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

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 mo...

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Vydané v:Energy systems (Berlin. Periodical) Ročník 15; číslo 1; s. 115 - 139
Hlavní autori: Pacaud, François, Carpentier, Pierre, Chancelier, Jean-Philippe, De Lara, Michel
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2024
Springer Nature B.V
Springer
Predmet:
Algorithms
Distributed generation
Dynamic programming
Economics and Management
Energy
Energy Policy
Energy Systems
Hot water heating
Independent variables
Mathematics
Operations Research/Decision Theory
Optimization
Optimization and Control
Original Paper
Predictive control
Random variables
Solar energy
Solar panels
Stochastic models
Uncertainty
Water tanks
MPC
SDDP
Electric microgrid
Multistage stochastic optimization
ISSN:1868-3967, 1868-3975
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1868-3967
1868-3975
DOI:10.1007/s12667-022-00522-7