Multi-Agent Reinforcement Learning Controller to Maximize Energy Efficiency for Multi-Generator Industrial Wave Energy Converter

Abstract

Waves in the oceans are one of the most significant renewable energy sources and are an excellent resource to tackle climate challenges through decarbonizing energy generation. Lowering the Levelized Cost of Energy (LCOE) for energy generation from ocean waves is critical for competitiveness with other forms of clean energy like wind and solar. It requires complex controllers to maximize efficiency for state-of-the-art multi-generator industrial Wave Energy Converters (WEC), which optimizes the reactive forces of the generators on multiple legs of WEC. This paper introduces Multi-Agent Reinforcement Learning controller (MARL) architectures that can handle these various objectives for LCOE. MARL can help increase energy capture efficiency to boost revenue, reduce structural stress to limit maintenance cost, and adaptively and proactively protect the wave energy converter from catastrophic weather events preserving investments and lowering effective capital cost. These architectures include 2-agent and 3-agent MARL implementing proximal policy optimization (PPO) with various optimizations to help sustain the training convergence in the complex hyperplane without falling off the cliff. Also, the design for trust assures the operation of WEC within a safe zone of mechanical compliance. As a part of this design, reward shaping for multiple objectives of energy capture and penalty for harmful motions minimizes stress and lowers the cost of maintenance. We achieved double-digit gains in energy capture efficiency across the waves of different principal frequencies over the baseline Spring Damper controller with the proposed MARL controllers.