Methods for Mitigating Uncertainty in Real-Time Operations of a Connected Microgrid

TL;DR

This paper compares various control strategies for microgrid energy management under uncertainty, demonstrating that deep reinforcement learning with implicit weather forecasts can outperform traditional methods and stochastic MPC in reducing operational costs.

Contribution

It introduces and evaluates a novel DRL-based control approach that implicitly forecasts weather, outperforming existing methods in real-time microgrid management.

Findings

DRL with implicit forecast reduces costs by 1.3% over stochastic MPC.

Stochastic MPC outperforms deterministic MPC with simple forecasts.

Weather-based control strategies enhance microgrid operation efficiency.

Abstract

In this paper, we compare the effectiveness of a two-stage control strategy for the energy management system (EMS) of a grid-connected microgrid under uncertain solar irradiance and load demand using a real-world dataset from an island in Southeast Asia (SEA). The first stage computes a day-ahead commitment for power profile exchanged with the main grid, while the second stage focuses on real-time controls to minimize the system operating cost. Given the challenges in accurately forecasting solar irradiance for a long time horizon, scenario-based stochastic programming (SP) is considered for the first stage. For the second stage, as the most recent weather conditions can be used, several methodologies to handle the uncertainties are investigated, including: (1) the rule-based method historically deployed on EMS, (2) model predictive controller (MPC) using either an explicit forecast or scenario-based stochastic forecast, and (3) Deep Reinforcement Learning (DRL) computing its own implicit forecast through a distribution of costs. Performances of these methodologies are compared in terms of precision with a reference control assuming perfect forecast -- i.e. representing the minimal achievable operation cost in theory. Obtained results show that MPC with a stochastic forecast outperforms MPC with a simple deterministic prediction. This suggests that using an explicit forecast, even within a short time window, is challenging. Using weather conditions can, however, be more efficient, as demonstrated by DRL (with implicit forecast), outperforming MPC with stochastic forecast by 1.3\%.