基于条件风险价值风险控制的多电源虚拟电厂机组动态聚合优化模型

Dynamic Aggregation Optimization Model for Multisource Units of VPP Based on CVaR Risk Control

摘要:

虚拟电厂存在源荷不确定性的问题会导致虚拟电厂在获取收益同时也要面对一定的风险损失。针对此问题，提出在日前从现有的多电源机组中选择可调容量合适的机组组建虚拟电厂，就能有效规避风险损失，使虚拟电厂的收益最大化。文中采用条件风险价值作为风险度量的指标，以运行收益最大化和风险损失最小化为优化目标，建立基于条件风险价值点（CVaR）风险控制的多电源虚拟电厂机组动态聚合优化模型。首先，采用场景技术来模拟虚拟电厂中源荷的不确定性；然后，在此基础上研究了虚拟电厂管理者的风险偏好对虚拟电厂机组选择的影响，以及环境惩罚成本和购电电价对虚拟电厂机组选择的影响；最后，通过算例仿真验证了该模型的正确性。结果表明，当虚拟电厂管理者选择合适的风险偏好时，虚拟电厂机组的动态聚合模型可有效降低虚拟电厂的风险损失以及提高虚拟电厂供能的稳定性。

Abstract:

Virtual power plant（VPP）has the problem of generation and load uncertainty, which will lead to VPP facing risk loss while gaining profit. To solve this problem, VPP can be set up by selecting units with suitable adjustable capacity from the existing multisource units in dayahead, so as to effectively avoid the risk loss and maximize the revenue of VPP. In this study,conditionalvalueatrisk（CVaR）is used as the index of risk measurement, and the optimization goal is to maximize the operating revenue and minimize the risk loss. The dynamic aggregation optimization model of multi-source units is established based on CVaR risk control. Firstly, scenario technology is used to simulate the uncertainty of generation and load in VPP. Then, the influence of risk preference of VPP manager, environmental penalty cost and purchasing electricity price on the units selection of VPP is studied. Finally, a simulation example is given to verify the correctness of the proposed model. The result shows that the dynamic aggregation model can effectively reduce the risk loss of VPP and improve the power supply stability of VPP when the VPP manager chooses the appropriate risk preference.