计及电量电价弹性的主动配电网多时间尺度优化调度

LIANG Ning

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计及电量电价弹性的主动配电网多时间尺度优化调度

更新时间：2025-08-13

- 计及电量电价弹性的主动配电网多时间尺度优化调度
- Automation of Electric Power Systems Vol. 42, Issue 12, Pages: 44-50(2018)
- 作者机构：
  
  武汉大学电气工程学院,湖北省,武汉市,430072
- 作者简介：
- 基金信息：
- DOI：
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- Published：2018
- 稿件说明：
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LIANG Ning. [J]. Automation of electric power systems, 2018, 42(12): 44-50.
DOI：

LIANG Ning. [J]. Automation of electric power systems, 2018, 42(12): 44-50. DOI：

摘要

电动汽车、可再生能源和储能的接入对配电网运行带来了新的挑战，若调度方法和模型制定不当，将影响到配电网的经济性和可靠性，以及电动车主参与调度的积极性。为此，提出了一种主动配电网多时间尺度优化调度方法。首先，在日前阶段构造了基于电量电价弹性的电动汽车充电模型，建立了一种主动配电网日前经济调度模型。然后，在实时阶段通过储能和电动汽车降低可再生能源预测误差对系统的影响。该方法在研究电量电价弹性对电动汽车充电影响机理的基础上，基于不同时间尺度可再生能源预测数据，决策电动汽车、储能和柔性负荷的调用。仿真结果表明，所提方法降低了配电网购电和电动汽车充电费用，减弱了可再生能源预测误差对配电网的影响，优化了负荷特性。

Abstract

The access of electric vehicles

renewable energy and energy storage to distribution network poses new challenges to the operation of distribution network. Without the proper scheduling method and model

the economy and reliability of distribution network will be affected. Worse

the enthusiasm of electric vehicle owners to participate in the dispatching will reduce. Accordingly

a optimal scheduling method with multiple time scale is proposed for the active distribution network. First

an electric vehicle charging model based on elastic electricity price is constructed. Meanwhile

a day-ahead economic scheduling model of active distribution network is established. Secondly

in the real-time scheduling stage

the bias of renewable energy forecast is reduced by using the energy storage and electric vehicles. Based on the renewable energy forecast results and the study of the influence mechanism of elastic electricity price on electric vehicle charging

the optimal operation of electric vehicle

storage and flexible load is obtained. Simulation results show that the proposed method can reduce the cost of purchasing electricity in the distribution network and electric vehicles charging

weaken the influence of the bias of renewable energy forecast on the distribution network and optimize the characteristics of load.

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