能源转型下基于碳排放与新能源阶梯惩罚的抽水蓄能双层优化研究

doi:10.19799/j.cnki.2095-4239.2024.0043

摘要/Abstract

摘要：

伴随“双碳”目标推进，高比例新能源并网，火电有序退役，抽蓄作为灵活性调节资源加快建设，合理规划是确保电力系统多能源稳定、可持续转型的重中之重。首先，本文针对高比例新能源场景，开展抽水蓄能容量规划及调度优化方法研究，以系统各电源全寿命周期成本、碳排放和弃电惩罚成本最小化为上层优化目标，以系统各电源碳排放量和风光出力波动最小化作为下层优化目标，构建抽水蓄能双层优化模型。然后，本文研究了星鸦算法与遗传算法、灰狼算法各自的优劣性，通过对星鸦算法参数自适应优化，得到具备更佳寻优能力与速度的算法，结合CPLEX对双层优化模型求解。最后，通过引入碳排放与新能源弃电阶梯惩罚机制，对模型上下层优化目标起到促进作用。算例结果表明，该区域电网中抽水蓄能需加快建设约74.21%，火电需合理退役约40.79%，同时构建模型能够有效降低系统综合成本约5.80%，减少弃风弃光约20.43%，降低系统碳排放量约25.96%，平抑风光出力波动约1.18%，验证了模型的有效性与合理规划的重要性，为电力系统中长期规划提供参考。

关键词: 高比例新能源, 抽水蓄能, 阶梯惩罚成本, 双层优化

Abstract:

With the implementation of the "double carbon" goal, increasing integration of new energy sources within grids, planned decommissioning of thermal power plants, and accelerated construction of pumped storage solutions as flexible regulating resources, rational planning has emerged as the most crucial factor ensuring the stability of power systems and guaranteeing the sustainable conversion of multiple energy forms. This study investigates optimization methods for pumped storage capacity planning and dispatching, considering scenarios of new energy utilization. Furthermore, it constructs a two-layer optimization model for pumped storage considering the minimization of the whole-life cycle costs, carbon emissions, and abandonment penalty costs of each power source as its upper-layer optimization objectives and the minimization of the carbon emissions and wind power fluctuations of each power source as its lower-layer optimization objectives. Subsequently, the study compares the star crow, genetic, and gray wolf algorithms, focusing on their advantages and disadvantages. By adaptively optimizing the parameters of the star crow algorithm, the study identifies the algorithm with superior optimization performance and speed and integrates it with CPLEX to solve the two-layer optimization model. Additionally, the upper and lower optimization objectives of the model are established by introducing a step penalty mechanism for carbon emissions and new energy abandonment. Experimental results reveal that regional power grids must accelerate the construction of pumped storage facilities by approximately 74.21% and reasonably decommission thermal power by approximately 40.79%. Remarkably, the developed model effectively reduces the comprehensive system cost by approximately 5.80%, decreases the abandonment rate of wind and solar power by approximately 20.43%, lowers carbon emissions by approximately 25.96%, and smooths out wind and solar power fluctuations by approximately 1.18%. These outcomes validate the effectiveness of the model and highlight the importance of rational planning, offering valuable references for the medium- and long-term planning of power systems.

Key words: high percentage of new energy, pumped storage, step penalty costs, two-tier optimization

中图分类号:

TM 74

侯世豪, 赵波, 张利. 能源转型下基于碳排放与新能源阶梯惩罚的抽水蓄能双层优化研究[J]. 储能科学与技术, 2024, 13(7): 2414-2424.

Shihao HOU, Bo ZHAO, Li ZHANG. Optimization study of a double-layer pumped storage model based on a step penalty mechanism for carbon emissions and new energy abandonment[J]. Energy Storage Science and Technology, 2024, 13(7): 2414-2424.

图/表 11

图1

图2

图3

表1

表2

图4

表3

表4

表5

图5

图6

参考文献 26

1	魏旭, 刘东, 高飞, 等. 双碳目标下考虑源网荷储协同优化运行的新型电力系统发电规划[J]. 电网技术, 2023, 47(9): 3648-3661. DOI: 10.13335/j.1000-3673.pst.2022.1966.
	WEI X, LIU D, GAO F, et al. Generation expansion planning of new power system considering collaborative optimal operation of Source-grid-load-storage under carbon peaking and carbon neutrality[J]. Power System Technology, 2023, 47(9): 3648-3661. DOI: 10.13335/j.1000-3673.pst.2022.1966.
2	韩肖清, 李廷钧, 张东霞, 等. 双碳目标下的新型电力系统规划新问题及关键技术[J]. 高电压技术, 2021, 47(9): 3036-3046. DOI: 10.13336/j.1003-6520.hve.20210809.
	HAN X Q, LI T J, ZHANG D X, et al. New issues and key technologies of new power system planning under double carbon goals[J]. High Voltage Engineering, 2021, 47(9): 3036-3046. DOI: 10.13336/j.1003-6520.hve.20210809.
3	李晖, 刘栋, 姚丹阳. 面向碳达峰碳中和目标的我国电力系统发展研判[J]. 中国电机工程学报, 2021, 41(18): 6245-6259. DOI: 10.13334/j.0258-8013.pcsee.210050.
	LI H, LIU D, YAO D Y. Analysis and reflection on the development of power system towards the goal of carbon emission peak and carbon neutrality[J]. Proceedings of the CSEE, 2021, 41(18): 6245-6259. DOI: 10.13334/j.0258-8013.pcsee.210050.
4	石文辉, 屈姬贤, 罗魁, 等. 高比例新能源并网与运行发展研究[J]. 中国工程科学, 2022, 24(6): 52-63. DOI: 10.15302/J-SSCAE-2022.07.006.
	SHI W H, QU J X, LUO K, et al. Grid-integration and operation of high-proportioned new energy[J]. Strategic Study of CAE, 2022, 24(6): 52-63. DOI: 10.15302/J-SSCAE-2022.07.006.
5	韩冬, 赵增海, 严秉忠, 等. 2021年中国抽水蓄能发展现状与展望[J]. 水力发电, 2022, 48(5): 1-4, 104. DOI: 10.3969/j.issn.0559-9342.2022.05.001.
	HAN D, ZHAO Z H, YAN B Z, et al. Status and prospect of China's pumped storage development in 2021[J]. Water Power, 2022, 48(5): 1-4, 104. DOI: 10.3969/j.issn.0559-9342.2022.05.001.
6	朱健宇, 潘学萍, 王正风, 等. 兼顾碳减排和新能源消纳的火电机组深度调峰与复合储能协调规划[J]. 电力自动化设备, 2024, 44(1): 17-23. DOI: 10.16081/j.epae.202303003.
	ZHU J Y, PAN X P, WANG Z F, et al. Coordinated planning of thermal generator deep peak regulation and composite energy storage considering carbon emission reduction and new energy consumption[J]. Electric Power Automation Equipment, 2024, 44(1): 17-23. DOI: 10.16081/j.epae.202303003.
7	刘忠, 陈星宇, 邹淑云, 等. 计及碳排放的风-光-抽水蓄能系统容量优化配置方法[J]. 电力系统自动化, 2021, 45(22): 9-18. DOI: 10.7500/AEPS20210221003.
	LIU Z, CHEN X Y, ZOU S Y, et al. Optimal capacity configuration method for wind-photovoltaic-pumped-storage system considering carbon emission[J]. Automation of Electric Power Systems, 2021, 45(22): 9-18. DOI: 10.7500/AEPS20210221003.
8	XU X, HU W H, CAO D, et al. Optimized sizing of a standalone PV-wind-hydropower station with pumped-storage installation hybrid energy system[J]. Renewable Energy, 2020, 147: 1418-1431. DOI: 10.1016/j.renene.2019.09.099.
9	王鹏,魏招毅,周保荣,等.考虑火电有序退役的源-网-储协同多阶段规划方法[J].中国电机工程学报,2024,44(04):1386-1397.DOI:10.13334/j.0258-8013.pcsee.222550.
	WANG P, WEI Z Y, ZHOU B R, et al. Multi-stage planning method for source-grid-storage coordination considering the orderly decommissioning of thermal power[J]. Proceedings of the CSEE,2024,44(04):1386-1397.DOI:10.13334/j.0258-8013.pcsee.222550.
10	任大伟, 金晨, 侯金鸣, 等. 基于时序运行模拟的新能源配置储能替代火电规划模型[J]. 中国电力, 2021, 54(7): 18-26. DOI: 10.11930/j.issn.1004-9649.202009017.
	REN D W, JIN C, HOU J M, et al. Planning model for renewable energy with energy storage replacing thermal power based on time series operation simulation[J]. Electric Power, 2021, 54(7): 18-26. DOI: 10.11930/j.issn.1004-9649.202009017.
11	罗仕华, 胡维昊, 黄琦, 等. 市场机制下光伏/小水电/抽水蓄能电站系统容量优化配置[J]. 电工技术学报, 2020, 35(13): 2792-2804. DOI: 10.19595/j.cnki.1000-6753.tces.191843.
	LUO S H, HU W H, HUANG Q, et al. Optimization of photovoltaic/small hydropower/pumped storage power station system sizing under the market mechanism[J]. Transactions of China Electrotechnical Society, 2020, 35(13): 2792-2804. DOI: 10.19595/j.cnki.1000-6753.tces.191843.
12	林俐, 李北晨, 孙勇, 等. 基于高比例新能源消纳的抽水蓄能容量多时间尺度迭代优化配置模型[J]. 电网与清洁能源, 2021, 37(1): 104-111.
	LIN L, LI B C, SUN Y, et al. Multi-time-scale iterative optimal configuration model of pumped storage capacity based on accommodation of high share new energy[J]. Power System and Clean Energy, 2021, 37(1): 104-111.
13	周业荣, 李相锐, 绳博宇, 等. 水风光蓄互补发电系统中风光容量配置研究[J]. 水利水电技术(中英文), 2023, 54(11): 1-14. DOI: 10.13928/j.cnki.wrahe.2023.11.001.
	ZHOU Y R, LI X R, SHENG B Y, et al. Study of wind-photovoltaic optimal capacity configuration in hydro-wind-photovoltaic-pumped storage complementary power generation system[J]. Water Resources and Hydropower Engineering, 2023, 54(11): 1-14. DOI: 10.13928/j.cnki.wrahe.2023.11.001.
14	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.
	LAN J, ZHU J Z, LI S L, 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.
15	林卓然, 王守相, 王绍敏, 等. 考虑阶梯型碳交易机制的区域电-热综合能源系统分布协同调度方法[J]. 电网技术, 2023, 47(1): 217-229. DOI: 10.13335/j.1000-3673.pst.2022.1077.
	LIN Z R, WANG S X, WANG S M, et al. Distributed coordinated dispatching of district electric-thermal integrated energy system considering ladder-type carbon trading mechanism[J]. Power System Technology, 2023, 47(1): 217-229. DOI: 10.13335/j.1000-3673.pst.2022.1077.
16	王俊伟, 任艺, 郭尊, 等. 基于综合需求响应和奖惩阶梯型碳交易的综合能源系统优化调度[J]. 储能科学与技术, 2022, 11(7): 2177-2187. DOI: 10.19799/j.cnki.2095-4239.2021.0692.
	WANG J W, REN Y, GUO Z, et al. Optimal scheduling of integrated energy system considering integrated demand response and reward and punishment ladder carbon trading[J]. Energy Storage Science and Technology, 2022, 11(7): 2177-2187. DOI: 10.19799/j.cnki.2095-4239.2021.0692.
17	甘友春, 王灿, 贺旭辉, 等. 考虑光热电站和柔性负荷的电氢热综合能源系统联合优化运行[J]. 广西师范大学学报(自然科学版), 2024, 42(2): 69-83. DOI: 10.16088/j.issn.1001-6600.2023050506.
	GAN Y C, WANG C, HE X H, et al. Joint optimal operation of integrated electricity-hydrogen-heat energy system considering concentrating solar power plant and flexible load[J]. Journal of Guangxi Normal University(Natural Science Edition), 2024,42(2): 69-83. DOI: 10.16088/j.issn.1001-6600.2023050506.
18	王海亮, 代璐, 陈兆乐, 等. 基于弃新能源惩罚成本的分段函数式计算方法[J]. 电工技术, 2022(16): 71-73, 76. DOI: 10.19768/j.cnki.dgjs.2022.16.021.
	WANG H L, DAI L, CHEN Z L, et al. Piecewise functional calculation method based on the penalty cost of abandoning new energy[J]. Electric Engineering, 2022(16): 71-73, 76. DOI: 10.19768/j.cnki.dgjs.2022.16.021.
19	张良, 郑丽冬, 冷祥彪, 等. 基于灰狼算法的风-光-抽水蓄能联合系统多目标优化策略研究[J/OL]. 上海交通大学学报, 1-24[2024-02-01].https://doi.org/10.16183/j.cnki.jsjtu.2023.049.
	ZHANG L, ZHENG L D, LENG X B, et al. Research on multi-objective optimization strategy of wind-photovoltaic-pumped storage combined system based on gray wolf algorithm[J/OL]. Journal of Shanghai Jiaotong University: 1-24[2024-02-01].https://doi.org/10.16183/j.cnki.jsjtu.2023.049.
20	刘福国, 张利孟, 刘颖. 单点法、两点法和多点法确定机组煤耗特性曲线研究[J]. 热能动力工程, 2021, 36(7): 73-80. DOI: 10.16146/j.cnki.rndlgc.2021.07.012.
	LIU F G, ZHANG L M, LIU Y. Determination of load characteristic of standard coal consumption rate in coal-fired generator unit by one-point, two-point and multi-point methods[J]. Journal of Engineering for Thermal Energy and Power, 2021, 36(7): 73-80. DOI: 10.16146/j.cnki.rndlgc.2021.07.012.
21	辛保安, 陈梅, 赵鹏, 等. 碳中和目标下考虑供电安全约束的我国煤电退减路径研究[J]. 中国电机工程学报, 2022, 42(19): 6919-6931. DOI: 10.13334/j.0258-8013.pcsee.221673.
	XIN B A, CHEN M, ZHAO P, et al. Research on coal power generation reduction path considering power supply adequacy constraints under carbon neutrality target in China[J]. Proceedings of the CSEE, 2022, 42(19): 6919-6931. DOI: 10.13334/j.0258-8013.pcsee.221673.
22	李雄威, 顾佳伟, 王昕, 等. 风光火蓄联合发电系统日前优化调度研究[J]. 水电与抽水蓄能, 2022, 8(3): 23-28. DOI: 10.3969/j.issn.2096-093X.2022.03.005.
	LI X W, GU J W, WANG X, et al. Research on optimal scheduling of wind-photovoltaic-thermal-pumped storage combined power generation system[J]. Hydropower and Pumped Storage, 2022, 8(3): 23-28. DOI: 10.3969/j.issn.2096-093X.2022.03.005.
23	岳晓宇. 面向含高比例风电电力系统的抽水蓄能电站调度策略研究[D]. 北京: 华北电力大学, 2021. DOI: 10.27140/d.cnki.ghbbu.2021.001333.
	YUE X Y. Research on the dispatching strategy of pumped-storage power plants for power systems with high proportions of wind power[D]. Beijing: North China Electric Power University, 2021.
24	ABDEL-BASSET M, MOHAMED R, JAMEEL M, et al. Nutcracker optimizer: A novel nature-inspired metaheuristic algorithm for global optimization and engineering design problems[J]. Knowledge-Based Systems, 2023, 262: 110248. DOI: 10.1016/j.knosys.2022.110248.
25	DAHOU A, EWEES A A, HASHIM F A, et al. Optimizing fake news detection for Arabic context: A multitask learning approach with transformers and an enhanced Nutcracker Optimization Algorithm[J]. Knowledge-Based Systems, 2023, 280: 111023. DOI: 10.1016/j.knosys.2023.111023.
26	孔芝, 杨青峰, 赵杰, 等. 基于自适应调整权重和搜索策略的鲸鱼优化算法[J]. 东北大学学报(自然科学版), 2020, 41(1): 35-43. DOI: 10.12068/j.issn.1005-3026.2020.01.007.
	KONG Z, YANG Q F, ZHAO J, et al. Adaptive adjustment of weights and search strategies-based whale optimization algorithm[J]. Journal of Northeastern University (Natural Science), 2020, 41(1): 35-43. DOI: 10.12068/j.issn.1005-3026.2020.01.007.

测试函数	指标	NOA	GWO	GA	ANOA
F1	最优函数值	300	300	512.4684	300
F1	最小运行时间	1.8357	3.0608	3.4058	1.3360

F2	最优函数值	400.3851	400.9161	408.3825	400.3851
F2	最小运行时间	1.3203	1.3242	3.0977	1.3011

F6	最优函数值	1.8003e+03	3.4049e+03	1.8740e+03	1.8078e+03
F6	最小运行时间	1.4040	3.1603	3.5852	1.3687

F9	最优函数值	2.5293e+3	2.5449e+3	2.5293e+3	2.5293e+3
F9	最小运行时间	1.8751	3.3305	3.8453	1.6004

机组	配置成本/(万元/MW)	运维成本/(万元/MWh)	生命周期/年	折算后日配置成本/(万元/MW)
抽水蓄能	600	0.03	40	0.041
风电	715	0.06	20	0.098
光伏	350	0.06	30	0.032
水电	300	0.01	30	0.141
火电	330	0.03	30	0.030

场景	综合成本/万元	弃风光惩罚成本/万元	碳排放惩罚成本/万元	碳排放量/吨	弃风弃光量/万千瓦时
初始场景	6619.70	398.84	111.33	21517.40	6721.23
仅采用碳排放阶梯惩罚场景	6548.77	386.74	107.95	20954.61	6586.73
仅采用新能源弃电阶梯惩罚场景	6463.96	425.41	105.19	17532.01	6393.44
采用新能源弃电与碳排放阶梯惩罚场景	6398.74	423.34	101.86	16975.97	6370.44

场景	综合成本/万元	弃风光惩罚成本/万元	碳排放惩罚成本/万元	碳排放量/吨	弃风弃光量/万千瓦时	火电机组容量/万千瓦	抽水蓄能容量/ 万千瓦
初始场景	6619.70	398.84	111.33	21517.40	6721.23	7712	861
NOA	6239.98	320.89	95.59	15931.36	5348.24	4706	1500
GWO	6278.20	320.89	95.59	15931.36	5348.24	5980	1500
GA	6359.06	322.18	95.72	15953.27	5369.72	8617	1490
ANOA	6235.78	320.89	95.59	15931.36	5348.24	4566	1500

[1]	聂子攀, 肖立业, 邱清泉, 张京业. 地下抽水蓄能发展综述[J]. 储能科学与技术, 2024, 13(5): 1606-1619.
[2]	赵添辰, 张弓, 张云飞, 侯世豪, 王婷婷. “双碳”目标下抽水蓄能提升系统保供能力的技术经济性研究[J]. 储能科学与技术, 2024, 13(3): 1059-1073.
[3]	肖厦颖, 范传光, 郭峰, 杨天鑫, 王栋, 黄云辉. 基于改进多目标粒子群算法的储能电站定容选址优化配置研究[J]. 储能科学与技术, 2024, 13(2): 503-514.
[4]	武鑫, 尚文举, 马志勇, 滕伟, 张爽, 罗海荣. 抽水蓄能-飞轮混合储能系统协调控制方法[J]. 储能科学与技术, 2023, 12(2): 468-476.
[5]	魏震波, 姚怡欣, 张雯雯, 罗紫航, 李银江, 任语杰. 基于完备集合经验模态分解的含抽蓄微电网混合储能容量优化配置[J]. 储能科学与技术, 2023, 12(11): 3414-3424.
[6]	刘连德, 何江, 周家旭, 李翠萍, 李凯强, 朱星旭, 严干贵, 李军徽. 含高比例风光发电的电力系统中抽蓄电站的优化控制策略[J]. 储能科学与技术, 2022, 11(7): 2197-2205.
[7]	李秀慧, 崔炎. 考虑调峰调频需求的新能源电网储能优化配置[J]. 储能科学与技术, 2022, 11(11): 3594-3602.
[8]	刘坚. 适应可再生能源消纳的储能技术经济性分析[J]. 储能科学与技术, 2022, 11(1): 397-404.
[9]	陈星宇, 刘忠, 寇攀高, 邹淑云, 潘宜桦. 离网型风-光-抽水蓄能恒压供电系统[J]. 储能科学与技术, 2021, 10(1): 355-361.
[10]	龚国仙, 吕静亮, 姜新建, 孙旭东. 参与一次调频的双馈式可变速抽水蓄能机组运行控制[J]. 储能科学与技术, 2020, 9(6): 1878-1884.
[11]	周　丹, 任志伟, 孙　可, 陈锡祥, 郑伟民. 复杂运营环境下快充型公交充电策略优化方法[J]. 储能科学与技术, 2020, 9(1): 195-203.
[12]	丁倩, 曾平良, 孙轶恺, 徐辰婧, 徐振超. 一种考虑可再生能源不确定性的分布式储能电站选址定容规划方法[J]. 储能科学与技术, 2020, 9(1): 162-169.
[13]	王婷婷, 曹飞, 唐修波, 李阳, 张昊晟. 利用矿洞建设抽水蓄能电站的技术可行性分析[J]. 储能科学与技术, 2019, 8(1): 195-200.
[14]	傅昊, 姜彤, 崔岩, 权超. 基于直线电机的虚拟抽水蓄能系统运行控制策略[J]. 储能科学与技术, 2019, 8(1): 98-107.
[15]	陈如波. 低碳集约型抽水蓄能电站在深圳的实践[J]. 储能科学与技术, 2017, 6(S1): 62-.