Controlling Microgrids Without External Data: A Benchmark of Stochastic Programming Methods

TL;DR

This paper introduces four stochastic control algorithms for microgrid energy management that operate solely on historical data, outperforming traditional MPC methods and matching the performance of ideal forecasts across diverse setups.

Contribution

The paper presents a novel benchmark of stochastic programming methods for microgrid control using only historical data, avoiding reliance on external forecasts.

Findings

Most effective algorithm reduces costs significantly compared to MPC with same uncertainty model.

Algorithms perform similarly to an ideal MPC with perfect forecasts.

Benchmark conducted across 61 microgrid configurations using EMSx dataset.

Abstract

Microgrids are local energy systems that integrate energy production, demand, and storage units. They are generally connected to the regional grid to import electricity when local production and storage do not meet the demand. In this context, Energy Management Systems (EMS) are used to ensure the balance between supply and demand, while minimizing the electricity bill, or an environmental criterion. The main implementation challenges for an EMS come from the uncertainties in the consumption, the local renewable energy production, and in the price and the carbon intensity of electricity. Model Predictive Control (MPC) is widely used to implement EMS but is particularly sensitive to the forecast quality, and often requires a subscription to expensive third-party forecast services. We introduce four Multistage Stochastic Control Algorithms relying only on historical data obtained from on-site measurements. We formulate them under the shared framework of Multistage Stochastic Programming and benchmark them against two baselines in 61 different microgrid setups using the EMSx dataset. Our most effective algorithm produces notable cost reductions compared to an MPC that utilizes the same uncertainty model to generate predictions, and it demonstrates similar performance levels to an ideal MPC that relies on perfect forecasts.