A Machine Intelligence based Storage Allocator for Virtual Power Plant Applications

B. Abegaz
Loyola University Chicago,
United States

Keywords: power grids, energy storage systems (ess), directional induction, virtual power plant (VPP), performance


A machine intelligence-based storage allocator is proposed to solve the problem of real-time and intelligent control of energy storage systems integrated into a virtual power plant. The virtual power plant has ten sets of 200 V, 500 Ah Lithium-ion grid tied energy storage systems with an additional capacity of 10kWh from electric vehicles. The proposed system computes the feasible storage allocation in a virtual power plant setting using temporal, forward and reverse directional inferences, taking the frequency of dispatch, state of charge, depth of discharge, overcharge, undercharge, and leakage conditions of the storage units into consideration. The results of the implementation show that the system could provide up to 49% improvements in the reliable performance of energy storage systems, thus enabling them to be used for virtual power plant applications. Suggestions to use the proposed allocator for voltage regulation as a means of paving the way towards a more sustainable and modern electric power grid are presented.