10.1007/s40095-019-0301-4

Simulation and optimisation study of the integration of distributed generation and electric vehicles in smart residential district

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  • Michela Longo*1,
  • Federica Foiadelli1,
  • Wahiba Yaïci2,
  1. Dipartimento di Energia, Politecnico di Milano, Milan, 20156, IT
  2. CanmetENERGY Research Centre, Natural Resources Canada, Ottawa, CA
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Published in Issue 2019-04-03

How to Cite

Longo, M., Foiadelli, F., & Yaïci, W. (2019). Simulation and optimisation study of the integration of distributed generation and electric vehicles in smart residential district. International Journal of Energy and Environmental Engineering, 10(3 (September 2019). https://doi.org/10.1007/s40095-019-0301-4
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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.

Keywords

  • Distributed generation,
  • Electric vehicles,
  • EV charging strategy,
  • Smart residential district,
  • Photovoltaic panels,
  • Micro-turbines,
  • Co-generation,
  • Genetic algorithm
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