Some question we will cover in our presentation:
- What would be the ideal KPI to measure success? Heads per vehicle?
- How we gain more space in our cities?
- What is the economic impact for countries without automotive industry and with automotive industry?
We will try to give answers and explain the complexity moving from the technical and economical perspective deeper in the mobility and e-mobility market.
In order to study the interactions between electric mobility and renewable energy plants as well as the optimum installation areas, a model based on a cellular approach for an urban distribution grid at medium voltage level (approximately 30.000 inhabitants) is developed during the FFG research project “Move2Grid”. This model simplifies the complex grid structure, reduces the calculation time and enables a temporally resolved load flow calculation with annual load profiles. Based on, this model the impact of an increasing number of charging stations and the interaction with photovoltaic potentials are studied for different E-mobility and photovoltaic potentials penetration rates. The required charging profiles are modelled by using traffic analyses and probabilistic approaches. The determination of temporally resolved photovoltaic potentials are based on data from the “Styrian Solar-Roof Cadaster”  and weather data from the Austrian Zentralanstalt für Meteorologie und Geodynamik (ZAMG) .
The aim of the proposed paper is to show the impact and interaction of E-mobility and photovoltaic potentials for different penetration rates of both. Besides the determination of the self-sufficiency level, load flow calculations with annual load profiles are performed and are benchmarked in term of equipment overloads and deviations of the voltage range. This is done by using a newly developed evaluation tool. With this tool a quick determination of areas which are susceptible for equipment overloads and deviations of the voltage range, as well as the exact time of them and their duration for further analyses becomes possible. In order to avoid equipment overloads and deviations of the voltage range and the associated necessary grid expansion, demand side measures and the use of electrical storages are implemented in the power grid model, followed by load flow calculations and analyses.
While it is often assumed in the academic literature that EVs will be charged slowly overnight at home, a significant proportion of EV charging could exist as ‘destination' charging while parked during their users’ visits to workplaces or amenities such as shopping centres, supermarkets, gyms, cinemas and motorway service stations - where cars are left for durations ranging from ten minutes to three hours. A move from a solely domestic charging-based EV uptake to one focused on the widespread availability of public charging could serve to enhance the convenience of EV usership, enable EV access to those without off-street parking (which applies to 43% of households in the UK) and has the potential to reduce system cost: according to findings from the 'My Electric Avenue' project, 32% of local electricity networks across GB will require intervention when 40% - 70% of customers have at-home EV charging. By instead encouraging users to charge away from home at their place of work or other places where they leave their car, the installation of charging infrastructure can be directed towards areas of greater spare capacity or with more potential for ‘smarter’ network operation which could allow a higher penetration of EV charging.
This paper presents a Monte Carlo (MC)-based method for the characterization of likely demand profiles of EV destination charging at these locations based on smartphone users’ anonymised positional data captured in the Google Maps Popular Times feature. Unlike the majority of academic works on the subject, which tend to rely on users’ responses to surveys, these data represent individuals’ actual movements rather than how they might recall or divulge them. Through a smart charging approach proposed in this paper, likely electrical demand profiles for EV destination charging at different amenities are presented.
The method is demonstrated by way of two case studies. Firstly, it is applied to a large GB shopping centre to show how the approach can be used to derive suitable specifications for large charging infrastructure to maximise revenue or EV service provision. Secondly, it is applied to a GB supermarket in a residential area to show how the approach can be used to examine network impact for a distribution-connected destination charging facility.
The project has started in january 2018 en will run until march 2021, will yield at project end a reduction in CO2 of 2400 ton/year and 12,4 MW of extra renewable energy production.
The first chapter describes the challenges for the power system and for grid operators when electrifying the transportation sector to meet international climate goals. In the second chapter, a standard load profile for the charging process of EVs based on assumptions for the future electricity demand of the transportation sector and a standard business load profile is introduced. The third chapter reflects today’s state-of-the-art grid integration of EVSE, whereas Chapter IV describes expected future levels of the grid integration of EVSE. Chapter V analyses the behavior of EVSE and EV in case of grid faults. A summary and outlook is given in Chapter VI.
This paper presents an aggregated approach to control a compound of charging stations with respect to network congestions. The main focus of the paper will be on the automated temporal shift of the charging power of pooled charging stations in order to avoid limitation violations of the corresponding network section.
From the ecological point of view, the benefits of electric transport using are obvious for large cities, but in general in the country they can be insignificant or absent at all, so the total ecological effect will depend not only on the technical characteristics of electric vehicles, but also of the structure of electric power generation by types of generation (thermal, nuclear, renewable energy sources) and used fuels. At the moment, it is recognized in Belarus that electric vehicles are the optimal type of passenger transport for a city usage that meets all the requirements of environmental and energy security. However, the number of persons wanting to buy them is very little, because today electric vehicles in Belarus are very expensive cars. An electric vehicle in Belarus even without additional payments is 1.5 times more expensive than a car with ICE.
The program for the development of charging infrastructure and electric vehicles in the Republic of Belarus contains two scenarios for electricity consumption by electric vehicles and electric buses in the Republic of Belarus until 2025 - optimistic and pessimistic.
It is assumed that the number of electric vehicles in Belarus by 2025 will be:
- according to optimistic scenario - 32.7 thousand units, incl. 30.82 thousand units of passenger electric cars and 1.88 thousand units of electric buses;
- according to pessimistic scenario - 9.96 thousand units, incl. 9.37 thousand units of passenger electric vehicles and 0.59 thousand units of electric buses.
In this connection the need to develop strategic solutions in the following areas has become more acute for the Belarusian transport sector:
Based on simulated driving profiles of electrical vehicles (EVs) and a perfect foresight approach the flexibility of the charging processes is determined in a way, that no user is restricted in his mobility needs. Regarding the start- and end time of each travel the ratio of parking time and required charging time is formed as an indicator of the available flexibility.
The driving profiles are used in a mixed integer linear program (MILP) to optimize the procurement of the required energy for charging the EVs. To different marketing alternatives are considered: in the first step, the optimization considers only the Day-Ahead Auction. The second step combines the Day-Ahead auction with the continuous Intraday market. This combination, denoted as Intraday Redispatch, leads to a significant reduction of costs due to the higher volatility at the continuous Intraday Market. The other major advantage of the Intraday Redispatch is the possibility to trade energy at the continuous Intraday Market until 30 Minutes before delivery, so deviations of the forecasted arrival times of the EVs can be compensated.
The simulation carried out with 50 electrical vehicles shows the realized cost savings for an optimized charging based on the considered short-term markets and the resulting effects on the distribution grid. The Day-Ahead optimizations reduces the charging costs to 30% of the average costs, with the Intraday Redispatch trading strategy the costs could be lowered to even negative costs.
However, the electricity supply of Swedish major cities is strained for a number of hours during a year. In Stockholm region, for example, network constraint can arise during high loads of about 100 hours distributed during a year. The network constraint issues may still persist, even after today’s ongoing power boost and network enhancement. With the massive penetration of electric vehicles (EVs), the charging pattern of EVs will pose a number of challenges to the city’s power supply system.
This paper addresses these aspects and looks at strategies of how to deal with the challenges brought by increasing EV penetration rates in urban environments. The paper will present the strategies resulting from a vision project within the E-Mobility programme performed at Vattenfall R&D, Sweden. The project studied possible charging scenarios in the case of a fully electric transport system in Stockholm city by the year 2030; assessed the effect on grid power loading by simultaneous charging activities of large number of EVs without steered charging; and analyzed typical demand of city residential power consumers.
The study points out that uncontrolled charging of large numbers of EVs in big cities would stress the electricity network, and the need of controlled charging is a key aspect in the strategy for smart charging of EVs in big cities. Based on the load demand analysis, the project also designs a strategic controlled charging solution by shifting EV load from high load hours to night and low load hours. The details of the analysis and charging strategies will be described and presented in the full paper.
In this paper, models of the power consumption of residential customers  are combined with models of the driving behavior and its corresponding charging power. Here, the differences of the simultaneity factors and the considered power consumption in distribution grid planning is analyzed for different methods of combination and for alternating charging powers. Additionally, the benefits of a probabilistic approach for distribution grid planning is highlighted. A comparison shows that the probabilistic approach is beneficial compared to a deterministic approach in case of a radial grid structure, where the deterministic approach tends to underestimate the line currents.
 G. Walker, Impact and Chances of Electric Mobility for the German Low Voltage Distribution Grids, Ph.D. dissertation, Institute of Power Transmission and High Voltage Technology, University of Stuttgart, Germany, 2017.
 CIGRE WG C6.19, Planning and Optimization Methods for Active Distribution Systems, CIGRE Technical Brochure, 2014, ISBN: 978-2-85873-289-0.
 S. Kippelt, C. Wagner, C. Rehtanz, Consideration of Electricity Applications in Distribution Grid Expansion Planning and the Role of Flexibility, International ETG Congress 2017, Bonn, Germany, 2017.
 D. Groß, P. Wiest, K. Rudion und A. Probst, Parametrization of Stochastic Load Profile Modeling Approaches for Smart Grid Simulations, in IEEE International Conference on Innovative Smart Grid Technologies (IEEE ISGT Europe 2017), Torino, Italy, 2017
This paper focusses on the use of sector coupling in the transport sector. In particular, it investigates the direct electrification of the transport sector. In addition, an analysis of the influence of the required energy supply infrastructures on the use of sector coupling technologies for decarbonisation of the transport sector in Germany is carried out. The aim is to examine not only the influence of infrastructures on the choice of sector coupling technologies, but also the impact of the sector coupling options on infrastructures.
To this end, in the framework of the German Kopernikus project ENavi, the energy system model TIMES is expanded with regard to the conceivable sector coupling technologies. In order to be able to adequately evaluate these technologies, a simplified representation of the required energy supply infrastructures will also be implemented. By varying the infrastructure parameters, scenario-based analyses will then be carried out to determine to what extent the infrastructures influence the selection of sector coupling paths and thus the possible composition of Germany’s future energy system.
The main findings indicate that the potential of sector coupling technologies is very much dependent on the choice of greenhouse gas emission reduction targets. In freight traffic in particular, these options only become attractive when ambitious targets are established. With regard to infrastructures, it can be said that a detailed assessment of the infrastructures has a great influence on the energy system. Furthermore, the impact of the use of sector coupling is far from negligible and requires more detailed research.
In the paper it will be shown total harmonic distortion (THD), sags, swells, flicker, and supra harmonics of the voltage. This quantities have been measured in combination with the charging current on several electric vehicles from at least two substations. The two examples are from one fast- (symmetric) and several normal-EV charging equipment (asymmetric).
The expected results are that the THD of the voltage is not much influenced from the electric vehicle, but there will be some unbalance in the slow-EV charging case, due to that the charger are one phase loads and they also charge at different times. It has been observed low distortion in the charging current due to the specific EV charger but no changes has been observed in the voltage quality (THD) compared to a substation without EV charging equipment. The voltage quality follows instead the “normal” pattern of other loads in the facility already present before the EV charging is started.
Even though it has not been possible to observe any problem at this facility so far, it is important to continue to do new measurements of EVs in weaker grids.
EVs can act as flexible loads and decentralised storage resources capable of providing additional flexibility to support power system operation. With smart charging, EVs could alternate their charging patterns to flatten peak demand, fill load valleys and support real-time balancing of the grids by adjusting their charging levels. Smart charging therefore is a way of optimizing the charging process according to distribution grids constraints and local renewable energy sources availability and customers’ preferences. Smart charging includes different technical charging options, from the simplest of deferring the EV charging from peak to off-peak periods, to providing close to real time balancing and ancillary services. Such mechanisms range from simply switching on and off the charging through unidirectional control of vehicles (also called V1G) that allows for increase or decrease of the rate of charging, to the technically challenging bidirectional vehicle-to-grid (V2G), which allows the EV to provide services to the grid in the discharge mode. Smart charging techniques differ from system to system: while it tries to capture the synergies with renewable energies, each of renewable energy sources present different availability patterns.
The paper will analyze in depth how smart charging can harness the synergies between EVs and renewable generation, providing insights on what would be the impact of smart charging in power systems (both V1G and V2G technologies). Based on a modeling exercise, the paper will look at two scenarios:
The project "BOB-Solingen" - the abbreviation BOB denotes the German words “Batterie-Oberleitungs-Bus” – is intended to electrify the entire public transport sector by introducing a new kind of trolleybuses, which will be able to travel regardless of the vital overhead line by means of the included battery. BOB is the result of combining the recognized trolleybus technology with the latest battery technology and the intelligent charging infrastructure, creating the next generation of buses which, as a matter of fact, are able to drive on routes with no power supply as well.
Moreover, the project is planned to integrate charging stations for electric vehicles (EV), decentralized renewable power generators such as photovoltaic (PV) systems as well as a stationary power storage system. The stationary storage will consist of used trolleybus batteries to increase their cost efficiency by establishing a second-life utilization concept.
The entire DC system will be transformed into a Smart-Trolleybus-System (STS) allowing an intelligent control and management of the power flow in the overall system. The Chair of Power System Engineering at the University of Wuppertal will develop and implement the essential automation system for the DC grid to use its existing overhead infrastructure as effective as possible within its physical limitations.
In order to realize an intelligent control of the grid, the load flow of the current grid (including the trolleybuses) as well as of the future grid (including BOB) has to be modeled and simulated. By means of the simulation, critical grid situations can be detected. These might occur more frequently in future due to the fact that e.g. the additionally implemented batteries can cause an increased number of peak loads which have to be handled.
This paper intends to simulate and evaluate the power profiles of all operating trolleybuses as well as the planned BOB based on different possible operation circumstances (e.g. stopping or not at traffic lights and bus stops, different stopping durations, traffic influences, variation in temperature and passenger numbers). The power profiles for both the buses and additional actuators within the DC grid, such as PV systems, charging stations and stationary power storage units will be presented and discussed.
The grid state is expected to be strongly fluctuating due to the increased number of loads and feeders, some of which operate bidirectionally. Load flow calculations will be the key to point out the actual grid state in order to enable the essential intelligent grid control. Performance and capability will be presented and evaluated in this paper.
With the upcoming new load of electric vehicles it is important to use its benefits such as load-shift, possibility to stop charging, or even up-load power to the grid. These capabilities can be used for tasks and deliveries such as; a) emergency power as a global system service, b) primary frequency control as a global system service, c) balancing to avoid regional and local bottlenecks as a regional and local system service, d) voltage control as a local system service, and e) local load-shift in order to use the subscription locally in the most optimal way.
In order to enable these upcoming future system services it is of high importance to drive the development of charging infrastructure as well as its communication infrastructure in this direction.
In the paper it is highlighted urging questions for the development of a smart communication infrastructure for the upcoming infrastructure associated with electric vehicles.
In future scenarios electric cars are expected to take an active part of solving the arising power system issues listed above a) – e). Electric cars need to help the future power system.
It is outlined the importance of standardization for the evolution of the electric infrastructure for electric cars.Also standards that opens up for controlled charging and future implementation of Vehicle to Grid (V2G) where electric cars feed power to the grid and/or Vehicle to Home (V2H) is treated.
The study was evaluated on 10 different Swedish simulated detached houses without electric heating, and using various combinations of charging powers and fuse limits. The results show that the worst house—the house that needed smart charging the most—needed postponed charging 8 days a year to avoid breaking the fuse. Moreover, postponed charging increased the charging duration, and thus inconvenience to the EV owners, by at most 4 hours. On the other hand, the capacity-filling charging scheme could increase or decrease the charging duration—compared to the uncontrolled charging. An increase is expected if the difference between the fuse size and the house load is smaller than the uncontrolled charging power. The charging duration will be shorter if the difference between the fuse size and the house load is larger than the comparable uncontrolled charging power.
The capacity-filling scheme proved to be more convenient, as it did not increase the charging duration by more than 3 minutes. Moreover, it reduced the charging duration for at least 198 days a year.
The results indicate that charging the EVs by the available capacity—the difference between fuse size and house load—is recommended compared to constraining the charging power.
In this paper, we use first hand data of a real EV fleet of Tesla vehicles and their historical driving patterns to develop a two-stage stochastic optimization problem. This model maximizes the profit of a risk-averse EV aggregator that aims to place optimal bids on the day ahead in both energy and Frequency Containment Reserve (FCR) markets. Only uni-directional charging is examined, while we take into account uncertainty from prices and vehicle utilization. Case studies are carried out modelling individual vehicle driving behavior in different Nordic price areas in both winter and summer.
We identify a strong alignment of EV availability and periods of high FCR prices. Results show that consumption is shifted largely towards early hours of the morning. When compared to a reference "cost of charging case", up to 50% of the cost of charging can be covered in Norway, while the entire cost is met in Sweden.
Whether charging infrastructure expansion is effective will be determined by future demand, the quality of the coordination between stakeholders and exploitation of potential synergies between public, semi-public and private charging infrastructure.
Today, charging infrastructure planning must include all interested parties, many of whom had not been considered until recently. These include municipalities, public works departments, distribution system operators, transit authorities, car-sharing companies, charging infrastructure operators, supermarket chains, and landlords.
Because any one stakeholder can now play multiple roles, the traditional channels and directions of communication in the energy industry will change.
Charging infrastructure must meet conflicting criteria. Decision-makers need a tool or suite of tools that helps them to balance these competing criteria. Any such tool must make electromobility development potential apparent, deliver reliable estimates of future demand and assist in coordinating planning, implementation and operation of charging infrastructure.
So far, most planning tools do not include the full spectrum of charging powers available between 2.7 kW and 350 kW, have not considered mid- and long-term time horizons for infrastructure development, and do not have the ability to directly include all the stakeholders in the planning process. Further, they typically do not integrate real-time data from monitoring of existing charging stations, a requirement for any demand-oriented infrastructure planning.
Successful integration and use of charging points also require coordination with on-site implementers, as well as consideration of specific site conditions and information about independent activity in semi-public and private spaces not traditionally gathered by governments and planners.
We introduce a set of quantitative and qualitative methods for producing robust planning guidance based on our experience in the German state of Brandenburg.
The city of Hamburg, Germany committed to buy exclusively emission free buses by 2020. Thus, public transportation companies as Hamburger Hochbahn AG (HOCHBAHN) must build a charging infrastructure for large electric bus fleets. Currently HOCHBAHN is planning an urban charging depot for 240 electric buses (EBs). This pilot project is the first large-scale infrastructure project for EB fleets in Germany. New electrical infrastructures for bus depots must comply with local grid capabilities. Furthermore, they have to fulfill highly individual boundary conditions and operational requirements. In the close future, most public transportation companies will face the challenge of developing electric infrastructures for EB fleets. This paper identifies the key components of electric infrastructures for bus depots based on the introduced concept. It outlines decision objectives, that describe the characteristics of a concept, and confronts them among themselves. The authors apply a sensitivity analysis to evaluate how dimensioning of components affects bus charging times and operation. The developed algorithm in this work uses real data and demonstrates that components can be downsized by 8\%. Furthermore, the method is extended to evaluate needed capacity for varying module sizes of concepts.
The INVADE project has pilots in The Netherlands. This paper will describe the large scale offices and parking lots case for semi private/public situations and the large scale public case with charge points in the public domain. Both sub-pilots have in common the lack of available information regarding the charging process as users and EVs are completely unknown. This makes the definition and formulation of the optimization problem more challenging and more dependent on the forecasting tools.
In such a situation, the DSO can face congestions due to simultaneous EV charges causing grid congestions or voltage limit violation in weak or remote areas. Therefore, the DSO could be interested in offering economic discounts to EV drivers if they reduce or even delay their charging processes when there is grid scarcity. Additionally, EV charging could be re-scheduled in some cases where time flexible customers want to utilize fluctuating prices in order to save charging costs.
The nature of the problem is that some information is not known with certainty at the moment decisions are made, and further, that information will be revealed successively. To handle this situation, usage of a rolling horizon principle has been selected, which implies that the process of receiving fresh data, updating predictions, making decisions and sending these to the local systems is repeated for each time-slot
The paper is structured as follows: The section 2 presents the Dutch case studies, section 3 presents the optimization problem mathematical formulation, section 4 shows the inputs and outputs obtained and the section 5 includes the paper conclusions.
For BEV-penetration levels of 5%, 10% and 21% and for charging stations (CS) of 11 and 22 kW the additional transformer loads and voltage drops for this suburban network are calculated.
The worst-case assumption of simultaneous charging of all BEVs in a string of 60 households (HH) is used as a starting point. In this case more than 5% penetration of BEVS will always lead to excessive voltage drop, if charging stations are not clustered close to the feeder of the string.
However, including the statistics of arrival time (with a maximum at 18.00 h) and the duration of individual charging events (as they depend on the statistics of daily driving distance; 70% of cars have driven less than 50 km per day in Germany), the maximum number of simultaneously charging vehicles is reduced significantly with a high confidence. For the example of 60 HH and 21% BEV penetration (which leads to 17 BEVs in the string), the number of simultaneously charging vehicles is decreased from 17 to 6 BEVS with a confidence of 99.7%.
In a final step, the impact of decentralized storage placed at each charging station is considered. With 5 kWh of local battery storage, which is assumed to cover the initial recharging effort, and considering the distribution of driving distances in Germany, only 34% of all BEVs need further recharging from the grid. For the above example of 17 BEVs in a string, a 5 kWh storage leads to a maximum of 3 simultaneously charging vehicles with more than 99.7% confidence. 10 kWh and 15 kWh of battery storage at each charging station leads to a maximum of 2 and 1 vehicle(s) with more than 99.7% confidence, respectively.
Using the same approach, 100% penetration with BEVs s and 15 kWh battery storage at each private charging station, of the 79 vehicles in the above string ouf the network, not more than 3 vehicles will be seen to charge from the grid simultaneously with a confidence of 99,7%.
Thus, it is very important to take the statistics of charging events and average usage of cars into account, to limit network-extension effort to a reasonable amount, while still providing a high service level for the customers.
TU Braunschweig’s buildings are distributed in various areas within the city. In total, the campus comprises four separate areas that can be viewed independently of each other. In addition to the central campus, there is a campus area located at east as well as at north. Each campus power grid includes medium voltage and low voltage level.
Simulations of the campus grid were carried out using DIgSILENT PowerFactory. A separate model is created for each campus grid. Load flow calculations can be carried out within a selected observation period.
In the first step a qualitative mapping of the grid structure including the specific parameters of cable and transformer is made using the existing planning documents from the building management department of the university. In the second step, the electrical load and generation profiles are implemented in the simulation model. The university has a comprehensive system of electrical meters distributed throughout the campus.
In order to make a forecast for the respective observation period, assumptions regarding the development of electromobility, renewable energy integration and energy efficiency improvements must be made for the simulation model.
With regard to the electromobility sector, studies were used to determine the demand. The results were compared with the parking situation as well as the grid operating resource limits. For PV integration, an analysis of all roof surfaces with consideration of the structurally loading of the roofs as well as the monument protection of individual buildings was carried out. Based on the expected improvements depending energy efficiency, the Federal Government's targets for 2020 (10 %) and 2050 (25 %) will be used as reference values.
In further investigations of the paper, the development of an energy supply concept is to be addressed in order to achieve the goals set by the Federal Government. For the observation periods 2020, 2030 and 2050, the influence of electromobility through different levels of penetration is evaluated. The effects on the university's distribution grids are investigated using the simulation model. Furthermore, the integration potential of electrical energy storage systems in combination with a charging management system is analyzed and presented.
How will the electro-mobility solutions for next generation customers of vehicles look like? Volvo Cars and RISE Viktoria are undertaking a pre-study with the main task to address additional research needs covering a situation where vehicles are finding charge spots, optimizes automatic charge control and enabling for prosumers to utilize own produced electricity as fuel.
This paper will explore some of the needs and possibilities opened up when transferring to next generation electromobility taking power production, and remote distribution of energy into consideration.
Battery storage systems are installed in Germany in more than 50% of all new residential PV installation. For residential electricity consumption of 10 to 12 kWh per day, a battery of 4-6 kWh leads to an optimum in terms of profitability (250-280 equivalent full cycles per year). Increasing the battery capacity will lead to lower specific cost per kWh of the battery and higher efficiency of the overall system, but will not lead to sufficient gain in self-consumption in order to pay for the additional invest (a 10 kWh battery leads to approx.180 cycles per year). The battery utilization can be proved, if the battery provides additional services for the network or for the customer.
Large batteries in domestic applications are not fully discharged during evening hours in summer. Hence charging a BEV during evening hours can take advantage of the surplus of stored electricity for large batteries. The conference paper will provide plots of battery utilization (in terms of equivalent full cycles per year) as it depend on PV size, local demand, daily driving distance and charging patterns (during day or evening; slow or fast charging). For the aforementioned scenario (10 to 12 kWh electricity consumption of the household; plus 10 kWh per day for charging of BEV during evening hours) a battery of 10 to 12 kWh will be cycled approx. 280 times per year and is therefore utilized well.
The final analysis of opportunities (saving through own consumption) leads to an indication of the allowed cost for the battery to provide a positive business case. This paper builds on previous publication of ZSW on PV storage systems, self-consumption, level of autarky and allowed battery cost for a positive business case. Those studies have been a result of simulations taking time series of measured yearly solar radiation and consumption as input data. Those results compared well with field test data.
Our study, performed by Ecofys and E.ON, specified key elements of economic and technical framework conditions to integrate electric vehicles and flexible demand units ‘grid friendly’. We focus on the regulation in Germany and use quantitative data from German distribution systems (E.ON), but our conclusions provide general principles for generic distribution systems. The identified instruments had the two main target functions: Use flexible load to increase the network utilisation (decrease cost per grid user) and avoid inefficient network extension. Therefore Ecofys, a Navigant company, identified specific problems and opportunities through an expanded use of flexibility and thus to answer the questions which regulatory flexibility instruments are needed in distribution networks.
For this purpose, we conduct an extensive analysis of recent studies, develop three flexibility models for an appropriate flexibility instrument and evaluate these using previously defined criteria. The instruments are based on two basic principles, that the distribution system operator is able to influence demand units in the low voltage systems via (1) providing a quota / schedule of maximum available capacity or (2) direct control (flexibility procurement). For each model we specified and described
Finally, we assessed the instruments for typical distribution network classes, based on a quantitative analysis of the population of demand units in the German distribution system. The results and conclusions highlight key elements of future regulation which can effectively and efficiently unlock the potentials of new demand units for flexibility in the distribution system.
 Ef.Ruhr (2017): Verteilnetzstudie für das Land Baden-Württemberg, available at https://um.baden-wuerttemberg.de/fileadmin/redaktion/m-um/intern/Dateien/Dokumente/5_Energie/Versorgungssicherheit/170413_Verteilnetzstudie_BW.pdf
We proposed Watt & Volt / Var control in which EV and PV are cooperated for maintaining voltage in the distribution feeder in the solar integration workshop 2017. In this paper, by Freq / Watt control contributing to the frequency regulation in the TSO level is also installed to in the EV inverter. A smart inverter that suppresses voltage fluctuation and frequency fluctuation by autonomous distributed control is to be successfully realized by the proposed control.
Voltage and power flow profile of a distribution feeder in which massive PVs and EVs are interconnected and global frequency deviation based on the supply and demand calculation are emulated in a real-time simulator at the same time. HIL test is carried out by use of the real-time simulator and two smart inverters of the PV and the EV. On the real-time simulator, Opal-RT, the typical distribution feeder in Japan was modified at 6600V level. The feeder length is set at 5km. 720 houses are interconnected to the feeder. In this paper, it is assumed that PVs and EVs are interconnected to all houses, and all the interconnection inverters have control capability. A thermal power generation, an electric power demand, power output of the PVs based on actual measurement are modified for supply and demand imbalance calculation. Calculated frequency deviation is applied to the modified voltage source of the distribution substation at the top of the distribution feeder. Two different type inverter system, TriphaseNV: PM15 and MITSUBISHI ELECTRIC: Smart V2H, are used as actual smart inverters installing proposed control strategies.
Effectiveness of autonomous distributed control by PVs and EVs on the distribution feeder was confirmed through the HIL tests. And, there ware no interference and unstable phenomena caused by multiple smart inverter control. It is said that smart inverters of PVs and EVs is very effective as control devices for TSO and DSO.
In order to reduce the amount of CO2, more and more transitions from classical mobility towards electric mobility are on the way. Besides the transition from internal combustion engine cars toward electrical powered cars, a huge amount of CO2 can be saved by replacing heavy good vehicles powered by Diesel with electric ones. This is in particular interesting, since more and more goods have to be transported on roads due to full capacity of rail-bound transports. This makes it inevitable to electrify highways in order to power the vehicles via overhead wires. The substations (rectifiers) which connects the DC overhead wires with the surrounding distribution AC networks could be used in a smart way to stabilize the power injection and hence the voltage on the overhead wires, as well as providing reactive power for the distribution network in order to compensate for their own influence and additionally provide system services. A project currently carried out at Fraunhofer IEE investigates on the potential of smart substations. Results will be shown at the workshop.
Use Cases and Simulation
Four worst case scenarios will be investigated (combinations of high and low consumption and generation). In these scenarios, the electrified highway will be weakly, mean and strongly used by the vehicles. Also one scenario will be performed without any influence from the electric vehicles. Each scenario will be simulated one time with and one time without a smart substation. The aim of the smart substations will be to control the voltage on both sides of the station to hold a certain value. We will also investigate if it is possible to generate ancillary services which can provide the DSO to the TSO (e.g. reactive power at PCC, voltage control …) using these substations. Also minimizing network losses and losses on the overhead wires is one focus in the simulations.
Expected Results and Outlook
We expect that the electrification of highways will have a significant influence on the supplying distribution grids. The influence will be strongly dependent on the dimension of the substations and the amount of vehicles using the highway (it also depends on the capacity and dimensioning of the vehicles themselves and their storage). Using conventional static substations can result in voltage problems for the distribution grids and also on the overhead wires. The latter can be omitted placing the substations such way, that full load scenarios from the vehicles are covered..
On the DC side smart substations can control the voltage at the upper limit of the designed voltage band. At the same power this leads towards a lower current and therefore a lower voltage drop on the overhead wires resulting in a lower number of substations needed. A cost benefit analysis of regular and smart substations will show the best solution from economical point of view.
Fraunhofer IEE is planning a new project for the macroeconomic optimization of the charging infrastructure and the distribution grid for fast developing e-mobility. Negotiations with potential partners to compose a consortium of distribution grid operators, car manufacturers and research organizations are already in a mature stage. The results should be achieved through an integrated analysis of the complete value chain of electric vehicle charging infrastructure. The aim is to enable the various stakeholders to offer the best solution in terms of overall economic efficiency, which optimally serves the needs and interests of vehicle users, vehicle and charging infrastructure manufacturers as well as grid operators and energy suppliers. During a planned project term of about four years frequent workshops with all stakeholders are planned. The consortium should use this format to gather as many information as possible and gain knowledge that is not in the focus of the project partners by nature.
The main result of the project is the description of optimal technology mix with regard to charging technology, ICT infrastructure, grid expansion and needs-based charging infrastructure development through holistic charging and grid operating strategies.
The aim of this paper and presentation is to introduce this new project and encourage stakeholder to participate in the planned workshops and give their input to the project.
The energy sector is changing drastically. Driven by the reduction of CO2-emissions is the production and supply of electrical energy shifting from central plants to a large amount of decentral, renewable and small ones. The fossil vehicle is replaced by electric or hybrid engines. Heat is less and less generated on the basis of oil/gas and more by electric heat pumps. In short, the demand for electrical energy and power will increase in short to medium term, despite increasing the efficiency of appliances. The distribution of electricity has previously been unidirectional, from large power plants down to the end-customer via the distribution grid and is today already bi-directional and very decentral. The private household is transforming from a pure energy consumer to a so called "prosumer" (producer / consumer), as they want to harvest or feed-in their own “green electricity”.
A large amount of electric vehicles will be introduced to the market in the near future. E-mobility will be affordable to the broad market. Many e-mobility associations are agreeing that despite the actual high demand for fast-charging points, most of the charging will be happening either at the private home or at the workplace (fleets), means on the level of the distribution grid.
E-Mobility and Wallboxes up to 22KW are increasing the electrical power requirements in a house and as well in the distribution grids, next to the high rate of simultaneity-effect. These vehicles are typically connected at least 8 hours at home (sleep) and / or 8 hours at the office (work), and therefore have a high potential for flexibility in their load-management. This flexiblity can be learned, preditect and used for peakshaving or shifting. Either via DR-models, power based tarifs or even dynamic grid tarifs depending on the chosen energy management method. If there is no charging-concept, then will the car be charged based on customer requirements, mostly like a mobile-phone (user centric charging). Home = plug in
Additional electrical-car charging and PV-generators will bring Power- and Voltage-violation in the distribution grid, if there is no active energy management. The distribution grid will reach its limits and has to be re-inforced with high amount of costs, which has to be paid by the society. A pure reinforcement without smart technologies will not work, according to many distribution system operators nor is it the economically best option in many cases.
New flexibility concepts for companies like Distribution System Operators, Utilities, Mobility-Service-Providers, etc. are requested for a hassle free implementation of e-mobility and the management of Energy Flows in the house as well at the distribution grid level.
“Solving the issues of renewable energy integration at the place of its origin – in the private household and the distribution grid.
Within the proposed paper, we will present an in-depth analysis of the energy and power flows of residential PV battery systems in combination with EV and local charging infrastructure. The analysis will focus on the potential to increase speed of charging in times of limited power availability from the public grid. Hence, the following four use cases are analysed for this purpose:
Therefore load flows in residential photovoltaic battery energy storage systems will be simulated for a multitude of individual residential buildings using minute resolved data of photovoltaic power plants, residential load profiles, charging and driving profiles of electric vehicles. Following this, a sensitivity analysis will be performed for the presented charging services. Due to the big seasonal differences in the service level of these use cases this paper finishes with a detailed seasonal analysis of the charging services.
Simultaneously, the transport sector with electric vehicles (EVs) is increasingly an important consumer of electricity. However, as fleets increase, and when EVs are immobilized and connected to the grid they can be used as controllable loads, charging in periods with higher renewable generation or lower prices, using the Grid to Vehicle (G2V) system. With adequate technology, in addition to absorbing power from the grid, vehicles can also use some of their storage capacity to inject energy into the grid, in order to ensure the balance between the generation and demand, using the Vehicle to Grid (V2G) system.
According to some authors, this operation would require a complex centralized management system that would command the dispatch per group of loads. Others argue that an effective policy of real-time pricing would be enough to motivate the charging and grid injection in the desirable periods. Other authors also advocate a mechanism with each load presenting the capacity to detect an increase in the voltage or slight frequency deviation in the local grid, using it as signal in the decision of charging or grid injection
However, with current electrochemical technologies, cells degrade according to age, discharge depth and number of charge/discharge cycles. Therefore, with V2G the number of charge/discharge cycles is accelerated and the degradation of cells has to be known accurately so that owners can be remunerated in order to feel encouraged to participate in V2G.
The implementation of inductive recharge systems introduces a great simplification in the operation of recharging the EVs, eliminating the cabling and the human intervention. The EV when not in use in the transport only needs to be immobilized on a point of transfer of energy and to allow (or not) the G2V or V2G operation, by pre-programmed indication of its owner, considering the percentage of available battery capacity, the minimum DoD, the operating period, the required SoC at the end of the period, etc. All these features are now easily implemented in smartphone APP, digital identification technology and wireless systems.
The full paper intends to discuss the technologies and methodologies for the implementation of Grid to Vehicle and Vehicle to Grid systems, as well as their potential benefits for the grid in a scenario with large-scale penetration of renewable generation.
Sharma Suman* , Prerna Jain†, and Rohit Bhakar†
* Department of Electrical Engineering, MNIT Jaipur, India. email@example.com
† Department of Electrical Engineering, MNIT Jaipur, India
Abstract: Various entities in electricity market can formulate self-scheduling problem to minimize the costs or maximize the profits. The System Operator (SO) perspective is to minimize the operating cost with shaved peaks and filled valleys. Profit maximization with Charging cost minimization is major concern of Electric Vehicle Aggregator (EVA) in scheduling problem. Coordinated dynamic charging strategy aids EVA to allocate power effectively considering techno-economic constraints. Charging rate modulation according to base load profile of SO and TOU prices as well, significantly enhance EV adoption under sustainable smart grid environment. Optimal Scheduling of EVA involves energy management and available capacity of EVs for participation in multiple markets. Proposed model increases flexibility for load shifting according to price-based demand response framework to increase economic efficiency and reducing the charging peak load simultaneously with satisfaction of EV owners.
Objectives and context
This paper proposes an Optimal TOU pricing based G2V scheduling Strategy of EVA for reserve and regulation markets considering their trade-offs. Profit assessment through multiple markets participation with simultaneous perception of customers and SO, is the challenge towards EVA. G2V scheduling is economically viable only when revenues earned by AS provision outweighs purchase costs for charging energy. In charging mode (G2V), EVA earns revenue for being on stand-by when providing down regulation. EVs utilize additional energy for transportation purposes and thus EVA does not entail additional revenue of energy sale. EVA increases the adoption of EVs by incentivizing EV owners through reduced charging cost (lower than residential electricity prices). TOU price signals are sent downstream to EV owners and charging requirement signals from EV owners dispatched upstream to the EVA. EVA procures DR at the least-cost without sacrificing benefit of consumers and SO. The EVA performs impact analysis of varying dispatch in response to variation in market prices of energy and AS. High value of price differential (difference of maximum price and minimum price) has the capability to compensate for the additional losses if any.
Joint optimization of charging cost and revenues
Optimal allocation of regulation and reserves
Fig. 1 Typical Architecture of Two-Stage Coordinated Dynamic Charge (Unidirectional G2V) Scheduling and Dispatch of EVA using TOU Pricing
We have developed some V2G control schemes through the HIL(Hardware In the Loop) tests using experimental batteries and stand-alone bi-directional inverters on control response, communication delay in the case of remote control, and power system frequency dynamics.
In this paper, the proposed V2G control schemes is implemented to the actual PEV and the V2G capable charging system as a first V2G system in Japan. Accuracy of the grid frequency and voltage measurements, response of the system, and communication capability, and so on, are verified on two different type PEV system. Effectiveness of the PEV-FFR(Fast Frequency Response) featuring quick response of the PEV and inverter system, PEV-LFC(Load Frequency Control) coordinating large-scale thermal generations, and combination control of the FFR and LFC dispatched to the multiple PEVs are evaluated. PEV-SIR(Synthetic Inertia Response), in which very quick system response in the frequency detection, control initiation, and physical responses is required, is also evaluated by the HIL test.
Overview of the HIL is as follows. The frequency fluctuations under massive PVs and PEVs integration are emulated by the power system real-time simulator (OPAL-RT Technology, OP5600). The power system model is based on a prefecture level with a population of about 9 million people. The power capacity of the PV is 20% of the total electricity demand at peak time, and the number of PEVs is 480,000, these values are target in 2030.
One PEV is Nissan LEAF(30[kWh]), the other is Nissan e-NV200(24[kWh]). These cars are connected to two different bi-directional power conditioners (Nichicon Corporation, NECST-TD1 & Mitsubishi Electric Corporation, SMART V2H System). It is possible to control charging / discharging of 3 [kW] by Nichicon’s one and 6 [kW] by Mitsubishi Electric’s one.
The frequency command value calculated by the real-time power system simulator is transmitted to the power amplifier (California Instruments, MX15, rated: 15 kVA). Then, the power amplifier outputs instantaneous voltages corresponding to the frequency deviations. The PEV controller (dSPACE, Micro Auto Box II) measures the frequency deviations and determines a charge / discharge power command to the PEV. Then the PEVs output the V2G power to the power amplifier via the PEV power conditioners. By feeding back V2G power measurements to the real-time simulator, the frequency fluctuations in the next step can be calculated in the real-time simulator. The FFR, LFC, and SIR control schemes are verified on the PEV and charger system.
In this paper, we present an analysis how hotels can address these challenges by adopting optimized energy management strategies for electric vehicle (EV) charging. A model predictive control approach is presented that is uses a hotel load forecast and a mobility forecast. A mixed integer linear program is developed and implemented to derive optimal charging and discharging schedules for EVs. The approach models in detail current EV charging constraints, e.g. minimum charging power requirements. Load forecasts are derived using a seasonal ARIMA model. Mobility forecasts are based on data collected in an ongoing field test with 12 hotels.
The presented case studies discuss several price incentives, e.g. market-oriented prices, and evaluate benefits from vehicle to grid operation for load management in the hotel and flexibility provision for the grid. Potential trade-offs for hotels and grid operators are analyzed and indications what EV penetration levels will result in additional grid reinforcements are provided for different hotel types.
One of the key questions in real estate is the economical effects to property owner, the tenants and the car drivers. The costs include the infrstructure such as cabling, the charging equipment, and energy and power costs, and possible upgrades to electrical supply. We looked at employee parking data collected from office sites and public garages mostly used for workplace parking, and looked at economical aspects as a whole. The costs are calculated both in base case where EV charging is used without any load management and with peak shaving with load management, including long term investment costs such as upgrades of electrical infrastructure. Further, we look at cost savings related to EVs, such as replacing ICE fuel costs with plug-in car electricity, and how it the economics work for all parties, including real estate owner, tenants, and EV charging service provider.
The important conclusion from the analysis is that when EV charging is made possible to all parking spaces in a cost-efficient way, plug-in cars become more popular, and the economics work, while at the same time reducing emissions.
In this paper we present a method for conducting grid integration studies in real integrated low- and medium-voltage grid models in the context of eMobility. A special focus is the comparison of a probabilistic distribution approach for BEV charging in low-voltage grids vs. the usage of simultaneity factors. The probabilistic method uses a pool of BEV charging profiles and places them randomly in an LV grid to derive worst-case situations. Necessary grid reinforcement and expansion as well as their cost are then estimated with an automated approach based on a heuristic optimization algorithm. Additionally we compare different charging scenarios like residential charging, public fast charging and dedicated grids for charging stations.
The results show, that the application of simultaneity factors can cause large deviations in regard to violations, as well as necessary grid reinforcement and expansion compared to the probabilistic approach. Especially local weak spots in LV feeders often cannot be identified when using common simultaneity factors for all BEVs in a low-voltage grid. This leads to a possible underestimation of reinforcement and expansion cost. Furthermore the cost of integrating charging infrastructure into the grid varies widely between the different scenarios considered.
The global proliferation of plug-in electric vehicles (PEV) poses a major a major challenge for operators of current and future distribution systems. If uncoordinated, the charging process of such a growing electrical fleet can lead to overloading of the main network’s assets, distribution transformers and head feeders, which may result in their overheating, deterioration, triggering of associated protections and their eventual risk of failure, compromising the stability and reliability of low voltage distribution networks. In order to mitigate the thermal impacts of uncontrolled domestic AC charging and to increase the housing capacity of PEVs in radial low voltage distribution systems, the present study compares the effectiveness and the performance of a centralized charging control algorithm based on three alternative optimization formulations that seek to minimize the total impact upon the car owners. A lineal, sequential quadratic and heuristic formulations of the optimization are suggested and compared in terms of the impact of the charging strategy on the end-user and the performance of the algorithm. The effectiveness of the proposed system is tested on a realistic simulation environment considering a residential low voltage grid facing a complete set of night charging episodes for incremental PEV penetrations levels. The network topology consists of 20 individual households fed by power underground cables and distributed among three main feeders where a maximum of 20 vehicles, modeled after the two commercially available and mass-produced cars, have been randomly assigned to the existing dwellings. Preliminary results reveal how a successful management of the network’s thermal constrains are obtained resulting in a minimal impact over the participating users under all three implementations. Considering the impact on the end-users the heuristic and linear formulations seem to result in lower values compared to those of the quadratic formulation.
Plug-in electric vehicles, Radial low voltage networks, Real-time control, Centralized thermal management, Active distribution networks, User impact minimization.
The studied network only registers a minor overloading episode affecting its distribution transformer and thus solely some minimal differences between the three implementations can be seen. Further results will be obtained and will include, the comparison of the performance of the solution for each formulation in terms of computation time and optimality of the solution. Furthermore, more dramatic scenarios such us increasing the thermal limitations of the network or the existing base load will be explored in order to observe significant network violations and so clearer distinctions between the methodologies can be exposed.
Traditional protocols and methods of load management do not scale for such building environment. A scalable method of doing energy management in EV charging is needed. We present a distributed collaborative algorithm which can implement energy and load management in presence of a large number of energy consumers and produces, as well as energy storage. The algorithm is based on collective situational awareness, including distribution of concerns amongst the components of the system, and is able to cope with an unreliable networking environment. We further present a domain specific mesh networking algorithm that allows the charging algorithm to perform better and/or under more demanding conditions than using previous standard meshing schemes.
The algorithm is based on each charging station exchanging information and trading resources such as capacity allocations between each other, typically over wireless network, avoiding single point of failure. We use radio broadcast domain to reduce transmission. For example, we first try to get a resource from local area, before resorting to a search further away. Locality allows very low latency, which allows charging stations to quickly react to increased power requirements. The algorithm is robust as it makes pessimistic assumptions until communications with peers confirms an action to be within available capacity.
The algorithm can work stand-alone, but its behaviour can be optimized by a cloud service which will pull in data from various sources, such as grid, weather services, and electricity markets. The cloud service can participate frequency markets through aggregators to allow large numbers of EVs to be used as part of smart grid, with minimum communication resources.
The algorithm was originally designed to be able handle very limited electrical infrastructure, such as block heater outlets present in Scandinavian countries, which often have power limits at 1kW or even less per parking space. However, the cloud-assisted capability to fine tune the power use and accommodate other loads in the building is valuable when developing EV charging services worldwide, as many existing buildings have very limited amount of power available, and the trend of introducing peak power based pricing will make peak shaving a commercial necessity. The system implements fairness as well as protecting the electrical system in addition to being smart grid and smart building capable.
The main challenge for many Distribution System Operators (DSOs) when it comes to the integration of Electric Vehicle (EV) charging on their grids is not a problem of energy but rather of power. Critical peak power (CPP), already a difficulty during winter months, is exacerbated by the increasing presence of EV charging stations as the use of electric mobility becomes widespread. As a result, DSOs are showing an increased demand for peak-shaving and peak-shifting technologies.
The project "Coordinating Power Control" ("Växlande Effektreglering" in Swedish, referred to as "VäxEl" in this paper), which began in January of 2017, is based on the increased interest of rural households for solar panels, home batteries, EVs and other "smart home" equipment. The ambition of the VäxEl project is to create a cost-effective optimization of a distribution grid by addressing various technical, regulatory, and psychological challenges.
By bringing together a community of market actors, including smart grid service providers, governmental regulatory bodies, research departments, and a local grid owner (DSO), VäxEl seeks to uncover and propose solutions to such challenges. In May of 2018, the International Smart Grid Action Network (ISGAN), a cooperation within the International Energy Agency (IEA), presented the VäxEl project with its prestigious Award of Excellence for world-leading activities in the area of smart grids for power flexibility.
Through partial funding provided by the Swedish Energy Agency and by assisting homeowners in applying for the economic assistance available to purchasers of EVs, home batteries, and solar arrays, VäxEl has been able to martial a formidable amount of power flexibility, including the installation of 500 connected water-based heating systems, 60 sites with rooftop solar panels (providing 200 kW of production), 36 kW of Electric vehicle charging, and 70 kWh of home energy storage (providing 60 kW of instant power flexibility).
This paper presents some of the progress made within the VäxEl project, primarily focusing on two key aspects: the modeling and design of an optimization algorithm for integrating the resources within the project for the purpose of reducing CPP, and the reduction in CPP achieved during February of 2018 by connected heating systems within the Upplands Energi electric grid.
E-mobility creates new and mobile volatile loads and feeds that will place an additional load on the grid and could require a large investment in network expansion. This makes e-mobility an important driver for forward-looking development of the grid in addition to the energy transition and the implementation of the European Single Market. Like other network users, e-mobility should also contribute to the provision of system services such as balancing power, frequency maintenance and reactive power supply. Smart charging is one of the key capabilities of e-mobility to provide flexibility, for example, by using electric vehicles as temporal energy storage that can feed energy back into the energy system when required.
The integration of e-mobility will occur mostly at low-voltage level. To avoid their overload, charging stations with capacities of 350 kW and over should be connected to the medium or higher voltage grids. If connection is only possible to the low-voltage grid, this type of charging stations should be combined with energy storage. In addition, three-phase charging system are preferred to avoid voltage imbalances in the network at every level. Market incentives or support programs should be based on three-phase charging.
A planning corridor is necessary for the ramp-up of e-mobility. An increasing number of electric vehicles translate into a greater likelihood of simultaneous charging that could cause bottlenecks in the network. Market-driven charging processes can lead to mass effects and create peaks of demand. As found by the meta-study commissioned by VDE|FNN and BDEW, grid-focused charging control is more efficient than market-led charging. Market signals must therefore be coupled with network-relevant parameters to find the most cost-efficient solution.
This paper summarizes current network conditions in Germany, explains the effects on the power grids and outlines the necessary next steps to facilitate e-mobility integration. Key findings from the meta-study commissioned by VDE|FNN and BDEW also provide a description of the critical factors for the integration of e-mobility and offer suggestions for future research.