针对不同类型电池储能系统的选型规划和充放电功率分配难以协调其技术性、经济性和环境性的问题,提出一种考虑风-光-储协同的电池储能系统综合评价体系,进而对电池储能系统充放电策略进行优化并选型。首先,建立电池储能系统全寿命周期内的效益模型,采用模糊层次分析法对上述电池储能系统的效益模型进行分层分析,将其划分成不同的准则和指标,并计算出其相应的权值;其次,建立风-光-储协同下电池储能系统的充放电功率优化模型,并将上述权值与电池储能系统全寿命周期内的经济性、技术性和环境性相结合,确定目标函数;再次,提出一种灰狼优化算法和莱维飞行策略相结合的混合优化算法,对电池储能系统一天内每小时充放电功率的分配进行优化,并得出电池储能系统的荷电状态曲线;最后,基于新疆某地的气象和负荷数据分析对比5种电池储能系统经济性、环境性和技术性的各项指标,根据算例结果为电池储能系统的综合选型方案和充放电功率分配提出建议,并验证所建模型的可行性和求解算法的优越性。
Abstract
Battery energy storage system （BESS） can be used to smooth out the fluctuations of the scenery, but because of the single evaluation index for their selection, it is difficult to coordinate the technical, economic and environmental aspects of different types of energy storage systems in their selection planning and charging/discharging power allocation. In this paper, a comprehensive evaluation system for BESS considering the synergy of "wind-photovoltaic-battery" is proposed, then the charging and discharging strategies for BESS are optimized and selected. Firstly, the benefits of the BESS are modeled over its whole life cycle, and fuzzy analytic hierarchy process（FAHP） is used to analyze the above benefits model of BESS in a hierarchical manner, dividing it into different criteria and indicators, and deriving their corresponding weights; Secondly, the charge/discharge power optimization model of the BESS under the synergy of "wind-photovoltaic-battery" is established, which is combined with the above weights, including the economic, technical and environmental aspects of the BESS during its whole life cycle then, the objective function is determine; Thirdly, in this paper ,a hybrid optimization algorithm combining Grey Wolf algorithm and Lévy flight strategy （LF-GWO） is proposed, which determines to optimize the distribution of charging and discharging power of BESS for each hour in a day, and derives the SOC of BESS; Finally, the example is used to analyze and compare the economic, environmental and technical indexes of five types of BESS based on the meteorological and load data of a place in Xinjiang, and the result of example makes suggestions for the selection scheme and charging/discharging power allocation of the BESS, which also verifies the feasibility of the proposed model and the superiority of the solution algorithm.
关键词
电池储能系统 /
风-光-储协同 /
模糊层次分析 /
混合优化算法
Key words
battery energy storage system /
wind-photovoltaic-battery synergy /
fuzzy analytic hierarchy process（FAHP） /
hybrid optimization algorithm
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 谢小荣, 马宁嘉, 刘威, 等. 新型电力系统中储能应用功能的综述与展望[J]. 中国电机工程学报, 2023, 43(1): 158-169.
XIE X R, MA N J, LIU W, et al.Functions of energy storage in renewable energy dominated power systems: review and prospect[J]. Proceedings of the CSEE, 2023, 43(1): 158-169.
[2] 郭苏, 何意, 阿依努尔·库尔班, 等. 基于多储能技术经济性比较的可再生能源发电系统多目标容量优化[J]. 太阳能学报, 2022, 43(10): 424-431.
GUO S, HE Y, AYNUR KURBAN, et al.Multi-objective capacity optimization of renewable energy power system considering techno-economic comparisons of various energy storage technologies[J]. Acta energiae solaris sinica, 2022, 43(10): 424-431.
[3] KOUSKSOU T, BRUEL P, JAMIL A, et al.Energy storage: applications and challenges[J]. Solar energy materials and solar cells, 2014, 120: 59-80.
[4] 郝婷, 樊小朝, 王维庆, 等. 阶梯式碳交易下考虑源荷不确定性的储能优化配置[J]. 电力系统保护与控制, 2023, 51(1): 101-112.
HAO T, FAN X C, WANG W Q, et al.Optimal configuration of energy storage considering the source-load uncertainty under ladder-type carbon trading[J]. Power system protection and control, 2023, 51(1): 101-112.
[5] 王凯, 延肖何, 蒋凯, 等. 考虑碳交易的风光储场站参与电力现货市场报价策略与调控方法[J]. 中国电机工程学报, 2023, 43(18): 7091-7103.
WANG K, YAN X H, JIANG K, et al.Bidding strategy and regulation method for the unified wind/photovoltaic/energy storage power stations in electricity spot market considering carbon trading[J]. Proceedings of the CSEE, 2023, 43(18): 7091-7103.
[6] 曹文思, 张敏, 黄慧. 基于混合算法的配电网中电池储能的多目标优化配置研究[J]. 太阳能学报, 2022, 43(5): 541-546.
CAO W S, ZHANG M, HUANG H.Research on multi-objective optimal configuration of battery energy storage in distribution network based on hybrid algorithm[J]. Acta energiae solaris sinica, 2022, 43(5): 541-546.
[7] 赵松, 张谦, 霍红岩, 等. 基于层次分析法AHP和TOPSIS的储能参与新能源一次调频选型方案[J]. 现代电力, 2024, 41(1): 134-143.
ZHAO S, ZHANG Q, HUO H Y, et al.Selection scheme for energy storage to participate in primary frequency regulation of new energy based on AHP and TOPSIS methods[J]. Modern electric power, 2024, 41(1): 134-143.
[8] 黎淑娟. 储能电池参与电网快速调频的综合选型与优化配置[D]. 长沙: 湖南大学, 2018.
LI S J.Selection and Optimization of Energy Storage Battery Participates in Power Grid Rapid Frequency Regulation[D]. Changsha: Hunan University, 2018.
[9] 向育鹏, 卫志农, 孙国强, 等. 基于全寿命周期成本的配电网蓄电池储能系统的优化配置[J]. 电网技术, 2015, 39(1): 264-270.
XIANG Y P, WEI Z N, SUN G Q, et al.Life cycle cost based optimal configuration of battery energy storage system in distribution network[J]. Power system technology, 2015, 39(1): 264-270.
[10] 赵立军, 张秀路, 韩丽维, 等. 基于多场景的配电网分布式光伏及储能规划[J]. 现代电力, 2022, 39(4): 460-468.
ZHAO L J, ZHANG X L, HAN L W, et al.Distributed photovoltaic generation and energy storage planning of distribution network based on multi scenarios[J]. Modern electric power, 2022, 39(4): 460-468.
[11] 王泽爽, 陈嘉俊, 朱建全, 等. 计及循环寿命的储能优化配置与运营策略[J]. 电力自动化设备, 2021, 41(10): 75-81.
WANG Z S, CHEN J J, ZHU J Q, et al.Optimal configuration and operation strategy of energy storage considering cycle life[J]. Electric power automation equipment, 2021, 41(10): 75-81.
[12] 肖钧文, 黄辉先, 刘晓舟. 大规模风光储并网碳经济性评估[J]. 太阳能学报, 2020, 41(6): 171-177.
XIAO J W, HUANG H X, LIU X Z.Carbon economy evaluation of large-scale wind-solar-storage power[J]. Acta energiae solaris sinica, 2020, 41(6): 171-177.
[13] 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.
[14] 郭之栋, 胡存刚, 芮涛, 等. 基于动态电价机制的用户侧分布式储能电能交易策略[J]. 中国电力, 2023, 56(1): 28-37.
GUO Z D, HU C G, RUI T, et al.User side distributed energy storage trading strategy based on dynamic electricity price mechanism[J]. Electric power, 2023, 56(1): 28-37.
[15] 周明, 郑雅楠, 李庚银, 等. 考虑分时电价和电能质量的大用户转运费用固定成本的综合分摊方法[J]. 中国电机工程学报, 2008, 28(19): 125-130.
ZHOU M, ZHENG Y N, LI G Y, et al.Synthetical method for allocating wheeling fixed cost for large consumers considering time-of-use pricing and power quality[J]. Proceedings of the CSEE, 2008, 28(19): 125-130.
[16] 季玉琦, 潘超, 肖晗, 等. 分布式电源电压支撑能力层次分析评价[J]. 电力系统保护与控制, 2021, 49(11): 163-171.
JI Y Q, PAN C, XIAO H, et al.Hierarchical analysis and evaluation of the voltage support capability of distributed generation[J]. Power system protection and control, 2021, 49(11): 163-171.
[17] DYASI P, NAICKER V.Application of the Analytic Hierarchy Process in selecting long-term storage option for spent nuclear fuel in South Africa[J]. Annals of nuclear energy, 2023, 187: 109784.
[18] 王长江, 李本新, 姜涛, 等. 基于改进模糊层次分析法的交直流受端电网交流故障筛选与排序[J]. 电网技术, 2021, 45(10): 4047-4056.
WANG C J, LI B X, JIANG T, et al.A contingency screening and ranking method based on improved fuzzy analytic hierarchy process for AC/DC receiving-end power grid AC faults[J]. Power system technology, 2021, 45(10): 4047-4056.
[19] 陆昊. 新型电力系统中储能配置优化及综合价值测度研究[D]. 北京: 华北电力大学, 2021.
LU H.Research on energy storage plan optimization and comprehensive value measurement in the new power system[D]. Beijing: North China Electric Power University, 2021.
[20] DUFO-LÓPEZ R, BERNAL-AGUSTÍN J L. Multi-objective design of PV-wind-diesel-hydrogen-battery systems[J]. Renewable energy, 2008, 33(12): 2559-2572.
[21] NGUYEN N T, MATSUHASHI R, VO T T B C. A design on sustainable hybrid energy systems by multi-objective optimization for aquaculture industry[J]. Renewable energy, 2021, 163: 1878-1894.
[22] MIRJALILI S, MIRJALILI S M, LEWIS A.Grey wolf optimizer[J]. Advances in engineering software, 2014, 69: 46-61.
[23] 王庆喜, 郭晓波. 基于莱维飞行的粒子群优化算法[J]. 计算机应用研究, 2016, 33(9): 2588-2591.
WANG Q X, GUO X B.Particle swarm optimization algorithm based on Levy flight[J]. Application research of computers, 2016, 33(9): 2588-2591.
基金
新疆维吾尔自治区重大科技专项项目（2022A01004-1）
PDF(1440 KB)
