Risk-Embedded Scheduling Optimization for a Virtual Power Plant Under Carbon Emission Trading Constraints

Virtual power plants (VPPs), as a novel integrated energy aggregation model, can effectively realize peak shaving and valley filling in the power system. This paper mainly investigates the model of a VPP consisting of distributed energy resources (DERs) as energy producers and converters, and an electric vehicle parking lot (EVPL) serving as both a battery storage system and an energy consumer. To mitigate the risks associated with uncertainties, a downside risk constraint method is concerned, which has not yet been applied to this model with multiple uncertainties. We concentrate on the risk-embedded scheduling of the VPP with EVPL, taking into account key uncertain factors, such as, the arrival and departure times of the EVs. Numerical results reveal that the primary factor affecting VPP profits is the instability of renewable energy. In our model, compared to risk-neutral strategies, risk-averse strategies lead to a reduction in average profits by 5.66%, highlighting the trade-off between minimizing risk and maximizing profits. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.