With the development of integrated energy systems (IES), the traditional demand response technologies for single energy that do not take customer satisfaction into account have been unable to meet actual needs. Therefore, it is urgent to study the integrated demand response (IDR) technology for integrated energy, which considers consumers’ willingness to participate in IDR. This paper proposes an energy management optimization method for community IES based on user dominated demand side response (UDDSR). Firstly, the responsive power loads and thermal loads are modeled, and aggregated using UDDSR bidding optimization. Next, the community IES is modeled and an aggregated building thermal model is introduced to measure the temperature requirements of the entire community of users for heating. Then, a day-ahead scheduling model is proposed to realize the energy management optimization. Finally, a penalty mechanism is introduced to punish the participants causing imbalance response against the day-ahead IDR bids, and the conditional value-at-risk (CVaR) theory is introduced to enhance the robustness of the scheduling model under different prediction accuracies. The case study demonstrates that the proposed method can reduce the operating cost of the community under the premise of fully considering users’ willingness, and can complete the IDR request initiated by the power grid operator or the dispatching department.
[1]
Jun Wang,et al.
Medium- and Long-Term Integrated Demand Response of Integrated Energy System Based on System Dynamics
,
2020,
Energies.
[2]
E. Hobman,et al.
Willingness to participate in direct load control: The role of consumer distrust
,
2017
.
[3]
Chongqing Kang,et al.
Review and prospect of integrated demand response in the multi-energy system
,
2017
.
[4]
Shahram Jadid,et al.
Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system
,
2015
.
[5]
Tao Ding,et al.
Integrated demand response for a load serving entity in multi-energy market considering network constraints
,
2019,
Applied Energy.
[6]
Wei Gu,et al.
A smart community energy management scheme considering user dominated demand side response and P2P trading
,
2020
.