioning

confidence: 99%

“…q m→n k and q n←m k respectively represent the thermal energy transmitted from node m to node n and the thermal energy received from node m by node n in the system at time k. µ m→n and f m→n , respectively, represent the line interaction loss and line loss from node m to node n in the system at time k. The superscript sh indicates the heat storage node. Formula (15) gives the load rate and start-up constraints of the cogeneration unit. Formula ( 16) is the climbing constraint of the cogeneration unit.…”

Section: Chp Node Modelmentioning

confidence: 99%

See 1 more Smart Citation

Edge–Cloud Collaborative Optimization Scheduling of an Industrial Park Integrated Energy System

Liu,

Song,

Xin

et al. 2024

Sustainability

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Due to the large proportion of China’s energy consumption used by industry, in response to the national strategic goal of “carbon peak and carbon neutrality” put forward by the Chinese government, it is urgent to improve energy efficiency in the industrial field. This paper focuses on the optimization of an integrated energy system with supply–demand coordination in an industrial park. This optimization is formulated as a “node-flow” model. Within the model, each node is designed according to the objective function of its own operation and the energy coupling relationship. The flow model is designed based on the energy flow interaction relationship between each node. Based on the “node-flow” model, an edge–cloud information interaction mechanism based on energy transfer balance between nodes is proposed to describe the way the system interacts with information, and a distributed iterative optimization algorithm based on edge–cloud collaboration is designed to realize the optimization decision of each node. The performance of the method proposed in this paper is demonstrated using a practical case study of an industrial park integrated energy system in Xinjiang. The results show that the proposed model can effectively improve the utilization efficiency of multi-energy synergy and complementation in the industrial park, and the proposed algorithm can shorten the solution time by more than 50% without significantly affecting the accuracy of the solution.

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