Integrating electric vehicles in electricity system models – representing individual driving patterns

This study takes initial steps in developing a method that includes a representation of road transportation demand on individual EV level (based on GPS driving measurements) in an optimisation electricity system model to also represent the spread in the individual driving patterns. The main conclusions are that different driving profiles do have an impact on the charging and discharging back to grid depending on the individual driving distance, battery capacity and driving profile. This have shown to have an impact on, e.g. investments in peak power and the potential role of EVs facilitating the integration of more intermittent renewable power

Electrification of Road Transportation - Implications for the Electricity System

It is incumbent upon the transport sector to reduce its CO2 emissions by replacing fossil fuels with low-carbon alternatives. Suggested strategies include electrification of the road transport sector through the use of electric vehicles (EVs) with static charging, electric road systems (ERS), and the use of electricity to produce fuels. Electrification of the transport sector will create new electricity load profiles that depend on the time of consumption and the amount of electricity used in EVs. EVs can also contribute with flexibility to the electricity system – a feature that will be of increasing importance with a higher share of variable renewable electricity (VRE) in the electricity system. The overall aim of this thesis is to investigate how electrification of the transport sector affects the electricity system with respect to the demands for energy and power on different geographical and temporal scales. In this work, a vehicle energy consumption model is developed to estimate the variations of the energy and power demands according to time and location for the transportation work on a highway (the E39 in Norway). Furthermore, charging of passenger EVs and ERS is included in several electricity system models, to investigate how EVs influence investments in electricity generation capacity and VRE integration. \ua0Our results, using the Norwegian E39 highway as a case study, indicate that electrification of the road transport entails large variations in the spatial and time distributions of the energy and power demands along the road. Installation of ERS on all the European (E) and National (N) roads in Sweden and Norway would encompass more than 50% of the national vehicle traffic. Implementation of ERS on 25% of the total E- and N-roads (~6,800 km) would be sufficient to cover 70% of the traffic on these roads and would connect most of the larger cities in Norway and Sweden through ERS.We conclude that with a cap on CO2 emissions from the European electricity system, which corresponds to 99% reduction by 2050, the demand from EV is mainly met by an increase in generation from VRE, e.g., solar power in regions with adequate solar conditions and wind power in regions with good wind conditions. Re-charging of EVs directly subsequent to driving or ERS will require increased investments in peak power (by up to ~15%), as well as, in thermal power plants compared to optimised EV charging. The model results show that an optimised charging strategy with vehicle-to-grid (i.e., discharging electricity back to the grid) that minimises the cost of the electricity system can: (i) avoid investments in other storage technologies; (ii) reduce the need for peak power capacity in the system; and (iii), for some regions, stimulate increased shares of VRE (mainly solar power), as compared to direct charging. This thesis also shows that it is important to represent the heterogeneity of individual driving patterns in electricity system optimisation models when the charging infrastructure is limited to the home location and a battery capacity of 30 kWh or less per vehicle

Electricity as an Energy Carrier in Transport: Cost and Efficiency Comparison of Different Pathways

This study includes a techno-economic assessment of different pathways of using electricity in passenger cars and short sea ships, with a special focus on electrofuels (i.e.fuels produced from electricity, water and CO2) and electric road systems (ERS). For passenger cars electro-diesel is shown to be cost-competitive compare to battery electric vehicles with larger batteries (BEV50kWh) and hydrogen fuel cell vehicles (FCEV), assuming optimistic cost for the electrolyser. ERS is shown to reduce the vehicle cost substantially compare to BEV50kWh and FCEV, but depend on a new large scale infrastructure. For ships it is shown that battery electric vessels with a relatively small battery has the lowest cost. Electro-diesel and hydrogen can compete with the battery options only when ships operate few days per year

Centralized and decentralized electrolysis-based hydrogen supply systems for road transportation – A modeling study of current and future costs

This work compares the costs of three electrolysis-based hydrogen supply systems for heavy road transportation: a decentralized, off-grid system for hydrogen production from wind and solar power (Dec-Sa); a decentralized system connected to the electricity grid (Dec-Gc); and a centralized grid-connected electrolyzer with hydrogen transported to refueling stations (Cen-Gc). A cost-minimizing optimization model was developed in which the hydrogen production is designed to meet the demand at refueling stations at the lowest total cost for two timeframes: one with current electricity prices and one with estimated future prices. The results show that: For most of the studied geographical regions, Dec-Gc gives the lowest costs of hydrogen delivery (2.2–3.3€/kgH2), while Dec-Sa entails higher hydrogen production costs (2.5–6.7€/kgH2). In addition, the centralized system (Cen-Gc) involves lower costs for production and storage than the grid-connected decentralized system (Dec-Gc), although the additional costs for hydrogen transport increase the total cost (3.5–4.8€/kgH2)

Centralized and decentralized electrolysis-based hydrogen supply systems for road transportation – A modeling study of current and future costs

This work compares the costs of three electrolysis-based hydrogen supply systems for heavy road transportation: a decentralized, off-grid system for hydrogen production from wind and solar power (Dec-Sa); a decentralized system connected to the electricity grid (Dec-Gc); and a centralized grid-connected electrolyzer with hydrogen transported to refueling stations (Cen-Gc). A cost-minimizing optimization model was developed in which the hydrogen production is designed to meet the demand at refueling stations at the lowest total cost for two timeframes: one with current electricity prices and one with estimated future prices. The results show that: For most of the studied geographical regions, Dec-Gc gives the lowest costs of hydrogen delivery (2.2–3.3€/kgH2), while Dec-Sa entails higher hydrogen production costs (2.5–6.7€/kgH2). In addition, the centralized system (Cen-Gc) involves lower costs for production and storage than the grid-connected decentralized system (Dec-Gc), although the additional costs for hydrogen transport increase the total cost (3.5–4.8€/kgH2)

Self-consumption and self-sufficiency for household solar producers when introducing an electric vehicle

The aim of this study was to analyse how electric vehicles (EVs) affect the levels of electricity self-consumption and self-sufficiency in households that have in-house electricity generation from solar photovoltaics (PV). A model of the household electricity system was developed, in which real-time measurements of household electricity consumption and vehicle driving, together with modelled PV generation were used as inputs. The results show that using an EV for storage of in-house-generated PV electricity has the potential to achieve the same levels of self-consumption and self-sufficiency for households as could be obtained using a stationary battery. As an example, the level of self-sufficiency (21.4%) obtained for the households, with a median installed PV capacity of 8.7 kWp, was the same with an EV as with a stationary battery with a median capacity of 2.9 kWh. However, substantial variations (up to 50% points) were noted between households, primarily reflecting driving profiles

To Represent Electric Vehicles in Electricity Systems Modelling-Aggregated Vehicle Representation vs. Individual Driving Profiles

This study describes, applies, and compares three different approaches to integrate electric vehicles (EVs) in a cost-minimising electricity system investment model and a dispatch model. The approaches include both an aggregated vehicle representation and individual driving profiles of passenger EVs. The driving patterns of 426 randomly selected vehicles in Sweden were recorded between 30 and 73 days each and used as input to the electricity system model for the individual driving profiles. The main conclusion is that an aggregated vehicle representation gives similar results as when including individual driving profiles for most scenarios modelled. However, this study also concludes that it is important to represent the heterogeneity of individual driving profiles in electricity system optimisation models when: (i) charging infrastructure is limited to only the home location in regions with a high share of solar and wind power in the electricity system, and (ii) when addressing special research issues such as impact of vehicle-to-grid (V2G) on battery health status. An aggregated vehicle representation will, if the charging infrastructure is limited to only home location, over-estimate the V2G potential resulting in a higher share (up to 10 percentage points) of variable renewable electricity generation and an under-estimation of investments in both short- and long-term storage technologies

Comparison and Analysis of GPS Measured Electric Vehicle Charging Demand: The Case of Western Sweden and Seattle

Electrification of transportation using electric vehicles has a large potential to reduce transport related emissions but could potentially cause issues in generation and distribution of electricity. This study uses GPS measured driving patterns from conventional gasoline and diesel cars in western Sweden and Seattle, United States, to estimate and analyze expected charging coincidence assuming these driving patterns were the same for electric vehicles. The results show that the electric vehicle charging power demand in western Sweden and Seattle is 50–183% higher compared to studies that were relying on national household travel surveys in Sweden and United States. The after-coincidence charging power demand from GPS measured driving behavior converges at 1.8\ua0kW or lower for Sweden and at 2.1\ua0kW or lower for the United States The results show that nominal charging power has the largest impact on after-coincidence charging power demand, followed by the vehicle’s electricity consumption and lastly the charging location. We also find that the reduction in charging demand, when charging is moved in time, is largest for few vehicles and reduces as the number of vehicles increase. Our results are important when analyzing the impact from large scale introduction of electric vehicles on electricity distribution and generation

Substation Placement for Electric Road Systems

One option to avoid range issues for electrified heavy vehicles, and the large individual batteries for each such vehicle, is to construct electric road systems (ERS), where vehicles are supplied with electricity while driving. In this article, a model has been developed that calculates the cost for supplying an ERS with electricity from a regional grid to a road in the form of cables and substations, considering the power demand profile for heavy transport. The modeling accounts for electric losses and voltage drop in cables and transformers. We have used the model to exhaustively compute and compared the cost of different combinations of substation sizes and locations along the road, using a European highway in West Sweden as a case study. Our results show that the costs for building an electricity distribution system for an ERS vary only to a minor extent with the location of substations (10% difference between the cheapest cost and the average cost of all configurations). Furthermore, we have varied the peak and average power demand profile for the investigated highway to investigate the impact of a specific demand profile on the results. The results from this variation show that the sum of the peak power demand is the most important factor in system cost. Specifically, a 30% change in the peak power demand for the road has a significant impact on the electricity supply system cost. A reduction in the geographical variation of power demand along the road has no significant impact on the electricity distribution system cost as long as the aggregated peak power demand for all road segments is held constant. The results of the work are relevant as input to future work on comparing the cost–benefit of ERS with other alternatives when reducing CO2 from road traffic—in particular from heavy road traffic

Large-scale implementation of electric road systems: Associated costs and the impact on CO2 emissions

This study investigates a large-scale implementation of an electric road system (ERS) in Norway and Sweden by identifying: (i) which roads; (ii) how much of the road network; and (iii) which vehicle types are beneficial to electrify based on an analysis of current road traffic volumes, CO2 emissions mitigation potential, and infrastructure investment costs. All the European (E) and National (N) roads in Norway and Sweden were included, while assuming different degrees of electrification in terms of the fraction of the road length with an ERS, prioritizing roads with high-traffic loads. The results show that implementing an ERS already for 25% of the E- and N-road lengths could result in electrification of 70% of the traffic on these roads, as well as 35% of the total vehicle kilometers in Norway and Sweden. The ERS will then connect some of the larger cities with ERS. Installation of ERS on all the E- and N-roads in the two countries would cover more than 60% of the CO2 emissions from all heavy traffic assuming all vehicles run on electricity. For roads with an average daily traffic of >6800 and >1200 vehicles per day, the costs of infrastructure investment are ∼0.03 € 2016 per vkm and ∼0.15 € 2016 per vkm, respectively. Thereby, for roads with high traffic volumes using an ERS, the total driving cost per km using an ERS (0.23–0.55 € 2016 per vkm) does not seem to be an issue. Light vehicles appear to be important bringing down the ERS infrastructure cost