CVaR-Based Optimization Framework for Virtual Power Plants Aggregation

Virtual power plant (VPP) technology mitigates the stochastic nature of distributed generation by aggregating distributed power sources, energy storage devices, and controllable loads for coordinated control. This paper studies the aggregation problem of multiple virtual power plants based on conditional value at risk (CVaR) risk control method. A controllable unit renter (CUR) is introduced, and the renter guides the virtual power plants to rent controllable units with suitable capacity and price for operation optimization by adjusting the unit rental price, so that the overall revenue is maximized. In this paper, a bi-level optimization model is established, in which the upper model is optimized with the objective of maximizing the operating revenue of CUR, and the lower model is optimized with the objective of minimizing the operating cost of the VPPs. The bi-level nonlinear optimization model is transformed into a single-level mixed-integer linear programming model to be solved by KKT, Big-M method, and strong dyadic theory.