Open AccessOptimal home energy management under hybrid photovoltaic‐storage uncertainty: a distributionally robust chance‐constrained approach
Author(s) -
Zhao Pengfei,
Wu Han,
Gu Chenghong,
HernandoGil Ignacio
Publication year - 2019
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2018.6169
Subject(s) - photovoltaic system , computer science , demand response , renewable energy , energy management , robust optimization , mathematical optimization , electricity , energy storage , automotive engineering , scheduling (production processes) , electricity generation , reliability engineering , operations research , environmental economics , engineering , power (physics) , energy (signal processing) , economics , electrical engineering , mathematics , statistics , physics , quantum mechanics
Energy storage and demand response (DR) resources, in combination with intermittent renewable generation, are expected to provide domestic customers with the ability to reducing their electricity consumption. This study 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 interventions, leading to different levels of customer weariness and consumption patterns. Thus, the DR is applied with efficient and specific control of domestic appliances through load shifting and curtailment. Regarding the uncertainty associated with the photovoltaic 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 optimisation, the ambiguity set is accurately built for this distributionally robust CC (DRCC) problem without the need for any probability distribution associated with uncertainty. Based on the greatly altered consumption profiles in this study, the proposed DRCC‐HEMS is proven to be optimally effective and computationally efficient while considering uncertainty.