Assisted Energy Management in Smart Microgrids

TL;DR

This paper explores energy management in smart microgrids by using forward contracts and machine learning to improve service availability and reduce costs amid renewable energy variability.

Contribution

It introduces a novel approach combining policy-based brokers and neural network-based learning brokers for efficient energy management in microgrids.

Findings

Neural network brokers reduce reimbursement costs over time.

Learning brokers maximize overall profit in microgrid energy management.

Forward contracts improve resource allocation under renewable energy uncertainty.

Abstract

Demand response provides utilities with a mechanism to share with end users the stochasticity resulting from the use of renewable sources. Pricing is accordingly used to reflect energy availability, to allocate such a limited resource to those loads that value it most. However, the strictly competitive mechanism can result in service interruption in presence of competing demand. To solve this issue we investigate on the use of forward contracts, i.e., service level agreements priced to reflect the expectation of future supply and demand curves. Given the limited resources of microgrids, service interruption is an opposite objective to the one of service availability. We firstly design policy-based brokers and identify then a learning broker based on artificial neural networks. We show the latter being progressively minimizing the reimbursement costs and maximizing the overall profit.