A Stochastic Game Framework for Efficient Energy Management in Microgrid Networks

Published in

arXiv.org(2020)

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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

References31 items

Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning

Incentive-based demand response for smart grid with reinforcement learning and deep neural network

Reinforcement learning for demand response: A review of algorithms and modeling techniques

Local Energy Trading Behavior Modeling With Deep Reinforcement Learning

Dynamic pricing for decentralized energy trading in micro-grids

A Stochastic Game Framework for Efficient Energy Management in Microgrid Networks

Published in

arXiv.org(2020)

External Links:

Generate Graph

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

Authors

Shravan Nayak

1 papers

C. Ekbote

1 papers

Annanya Pratap Singh Chauhan

1 papers

References31 items

Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning

Incentive-based demand response for smart grid with reinforcement learning and deep neural network

Reinforcement learning for demand response: A review of algorithms and modeling techniques

Local Energy Trading Behavior Modeling With Deep Reinforcement Learning

Dynamic pricing for decentralized energy trading in micro-grids

Raghuram Bharadwaj Diddigi

1 papers

Prishita Ray

1 papers

Abhinava Sikdar

1 papers

Sai Koti Reddy Danda

1 papers

S. Bhatnagar

1 papers

A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach

Reinforcement Learning-based Energy Trading for Microgrids

A unified decision making framework for supply and demand management in microgrid networks

Risk-Averse Energy Trading in Multienergy Microgrids: A Two-Stage Stochastic Game Approach

Energy Trading Game for Microgrids Using Reinforcement Learning

Deep Reinforcement Learning Solutions for Energy Microgrids Management

Incentivizing Energy Trading for Interconnected Microgrids

Dynamic Pricing and Energy Consumption Scheduling With Reinforcement Learning

Reinforcement Learning in Energy Trading Game Among Smart Microgrids

Multiagent cooperation and competition with deep reinforcement learning

Distributed Optimal Energy Management in Microgrids

The microgrid simulation tool RAPSim: Description and case study

Playing Atari with Deep Reinforcement Learning

Distributed Energy Trading: The Multiple-Microgrid Case

Multiobjective Intelligent Energy Management for a Microgrid

Energy Management in Microgrids Using Demand Response and Distributed Storage—A Multiagent Approach

An Analysis of Stochastic Game Theory for Multiagent Reinforcement Learning

Looking more closely at upcoming and established immunotherapy standards S 73 Oncogene-driven lung cancer : EGFR , METex 14 , ROS 1 , RET S 75 SCLC : prognostic determinants and new treatment modalities S 77

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 )

Field of Study

EngineeringComputer Science

Journal Information

Name

ArXiv

Volume

abs/2005.00687

Venue Information

Name

arXiv.org

Type

URL

https://arxiv.org

Alternate Names

ArXiv