Optimal home energy management under hybrid photovoltaic-storage uncertainty: a distributionally robust chance-constrained approach

Pengfei Zhao, Han Wu, Chenghong Gu, Ignacio Hernando Gil

Research output: Contribution to journalArticle

Abstract

Energy storage and demand response resources, in combination with intermittent renewable generation, are expected to provide domestic customers with the capability of reducing their electricity consumption. This paper highlights the role that an intelligent battery control, in combination with solar generation, could play to increase renewable uptake while reducing customers’ electricity bills without intruding on people’s daily life. The optimal performance of a home energy management system (HEMS) is investigated through a range of demand-response (DR) interventions, leading to different levels of customer weariness and consumption patterns. Thus, DR is applied with efficient and specific control of domestic appliances through load shifting and curtailment. Regarding the uncertainty associated with PV generation, a chance-constrained (CC) optimal scheduling is considered subject to the operation constraints from each power component in the HEMS. By applying distributionally robust optimization (DRO), the ambiguity set is accurately built for this distributionally robust chance-constrained (DRCC) problem without the need of any probability distribution associated with uncertainty. Based on the greatly altered consumption profiles in this paper, the proposed DRCC-HEMS is proven to be optimally effective and computationally efficient while considering uncertainty.
LanguageEnglish
Number of pages9
JournalIET Renewable Power Generation
Early online date10 May 2019
DOIs
StatusE-pub ahead of print - 10 May 2019

Cite this

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title = "Optimal home energy management under hybrid photovoltaic-storage uncertainty: a distributionally robust chance-constrained approach",
abstract = "Energy storage and demand response resources, in combination with intermittent renewable generation, are expected to provide domestic customers with the capability of reducing their electricity consumption. This paper highlights the role that an intelligent battery control, in combination with solar generation, could play to increase renewable uptake while reducing customers’ electricity bills without intruding on people’s daily life. The optimal performance of a home energy management system (HEMS) is investigated through a range of demand-response (DR) interventions, leading to different levels of customer weariness and consumption patterns. Thus, DR is applied with efficient and specific control of domestic appliances through load shifting and curtailment. Regarding the uncertainty associated with PV generation, a chance-constrained (CC) optimal scheduling is considered subject to the operation constraints from each power component in the HEMS. By applying distributionally robust optimization (DRO), the ambiguity set is accurately built for this distributionally robust chance-constrained (DRCC) problem without the need of any probability distribution associated with uncertainty. Based on the greatly altered consumption profiles in this paper, the proposed DRCC-HEMS is proven to be optimally effective and computationally efficient while considering uncertainty.",
author = "Pengfei Zhao and Han Wu and Chenghong Gu and {Hernando Gil}, Ignacio",
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AU - Hernando Gil, Ignacio

PY - 2019/5/10

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AB - Energy storage and demand response resources, in combination with intermittent renewable generation, are expected to provide domestic customers with the capability of reducing their electricity consumption. This paper highlights the role that an intelligent battery control, in combination with solar generation, could play to increase renewable uptake while reducing customers’ electricity bills without intruding on people’s daily life. The optimal performance of a home energy management system (HEMS) is investigated through a range of demand-response (DR) interventions, leading to different levels of customer weariness and consumption patterns. Thus, DR is applied with efficient and specific control of domestic appliances through load shifting and curtailment. Regarding the uncertainty associated with PV generation, a chance-constrained (CC) optimal scheduling is considered subject to the operation constraints from each power component in the HEMS. By applying distributionally robust optimization (DRO), the ambiguity set is accurately built for this distributionally robust chance-constrained (DRCC) problem without the need of any probability distribution associated with uncertainty. Based on the greatly altered consumption profiles in this paper, the proposed DRCC-HEMS is proven to be optimally effective and computationally efficient while considering uncertainty.

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