Stochastic optimal control of a domestic microgrid equipped with solar panel and battery

TL;DR

This paper compares stochastic and deterministic optimization methods for managing a domestic microgrid with solar panels and batteries, demonstrating that stochastic methods slightly outperform deterministic ones in cost efficiency.

Contribution

It introduces a comparison between SDDP and MPC algorithms for microgrid energy management, highlighting the benefits of stochastic modeling over deterministic forecasts.

Findings

SDDP outperforms MPC by a few percent in cost savings.

Optimization algorithms significantly outperform heuristic methods.

Stochastic modeling provides better handling of uncertainties.

Abstract

Microgrids are integrated systems that gather and operate energy production units to satisfy consumers demands. This paper details different mathematical methods to design the Energy Management System (EMS) of domestic microgrids. We consider different stocks coupled together - a battery, a domestic hot water tank - and decentralized energy production with solar panel. The main challenge of the EMS is to ensure, at least cost, that supply matches demand for all time, while considering the inherent uncertainties of such systems. We benchmark two optimization algorithms to manage the EMS, and compare them with a heuristic. The Model Predictive Control (MPC) is a well known algorithm which models the future uncertainties with a deterministic forecast. By contrast, Stochastic Dual Dynamic Programming (SDDP) models the future uncertainties as probability distributions to compute optimal policies. We present a fair comparison of these two algorithms to control microgrid. A comprehensive numerical study shows that i) optimization algorithms achieve significant gains compared to the heuristic, ii) SDDP outperforms MPC by a few percents, with a reasonable computational overhead.