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Application of an optimization-based curtailment service provider in real-time simulation
- Source :
- Energy Informatics, Vol 1, Iss 1, Pp 1-17 (2018), Repositório Científico de Acesso Aberto de Portugal, Repositório Científico de Acesso Aberto de Portugal (RCAAP), instacron:RCAAP
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- The use of demand response programs and distributed renewable energy resources are intensively discussed. These concepts play a key role in the distribution network, especially smart grids and microgrids. Nowadays, most of the implemented demand response programs are considered for large-scale resources, which make small and medium resources unable to participate in electricity market negotiations. In order to overcome this barrier, a third-party entity, namely an aggregator, can be considered as an intermediate player between the demand side and grid side. For this purpose, curtailment service provider is considered as an aggregator, which aggregates small and medium-scale resources, who do not have adequate capacity of reduction or generation and allow them to participate in wholesale electricity markets as a unique resource. However, before massive implementation of business models, the performance of the curtailment service provider should be adequately surveyed and validated in order to prevent future problems. This paper proposes a real-time simulation model of a curtailment service provider, which employs several real and laboratory hardware equipment considered as hardware-in-the-loop in the real-time simulator. Furthermore, an optimization problem is developed for a curtailment service provider in order to optimally schedule the available resources including several demand response programs and distributed renewable resources, aiming at minimizing its operation costs. The implemented case study considers a distribution network with 20 consumers and prosumers, and 26 renewable-based producers including wind and photovoltaic generation, where the developed model is performed in real-time for 12 min and behaviors of small and medium prosumers and producers is surveyed.<br />The present work was done and funded in the scope of the following project: NetEfficity Project (P2020 - 18015), co-funded by Portugal 2020, Fundo Europeu de Desenvolvimento Regional (FEDER) through Programa Operacional Competitividade e Internacionalização (PO CI); and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE pro-gram and by National Funds through FCT.
- Subjects :
- Optimization
Schedule
Computer Networks and Communications
Computer science
020209 energy
Energy Engineering and Power Technology
02 engineering and technology
lcsh:HD9502-9502.5
computer.software_genre
7. Clean energy
News aggregator
Demand response
Resource (project management)
0202 electrical engineering, electronic engineering, information engineering
Electricity market
Curtailment service provider
020208 electrical & electronic engineering
Service provider
Grid
lcsh:Energy industries. Energy policy. Fuel trade
Real-time simulation
Smart grid
Risk analysis (engineering)
Hardware-in-the-loop
computer
Information Systems
Subjects
Details
- ISSN :
- 25208942
- Volume :
- 1
- Database :
- OpenAIRE
- Journal :
- Energy Informatics
- Accession number :
- edsair.doi.dedup.....484e18389345022a3e56665f2800ec96