Energy Route Multi-Objective Optimization of Wireless Power Transfer Network: An Improved Cross-Entropy Method

This paper identifies the Wireless Power Transfer Network (WPTN) as an ideal model for long-distance Wireless Power Transfer (WPT) in a certain region with multiple electrical equipment. The schematic circuit and design of each power node and the process of power transmission between the two power nodes are elaborated. The Improved Cross-Entropy (ICE) method is proposed as an algorithm to solve for optimal energy route. Non-dominated sorting is introduced for optimization. A demonstration of the optimization result of a 30-nodes WPTN system based on the proposed algorithm proves ICE method to be efficacious and efficiency

Model-Free Reinforcement Learning Mining the Optimal Policy in DICE Model

Economic dynamic models of climate change usually involve many variables, complex dynamics and uncertainties. However, the limited computing tools do not have abilities to include these factors, so the complexity of the models is often reduced. In this paper, I investigated if the model-free deep reinforcement learning (RL) approach can provide a viable alternative in finding optimal strategies in IAM models with multi-objectives. As the first step to promote RL in large-scale IAM models, I adapted an IAM based on DICE baseline into an OpenAI Gym environment. I use stable_baseline 3 as the RL framework, and apply Soft Actor Critic (SAC), which is one of the most advanced model-free RL training algorithms nowadays. Policy was learned through interactions with the environment without knowledge of model dynamics. After 88,000 timesteps, I got a learning strategy in my model, which consists of annual abatement rate and consumption. The results are compared with the baseline policy data in DICE 2013, showing a high consistency and accuracy. It demonstrates the potential of RL framework for economic dynamics