• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2022, Vol. 43 ›› Issue (1): 38-48.doi: 10.12204/j.issn.1000-7229.2022.01.005

• Integrated Multiple Energy and Information Technologies in Enabling Planning and Operationof Energy Internet•Hosted by Associate Professor LIU Yang and Dr. HAN Fujia• • Previous Articles     Next Articles

Source-Load Collaborative Optimization Method of Integrated Energy System Based on Service Provider Guidance

LIAO Zongyi(), WAN Wenlue, CHEN Xi, CHEN Lan   

  1. School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China
  • Received:2021-07-22 Online:2022-01-01 Published:2021-12-21
  • Contact: LIAO Zongyi E-mail:1056406531@qq.com
  • Supported by:
    Major Project of Artificial Intelligence Technology Innovation of Chongqing(cstc2017rgzn-zdyf0120);Key Project of Graduate Innovation of Chongqing University of Technology(clgycx20201004)


As the energy market gradually opens up, multiple market factors have been involved in the traditional centralized optimal dispatching approach. In this context, the paper takes the optimal operation of community integrated energy system (CIES) as an application scenario and proposes a source-load collaborative optimization model under the guidance of an integrated energy service provider (IESP). On the demand-side, an integrated demand response (IDR) strategy is established according to the transferable electricity load and gas load as well as the virtual heat storage characteristics of the enclosed structures in the buildings. On the supply-side, an integrated energy service provider is introduced to replace the energy networks and lead the collaborative and flexible trade of multiple energies. Besides, it is able to comprehensively evaluate the community’s energy demand, respond feedback and the situations of interactive power of major grid and tie lines in the community so as to optimize the electricity-gas combined price signals. The bi-level optimization algorithm of particle swarm optimization combined with mixed integer linear programming is used to optimize the price signal of the upper service provider and the demand response and economic dispatching of the lower community. Considering the mutual influence in the process of bilateral interaction between supply-side and demand-side, the interactive strategy of all parties in pursuit of interest goals is solved by cycle iteration. Numerical simulation shows that the model can be used to exploit response potential in the participation satisfaction of the community and energy economy, realize“peak-shaving and valley filling”in the community and the main network, optimize the configuration of energy resources, and increase the overall economy of the system.

Key words: integrated energy service provider (IESP), integrated demand response (IDR), price mechanism, bi-level optimization, peak-shaving and valley filling

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