考虑分层碳排放的多时间尺度光储充一体站日前运行策略

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Abstract In the context of “dual carbon” goal, integrated photovoltaic storage and charging station (IPSCS) can effectively solve the problem of independent operation of multiple types of flexible resources. To enhance the collaborative consumption capacity of source load and reduce carbon emissions, this paper proposes a multi-timescale day-ahead operation strategy for IPSCS that considers carbon emission stratification and source load interaction. Firstly, hybrid prediction model and Monte Carlo method are applied to historical data to derive typical photovoltaic output and electric vehicle load scenarios, respectively. Secondly, the objective function of the mathematical model of the operation strategy of the integrated station is minimum comprehensive planning cost, minimum carbon emission, and maximum utilization of new energy. The model is solved using an improved whale optimization algorithm (IWOA) incorporating an opposition-based learning strategy and diversity variation processing. Finally, the rationality and effectiveness of the proposed strategy are verified through case analysis.

Key words： integrated photovoltaic storage and charging station (IPSCS) carbon emissions multi-timescale improved whale optimization algorithm

Received: 18 July 2024

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	PENG Cheng
	XU Jianyong
	ZHAO Shuqi
	XU Jianjun

Cite this article:

PENG Cheng,XU Jianyong,ZHAO Shuqi等. Day-ahead operation strategy for a multi-timescale integrated photovoltaic storage and charging station considering carbon emission stratification[J]. Electrical Engineering, 2025, 26(1): 1-13.

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https://dqjs.cesmedia.cn/EN/Y2025/V26/I1/1

[1] LI Junjie, LIANG Mei, CHENG Wanjing, et al.Life cycle cost of conventional, battery electric, and fuel cell electric vehicles considering traffic and envi- ronmental policies in China[J]. International Journal Hydrogen Energy, 2021, 46(14): 9553-9566.
[2] 李玉齐, 张健. 关于微网的新型配电系统研究概述与思考[J]. 电气技术, 2020, 21(5): 1-5, 14.
[3] GNANN T, FUNKE S, JAKOBSSON N, et al.Fast charging infrastructure for electric vehicles: today’s situation and future needs[J]. Transportation Research Part D: Transport and Environment, 2018, 62(9): 314-329.
[4] 陈嘉鹏, 汤乃云, 王雪松. 基于电动汽车入网特性的电网经济调度研究[J]. 电气技术, 2019, 20(3): 24-30, 36.
[5] YAN Qin, ZHANG Bei, KEZUNOVIC M.Optimized operational cost reduction for an EV charging station integrated with battery energy storage and PV generation[J]. IEEE Transactions on Smart Grid, 2019, 10(2): 2096-2106.
[6] HAFEZ O, BHATTACHARYA K.Queuing analysis based PEV load modeling considering battery charging behavior and their impact on distribution system operation[J]. IEEE Transactions on Smart Grid, 2018, 9(1): 261-273.
[7] 禹威威, 刘世林, 陈其工, 等. 考虑需求侧管理的光伏微电网多目标优化调度方法[J]. 太阳能学报, 2017, 38(11): 2972-2981.
[8] 蒋甜甜. 基于需求响应的光储充一体化系统多目标优化运行研究[D]. 郑州: 郑州大学, 2020.
[9] 薛贵挺, 汪柳君, 刘哲, 等. 考虑碳排放的光储充一体站日前运行策略[J]. 电力系统保护与控制, 2022, 50(7): 103-110.
[10] 陈奎, 马子龙, 周思宇, 等. 电动汽车两阶段多目标有序充电策略研究[J]. 电力系统保护与控制, 2020, 48(1): 65-72.
[11] DOMINGUEZ-NAVARRO J A, DUFO-LOPEZ R, YUSTA-LOYO J M, et al. Design of an electric vehicle fast-charging station with integration of renewable energy and storage systems[J]. International Journal of Electrical Power & Energy Systems, 2019, 105: 46-58.
[12] 陈忠华. 配置储能的电动汽车快速充电站优化设计与能量管理策略研究[D]. 杭州: 浙江大学, 2020.
[13] 梁振锋, 祁芙蓉, 王德意, 等. 考虑应急电源功能的光储充放电站配置方法研究[J]. 电网技术, 2023, 47(8): 3376-3384.
[14] 吴凡, 周云, 冯冬涵, 等. 光储充一体化快充站日前运行策略[J]. 电测与仪表, 2021, 58(12): 104-109.
[15] GUI Qiang, SU Hao, FENG Donghan, et al.A novel linear battery energy storage system (BESS) life loss calculation model for BESS-integrated wind farm in scheduled power tracking[C]//8th Renewable Power Generation Conference (RPG 2019), Shanghai, China, 2019: 1-8.
[16] 周显春. ARMA融合CNN-LSTM的传感器流数据异常检测方案[J]. 国外电子测量技术, 2022, 41(4): 55-61.
[17] 张童彦, 廖清芬, 唐飞, 等. 基于气象资源插值与迁移学习的广域分布式光伏功率预测方法[J]. 中国电机工程学报, 2023, 43(20): 7929-7939.
[18] 严璐晗, 林培杰, 程树英, 等. 基于增量学习的CNN-LSTM光伏功率预测[J]. 电气技术, 2024, 25(5): 31-40.
[19] 鲍志伟. 具有储能系统的快速充电站优化能量管理策略研究[D]. 北京: 北京交通大学, 2016.
[20] 程苒, 段瑶, 吴亚雄, 等. 考虑等效循环放电深度约束的配电网储能规划[J/OL]. 南方电网技术, 1-10 [2024-09-05]. https://kns-cnki-net.webvpn.nepu.edu.cn/kcms/detail/44.1643.TK.20231017.1524.004.html.
[21] 陈文溆乐, 向月, 彭光博, 等. “双碳”目标下电力系统供给侧形态发展系统动力学建模与分析[J]. 上海交通大学学报, 2021, 55(12): 1567-1576.
[22] 张淑清, 苑世钰, 姚玉永, 等. 基于ICEEMD及AWOA优化ELM的机械故障诊断方法[J]. 仪器仪表学报, 2019, 40(11): 172-180.
[23] 李畸勇, 张伟斌, 赵新哲, 等. 改进鲸鱼算法优化支持向量回归的光伏最大功率点跟踪[J]. 电工技术学报, 2021, 36(9): 1771-1781.
[24] MAHDAVI S, RAHNAMAYAN S, DEB K.Opposition based learning: a literature review[J]. Swarm and Evolutionary Computation, 2018, 39: 1-23.