Towards Sustainable Energy-Efficient Communities Based on a Scheduling Algorithm
| Autor: | Esther Palomar, Carlos Oliveira Cruz, Alfredo Gardel, Ignacio Bravo |
|---|---|
| Přispěvatelé: | Universidad de Alcalá. Departamento de Electrónica |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Mains electricity
Computer science Energy management Scheduling algorithm 020209 energy 02 engineering and technology lcsh:Chemical technology Biochemistry Article Analytical Chemistry Scheduling (computing) Demand response 0202 electrical engineering electronic engineering information engineering consumer preferences lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Consumer behaviour Consumer preferences business.industry Cooperative smart community 020208 electrical & electronic engineering cooperative smart community scheduling algorithm renewables Environmental economics Atomic and Molecular Physics and Optics Renewable energy Sustainable energy Incentive Electrónica Renewables Electronics business Efficient energy use |
| Zdroj: | Sensors, Vol 19, Iss 18, p 3973 (2019) Sensors (Basel, Switzerland) Sensors Volume 19 Issue 18 e_Buah Biblioteca Digital Universidad de Alcalá instname |
| ISSN: | 1424-8220 |
| Popis: | The Internet of Things (IoT) and Demand Response (DR) combined have transformed the way Information and Communication Technologies (ICT) contribute to saving energy and reducing costs, while also giving consumers more control over their energy footprint. Unlike current price and incentive based DR strategies, we propose a DR model that promotes consumers reaching coordinated behaviour towards more sustainable (and green) communities. A cooperative DR system is designed not only to bolster energy efficiency management at both home and district levels, but also to integrate the renewable energy resource information into the community&rsquo s energy management. Initially conceived in a centralised way, a data collector called the &ldquo aggregator&rdquo will handle the operation scheduling requirements given the consumers&rsquo time preferences and the available electricity supply from renewables. Evaluation on the algorithm implementation shows feasible computational cost (CC) in different scenarios of households, communities and consumer behaviour. Number of appliances and timeframe flexibility have the greatest impact on the reallocation cost. A discussion on the communication, security and hardware platforms is included prior to future pilot deployment. |
| Databáze: | OpenAIRE |
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