A Stochastic Game Framework for Efficient Energy Management in Microgrid Networks (2020-02-06T00:00:00.000000Z)

TL;DR

A novel approach that makes use of independent learners Deep Q-learning algorithm to solve the problem of energy management in microgrid networks in the framework of stochastic games is proposed.

Abstract

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.

Authors

Shravan Nayak

1 papers

C. Ekbote

1 papers

Annanya Pratap Singh Chauhan

1 papers

TL;DR

Abstract

Authors

References31 items

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Distributed Energy Trading in Microgrids: A Game-Theoretic Model and Its Equilibrium Analysis

Time state ( t ): This is the time interval of the day at which the decision is taken by the microgrid

report the average rewards obtained by the dynamic pricing and constant pricing policies over the last 50 , 000 iterations (after convergence)

This empirically shows that following a DPP as compared to a CPP proves to be more advantageous to a majority of the microgrids, when energy trading occurs between microgrids

We empirically show that the proposed dynamic pricing setup ensures more reward for most of the participating microgrids

We can observe from Figure 2 that the agents learn to schedule the ADL demands at different times which shows that our model is capable of shifting power consumption from the peak demand time

ADL state ( ADL it ): This state component jointly signiﬁes the ADL loads that are remaining and the adl loads that have been completed by the adl agent, till the current time step

The Net Energy ( ne it ): This is a cumulative sum of battery value ( b i t ) and generated renewable energy ( re i t ) subtracted by the Non-ADL demand ( d it )

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