Optimal planning of distributed generation with application of multi-objective algorithm including economic, environmental and technical issues with considering uncertainties

This paper proposes a stochastic multi-objective model for integration of distributed generations (DGs) in distribution networks. The proposed model determines the optimal location and size of DGs by optimising different objective functions dependently and simultaneously subject to the operating con...

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Vydáno v:International journal of ambient energy Ročník 38; číslo 8; s. 849 - 857
Hlavní autoři: Karimi, Masoumeh, Jannati Oskuee, Mohammad Reza, Amroony Boushehry, Mohammad Javad, Najafi Ravadanegh, Sajad
Médium: Journal Article
Jazyk:angličtina
Vydáno: Taylor & Francis 17.11.2017
Témata:
Distributed generation sitting and sizing
electric distribution system
genetic algorithm
multi-objective
scenario based-stochastic programming
uncertainty modelling
ISSN:0143-0750, 2162-8246
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Shrnutí:This paper proposes a stochastic multi-objective model for integration of distributed generations (DGs) in distribution networks. The proposed model determines the optimal location and size of DGs by optimising different objective functions dependently and simultaneously subject to the operating constraints. If proper sizes of DGs are located in suitable sites and are also managed properly they can improve integrity, reliability and efficiency of the system. Regarding the widespread impact of uncertainties, some strategies must be devised in order to incorporate them well into power system modelling and hence achieve the best possible strategy to be adopted which its characteristics keep closer to reality. The most important uncertainties in network planning are load forecasting and market price errors. The proposed scheme is solved using non-dominated sorting genetic algorithms II, allowing the distribution company (DisCo) to exercise his/her personal preferences. To validate the effectiveness of the proposed scheme, simulations are carried out on a 33-bus distribution network and finally the attained results are discussed.
ISSN:0143-0750
2162-8246
DOI:10.1080/01430750.2016.1222955