Energy Reports (Nov 2022)

Multi-energy coordinated and flexible operation optimization and revenue reallocation models for integrated micro energy system considering seasonal and daily load characteristics of different buildings

  • Hongyu Lin,
  • Qingyou Yan,
  • Xueting Li,
  • Jialu Dang,
  • Shenbo Yang,
  • De Gejirifu,
  • Lujin Yao,
  • Yao Wang

Journal volume & issue
Vol. 8
pp. 12583 – 12597

Abstract

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For pursuing economic and environmental-friendly goals, this paper constructs a regional integrated micro energy system where electricity, heat, cooling, and gas energies are interacted and integrated among buildings. First, independent and collaborative operations of micro energy systems are proposed. Second, seasonal and daily power, heat, and cooling loads of a residential building, a business building, and a mall are depicted. Third, in order to minimize the operation cost, energy consumption and CO2 emission, a multi-energy coordinated flexible operation optimization model of integrated micro energy system is established, and the chaotic particle swarm optimization algorithm is applied to solve the optimization model. A benefit evaluation model is built based on perspectives of energy, economy, and environment to evaluate the optimization results. Then, Shapley method is improved by integrating it with cloud focus theory to more fairly allocate optimized benefits, and influencing indexes for adjusting the weights of participants are proposed. Finally, a case study is conducted. The results indicate that: (1) the integrated micro energy system enabled surplus energy inter-supply among the subsystems, thus realizing interconnection and energy complementarity between micro energy systems; (2) seasonal load characteristics showed the differences among the building subsystems, where the winter load showed more flexibility in energy conversion and inter-supply among the subsystems than the summer load; (3) the improved Shapley method are more effective and fairer for benefit allocation, based on different level of importance of participants; (4) Chaotic particle swarm optimization algorithm are more superior in terms of calculation efficiency and accuracy for optimization solutions.

Keywords