Day-ahead energy pricing and management method for regional integrated energy systems considering multi-energy demand responses

被引:48
作者
Zhu, Xu [1 ]
Sun, Yuanzhang [1 ]
Yang, Jun [1 ]
Dou, Zhenlan [2 ]
Li, Gaojunjie [1 ]
Xu, Chengying [1 ]
Wen, Yuxin [1 ]
机构
[1] Wuhan Univ, Sch Elect Engn & Automation, Wuhan 430072, Peoples R China
[2] State Grid Shanghai Municipal Elect Power Co, Shanghai 200023, Peoples R China
关键词
Demand response; Data center; Regional integrated energy system; Energy trading; Stackelberg game; ELECTRICITY; MARKET; NETWORK;
D O I
10.1016/j.energy.2022.123914
中图分类号
O414.1 [热力学];
学科分类号
摘要
By coupling multiple energy networks, an integrated energy system can realize the conversion and complementarity of various energy sources. The development of regional integrated energy systems (RIESs) has brought both opportunities and challenges to the energy market. Without a reasonable integrated energy day-ahead trading structure, it is difficult for energy providers to make profits and for consumers to achieve economic energy utilization. At the same time, various user-side energy loads are controllable, and it is necessary to comprehensively consider multi-energy demand responses in energy trading and management. To address the above issues, the trading behaviors of main market players are clarified, and an integrated energy pricing and management strategy is proposed in this paper. Multi energy demand response models for data centers, electric vehicles (EVs) and air conditioning loads (ACLs) are also established. Considering multi-energy demand responses, a day-ahead energy pricing and management model for RIESs is proposed. The model is a bilevel Stackelberg game optimization model, in which the upper level considers profit maximization of energy service provider (ESP) while the lower level deals with the cost minimization of energy consumer (EC). Based on simulation analysis, the feasibility of the proposed model is verified. The trading scheme optimized by this model can benefit both ESP and EC. Compared with a traditional integrated energy trading structure, the profit source of ESP is enlarged. The simulation results also show that the application of multi-energy demand responses can improve the economic and collaborative operation effect of RIESs and reduce carbon emissions. (c) 2022 Elsevier Ltd. All rights reserved.
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页数:24
相关论文
共 39 条
[1]   Distribution Locational Marginal Pricing (DLMP) for Congestion Management and Voltage Support [J].
Bai, Linquan ;
Wang, Jianhui ;
Wang, Chengshan ;
Chen, Chen ;
Li, Fangxing .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2018, 33 (04) :4061-4073
[2]   Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty [J].
Bai, Linquan ;
Li, Fangxing ;
Cui, Hantao ;
Jiang, Tao ;
Sun, Hongbin ;
Zhu, Jinxiang .
APPLIED ENERGY, 2016, 167 :270-279
[3]   Assessing consumer benefits in the Ontario residential retail natural gas market: Why marketer entry did not help [J].
Bloemhof, Barb .
ENERGY POLICY, 2017, 109 :555-564
[4]  
Drysdale B, 2013, ENERG ENVIRON-UK, V23, P1471
[5]   Bargaining-based cooperative energy trading for distribution company and demand response [J].
Fan, Songli ;
Ai, Qian ;
Piao, Longjian .
APPLIED ENERGY, 2018, 226 :469-482
[6]   Planning Method of Regional Integrated Energy System considering Demand Side Uncertainty [J].
Ge, Shaoyun ;
Liu, Xiaoou ;
Ge, Lukun .
INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS, 2019, 20 (01)
[7]   Optimal operation for integrated energy system considering thermal inertia of district heating network and buildings [J].
Gu, Wei ;
Wang, Jun ;
Lu, Shuai ;
Luo, Zhao ;
Wu, Chenyu .
APPLIED ENERGY, 2017, 199 :234-246
[8]   Colocation Data Center Demand Response Using Nash Bargaining Theory [J].
Guo, Yuanxiong ;
Li, Hongning ;
Pan, Miao .
IEEE TRANSACTIONS ON SMART GRID, 2018, 9 (05) :4017-4026
[9]  
Hossein Aghamohammadloo, 2021, ENERGY, V234
[10]  
Hou H, 2021, ENERGY CONV EC, V2, P122