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Article: An integrated smart home energy management model based on a pyramid taxonomy for residential houses with photovoltaic-battery systems

TitleAn integrated smart home energy management model based on a pyramid taxonomy for residential houses with photovoltaic-battery systems
Authors
KeywordsSHEM
Taxonomy
Probabilistic forecasting
User preference inference
Two-stage stochastic programming
Issue Date2021
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/apenergy
Citation
Applied Energy, 2021, v. 298, p. article no. 117159 How to Cite?
AbstractSmart home energy management (SHEM) with residential photovoltaic (PV)-battery systems is a complicated issue with different facets. An integrated SHEM model covering the essential functions is missing. Meanwhile, residential PV-battery systems' optimal operations with renewable energy exchanges and imperfect forecasts are still open challenges. In this study, the research activities in SHEM are firstly organized by a pyramid with four functional layers: (i) Monitoring; (ii) Analyzing and forecasting; (iii) Scheduling; and (iv) Coordinating, which can serve as a standard pathway for developing SHEM. Second, guided by the pyramid taxonomy, an integrated SHEM model is developed for residential houses with PV-battery systems. Assuming a perfect Monitoring layer, we obtain the probabilistic load/PV forecasts and user preference vectors of shiftable appliances based on historical data. Then, we develop a two-stage stochastic programming model for optimal scheduling of single houses with a grid-connected PV-battery system, incorporating the probabilistic forecasts and user preference vectors. A retail electricity market with day-ahead (DA) and real-time (RT) markets is employed for leveraging imperfect forecasts. Finally, we design a distributed coordinating algorithm - Asynchronous Scheduling and Iterative Pricing for PV power-sharing among multiple prosumers based on the single-house scheduling model. Numerical simulations based on realistic loads and PV generation data validated the two-stage stochastic programming model's economic superiority and the distributed PV power-sharing approach compared with the rule-based dispatching and selfish scheduling strategies. We concluded that 1) the modeling of load/PV forecast uncertainties is valuable than averaging or ignoring them, 2) the two-stage stochastic programming model and the DA-RT retail electricity market are beneficial for utilizing imperfect forecasts, and 3) coordinating multiple prosumers could benefit each household by sharing PV and battery investments for revenue or trading with local small prosumers for cost reductions.
Persistent Identifierhttp://hdl.handle.net/10722/300687
ISSN
2023 Impact Factor: 10.1
2023 SCImago Journal Rankings: 2.820
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZheng, Z-
dc.contributor.authorSun, Z-
dc.contributor.authorPan, J-
dc.contributor.authorLuo, X-
dc.date.accessioned2021-06-18T14:55:34Z-
dc.date.available2021-06-18T14:55:34Z-
dc.date.issued2021-
dc.identifier.citationApplied Energy, 2021, v. 298, p. article no. 117159-
dc.identifier.issn0306-2619-
dc.identifier.urihttp://hdl.handle.net/10722/300687-
dc.description.abstractSmart home energy management (SHEM) with residential photovoltaic (PV)-battery systems is a complicated issue with different facets. An integrated SHEM model covering the essential functions is missing. Meanwhile, residential PV-battery systems' optimal operations with renewable energy exchanges and imperfect forecasts are still open challenges. In this study, the research activities in SHEM are firstly organized by a pyramid with four functional layers: (i) Monitoring; (ii) Analyzing and forecasting; (iii) Scheduling; and (iv) Coordinating, which can serve as a standard pathway for developing SHEM. Second, guided by the pyramid taxonomy, an integrated SHEM model is developed for residential houses with PV-battery systems. Assuming a perfect Monitoring layer, we obtain the probabilistic load/PV forecasts and user preference vectors of shiftable appliances based on historical data. Then, we develop a two-stage stochastic programming model for optimal scheduling of single houses with a grid-connected PV-battery system, incorporating the probabilistic forecasts and user preference vectors. A retail electricity market with day-ahead (DA) and real-time (RT) markets is employed for leveraging imperfect forecasts. Finally, we design a distributed coordinating algorithm - Asynchronous Scheduling and Iterative Pricing for PV power-sharing among multiple prosumers based on the single-house scheduling model. Numerical simulations based on realistic loads and PV generation data validated the two-stage stochastic programming model's economic superiority and the distributed PV power-sharing approach compared with the rule-based dispatching and selfish scheduling strategies. We concluded that 1) the modeling of load/PV forecast uncertainties is valuable than averaging or ignoring them, 2) the two-stage stochastic programming model and the DA-RT retail electricity market are beneficial for utilizing imperfect forecasts, and 3) coordinating multiple prosumers could benefit each household by sharing PV and battery investments for revenue or trading with local small prosumers for cost reductions.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/apenergy-
dc.relation.ispartofApplied Energy-
dc.subjectSHEM-
dc.subjectTaxonomy-
dc.subjectProbabilistic forecasting-
dc.subjectUser preference inference-
dc.subjectTwo-stage stochastic programming-
dc.titleAn integrated smart home energy management model based on a pyramid taxonomy for residential houses with photovoltaic-battery systems-
dc.typeArticle-
dc.identifier.emailPan, J: jpan@cs.hku.hk-
dc.identifier.authorityPan, J=rp01984-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.apenergy.2021.117159-
dc.identifier.scopuseid_2-s2.0-85107816198-
dc.identifier.hkuros323043-
dc.identifier.volume298-
dc.identifier.spagearticle no. 117159-
dc.identifier.epagearticle no. 117159-
dc.identifier.isiWOS:000675857000004-
dc.publisher.placeUnited Kingdom-

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