Capacity optimal allocation of hybrid energy storage in DC distribution network based on Ensemble Empirical Mode Decomposition

Abstract

In response to fluctuations in the power levels within the link connecting the direct current transmission system to the upper-level power grid, we propose an optimization approach for determining the ability of a Hybrid Energy Storage System (HESS). Our first step involves calculating the power agreement for the link and the total power requirements for the HESS, taking into account the system’s net load power and the dynamic pricing of electricity based on time-of-use. Subsequently, we utilize the Ensemble Empirical Mode Decomposition (EEMD) technique to break down the total energy generated by the HESS into its constituent components. Incorporating instructions for both powering up and losing power of the HESS across varying filter orders, we solve the capacity allocation model for the HESS using a particle swarm algorithm, with rated power and rated capacity as decision variables. Finally, we identify the filter order that yields the best objective function value and the corresponding energy storage configuration scheme. An illustrative analysis, conducted on an enhanced IEEE-14 node DC distribution network, demonstrates that the proposed HESS configuration effectively mitigates tie-line fluctuations and enhances system stability.

Keywords

• DC distribution network
• EEMD
• HESS
• Capacity optimization