Optimal operation strategy for building users considering asynchronous information release in multi-type demand response markets to mitigate building-grid interaction risks

Building users, as ideal demand response participants, have been proven to effectively adapt to the time-of-use (TOU) pricing and provide multiple auxiliary services. The asynchronous release of TOU pricing and peak shaving ancillary service information by the grid operator leads to challenges in flexible resource allocation for building users. Research indicates that the lack of a flexibility resource allocation strategy not only significantly reduces the benefits for building users but also increases the risk of flexibility resource shortages in random demand response scenarios. To address this issue, this study explores multi-market demand response strategies for building users. First, a hybrid scenario theoretical optimal dispatch method is proposed to quantify the theoretically optimal economic benefits and load reduction capability when participating in both TOU pricing and peak shaving. Then, considering the unpredictability of peak shaving events, a reserved peak shaving capability optimal dispatch method is developed, which is particularly suitable for real-world market environments. This method introduces a power reserve coefficient to allocate the available capability of stationary energy storage devices across different market scenarios. Lastly, the proposed models are applied to the electricity market in Shenzhen, China. The results indicate that optimizing solely for economic efficiency in a single scenario leads to a theoretical economic loss of 5.6 % and a peak shaving capability loss of nearly 900 kW. The reserved peak shaving capability optimal dispatch method achieves an increase in declared response quantities ranging from 79 kW to 448 kW. The effectiveness of the method is further validated by adjusting the frequency of peak shaving events, achieving a maximum operating cost reduction of 5.66 %. Finds show that the proposed method can enhance the load reduction capability of building users in response to random peak shaving events and improve the economic benefits of hybrid scenarios.