DPV and HESS Joint Optimal Configuration in Distribution Network Considering Renewable Energy Consumption

The vision of a high proportion of renewable energy development has posed numerous challenges to the planning and operation of power systems, necessitating the simultaneous construction of flexible resources such as ESS. Therefore, a joint optimal allocation method for DPV and HESS in the distribution network is proposed, taking into account the renewable energy consumption. This method is designed to meet the requirements of improving renewable energy consumption, reducing energy storage allocation cost, and improving system flexibility. Firstly, we utilize the LHS method based on the probability distribution of DPV and load to make sure sample uniformly. Subsequently, the time correlation information between DPV output and load demand is extracted based on the Cholesky decomposition method, and typical system operation scenarios after distributed photovoltaic access are constructed. Secondly, considering the characteristics such as flexible power matching and energy spatiotemporal transfer of each part in HESS, the DPV power curve to be transferred is decomposed according to the fluctuation frequency by the EMS using the method of ELMD. After that, PPMCC is employed to reconstruct the decomposed components to obtain high, medium and low frequency components. Subsequently, a power allocation strategy of each device in HESS is formulated. A two-layer optimization model is thus established with the maximum installed distributed photovoltaic capacity and the minimum investment cost as the upper optimization objectives, while the minimum system operating cost and the maximum renewable energy consumption in each typical scenario as the lower optimization objectives. The PSO and mathematical programming algorithms are utilized to solve the upper and lower layers of this model, enabling us to obtain the optimal configuration capacity of DPV and HESS as well as the optimal operation results of power allocation of HESS in each scenario. The simulation results verify the effectiveness of this proposed strategy and method.