Ønsker du å delta i prosjektet og har en Nissan Leaf eller Nissan e-NV200? 

Vi samler fremdeles data og om bilen din er registrert som deltaker i prosjektet hører vi gjerne fra deg. Vi vil lade batteriet i bilen din noe opp og ut en til to ganger om dagen, omtrent som om du benyttet bilen til daglig. Utladning vil ikke gå under 20 % av batteriets kapasitet og opplading ikke over 80 %, oftest fluktuere rundt 50 % batterilading slik at vi ikke påvirker kapasiteten til batteriet.

Parkeringsplassene er i parkeringshuset P10 nær inngangen og således av de mest attraktive på Gardermoen.

Send en epost til thomas.martinsen@nmbu.no med informasjon om når du planlegger å parkere bilen på Gardermoen, så vil vi komme tilbake til deg med informasjon om deltakelse. 

• Bakgrunn

  Andelen fornybar energi øker, både nasjonalt og globalt. Kraft fra fornybar energi er ofte væravhengig samtidig som skifte fra bruk av fossil energi til økt bruk av elektrisitet øker variasjon i etterspørsel. Tilsammen blir det mer utfordrende å holde elforsyningen stabil. I prosjektet «Nettbalansering fra store parkeringsanlegg med el-biler og næringsbygg»" vil vi undersøke om energilagring i batteriene i parkerte elbiler og styring av energibruk i bygninger kan bidra til en mer fleksibel og dermed økonomisk energibruk. Vi vil både bruke batteriene i parkerte elbiler som midlertidig energilager, og styre energikrevende utstyr i næringsbygg. Da kan vi bruke mye elektrisitet om natten og mindre om morgenen og ettermiddagen. Det reduserer effekttopper og dermed behov forutbygging av nettet, som er kostbart og krever areal.

• Mål

  Vi bruker Oslo lufthavn Gardermoen som case. På flyplasser er det forutsigbart hvor lenge bileierne ønsker å la bilen stå parkert og dermed hvor lenge el-bilbatteriene er tilgjengelig. Vi har laget en modell som sikrer bileier lading samtidig som vi optimaliserer bruk av energi-lageret. Vi har utviklet et program for å beregne potensialet for fleksibilitet. Videre har vi analysert økonomiske fordeler både samfunnsøkonomisk og privatøkonomisk for aktørene.

• Resultater

  Enhancing peak electricity demand forecasting for commercial buildings using novel LSTM loss functions

  Heidi S. Nygård, Sigurd Grøtan, Kjersti Rustad Kvisberg, Leonardo RYdin Gorjão, Thomas Martinsen
  Electric Power Systems Research, Volume 246 (2025), 111722
  https://doi.org/10.1016/j.epsr.2025.111722

  Abstract
  The increasing integration of variable renewable energy necessitates new measures for grid balancing and cost optimization. Consumers can contribute through demand response, provided accurate predictions, particularly during peak periods. This study aims to enhance electricity demand forecasting using a Long Short-Term Memory (LSTM) neural network with two novel loss functions: Weighted Mean Squared Error (WMSE) and Negative Log Likelihood (NLL). WMSE emphasizes high-demand periods, while NLL captures prediction uncertainty. The model, trained on two years of hourly data for Oslo Airport Gardermoen (Norway), incorporates temporal features, historical demand, passenger numbers, and outdoor air temperature. Optimal model architecture is determined through grid search and cross-validation. Results reveal that top-performing model configurations have minimal architecture, suggesting that a simple model is sufficient for capturing temporal and seasonal demand variations. Models trained with WMSE and NLL demonstrate reliable peak predictions and valuable uncertainty estimations, with the top performing model achieving a Mean Absolute Percentage Error (MAPE) of 5.54 ± 1.00 %. Visual inspections confirm reproducible daily demand patterns, including characteristic dual peaks and weekday-weekend distinctions. This research demonstrates that LSTM models are effective and easy to use for electricity demand forecasts, empowering consumers in making informed decisions about flexibility management and demand response strategies.

  Multi-year analysis for optimal behind-the-meter battery storage sizing and scheduling: A Norwegian case study

  Muhammad Tabish Parray, Thomas Martinsen
  Journal of Energy Storage (2025), 115304
  
  https://doi.org/10.1016/j.est.2025.115304

  Abstract
  Electricity can be a significant cost for large commercial/industrial consumers, and optimal dispatch of behind-the-meter battery storage systems (BTM-BSS) have the potential to reduce these costs. Using the Oslo airport in Norway as a case study, this study proposes a Mixed-Integer Nonlinear Programming (MINLP) optimization model for monthly BTM-BSS dispatch, to explore the potential electricity cost savings. The model schedules the BTM-BSS dispatch for each month between 2015 and 2022 (on an hourly basis). The results demonstrate the financial viability of BTM-BSS. The paper shows that the potential operational savings obtained are dependent on tariff (both energy costs and power costs) set by the regulator, and power charges reduction is prioritized over energy charges reduction. The study further presents a methodology to calculate the optimal BTM-BSS size for the system, based on capital costs, multi-year electricity tariffs and energy demand. By employing a multiyear analytical approach, the study uncovers that the optimal BTM-BSS sizing distinctly varies from what is suggested by a single-year analysis, highlighting the importance of long-term data consideration. The optimal battery size of 3 MW/12MWh for a 267MWh average daily system demand, reduces the peak load of the system by 9.35 % on average and the total electricity costs by 6.4 %. The break-even period is 8.9 years, yielding a Return on Investment (ROI) of 124 %. A sensitivity analysis examining how BTM-BSS profitability and battery dispatch curves vary with electricity tariffs (power charges, hourly energy price) and battery operational constraints is also presented. Battery degradation is also introduced in a scenario to quantify its impact on the BTM-BSS operations and profitability. Overall, the findings highlight the potential benefits of optimally operating BTM-BSS in real-world settings and the importance of sizing the BTM-BSS systems on multiyear datasets.

  The grid benefits of vehicle-to-grid in Norway and Denmark: An analysis of home- and public parking potentials

  Niels Oliver Nagel, Eirik Ogner Jåstad, Thomas Martinsen
  Energy 293 (2024), 130729
  https://doi.org/10.1016/j.energy.2024.130729

  Abstract
  This study provides an analysis of the potential benefits of bi-directional charging of electric vehicles (V2G) and its implications for the energy sector using the Balmorel energy system model. We compare results from V2G scenarios to those of smart charging scenarios, specifically focusing on the target year 2040. The study explores specific cases: large-scale adoption of V2G for home charging and small-scale adoption at public airport parking lots. Two market regions, Norway and Denmark, are in focus and shed light on how different energy mixes affect the results. Findings show that large-scale V2G adoption has the potential to significantly affect prices in the analyzed price regions. V2G helps decrease price volatility, reduce peak and valley electricity prices, reduce renewable curtailment, and reduce the cost of operation in the energy system. The spread between charging and discharging prices is highest in the case of small-scale adoption and in regions with less inherent power system flexibility. Large-scale adoption leads to diminishing V2G returns. Also, more inherent power system flexibility competes with V2G. In the flexible Norwegian price regions, V2G may increase average electricity prices, leading to a tradeoff between societal and consumer welfare.

  Smart charging of electric vehicles based on scheduling theory

  Heidi S. Nygård, Ingrid Maria Mørch, Olvar Bergland
  27th International Conference on Electricity Distribution (CIRED 2023), pp 3889-3893

  Abstract
  Reducing energy related costs by better timing demand for power is a rapidly growing concept due to increased and more fluctuating energy prices. One way to achieve this is through smart charging of electric vehicles (EVs). In the present work, a python-based program has been developed based on scheduling theory and tailored to the specific objectives of charging at a long-term parking facility. Basic building blocks of the theory were assessed to explore the possibility of combining them into a useful model of the problem. The simulation yields example schedules where charging is restricted to night-time and no more EVs than necessary are charged simultaneously, without compromising the constraint of fully charged vehicles at predefined due dates.

  Load scheduling and V2G to minimize power demand - exploring potential for airport parking facility, Norway

  Thomas Martinsen, Georg Devik, Heidi S. Nygård
  27th International Conference on Electricity Distribution (CIRED 2023), pp 3324-3327

  Abstract
  Electrification of transport introduces challenges but also opportunities. The challenges are increased power demand and possibly grid investment causing additional power demand cost for charging. However, electric vehicles are idle for long time periods, particularly at airport parking. This provides opportunities for scheduled charging in order to minimize power charges. We have developed an algorithm giving priority to charging the electric vehicle(s) closest to departure while minimizing monthly average power demand. We are applying the algorithm to data from a parking facility in the Oslo airport vicinity. We explore the potential for reaching the minimum average monthly power demand through scheduling and discuss briefly the cost and benefits of introducing V2G. The results indicate that all EVs may be charged with only a small increase in the monthly average power demand and V2G would not provide further benefits for the parking facility owner with the current power tariff in Norway.

  The potential for load shifting with flexible EV charging at Oslo Airport Gardermoen

  Åshild Grøtan, Thomas Martinsen, Rasmus Gåsemyr Tveitane, Heidi S. Nygård
  CIRED Port Workshop 2022: E-mobility and power distribution systems, p. 975-979

  Abstract
  Flexible charging as a load-shifting asset was explored using energy data from a parking facility with electric vehicle (EV) chargers at Oslo Airport Gardermoen (OSL) in Norway. The parking facility is an interesting candidate for flexible charging, as 60% of the charging sessions last for more than 24 hours. We investigated the potential to reduce the building’s peak load using scheduled EV charging. Monte Carlo simulations were used to model load shifting of EV charging based on historical parking data. Several scenarios were simulated, with variations in charging power, volume, duration, and the number of charging sessions. In every scenario the method successfully reduced the building’s highest loads, and in most scenarios the simulation almost completely avoided charging during the peak load hours. Overall, the study shows that there is a large amount of power flexibility in parked EVs at OSL, and that relatively simple distribution methods for charging can reduce the highest loads. Even with a larger number of charging sessions, the method used in this study would lower peak loads, although the uncertainties are greater for these scenarios.

  Optimizing smart and bidirectional charger allocation in a behind-the-meter setting: a real-world data-driven Norwegian case study for growing electric vehicle adoption

  Muhammad Tabish Parray , Thomas Martinsen , Eirik Ogner Jåstad 
  
  Journal of Energy Storage, Volume 142, 10 January 2026, 119615
  https://doi.org/10.1016/j.est.2025.119615

  Abstract
  As Electric Vehicle (EV) adoption accelerates, expanding the necessary charging infrastructure presents a significant cost, particularly the chargers themselves. This study analyses the long-term economics of using smart, bidirectional, or a combination of both charger types. A key finding is that deploying a mix of smart and bidirectional chargers is more profitable than using a single type, with a near-optimal proportion of around 20% bidirectional chargers for 2021–2023. This optimal percentage decreases with higher EV adoption over time, reaching as low as 5% by 2040. The study assumes optimal charge/discharge management, using Oslo Airport's long-term parking as a large-scale, real-world case study with actual electricity demand data, electricity tariffs, parking data, efficiency losses and Norwegian EV growth models. It demonstrates that both smart and bidirectional EV charging increase savings through efficient energy arbitrage and significant peak demand reduction, consistently achieving a Return on Investment (ROI) greater than 100% in most scenarios. It also provides a methodology to calculate the ideal ratio of these chargers for a given EV adoption in the car segment. The study further highlights how savings are significantly influenced by electricity tariffs, demand patterns and EV charger costs. The research quantifies how investments in optimally sized EV charging infrastructure provide better ROI and breakeven times than optimally sized battery storage systems.
  
  

• Nyheter/omtale

  • NeX2G ble presentert på Smartgrid-dagen 2024 ved NMBU. Nyhetsartikkel om arrangementet her.
  • Smartgridsenteret arrangerte et webinar om elbilladere som en del av energisystemet og i den sammenheng inviterte vi NMBU og ABB for å snakke om dette. Opptak av webinaret finnes her.
  • Kan parkerte elbiler bidra til mer bærekraftig bruk av energi?
    Ett nytt prosjekt skal undersøke om batteriene i parkerte elbiler kan brukes til en mer fleksibel og økonomisk energibruk.  
  • Snart kan du få betalt for å parkere på flyplassen
    Til et pilotprosjekt på parkeringsplassen P10 på Oslo lufthavn jakter et team med forskere ved Norges miljø- og biovitenskapelige universitet (NMBU) elbileiere som skal på ferie neste år
  • Soon you can get paid to park at the airport

• Masteroppgaver

  Vi tilbyr masteroppgaver i dette prosjektet for studenter ved MINA og REALTEK.

  

  Tidligere masteroppgaver

  • Utfordringer og muligheter med elbiler og effektivisering i fremtidens energisystem (Marie Eide Roalkvam, 2024)
  • Predicting electricity demand using machine learning: Case study of Oslo Airport Gardermoen (Sigurd Grøtan, 2023)
  • Simulering av styringsalgoritmer for lastflytting av industrielle varmtvannsberedere på Oslo Lufthavn Gardermoen (Njål Kolberg Olsen, 2023)
  • Zero emission airport, OSLO Gardermoen - future energy use and combination of energy carriers (Lenka Ouro Akpo Vrabel, 2023)
  • Kostnadsutvikling og potensiale for toveis veggladere og V2G-teknologi: En analyse av komponentene og mulig kostandsutviklingen og potensialet for bidrag til strømforsyningen fra elbilparken (Tor Alexander Guldal, 2023)
  • Utfordringer i distribusjonsnettet ved elektrifisering av veitrafikken (Tomas Løkken, 2023)
  • Potensial for redusert effektuttak ved ladeplan av elbiler på en langtirdsparkeringsplass: en studie av Gardermoen Parkering (Georg Devik, 2022)
  • Prediksjon av elektrisitetsbruk i næringsbygg: Casestudie av Oslo Lufthavn Gardermoen (Kjersti Rustad Kvisberg, 2022)
  • Deep reinforcement learning for vehicle-to-grid control of long-term parked EVs: A case study of Oslo airport Gardermoen (Filip Kristoff Ørn, 2022)
  • Power Peak Shaving: How to Schedule Charging of Electric Vehicles and Organize Mutually Beneficial Vehicle to Grid (V2G) (Ingrid Maria Mørch, 2022)
  • Importance of drivers and barriers for V2G? (Tamara Nørreskov Aasebøe, 2021)
  • Fleksibilitet i parkerte elbiler ved næringsbygg : en casestudie av Oslo lufthavn Gardemoen (Rasmus Gåsemyr Tveitane, 2021)

  Pågående masteroppgaver:

• Deltakende forskere

  Prosjektet koordineres av MINA, og er et samarbeid mellom MINA og REALTEK ved NMBU.

  Eksterne partnere:

  • Statnett
  • OsloMet
  • Avinor
  • Eliva
  • lnett

  

  

  

  

  

  Deltakende NMBU-forskere

  Mads Greaker

  OsloMet