MSc presentation by Winnie Adhiambo Apiyo

MSc presentation by Winnie Adhiambo Apiyo Winnie Adhiambo Apiyo, MSc Fellow in Sustainable Energy Engineering at Reykjavík University gave a presentation

MSc presentation by Winnie Adhiambo Apiyo

Winnie Adhiambo Apiyo, MSc Fellow in Sustainable Energy Engineering at Reykjavík University gave a presentation on her MSc project on Monday 27 May, 2019 at 11:00 at Reykjavík University.  Due to the sensitiviy of the data her presentation was to a closed audience.  

The title of the project is:

Impact analysis of electric vehicles charging on the Icelandic power system

Winnie's supervisor:

Dr. Ragnar Kristjánsson, assistant Professor, Reykjavík University.

The external examiner was Gnýr Guđmundsson, Head of network analysis and planning, Landsnet.

Abstract:

Increased adoption of electromobility in the form of Battery Electric Vehicles and Plug-in Hybrid Electric Vehicles is anticipated in Iceland over the next few years. Electrified transport will lead to increases in system peaks that are higher than the corresponding increases in annual electricity demand. The objective of this study is to assess the likely incremental impact of EVs on both the transmission and distribution networks through evaluating the network reinforcements needed to support the increase in electricity demand and quantify associated costs. This is done by extensive analyses of large datasets of the transmission and distribution grids and transportation data. The country depends on imported petroleum fuels to meet its transport fuel demand. Transition to EVs is of particular interest for Iceland as electricity can be supplied from low cost clean renewable energy resources. To evaluate how the transition to EVs will impact the system maximum load, four load profiles are defined: BAU, PROPOSAL, PREMIUM and BAN scenarios. The load profile models used for scenario analysis is done by incorporating key fiscal parameters including different taxes on vehicle usage pattern and upfront purchase cost, petroleum fuel tax levies, vehicle tax exempting, extra fees and subsidies. Realistic charging profiles of EVs are based on real-life driving data from different traffic zones. The fleet number in each area is estimated based on the population and commercial density of electricity consumption in the regions. This EV load growth is studied in three different loads forecasted assumptions or scenarios: Base case scenario, Upgraded system scenario and the slow progress energy forecast scenario. The scenarios are analysed using two separate Icelandic power system models. The reinforcement needs are quantified for up to 32 years. The year 2018 is assumed to be the first year PHEVs and BEVs are implemented, while 2050 would allow the sufficient technology time to penetrate the Icelandic vehicle fleet fully. The 2018-2050 long term plan takes a strategic view of how the network should be developed to meet future objectives. Five generation portfolios in different geographical locations are defined to cater for the increasing demand as a result of EVs uptake. The different production locations should put various stress on the power system. Steady state power system analysis is carried out using simulations on mathematical models of electrical power and power system components, which play an essential role in both operational control and planning by developing the required mathematical models and then using these models to perform power flow and contingency analysis. The models used in this report is the Icelandic base model that simulates a winter period when the load was at its peak and a model that modifies the base model by implementing all the changes as per Landsnet’s Network Development Plan 2018-2027. The models are developed in MATPOWER and MATLAB used in automating and simulations. The effect of the electric vehicles in different distinct areas in Iceland are investigated by monitoring thermal and voltage constraints violations in the power system. From the results obtained, it is possible to conclude that there is a significant rise in the network peak load. This increase in peak load will originate large voltage drops and the overloading of some network branches.


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