A data-driven rolling optimization control approach for building energy systems that integrate virtual energy storage systems

Mu, Yunfei; Xu, Yuanxin; Zhang, Jiarui; Wu, Zeqing; Jia, Hongjie; Jin, Xiaolong; Yan, Qingdong

doi:10.1016/j.apenergy.2023.121362

Cited by 11 publications

(3 citation statements)

References 64 publications

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“…The operating characteristics of ES are represented by (23). The operational characteristics of HSHN are given by Equations ( 1)- (19).…”

Section: Structure Of the System And Device Modelingmentioning

confidence: 99%

“…where ε gb and ε eb are the power margin factors for GB and EB, respectively. The heating network constraints and equipment constraints are given in Equations ( 1)- (23). In order to increase the margin for the heat storage regulation of the heating network in intra-day scheduling, a margin of 1 • C is set for both the upper and lower limits of the pipeline temperature.…”

Section: Day-ahead Scheduling Modelmentioning

confidence: 99%

“…The concept of virtual energy storage was explored through the optimization of controllable loads within the system. These loads included centrally controlled air conditioners [19], electric vehicles engaged in orderly charging and discharging [20], customer demand response [21], thermal inertia of buildings [22], and user thermal comfort [23]. This optimization effectively assisted in peak shaving and valley filling within the power system, all without the need for additional energy storage devices.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Time-Scale Optimal Scheduling of Integrated Energy System Considering Transmission Delay and Heat Storage of Heating Network

Wang,

Zeng,

Wan

2023

Sustainability

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In the integrated energy system, significant potential exists for the regulation of the heat storage capacity within the heating network. In relation to this attribute, the establishment of the quasi-dynamic model for the heating network is accomplished through the utilization of the fictitious node method. Additionally, a method is introduced to quantify the heat storage within the heating network. Moreover, a multi-time-scale scheduling approach is proposed for the integrated energy system, with consideration given to the heat storage of the heating network. During the day-ahead scheduling phase, the active regulation of the heat storage within the heating network is carried out to enhance the economy of system operation. Transitioning to the intra-day upper scheduling phase, the heat storage capacity of the heating network is utilized to eliminate the transmission delay effect, thereby achieving the coordinated scheduling of both electricity and heat. Shifting to the intra-day lower scheduling phase, the heat storage capacity of the heating network is utilized to enhance the operational flexibility of the power system. Simulation experiments demonstrate that the coordinated scheduling of electricity and heat in the integrated energy system can be effectively achieved through the utilization of the fictitious node method. Furthermore, the proposed multi-time-scale scheduling method, making full use of the heat storage characteristics of the heating network, can effectively suppress fluctuations in the new energy output and load demand while taking the economy into account. In this paper, it results in a 5.9% improvement in system operating economics and possesses the capacity to mitigate wind power fluctuations with an error rate of approximately 20%. This capability significantly enhances the integration of wind power as a sustainable energy source.

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“…The operating characteristics of ES are represented by (23). The operational characteristics of HSHN are given by Equations ( 1)- (19).…”

Section: Structure Of the System And Device Modelingmentioning

confidence: 99%

Section: Day-ahead Scheduling Modelmentioning

confidence: 99%