基于改进野狗优化算法的电动汽车调峰策略

doi:10.19799/j.cnki.2095-4239.2023.0116

摘要/Abstract

摘要：

针对新能源接入导致电网峰谷差变大的问题，同时考虑分时电价和碳收益的影响，本工作提出基于改进野狗优化算法(IDOA)的电动汽车调峰策略。首先，设计执行策略动态选择的IDOA，以提升原始野狗优化算法的寻优精度和寻优速度；其次，建立考虑负荷峰谷差、充电成本、放电收益和出售碳配额收益的电动汽车参与调峰优化调度模型，并以惩罚项的形式将约束条件引入优化调度模型形成寻优价值函数，使用IDOA求解该价值函数；最后，对提出的IDOA和优化调度模型进行了仿真验证，结果表明，与其他4种算法相比，IDOA在寻优速度、准确性和鲁棒性上均具有良好效果，IDOA求解调峰模型降低了电网负荷峰谷差，同时也减轻了车主用车成本。

关键词: 电动汽车, 调峰策略, 碳收益, 改进野狗优化算法, 优化调度

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

中图分类号:

TM 715

蔡新雷, 祝锦舟, 刘霡, 刘佳乐, 孟子杰, 余洋. 基于改进野狗优化算法的电动汽车调峰策略[J]. 储能科学与技术, 2023, 12(6): 1913-1919.

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.

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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

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