What is it about?

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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Why is it important?

1) The CAC apparent temperature model determines the CAC operating comfort range based on the temperature felt by the human body, rather than the traditional air temperature, which improves the accuracy of CAC PSC evaluation. 2) The CAC response operation and participation constraint model comprehensively considered the factors such as mall passenger flow, air conditioning life, outdoor temperature, and incentives. The response characteristics of CAC obtained from commercial users rather than residential users improved the accuracy of PSC evaluation. 3) The PSC economic allocation model of the CAC aggregator fully considers the response characteristics of different CACs to allocate the peak shaving tasks of the power grid. Therefore, the load aggregator can meet the PSD of RDPS with the minimum economic cost, and further improve the economy of peak shaving. 4) The multi-time scale PSC evaluation model gives the PSCs of different time scales of CACL in detail, meeting the demand of RDPS for peak shaving. For example, the online evaluation model gives CACL’s fast PSC and sustainable PSC under the emergency peak shaving situation. The intraday evaluation model gives the residual PSC and continuous PSC after the CACL responds to the power grid peak shaving. The day-ahead evaluation model provides the CACL PSC under different weather, temperature, and other scenarios.


For the RDPS, considering apparent temperature and business income factors, this paper constructs CACL online, intraday and day-ahead multi-time-scale PSC evaluation models. This model can effectively relate and coordinate processes on each time scale and quantify the trends of available PSCs related to the CACL distribution and use processes. The following main conclusions can be drawn: 1) The economic distribution model of a PSC considers the constraints of CAC operation and commercial income, reasonably distributes the PSC of the CAC, and maximises the income of a load aggregator to meet the PSD of the power grid. 2) The online scheduling time scale model can quantify the CAC’s rapid peak shaving and continuous PSC under different incentives for an emergency peak shaving situation. This information can improve the ability of the RDPS to manage short-term plan defaults, excessive power fluctuations of renewable energy sources or other emergency peak-shaving needs in the current period. 3) On the pre-scheduling time scale, the model can quantify the residual PSC and continuous PSC of a CACL after responding to peak shaving commands. Such information can improve the ability of the RDPS to manage continuous correction plan deviations and continuous power fluctuations. 4) On the day-ahead dispatching time scale, the model can evaluate the PSC of a CACL considering the peak shaving factors and determine the PSC under four typical scenarios. Such information can improve the quality of RDPS day-ahead planning and help to alleviate the burden of large power grid regulation.

rui Zhang
Harbin Institute of Technology

Read the Original

This page is a summary of: Capacity evaluation of central air conditioning load participating in peak shaving of renewable dominated power systems, IET Generation Transmission & Distribution, November 2022, the Institution of Engineering and Technology (the IET), DOI: 10.1049/gtd2.12672.
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