en.Wedoany.com Reported - To address issues such as lagging regulation response in centralized heating systems and uneven indoor temperatures, researchers have proposed a centralized heating peak-shaving system coupled with distributed green electricity. By introducing a "cost-comfort" dual-factor model to characterize users' bounded rational behavior, a bi-level pricing and scheduling optimization framework for enterprises and users was constructed, achieving synergistic improvements in green electricity consumption and heating revenue. Simulation results based on a residential community in Xi'an show that the daily average green electricity consumption power reaches 10 to 18 megawatts, heating enterprise operating costs are reduced by 36.5%, and the user temperature compliance rate significantly increases from 2.9% to 90.3%.
Centralized heating is a infrastructure for winter livelihood security in northern Chinese cities and towns. However, due to factors such as long pipeline transmission distances, hydraulic and thermal regulation exhibits significant time lag. Imbalances in terminal hydraulics and heat frequently cause indoor overheating or overcooling, resulting in low temperature compliance rates and severe energy waste. To enhance regulation capabilities, centralized heating peak-shaving systems have gradually become one of the main forms of heating in the north. This system uses centralized heating to supply base heat load and deploys fast-response heat sources on the demand side to accommodate load fluctuations.
Traditional peak-shaving systems often add heat sources at the community or secondary network side and implement unified control. While they play a role in alleviating terminal heating deviations, their regulation strategies are based on group average demand and cannot meet individual user thermal comfort preferences. Building on this, distributed peak-shaving systems achieve personalized regulation through user-side heat sources and demand response, offering advantages such as high flexibility and low heat loss. Against the backdrop of a high proportion of renewable energy integration into the grid, utilizing green electricity in distributed peak-shaving systems can not only promote deep interaction between buildings and the grid and enhance green electricity consumption, but also encourage residents to deeply participate in grid demand response, reducing grid fluctuations. Relevant national-level documents in China have also explicitly proposed promoting the integration of green electricity with heating systems, prioritizing support for green electricity consumption in non-electric fields such as heating.
However, in existing studies, user-side response behavior is often idealized as a fully rational and fully executable process, neglecting cognitive biases in actual decision-making. To address this, the study, based on modeling the thermal response of users with bounded rationality, constructs a bi-level optimization scheduling framework for heating-green electricity coupled peak-shaving, and solves and validates it under the goal of maximizing revenue. Using 171 user questionnaires from Xi'an, the study quantifies heat pricing and comfort preferences, and divides users into four typical categories through K-means clustering, providing a basis for differentiated heat pricing mechanisms. The bi-level optimization model treats enterprises as leaders and users as followers. The upper-level heating and power supply enterprises independently optimize the unit heating price and time-of-use electricity price under policy and market constraints, while the lower-level users optimize satisfaction by weighing heat price against comfort, thereby achieving dynamic supply-demand feedback and closed-loop regulation.
Simulation results indicate that after optimization with the proposed model, non-renewable energy usage decreases from 39 MW to approximately 27 MW, enterprise revenue improves significantly, with power supply and heating enterprise revenues increasing by 135% and 101% respectively, and the proportion of green electricity revenue reaching 78%. The user temperature compliance rate rises from 2.9% to 90.3%. The system can guide different types of users to participate in regulation during periods of high green electricity output, balancing comfort and economic affordability, and overall fairness is improved.
The research team stated that future work will further explore robustness under extreme weather conditions, cross-day user behavior modeling, and promotion scenarios under complex market mechanisms.
