Peak shaving strategy of electric vehicles based on an improved Dingo optimization algorithm

doi:10.19799/j.cnki.2095-4239.2023.0116

Abstract

Abstract:

Aiming at the problem that the new energies lead to an increase in the peak-valley difference of the power grid and considering the influence of time-of-use electricity price and carbon income, this study proposes an electric vehicle peak-shaving strategy based on the Improved Dingo Optimization Algorithm (IDOA). First, the IDOA dynamically selected by the execution strategy is designed to improve the optimization accuracy and speed of the original Dingo optimization algorithm. Second, an optimal scheduling model of electric vehicles participating in peak load regulation is established, considering the peak-valley difference in load, charging cost, discharging income, and sales carbon quota income. The constraint conditions are introduced into the optimal scheduling model as a penalty term to form the optimization value function that IDOA solves. Finally, the proposed IDOA and optimal scheduling model are simulated and verified. The results show that IDOA has good results in optimization speed, accuracy, and robustness compared to the other four algorithms. The peaking model solved by IDOA reduces the peak-valley difference of power grid load and lowers car owners' costs.

Key words: electric vehicles, peak regulation strategy, carbon earnings, improved dingo optimization algorithm, optimal schedule

CLC Number:

TM 715

Xinlei CAI, Jinzhou ZHU, Mai LIU, Jiale LIU, Zijie MENG, Yang YU. Peak shaving strategy of electric vehicles based on an improved Dingo optimization algorithm[J]. Energy Storage Science and Technology, 2023, 12(6): 1913-1919.

Figures/Tables 5

Table 1

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Table 2

References 23

1	马静, 沈玉明, 荣秀婷, 等. 考虑储能用户与新能源双边交易调峰服务的电力系统联合运营模式[J]. 电力自动化设备, 2023, 43(1):113-120.
	MA J, SHEN Y M, RONG X T, et al. Joint operation mode of power system considering bilateral peak regulation service transaction between energy storage users and new energy[J]. Electric Power Automation Equipment, 2023, 43(1):113-120.
2	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.
	LI X H, CUI Y. 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.
3	刘兴龙. 分散决策下水电站群发电效益分摊机制及调峰方法研究[D]. 武汉: 华中科技大学, 2020.
	LIU X L. Study on sharing mechanism and peak shaving method of power generation benefits of hydropower stations under decentralized decision-making[D]. Wuhan: Huazhong University of Science and Technology, 2020.
4	余洋, 张瑞丰, 陆文韬, 等. 基于稳定经济模型预测控制的集群电动汽车辅助电网调频控制策略[J]. 电工技术学报, 2022, 37(23): 6025-6040.
	YU Y, ZHANG R F, LU W T, et al. Auxiliary frequency regulation control strategy of aggregated electric vehicles based on Lyapunov-based economic model predictive control[J]. Transactions of China Electrotechnical Society, 2022, 37(23): 6025-6040.
5	韩帅, 孙乐平, 卢健斌, 等. 含电动汽车的气电互联虚拟电厂区间多目标优化调度策略[J]. 储能科学与技术, 2022, 11(5): 1428-1436.
	HAN S, SUN L P, LU J B, et al. Multi-objective optimal dispatch strategy of gas-electric interconnected virtual power plant interval with electric vehicles[J]. Energy Storage Science and Technology, 2022, 11(5): 1428-1436.
6	楚岩枫, 朱天聪. 基于Bass模型和GM(1, 1)模型的我国电动汽车保有量预测研究[J]. 数学的实践与认识, 2021, 51(11): 21-32.
	CHU Y F, ZHU T C. Research on forecast of electric vehicle's ownership in China based on bass model and GM(1, 1)model[J]. Mathematics in Practice and Theory, 2021, 51(11): 21-32.
7	张岩, 韩伟, 宋闯, 等. 含电动汽车的光储充一体化电站设施规划与运行联合优化[J]. 储能科学与技术, 2022, 11(5): 1502-1511.
	ZHANG Y, HAN W, SONG C, et al. Joint planning and operation optimization of photovoltaic-storagecharging integrated station containing electric vehicles[J]. Energy Storage Science and Technology, 2022, 11(5): 1502-1511.
8	常硕, 牛玉刚, 陈凯炎. 多目标约束的电动汽车实时调度策略[J]. 华东理工大学学报(自然科学版), 2021, 47(4): 465-474.
	CHANG S, NIU Y G, CHEN K Y. Real-time scheduling strategy for electric vehicles with multi-objective constraints[J]. Journal of East China University of Science and Technology, 2021, 47(4): 465-474.
9	姚伟锋. 考虑电动汽车广泛接入的电力系统规划与运行策略[D]. 杭州: 浙江大学, 2014.
	YAO W F. Power system planning and operation strategy considering the wide access of electric vehicles[D]. Hangzhou: Zhejiang University, 2014.
10	孙波, 廖强强, 谢品杰, 等. 车电互联削峰填谷的经济成本效益分析[J]. 电网技术, 2012, 36(10):30-34.
	SUN B, LIAO Q Q, XIE P J, et al. A cost-benefit analysis model of vehicle-to-grid for peak shaving[J]. Power System Technology, 2012, 36(10): 30-34.
11	程杉, 王贤宁, 冯毅煁, 等. 基于CPLEX与MATLAB的电动汽车充电站优化调度仿真系统[J]. 电网与清洁能源, 2018, 34(1):123-127, 136.
	CHENG S, WANG X N, FENG Y C, et al. Optimal simulation system of electric vehicle charging station based on CPLEX and MATLAB[J]. Advances of Power System & Hydroelectric Engineering, 2018, 34(1):123-127, 136.
12	吴芳柱, 田园. 基于分时电价的电动汽车有序充放电策略研究[J]. 电工电气, 2022(4): 21-25.
	WU F Z, TIAN Y. Coordinated charging strategy for electric vehicle charging and discharging based on time-use price[J]. Electrotechnics Electric, 2022(4): 21-25.
13	王红君, 吴浩, 赵辉, 等. 电动汽车参与电网调峰的模型及调度策略研究[J]. 电源技术, 2015, 39(5): 992-993, 1125.
	WANG H J, WU H, ZHAO H, et al. Model of electric vehicle participating power grid peak shaving and scheduling strategy[J]. Chinese Journal of Power Sources, 2015, 39(5): 992-993, 1125.
14	许翔泰, 张仰飞, 陈光宇, 等. 计及光伏发电的电动汽车充电优化调度研究[J]. 智慧电力, 2019, 47(10): 44-50.
	XU X T, ZHANG Y F, CHEN G Y, et al. Optimal scheduling of charging for electric vehicle considering photovoltaic power generation[J]. Smart Power, 2019, 47(10): 44-50.
15	FATHY A, REZK H, YOUSRI D, et al. Recent approach of wild horse optimizer for identifying the optimal parameters of high efficiency triple-junction solar system[J]. IET Renewable Power Generation, 2023, 17(4): 856-872.
16	FATHY A, REZK H, YOUSRI D, et al. Real-time bald eagle search approach for tracking the maximum generated power of wind energy conversion system[J]. Energy, 2022, 249: doi: 10.1016/j.energy.2022.123661.
17	WANG K, WANG J Z, ZENG B, et al. An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization[J]. Applied Energy, 2022, 314: doi: 10.1016/j.apenergy.2022.118938.
18	MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67.
19	杨海柱, 田馥铭, 张鹏, 等. 基于CEEMD-FE和AOA-LSSVM的短期电力负荷预测[J]. 电力系统保护与控制, 2022, 50(13):126-133.
	YANG H Z, TIAN F M, ZHANG P, et al. Short-term load forecasting based on CEEMD-FE-AOA-LSSVM[J]. Power System Protection and Control, 2022, 50(13):126-133.
20	梁恩豪, 孙军伟, 王延峰. 基于自适应樽海鞘算法优化BP的风光互补并网发电功率预测[J]. 电力系统保护与控制, 2021, 49(24): 114-120.
	LIANG E H, SUN J W, WANG Y F. Wind and solar complementary grid-connected power generation prediction based on BP optimized by a swarm intelligence algorithm[J]. Power System Protection and Control, 2021, 49(24): 114-120.
21	潘振宁, 张孝顺, 余涛, 等. 大规模电动汽车集群分层实时优化调度[J]. 电力系统自动化, 2017, 41(16):96-104.
	PAN Z N, ZHANG X S, YU T, et al. Hierarchical real-time optimized dispatching for large-scale clusters of electric vehicles[J]. Automation of Electric Power Systems, 2017, 41(16):96-104.
22	CUI M J, WANG J H, TAN J, et al. A novel event detection method using PMU data with high precision[J]. IEEE Transactions on Power Systems, 2019, 34(1): 454-466.
23	罗敏, 赵伟, 林国营, 等. 基于电网峰谷分时电价联动的电动汽车有序充电电价研究[J]. 电器与能效管理技术, 2015(24): 78-82, 92.
	LUO M, ZHAO W, LIN G Y, et al. Research on coordinated charging price of electric vehicle based on users' price sensitivity[J]. Low Voltage Apparatus, 2015(24): 78-82, 92.

优化算法	指标	标准函数计算的值
优化算法	指标	F1	F2	F3	F4	F5	F6
IDOA	AVE	1.08×10^-153	7.19×10^-137	1.36×10^-54	2.81×10^-31	26.6084	6.30×10^-10
	STD	5.93×10^-153	3.94×10^-136	6.42×10^-54	1.43×10^-30	0.0464	2.08×10^-10
	RT/s	0.1407	0.1459	0.2955	0.1428	0.1803	0.2226
DOA	AVE	6.68×10^-49	1.27×10^-31	4.55×10^-22	1.19×10^-29	28.828	3.4983
	STD	3.66×10^-48	6.16×10^-31	2.49×10^-21	4.44×10^-29	0.0507	1.2277
	RT/s	0.38	0.4092	0.7103	0.4267	0.4282	0.3761
AOA	AVE	6.11×10^-29	7.41×10^-19	0.0056	0.0196	28.0941	1.5208
	STD	3.35×10^-28	4.06×10^-18	0.0144	0.0217	0.3403	0.2016
	RT/s	0.6613	0.6655	0.4435	0.4831	0.4677	0.1608
SSA	AVE	1.15×10^-8	6.79×10^-6	1.40×10^-9	1.40×10^-5	29.092	3.4242
	STD	2.43×10^-9	1.39×10^-6	4.83×10^-10	2.76×10^-6	63.5789	1.2836
	RT/s	0.3064	0.1933	0.9042	0.1874	0.2334	0.3903
GWO	AVE	7.05×10^-41	5.49×10^-24	4.04×10^-12	2.83×10^-10	28.8314	0.1617
	STD	9.93×10^-41	3.62×10^-24	1.31×10^-11	3.21×10^-10	0.6352	0.2293
	RT/s	0.2274	0.259	0.6101	0.2261	0.2681	0.2274

比较项目	峰谷差/kW	峰谷差率/%	车主收益/元
原始负荷	1395.6829	29.52	—
GWO寻优后	1316.5284	28.18	1134.83
AOA寻优后	1295.3162	28.14	1044.56
SSA寻优后	1291.0415	27.86	1071.40
DOA寻优后	1149.4061	25.59	1102.36
IDOA寻优后	1136.0828	25.29	1232.66

[1]	Guanghua WU, Hongsheng LI, Fei LI, Bo CHEN, Shike ZHANG. Research on the prediction of carbon emissions in the whole life cycle of electric vehicles considering time correlation [J]. Energy Storage Science and Technology, 2022, 11(7): 2206-2212.
[2]	Yan ZHANG, Wei HAN, Chuang SONG, Shuangyi YANG. Joint planning and operation optimization of photovoltaic-storage- charging integrated station containing electric vehicles [J]. Energy Storage Science and Technology, 2022, 11(5): 1502-1511.
[3]	Jun WANG, Lin RUAN, Yanliang QIU. Research progress on rapid heating methods for lithium-ion battery in low-temperature [J]. Energy Storage Science and Technology, 2022, 11(5): 1563-1574.
[4]	Xiaogang WU, Zhihao CUI, Yizhao SUN, Kun ZHANG, Jiuyu DU. Charging strategy and thermal management technology of power battery in high power charging process of electric vehicle [J]. Energy Storage Science and Technology, 2021, 10(6): 2218-2234.
[5]	ZHU Zhangtao1, CHEN Haojie2, DAI Junjie1, LI Weibin1, LI Xue2 . Probabilistic load flow calculation of distribution network with wind power and electric vehicles based on space transform#br# [J]. Energy Storage Science and Technology, 2017, 6(1): 127-134.
[6]	LI Hong. Project “High energy density lithium batteries for long range EV” [J]. Energy Storage Science and Technology, 2016, 5(6): 915-918.
[7]	CHEN Yongchong, WANG Qiuping. V2G or VEG?An investigation into the business model for future electric vehicles based on technological developments [J]. Energy Storage Science and Technology, 2013, 2(3): 307-311.