Online battery scheduling for grid-connected photo-voltaic systems

Abstract

Abstract We study the problem of determining optimum policy for managing battery energy storage system (BESS) in grid-connected photo-voltaic (PV) systems, where the stochastic electricity demands from the load are met from three sources: grid, PV energy, and BESS. BESS is used either to store excess energy generated from PV systems for later use, or to purchase energy from the grid when the time-of-use (TOU) pricing is lower. The objective is to identify the optimum charging/discharging schedule of BESS so that the long-term cost of energy purchased from the grid is minimized. The stochastic variabilities in loads and PV energy are captured by employing probabilistic models of periodic stochastic process with parameters estimated using historical data. The optimization problem is formulated under the framework of periodic discounted Markov decision process (MDP), and the problem formulation includes the aging effects of batteries and solar panels. The online optimization problem is solved by adopting a policy iteration approach tailored for periodic MDP. The proposed online scheduling algorithm provides periodic policies for a period of 24-hour, where the system model is updated every day based on load and PV energy from the previous day in a rolling horizon fashion. Simulation results demonstrate that the proposed algorithm can achieve a 41.6% reduction in annual utility bills compared to conventional systems without PV and BESS, thus ascertaining the values of installing BESS and PV systems.