考虑调峰调频需求的新能源电网储能优化配置

doi:10.19799/j.cnki.2095-4239.2022.0331

摘要/Abstract

摘要：

风电、光伏等新能源大规模接入电网带来电力电量不平衡问题。对此，本工作提出考虑新能源电网调峰调频需求的储能优化配置方法。首先，建立典型日多时间尺度运行模拟模型用于量化储能参与系统调峰和频率响应的贡献，即兼顾碳足迹约束的日前调峰和兼顾联络线偏差控制的日内调频。然后，建立储能配置-运行双层优化模型，上层配置模型以系统年运行成本与储能等值年投资成本总和最小为目标决策储能容量，下层运行模型通过两阶段时序模拟刻画不同典型日下的多时间尺度运行。该双层优化问题通过差分进化算法+Gurobi求解器的混合算法求解。最后，算例表明考虑储能参与双重应用场景的配置结果更为合理，系统运行总成本更低、碳排放更少。

关键词: 储能配置, 新能源电网, 调峰-调频两阶段运行模拟, 双层优化

Abstract:

The issue of power imbalance is brought on by the large-scale access to wind power, photovoltaic and other renewable energy sources in the system. In this paper, we suggested a method to optimize energy storage allocation considering peak and frequency regulation demands. In order to quantify the contribution of energy storage to system peaking and frequency response, i.e., intra-day frequency regulation with contact line deviation control and day-ahead peaking with carbon footprint constraints, a typical daily multi-timescale operation simulation model is first established. Then, a two-layer energy storage configuration-operation optimization model is established. The lower model is a simulation of different typical day operations, solved by a hybrid algorithm of differential evolutionary algorithm & Gurobi solver. The upper model aims to minimize the sum of typical day-set operation cost and energy storage investment cost to determine the energy storage capacity. Finally, the calculation example demonstrates that the configuration results taking into account the dual application scenarios with energy storage participation are more reasonable, the overall system operation cost is lower, and the carbon emission is less.

Key words: energy storage configuration, renewable energy grid, two-stage operation simulation of peak-frequency regulation, two-layer optimization

中图分类号:

TM 73

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.

Xiuhui LI, Yan CUI. Optimal allocation of energy storage in renewable energy grid considering the demand of peak and frequency regulation[J]. Energy Storage Science and Technology, 2022, 11(11): 3594-3602.

图/表 15

图1

图2

图3

图4

表1

储能系统经济参数"

储能节点	单位功率价格/( $美元 / M W$ )	单位容量价格/( $美元 / M W h$ )	寿命/a	单位运维成本/( $美元 / M W$ )
$B$	$1.79 × 105$	$4.29 × 105$	$10$	$6.1 × 104$

表1

表2

各能源类型CO2 当量值"

能源类型	$t C O 2 e q / M W h$
火电	0.82
风电	0.01
光伏	0.05

表2

图5

图6

表3

3种方案配置结果对比"

场景	储能系统功率/ $M W$	储能系统容量/ $M W h$	储能年均投资/美元	系统弃风光成本/美元	系统调峰成本/美元	系统调频成本/美元	系统年总成本/美元
$1$	$59$	$182$	1.23×10⁷‍	1.73×10⁷	2.62×10⁸	9.94×10⁴	2.74×10⁸‍
$2$	$50$	$221$	1.23×10⁷‍	5.34×10⁷	2.98×10⁸‍	1.12×10⁶‍	3.13×10⁸‍
$3$	$0$	$0$	$0$	7.53×10⁷	3.2×10⁸‍	1.12×10⁶‍	3.21×10⁸‍

表3

图7

图8

图9

图10

图11

图12

参考文献 23

1	IRENA.Renewable capacity statistics 2020[R/OL]. AbuDhabi:International Renewable Energy Agency (IRENA),2020[2021-12-01]. https://www.irena.org/publications/2020/Mar/Renewable-Capacity-Statistics-2020.
2	王仁顺, 赵宇, 马福元, 等. 受端电网高比例可再生能源消纳的运行瓶颈分析与储能需求评估[J]. 电网技术, 2022, 46(10): 3777-3787.
	WANG R S, ZHAO Y, MA F Y, et al. Operation bottleneck analysis and energy storage demand assessment of high proportion of renewable energy consumption in receiver power grid [J]. Power System Technology, 2022, 46(10): 3777-3787.
3	周校聿, 刘娆, 鲍福增, 等. 百兆瓦级储能参与电网双重辅助服务调度的联合优化模型[J]. 电力系统自动化, 2021, 45(19): 60-69.
	ZHOU X Y, LIU R, BAO F Z, et al. Joint optimization model for hundred-megawatt-level energy storage participating in dual ancillary services dispatch of power grid[J]. Automation of Electric Power Systems, 2021, 45(19): 60-69.
4	李建林, 崔宜琳, 王力. 储能学科建设探索及相关建议[J]. 中国电机工程学报: doi: 10.13334/j.0258-8013.pcsee.212203.
	LI J L, CUI Y L, WANG L. Research on energy storage discipline construction and related suggestions[J]. Proceedings of the CSEE: doi: 10.13334/j.0258-8013.pcsee.212203.
5	李建林, 武亦文, 王楠, 等. 吉瓦级电化学储能电站研究综述及展望[J]. 电力系统自动化, 2021, 45(19): 2-14.
	LI J L, WU Y W, WANG N, et al. Review and prospect of gigawatt-level electrochemical energy storage power station[J]. Automation of Electric Power Systems, 2021, 45(19): 2-14.
6	李军徽, 张嘉辉, 李翠萍, 等. 参与调峰的储能系统配置方案及经济性分析[J]. 电工技术学报, 2021, 36(19): 4148-4160.
	LI J H, ZHANG J H, LI C P, et al. Configuration scheme and economic analysis of energy storage system participating in grid peak shaving[J]. Transactions of China Electrotechnical Society, 2021, 36(19): 4148-4160.
7	刘洪波, 张崇, 孙同, 等. 高风电渗透率下考虑电网频率支撑需求的储能系统配置方法[J]. 电力自动化设备, 2021, 41(10): 36-43.
	LIU H B, ZHANG C, SUN T, et al. Configuration method of energy storage system considering grid frequency support demand under high wind power penetration[J]. Electric Power Automation Equipment, 2021, 41(10): 36-43.
8	刘文龙, 耿光超, 陈义宣, 等. 抑制超低频振荡的储能控制策略[J]. 电网技术, 2020, 44(5): 1689-1696.
	LIU W L, GENG G C, CHEN Y X, et al. Control strategy of energy storage system for ultra-low frequency oscillation suppression[J]. Power System Technology, 2020, 44(5): 1689-1696.
9	彭春华, 陈露, 张金克, 等. 基于分类概率机会约束IGDT的配网储能多目标优化配置[J]. 中国电机工程学报, 2020, 40(9): 2809-2819.
	PENG C H, CHEN L, ZHANG J K, et al. Multi-objective optimal allocation of energy storage in distribution network based on classified probability chance constraint information gap decision theory[J]. Proceedings of the CSEE, 2020, 40(9): 2809-2819.
10	彭穗, 龚贤夫, 刘新苗, 等. 计及直流调节能力的含风电电力系统储能优化配置[J]. 中国电力, 2022, 55(1): 37-45.
	PENG S, GONG X F, LIU X M, et al. Optimized energy storage configuration of wind power integrated power system considering DC regulation capacity[J]. Electric Power, 2022, 55(1): 37-45.
11	CAO M J, XU Q S, QIN X Y, et al. Battery energy storage sizing based on a model predictive control strategy with operational constraints to smooth the wind power[J]. International Journal of Electrical Power & Energy Systems, 2020, 115: doi: 10.1016/j.ijepes.2019.105471.
12	林哲, 胡泽春, 宋永华. 考虑N-1准则的配电网与分布式储能联合规划[J]. 中国电机工程学报, 2021, 41(13): 4390-4403.
	LIN Z, HU Z C, SONG Y H. Joint planning of distribution network expansion and distributed energy storage systems under N-1 criterion[J]. Proceedings of the CSEE, 2021, 41(13): 4390-4403.
13	YAN N, ZHANG B, LI W, et al. Hybrid energy storage capacity allocation method for active distribution network considering demand side response[J]. IEEE Transactions on Applied Superconductivity, 2019, 29(2): 1-4.
14	ENGELS J, CLAESSENS B, DECONINCK G. Optimal combination of frequency control and peak shaving with battery storage systems[J]. IEEE Transactions on Smart Grid, 2020, 11(4): 3270-3279.
15	刁涵彬, 李培强, 王继飞, 等. 考虑电/热储能互补协调的综合能源系统优化调度[J]. 电工技术学报, 2020, 35(21): 4532-4543.
	DIAO H B, LI P Q, WANG J F, et al. Optimal dispatch of integrated energy system considering complementary coordination of electric/thermal energy storage[J]. Transactions of China Electrotechnical Society, 2020, 35(21): 4532-4543.
16	SHI Y Y, XU B L, WANG D, et al. Using battery storage for peak shaving and frequency regulation: Joint optimization for superlinear gains[J]. 2018 IEEE Power & Energy Society General Meeting (PESGM), 2018: 1.
17	陈长青, 李欣然, 张冰玉, 等. 基于多时间尺度的储能调峰调频协同控制策略[J]. 电力系统保护与控制, 2022, 50(5): 94-105.
	CHEN C Q, LI X R, ZHANG B Y, et al. Energy storage peak and frequency modulation cooperative control strategy based on multi-time-scale[J]. Power System Protection and Control, 2022, 50(5): 94-105.
18	车泉辉, 吴耀武, 祝志刚, 等. 基于碳交易的含大规模光伏发电系统复合储能优化调度[J]. 电力系统自动化, 2019, 43(3): 76-82, 154.
	CHE Q H, WU Y W, ZHU Z G, et al. Carbon trading based optimal scheduling of hybrid energy storage system in power systems with large-scale photovoltaic power generation[J]. Automation of Electric Power Systems, 2019, 43(3): 76-82, 154.
19	LI F X, BO R. Small test systems for power system economic studies[J]. IEEE PES General Meeting, 2010: 1-4.
20	李彦晨, 贾燕冰, 谢栋, 等. 计及电能质量影响的配电网风储优化配置[J/OL]. 电网技术: 2022[2022-06-05]. https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CAPJ&dbname=CAPJLAST&filename= DWJS20220413002&uniplatform=NZKPT&v=qD8pKR4Ao-AaMKR0 ZJjahyU3sbeU82LLoZtC8zz18Kr_Zq6noWUU_X3xgFH7VUfY.
	LI Y C, JIA Y B, XIE D, et al. Optimal configuration of wind storage in distribution network considering influence of power quality[J/OL]. Power Grid Technology: 2022[2022-06-05]. https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CAPJ&dbname=CAPJLAST&filename=DWJS20220413002&uniplatform=NZKPT&v= qD8pKR4Ao-AaMKR0ZJjahyU3sbeU82LLoZtC8zz18Kr_Zq6noWU U_X3xgFH7VUfY.
21	TREVINO-MARTINEZ S, SAWHNEY R, SHYLO O. Energy-carbon footprint optimization in sequence-dependent production scheduling[J]. Applied Energy, 2022, 315: doi: 10.1016/j.apenergy.2022.118949.
22	郑乐, 胡伟, 陆秋瑜, 等. 储能系统用于提高风电接入的规划和运行综合优化模型[J]. 中国电机工程学报, 2014, 34(16): 2533-2543.
	ZHENG L, HU W, LU Q Y, et al. Research on planning and operation model for energy storage system to optimize wind power integration[J]. Proceedings of the CSEE, 2014, 34(16): 2533-2543.
23	邵振, 邹晓松, 袁旭峰, 等. 基于改进多目标粒子群优化算法的配电网削峰填谷优化[J]. 科学技术与工程, 2020, 20(10): 3984-3989.
	SHAO Z, ZOU X S, YUAN X F, et al. Optimization of peak load shifting in distribution network based on improved MOPSO algorithm[J]. Science Technology and Engineering, 2020, 20(10): 3984-3989.

[1]	曾伟, 熊俊杰, 李建林, 马速良, 武亦文. 基于权重自适应鲸鱼优化算法的多能系统储能电站最优配置[J]. 储能科学与技术, 2022, 11(7): 2241-2249.
[2]	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.
[3]	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.
[4]	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.