A Stochastic Game Framework for Efficient Energy Management in Microgrid Networks
| Autor: | Annanya Pratap Singh Chauhan, Prishita Ray, Abhinava Sikdar, Sai Koti Reddy Danda, Shalabh Bhatnagar, Chanakya Ajit Ekbote, Shravan Nayak, Raghuram Bharadwaj Diddigi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Operations research Energy management business.industry Computer science 020209 energy 020208 electrical & electronic engineering Stochastic game 02 engineering and technology Systems and Control (eess.SY) Electrical Engineering and Systems Science - Systems and Control Scheduling (computing) Renewable energy Computer Science - Computer Science and Game Theory Dynamic pricing 0202 electrical engineering electronic engineering information engineering FOS: Electrical engineering electronic engineering information engineering Microgrid business Database transaction Efficient energy use Computer Science and Game Theory (cs.GT) |
| Zdroj: | ISGT-Europe |
| Popis: | We consider the problem of energy management in microgrid networks. A microgrid is capable of generating a limited amount of energy from a renewable resource and is responsible for handling the demands of its dedicated customers. Owing to the variable nature of renewable generation and the demands of the customers, it becomes imperative that each microgrid optimally manages its energy. This involves intelligently scheduling the demands at the customer side, selling (when there is a surplus) and buying (when there is a deficit) the power from its neighboring microgrids depending on its current and future needs. Typically, the transaction of power among the microgrids happens at a pre-decided price by the central grid. In this work, we formulate the problems of demand and battery scheduling, energy trading and dynamic pricing (where we allow the microgrids to decide the price of the transaction depending on their current configuration of demand and renewable energy) in the framework of stochastic games. Subsequently, we propose a novel approach that makes use of independent learners Deep Q-learning algorithm to solve this problem. Through extensive empirical evaluation, we show that our proposed framework is more beneficial to the majority of the microgrids and we provide a detailed analysis of the results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|