考虑电动汽车灵活储能的微电网双重激励优化调度

doi:10.19799/j.cnki.2095-4239.2023.0077

摘要/Abstract

摘要：

针对电力系统采用分时电价单一激励下电动汽车有序充电难以应对风力发电与日内负荷的供需变化，从而形成峰谷差的问题，提出一种基于电动汽车灵活储能的分时电价与动态碳配额双重激励调度策略。首先，根据蒙特卡洛对电动汽车充电负荷进行预测，并建立其电池损耗模型。在此基础上，根据日前预测中各个时段不同供电设备的出力以及碳排放与碳配额的占比，在考虑微电网发电成本以及用户期望荷电状态的前提下以微电网并网功率均方差最小和用户收益最大为优化目标。通过动态调整分时电价和阶梯碳价，并运用优化的灰狼算法对模型进行求解，制定出合理的充放电计划以充分发挥电动汽车作为柔性负荷的特点，从而实现平抑微电网负荷曲线波动的目的。最后，将所提策略与无激励和单一激励策略进行仿真分析对比，结果显示负荷峰谷差分别降低了30.1%和18.6%，验证了其有效性和优越性。同时车主用户收益的提高和碳排放量的降低，验证了电动汽车的环保特性需要与清洁能源协同发展。

关键词: 电动汽车, 双重激励, 微电网, 碳配额, 阶梯碳价

Abstract:

A dual incentive adjustment strategy is proposed based on flexible energy storage of electric vehicles to address the problem of peak valley differences caused by the difficulty in orderly charging of electric vehicles under a single incentive, such as time-of-use electricity pricing, within the power system, which is difficult to cope with the supply and demand fluctuations caused by wind power generation and daily load patterns. To address this issue, our strategy incorporates several key elements. Firstly, the charging load of electric vehicles is predicted based on Monte Carlo simulations, and a battery loss model is established. On this basis, the optimization objectives are defined by considering the output of different power supply equipment in different periods, the proportion of carbon emissions, carbon quotas in the daily forecast, the cost of microgrid power generation, and the expected state of charge of users to minimize the mean square deviation of microgrid connected to power while maximizing user benefits. By dynamically adjusting the time-of-use electricity price and implementing a tiered carbon price, the optimized Grey Wolf algorithm is used to solve the model and formulate a reasonable charging and discharging plan to fully utilize the characteristics of electric vehicles as flexible loads, thereby suppressing the fluctuations in the load curve of the microgrid. Finally, simulation analysis is conducted to compare the proposed strategy with both non-incentive and single-incentive strategies, and the results showed that the peak-to-valley load difference decreases by 30.1% and 18.6%, respectively, verifying the effectiveness and superiority of our approach. Furthermore, the improvement of the owner-user benefits and the reduction of carbon emissions have verified that the environmental characteristics of electric vehicles require coordinated development with clean energy.

Key words: electric vehicle, dual incentive, microgrid, carbon quota, ladder carbon valence

中图分类号:

TM 73

张响, 段俊东, 康博阳. 考虑电动汽车灵活储能的微电网双重激励优化调度[J]. 储能科学与技术, 2023, 12(8): 2556-2564.

Xiang ZHANG, Jundong DUAN, Boyang KANG. Dual incentive optimal scheduling of microgrid considering flexible energy storage of electric vehicles[J]. Energy Storage Science and Technology, 2023, 12(8): 2556-2564.

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碳配额相关参数	数值
微燃机单位电量的碳配额/(kg/kW)	0.6112
微燃机单位电量的碳排放/(kg/kW)	0.7109
燃油车每公里的碳排放量/(kg/km)	0.197
电网购电单位电量的碳配额/(kg/kW)	0.7201
电网购电单位电量的碳排放/(kg/kW)	0.8891
碳排放区间/kg	40
电网购电价格/(元/kWh)	0.8

具体时段	电价/(元/kWh)
1，2，3，4，5，6，7，24	0.25
8，9，10，11，14，15，16，17	0.5
12，13，18，19，20，21，22，23	0.8

具体时段	碳价/(元/kWh)
1，2，3，4，5，6，7，8，22，23，24	0.20
21	0.24
10，12，13，17，19	0.26
9，11，14，15，16，18，20	0.28

场景	考虑分时电价	考虑碳交易
1	×	×
2	√	×
3	√	√

场景	负荷峰谷差/kW	用户收益/元	微电网运行费用/元
场景1	2655.6	—	54353
场景2	2280.2	1549.3	57262
场景3	1856.2	3683.2	54390