An alternative optimal strategy for stochastic model predictive control of a residential battery energy management system with solar photovoltaic

Scenario-based stochastic model predictive control traditionally considers the optimal strategy to be the expectation of the optimal strategies across all scenarios. However, while the stochastic problem involving uncertainties can be substantiated by a large number of scenarios, the expectation of...

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Bibliographic Details
Published in:Applied energy Vol. 283; p. 116289
Main Authors: van der Meer, Dennis, Wang, Guang Chao, Munkhammar, Joakim
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.02.2021
Subjects:
autocorrelation
batteries
case studies
energy
Engineering Science with specialization in Civil Engineering and Built Environment
Gradient boosting
management systems
Multivariate forecasting
prediction
Quantile regression
Scenario based
solar collectors
solar energy
Stochastic model predictive control
stochastic processes
Sweden
Teknisk fysik med inriktning mot byggteknik och byggd miljö
variance covariance matrix
Sweden
Stochastic model predictive control
Scenario based
Quantile regression
Multivariate forecasting
Gradient boosting
ISSN:0306-2619, 1872-9118, 1872-9118
Online Access:Get full text
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Summary:Scenario-based stochastic model predictive control traditionally considers the optimal strategy to be the expectation of the optimal strategies across all scenarios. However, while the stochastic problem involving uncertainties can be substantiated by a large number of scenarios, the expectation of the respective optimal control strategies derived from all scenarios as the optimal control strategy to the problem is challenging to justify. We therefore propose a different approach in which we artfully have the optimization program find the common optimal strategy across all scenarios for the first prediction step at each sample time, which, if it exists, yields the true optimal strategy with greater confidence. We demonstrate the efficacy of the proposed formulation through a case study of a research villa in Borås, Sweden, that is equipped with a battery and a photovoltaic system. We compute a covariance matrix that contains time-dependent information of the data and use it to generate autocorrelated scenarios from the probabilistic forecasts that serve as the uncertain input to the energy management system. We justify the credibility of the optimal solution derived from the proposed formulation with compelling reasoning and quantitative results such as improved self-consumption of photovoltaic power. •We reformulate the scenario-based stochastic model predictive control algorithm.•We add a constraint that forces the control strategy to be equal across scenarios.•The reformulation is tested on a system with a battery, load and PV system.•The probabilistic forecasts and multivariate forecasts are thoroughly evaluated.•The reformulation results in increased profit, self-consumption and self-sufficiency.
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ISSN:0306-2619
1872-9118
1872-9118
DOI:10.1016/j.apenergy.2020.116289