考虑容量电价的风光火储能源基地多目标优化调度

doi:10.3969/j.issn.2097-0706.2025.07.001

摘要/Abstract

摘要：

建设能源基地是促进我国能源产业转型升级的重大战略布局，为了推动能源基地高质量发展，探索基地最优调度方案尤为重要。针对蒙西某能源基地，引入容量电价和深度调峰补偿电价，采取阶梯式爬坡速率约束和模糊处理方法，构建了一种考虑基地收益的能源基地多目标优化调度模型。根据当地新能源发电特性进行优化调度，仿真结果表明：净负荷方差最小方案调峰压力最小，保障了电网安全；新能源消纳最多方案风光弃能率在2.26%以下，利于新能源发展；总成本最低方案通过减少风光并网量降低了基地运行成本；基地总收益最大方案较其余方案日收益增加0.52%~24.12%，相较于新能源消纳最多方案，风光消纳量损失最少，兼顾了能源基地整体经济性和新能源消纳水平。该优化调度模型能够在考虑基地收益的前提下，根据综合能源基地条件有效实现多目标优化调度，可为类似能源基地的优化调度提供参考。

关键词: 风光火储系统, 综合能源基地, 容量电价, 深度调峰补偿电价, 新能源消纳, 多目标优化调度

Abstract:

The construction of large energy bases is a major strategic layout to promote the transformation and upgrading of China's energy industry. Exploring the optimal scheduling scheme is particularly important for the high-quality development of bases. Capacity tariff and deep peak-load regulation compensation are introduced to a large energy base in western Mongolia， and a multi-objective optimal dispatch model of the base considering its revenue is constructed with step climbing rate constraints and fuzzy processing methods. The optimal schedule schemes are carried out according to the characteristics of local new energy generation. The simulation results show that the scheduling scheme aiming for the smallest net load variance has the smallest regulation pressure， which can ensure the security of the power grid； the scheme aiming for accommodating the most new energy can keep the new energy abandonment rate below 2.26% ， which is conducive to the development of new energy； the scheme aiming for the lowest total cost can reduce the operating cost of the base by decreasing the amount of grid-connected wind and solar power； and the scheme aiming for the highest revenue has a daily revenue 0.52%-24.12% higher than that of the rest of the schemes and has a second-highest new energy accommodating rate among all the schemes. The last scheme well balances the overall economy and new energy consumption level of the energy base. The optimal scheduling model can effectively realize multi-objective optimal scheduling according to the economic conditions of energy bases. The study provides reference for the optimal scheduling for similar energy bases.

Key words: wind-photovoltaic-thermal-storage system, integrated energy base, capacity tariff, deep peak-load regulation compensation, new energy consumption, multi-objective optimal dispatch

中图分类号:

TK01

洪春雪, 肖海平, 谭甲群, 吕如轩, 陈燕鹏, 巨星. 考虑容量电价的风光火储能源基地多目标优化调度[J]. 综合智慧能源, 2025, 47(7): 1-11.

HONG Chunxue, XIAO Haiping, TAN Jiaqun, LYU Ruxuan, CHEN Yanpeng, JU Xing. Multi-objective optimal schedule of a wind-photovoltaic-thermal-storage energy base considering capacity tariffs[J]. Integrated Intelligent Energy, 2025, 47(7): 1-11.

图/表 10

图1

图2

表1

图3

表2

图4

表3

图5

表4

图6

参考文献 27

[1]	国家能源局. 2023年能源工作指导意见[EB/OL].(2023-04-06)[2024-05-20]. https://zfxxgk.nea.gov.cn/2023-04/06/c_1310710616.htm.
[2]	李雄威, 王昕, 顾佳伟, 等. 考虑火电深度调峰的风光火储系统日前优化调度[J]. 中国电力, 2023, 56(1):1-7,48.
	LI Xiongwei, WANG Xin, GU Jiawei, et al. Day-ahead optimal dispatching of wind-solar-thermal power storage system considering deep peak shaving of thermal power[J]. Electric Power, 2023, 56(1):1-7,48.
[3]	黄银恒, 李猛, 庞毅, 等. 区域综合能源系统容量配置和调度策略优化方法研究[J]. 综合智慧能源, 2023, 45(6): 34-41. doi: 10.3969/j.issn.2097-0706.2023.06.005
	HUANG Yinheng, LI Meng, PANG Yi, et al. Research on optimization method for capacity allocation and scheduling strategy of regional integrated energy systems[J]. Integrated Intelligent Energy, 2023, 45(6): 34-41. doi: 10.3969/j.issn.2097-0706.2023.06.005
[4]	黎静华, 朱梦姝, 陆悦江, 等. 综合能源系统优化调度综述[J]. 电网技术, 2021, 45(6):2256-2272.
	LI Jinghua, ZHU Mengshu, LU Yuejiang, et al. Review on optimal scheduling of integrated energy systems[J]. Power System Technology, 2021, 45(6): 2256-2272.
[5]	叶泽, 李湘旗, 姜飞, 等. 考虑最优弃能率的风光火储联合系统分层优化经济调度[J]. 电网技术, 2021, 45(6):2270-2280.
	YE Ze, LI Xiangqi, JIANG Fei, et al. Hierarchical optimization economic dispatching of combined wind-PV-thermal-energy storage system considering the optimal energy abandonment rate[J]. Power System Technology, 2021, 45(6): 2270-2280.
[6]	吴庆泽, 吕丽霞, 刘长良, 等. 多模式风光火储系统多目标优化调度[J/OL]. 华北电力大学学报(自然科学版):1-8(2022-10-09)[2024-05-20]. http://kns.cnki.net/kcms/detail/13.1212.TM.20221008.1337.006.html.
	WU Qingze, LU Lixia, LIU Changliang, et al. Multi-objective optimization scheduling of multi-mode solar energy fire storage system[J/OL]. Journal of North China Electric Power University(Natural Science Edition):1-8 (2022-10-09)[2024-05-20]. http://kns.cnki.net/kcms/detail/13.1212.TM.20221008.1337.006.html.
[7]	檀勤良, 丁毅宏, 李渝, 等. 考虑经济-环境平衡的风光火联合外送调度策略多目标优化[J]. 电力建设, 2020, 41(8):129-136. doi: 10.12204/j.issn.1000-7229.2020.08.015
	TAN Qinliang, DING Yihong, LI Yu, et al. Multi-objective optimization of combined wind-solar-thermal power dispatching strategy considering economic-environmental equilibrium[J]. Electric Power Construction, 2020, 41(8): 129-136. doi: 10.12204/j.issn.1000-7229.2020.08.015
[8]	WANG X B, CHANG J X, MENG X J, et al. Short-term hydro-thermal-wind-photovoltaic complementary operation of interconnected power systems[J]. Applied Energy, 2018, 229: 945-962.
[9]	WANG F J, XIE Y C, XU J P. Reliable-economical equilibrium based short-term scheduling towards hybrid hydro-photovoltaic generation systems: Case study from China[J]. Applied Energy, 2019, 253: 113559.
[10]	FAN G Z, PENG C H, WANG X K, et al. Optimal scheduling of integrated energy system considering renewable energy uncertainties based on distributionally robust adaptive MPC[J]. Renewable Energy, 2024, 226:120457.
[11]	XIAO H, LONG F Y, ZENG L J, et al. Optimal scheduling of regional integrated energy system considering multiple uncertainties and integrated demand response[J]. Electric Power Systems Research, 2023, 217:109169.
[12]	林俐, 田欣雨. 基于火电机组分级深度调峰的电力系统经济调度及效益分析[J]. 电网技术, 2017, 41(7):2255-2263.
	LIN Li, TIAN Xinyu. Analysis of deep peak regulation and its benefit of thermal units in power system with large scale wind power integrated[J]. Power System Technology, 2017, 41(7): 2255-2263.
[13]	赵书强, 吴杨, 李志伟, 等. 考虑风光出力不确定性的电力系统调峰能力及经济性分析[J]. 电网技术, 2022, 46(5):1752-1761.
	ZHAO Shuqiang, WU Yang, LI Zhiwei, et al. Analysis of power system peaking capacity and economy considering uncertainty of wind and solar output[J]. Power System Technology, 2022, 46(5): 1752-1761.
[14]	李军徽, 张嘉辉, 穆钢, 等. 储能辅助火电机组深度调峰的分层优化调度[J]. 电网技术, 2019, 43(11):3961-3970.
	LI Junhui, ZHANG Jiahui, MU Gang, et al. Hierarchical optimization scheduling of deep peak shaving for energy-storage auxiliary thermal power generating units[J]. Power System Technology, 2019, 43(11): 3961-3970.
[15]	国家发展改革委, 国家能源局. 国家发展改革委国家能源局关于建立煤电容量电价机制的通知[EB/OL].(2023-11-08)[2024-05-20]. https://www.gov.cn/zhengce/zhengceku/202311/content_6914744.htm.
[16]	李铁, 李正文, 杨俊友, 等. 计及调峰主动性的风光水火储多能系统互补协调优化调度[J]. 电网技术, 2020, 44(10):3622-3630.
	LI Tie, LI Zhengwen, YANG Junyou, et al. Coordination and optimal scheduling of multi-energy complementary system considering peak regulation initiative[J]. Power System Technology, 2020, 44(10):3622-3630.
[17]	安磊, 王绵斌, 齐霞, 等. “风、光、火、蓄、储”多能源互补优化调度方法研究[J]. 可再生能源, 2018, 36(10):1492-1498.
	AN Lei, WANG Mianbin, QI Xia, et al. Optimal dispatching of multi-power sources containing wind/photovoltaic/thermal/hydro-pumped and battery storage[J]. Renewable Energy Resources, 2018, 36(10): 1492-1498.
[18]	WU X N, LIAO B R, SU Y G, et al. Multi-objective and multi-algorithm operation optimization of integrated energy system considering ground source energy and solar energy[J]. International Journal of Electrical Power and Energy Systems, 2023, 144: 108529.
[19]	蓝静, 朱继忠, 李盛林, 等. 考虑碳惩罚的电化学储能消纳风光与调峰研究[J]. 综合智慧能源, 2022, 44(1): 9-17. doi: 10.3969/j.issn.2097-0706.2022.01.002
	LAN Jing, ZHU Jizhong, LI Shenglin, et al. Research on electrochemical energy storage to assist new energy consumption and peak load regulation considering carbon penalty[J]. Integrated Intelligent Energy, 2022, 44(1):9-17. doi: 10.3969/j.issn.2097-0706.2022.01.002
[20]	张荣权, 李刚强, 卜思齐, 等. 基于自适应学习率萤火虫算法的多能源系统联合优化调度[J]. 综合智慧能源, 2022, 44(7): 49-57. doi: 10.3969/j.issn.2097-0706.2022.07.006
	ZHANG Rongquan, LI Gangqiang, BU Siqi, et al. Economic operation of a multi-energy system based on adaptive learning rate firefly algorithm[J]. Integrated Intelligent Energy, 2022, 44(7): 49-57. doi: 10.3969/j.issn.2097-0706.2022.07.006
[21]	德格吉日夫, 田雪沁, 王新雷, 等. 计及运行成本与排放量的风光火储联合外送调度多目标优化模型研究[J]. 电网与清洁能源, 2022, 38(6):121-128.
	Degejirifu, TIAN Xueqin, WANG Xinlei, et al. Research on multi-objective optimization model of the combined outward transmission dispatching of wind,solar,thermal-power and storage considering operation cost and emission[J]. Power System and Clean Energy, 2022, 38(6):121-128.
[22]	徐伟强. 考虑深度调峰和风光不确定性的风-光-火-储优化调度研究[D]. 兰州: 兰州理工大学, 2023.
	XU Weiqiang. Research on optimal dispatching of wind-photovoltaic-thermal and energy storage considering deep peaking and wind and photovoltaic power uncertainty[D]. Lanzhou: Lanzhou University of Technology, 2023.
[23]	张国斌, 陈玥, 张佳辉, 等. 风-光-水-火-抽蓄联合发电系统日前优化调度研究[J]. 太阳能学报, 2020, 41(8): 79-85.
	ZHANG Guobin, CHEN Yue, ZHANG Jiahui, et al. Research on optimization of day-ahead dispatching of wind power-photovoltaic hydropower-thermal power-pumped storage combined power generation system[J]. Acta Energiae Solaris Sinica, 2020, 41(8):79-85.
[24]	王鑫, 陈祖翠, 卞在平, 等. 基于粒子群优化算法的智慧微电网风光储容量优化配置[J]. 综合智慧能源, 2022, 44(6): 52-58. doi: 10.3969/j.issn.2097-0706.2022.06.006
	WANG Xin, CHEN Zucui, BIAN Zaiping, et al. Optimal allocation of a wind-PV-battery hybrid system in smart microgrid based on particle swarm optimization algorithm[J]. Integrated Intelligent Energy, 2022, 44(6): 52-58. doi: 10.3969/j.issn.2097-0706.2022.06.006
[25]	胡勇. 基于汽轮机蓄能特性的大型火电机组快速变负荷控制研究[D]. 北京: 华北电力大学, 2015.
	HU Yong. Research on rapid load adjustment control of large capacity coal-fired power plant based on the characteristic of turbine stored energy[D]. Beijing: North China Electric Power University, 2015.
[26]	崔杨, 周慧娟, 仲悟之, 等. 考虑源荷两侧不确定性的含风电电力系统低碳调度[J]. 电力自动化设备, 2020, 40(11):85-93.
	CUI Yang, ZHOU Huijuan, ZHONG Wuzhi, et al. Low-carbon scheduling of power system with wind power considering uncertainty of both source and load sides[J]. Electric Power Automation Equipment, 2020, 40(11):85-93.
[27]	MA T, YANG H X, LU L, et al. Pumped storage-based standalone photovoltaic power generation system: Modeling and techno-economic optimization[J]. Applied Energy, 2015, 137: 649-659.

机组	出力上、下限/MW	燃料成本系数
机组	出力上、下限/MW	a_i	b_i	c_i
^#1	400/120	1.21×10^-5	0.288	8.8
^#2	350/105	2.17×10^-5	0.290	7.2
^#3	300/90	3.42×10^-5	0.292	5.2
^#4	150/45	6.63×10^-5	0.306	3.5

方案	系统总收益/万元	火电成本/万元	运行总成本/万元	净负荷平方差/MW²	弃风弃光成本/万元
1（净负荷方差最小）	274.12	434.43	485.38	14 888.57	13.28
2（新能源消纳最多）	283.72	433.65	488.11	20 757.13	3.38
3（总成本最低）	334.00	278.33	325.23	18 944.06	9.70
4（总收益最大）	347.26	276.58	333.81	19 433.49	4.26

方案	系统总收益/万元	火电成本/万元	运行总成本/万元	净负荷平方差/MW²	弃风弃光成本/万元
1（净负荷方差最小）	252.85	429.97	488.45	4 622.05	10.15
2（新能源消纳最多）	331.62	257.64	323.86	12 779.17	2.03
3（总成本最低）	330.00	259.21	303.92	8 102.55	7.10
4（总收益最大）	333.22	257.65	317.48	11 230.48	2.03

方案	系统总收益/万元	火电成本/万元	运行总成本/万元	净负荷平方差/MW²	弃风弃光成本/万元
1（净负荷方差最小）	236.21	433.01	477.43	5 651.75	0.58
2（新能源消纳最多）	235.76	439.77	490.12	18 371.56	0.00
3（总成本最低）	298.51	281.68	309.85	11 796.71	0.04
4（总收益最大）	300.07	281.59	322.15	13 371.92	0.00