Data-driven and physical model-based evaluation method for the achievable demand response potential of residential consumers' air conditioning loads

Song, Zuwei; Shi, Jing; Li, Shujian; Chen, Zexu; Jiao, Fengshun; Yang, Wangwang; Zhang, Zitong

doi:10.1016/j.apenergy.2021.118017

Cited by 26 publications

(5 citation statements)

References 36 publications

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“…However, the results are approximate and the time frame is annual. In addition to resident consumption patterns, study [12] considers users' wishes when assessing the potential for time-varying demand response from air conditioners. In study [13], considering the impact of user consumption on demand response potential, thermal sensitivity was used to quantify the above factors and combined with neural network algorithm to calculate the demand response potential of users.…”

Section: Evaluation Of Individual Resource Potential For Demand Responsementioning

confidence: 99%

Towards a Sustainable Power System: A Three-Stage Demand Response Potential Evaluation Model

Tan,

You,

et al. 2024

Sustainability

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Developing flexible resources is a key strategy for advancing the development of new power systems and addressing the issue of climate change. Demand response is a crucial flexibility resource that is extensively employed due to its sustainability and economy. This work develops a three-stage demand response potential evaluation model based on “theoretical potential–realizable potential–multi-load aggregation potential” in response to the issues of inadequate consideration of numerous complicated agents and time in previous research. Firstly, the traditional method calculates the theoretical maximum demand response potential of a single industry in each period. Based on this, the industry characteristics are taken into account when establishing the demand response potential evaluation model. Lastly, the time variation of the demand response potential is taken into consideration when evaluating the demand response potential of multiple load aggregation. For the analysis, three industries are chosen as examples. The results show that the potential of peak shaving and valley filling obtained by using the model is smaller than that of the traditional method, the reduction range of peak cutting demand response potential calculated by multi-load aggregation is 19–100%, and the reduction range of valley filling demand response potential is 20–89%. The results are closer to reality, which is conducive to improving the accuracy of relevant departments in making relevant decisions and promoting the sustainable development of a new power system.

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Section: Evaluation Of Individual Resource Potential For Demand Responsementioning

confidence: 99%

Towards a Sustainable Power System: A Three-Stage Demand Response Potential Evaluation Model

Tan,

You,

et al. 2024

Sustainability

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“…Currently, research on credit evaluation systems for VPP users mainly focused on user credit evaluation. There have been studies on the evaluation of demand response capability and peak shaving potential [1][2][3]. Ren et al [4] proposed key indicators for measuring the peak-shaving potential of user demand response and quantitatively evaluated the peak-shaving ability of users based on actual measurement data.…”

Section: Related Workmentioning

confidence: 99%

Knowledge graph-based real-time reliable credit management system for virtual power plant users

Yu,

Hu,

Renwei Cao

et al. 2023

Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023)

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This paper proposes a user credit management system based on knowledge graph for virtual power plants (VPP), which allows the VPP operators to set up a VPP user credit evaluation model with many useful functionalities, such as analyzing and evaluating user credit, realizing secure data transmission and multi-dimensional, accuracy, and real-time evaluation. Furthermore, it selects the optimal set of users to participate in the system and manages the user credit blacklist. Our analysis shows that it is both flexible and reliable.

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“…FIGURE 19 Remaining PSC of the CACL after changing peak shaving commands FIGURE 20 Remaining PSC of the CACL after changing the peak shaving command at subsequent times, the indoor temperature of the building and the start and stop operational state of CAC change compared with that before the peak shaving command, and the subsequent residual PSC of CAC changes. For example, at 04:00 PM, the residual PSC changes from 19.52 to 21.59 MW; at 07:00 PM, the residual PSC changes from 6.97 to 11.12 MW.…”

Section: Evaluation Of Continuous Psc Based On a Pre-scheduling Time ...mentioning

confidence: 99%

“…In contrast, in the actual response process of CAC, they were more based on apparent temperature [16,17], leading to the deviation between the evaluation results and the actual response [18]. Literatures [19,20] have studied the willingness to respond to residential CAC on family income, user education level, the average age of users, social psychology and other aspects. However, residential and commercial users have different response characteristics, and such methods are difficult to apply to commercial users.…”

Section: Introductionmentioning

confidence: 99%

Capacity evaluation of central air conditioning load participating in peak shaving of renewable dominated power systems

Zhang

Tang

et al. 2022

IET Generation Trans & Dist

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The development of renewable‐dominated power systems (RDPS) in the context of pursuing deep decarbonisation targets necessitates suitable exploitation of the peak shaving capacity (PSC) of central air conditioning loads (CACL) in multiple time scales. Based on the evaluation framework of CACL participating in the RDPS PSC and the CAC apparent temperature model and its operation mode, the CACL PSC evaluation model related to the resource allocation in multiple time scales is proposed. For the time scale of online dispatching, the capacity evaluation model for rapid CACL response to power system emergency peak shaving command and its continuous response potential is constructed. The method of optimising CACL PSC based on the load aggregator model is developed. For the intraday pre‐scheduling time scale, the evaluation model of CACL's remaining capacity after undertaking the peak shaving task and the ability to respond continuously to the peak shaving command is constructed. For the day‐ahead dispatching time scale, the PSC of CACL under different weather conditions, temperatures, holidays, and other typical scenarios is evaluated. Finally, case studies carried out on 14 central air‐conditioned buildings in Shanghai city comprehensively validate the effectiveness of the proposed CACL PSC evaluation model.

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Data-driven and physical model-based evaluation method for the achievable demand response potential of residential consumers' air conditioning loads

Cited by 26 publications

References 36 publications

Towards a Sustainable Power System: A Three-Stage Demand Response Potential Evaluation Model

Towards a Sustainable Power System: A Three-Stage Demand Response Potential Evaluation Model

Knowledge graph-based real-time reliable credit management system for virtual power plant users

Capacity evaluation of central air conditioning load participating in peak shaving of renewable dominated power systems

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