When converting bus fleets from fossil-fuelled to emission-free drives, transport companies have to face major challenges in optimum system design. One approach for bridging the gap so far is using auxiliary fossil-fuelled heating in electric buses in order to increase the available range to the required minimum. However, this can only be a short-term solution on the way to emission-free transport systems until batteries provide enough range even with electric heating. This paper provides a modelling and evaluation approach to compare several battery-electric bus systems with either fossil-fuelled or electric heating with regard to vehicle demand, costs and CO2 emissions. For this purpose vehicles, required infrastructure, bus operation and fuel or electricity consumption are modelled for costs and emissions respectively. The use of data derived from several years of bus operation with different propulsion systems and battery-electric buses, in particular, allows results with a certain validity to be optained, although assumptions were still made in both cost and emission analyses. It is shown that in some cases, a shift to electric heating can be accomplished without additional vehicle demand, only a few percent of additional costs and a significant reduction of emissions. But depending on the vehicle and battery type, the additional emissions for increased vehicle demand sometimes almost overcompensate the emissions saved from heating and hence, overall savings are very small.
This paper discusses the dependencies and interactions between the gains and losses when shifting to electric heating and throws a spotlight on the critical questions that need to be investigated and answered before making such a decision. Another finding is that emission-free bus systems with electric heating are only economical compared to diesel buses at a price of about 800 to 1,200 euros per ton CO2. The current CO2 price for consumers and industry in Germany in 2021 is 25 euros per ton. Thus, CO2 pricing needs to be changed radically in order to give economic incentives for a transition to emission-free bus systems.
Reducing grid peak load through smart charging strategies and battery energy storage systems
A high electric vehicle penetration in urban distribution grids leads to challenges, such as line over loading for the grid operator. In such a case smart charging strategies or the installation of grid-integrated storage systems represent an alternative to conventional grid reinforcement. This paper examines the influence of smart charging strategies at electric vehicle charging parks to the peak grid load. Furthermore, the battery energy storage systems (BESSs) with various capacities located at these charging parks are simulated with the aim to reduce the impact to the grid.
The data for the charging activities of the electric vehicles is simulated using the simulation tool ‘SimBEV’. Additionally to an immediate recharge, different smart charging strategies implemented there. The resulting power time-series at the charging stations are integrated in a synthetic example grid using the open-source tool “open_BEA”, which combines previously disjoint tools to enable accurate co-simulations of BESSs and distribution grids. The power flow analysis is conducted using the open-source tool ‘eDisGo’ and the behavior, which includes capacity decrease and battery as well as power electronic losses of the BESSs is analyzed using the open-source tool “SimSES”.
Results show that with controlled charging strategies the capacity of the storage systems at the charging parks can be reduced from 2 MWh to 600 kWh while achieving the same reduction of peak load at the point of common coupling.
HPEVCS - High Power Electric Vehicle Charging Stations
The rising of electric vehicle (EV) adoption as a tool in the energy transition requires the development of a widespread charging infrastructure. Considering that the EV adoption depends also on the reduction of the 'consumer anxiety' regarding charging access (availability within battery range) and charging time, the infrastructure must be deployed at a faster pace than the increasing EV on the roads, in order to provide a successful transition. On the other hand, the application of classic solutions for energy supply for this purpose are time consuming, delaying the transition. Moreover, they can be costly when grid reinforcements are needed, mainly outside the urban centres, preventing a global coverage for EV at a price that society is willing to accept.
In REN, the Portuguese TSO (Transmission System Operators), fostering the development of an innovation culture, a new grid architectural solution was created which is believed to fit the above-mentioned challenges.
REN solution enables charging electric vehicles directly from the Transmission Grid. This is achieved through a simple installation based on a tap connection to a VHV (Very High Voltage) existing line and a new substation concept based on VHV/LV transformers that supply with low voltage an EV charging station with high power availability. As a regular substation, this installation can be remotely operated and monitored.
The modular design allows scalability (by adding more transformers and chargers), aligned with the growth of the EV fleet thus minimizing the risk of investment. Finally, as we see the charger power development increasing in order to decrease the charging time, the future charger replacements will not result in grid reinforcements, which represents a long-lasting solution.
In 2018, a demonstration project was carried out in Lisbon area as a proof of concept using a 220 kV overhead line.
This solution positions itself as complementary to the mainstream charging infrastructure in the urban centres, with the following differentiating factors:
The solution is also suitable for tailor-made applications such as the electrification of public transport or company fleets, large parking lots in city suburbs or intermodal parking facilities, amongst others.
It provides high flexibility for charging needs, by allowing high simultaneity of EV charging without the risk of overloading the grid. In this way, it avoids the need for peak shaving in grid management or smart charging.
The HPEVCS European patented solution creates an opportunity to position TSO directly as a major player in the energy transition supporting the decarbonisation of the transport sector in a competitive way by using the existing grid.
Based on a sustainable development scenario with a 30% Electric Vehicle (EV) market share by 2030 the International Energy Agency projects a rise of grid challenges. In parallel, Electric Vehicle Supply Equipment (EVSE) is evolving to answer these growing needs. Efforts toward its standardization and association with smart charging strategies are being made to support grid integration while minimizing costs. Still, specific testing of EVSE technologies has yet to be established.
Here, we model the digital twin of an EV and build a comprehensive Power Hardware-In-the-Loop (PHIL) test bench. Used for EVSE conformity validation, this testing setup contributes as well to grid stability evaluation. First, we developed an EV model enabling uni- and bi-directional scenarios. Then, we built a comprehensive PHIL setup integrating our EV model, a 22kW charging unit with a Type-2 connector, and a load emulator.
Using this setup, automated procedures are established to test the charging station functionalities. Communication protocol and main mechanisms, such as defined in IEC 61851-1, are evaluated based on proposed key performance indicators. Furthermore, grid integration simulation is carried out to benchmark EV charging control strategies using a low voltage grid with representative loads as well as sources such as household loads, Photovoltaics (PV), and EVs. Regulating local bus voltages, control schemes with different access levels to grid status are designed and evaluated. We found that increased information access leads to reduced voltage deviations at the buses as well as improved power loss mitigation.
In this paper we describe the methodology of integrating electromobility charging infrastructure forecasts in the grid models of LEW Verteilnetz GmbH (LVN). Based on the scenarios of 1, 3 and 7 million electric vehicles in Germany, Westenergie Netzservice GmbH forecasts the charging facilities in the four categories Commercial, Domestic, SemiPublic and Public, both in 100x100 m tiles and at street level in the supply area of LVN. We present the basic procedure for allocating the forecasted number of charging facilities to house connection properties and stations in those four categories. Based on this, we generate forecast power values for the different categories depending on the number of charging facilities using simultaneity curves. Finally, we demonstrate the utilisation of the forecast data in selected use cases.
Charging Electric Vehicle Fleets requires expensive charging infrastructure and electricity grid upgrades; however, the costs can be mitigated by smart charging – planning the charging power over time. EV fleets present unique smart charging challenges and opportunities compared to private cars. Smart Charging varies in complexity from instructions on where to park returning vehicles, to integrated software systems monitoring the vehicles and utility prices and directing the parking and charging process real time. This paper presents a range of inputs and outputs which can be used for smart charging, presents a categorization of the levels of smart charging systems, and evaluates the potential cost savings for each level in an example case using ChargeSim fleet charging analysis and simulation software. In the example case, unmanaged charging could cost 70% more than theoretical minimum achievable with an energy storage system, however even a simple smart charging system could reduce the excess cost to only 13%.
In the course of the energy transition, both the energy sector and the logistics industry are facing radical changes. Providing renewable energy is subject to natural fluctuations, which leads to continuous over- and undersupply. Besides, the insufficiency of clarity concerning the requirements of renewable energy as well as the extent of charging networks poses a tremendous problem. Especially in terms of mobility, many questions remain unaddressed. Despite the immense benefits of electrification within the industrial freight transport, companies have serious concerns about converting their fleets. The lack of transparency regarding the current status of charging infrastructure and capacity as well as its possibilities to expand, causes the inadequate acceptance of electric mobility in multimodal logistics chains. In order to profit from the far-reaching potential of the energy sector, a synergistic interaction of the “energy” and “mobility” sectors has to be conceived.
The project “iP4MoVE”, which is funded by the European Union, addresses exactly that synergistic interaction of “energy” and “mobility” and aims at the development of a platform to support the conversion towards electric mobility in the logistics industry. Thus, the platform provides relevant information regarding logistics energy demands and on the other hand the availability of energy and charging points as well as current prices. Therefore, the conception and development of energy districts at suitable locations is required. Appropriate locations for energy districts result from the demands of loading park operators, logistics service providers, as well as energetic and logistic requirements. On basis of typical transport routes and renewable energy productions, ideal locations and optimal storage capacities for locally specified energy districts will be identified. Furthermore, the charging process at the charging parks will be designed in a standardized and simple accounting procedure. Considering all public charging parks the routing process in the logistics sector will be designed and optimized based on time, economic and ecological factors. Besides, the optimal degree of electrification of vehicle fleets for logistics service providers can be determined. The project “iP4MoVE” connects the sectors mobility and energy and aims to create a new business model in the logistics industry to make it more sustainable.
In particular, this conference paper focuses on the current status regarding the development of a sustainable route planning as well as the identification of suitable locations for charging park infrastructures as a central prerequisite for this purpose. Therefore, the current charging park infrastructure and the provided charging capacity will be analysed.
The urgent need to reduce carbon emissions resulting in decentralized renewable energy systems also encourages the establishment of energy communities where residential and/or commercial consumers can actively participate in the generation, consumption, or provision of flexibility of electric energy. The integration of electric mobility within these energy communities is of particular interest as its increasing load will impact grid stability and therefore the power grid’s and components’ sizing and operation. With this work, we provide a holistic overview of research activities on the integration
of electric vehicles in energy communities that supports researchers and practitioners with the identification of relevant topics and research gaps. We identify seven research clusters by hierarchical clustering analysis. Relevant topics include smart charging, vehicle-to-x, and considerations of uncertainty. Future research should focus on open-source models and the synthesis of the knowledge base from the extensive body of literature.
With the increasing adoption of electric vehicles (EV), the electricity grid is majorly impacted due to its uncertain charging requirements, especially when there is a high penetration of distributed renewable energy sources such as photovoltaic systems (PV). Along with solutions including intelligent control of electric load with a battery energy storage system (BESS), an optimal design of the EV charging infrastructure is vital. Simulative analysis could help to evaluate the costs, self-sufficiency, self-consumption and grid impact indicators. However, grid impact indicators have not been evaluated for EV charging stations so far. This paper deals with a DC-coupled EV charging infrastructure that is connected to a PV array, BESS and the electricity grid. The system is evaluated for a workplace environment. The charging infrastructure includes a variable number of AC and DC charging points. A load shifting algorithm is introduced in case the PV, BESS and grid inverter cannot cover the load. Furthermore, a charging algorithm which maximizes self-consumption is introduced. Economic optima with and without the charging strategy are used as reference systems for evaluating the grid inverter’s and battery’s sizes as well as number of charging points influencing electricity costs and grid impact indicators. The results show that according to seasons in Germany, the southerly oriented PV of 15° tilt cannot cover the load between November and February and depends on the grid irrespective of the number of charging points and battery size. With the help of a self-consumption maximizing charging strategy, the grid impact can be significantly reduced. The charging strategy has a far more positive influence than the variation of component sizes. A BESS can slightly increase the charging strategy’s positive influence but has not shown economical advantage in the considered scenarios.
Most of the electric vehicle (EV) charging will take place at home, and most of the charging will occur after work in the evening if not a smart control scheme is introduced. This results in high powers during a few hours in the households driving costs, but also resulting in significant voltage drops in the low voltage grid. In addition, the expansion rate of installation of photovoltaics (PV) in private houses increases. These installations produce power at daytime and in the spring to autumn when little power is used in northern countries like Sweden where heating is a more significant energy consumer than cooling. During the hours of large PV production, the entire neighborhood soon becomes a net consumer feeding power to the distribution grid via the transformer station, resulting in high voltages in the grid. The combination of PV and EV charging in the low voltage grid is thereby a new and challenging task for the energy companies.
This work investigates how PV and EV charging affect the low voltage grids. Data for an existing grids consisting of 29 households in a Swedish suburban area is used. In addition to cable and transformer data, hourly data of the household electric consumption for one year is used in the investigations. It is shown the deviations from the nominal voltage by both the EV charging and PV installations are significant, but also that the variations differ depending on where in the grid the EVs or PVs are added. The methodology used in the analysis is to systematically find the worst and best households, one at the time, in a grid perspective of adding an EV respectively PV. Thereby the impact on deviations from the nominal voltage in the grid can be found for different penetrations rates of EV and PV installations for the grid.
The potential of smart control of the EV charging to minimize the voltage variations in the electric grid is investigated using an optimal control algorithm based on the data described above. The optimisation results show that simultaneous control of several vehicles in the grid can significantly reduce voltage variations in the grid. The number of vehicles needed to achieve a certain decrease in the voltage drop for a grid is approximately halved using V2G. Simultaneous optimisation of charging profiles is an alternative to upgrading the existing grid infrastructure to avoid large voltage variations, and is important to consider when, e.g., designing new price tariffs. In the wide introduction of EVs and PVs, smart charging has a significant impact on the voltage stability and the results are promising.
Electric vehicles (EVs) can participate in various markets through a vehicle-to-grid (V2G) interface. Aggregators can combine the individual contributions of EVs to offer them, for example, on the frequency containment reserve (FCR) market or to use them for arbitrage trading. A simple approach is combining EVs in random fashion until the pool is able to reach the threshold for a service of choice. Alternatively, aggregators can compose their pools in smart fashion and include only EVs that contribute significantly to the pool’s performance. In a previous publication, we have shown that optimizing the aggregated pools of commercial vehicles for the provision of FCR or arbitrage trading can increase revenues by up to 7-fold.
In this work, we analyze the optimally composed pools and show that large vehicle batteries in the order of 80 kWh are particularly useful for arbitrage trading, while FCR provision is also possible with medium-sized EV batteries in the range of 30 kWh due to the small cycle depths. The inclusion of EVs with very small vehicle batteries around 20 kWh in aggregated pools is neither economically optimal for arbitrage trading nor for FCR provision. An analysis of the economic sectors of the commercial EVs selected for the optimal EV pools shows that some economic sectors are more suitable for V2G than others: In particular EVs of the sector "human health and social work activities" are unsuitable for V2G provision due to regular and long travel times during the day. In contrast, EVs from the "manufacturing" sector are particularly well represented in all applications and the "transportation and storage" sector in the arbitrage application. In addition to these analyses of the optimized pools, we reveal that a reduction in the required minimum power and increments would make the FCR market even more attractive to EV pools by increasing revenues by 50% to 66%. It would also better exploit the potential of EVs, as increments could be better utilized than they are in the current 1 MW minimum power requirement in central Europe.
Vehicle to grid (V2G) enables active participation of electric vehicles in the energy system and thus can bring advantages for electricity grid operation and renewable integration. For this technological innovation to be adopted by a great majority a high user involvement in V2G is required. Thus, limitations need to be considered, such as the security range, which gains relevance in the context of V2G, since this range must not be undercut during the charging and discharging process. In this paper we investigate the desired security range in the light of user experience and the place of living. Results indicate, that security range requirements are high, independent of user experience and the place of living.
The interaction between electric vehicles (EV) and the future energy system is subject of current research in the field of energy system analysis. EVs represent an additional electrical load on the one hand and a potential flexibility provider through smart charging on the other. Feedback effects on the energy system and potential benefits of intelligently charged EVs depend on a variety of technical parameters as well as the individual driving behavior of vehicle owners. Since no sufficient data on EV users driving behavior is currently available, synthetic profiles have to be used. In this paper we propose a methodological approach that combines the mobility data of the two main household travel surveys in Germany - the Mobility in Germany 2017 and the German Mobility panel - to synthesize annual mobility profiles that represent the German mobility behavior. To guarantee statistical soundness, the methodology requires a large number of individual profiles used for further evaluations. Computational power however limits the maximum number of usable profiles. In the context of this paper, we assess and compare potential revenues of a price optimized unidirectional and bidirectional charging strategy. Those evaluations are carried out for 10,000 profiles with the linear optimization model eFLAME. Resulting revenues and vehicle-specific indicators such as equivalent full cycles (EFC) and charging/discharging hours serve as a reference for further evaluations with a reduced number of profiles. To reduce that number, we compare two distinct methodological approaches. The first approach is based on randomly drawing an increasing number of profiles, while the second is based on applying various clustering algorithms to specifically identify representative profiles. In the context of clustering algorithms, we test and compare distinct feature definitions, preanalysis methods and include a principal component analysis (PCA) to identify the best cluster of representative profiles. To assess the validity of each approach, we use the deviation of 16 key indicators from the reference simulation run with 10,000 profiles.
When considering randomly drawn profiles, we identified a minimum number of 1,000 profiles to adequately represent the German mobility behavior and keep deviations for all 16 key indicators low. The use of cluster algorithms can reduce this number even further. Even with a minimum of 10 identified representative profiles, deviations for most key indicators are comparatively low. The reduction of necessary profiles drastically reduces the computational effort e.g., allowing more comprehensive and complex sensitivity analysis. Moreover, the small number of profiles allows the direct integration into the system of equations of an energy system model. Such an approach can be used to assess the dynamic interactions of EVs with the future energy system and potential system benefits of intelligently charged vehicles.
Higher loads through the increasing spread of electric vehicles and according charging points as well as heat pumps will put the distribution grid under pressure in the near future. Volatile supply from growing PV generation makes it even harder to guarantee secure grid operation. The provision and use of flexibility, i.e. opportunities to decrease and increase loads and generation at certain times, is one way to stabilise the distribution grid operation [1].
In the work presented here, we propose a local flexibility market as a mechanism to synchronise demand for flexibility on the side of the distribution service operator (DSO) with flexibility offers by end-users. The flexibility market platform collects expected flexibility demand from the DSO, asks home energy management systems (HEMS) at the prosumers’ premises to offer flexibility through a two-stage optimisation, and performs a matching of both based on their location in the grid. Afterwards, information is provided to HEMS whether their offer was accepted. Furthermore, we introduce generic flexibility market products to allow day ahead and intraday flexibility matching, for instance.
To achieve reliable information exchange, we adopt established data models like the common information model (CIM) for flexibility demand and the universal smart energy framework (USEF) flexibility trade protocol (UFTP) for offers and orders, allowing the definition of alternative flexibility options with varying energy quantities, expiration time, price tags, and sanction prices. The matching of demand and offer is done by mixed integer linear programming (MILP) constrained by the fulfillment of demand and with the objective of cost-optimality across alternative flexibility options.
We operate the flexibility market in a dockerised simulation environment and show its feasibility to manage flexibility for high numbers of HEMS in the distribution grid. The simulation enables user authentication and authorisation, simulation time management with accelerated execution, and GUI inspection. We analyse performance and the impact of prosumers' habits on the effectiveness of congestion mitigation and flexibility costs.
Simulation results indicate that resulting costs depend on prosumer preferences and managed devices. Flexibility costs indeed depend on the availability of electric vehicles. Still, more work has to be done to investigate the impact of grid size and structure and of prosumer’s bidding behaviour in such a locally restricted market area. An application in field tests as another next step is facilitated by the already distributed simulation environment and use of enhanced communication protocols.
[1] Netzengpässe als Herausforderung für das Stromversorgungssystem. Akademienprojekt Energiesysteme der Zukunft, 2020, https://energiesysteme-zukunft.de/publikationen/stellungnahme-netzengpaesse
Power Quality of Charging and Discharging of Automobile Batteries Used as Power System Energy Storage
Ewald F. Fuchs University of Colorado at Boulder (presently on Sabbatical in Munich, Germany)
Abstract—Photovoltaic (PV) and windpower (WP) plants intermittently generate electricity which cannot be always accepted by power system loads as is evident from the most recent net-electricity generation in Germany [1]. To ease the limitation of accepting power generated from renewable energy sources, automobile batteries could be used to store electric energy if cars are not in use. Power quality of non-ideal lithium–ion battery charging is explored, and power quality issues of charged batteries’ energy delivery to the power system is investigated. During charging their non-ideal internal parameters (e.g. capacitances, resistances) cause harmonics within the distribution system and vice versa, pulse-width- modulation (PWM) of current-controlled voltage-source inverters supplying energy from the batteries to the distribution system. Power quality occurring when a 100 kWh car battery either absorbs or supplies energy from the three-phase distribution system via Δ-Y transformer supplying a diode rectifier in series with a thyristor to charge a lithium-ion battery--alternatively a diode rectifier in series with a MOSFET--is investigated. For a single-phase system a transformer supplies a diode bridge in series with a MOSFET. Transient analyses are performed with PSPICE. A PV system on a single-family house at 48o latitude supplies electric energy to the residence: generation is largest during summer and meager during winter, limiting the use of batteries for storage because no battery can store the energy from summer for use in winter. As expected, PV generation depends upon geographic latitude: for example, at 48o the PV system generates about 70% of that at 40o. Obviously feasibility of electricity storage from PV sources is not the same all over the world. WP has similar problems and it is well-known that “Dunkelflaute“ is detrimental to storage at small-scale (e.g., automobile batteries). Also detrimental is too much wind, one striking example being the recent shutdown/outage of the US State of Texas’ electricity grid (Electric Reliability Council of Texas, or ERCOT). {Private communication of W. M. Grady [2]}. It is recommended to rely mostly on large-scale hydro plants (pump storage plants in Bavaria at Jochberg and in Baden-Württemberg at Hotzenwald), as well as not locally confined biogas and hydrogen storage plants. One promising storage for mobile transportation–be it bus, train or cars--and distribution systems is the fuel cell. Its discharging performance is dependent upon its non-ideal parameters such as capacitances, resistances and time-dependent low-frequency pulsations modeled by a voltage source vpm(t).
References
1) https://energy-charts.info/charts/power/chart.htm?l=de&c=DE&interval=month
2) ERCOT_April_15_WIND_SOLAR_Drop.pptx