Optimal Power Flow Management for Grid Connected PV Systems With Batteries

TLDR

This study uniquely incorporates battery ageing into the optimization and adopts a day‑ahead power‑management approach. The goal is to enable high PV penetration by providing low‑cost peak‑shaving service through optimal power management of grid‑connected PV systems with storage. A power supervisor using an optimal predictive scheduling algorithm based on dynamic programming, benchmarked against rule‑based control, is evaluated through simulations and real‑day tests. Simulations demonstrate cost‑effective peak shaving and reduced grid fluctuations, while real‑world tests show that forecast accuracy limits schedule efficiency, indicating a need for further reactive‑power optimization.

Abstract

This paper presents an optimal power management mechanism for grid connected photovoltaic (PV) systems with storage. The objective is to help intensive penetration of PV production into the grid by proposing peak shaving service at the lowest cost. The structure of a power supervisor based on an optimal predictive power scheduling algorithm is proposed. Optimization is performed using Dynamic Programming and is compared with a simple ruled-based management. The particularity of this study remains first in the consideration of batteries ageing into the optimization process and second in the “day-ahead” approach of power management. Simulations and real conditions application are carried out over one exemplary day. In simulation, it points out that peak shaving is realized with the minimal cost, but especially that power fluctuations on the grid are reduced which matches with the initial objective of helping PV penetration into the grid. In real conditions, efficiency of the predictive schedule depends on accuracy of the forecasts, which leads to future works about optimal reactive power management.