Multi-objective optimization scheduling for integrated electricity and heating system including hybrid power flow constraints

被引：0
作者：
Si F.-Y. [1 ]
Han Y.-H. [2 ]
Yuan H.-T. [2 ]
Wang J.-K. [1 ]
Zhao Q. [3 ]
机构：
[1] College of Information Science and Engineering, Northeastern University, Shenyang
[2] School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao
[3] School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao
来源：
Kongzhi yu Juece/Control and Decision | 2021年 / 37卷 / 01期
关键词：
Epsilon constraint algorithm; Hybrid power flow; Integrated electricity and heating system; Pareto front; Technical dissatisfaction;
D O I：
10.13195/j.kzyjc.2020.0913
中图分类号：
学科分类号：
摘要：
The problems of multi-energy optimal management and hybrid power flow are investigated to improve the reliability of the energy network. Aiming at multi-energy network constraints and their coupling characteristics, an integrated electricity and heating system is established to integrate multi-energy networks. The fuzzy soft constraint is constructed to quantify the technical dissatisfaction of the networks. A multi-objective optimization scheduling strategy is proposed to minimize the operation cost and technical dissatisfaction of the system with hybrid power flow constraints. An epsilon constraint algorithm is adopted to obtain the Pareto front of the proposed multi-objective optimization problem. Results from the case study indicate that the proposed model and algorithm can effectively improve the quality of energy supply and the accuracy of optimal decisions. It further reflects the benefits of the proposed scheme in the aspects of the economy, quality and complex constraints, as well as ensuring the economical and stable operation of the system. © 2022, Editorial Office of Control and Decision. All right reserved.
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页码：97 / 107
页数：10
相关论文
共 32 条
[1]  
Shui Y, Liu J Y, Gao H J, Et al., A distributionally robust coordinated dispatch model for integrated electricity and heating systems considering uncertainty of wind power, Proceedings of the CSEE, 38, 24, pp. 7235-7247, (2018)
[2]  
Huang D W, Huang Z, Ge W C, Et al., Analysis on electric power flexible regulation capacity for combined heat and power units in heat-led mode, Automation of Electric Power Systems, 42, 24, pp. 27-35, (2018)
[3]  
Yang S, Chen L, Xu F, Et al., Optimal dispatch model of wind power accommodation in integrated electrical-thermal power system based on power flow model, Power System Technology, 42, 2, pp. 417-426, (2018)
[4]  
Zhou H S, Li Z G, Zheng J H, Et al., Robust scheduling of integrated electricity and heating system hedging heating network uncertainties, IEEE Transactions on Smart Grid, 11, 2, pp. 1543-1555, (2020)
[5]  
Mancarella P., MES (multi-energy systems): An overview of concepts and evaluation models, Energy, 65, pp. 1-17, (2014)
[6]  
Yang H M, Xiong T L, Qiu J, Et al., Optimal operation of DES/CCHP based regional multi-energy prosumer with demand response, Applied Energy, 167, pp. 353-365, (2016)
[7]  
Sun P, Teng Y, Leng O Y, Et al., Coordinated optimization of combined heat and power systems considering multiple thermal inertia of heating system, Proceedings of the CSEE, 40, 19, pp. 6059-6071, (2020)
[8]  
Chen Y, Wei W, Liu F, Et al., A multi-lateral trading model for coupled gas-heat-power energy networks, Applied Energy, 200, pp. 180-191, (2017)
[9]  
Zhang G, Zhang F, Zhang L, Et al., Two-stage robust optimization model of day-ahead scheduling considering carbon emissions trading, Proceedings of the CSEE, 38, 18, pp. 5490-5499, (2018)
[10]  
Chu X L, Yang D., Multi-objective programming for building clusters combined cooling, heating and power system under uncertainty, Control and Decision, 35, 1, pp. 195-204, (2020)
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