A Reinforcement Learning-Based Decision System for Electricity Pricing Plan Selection by Smart Grid End Users

With the development of deregulated retail power markets, it is possible for end users equipped with smart meters and controllers to optimize their consumption cost portfolios by choosing various pricing plans from different retail electricity companies. This article proposes a reinforcement learning-based decision system for assisting the selection of electricity pricing plans, which can minimize the electricity payment and consumption dissatisfaction for individual smart grid end user. The decision problem is modeled as a transition probability-free Markov decision process (MDP) with improved state framework. The proposed problem is solved using a Kernel approximator-integrated batch Q-learning algorithm, where some modifications of sampling and data representation are made to improve the computational and prediction performance. The proposed algorithm can extract the hidden features behind the time-varying pricing plans from a continuous high-dimensional state space. Case studies are based on data from real-world historical pricing plans and the optimal decision policy is learned without a priori information about the market environment. Results of several experiments demonstrate that the proposed decision model can construct a precise predictive policy for individual user, effectively reducing their cost and energy consumption dissatisfaction.

Automated summary

This article proposes a reinforcement learning-based decision system to help end users optimize their consumption cost portfolios by choosing various pricing plans from different retail electricity companies. The algorithm improves computational and prediction performance by using an improved state framework and a batch Q-learning algorithm integrated with a Kernel approximator. Results show that the proposed decision model effectively reduces cost and energy consumption dissatisfaction for individual users.