Optimization of a domestic microgrid equipped with solar panel and battery: Model Predictive Control and Stochastic Dual Dynamic Programming approaches

TL;DR

This paper compares Model Predictive Control and Stochastic Dual Dynamic Programming for managing a solar-powered microgrid with storage, demonstrating that both outperform rule-based policies, with SDDP generally providing better results.

Contribution

It introduces a fair comparison scheme between MPC and SDDP for microgrid management, highlighting the advantages of stochastic modeling over deterministic forecasts.

Findings

SDDP outperforms MPC in most scenarios

Both algorithms significantly outperform rule-based policies

SDDP shows better average and out-of-sample performance

Abstract

In this study, a microgrid with storage (battery, hot water tank) and solar panel is considered. We benchmark two algorithms, MPC and SDDP, that yield online policies to manage the microgrid, and compare them with a rule based policy. 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 stagewise independent random variables with known probability distributions. We present a scheme, based on out-of-sample validation, to fairly compare the two online policies yielded by MPC and SDDP. Our numerical studies put to light that MPC and SDDP achieve significant gains compared to the rule based policy, and that SDDP overperforms MPC not only on average but on most of the out-of-sample assessment scenarios.