Referring to the Project Objectives, the project required 2 separate modelling stages before the Ecovillage demand could be calculated for each of the electric vehicle adoption rates. The new demand profiles would then be simulated in a further software to analyse the supply/demand against the KPI. The reliance on software capabilities meant the selection of appropriate software was pivotal in our ability to complete our Project Aims.
EV Charging Demand Modelling
After research, there was found that the most tools available [1], [2], [3] only calculate one of the needed parameters (i.e. time needed to full charge only, time needed from a specific SOC to another, etc.). Another significant drawback, was that these calculators were unable to generate time-step series demand or time-step series SOC curves, a feature that would allow the simulation of different scenarios with batteries charging from different initial SOC. This type of random demand for every EV battery is closer to real life conditions, rather than the grouping of vehicles according to their different SOC.
The reasons stated above, led to the choice of Matlab Simulink as the tool most suitable to model the chosen EV batteries charging demand. Simulink has the advantages of:
The reasons stated above, led to the choice of Matlab Simulink as the tool most suitable to model the chosen EV batteries charging demand. Simulink has the advantages of:
- Customisability in terms of its generic models.
- Big library of components that can be used.
- Can combine, process and output data of signals in time-step series in Matlab files/Excel sheets.
- Can measure several different sizes.
- Offers programmable models ability with customisable behaviour.
Ecovillage Residents Travel Profile Estimation
All the information for Ecovillage Residents transportation activity were available in form of statistical data. These data contained information regarding: the amount of people that travel to work, people that work from home, distances travelled on car/foot/bicycles, the amount of people that use the bus etc. For these data processing, there was used Microsoft Excel, as it is the most widely available software for this purpose. Some of the features that Excel has for this task are:
- Data from Simulink models could be exported and then used in Excel for the new demand profile estimation.
- Excel allows the graphical representation of processed data in graphs that contain multiple datasets.
- Offers the feature of creation of algorithms within cells, that can be copied/modified easily by the user, giving user a view of the data at the time of processing.
- Data from the most energy modelling softwares can be exported/imported in Excel spreadsheets form, so their processing is easier.
Modelling of Energy Demand-Generation
There is a vast majority of software available for energy modelling of microgrids. Such software are MERIT, RET Screen, HOMER Pro, etc. For our case, the chosen software was HOMER Pro. HOMER Pro offers a wide variety of advantages such as:
- Large components library. Most of these components have lots of parameters that can be customised.
- It can export results, which can be easily processed on Excel.
- It is a worldwide, commonly used software with a big community that offers advice or guidance, regarding incurring problems in modelling.
- It can simulate thermal apart from electrical networks. So there could be potientially built a model in the future, examining the energy alternatives for Findhorn Ecovillage in a more hollistic aspect.
- The feature of financial analysis of components. This analysis could be later carried out to assess the long and short terms of EV adoption financial impacts.
- Various Energy studies for Findhorn Ecovillage, were conducted by the University of Strathclyde in the previous years [4], [5], so there was plenty of information available regarding Findhorn electricity demand modelling.
References
[1]. UK Government, G. (2017). Home Charging Claculator. Retrieved from www.goultralow.com: https://www.goultralow.com/electric-car-savings/home-charging-calculator/
[2]. EV Calculator. (n.d.). Retrieved from www.evconvert.com: http://www.evconvert.com/tools/evcalc/
[3]. Kiss, T. (2018). EV Charge Time Calculator. Retrieved from www.leccy.net: https://leccy.net/
[4]. De Bartolo, C. , & Tuohy, P. G. (2015). Master Thesis: "Strategies for harnessing and integrating renewables". Glasgow: University of Strathclyde: Department of Mechanical and Aerospace Engineering.
[5]. Tuohy, P. G., De Bartolo, C., McGhee, R., Kim, J., Peacock, A. D., & Owens, E. (n.d.). DESIGN OF FUTURE GRID CONNECTED LOCAL ENERGY NETWORKS WITH RENEWABLES, LOAD SHIFTING AND STORAGE. Glasgow, Edinburgh: Energy Systems Research Unit University of Strathclyde, Energy Academy Herriot Watt University.
[2]. EV Calculator. (n.d.). Retrieved from www.evconvert.com: http://www.evconvert.com/tools/evcalc/
[3]. Kiss, T. (2018). EV Charge Time Calculator. Retrieved from www.leccy.net: https://leccy.net/
[4]. De Bartolo, C. , & Tuohy, P. G. (2015). Master Thesis: "Strategies for harnessing and integrating renewables". Glasgow: University of Strathclyde: Department of Mechanical and Aerospace Engineering.
[5]. Tuohy, P. G., De Bartolo, C., McGhee, R., Kim, J., Peacock, A. D., & Owens, E. (n.d.). DESIGN OF FUTURE GRID CONNECTED LOCAL ENERGY NETWORKS WITH RENEWABLES, LOAD SHIFTING AND STORAGE. Glasgow, Edinburgh: Energy Systems Research Unit University of Strathclyde, Energy Academy Herriot Watt University.
UNIVERSITY OF STRATHCLYDE
Address:
16 Richmond St
Glasgow G1 1XQ
United Kingdom
Phone:
+ 44 141 552 4400
Address:
16 Richmond St
Glasgow G1 1XQ
United Kingdom
Phone:
+ 44 141 552 4400