Abstract
Local energy storage and smart energy scheduling can be used to flatten energy profiles with undesirable peaks. Extending a recently developed model to allow controllable loads, we present Centralized and Decentralized Model Predictive Control algorithms to reduce these peaks. Numerical results show that the additional degree of freedom leads to improved performance.
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Braun, P., Grüne, L., Kellett, C.M., Weller, S.R., Worthmann, K. (2016). Model Predictive Control of Residential Energy Systems Using Energy Storage and Controllable Loads. In: Russo, G., Capasso, V., Nicosia, G., Romano, V. (eds) Progress in Industrial Mathematics at ECMI 2014. ECMI 2014. Mathematics in Industry(), vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-23413-7_85
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DOI: https://doi.org/10.1007/978-3-319-23413-7_85
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