Ecosystem based Opportunity Identification and Feasibility Evaluation for Demand Side Management Solutions

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Abstract

Demand response is important to integrate renewable energy sources into the energy system, and maintain frequency stability and security of supply. Demand-side management (DSM) is essential for energy consumers to participate in demand response. The adoption of DSM solutions relies on financial incentives, technology readiness, regulatory support, but also the adoption environment and conditions that vary in different regions. However, no available method facilitates this investigation process. This paper uses a case of residential DSM solutions in Denmark to introduce an ecosystem-based instrument that can systematically identify regional needs and evaluate potential DSM solutions. The case study shows several types of DSM appliances and services have been offered to residential consumers, covering certain parts of both implicit and explicit demand response. However, none of the existing DSM solutions has fully managed to target any identified opportunities. Furthermore, three aspects should be focused for promoting the adoption of the DSM solutions: 1) financial incentives for acquiring DSM-enabled technologies; 2) adoption of distributed energy resources; and 3) big data and AI enabled automatic load control.

OriginalsprogEngelsk
Titel2023 IEEE Conference on Power Electronics and Renewable Energy, (CPERE)
Antal sider6
ForlagIEEE
Publikationsdato2023
ISBN (Elektronisk)9781665452335
DOI
StatusUdgivet - 2023
Begivenhed2023 IEEE Conference on Power Electronics and Renewable Energy, CPERE 2023 - Luxor, Egypten
Varighed: 19. feb. 202321. feb. 2023

Konference

Konference2023 IEEE Conference on Power Electronics and Renewable Energy, CPERE 2023
Land/OmrådeEgypten
ByLuxor
Periode19/02/202321/02/2023
SponsorIEEE Power Electronics Society (PELS), OPAL-RT Technologies, Power Electronics Modules (PEModule), RTDS Technologies Inc., Smart Systems Scientific and Lab Equipment

Bibliografisk note

Funding Information:
ACKNOWLEDGMENT This paper is part of the IEA EBC Annex 81 Data-Driven Smart Buildings project, funded by EUDP (project number: 64019-0539) and part of the national project-Digital Energy Hub funded by the Danish Industry Foundation.

Publisher Copyright:
© 2023 IEEE.

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