Energy Management System for an Industrial Microgrid Using Optimization Algorithms-Based Reinforcement Learning Technique

Saugat Upadhyay, Ibrahim Ahmed and Lucian Mihet-Popa ()
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Saugat Upadhyay: Faculty of Information Technology, Engineering and Economics, Østfold University College, Kobberslagerstredet 5, 1671 Fredrikstad, Norway
Ibrahim Ahmed: Faculty of Information Technology, Engineering and Economics, Østfold University College, Kobberslagerstredet 5, 1671 Fredrikstad, Norway
Lucian Mihet-Popa: Faculty of Information Technology, Engineering and Economics, Østfold University College, Kobberslagerstredet 5, 1671 Fredrikstad, Norway

Energies, 2024, vol. 17, issue 16, 1-18

Abstract: The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront, and the research interest in microgrids that rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper builds on the existing research framework by combining PPO with machine learning-based load forecasting to produce an optimal solution for an industrial microgrid in Norway under different pricing schemes, including day-ahead pricing and peak pricing. It addresses the peak shaving and price arbitrage challenges by taking the historical data into the algorithm and making the decisions according to the energy consumption pattern, battery characteristics, PV production, and energy price. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared with logic-based optimization. These findings contribute to digitalization and decarbonization of energy technology, and support the fundamental goals and policies of the European Green Deal.

Keywords: EMS; PPO; BESS; optimization algorithm; peak shaving; price arbitrage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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