Publication
An Aggregation Model for Energy Resources Management and Market Negotiations
| dc.contributor.author | Abrishambaf, Omid | |
| dc.contributor.author | Faria, Pedro | |
| dc.contributor.author | Spínola, João | |
| dc.contributor.author | Vale, Zita | |
| dc.date.accessioned | 2021-03-08T18:03:28Z | |
| dc.date.available | 2021-03-08T18:03:28Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Currently the use of distributed energy resources, especially renewable generation, and demand response programs are widely discussed in scientific contexts, since they are a reality in nowadays electricity markets and distribution networks. In order to benefit from these concepts, an efficient energy management system is needed to prevent energy wasting and increase profits. In this paper, an optimization based aggregation model is presented for distributed energy resources and demand response program management. This aggregation model allows different types of customers to participate in electricity market through several tariffs based demand response programs. The optimization algorithm is a mixed-integer linear problem, which focuses on minimizing operational costs of the aggregator. Moreover, the aggregation process has been done via K-Means clustering algorithm, which obtains the aggregated costs and energy of resources for remuneration. By this way, the aggregator is aware of energy available and minimum selling price in order to participate in the market with profit. A realistic low voltage distribution network has been proposed as a case study in order to test and validate the proposed methodology. This distribution network consists of 25 distributed generation units, including photovoltaic, wind and biomass generation, and 20 consumers, including residential, commercial, and industrial buildings. | pt_PT |
| dc.description.sponsorship | This work has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAMGO). This work also received funding from the following projects: NETEFFICITY Project (ANI | P2020 – 18015); and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.25046/aj030227 | pt_PT |
| dc.identifier.issn | 2415-6698 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/17311 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | ASTES | pt_PT |
| dc.relation | Enabling Demand Response for short and real-time Efficient And Market Based smart Grid Operation - An intelligent and real-time simulation approach | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Demand response | pt_PT |
| dc.subject | Agregator | pt_PT |
| dc.subject | Distributed Generation | pt_PT |
| dc.subject | Smart grid | pt_PT |
| dc.subject | K-Mean Clustering | pt_PT |
| dc.title | An Aggregation Model for Energy Resources Management and Market Negotiations | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Enabling Demand Response for short and real-time Efficient And Market Based smart Grid Operation - An intelligent and real-time simulation approach | |
| oaire.awardURI | info:eu-repo/grantAgreement/EC/H2020/641794/EU | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F00760%2F2013/PT | |
| oaire.citation.endPage | 237 | pt_PT |
| oaire.citation.issue | 2 | pt_PT |
| oaire.citation.startPage | 231 | pt_PT |
| oaire.citation.title | Advances in Science, Technology and Engineering Systems Journal | pt_PT |
| oaire.citation.volume | 3 | pt_PT |
| oaire.fundingStream | H2020 | |
| oaire.fundingStream | 5876 | |
| person.familyName | Abrishambaf | |
| person.familyName | Faria | |
| person.familyName | Vale | |
| person.givenName | Omid | |
| person.givenName | Pedro | |
| person.givenName | Zita | |
| person.identifier | 632184 | |
| person.identifier.ciencia-id | 7F1A-B942-5BD2 | |
| person.identifier.ciencia-id | B212-2309-F9C3 | |
| person.identifier.ciencia-id | 721B-B0EB-7141 | |
| person.identifier.orcid | 0000-0002-4249-8367 | |
| person.identifier.orcid | 0000-0002-5982-8342 | |
| person.identifier.orcid | 0000-0002-4560-9544 | |
| person.identifier.rid | A-5824-2012 | |
| person.identifier.scopus-author-id | 57189232486 | |
| person.identifier.scopus-author-id | 7004115775 | |
| project.funder.identifier | http://doi.org/10.13039/501100008530 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | European Commission | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
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