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.
Highlights
An energy system is defined as the totality of energy sources, energy conversion plants and storage devices together with users and infrastructures, which guarantee energy transmission and distribution [1].Energy sources can be either renewable, with rates of regeneration which counterbalance their consumption, or non-renewable, with regeneration occurring in a time scale that is not comparable with that of human activities
This paper addresses the subject at a district level by considering a residential neighbourhood of a big metropolitan area as case study, and proposing a model for a smart residential district that is efficiently run and optimises energy use [20, 21]
As it has been explained in the previous chapter, the demand includes households, private and shared electric vehicles (EVs) while the power generation is related to PV panels and micro-turbines operating in a co-generative mode
Summary
Energy sources can be either renewable, with rates of regeneration which counterbalance their consumption, or non-renewable, with regeneration occurring in a time scale that is not comparable with that of human activities. Nonrenewable energy sources include fossil fuels such as oil and coal, and nuclear resources; renewable energy derives from sunlight, wind, rain, tide, geothermal heat and biomass. Wood and traditional biomass were principally employed as fuels, not comparable to non-renewable energy, the exploitation of renewable energy sources (RES) became significant at the start of the twenty-first century. In Africa and North America, natural gas and oil have a primary role; coal and hydropower are present in the energy mix, together with nuclear and renewables in a small portion (mainly in North America). The Middle East relies almost completely on oil and natural gas, being a region with substantial reserves. Europe and Eurasia prove to be the regions where renewable energy is most widely
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