Multi-Agent Q-Learning for Minimizing Demand-Supply Power Deficit in Microgrids

TL;DR

This paper applies multi-agent Q-learning to optimize power distribution in microgrids, aiming to minimize demand-supply deficits while considering renewable energy uncertainty and production costs.

Contribution

It introduces a distributed multi-agent Q-learning approach for demand-supply balancing in microgrids with renewable energy and cost considerations.

Findings

Effective reduction in demand-supply deficit achieved

Adaptive power management under renewable energy uncertainty

Incorporation of cost constraints improves operational efficiency

Abstract

We consider the problem of minimizing the difference in the demand and the supply of power using microgrids. We setup multiple microgrids, that provide electricity to a village. They have access to the batteries that can store renewable power and also the electrical lines from the main grid. During each time period, these microgrids need to take decision on the amount of renewable power to be used from the batteries as well as the amount of power needed from the main grid. We formulate this problem in the framework of Markov Decision Process (MDP), similar to the one discussed in [1]. The power allotment to the village from the main grid is fixed and bounded, whereas the renewable energy generation is uncertain in nature. Therefore we adapt a distributed version of the popular Reinforcement learning technique, Multi-Agent Q-Learning to the problem. Finally, we also consider a variant of this problem where the cost of power production at the main site is taken into consideration. In this scenario the microgrids need to minimize the demand-supply deficit, while maintaining the desired average cost of the power production.