A dynamic decision-making method for energy transaction price of CCHP microgrids considering multiple uncertainties

被引:0
|
作者
Zhao, Huiru [1 ]
Wang, Xuejie [1 ]
Wang, Yuwei [2 ]
Li, Bingkang [1 ]
Lu, Hao [1 ]
机构
[1] North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
[2] North China Elect Power Univ, Dept Econ Management, Baoding 071003, Hebei, Peoples R China
基金
中国国家自然科学基金;
关键词
Combined cooling heating and power; microgrid; Load service entity; Robust optimization; Dynamic transaction price decision; DISTRIBUTION-SYSTEM; OPERATION STRATEGY; POWER; MANAGEMENT; MODEL; OPTIMIZATION; ALGORITHM; DESIGN;
D O I
10.1016/j.ijepes.2020.106592
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Combined cooling, heating and power microgrid (CCHP microgrid) system, which has the characteristics of clean production and multi-energy comprehensive supply, has attracted great attention. Through the integration of renewable energy, etc., the electricity retailer can form a load service entity (LSE) to provide services. Transactions can be made between CCHP microgrid and LSE. However, how to determine the transaction price between them is still a problem. In addition, both the CCHP microgrid and LSE contain renewable energy with uncertain output, which affects the normal operation of the system. In this paper, we propose a dynamic decision-making method for the energy transaction price considering multiple uncertainties. According to the random output of renewable energy, the uncertainty set is constructed. Furthermore, a robust optimal dispatching model is established. According to the dispatching results, the optimal transaction price is searched by using the salp swarm algorithm (SSA). It can be seen from the simulation results: 1) This price decision method can effectively reduce the operating cost of the system; 2) The LSE acts as an energy exchange center in the entire system; 3) Using robust optimization for energy dispatching can maximize the consumption of renewable energy.
引用
收藏
页数:21
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