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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.
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment
FingerprintDive into the research topics of 'Optimal home energy management under hybrid photovoltaic-storage uncertainty: a distributionally robust chance-constrained approach'. Together they form a unique fingerprint.
- 1 Finished
Fellowship - Multi-Vector Energy Distribution System Modelling and Optimisation with Integrated Demand Side Response
1/09/14 → 31/08/17
Project: Research council