Robust Optimization Aggregation Method for Distributed Resources in Virtual Power Plants Considering Internal Load Forecasting Errors

A virtual power plant (VPP) coordinates internal power output through resource aggregation to maximize its own revenue. Existing aggregation methods overlook the impact of internal load forecasting errors within the VPP, which, under complex climate conditions, may lead to reduced coordination efficiency due to load fluctuations, thereby affecting overall economic performance. To address this issue, an objective function was constructed that comprehensively considers electricity sales revenue, power supply revenue, and resource costs, with a load disturbance term introduced to characterize the influence of load forecasting errors. A dynamic robust budget adjustment mechanism was employed, which adapts the robust budget based on the standard deviation of historical load errors to balance robustness and economic efficiency. Furthermore, the operating constraints of distributed resources were analyzed under multiple external factors, including power balance, small unit output, storage device power, and energy deviation. Accordingly, a distributed resource robust optimization aggregation model considering internal load errors was established. Case studies demonstrate that the proposed method can effectively reduce market penalties caused by internal load errors and ensure the robustness and feasibility of resource aggregation schemes under complex environments.