The Potential of Vehicle-to-Home Integration for Residential Prosumers: A Case Study

Brennenstuhl, Marcus; Otto, Robert; Elangovan, Pawan Kumar; Eicker, Ursula Article — Published Version The Potential of Vehicle-to-Home Integration for Residential Prosumers: A Case Study Smart Grids and Sustainable Energy Provided in Cooperation with: Springer Nature Suggested Citation: Brennenstuhl, Marcus; Otto, Robert; Elangovan, Pawan Kumar; Eicker, Ursula (2024) : The Potential of Vehicle-to-Home Integration for Residential Prosumers: A Case Study, Smart Grids and Sustainable Energy, ISSN 2731-8087, Springer Nature Singapore, Singapore, Vol. 9, Iss. 1, https://doi.org/10.1007/s40866-024-00206-4 This Version is available at: https://hdl.handle.net/10419/315875 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Smart Grids and Sustainable Energy (2024) 9:25 https://doi.org/10.1007/s40866-024-00206-4 ORIGINAL PAPER The Potential of Vehicle-to-Home Integration for Residential Prosumers: A Case Study Marcus Brennenstuhl1·Robert Otto1·Pawan Kumar Elangovan1·Ursula Eicker2 Received: 8 May 2023 / Accepted: 28 April 2024 / Published online: 18 May 2024 © The Author(s) 2024 Abstract The transition of the transport sector to e-mobility poses various challenges but also provides great flexible load and supply potential and thus enables a stronger coupling of the transport sector with other sectors. If emerging opportunities such as bidirectionalcharginginthecontextofVehicle-to-HomeandVehicle-to-Gridapplicationsareutilised,apreviouslyunimagined load management and storage potential can be tapped. This can transform e-mobility from an additional burden to the grid to a grid-supporting factor that enables greater integration of renewable energies and reduces additional investments in infrastructure like grid expansion and stationary storage systems. In order to investigate this potential, within this work we examine simulation based various Vehicle-to-Home (PV self-consumption, load shifting due to flexible electricity tariff) and Vehicle-to-Grid (secondary reserve) scenarios for different driving profiles for a residential building with heat pump, PV system and optionally a small wind turbine. In addition, a charge load optimisation is carried out using a genetic algorithm. The energy quantities, saving potential and additional number of battery cycles are quantified. The results show that, despite additional battery degradation, significant financial incentives can be achieved. Keywords Vehicle-to-Home ·Vehicle-to-Grid ·Bidirectional charging ·Chargeload management ·Heat pump · Genetic algorithm ·Small wind turbine and PV self-consumption Introduction The coupling of the transport and electricity sectors is currently emerging due to the available technologies and political incentives, especially in the passenger car sector. It is predicted that the share of electric vehicles (BEV, Battery Electric Vehicle) and e-hybrid vehicles (PHEV, Plug-in BMarcus Brennenstuhl [email protected] Robert Otto [email protected] Pawan Kumar Elangovan pawan.elangov[email protected] Ursula Eicker [email protected] 1Center for Sustainable Energy Technology, University of Applied Sciences Stuttgart, Schellingstraße 24, 70174 Stuttgart, Baden-Wuerttemberg, Germany 2Canada Excellence Research Chair Next Generation Cities, Concordia University, 1455 Boul. de Maisonneuve Ouest, QC H3G 1M8 Montréal, Québec, Canada HybridElectricVehicle)inGermanywillincreasefrom1.2% in 2020 to 24.4% in 2030. This corresponds to 11.6 million vehicles in 2030 [1]. This results in increased electricity consumption and potential bottlenecks during peak charging times. It is predicted that for BEVs (passenger cars), an additional 44 TWh of electricity per year (70 TWh for all e-mobility without rail transport) will have to be generated by 2030 [2]. Equally, this also offers enormous potential. According to Figgener et al. [3], the 1,270,000 BEVs and PHEVs registered in Germany by the end of 2021 had a cumulative battery capacity of 39.6 GWh in conjunction with a total possible AC charging capacity of 7.7 GW and a DC charging capacity of 51.8 GW with a PHEV share of approx. 50%. This means that BEVs and PHEVs already have a storage and performance potential similar to that of all pumped storage power plants installed in Germany, which have a storage potential of 39 GWh and a power generation potential of 6.2 - 6.7 GW [4,5]. If we assume this share of PHEVs and the average battery capacity installed per vehicle would be constant up to the year 2030, then in conjunction with the predicted 11.6 million vehicles [1], this would result in an installed capacity of 457.4 GWh, an AC charging capacity of 123 Smart Grids and Sustainable Energy (2024) 9:25 88.9 GW (standard household wallbox), and a DC charging capacity of 598.3 GW. If only 10% of this installed storage capacity were used, 45.7 GWh would be available in 2030. For comparison, the total installed power plant based electricity generation capacity in Germany is currently 223 GW [6]. In order to capture this potential, bidirectional charging can be used, for example, in the context of Vehicle-to-Grid andVehicle-to-Home.InthecontextofVehicle-to-Grid,electricity is supplied to the electricity grid, e.g., to smooth peak loads or to provide balancing power. By Vehicle-toHome, electricity is supplied to a building, e.g., to cover the household electricity demand or the electricity demand of a heat pump. In an emergency, this could also serve to supply power in the event of a grid failure. A BEV could supply a multi-person household with electricity for up to a week. In combination with a PV system or a small wind turbine, Vehicle-to-Home can lead to a significant increase in selfconsumption and self-sufficiency [7]. This potential is also shown in connection with heat pumps. In Arnaudo et al. [8], for example, it is shown on the basis of simulations that bidirectionalcharginginconjunctionwithheat pumpscanrelieve theexistinggrid infrastructure and thus enabletheintegration of heat pumps. Basically, it can be said that Vehicle-to-Grid can improve the efficiency and cost-effectiveness of electricity grids and save CO2 emissions [9]. In Sovacool et al. [9], a wide range of business areas are identified that go far beyond vehicle owners and electricity suppliers as well as grid services and can represent a comprehensive value chain. In Otto et al. [10] the possibilities of bidirectional charging in the context of parking garages and a model of charge load predictionwereinvestigated.Despiteallthesepromisingfactors, there are major hurdles that prevent a wide spread utilisation. Besides the lack of standardisation, which is currently being taken care of, the fear of battery degradation and permanent damage due to additional battery cycles, as well as uncertainties considering inverter efficiencies, especially for small fluctuating loads, deter many potential users. Battery Degradation Due to Bidirectional Charging Batterydegradationis one issuethat hasnot yetbeen clarified in the context of bidirectional charging. The main problem with this topic is that no practical long-term experience is available. So far, studies have been carried out on the basis of laboratory tests and simulations, which have come to very different and sometimes contradictory conclusions. In Wang et al. [11], for example, the battery degradation for various Vehicle-to-Grid services such as control power and peak load smoothingwasinvestigatedusing a semi-empiricalmodel. In the worst case, an additional reduction in battery capacity of 3.6% was observed over a period of ten years due to the provision of balancing power, and a reduction of 5.6% due to the provision of peak load smoothing. Furthermore, the study found a degradation cost of $ 0.20 for providing two hours of balancing power, $ 0.38 for providing two hours of peak shaving, and $ 1.18 for load shifting over 24 hours. A comprehensive literature review by Thompson et al. [12], focusing on the behaviour of different battery technologies, comes to the conclusion that with regard to ageing through charging cycles, the amount of energy extracted is fundamentally more decisive than the number of cycles. However, the way in which the energy is extracted (discharge power, cell temperature) also plays an important role. Thompson et al. conclude that dedicated implementation of bidirectional charging through continuous battery monitoring and controlled charging and discharging can actually increase battery life. In Shinzaki et al. [13], a field test with a PHEV was conducted over several months, and it was shown that bidirectional charging had very little or no negative effect on the lifetime of the vehicle battery due to the relatively low energy throughput. In Lunz et al. [14], the effects of bidirectional charging were investigated in a simulation-based manner. The study concluded that bidirectional charging can significantly increase battery life expectancy due to the constantmonitoring of the batteryand thereduced battery charge and discharge times at high SOC. In Uddin et al. [15], investigations were carried out based on a comprehensive battery degradationmodel.Itwasalsoconcludedthatintelligentbidirectional charging can reduce battery ageing and thus the capacity loss of BEVs by up to 9.1% and the power loss by up to 12.1%. In Lehtola et al. [16], measurement data, driving data, and data from Vehicle-to-Grid operation were combined with a battery ageing model. It was found that the decisive factors that influence calendar ageing are time, temperature, and state of charge. Cycle ageing is defined by the number of cycles, the depth of discharge, and the charging rate. An important finding of this work is that at full battery capacity, the battery retains less than 80% of its initial capacity after less than 1000 cycles, whereas if the battery is used in a range between 40% and 60% of SOC, 86.7% of the original capacity is still left after 3000 cycles. This is particularly relevant with regard to cycles in the context of bidirectional charging, as only 5 kWh - 10 kWh, which corresponds to 10% - 15% of the battery capacity for common larger BEV batteries, are required for Vehicle-to-Home applications. With optimal charging management and bidirectional operation in this SOC range, cycle-related battery ageing and the resulting costs could be significantly reduced. Inverter Efficiency Regarding the efficiency of AC-DC inverters in BEVs in the context of bidirectional charging, a wide range of statements 123 25 Page 2 of 23 Smart Grids and Sustainable Energy (2024) 9:25 can be found in research publications. Thingvad et al. [17] haveinvestigatedacommerciallyavailableinverteras usedin BEVs with regard to the provision of positive and negative primary control power. They came to the conclusion that efficiency varies greatly depending on the power called up or fed into the grid. They found poor efficiencies of less than 50% at low power (0 - 2 kW) and good efficiencies of 90% at nominal power for the charging and discharging processes. Basically, they conclude that there is a need for improvement in terms of efficiency for the application of bidirectional charging on a broad scale. Videgain Barranco et al. [18] investigated the charging and discharging behaviour of a Nissan Leaf ZE1 in connection with a 3-phase 10 kW CHAdeMO charging connection under laboratory conditions. Efficiencies between 77.6% at 2 kW charging and discharging power and 81.5% at 7 kW charging and discharging power were determined for combined charging and discharging for Vehicle-to-Home applications. However, a constant power extraction was assumed in this test series. In Schram et al. [19], the power-dependent efficiencies for AC chargingand dischargingofaNissan LeafandaRenault ZOE were determined. The efficiencies determined ranged from 78% for charging and discharging with 2.8 kW to 86.5% for charging and discharging with 11.0 kW. Correia et al. [20] were able to show, however, with regard to bidirectional DC charging, that a significant improvement in efficiency could be achieved by using a dedicated inverter. Measurements with a conventional inverter at 2.5 kW charging and discharging power resulted in an overall efficiency of 64.6%, and at 10 kW charging and discharging power in an overall efficiency of 80.4%. With inverters based on silicon carbide, on the other hand, 90.9% efficiency could be achieved at 2.5 kW and 91.2% efficiency at 10 kW charging and discharging power. Basically, the findings so far show that AC as well as DC inverters suffer from efficiency loss at low power levels and high power fluctuations. This is due to the fact that the inverters installed have not yet been optimised for bidirectional charging and the corresponding power spectrum. The obstacles to this are not so much on the technical level as on the economic level, since without widespread use of bidirectional charging, the costs of development and the installation of corresponding optimised inverters on the side of vehicle manufacturers cannot be justified. Nevertheless, already in the power spectrum that is relevant, e.g., for the operation of heat pumps in residential buildings (2 kW - 5 kW power input), acceptable efficiencies in the region of 80% are achieved for combined charging and discharging. Interestingly, this would make Vehicle-to-Grid or Vehicle-toHomesystemswithanefficiencyof70%-80%[21]similarin efficiency to dedicated pumped storage power plants, which have an efficiency of 70% for older plants and 83% for the newest ones [22]. Aim of this Work Within this work, we examine the potential of an optimisation of Vehicle-to-Home and Vehicle-to-Grid scenarios on a single building level regarding PV and small wind power self-consumption,flexibleelectricitytariffandnegativeautomatic Frequency Restoration Reserve (aFRR), also known as secondary reserve. For this investigation, a digital twin of a residential building with an energy efficient building standard, heat pump, PV system, and in one use case with an additional small wind turbine is created. The building and its systems are resembled as white box models and calibrated and validated based on detailed monitoring data. For each use case and two different driving profiles, a dynamic co-simulation on a yearly basis with a one minute time resolution is carried out. For each day of the year, the charge load management is optimised by a genetic algorithm and compared to normal operation without bidirectional charging and optimisation. The aim is to show the possible technical and economic benefits of bidirectional charging in the context of Vehicle-to-Home and Vehicle-to-Grid. It is shown that substantial financial gains can be made and a grid supportive role can be fulfilled even with frequent BEV usage. In addition, the impact on battery life with respect to the additional battery cycles required is examined and evaluated. Methodology This work is based on a residential building from a positive energy settlement in the German municipality of Wüstenrot. The building that was constructed in 2013 is equipped with a heat pump connected to a cold local district heating network, two thermal buffer storage tanks, and a PV system. The detailed system parameters can be found in Fig. 1. Highresolution measurement data of all relevant energy flows was collected from this building over several years. Based on this, a white box model was created in the INSEL simulation environment, calibrated with measured data, and validated. This calibration and validation methodology is described in detail in [23]. More details about the arrangement of the cold district heating grid and the plus energy settlement can be found in [24]. Modelling and Optimisation Approach Inordertooptimisetheflexibilitypotential, the dynamic simulation model is coupled with a metaheuristic optimisation based on a genetic algorithm. The aim of this approach is to optimisethe bidirectional charging anddischargingof a BEV with regard to various criteria. In doing so, schedules for a certain time horizon are created and automatically updated 123 Page 3 of 23 25 Smart Grids and Sustainable Energy (2024) 9:25 Fig. 1Building system specifications Building ID 12 Heat pump Waterkotte Modell DS 5023.5Ai, 22.2 kW Thermal buffer storage DHW 400l; Heating 1000l Battery storage None Installed PV power 13.64 kWp PV orientation 58.4 m² (48 mod.) orientation 180°, tilt 15°; 49.5 m² (40 mod.) orientation 0°, tilt 15° PV manufacturer and model Solar Frontier Typ SF155-L Residential useable area 285.13 m² Heating demand 22,696 kWh based on weather and demand forecast data. A time horizon of 24 hours is considered here. However, this is scaleable in terms of time, so that operation can also be optimised at significantly shorter (hourly) intervals. A genetic algorithm based on the DEAP toolbox in Python [25]isusedtovarythe charging and discharging states of the BEV. This is implemented as an INSEL-Python co-simulation. Regarding the examined building, the heating demand and thusthe electricity demand of theheat pump aswell asthe PV electricity generation are simulated dynamically. The householdelectricitydemandisincludedbasedonmeasuredvalues that were collected in 5 s intervals. The vehicle battery is also dynamically resembled in the INSEL model. The model configuration is shown in Fig. 2. If the BEV is used (availability is determined based on specific driving profiles, see chapter “Driving Profiles”) it’s battery capacity is excluded from the model, and the consumed amount of energy is transferred to the modelled vehicle’s battery as an energy debt, which is to be charged as efficiently as possible by the charge load optimisation. In order to reduce the settling time of the model, parts of the model are parameterised with measured values at each simulation start, which reflect the actual state of the building and it’s systems. A settling time of the model of 3 hours was determined based on an iterative study. Furthermore, control intervals of 5 min are selected to ensure a high amount of flexibility without too small charge and discharge intervals. In order to consider the rebound effect, the subsequent 3 h after the 24 h optimisation are also included in the energy flow balance. Regarding mobility prediction accuracy, for simplification reasons, it is assumed that departure times are available to the optimisation algorithm in advance. In reality, this could be realised, e.g., via an app in which users specify the departure times in advance, or also by a self-learning algorithm. The charging and discharging states that are used by the optimisation are as follows: Weather data: temperature, global radiation, wind speed PV-system (Two diode model, parameter fit) Inverter (parameter fit) Household electricity demand Grid demand and infeed Dynamic building model Temperature cold district heating grid Household DHW demand BEV battery (no electrochemical model) Thermal multi layer DHW storage Thermal multi layer space heating storage Heat pump (characteristic curve model) Global radiation Temperature Wind speed Electricity Small wind turbine (characteristic curve model) Fig. 2 Energy system model scheme 123 25 Page 4 of 23 Smart Grids and Sustainable Energy (2024) 9:25 •Allow discharging by household and heat pump electricity:Yes/No. •Allow charging by PV / small wind turbine electricity: Yes / No. •Allow charging by grid electricity: Yes / No. In the following scenarios, the possible daily optimisation potential is determined by the simulation. For each day of the year, a demand and generation simulation as well as an optimisation based on the weather data forecast for that day are performed. These results are then compared with the data that was measured in reality at that time. Driving Profiles To represent the BEV, annual load profiles are first created in one-minute resolution using a mobility generator tool developed within the Smart2Charge project, based on representative profiles from [26–28]. Profiles such as daily commuting to work or usage as a secondary car with a high rateofavailabilityareusedandinvestigated.Theprofilesgenerated by the mobility generator are first available as weekly profiles. To create annual profiles, these are randomised. The start or arrival time is redetermined using a Gaussian normal distribution within +/- 30 min around the original time. For each driving event, a randomly generated value between -1/5 and 1/5 of the energy quantity required according to the driving profile is added to the balance. These load profiles are then coupled with the dynamic building model. In the following, the investigated weekly driving profiles are shown with regard to BEV availability and energy consumption. Figure 3a and b show a driving profile that includes regular commuting to work, while Fig. 4a and b show a driving profile that corresponds to usage as a secondary car with a significantly higher time spent at home. Optimisation Scenarios Based on the previously described simulation and optimisation approach, the the following scenarios are examined and optimised: •PV self-consumption. •Vehicle-to-Home(PVandwindpowerself-consumption). •Vehicle-to-Home (flexible electricity tariff). •Vehicle-to-Grid (aFRR). The aim thereby is to reduce peak demand, the amount of electricity that is fed into the grid and thus grid load, as well as the operation costs for the building owner. PV Self-consumption The goal of PV self-consumption optimisation is to minimise the cumulative amount of electricity drawn from the grid. This includes BEV electricity demand, heat pump electricity demand, as well as household electricity demand. Basically, under German regulatory conditions, it is desirable to consume as much PV electricity as possible oneself, as the household electricity price in Germany today is more than four times higher than the fixed feed-in tariff for small (smaller than 10 kWp) newly installed PV systems. This results in the following Eq. 1as the optimisation objective function for which the result is to be minimised: M=optend optstart+3h(Qel_Hh +Qel_HP +Qel_BEV) ∗mtarif f −(Qel_PV ∗mPV_togrid)(1) Where M[C] is the total cost or profit from operation, Qel_Hh [kWh] is the household electricity demand for each Monday Tuesday Wednesday Thursday Friday Saturday Sunday Availability (a) Availability Monday Tuesday Wednesday Thursday Friday Saturday Sunday Demand [kWh] (b) Demand Fig. 3 Weekly usage and consumption profile, type work commute 123 Page 5 of 23 25 Smart Grids and Sustainable Energy (2024) 9:25 Monday Tuesday Wednesday Thursday Friday Saturday Sunday (a) Availability Demand [kWh] Monday Tuesday Wednesday Thursday Friday Saturday Sunday (b) Demand Fig. 4 Weekly usage and consumption profile, type secondary vehicle time step, Qel_HP [kWh] is the electricity demand of the heat pump for each time step, Qel_BEV [kWh] is the electricity demand of the BEV for each time step, and Qel_PV [kWh] is the amount of electricity from the PV system that is fed into the grid for each time step. mtarif f is the electricity purchase tariff [C/kWh] and mPV_togrid is the feed-in tariff of the PV electricity [C/kWh]. Vehicle-to-Home (PV and Wind Power Self-consumption) The aim of the PV and wind power self-consumption optimisation is to minimise the cumulative amount of electricity drawn from the grid. This includes the BEV electricity demand, the heat pump electricity demand, as well as the household electricity demand. Thisresults in the followingEq. 2as theobjective function for which the result is to be minimised. M=optend optstart+3h(Qel_Hh +Qel_HP +Qel_BEV)∗mtarif f − Qel_PV ∗mPV_togrid −Qel_Wind ∗mWind_togrid(2) Where M[C] is the total cost or profit from operation, Qel_Hh [kWh] is the household electricity demand for each timestep, Qel_HP [kWh]isthe heat pumpelectricity demand for each time step, Qel_BEV [kWh] is the electricity demand of the BEV for each time step, Qel_PV [kWh] is the amount of electricity from the PV system that is fed into the grid for each time step, and Qel_Wind [kWh] is the amount of electricity from the small wind turbine that is fed into the grid for each time step. mtarif f is the electricity purchase tariff [C/kWh], mPV_togrid is the feed-in tariff for PV electricity [C/kWh] and mWind_togrid is the feed-in tariff for wind electricity [C/kWh]. Vehicle-to-Home (Flexible Electricity Tariff) In order to investigate the load shifting possibilities of Vehicle-to-Home applications in conjunction with a flexible electricity tariff, a ToU tariff is implemented in the model that uses a dynamic network fee. PV electricity generation is thus not considered in order to better evaluate the effects of the flexible tariff. The tariff approach is described in [29]. For the ToU variant with a variable network fee, a fixed price component of the grid fee is included in the calculation that is linked to the distribution grid load in order to counteract the grid load and, at the same time, increase the incentive for end customers compared to the daily variation of electricity exchange prices. This variable component is set at three price levels. 2.75 times, 1.3 times, and 0.3 times the original level of the variable component of the network fee. It is envisaged that in total, the variable network fee will be equal to the original static amount to be paid. [29] Applying the network fee for the year 2022 and the dayahead exchange electricity prices from calendar week 25 of 2022 using this methodology results in the ToU tariff shown in Fig. 5. 0 10 20 30 40 50 60 Price [ct/kWh] Time Day-ahead stock price Other fees excl. network fee Dynamic network fee Flexible tariff price Fig. 5 Flexible ToU electricity tariff 123 25 Page 6 of 23 Smart Grids and Sustainable Energy (2024) 9:25 The aim of optimising the use of a flexible electricity tariff is to minimise the use of grid electricity from periods with high tariff prices to periods with lower prices. This includes the BEV electricity demand, the heat pump electricity demand, as well as the household electricity demand. This results in the following Eq. 3as the objective function for which the result is to be minimised. M=optend optstart+3h (Qel_Hh+Qel_HP+Qel_BEV)∗mtarif_flex (3) Where M[C]isthetotalcostorprofitofoperation, Qel_Hh [kWh] is the electricity demand of the household for each time step, Qel_HP [kWh] is the electricity demand of the heat pump for each time step, and Qel_BEV [kWh] is the electricity demand of the BEV for each time step. mtarif_flex is the flexible electricity tariff [C/kWh]. Vehicle-to-Grid (aFRR) To determine the economic potential of BEV participation in the aFRR market, quarter-hourly aFRR demand data [30] were used for the year 2019 and merged with the corresponding price data of the aFRR power and balancing energy market (4h resolution) [30]. The assumption was made that negative aFRR can be provided for the full period that the vehicle is at home and that the electricity demand is solely fulfilled by aFRR. The revenues from participation in the power market are not included in the balance because they are assumed to be negligible in this context. The adopted participation conditions are that the lowest price is offered on the balancing energy market in order to be activated as often as possible. Thus, the case studied here represents the highest possible number of activations and thus the greatest possible flexibility that must be provided. The household, heat pump, and driving demands are fulfilled by the energy temporarily stored in the vehicle battery. This is done for all timestepswheretheaFRRdemandisnegative.Forelectricity purchases that fall outside of this time period, an electricity purchase price of 0.42 C/kWh is applied. During periods when the vehicle is not available, participation in the aFRR market is excluded. The cash flow is calculated according to the following Eq. 4: M=optend optstart+3hQel_aFRR ∗(maFRR +mc) ∗(VAT+1)+Qel_tarif f ∗mtarif f (4) Where M[C]istheincomeorcost,maFRR [C/kWh]isthe offered aFRR balancing energy marked price, mc [C/kWh] is the sum of surcharges and taxes of 0.135 C/kWh (see also Table 1), mtarif f is the standard electricity tariff amounting to 0.42 C/kWh, Qel_aFRR is the amount of delivered aFRR electricity[kWh] and Qel_tarif f [kWh]isthe amount of tariff electricity purchased. The VAT is assumed to be 19%. Results Vehicle-to-Home: PV Self-consumption In the following, two different driving profiles (first car, commuting to work, and secondary car with less frequent use) are investigated for optimising PV self-consumption. The aim is to keep the SOC in the range between 40% and 60%, which enables a high cycle tolerance and thus low battery degradation. Driving Profile First Car, Commuting to Work, 80 kWh Storage Size, 10 kWh Storage Usage Table 2shows the monthly cost reduction resulting from the optimised operation and the avoided electricity purchase costs. Opt means the predicted improvement, Meas Opt means the improvement that would occur taking into account theforecastuncertaintywithregardtoglobalradiation,ambient temperature, electricity consumption, DHW demand and the associated electricity consumption of the heat pump. As expected, there is a significant savings potential of up to 32.5% in the summer months and in the transitional period, taking into account the forecast deviation. in the winter months, the optimisation achieves negligible results due to the low PV yield. For this use case, the calculated annual cost savings without taking the forecast deviation into account is 303.1 C. Taking the forecast deviation into account, it is 242.0 C. The number of battery cycles would be 45.5 with optimised Vehicle-to-Home operation and 31.7 without. This would Table 1 Surcharges and taxes for electricity purchase as of September 2022 EEG reallocation charge No longer required as of 07/01/2022 CHP surcharge 3.78 C/MWh §19 StromNEV-reallocation 4.37 C/MWh Offshore apportionment of liability 4.20 C/MWh Reallocation charge for switchable loads 0.03 C/MWh Network fee 80.80 C/MWh Electricity tax 20.05 C/MWh VAT 21.51 C/MWh Total 134.74 C/MWh 123 Page 7 of 23 25 Smart Grids and Sustainable Energy (2024) 9:25 Table 2Monthly cost reduction through Vehicle-to-Home, with optimisation of PV self-consumption (driving profile of first car, commuting to work) Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Opt 0.1% 3.7% 8.6% 18.0% 28.2% 52.3% 50.8% 48.6% 22.5% 8.2% 0.5% 0.0% Meas Opt 0.1% 3.4% 8.2% 21.4% 32.5% 21.2% 21.4% 31.5% 18.7% 9.0% 1.2% 0.0% correspond to 13.8 additional cycles. The optimised operation could save the purchase of 661.1 kWh of grid electricity. Assuming a life span of 3,000 cycles, which might be possible with this mode of operation, and a price of 20,000 Cfor a replacement battery, this would result in 92.2 Cof damage to the battery. Increasing the usable capacity to 20 kWh resulted in a further reduction of purchased grid electricity of 29%. This would save 344.5 Cper year, taking into account the forecast uncertainty. The number of charging cycles increases by another 3.5 cycles to 49. However, when using 20 kWh and thus 25% of the SOC, it is no longer possible to keep the SOC in the optimal range for the battery, which can increase the cycle-related degradation of the battery. Figures 6(normal operation) and 7(optimised operation) show the difference between normal and optimised operation for one weekday in the transition period. The driving profile here defines that during a large part of the time with PV yield, the vehicle is not available. Nevertheless, a significant part of grid power consumption can be avoided through optimisation. In the morning hours, for example, the heat pump is powered by the vehicle battery. After returning from the work trip, the battery is recharged with the remaining available PV electricity. All in all, the SOC can be kept in the ideal range between 40% and 60% despite the provision of heat pump and household electricity andajourneyofapprox.50kilometres.Itcanalsobeseenthat the operating costs are significantly reduced by the reduction in grid electricity consumption and that a slight profit can even be achieved in the balance of fed-in and purchased grid electricity on the day shown. Driving Profile Secondary Car, 80 kWh Storage Size, 10 kWh Storage Usage Table 3shows the monthly cost reduction resulting from the optimised operation and the avoided electricity purchase costs. As expected, there is a significant savings potential of up to 52,1% in the summer months and in the transition period, taking into account the forecast deviation. in the winter months, this operational optimisation results in only a small improvement due to the low PV yield. For this use case, the calculated annual cost savings without taking the forecast deviation into account is 547.7 C. Taking the forecast deviation into account, this is 484.2 C. The forecast deviation therefore plays a smaller role here due to the greater availability of the vehicle battery and thus a greater flexibility. Taking the forecast deviation into account, the number of battery cycles would be 48.6 with Vehicle-toHome operation and 18.2 without. This would correspond to 30.4 additional cycles. The optimised operation could save the purchase of 1380.1 kWh of grid electricity. Assuming a battery life of 3,000 cycles, which might be possible with this mode of operation, and a price of 20,000 Cfor a replacement battery, this would result in 202.7 Cof damage to the Fig. 6 Daily energy and cost balance driving profile commutingtowork 123 25 Page 8 of 23 Smart Grids and Sustainable Energy (2024) 9:25 to 650.2 C, taking into account the forecast uncertainty. The number of cycles increases by 11.2 to 72.5, which would correspond to a battery damage of 362.0 C. Figures 16 (normal operation) and 17 (optimised operation) show the difference between normal and optimised operation in the transition period. The driving profile makes the vehicle available for a large part of the time. It is shown that the battery storage is loaded at times of low electricity tariff prices in order to cover the household and partly the heat pump electricity demand. Heat pump operation in the morning is fed by tariff electricity as long as the tariff price is low. After that, it is covered by the vehicle battery. In total, despite the provision of heat pump and household electricity as well as the demand of the BEV, the SOC can be kept in the ideal range between 40% and 60%. It is also shown that operating costsarereducedbypurchasingelectricityatmorefavourable conditions. Vehicle-to-Grid (aFRR) In the following, two different driving profiles (first car, commuting to work and secondary car with frequent leisure use) areinvestigatedwithregardtotheprovisionofnegativeaFRR power. The charge control system is designed to keep the SOC between 40% and SOC 60% in order to achieve a high cycle stability and thus lower losses due to battery degradation. However, the lower SOC limit may be undershot by BEV driving. For each driving profile, the daily initial SOC, which corresponds to the targeted SOC at the end of the day, andthechargingpowerarevariedtofindthe combinationthat offers the greatest flexibility and can thus meet all negative aFRR activations on as many days as possible. Driving Profile First Car, Commuting to Work, 80 kWh Storage Size The results of varying the charging power and the daily start and end capacity of the vehicle battery, sorted by the least number of days on which the negative aFRR activations cannot be met, are shown in Fig. 23. The best case here is a daily initial SOC of 40% in conjunction with 2 kW of charging power. In this case, not all aFRR activations could be fulfilled on 35 days. Only three of these days are on weekends. On 28 days, the final capacity of the vehicle battery would be higher than the next day’s required initial capacity, which on the one hand means that additional power has been stored that would further improve the economics, but on the other hand may also result in limiting the potential of providing negative aFRR the next day. Table 8shows the monthly cost reductionthat resultsfromobtainingnegativeaFRRandelectricity purchase costs that are avoided as a result. This shows a savings potential of up to 30.4%, taking into account forecast uncertainty. The seasonal differences can be explained by the fact that in the winter months, the electricity consumption is higher due to the heat pump operation, and therefore the relative improvement is lower. For this use case, the calculated annual cost savings without considering the forecast deviation is 1387.5 C. Considering the forecast deviation, this would be 1154.0 C. The number of battery cycles would be 85.9 with aFRR activations and 31.7 without. This would correspond to 54.6 additional cycles. Assuming a battery life of at least 3,000 cycles, which might be possible with this operating mode, and a price of 20,000 Cfor a replacement battery, this would result in 364 Cof damage to the battery. However, it is not possible to stay consistently in the range between 40% and 60%with this drivingprofile.If the upper limitofamaximum Fig. 16 Daily energy and cost balance driving profile secondary car, flexible electricity tariff 123 Page 15 of 23 25 Smart Grids and Sustainable Energy (2024) 9:25 Fig. 17 Daily energy and cost balance driving profile secondary car, Vehicle-to-Home, optimised for flexible electricity tariff SOC of 60% is met, the SOC drops to as low as 30% on some days due to the additional energy demand of driving. Thiscouldbecompensatedbypurposefulchargingbeforethe start of the trip, but this would mean a higher tariff electricity demand. In Figs. 18 (normal operation) and 19 (aFRR fulfilment), the difference between normal and operation with aFRR fulfilment is shown for a weekday in the transition period. Due to the driving profile, the vehicle is not available during the day. It can be seen that much of the grid electricity demand can be replaced by providing negative aFRR. Similarly, with the charging power reduced to 2 kW, the SOC of the vehicle battery is consistently in the optimal operating range. It is also shown that operating costs are significantly reduced by obtaining electricity through negative aFRR. Profile Secondary Car, 80 kWh Storage Size The results of varying the charging power and the daily start and end SOC of the vehicle battery, sorted by the least number of days on which the negative aFRR activations cannot be met, are shown in Fig. 24. The most favourable case is represented by a daily initial SOC of 40% in conjunction with 2 kW of charging power. In this case, not all aFRR activations could be fulfilled on 21 days. On 42 days, the final SOC of the vehicle battery would be higher than the required start SOC of the next day, which on the one hand means that additional power has been stored that would further improve the economic efficiency, but on the other hand may also lead to a limited potential of providing negative aFRR on the next day. Table 9shows the monthly cost reduction that results from obtaining negative aFRR and thus avoiding the purchase of tariff electricity. This shows a savings potential of upto 40.7%, takinginto accountthe forecastuncertainty. The seasonal differences can be explained by the fact that in the winter months, the electricity consumption is higher due to the heat pump operation, and thus the relative improvement is lower. For this use case, the calculated annual cost savings without considering the forecast deviation is 1709.2 C. Including the forecast deviation, this would be 1490.5 C. The number of battery cycles would be 91.4 with aFRR fulfilment and 18.2 without. This would correspond to 73.2 additional cycles. Assuming a battery life of at least 3,000 cycles, which might be possible with this operating mode, and a price of 20,000 Cfor a replacement battery, this would result in 488.0 Cin damage to the battery. However, even with this driving profile, it is not possible to consistently stay within the range between 40% and 60%. If the upper limit of a SOC of 60% has to be met, on some days the SOC drops to as low as 35% due to the additional energy demand of the trips. This could becompensated by purposeful chargingbeforethestartofthe trip, but this would mean a higher tariff electricity demand. In Figs. 20 (normal operation) and 21 (aFRR fulfilment), the difference between normal and operation with aFRR fulfilTable 8 Monthly cost reduction from Vehicle-to-Home and Vehicle-to-Grid operation when providing negative aFRR (driving profile first car, commute to work) Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Opt 12.5% 17.8% 22.2% 21.0% 19.0% 30.3% 27.1% 30.4% 29.0% 25.6% 18.6% 21.2% Meas Opt 11.8% 18.1% 20.7% 20.8% 20.9% 30.4% 22.4% 30.2% 24.0% 24.7% 17.1% 17.6% 123 25 Page 16 of 23 Smart Grids and Sustainable Energy (2024) 9:25 Fig. 18 Daily energy and cost balance driving profile first car, commute to work, Vehicle-to-Home and Vehicle-to-Grid ment is shown for a weekday in the transition period. Due to the driving profile, the vehicle is unavailable twice during the day for shorter periods of time. It can be seen that a large part of the tariff electricity consumption, especially that caused by the cycling of the heat pump, can be replaced by providing negative aFRR. However, the SOC drops to as low as 38% due to the two trips. It can also be seen that the operating cost drops significantly by providing negative aFRR. Discussion The model-based investigation of optimised charge and discharge load management for different Vehicle-to-Home and Vehicle-to-Grid application scenarios shows that the forecast uncertainty of heat pump and household power demand as well as of PV power generation plays a measurable but subordinate role. This can be mainly attributed to the flexibility offered by the vehicle battery (capacity, activation speed, maximum charging, and discharging power). For all scenarios investigated, it is also shown that the revenue is greater than the determined potential damage to the vehicle battery due to additional cycles. An overview of the potential savings of the individual variants with and without additional costs duetobatterydegradationisgiveninFig.22.Here,thevariant of participating in the negative aFRR balance energy market offers the greatest financial potential but also the greatest planning uncertainty since the actual number of activations that will occur and the expected prices are difficult to predict. In this context, it should also be taken into account that, with the determined cycle numbers, the BEV would have to bein operation for morethan 10 yearswith aconservativelife expectancy of 1000 cycles, and for more than 20 years with Fig. 19 Daily energy and cost balance driving profile first car, commute to work, Vehicle-to-Home, and Vehicle-to-Grid optimised for providing negative aFRR 123 Page 17 of 23 25 Smart Grids and Sustainable Energy (2024) 9:25 Table 9Monthly cost reduction from Vehicle-to-Home and Vehicle-to-Grid operation when providing negative aFRR (driving profile secondary car) Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Opt 14.9% 23.8% 28.7% 27.9% 29.1% 42.6% 37.9% 41.6% 36.0% 35.9% 22.5% 17.9% Meas Opt 14.3% 25.6% 28.4% 28.1% 31.4% 40.7% 36.7% 40.6% 29.8% 35.8% 20.5% 20.2% Fig. 20 Daily energy and cost balance driving profile secondary car, Vehicle-to-Home and Vehicle-to-Grid Fig. 21 Daily energy and cost balance driving profile secondary car, Vehicle-to-Home and Vehicle-to-Grid optimised for providing negative aFRR Fig. 22 Annual cost reduction from various Vehicle-to-Home and Vehicle-to-Grid applications Driving profile PV selfconsumption [€/a] PV and wind selfconsumption [€/a] Flexible tariff [€/a] aFRR [€/a] First car (commute to work) 242.0 341.5 201.4 1154.0 Secondary car (no commute) 484.2 647.6 444.5 1490.5 First car (commute to work) 149.8 175.5 81.3 790.0 Secondary car (no commute) 281.5 271.6 157.2 1002.5 Without costs due to additional battery cycles With costs due to additional battery cycles 123 25 Page 18 of 23 Smart Grids and Sustainable Energy (2024) 9:25 a more optimistic life expectancy of 2000 to 3000 cycles, until the battery capacity drops below 80%. In this period, the vehicle is already depreciated on the balance sheet, and it is questionable whether the costs incurred by the additional cycles must be included in the balance. According to the German Federal Motor Transport Authority, 91.7% of passenger cars on Germany’s roads in 2021 were less than 20 years old [31] and the German Federal Ministry of Finance specifies a useful life of 6 years for passenger cars in its tables of depreciation for wear and tear (AfA) [32]. At the same time, it is possible that supervised operation of the vehicle battery in the range of the optimum SOC can even positively influence its service life and offset the negative effects of the additional cycles. Vehicle-to-Home (self-consumption): With optimised charge and discharge load management with respect to PV self-consumption and the combined PV and windself-consumption, a significant annual savingspotential isshownthatismainlydependet on the drivingprofile and the availability of the BEV. At the same time, if the additional battery ageing is taken into account, the savings potential would be approximately halved. An increase of the used storage capacity to 20 kWh compared to the 10 kWh investigated brings only a slight improvement. The forecast uncertainty has a greater impact in terms of yield reduction for driving profiles with greater absence durations due to the lower flexibility. The forecast uncertainty of small wind generation was not considered. This might be counteracted in a real implementation by using rule-based logic or model predictive control (MPC). Vehicle-to-Home (flexible electricity tariff): The usage of a flexible electricity tariff in combination with optimised charge and discharge load management leads to significantly more cycles with similar cost savings compared to the optimised self-consumption use case. This can be explained by the fact that a significantly larger amount of energy is shifted, but the financial incentive is lower compared to self-consumption. In terms of grid supportive operation, however, this variant is recommendable. Thus, depending on the driving profile, 15% - 33% of the total grid electricity demand of building and BEV could be shifted to times of low tariff prices and thereby low grid stress. With regard to the used capacity of the vehicle battery, a utilisation of a larger share than the considered 10 kWh is reasonable. In order to limit the use of the vehicle battery to the range between 40% and 60% of the SOC, a maximum of 16 kWh should be used in the considered case of an 80 kWh vehicle battery. Vehicle-to-Grid (aFRR): This variant offers the greatest potential, financially speaking, but it is highly dependent on the bidding strategy. In reality, vehicles would be activated less often, resulting in lower savings but also less flexibility needed and a lower cycle load. At the same time, to participate in the aFRR market, the minimum power of 1 MW requires a pool operation equivalent to more than 500 vehicles at the determined optimal charging power of 2 kW. This would also imply additional costs for an aggregator and platform infrastructure. Equally, however, a pool operation can compensate for situations where a vehicle cannot fulfil all activations and thus avoid compensation payments. Basically, the study showed that a rather low charging power is useful to provide more flexibility and thus to be able to fulfil all aFRR activations. The study did not take into account the aFRR power market price since the revenues achievable with this use case do not play a significant role. Conclusion In this research, we examined the potential of bidirectional charging for different Vehicle-to-Home and Vehicle-to-Grid applications in the context of a residential building with heat pump, PV, and in one use case, small wind power generation. Inparticular,thepotentialofoptimisedbidirectionalcharging with regard to PV and small wind power self-consumption, a flexible ToU electricity tariff, and negative automatic Frequency Restoration Reserve (aFRR) was investigated. The examined use cases and applications have shown that there is significant potential regarding (optimised) bidirectional charging in the context of Vehicle-to-Grid and Vehicle-toHome operations on a technical as well as on an economical level. It has been shown that, regarding self-consumption of PV and wind power in between 150 C and 272 C could be saved per year when the damage to the vehicle battery due to additional cycles is included. The implementation of a ToU electricity tariff was able to create an incentive to shift up to one-third of the building’s demand to hours of less net load. However, it proved to be more difficult to provide sufficient financial incentives compared to the other use cases. Out of the examined use cases the participation in the negative aFRR market offered the highest financial potential. Thereby only the participation in energy balance market made a relevant economical sense. Gains in aFRR power market were minor also because the aim of this use case was not to provide reserve power with an existing power plant but to obtain electricity cheaply. Interestingly, only a fraction of the BEV stock predicted until 2030 would be sufficient to fulfil a large part of the negative aFRR tendered in Germany. This could be extended to the additional provision of positive aFRR, which was not considered in this study since the primary goal here was to consume surplus electricity. In future research, it would be interesting to expand this study to other internationally important markets. It would also be beneficial to 123 Page 19 of 23 25 Smart Grids and Sustainable Energy (2024) 9:25 consider the influence on economic efficiency through additionalparameterssuchasdemand(kW)andpowerfactor(PF) as components of charge. In addition, it would be compelling to examine the effect of an additional stationary battery storage on the results. This study is only a simulation-based investigation. In order to transfer these results into practice, various assumptions, such as charging behaviour (response speed, ramp up speed), inverter efficiency, and battery ageing, need to be further investigated in field trials to better understand them and validate the benefits identified here. Also, unforeseen usage of the BEV that might impact the savings potential has not been considered so far. To tap the full potential of bidirectional charging, general conditions must be changed. Uniform standards for bidirectional charging must be finalised and rolled out sooner than later to prevent the majority of the future BEV stock from being sold incompatible.Also, the awarenessandeducationofBEVcustomers regarding battery ageing must be targeted. In addition, better inverters must be installed by the car manufacturers that provide higher efficiencies at low power levels (e.g. 0 - 3 kW). Appendix A: Vehicle-to-Grid (aFRR) Fig. 23 Vehicle-to-Grid parameter variation: profile first car,commutetowork,80kWh storage size Daily start charge state [% SOC] aFRR power [kW] Cost reduction [€] Days without aFRR fullfilment Addictional battery cycles 40% 2 1388 35 54 43% 2 1170 52 58 40% 1 844 58 31 40% 3 1666 58 62 45% 2 1034 71 58 48% 2 1008 72 61 43% 3 1481 73 63 45% 3 1354 81 63 50% 2 961 83 61 43% 1 622 91 34 53% 2 915 94 62 48% 3 1235 98 59 40% 4 1448 112 53 45% 1 504 112 36 55% 2 809 118 59 48% 1 458 127 37 50% 3 1026 128 52 43% 4 1253 130 51 53% 1 446 133 42 50% 1 446 134 39 55% 1 435 140 43 58% 1 407 141 45 58% 2 664 152 53 45% 4 1026 162 45 53% 3 832 163 47 48% 4 848 183 39 40% 5 921 195 33 50% 4 740 198 36 43% 5 810 204 31 55% 3 594 205 36 45% 5 702 215 29 48% 5 528 240 23 53% 4 449 246 24 58% 3 391 249 28 50% 5 384 263 18 55% 4 309 270 20 53% 5 297 282 15 58% 4 200 301 14 55% 5 200 303 11 58% 5 97 323 6 60% 3 13 337 6 60% 2 12 338 7 60% 1 2 346 1 60% 4 1 346 2 60% 5 4 346 1 123 25 Page 20 of 23 Smart Grids and Sustainable Energy (2024) 9:25 Fig. 24 Vehicle-to-Grid parameter variation: profile secondary car, 80 kWh storage size Daily start charge state [% SOC] aFRR power [kW] Cost reduction [€] Days without aFRR fullfilment Addictional battery cycles 40% 2 1709 21 74 43% 2 1455 31 77 45% 2 1344 39 80 40% 1 986 40 40 48% 2 1273 48 82 50% 2 1211 63 82 40% 3 1900 65 85 43% 1 731 69 43 43% 3 1754 73 88 53% 2 1134 78 82 45% 1 617 83 47 45% 3 1574 92 83 50% 1 541 109 51 55% 2 945 113 77 48% 1 549 114 46 53% 1 518 118 52 48% 3 1383 120 77 58% 1 493 123 58 55% 1 503 125 54 58% 2 778 151 69 50% 3 1140 155 68 40% 4 1332 164 62 43% 4 1220 174 60 53% 3 907 184 58 45% 4 1077 190 56 48% 4 952 205 51 55% 3 624 227 46 40% 5 855 232 40 50% 4 735 234 41 43% 5 709 244 35 58% 3 460 256 37 45% 5 588 261 30 53% 4 497 265 31 48% 5 445 276 25 55% 4 347 287 24 50% 5 336 292 20 53% 5 242 306 15 58% 4 191 308 16 55% 5 187 313 13 58% 5 130 324 10 60% 2 19 329 12 60% 3 11 337 7 60% 1 2 345 1 60% 4 2 346 1 60% 5 5 346 2 Acknowledgements This work emanated from research that was conducted with the financial support of the Federal Ministry for Economic Affairs and Climate Action grant number 03ET1116A within the research project Smart2Charge Author Contributions All authors contributed equally to this work Funding Open Access funding enabled and organized by Projekt DEAL. This work was funded by the Federal Ministry for Economic Affairs and Climate Action grant number 03ET1116A within the research project Smart2Charge Declarations Conflicts of interest No conflict of interest / competing interests exist regarding this work Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, 123 Page 21 of 23 25 Smart Grids and Sustainable Energy (2024) 9:25 unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitteduse,youwillneedtoobtainpermissiondirectlyfromthecopyright holder. To view a copy of this licence, visit http://creativecomm ons.org/licenses/by/4.0/. References 1. Statista GmbH (2022) Anteil der Elektroautos am Bestand der Personenkraftwagen in Deutschland von 2018 bis 2020 und Prognose bis 2030. https://de.statista.com/statistik/daten/studie/ 1202904/umfrage/anteil-der-elektroautos-am-pkw-bestand-indeutschland/ 2. Kemmler A, Wünsch A, Burret H (2021) Entwicklung des Bruttostromverbrauchs Bis 2030, pp 1–5. Prognos AG, Basel, Schweiz. https://www.bmwi.de/Redaktion/DE/Downloads/E/prognosbruttostromverbrauch-2018-2030.pdf?__blob=publicationFile& v=2 3. Figgener J, Hecht C, Haberschusz D, Bors J, Spreuer KG, Kairies K-P, Stenzel P, Sauer DU (2023) The development of battery storage systems in Germany: a market review (status 2022). Technical report. https://arxiv.org/ftp/arxiv/papers/2203/2203.06762.pdf 4. Stenzel P, Kotzur L, Stolten D, Figgener J, Haberschusz D, Sauer DU, Gottke V, Valentin F, Felten A, Schäfer F, Zunft S (2021) Energiespeicher - Batterien, Wasserstoff. Wärme. BWK: das Energie-Fachmagazin 73(Januar 2021):20–34 5. dena (2022) Wasserkraft als Stromspeicher. https://www.dena. de/themen-projekte/energiesysteme/flexibilitaet-und-speicher/ pumpspeicher/ 6. Umwelt Bundesamt (2022) Kraftwerksleistung in Deutschland. https://www.umweltbundesamt.de/bild/kraftwerksleistungin-deutschland 7. Alirezaei M, Noori M, Tatari O (2016) Getting to net zero energy building: investigating the role of vehicle to home technology. Energy and Buildings 130:465–476. https://doi.org/10.1016/ J.ENBUILD.2016.08.044 8. Arnaudo M, Topel M, Laumert B (2020) Vehicle-To-Grid for peak shaving to unlock the integration of distributed heat pumps in a Swedish neighborhood. Energies 13(7). https://doi.org/10.3390/ en13071705 9. Sovacool BK, Kester J, Noel L, Zarazua de Rubens G (2020) Actors, business models, and innovation activity systems for vehicle-to-grid(V2G)technology: a comprehensivereview.Renew Sustain Energy Rev 131(109963). https://doi.org/10.1016/j.rser. 2020.109963 10. Otto R, Brennenstuhl M, Duminil E, Uckelmann D, Schröter B (2022) A case study on energy management and charging monitoring of battery electric vehicles in parking garages. 6th E-Mobility Power System Integration Symposium: Proceedings, The Hague. https://doi.org/10.1049/icp.2022.2708 11. Wang D, Coignard J, Zeng T, Zhang C, Saxena S (2016) Quantifying electric vehicle battery degradation from driving vs. vehicleto-grid services. J Power Sources 332:193–203. https://doi.org/10. 1016/j.jpowsour.2016.09.116 12. Thompson AW (2018) Economic implications of lithium ion battery degradation for Vehicle-to-Grid (V2X) services. J Power Sources 396:691–709. https://doi.org/10.1016/J.JPOWSOUR. 2018.06.053 13. Shinzaki S, Sadano H, Maruyama Y, Kempton W (2015) Deployment of Vehicle-to-Grid technology and related issues. SAE Technical Papers (April). https://doi.org/10.4271/2015-01-0306 14. Lunz B, Yan Z, Gerschler JB, Sauer DU (2012) Influence of plug-in hybrid electric vehicle charging strategies on charging and battery degradation costs. Energy Policy 46:511–519. https://doi.org/10. 1016/J.ENPOL.2012.04.017 15. Uddin K, Jackson T, Widanage WD, Chouchelamane G, Jennings PA, Marco J (2017) On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integratedvehicleandsmart-gridsystem.Energy133:710–722.https:// doi.org/10.1016/J.ENERGY.2017.04.116 16. Lehtola TA, Zahedi A (2021) Electric vehicle battery cell cycle aging in vehicle to grid operations: a review. IEEE J Emerg Sel Top Power Electron 9(1):423–437. https://doi.org/10.1109/JESTPE. 2019.2959276 17. Thingvad A, Ziras C, Hu J, Marinelli M (2017) Assessing the energy content of system frequency and electric vehicle charging efficiency for ancillary service provision. 2017 52nd Internationaluniversitiespowerengineeringconference,UPEC2017,1–6. https://doi.org/10.1109/UPEC.2017.8231947 18. Videgain Barranco P, Covrig CF (2021) Vehicle-to-Grid and / or Vehicle-to-Home round-trip efficiency. Technical report, Brussels. https://doi.org/10.2760/997207 19. Schram W, Brinkel N, Smink G, Van Wijk T, Van Sark W (2020) Empirical evaluation of V2G round-trip efficiency. SEST 2020 - 3rd International conference on smart energy systems and technologies, 2–7. https://doi.org/10.1109/SEST48500.2020.9203459 20. Correia AFM, Moura P, Almeida AT (2022) Technical and economic assessment of battery storage and Vehicle-to-Grid systems in building microgrids. Energies 15(23). https://doi.org/10.3390/ en15238905 21. Noel L, Rubens GZd, Kester J, Sovacool BK (2019) Vehicleto-Grid: A Sociotechnical Transition Beyond Electric Mobility. Springer, Cham, Switzerland 22. Schweizer Bundesamt für Energie BFE (2008) Bestimmung von Wirkungsgraden bei Pumpspeicherung in Wasserkraftanlagen. Technical report. https://www.newsd.admin.ch/newsd/message/ attachments/11518.pdf 23. Brennenstuhl M, Lust D, Pietruschka D, Schneider D (2021) Demand side management based Power-to-Heat and Power-to-Gas optimization strategies for PV and wind self-consumption in a residential building cluster. Energies 14(20). https://doi.org/10.3390/ en14206712 24. BrennenstuhlM,ZehR,Otto R,Pesch R,Stockinger V,Pietruschka D (2019) Report on a plus-energy district with lowtemperature DHC network, novel agrothermal heat source, and applied demand response. Appl Sci. https://doi.org/10.3390/app9235059 25. DEAP (2021) DEAP - Distributed Evolutionary Algorithms in Python. https://deap.readthedocs.io/en/master/ 26. infas (2017) Mobilität in Deutschland. https://www.mobilitaet-indeutschland.de/ 27. Bundesministerium für Digitales und Verkehr (2021) Deutsches Mobilitätspanel (MOP). https://bmdv.bund.de/SharedDocs/DE/ Artikel/G/deutsches-mobilitaetspanel.html 28. Bundesministerium für Digitales und Verkehr (2023) Mobilität in Deutschland (MiD). https://bmdv.bund.de/SharedDocs/DE/ Artikel/G/mobilitaet-in-deutschland.html 29. Schmidt J, Yadack M, Eicker U (2018) Regulatory uncertainty and simulations of novel electricity tariffs for households : applications in Germany and demand elasticity. In: 2018 IEEE International conference on engineering, technology and innovation (ICE/ITMC) 30. 50Hertz Transmission GmbH, Amprion GmbH, TenneT TSO GmbH, TransnetBW GmbH (2023) Regelleistung.net Datencenter. https://www.regelleistung.net/ext/tender/ 31. Kraftfahrt-Bundesamt (2021) Kurzbericht Durchschnittsalter der Personenkraftwagen. Technical report. https://www.kba.de/ DE/Statistik/Fahrzeuge/Bestand/Fahrzeugalter/2021/2021_b_ kurzbericht_fz_alter_pdf.pdf?__blob=publicationFile&v=2 123 25 Page 22 of 23 Smart Grids and Sustainable Energy (2024) 9:25 32. Bundesministerium der Finanzen (2000) AfA-Tabelle für die allgemein verwendbaren Anlagegüter. https://www. bundesfinanzministerium.de/Content/DE/Standardartikel/ Themen/Steuern/Weitere_Steuerthemen/Betriebspruefung/AfATabellen/Ergaenzende-AfA-Tabellen/AfA-Tabelle_AV.pdf?__ blob=publicationFile&v=3 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 123 Page 23 of 23 25