Scholarlink

<p>The significant volatility of distributed generation and the uncoordinated charging behavior of Electric Vehicles (EVs) exacerbate the peak-valley disparity in industrial park distribution networks, adversely affecting the stable operation of power systems. To address this issue, this paper proposes a two-stage optimal scheduling strategy for peak shaving and valley filling, taking into account Photovoltaic (PV) systems, EVs, and Battery Energy Storage Systems (BESS). In the first stage, a Long Short-Term Memory (LSTM) network forecasts the short-term PV output. Based on the forecasted PV generation and time-of-use electricity pricing, a heuristic rule-based weight allocation method is introduced to determine the charging schedule of EVs. In the second stage, considering charging/discharging efficiency, capacity constraints, and the full life-cycle cost of BESS, an optimal scheduling model is developed to minimize the variance of grid load. An improved hybrid Genetic Algorithm (GA) is adopted to solve the model, effectively reducing peak-valley load differences. The proposed method is evaluated using real load data from an industrial park, demonstrating that the peak load is reduced from 958.4 to 917.2&#xa0;kW, while the valley load increases from 61 to 124.5&#xa0;kW, achieving an 11.7% reduction in peak-valley disparity and significantly improving the load curve.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Two-Stage Collaborative Scheduling Strategy for Peak Shaving and Valley Filling in Distribution Networks Considering PV, EV, and BESS

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月17日

查看全文
  • Xinyu You,
  • Yang Wang,
  • Zihong Song,
  • Zhukui Tan,
  • Yongxiang Cai,
  • Mingjun He,
  • Feng Xiao

摘要

The significant volatility of distributed generation and the uncoordinated charging behavior of Electric Vehicles (EVs) exacerbate the peak-valley disparity in industrial park distribution networks, adversely affecting the stable operation of power systems. To address this issue, this paper proposes a two-stage optimal scheduling strategy for peak shaving and valley filling, taking into account Photovoltaic (PV) systems, EVs, and Battery Energy Storage Systems (BESS). In the first stage, a Long Short-Term Memory (LSTM) network forecasts the short-term PV output. Based on the forecasted PV generation and time-of-use electricity pricing, a heuristic rule-based weight allocation method is introduced to determine the charging schedule of EVs. In the second stage, considering charging/discharging efficiency, capacity constraints, and the full life-cycle cost of BESS, an optimal scheduling model is developed to minimize the variance of grid load. An improved hybrid Genetic Algorithm (GA) is adopted to solve the model, effectively reducing peak-valley load differences. The proposed method is evaluated using real load data from an industrial park, demonstrating that the peak load is reduced from 958.4 to 917.2 kW, while the valley load increases from 61 to 124.5 kW, achieving an 11.7% reduction in peak-valley disparity and significantly improving the load curve.