Equivalent modeling of active distribution network considering the spatial uncertainty of renewable energy resources

Abstract Modeling the uncertainties of active distribution networks (ADNs) is essential to dispatch process. Existing techniques focus on the uncertainty of the forecasting errors of distributed generation (DG) caused by changing weather conditions. This paper proposes an equivalent modeling approach for ADNs considering the spatial uncertainty of DG, which is caused by the dispersed locations of distributed renewable energy resources. The boundary injections of this model are composed of deterministic and uncertain components. The deterministic component is calculated based on the fitted power characteristics, while the uncertain component is described using a probability distribution. Model parameters of both components are estimated using an enhanced reinforcement learning algorithm to track the time-varying equivalent loads and DG. Simulation results demonstrate that the equivalent ADN model can accurately represent the aggregated feature of the ADN considering the spatial uncertainty caused by distributed renewable energy resources.