Medium and Long-Term Transaction Model for Power Selling Companies Considering Dynamic Adjustment of Performance Guarantee Quota

doi:10.12204/j.issn.1000-7229.2024.02.014

Abstract

Abstract:

The current subjectively fixed performance guarantee quota model lacks objectivity in the cost calculation of performance guarantees for power-selling companies, which affects transaction decisions. Therefore, this study proposes a medium- and long-term transaction optimization decision-making model for power-selling companies based on a dynamic performance guarantee quota. First, a quota adjustment coefficient was proposed, considering the credit overdraft level, market loss level, and national policy inclination of the power-selling company. Subsequently, a dynamic performance guarantee quota model was constructed. Second, to maximize the profits of power-selling companies, a medium- and long-term trading optimization decision-making model was constructed, considering the impact of changes in the cost of performance guarantee on the trading decisions. This model incorporates the dynamic adjustment of the performance guarantee quota. Finally, by analyzing the dynamic and fixed performance guarantee quota, performance guarantee costs, and transaction profits of power-selling companies participating in medium- and long-term transactions under multiple scenarios, the suitability and effectiveness of the proposed optimization decision model for the medium- and long-term transactions of power-selling companies based on the dynamic performance guarantee quota were verified.

Key words: power selling company, dynamic performance guarantee quota, quota adjustment coefficient, medium- and long-term transaction optimization decision-making

CLC Number:

TM73

WU Jingbin, YANG Hongming, SHENG Yi, XIANG Sheng, MENG Ke. Medium and Long-Term Transaction Model for Power Selling Companies Considering Dynamic Adjustment of Performance Guarantee Quota[J]. ELECTRIC POWER CONSTRUCTION, 2024, 45(2): 160-170.

Figures/Tables 11

Table 1 Difference between fixed and dynamically adjusted performance guarantee quota

Fig.1 Dynamic adjustment quota-based performance guarantee monthly amount payment process for power selling company

Fig.2 BFO-PSO hybrid algorithm solving process for transaction results of power selling companies

Table 2 Basic data settings of quota adjustment coefficient evaluation factors

Table 3 Weight of each evaluation factor

Table 4 Value range of various evaluation factors for quota adjustment coefficient

Fig.3 Relationship between the quota adjustment coefficient and the actual payment and theoretical performance guarantee quota of power selling company before and after dynamic design

Table 5 Parameter settings for performance guarantee quota of power selling companies

Fig.4 Optimization decision result of monthly transaction price of power selling company

Fig.5 Optimization decision result of monthly transaction quantity of power selling company

Table 6 Monthly performance guarantee quota, cost and transaction profit of power selling company before and after adjustment

References 43

[1]	赵晨曦, 过涵. 国际工程中履约保证理论释义及应用辨析[J]. 工程经济, 2021, 31(11): 60-65.
	ZHAO Chenxi, GUO Han. Theory interpretation and application analysis of performance security in international engineering[J]. Engineering Economy, 2021, 31(11): 60-65.
[2]	SHEN Y, BAO Z F, QIN X L, et al. Adaptive task scheduling strategy in cloud: when energy consumption meets performance guarantee[J]. World Wide Web, 2017, 20(2): 155-173. doi: 10.1007/s11280-016-0382-4 URL
[3]	CHANG L H, WU J Y. An improved RIP-based performance guarantee for sparse signal recovery via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2014, 60(9): 5702-5715. doi: 10.1109/TIT.18 URL
[4]	TAO X Y, OTA K, DONG M X, et al. Performance guaranteed computation offloading for mobile-edge cloud computing[J]. IEEE Wireless Communications Letters, 2017, 6(6): 774-777. doi: 10.1109/LWC.2017.2740927 URL
[5]	PAN X J, LI S D. Dynamic optimal control of process-product innovation with learning by doing[J]. European Journal of Operational Research, 2016, 248(1): 136-145. doi: 10.1016/j.ejor.2015.07.007 URL
[6]	崔东君, 和敬涵, 程晓春, 等. 考虑可再生能源配额分配的电力零售市场设计[J]. 四川电力技术, 2021, 44(1): 70-76.
	CUI Dongjun, HE Jinghan, CHENG Xiaochun, et al. Design of electricity retail market considering quota allocation of renewable energy[J]. Sichuan Electric Power Technology, 2021, 44(1): 70-76.
[7]	林新, 徐宏, 朱策, 等. 电力市场合规管理建设探究[J]. 电网技术, 2022, 46(1): 28-38.
	LIN Xin, XU Hong, ZHU Ce, et al. Construction of compliance management in electricity market[J]. Power System Technology, 2022, 46(1): 28-38.
[8]	曹昉, 罗进奔, 丁国华, 等. 面向电力市场环境的售电公司退出机制设计及退出险模型[J]. 电网技术, 2022, 46(12): 4729-4741.
	CAO Fang, LUO Jinben, DING Guohua, et al. Exit mechanism design and exit insurance model of electricity retailers facing electricity market[J]. Power System Technology, 2022, 46(12): 4729-4741.
[9]	江昕玥, 侯佳萱, 吴华华, 等. 澳大利亚电力监管机制及其对中国的启示[J]. 电力系统自动化, 2021, 45(14): 1-12.
	JIANG Xinyue, HOU Jiaxuan, WU Huahua, et al. Electricity regulatory mechanisms in Australia and its enlightenment to China[J]. Automation of Electric Power Systems, 2021, 45(14): 1-12.
[10]	刘连光, 潘明明, 田世明, 等. 考虑源网荷多元主体的售电竞争非合作博弈方法[J]. 中国电机工程学报, 2017, 37(6): 1618-1626.
	LIU Lianguang, PAN Mingming, TIAN Shiming, et al. A non-cooperative game analysis of an competitive electricity retail considering multiple subjects of source-grid-load[J]. Proceedings of the CSEE, 2017, 37(6): 1618-1626.
[11]	ZHANG Z, JIANG Y C, LIN Z Z, et al. Optimal alliance strategies among retailers under energy deviation settlement mechanism in China’s forward electricity market[J]. IEEE Transactions on Power Systems, 2020, 35(3): 2059-2071. doi: 10.1109/TPWRS.59 URL
[12]	CUI Q, WANG X L, WANG X F, et al. Residential appliances direct load control in real-time using cooperative game[J]. IEEE Transactions on Power Systems, 2015, 31(1): 226-233. doi: 10.1109/TPWRS.2015.2391774 URL
[13]	SIRIN S M, GONUL M S. Behavioral aspects of regulation: a discussion on switching and demand response in Turkish electricity market[J]. Energy Policy, 2016, 97: 591-602. doi: 10.1016/j.enpol.2016.08.005 URL
[14]	唐学军, 韩佶, 苗世洪, 等. 基于需求侧响应的多类型负荷协调控制模型[J]. 电力系统保护与控制, 2017, 45(16): 116-123.
	TANG Xuejun, HAN Ji, MIAO Shihong, et al. Coordinated control model of multi-type load based on demand response[J]. Power System Protection and Control, 2017, 45(16): 116-123.
[15]	程志友, 汪德胜. 基于机器学习与疫情关联特征的短期负荷预测[J]. 电力系统保护与控制, 2022, 50(23): 1-8.
	CHENG Zhiyou, WANG Desheng. Short-term load forecasting based on machine learning and epidemic association features[J]. Power System Protection and Control, 2022, 50(23): 1-8.
[16]	黄冬梅, 陈欢, 王宁, 等. 基于自适应图注意力网络的短期用户负荷预测[J]. 电力系统保护与控制, 2023, 51(20): 140-149.
	HUANG Dongmei, CHEN Huan, WANG Ning, et al. Short-term user load prediction based on an adaptive graph attention network[J]. Power System Protection and Control, 2023, 51(20): 140-149.
[17]	郭曼兰, 陈皓勇, 肖文平, 等. 计及浮动高峰电价的购售双方优化模型[J]. 南方电网技术, 2021, 15(5): 79-88.
	GUO Manlan, CHEN Haoyong, XIAO Wenping, et al. Optimization model for both purchase and sale considering variable peak price[J]. Southern Power System Technology, 2021, 15(5): 79-88.
[18]	FANG X, HU Q R, LI F X, et al. Coupon-based demand response considering wind power uncertainty: a strategic bidding model for load serving entities[J]. IEEE Transactions on Power Systems, 2016, 31(2): 1025-1037. doi: 10.1109/TPWRS.2015.2431271 URL
[19]	YANG J J, ZHAO J H, WEN F S, et al. A model of customizing electricity retail prices based on load profile clustering analysis[J]. IEEE Transactions on Smart Grid, 2019, 10(3): 3374-3386. doi: 10.1109/TSG.5165411 URL
[20]	CHAI B, CHEN J M, YANG Z Y, et al. Demand response management with multiple utility companies: a two-level game approach[J]. IEEE Transactions on Smart Grid, 2014, 5(2): 722-731. doi: 10.1109/TSG.2013.2295024 URL
[21]	GOOD N. Using behavioural economic theory in modelling of demand response[J]. Applied Energy, 2019, 239: 107-116. doi: 10.1016/j.apenergy.2019.01.158
[22]	WANG J, ZHOU M C, GUO X W, et al. Multiperiod asset allocation considering dynamic loss aversion behavior of investors[J]. IEEE Transactions on Computational Social Systems, 2019, 6(1): 73-81. doi: 10.1109/TCSS.6570650 URL
[23]	张智, 卢峰, 林振智, 等. 考虑用户有限理性的售电公司峰谷组合电力套餐设计[J]. 电力系统自动化, 2021, 45(16): 114-123.
	ZHANG Zhi, LU Feng, LIN Zhenzhi, et al. Peak-valley combination electricity package design for electricity retailer considering bounded rationality of consumers[J]. Automation of Electric Power Systems, 2021, 45(16): 114-123.
[24]	赵璇, 蒋传文, 王旭, 等. 计及售电公司风险偏好和用户需求响应的售电公司购售电决策方法[J]. 电气自动化, 2019, 41(3): 85-87, 102.
	ZHAO Xuan, JIANG Chuanwen, WANG Xu, et al. Decision-making method for power purchasing and selling of the power purchasing company considering its risk preference and user’s demand response[J]. Electrical Automation, 2019, 41(3): 85-87, 102.
[25]	LIU J Q, GAO W, LI D, et al. An incentive mechanism combined with anchoring effect and loss aversion to stimulate data offloading in IoT[J]. IEEE Internet of Things Journal, 2019, 6(3): 4491-4511. doi: 10.1109/JIoT.6488907 URL
[26]	戴尚文, 张利, 刘宁宁, 等. 考虑可再生能源消纳责任的售电公司购电决策分析[J]. 中国电力, 2021, 54(9): 156-164.
	DAI Shangwen, ZHANG Li, LIU Ningning, et al. Energy purchasing strategy of electricity retailer considering the responsibility of renewable energy consumption[J]. Electric Power, 2021, 54(9): 156-164.
[27]	WEI W, LIU F, MEI S W. Energy pricing and dispatch for smart grid retailers under demand response and market price uncertainty[J]. IEEE Transactions on Smart Grid, 2015, 6(3): 1364-1374. doi: 10.1109/TSG.2014.2376522 URL
[28]	YANG H M, ZHANG J, QIU J, et al. A practical pricing approach to smart grid demand response based on load classification[J]. IEEE Transactions on Smart Grid, 2018, 9(1): 179-190. doi: 10.1109/TSG.2016.2547883 URL
[29]	贾晨, 杜欣慧, 姚宏民. 计及不确定因素的售电公司动态购电决策多目标优化研究[J]. 电力系统保护与控制, 2019, 47(21): 134-143.
	JIA Chen, DU Xinhui, YAO Hongmin. Multi-objective optimization research on dynamic procurement strategy for electricity retailers considering uncertainty factors[J]. Power System Protection and Control, 2019, 47(21): 134-143.
[30]	DOWNWARD A, YOUNG D, ZAKERI G. Electricity retail contracting under risk-aversion[J]. European Journal of Operational Research, 2016, 251(3): 846-859. doi: 10.1016/j.ejor.2015.11.040 URL
[31]	杨洪明, 吴静斌. 基于可动态调整风险偏好因子的发/售电一体型集团公司中长期交易合作博弈决策[J]. 电力建设, 2023, 44(5): 16-27.
	YANG Hongming, WU Jingbin. Medium and long-term trading cooperation game decision of power generating & trading integrated company based on dynamically adjusted risk aversion factor[J]. Electric Power Construction, 2023, 44(5): 16-27.
[32]	吴玲, 刘浩, 刘秋华, 等. 可再生能源配额制下售电公司多市场交易决策[J]. 电力建设, 2023, 44(7): 1-10. doi: 10.12204/j.issn.1000-7229.2023.07.001
	WU Ling, LIU Hao, LIU Qiuhua, et al. Research on the multi-market decisions of electricity retailers under renewable portfolio standards[J]. Electric Power Construction, 2023, 44(7): 1-10. doi: 10.12204/j.issn.1000-7229.2023.07.001
[33]	汪元芹, 刘敏. 考虑现货电价及分布式电源不确定性的售电商购售电决策模型[J]. 电力科学与工程, 2021, 37(11): 62-71.
	WANG Yuanqin, LIU Min. Electricity purchase and sale decision modelling for electricity retailers considering the uncertainty of spot electricity price and distributed power generation[J]. Electric Power Science and Engineering, 2021, 37(11): 62-71.
[34]	王进猛, 吴小强. 税率调整对外资企业跨国关联交易的影响研究: 基于2008年企业所得税改革的准自然实验分析[J]. 国际税收, 2023(1): 63-74.
	WANG Jinmeng, WU Xiaoqiang. A research on the impacts of tax rate adjustment on cross-border related-party transactions of foreign-funded enterprises: a quasi-natural experimental study based on the 2008 enterprise income tax reform[J]. International Taxation in China, 2023(1): 63-74.
[35]	裘华东, 段光, 牛东晓. 基于ICWME的电网售电公司运营效益评价[J]. 技术经济与管理研究, 2019(4): 17-22.
	QIU Huadong, DUAN Guang, NIU Dongxiao. Evaluation for operating efficiency of electricity-retailing companies of power gri based on ICWME[J]. Journal of Technical Economics & Management, 2019(4): 17-22.
[36]	赵宾华, 黄金华, 李占斌, 等. 基于AHP-熵值法组合赋权的甘肃省城市生态用水安全综合评价及影响因素分析[J]. 水土保持通报, 2023, 43(1): 167-173, 213.
	ZHAO Binhua, HUANG Jinhua, LI Zhanbin, et al. Comprehensive evaluation and influencing factor analysis of urban ecological water security in Gansu province based on AHP-entropy method[J]. Bulletin of Soil and Water Conservation, 2023, 43(1): 167-173, 213.
[37]	代革联, 高幸, 姬中奎, 等. 组合赋权法在含水层富水性评价中的应用[J]. 煤炭技术, 2022, 41(11): 82-86.
	DAI Gelian, GAO Xing, JI Zhongkui, et al. Application of combined weighting method in evaluation of water richness of aquifer[J]. Coal Technology, 2022, 41(11): 82-86.
[38]	REIG-MULLOR J, PLA-SANTAMARIA D, GARCIA-BERNABEU A. Extended fuzzy analytic hierarchy process (E-FAHP): a general approach[J]. Mathematics, 2020, 8(11): 2014. doi: 10.3390/math8112014 URL
[39]	崔杨, 修志坚, 刘闯, 等. 计及需求响应与火-储深度调峰定价策略的电力系统双层优化调度[J]. 中国电机工程学报, 2021, 41(13): 4403-4415.
	CUI Yang, XIU Zhijian, LIU Chuang, et al. Dual level optimal dispatch of power system considering demand response and pricing strategy on deep peak regulation[J]. Proceedings of the CSEE, 2021, 41(13): 4403-4415.
[40]	覃源, 吕杲, 关科, 等. 基于熵权法的混凝土耐久性评价及寿命预测[J]. 硅酸盐学报, 2023, 51(5): 1344-1354.
	QIN Yuan, LV Gao, GUAN Ke, et al. Durability assessment and life prediction of concrete by entropy weight method[J]. Journal of the Chinese Ceramic Society, 2023, 51(5): 1344-1354.
[41]	梁冠军, 尤超, 张勇, 等. 基于FAHP的既有房屋改建施工安全风险评估模型及应用[J]. 西昌学院学报(自然科学版), 2023, 37(2): 28-33.
	LIANG Guanjun, YOU Chao, ZHANG Yong, et al. Risk assessment model and its application on existing building reconstruction based on FAHP[J]. Journal of Xichang University (Natural Science Edition), 2023, 37(2): 28-33.
[42]	曾博, 白婧萌, 郭万祝, 等. 智能配电网需求响应效益综合评价[J]. 电网技术, 2017, 41(5): 1603-1612.
	ZENG Bo, BAI Jingmeng, GUO Wanzhu, et al. Comprehensive evaluation for benefits of demand response in smart distribution networks[J]. Power System Technology, 2017, 41(5): 1603-1612.
[43]	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.