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Conference Papers Year : 2022

CANO: A lightweight CArbon emission and inhabitants' energy Needs Optimisation model

Paul Ortiz
Eric Rondeau

Abstract

Greenhouse gas emissions (GHGE) can be attributed at 30% to the residential sector in Europe. Several solutions are possible to reduce these emissions, whether by improving the insulation of houses or by increasing the e ciency of the integration of renewable energy and storage resources. The research presented in this paper falls within the scope of the last alternative (i.e., management of renewable energy and storage resources). Most of today's energy management systems rely on mathematical programming or metaheuristics to optimize the storage and release of energy. While they provide high-quality optimum or near-optimal solutions, they are computational-intensive and time-consuming, which make them non-suitable to be deployed at edge architecture. To overcome this limitation, this paper suggests adopting a rule-based optimization approach, proposing a lightweight model seeking to reduce GHGE, while meeting the inhabitants' energy needs. This new model, which is an evolution of the model proposed by the European project RED WoLF, is called CANO (standing for "lightweight CArbon emission and inhabitants' energy needs optimization model"). This paper presents, evaluate, and compares CANO to the RED WoLF algorithms based on two real datasets from two di↵erent countries (UK and France). The results evidence that CANO outperforms RED WoLF, and most importantly that it can satisfy all heating demands, which was not the case with the initial version of RED WoLF (only satisfying 80% and 96% of the demands respectively in France and the UK scenarios).
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Dates and versions

hal-03699852 , version 1 (20-06-2022)

Identifiers

  • HAL Id : hal-03699852 , version 1

Cite

Paul Ortiz, Sylvain Kubler, Eric Rondeau. CANO: A lightweight CArbon emission and inhabitants' energy Needs Optimisation model. 6th IFAC Symposium on Telematics Applications, TA'2022, Jun 2022, Nancy, France. ⟨hal-03699852⟩
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