灵活性(工程)
电
需求响应
负荷转移
热能
汽车工程
工艺工程
环境经济学
环境科学
材料科学
工程类
机械工程
经济
电气工程
管理
作者
Yongli Wang,Yiwen Li,Yunfei Zhang,Miaomiao Xu,Dexin Li
标识
DOI:10.1016/j.applthermaleng.2024.122640
摘要
At present, there is a problem in the park-level integrated energy system that the output of new energy does not completely match the user's energy demand period, resulting in a high abandonment rate of new energy and low economic benefits. Comprehensive demand response can collaboratively optimize multiple resources on the demand side, alleviate the contradiction between supply and demand, and effectively solve this problem. Taking into account the peak and valley complementary characteristics of electricity and heating loads in time distribution, this paper innovatively proposes an integrated energy system operation method in which electric and heating loads collaboratively participate in demand response, effectively reducing total operating costs and carbon emissions. First, the synergistic mechanism of electric and heating loads is analyzed, and a demand response strategy for electric and heating loads is proposed. Secondly, based on the difference in sensitivity of the electric load to the same electricity price and the flexibility of the thermal load, the electric load and thermal load demand response models are constructed respectively. Then, the electrothermal demand response collaborative model is used as the energy balance constraint to construct an optimized operation model that is economical and environmentally friendly. Finally, the CPLEX solver is called based on the MATLAB platform to obtain the solution set, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to obtain the optimal solution. The simulation analysis results show that compared to considering the demand response of electric load or heating load alone, considering the demand response synergy of electric and heating loads can smooth the load curve, alleviate peak load regulation pressure, and improve system flexibility. At the same time, it can also save costs by 8.27% and 6.8%, reduce carbon emissions by 3.23% and 2.06%, and increase new energy consumption rates by 5.36% and 3.25%.
科研通智能强力驱动
Strongly Powered by AbleSci AI