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<ArticleSet>
<Article>
<Journal>
<PublisherName>OICC Press</PublisherName>
<JournalTitle>International Journal of Energy and Environmental Engineering</JournalTitle>
<Issn>2251-6832</Issn>
<Volume>10</Volume>
<Issue>3 (September 2019)</Issue>
<PubDate PubStatus="epublish">
<Year>2019</Year>
<Month>04</Month>
<Day>03</Day>
</PubDate>
</Journal>
<ArticleTitle>Simulation and optimisation study of the integration of distributed generation and electric vehicles in smart residential district</ArticleTitle>
<VernacularTitle></VernacularTitle>
<FirstPage></FirstPage>
<LastPage></LastPage>
<ELocationID EIdType="doi">10.1007/s40095-019-0301-4</ELocationID>
<Language>EN</Language>
<AuthorList>
<Author>
<FirstName>Michela</FirstName>
<LastName>Longo</LastName>
<Affiliation>Dipartimento di Energia, Politecnico di Milano, Milan, 20156, IT</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
<Author>
<FirstName>Federica</FirstName>
<LastName>Foiadelli</LastName>
<Affiliation>Dipartimento di Energia, Politecnico di Milano, Milan, 20156, IT</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
<Author>
<FirstName>Wahiba</FirstName>
<LastName>Yaïci</LastName>
<Affiliation>CanmetENERGY Research Centre, Natural Resources Canada, Ottawa, CA</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
</AuthorList>
<PublicationType>Journal Article</PublicationType>
<History>
<PubDate PubStatus="received">
<Year>2019</Year>
<Month>04</Month>
<Day>03</Day>
</PubDate>
</History>
<Abstract>Abstract
This paper presents an optimisation methodology for simulating the integration of distributed generation and electric vehicles (EVs) in a residential district. A model of a smart residential district is proposed. Different charging scenarios (CS) for private cars are considered for simulating different power demand distributions during the day. Four different case studies are investigated, namely the Base Case, in which no EVs are present in the district and three study cases with different CSs. A global optimisation method based on a genetic algorithm approach was applied on the model to find the total power from PV panels installed and co-generative micro gas turbines while minimising the annual energy cost in the district for the four different scenarios. In conclusion, the results showed that the use of EVs in the district introduces considerable savings with respect to the Base Case. Moreover, the impact of the chosen CS is nearly insignificant under a purely economic perspective even if it is relevant for grid management. Additionally, the optimum amounts of installed power vary in a limited range if the distance travelled by EVs, users’ departure and arrival time change broadly.</Abstract>
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<Object Type="keyword">
<Param Name="value">Distributed generation</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Electric vehicles</Param>
</Object>
<Object Type="keyword">
<Param Name="value">EV charging strategy</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Smart residential district</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Photovoltaic panels</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Micro-turbines</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Co-generation</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Genetic algorithm</Param>
</Object>
</ObjectList>
</Article>
</ArticleSet>