Stochastic energy management of DC photovoltaic microgrids using Markov decision process

Abstract

The increasing reliance on renewable energy sources, particularly photovoltaic (PV) systems, in off-grid applications presents a critical challenge: managing energy supply under random load behavior and intermittent resource availability. Autonomous PV microgrids encounter significant stability and efficiency challenges stemming from the intrinsic unpredictability of energy generation and consumption. While existing research has explored various control strategies, significant gaps remain in real-time power demand estimation and adaptive energy management under stochastic load conditions. This study addresses these challenges by proposing a stochastic predictive control (SPC) approach, integrating a Markov decision process (MDP) to enhance energy management decision-making. This approach optimizes the real-time balance between power generation, load consumption, and energy storage, even under unpredictable load variations. The simulation results in realistic scenarios demonstrate the effectiveness of the proposed method in stabilizing the microgrid, reducing oscillations, and managing battery charge and discharge cycles. By providing a robust and adaptive solution, this study advances the field of autonomous PV DC microgrids, improving system resilience and energy utilization. The findings have significant implications for enhancing the performance and reliability of off-grid renewable energy applications. • A new stochastic predictive control-based energy management strategy is developed for PV microgrid systems with unpredictable consumption. • The system's stability is ensured under stochastic operation. • The proposed approach improves the efficiency of the PV microgrid in stochastic scenarios and outperforms existing algorithms. • The efficiency of the control strategy is tested under real-time climatic profiles and unpredictable load behavior.

Topics & Concepts