| 引用本文: | 田富豪,包铭磊,惠恒宇,等.考虑多重不确定性的虚拟电厂可信备用评估[J].电力系统保护与控制,2025,53(10):45-56.[点击复制] |
| TIAN Fuhao,BAO Minglei,HUI Hengyu,et al.Reserve credit evaluation of virtual power plants considering multiple uncertainties[J].Power System Protection and Control,2025,53(10):45-56[点击复制] |
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| 摘要: |
| 虚拟电厂(virtual power plants, VPP)可为电网运行提供容量可观的备用资源。准确评估和量化VPP的备用是VPP参与电网调控的关键。然而，分布式新能源出力、负荷用电、电价等具有较强的不确定性，可能导致传统基于确定性方法的备用评估结果不可靠。为此，结合VPP的特征，提出了考虑多重不确定性的可信备用定义及评估方法。首先，给出了VPP架构和可信备用定义。然后，构建了考虑各类资源聚合的VPP提供备用的模型。通过蒙特卡洛模拟各类不确定因素，利用核密度估计法确定具有不同置信度的可信备用集合，有效量化了VPP的可信备用。最后，以典型VPP为例进行仿真，验证了所提方法能够有效地刻画多重不确定性条件下VPP所能提供备用的概率特性，能够为调度机构提供更全面可靠的备用信息。 |
| 关键词: 可信备用 虚拟电厂 不确定性 蒙特卡洛 核密度估计 |
| DOI：10.19783/j.cnki.pspc.240932 |
| 投稿时间：2024-07-16修订日期：2024-10-24 |
| 基金项目:江苏省自然科学基金项目资助(BK20232026) |
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| Reserve credit evaluation of virtual power plants considering multiple uncertainties |
| TIAN Fuhao1,BAO Minglei2,HUI Hengyu2,QIU Yutao2,3,DING Yi2 |
| (1. College of Engineers, Zhejiang University, Hangzhou 310015, China; 2. College of Electrical Engineering, Zhejiang
University, Hangzhou 310027, China; 3. State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310007, China) |
| Abstract: |
| Virtual power plants (VPP) can provide considerable reserve capacity for power grid operation. Accurately evaluating and quantifying the reserve capacity of VPP is key to their participating in power grid regulation. However, the strong uncertainties associated with distributed renewable energy output, load consumption, electricity price, and other factors can lead to unreliable results when using traditional deterministic methods for reserve evaluation. To address this, a definition and evaluation method for reserve credit under multiple uncertainties is proposed based on the characteristics of VPP. First, the framework of VPP and the definition of reserve credit are introduced. Then, a VPP reserve provision model is constructed considering various resource aggregation. By applying Monte Carlo to model multiple uncertainties and using kernel density estimation, a set of reserve credit with different confidence levels is derived, effectively quantifying the reserve credit of VPP. Finally, a case study on a typical VPP is conducted. It demonstrates that the proposed method can effectively reflect the probability characteristics of the reserves that VPP can provide considering multiple uncertainties, providing dispatch agencies with more comprehensive and reliable reserve information. |
| Key words: reserve credit virtual power plant (VPP) uncertainty Monte Carlo kernel density estimation |
