Optimal Purchase Decision of Henan Electric Power Company Considering Qinghai Direct Current

doi:10.11985/2022.04.032

Abstract

Abstract:

A coordinated decision-making method for provincial power companies in multistage and multi-time scale markets is proposed. Firstly, the optimization space of annual and monthly power purchase is identified based on the medium and long term power balance. Secondly, on the basis of the existence of power purchase optimization space, based on the two-latitude risk analysis method, the power purchase optimization of provincial power companies considering the risk and cost of power purchase is carried out. Taking the minimum power purchase cost as the goal and adding the risk constraint, the optimization model considering both the minimum power purchase cost and the minimum risk is constructed. Finally, according to the construction of supporting DC power supply in Qinghai province and the current situation and development of Henan power grid, taking Qinghai-Henan ±800 kV UHVDC transmission project as an example, the power purchase strategy of Henan Electric Power Company in 2020-2025 after Qinghai DC is put into operation is optimized and the feasibility and effectiveness of the proposed method are verified.

Key words: Qinghai-Henan UHVDC, electricity purchasing strategy, risk management

CLC Number:

TM561

LI Hujun, ZHAO Wenjie, BAI Hongkun, WANG Tianliang, LI Qian, JIANG Xin. Optimal Purchase Decision of Henan Electric Power Company Considering Qinghai Direct Current[J]. Journal of Electrical Engineering, 2022, 17(4): 300-308.

Figures/Tables 7

References 20

[1]	国家电网有限公司. 国家电网青海—河南±800千伏特高压直流工程启动送电[EB/OL]. [2020-7-17]. http://www.sasac.gov.cn/n2588025/n2588124/c15129733/content.html.
	State Grid. State Grid Qinghai-Henan ±800 kV UHV DC project starts power transmission[EB/OL]. [2020-7-17]. http://www.sasac.gov.cn/n2588025/n2588124/c15129733/content.html.
[2]	马青. 四川水电年度电力市场交易价差分析[J]. 水力发电, 2019, 45(8):103-106.
	MA Qing. Analysis of transaction price difference for hydropower plants in Sichuan annual electricity market[J]. Water Power, 2019, 45(8):103-106.
[3]	李笑蓉, 白格平, 赵国梁, 等. 省间临时电力交易结算方法[J]. 中国电力, 2009, 42(10):69-72.
	LI Xiaorong, BAI Geping, ZHAO Guoliang, et al. Research on the settlement method of province-crossing temporary electricity exchange[J]. Electric Power, 2009, 42(10):69-72.
[4]	刘闯, 李凤婷, 晁勤, 等. 考虑清洁能源消纳的多边发电权交易新型模式[J]. 电力自动化设备, 2021, 41(1):92-98.
	LIU Chuang, LI Fengting, CHAO Qin, et al. Novel transaction mode of multilateral power generation rights considering clean energy consumption[J]. Electric Power Automation Equipment, 2021, 41(1):92-98.
[5]	刘妍, 谭建成. 基于区块链技术的大用户直购电模式实现研究[J]. 电力信息与通信技术, 2018, 16(7):94-100.
	LIU Yan, TAN Jiancheng. Appliccation of block chain technology in large consumers’ direct purchase[J]. Electric Power Information and Communication Technology, 2018, 16(7):94-100.
[6]	姜欣, 刘萌, 王天梁, 等. 电网侧储能电站参与现货联合市场的竞价策略[J]. 电网技术, 2021, 45(9):3398-3408.
	JIANG Xin, LIU Meng, WANG Tianliang, et al. Bidding strategy for grid-side energy storage power stations to participate in the spot joint market[J]. Power System Technology, 2021, 45(9):3398-3408.
[7]	周鑫. 电力市场下电网公司购电风险管理分析[D]. 长沙: 长沙理工大学, 2019.
	ZHOU Xin. Analysis of power purchase risk management of grid corporation under electricity market[D]. Changsha: Changsha University, 2019.
[8]	程雪儿. 电力市场环境下的市场风险研究[J]. 机电信息, 2018(6):97-98.
	CHENG Xueer. Research on market risk in electricity market environment[J]. Mechanical and Electrical Information, 2018(6):97-98.
[9]	虞思敏. 基于CVaR的省级电网公司购电组合优化[J]. 电工电气, 2017(1):26-29.
	YU Simin. Power purchase combination optimization of provincial power grid corp based on conditional value at risk[J]. Electrotechnics Electric, 2017(1):26-29.
[10]	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.
[11]	严磊, 罗晓伊, 佟如意, 等. 基于市场风险和风险偏好的购电商最优决策研究[J]. 四川电力技术, 2018, 41(6):47-51.
	YAN Lei, LUO Xiaoyi, TONG Ruyi, et al. Optimal decision of electricity retailers based on market risk and risk appetite[J]. Sichuan Electric Power Technology, 2018, 41(6):47-51.
[12]	赵阳, 蒋传文, 赵岩, 等. 基于偏度-加权条件风险价值的售电公司动态购电策略[J]. 电网与清洁能源, 2017, 33(1):130-136.
	ZHAO Yang, JIANG Chuanwen, ZHAO Yan, et al. Skewness-WCVaR based dynamic procurement strategy for electricity retailers[J]. Power System and Clean Energy, 2017, 33(1):130-136.
[13]	方日升. 考虑风电的省级电网多类市场购电风险研究[J]. 电网与清洁能源, 2017, 33(4):113-118,124.
	FANG Risheng. Research on multi-market power purchasing risk of provincial grid considering wind power[J]. Power System and Clean Energy, 2017, 33(4):113-118,124.
[14]	陈秀兰, 林芬, 王良缘, 等. 考虑可再生能源配额制的售电公司组合购电优化研究[J]. 福州大学学报, 2019, 47(2):199-205.
	CHEN Xiulan, LIN Fen, WANG Liangyuan, et al. Optimization of portfolio purchase for electricity retailers considering renewable portfolio standard[J]. Journal of Fuzhou University, 2019, 47(2):199-205.
[15]	梁富光. 考虑买方灵活合约的购电配比优化及风险技术分析[J]. 电力学报, 2019, 34(1):93-100.
	LIANG Fuguang. Purchasing portfolio optimization and risk technology analysis considering flexible electricity contracts[J]. Journal of Electric Power, 2019, 34(1):93-100.
[16]	韩彬, 燕京华, 孙振, 等. 福建电力现货市场初期模式探析[J]. 电力系统自动化, 2021, 45(7):170-175.
	HAN Bin, YAN Jinghua, SUN Zhen, et al. Analysis on initial mode of electricity spot market in Fujian of China[J]. Automation of Electric Power Systems, 2021, 45(7):170-175.
[17]	张兰, 张经纬, 肖云鹏, 等. 新加坡电力市场需求响应机制及对中国电力市场建设的启示[J]. 中国电力, 2020, 53(9):98-106.
	ZHANG Lan, ZHANG Jingwei, XIAO Yunpeng, et al. Demand response mechanism in Singapore electricity market and its enlightenment to China’s power market construction[J]. Electric Power, 2020, 53(9):98-106.
[18]	李丹, 喻乐, 施磊, 等. 一种基于量价结构的购售电策略优化分析与辅助决策方法[J]. 电气工程学报, 2020, 15(2):92-97.
	LI Dan, YU Le, SHI Lei, et al. Optimal analysis and aided decision-making method for power purchase and sales strategy based on volume and price structure[J]. Journal of Electrical Engineering, 2020, 15(2):92-97.
[19]	伍志韬, 杜伟, 刘蕾蕾, 等. 恶意攻击下的电力耦合网络风险传播模型研究[J]. 电网技术, 2020, 44(6):2045-2052.
	WU Zhitao, DU Wei, LIU Leilei, et al. Risk propagation model of power coupled networks under malicious attack[J]. Power System Technology, 2020, 44(6):2045-2052.
[20]	王海冰, 王简, 王承民, 等. 计及风险约束的虚拟电厂能量管理建模[J]. 中国电机工程学报, 2017, 37(20):5942-5950.
	WANG Haibing, WANG Jian, WANG Chengmin, et al. Risk-constrained energy management modeling of virtual power plant[J]. Proceedings of the CSEE, 2017, 37(20):5942-5950.

电量平衡		年份
电量平衡		2020年	2021年	2022年	2023年	2024年	2025年
需电量		3 760	3 930	4 100	4 270	4 440	4 610
省内	燃煤	2 343	2 343	2 343	2 347	2 417	2 488
	燃气	106	106	134	134	134	134
	常规水电	76	76	76	76	76	76
	抽水蓄能	19	32	36	36	36	46
	风电	109	164	213	242	269	299
	光伏	66	66	66	66	66	66
	非统调	169	173	177	182	186	190
省外	三峡水电	72	72	72	72	72	72
省外	哈郑直流	440	440	440	440	440	440
电量缺口		361	458	543	675	743	799

月份	2020年	2021年	2022年	2023年	2024年	2025年
1月	32.9	41.8	49.4	61.4	67.6	72.5
2月	26.2	33.1	39.0	48.4	53.2	57.0
3月	29.0	36.8	43.5	54.1	59.6	64.0
4月	26.1	33.0	39.0	48.5	53.4	57.3
5月	27.3	34.6	40.9	50.9	56.1	60.2
6月	29.4	37.6	44.7	55.8	61.6	66.3
7月	36.2	46.2	55.0	68.4	75.5	81.2
8月	34.9	44.5	53.0	66	72.8	78.4
9月	28.2	36.1	42.9	53.4	59.0	63.5
10月	28.0	35.4	41.9	52.1	57.3	61.5
11月	29.1	36.9	43.5	54.0	59.4	63.8
12月	33.4	42.3	49.9	61.9	68.0	73.0

电量情况		年份
电量情况		2020年	2021年	2022年	2023年	2024年	2025年
一、需电量		3 760	3 930	4 100	4 270	4 440	4 610
二、省内优先电量	1. 常规水电	75.5	75.5	75.5	75.5	75.5	75.5
	2. 统调光伏	108.7	164.1	212.9	242.0	269.2	298.9
	3. 统调风电	66.1	66.1	66.1	66.1	66.1	66.1
	4. 非统调	168.8	173.0	177.1	182.0	186.1	190.3
	5. 热电联产机组	99.9	99.9	99.9	99.9	99.9	99.9
	6. 抽蓄机组	18.7	31.5	35.8	35.8	35.8	45.7
	7. 调峰机组	52.3	64.6	75.3	81.8	87.8	94.3
三、直接交易电量		1 947.7	2 035.7	2 123.8	2 211.9	2 299.9	2 388.0
四、外购电量	1. 三峡	72	72	72	72	72	72
	2. 天中直流	440	440	440	440	440	440
	3. 青海直流	187.4	272.4	386.8	419.0	419.0	419.0
五、现货购电空间		522.8	435.1	334.8	344.1	388.7	420.3

时间	2020年			2021年			2022年			2023—2025年
时间	拟送	合约	现货	拟送	合约	现货	拟送	合约	现货	拟送	合约
1月	17.36	17.36	0.00	25.23	25.23	0.00	34.33	34.33	0.00	34.33	34.33
2月	15.68	13.48	2.20	22.79	19.60	3.19	31.01	27.29	3.72	31.01	31.01
3月	17.36	14.93	2.43	25.23	21.70	3.53	34.33	30.21	4.12	34.33	34.33
4月	16.80	14.45	2.35	24.42	21.00	3.42	33.23	29.24	3.99	33.23	33.23
5月	17.36	14.93	2.43	25.23	21.70	3.53	34.33	30.21	4.12	34.33	34.33
6月	16.80	14.45	2.35	24.42	21.00	3.42	33.23	29.24	3.99	33.23	33.23
7月	18.35	18.35	0.00	26.67	26.67	0.00	41.70	41.70	0.00	41.70	41.70
8月	18.35	18.35	0.00	26.67	26.67	0.00	41.70	41.70	0.00	41.70	41.70
9月	16.80	16.80	0.00	24.42	24.42	0.00	33.23	33.23	0.00	33.23	33.23
10月	17.36	14.93	2.43	25.23	21.70	3.53	34.33	30.21	4.12	34.33	34.33
11月	16.80	14.45	2.35	24.42	21.00	3.42	33.23	29.24	3.99	33.23	33.23
12月	17.36	14.93	2.43	25.23	21.70	3.53	34.33	30.21	4.12	34.33	34.33
全年	206	187	19	300	272	28	419	387	32	419	419