Optimal Model for CCHP of Power Retailer Considering the Daily Deviation Penalty

doi:10.12204/j.issn.1000-7229.2020.10.011

Abstract

Abstract:

The power retailers face the risk of electricity deviation penalty in the transitional period before the formation of the spot market of electric power in China. In this paper, the CCHP system is provided as an effective means to reduce the deviation power because of its controllable output. At the same time, the rolling optimal model of the CCHP considering the cost of deviation power penalty is proposed under the daily deviation penalty mechanism, which solves the operability problem of reducing the deviation power. The simulation is performed on the basis of the load of a power retailer. The simulation results show that, no matter in the case of positive deviation or negative deviation, by rolling optimization of the operation mode of CCHP, the cost of deviation penalty can be reduced and the overall revenue of the power retailer can be improved.

Key words: deviation penalty, power retailer, CCHP system, rolling optimization

CLC Number:

TM73

YU Li, LI Chengxin, ZHANG Yu, LIU Yicheng. Optimal Model for CCHP of Power Retailer Considering the Daily Deviation Penalty[J]. ELECTRIC POWER CONSTRUCTION, 2020, 41(10): 98-105.

Figures/Tables 12

Fig.1 CCHP system structure of power retailer

Fig.2 Flow chart of rolling optimization strategy

Fig.3 Power load forecasting and actual data of the power retailer

Fig.4 Forecasting curve of cooling and hot water load of the power retailer

Table 1 Parameters of CCHP system

Table 2 Time-of-use electricity price and natural gas price

Table 3 Relevant price parameters of the power retailer

Fig.5 Optimal output scheme of CCHP system under mode 2 in predicted load situation

Table 4 Profit comparison between mode 1 and 2 under different forecasting errors

Fig.6 Output comparison of CCHP system under mode 2 and 3 in actual load situation 1

Table 5 Revenue of mode 1, 2 and 3 in actual load situation 1

Fig.7 Revenue of mode 2 and 3 in actual load situation 2

References 20

[1]	关于进一步深化电力体制改革的若干意见(中发〔2015〕9号)[Z]. 2015.
[2]	王星华, 刘升伟, 陈豪君 , 等. 考虑用户差异性的售电公司需求响应电价模型[J]. 电力建设, 2019,40(9):116-123.
	WANG Xinghua, LIU Shengwei, CHEN Haojun , et al. Demand response pricing model for power sales companies considering users differences[J]. Electric Power Construction, 2019,40(9):116-123.
[3]	许传龙, 张粒子, 陈大宇 , 等. 基于预招标的月度偏差电量平衡机制及其多周期发电调度优化模型[J]. 中国电机工程学报, 2019,39(17):5085-5094.
	XU Chuanlong, ZHANG Lizi, CHEN Dayu , et al. A monthly balancing mechanism based on pre-bidding and its multi-period generation schedule optimization model[J]. Proceedings of the CSEE, 2019,39(17):5085-5094.
[4]	刘敦楠, 汤洪海, 杨沫 , 等. 促进新能源消纳的电力交易偏差结算补偿机制[J]. 电力系统自动化, 2017,41(24):105-111.
	LIU Dunnan, TANG Honghai, YANG Mo , et al. Settlement compensation mechanism of power deviation with promotion of renewable energy consumption[J]. Automation of Electric Power Systems, 2017,41(24):105-111.
[5]	石海波 . PCA-SVM在电力负荷预测中的应用研究[J]. 计算机仿真, 2010,27(10):279-282.
	SHI Haibo . Power load forecasting based on principal component analysis and support vector machine[J]. Computer Simulation, 2010,27(10):279-282.
[6]	姚李孝, 刘学琴 . 基于小波分析的月度负荷组合预测[J]. 电网技术, 2007,31(19):65-68.
	YAO Lixiao, LIU Xueqin . A wavelet analysis based combined model for monthly load forecasting[J]. Power System Technology, 2007,31(19):65-68.
[7]	白登辉 . 计及偏差电量考核机制的人工神经网络售电量预测模型[J]. 电工电能新技术, 2020,39(6):58-66.
	BAI Denghui . Artificial neural network electricity sales forecast model considering deviation electricity assessment mechanism[J]. Advanced Technology of Electrical Engineering and Energy, 2020,39(6):58-66.
[8]	郭曼兰, 陈皓勇, 张聪 , 等. 偏差电量考核机制下售电公司的最优经营策略[J]. 电力系统自动化, 2017,41(20):17-25.
	GUO Manlan, CHEN Haoyong, ZHANG Cong , et al. Optimal marketing strategy of retailers under energy deviation penalty[J]. Automation of Electric Power Systems, 2017,41(20):17-25.
[9]	喻小宝, 谭忠富, 马佳乐 , 等. 计及需求响应的售电公司正偏差电量考核优化模型[J]. 电力系统自动化, 2019,43(7):120-128.
	YU Xiaobao, TAN Zhongfu, MA Jiale , et al. Optimal model for positive deviation penalty of power retailers considering demand response[J]. Automation of Electric Power Systems, 2019,43(7):120-128.
[10]	张涛, 王成, 王凌云 , 等. 偏差电量考核机制下含DG的售电公司多目标优化调度模型[J]. 电工技术学报, 2019,34(15):3265-3274.
	ZHANG Tao, WANG Cheng, WANG Lingyun , et al. Multi-objective optimal dsipatching model of electricity retailers with distributed generator under energy deviation penalty[J]. Transactions of China Electrotechnical Society, 2019,34(15):3265-3274.
[11]	高瑞, 郭红霞, 杨苹 , 等. 基于用户侧可调节资源的售电公司实时市场偏差电量管控策略[J]. 电力建设, 2019,40(6):114-122.
	GAO Rui, GUO Hongxia, YANG Ping , et al. Research on control strategy of spot market deviation of electricity sales company based on user-side adjustable resources[J]. Electric Power Construction, 2019,40(6):114-122.
[12]	杨萌, 艾欣, 唐亮 , 等. 计及风险规避的售电公司平衡市场优化交易策略研究[J]. 电网技术, 2016,40(11):3300-3308.
	YANG Meng, AI Xin, TANG Liang , et al. Optimal trading strategy in balancing market for electricity retailer considering risk aversion[J]. Power System Technology, 2016,40(11):3300-3308.
[13]	杨阳方, 刘继春 . 计及电量互保策略的售电公司两阶段日前决策方法[J]. 电力系统自动化, 2017,41(24):120-128.
	YANG Yangfang, LIU Jichun . Two-stage decision-making method of retailers considering power transfer strategy[J]. Automation of Electric Power Systems, 2017,41(24):120-128.
[14]	贾晨, 杜欣慧 . 中长期交易机制下售电公司购电策略优化[J]. 中国电力, 2019,52(9):140-147.
	JIA Chen, DU Xinhui . Optimization of electricity purchasing strategy for electricity retailers under the medium and long-term trading mechanism[J]. Electric Power, 2019,52(9):140-147.
[15]	胡晨, 杜松怀, 苏娟 , 等. 新电改背景下我国售电公司的购售电途径与经营模式探讨[J]. 电网技术, 2016,40(11):3293-3299.
	HU Chen, DU Songhuai, SU Juan , et al. Preliminary research of trading approach and management modes of Chinese electricity retail companies under new electricity market reform[J]. Power System Technology, 2016,40(11):3293-3299.
[16]	LI F, SUN B, ZHANG C H , et al. Operation optimization for combined cooling, heating, and power system with condensation heat recovery[J]. Applied Energy, 2018(230):305-316.
[17]	马佳乐, 谭忠富, 喻小宝 , 等. 计及实时电量交易的售电公司日偏差电量考核优化模型[J]. 电力建设, 2019,40(2):11-19.
	MA Jiale, TAN Zhongfu, YU Xiaobao , et al. Optimal model for daily deviation penalty of electricity retailer considering real-time electricity trading[J]. Electric Power Construction, 2019,40(2):11-19.
[18]	LI M, MU H L, LI N , et al. Optimal design and operation strategy for integrated evaluation of CCHP (combined cooling heating and power) system[J]. Energy, 2016(99):202-220.
[19]	SRINIVAS M, PATNAIK L M . Adaptive probabilities of crossover and mutation in genetic algorithms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1994,24(4):656-667. doi: 10.1109/21.286385 URL
[20]	LI Z M, XU Y . Optimal coordinated energy dispatch of a multi-energy microgrid in grid-connected and islanded modes[J]. Applied Energy, 2018,210:974-986. doi: 10.1016/j.apenergy.2017.08.197 URL