储能科学与技术

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考虑电动汽车的新型电力系统源荷日前-日内低碳优化调度

李若琼1, 司宇杰1, 李欣2   

  1. 1.兰州交通大学自动化与电气工程学院，甘肃 兰州 730070
    2.兰州交通大学新能源与动力工程学院，甘肃 兰州 730070
  • 收稿日期:2025-02-14 修回日期:2025-01-07
  • 基金资助:
    项目编号），重点项目（项目编号

A novel day-ahead-intraday low-carbon optimal source-load dispatch method for power system considering the demand response characteristics of electric vehicles is proposed

Ruoqiong LI1, Yujie SI1, Xin LI2   

  1. 1.School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China
    2.School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China
  • Received:2025-02-14 Revised:2025-01-07

摘要：

电动汽车作为柔性负荷参与新型电力系统的优化调度可提高新能源消纳，为加快构建新型电力系统提供了一种新途径。针对电动汽车同时参与价格型需求响应与激励型需求响应的新型电力系统源荷低碳优化调度问题，本文基于麻雀搜索算法优化卷积长短时记忆神经网络，给出一种考虑电动汽车需求响应特性的新型电力系统源荷日前-日内低碳优化调度方法。首先，对新能源和基本负荷历史数据利用麻雀搜索算法优化卷积长短时记忆神经网络进行预测，降低源荷两侧的不确定性对新型电力系统日前-日内优化调度的影响；其次，以电动汽车参与需求响应的充电特性，将电动汽车分为三类充电模式，考虑阶梯式碳交易的系统总成本和污染气体排放最优为目标构建源荷互动的日前-日内两阶段低碳环境经济调度模型；最后，利用改进多目标灰狼算法对模型进行求解。算例分析选取典型日的光伏、风电与负荷数据，并综合考虑了电动汽车不同充电模式的需求响应特性。通过4种运行场景下的优化调度结果可知，场景4与场景1相比，较传统方法总成本降低10.3%、污染物排放减少10.9%、新能源消纳提高4.2%，日前-日内低碳优化调度方法可有效提高新能源消纳和新型电力系统的低碳环境经济综合效益。

关键词: 电动汽车, 源荷预测, 新型电力系统, 日前-日内优化调度, 低碳优化调度

Abstract:

The participation of electric vehicles as flexible loads in the optimal scheduling of new power systems can improve the consumption of new energy and provide a new way to speed up the construction of new power systems. Aiming at the low-carbon optimal scheduling problem of new power system sources and loads in which electric vehicles participate in both price-based demand response and incentive demand response, this paper proposes a new day-ahead-day low-carbon optimal scheduling method of power system sources and loads considering the demand response characteristics of electric vehicles based on the sparrow search algorithm to optimize the convolutional long short-term memory neural network. Firstly, the historical data of new energy and basic load are predicted by using the sparrow search algorithm to optimize the convolutional long-term and short-term memory neural network, so as to reduce the influence of the uncertainty on both sides of the source and load on the day-ahead-day optimal scheduling of the new power system. Secondly, based on the charging characteristics of electric vehicles participating in demand response, electric vehicles are divided into three types of charging modes. Considering the total system cost of ladder-type carbon trading and the optimal emission of polluting gases, a two-stage low-carbon environmental economic dispatch model of source-load interaction is constructed. Finally, the improved multi-objective grey wolf algorithm is used to solve the model. The example analysis selects the photovoltaic, wind power and load data of typical days, and comprehensively considers the demand response characteristics of different charging modes of electric vehicles. Through the optimal scheduling results under four operating scenarios, it can be seen that compared with scenario 1, the total cost of scenario 4 is reduced by 10.3 %, the pollutant emission is reduced by 10.9 %, and the new energy consumption is increased by 4.2 %. The day-ahead-day low-carbon optimal scheduling method can effectively improve the low-carbon environmental and economic comprehensive benefits of new energy consumption and new power systems.

Key words: electric vehicles, source-load prediction, new power system, day-ahead-intraday optimal scheduling, low carbon optimal scheduling

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