Grid-connected cabin preheating of Electric Vehicles in cold climates – A non-flexible share of the EV energy use

The number of EVs is increasing globally. In cold climates, it is generally recommended to use electricity from the grid to preheat the EV cabin before using the car, to extend driving ranges, to ensure comfort, and for safety. A majority of such preheating sessions are happening in the morning hours during the winter, when there is also a high demand for other energy use. It is thus important to understand the power loads for grid-connected preheating of EV cabins. This work presents an experimental study, with 51 preheating sessions of five typical EV models during different outdoor temperatures. The results of the study showed that during the preheating sessions, most of the EVs had a power use of between 3 and 8 kW initially, which was reduced to about 2 to 4 kW after a 10 to 20 min initial period. For most of the sessions, the preheating lasted between 15 and 45 min. The preheating energy use was found to be up to 2 kWh for most EVs, with a maximum of 5 kWh. Multiple linear regression models were developed, to investigate the relationship between various variables and the energy use for preheating. Finally, hourly energy loads for EV cabin preheating were compared to other energy loads in apartment buildings. The power and energy loads for preheating EV cabins are affected by a number of parameters, such as the specific EV, charge point, preheating duration, temperature levels, and user habits.publishedVersio

A method for generating complete EV charging datasets and analysis of residential charging behaviour in a large Norwegian case study

Electric vehicles (EVs) are part of the solution to achieve global carbon emissions reduction targets, and the number of EVs is increasing worldwide. Increased demand for EV charging can challenge the grid capacity of power distribution systems. Smart charging is therefore becoming an increasingly important topic, and availability of high-grade EV charging data is needed for analysing and modelling of EV charging and related energy flexibility. This study provides a set of methodologies for transforming real-world and commonly available EV charging data into easy-to-use EV charging datasets necessary for conducting a range of different EV studies. More than 35,000 residential charging sessions are analysed. The datasets include realistic predictions of battery capacities, charging power, and plug-in State-of-Charge (SoC) for each of the EVs, along with plug-in/plug-out times, and energy charged. Finally, we analyse how residential charging behaviour is affected by EV battery capacity and charging power. The results show a considerable potential for shifting residential EV charging in time, especially from afternoon/evenings to night-time. Such shifting of charging loads can reduce the grid burden resulting from residential EV charging. The potential for a single EV user to shift EV charging in time increases with higher EV charging power, more frequent connections, and longer connection times. The proposed methods provide the basis for assessing current and future EV charging behaviour, data-driven energy flexibility characterization, analysis, and modelling of EV charging loads and EV integration into power grids.publishedVersio

Montana Kaimin, October 23, 2008

Student newspaper of the University of Montana, Missoula.https://scholarworks.umt.edu/studentnewspaper/6217/thumbnail.jp

Energy profiles and electricity flexibility potential in apartment buildings with electric vehicles – A Norwegian case study

Energy flexibility in buildings has the potential to reduce the grid burden of neighbourhoods, yet its practical implementation remains limited. This paper presents a data-based case study from Norway, examining the electricity flexibility potential of electric vehicles, within the context of apartment building loads and PV generation. The results highlight the significant electricity flexibility potential in apartment buildings with EVs, where EV charging can be shifted in time by means of a shared energy management system. Energy profiles are presented, showing how EV charging can increase the average electricity use in apartments by a factor of 1.5 and the power use by a factor of 3.5 to 8.6. Furthermore, the study demonstrates how electricity flexibility KPIs of optimised EV charging in apartment buildings are affected by different energy tariffs, PV generation, V2G technology, and the location of the billing meters. The simulated scenarios showed a maximum reduction of peak loads of 45 %, while a maximum of 38 % of the EV charging was covered by PV generation. The study confirms that residential EV charging emerges as a viable frontrunner in the practical realization of end-user flexibility, paving the way for effective solutions in real-life applications.publishedVersio

Stochastic load profile generator for residential EV charging

Electric vehicle (EV) charging loads have an impact on the power grid, but also represent a potential for energy flexibility. There is a need for EV data to evaluate effects on the power grid and optimal EV charging strategies. A stochastic bottom-up model is developed for residential EV charging, taking outdoor temperatures into account. The model input is based on real-world data from residential charging in Norway. The load profile generator provides hourly load profiles for any number and combination of small and large EVs, assuming immediate charging after plug-in. It is found that the model generates realistic load profiles for residential EV charging, reflecting today’s charging patterns. Data generated can be used for load and flexibility simulations for residential EV charging.publishedVersio

Preliminary toolkit for goals and KPIs

PI-SEC er et norsk forskningsprosjekt som påløper i tidsrommet april 2016 til mars 2019. Prosjektet er finansiert av Norges forskningsråd. PI-SEC står for "Planning Instruments for Smart Energy Communities", og prosjektet har som mål å utvikle effektive planleggingsinstrumenter for integrering av energispørsmål på områdenivå. Prosjektet vil øke kunnskapen rundt hvilke parametere som er viktige for byer med fokus på smart og bærekraftig energi, samt hvordan disse kan kobles med planlegging, drift og monitorering av nye og eksisterende områder. Forskningspartnerne er NTNU og SINTEF Byggforsk, i samarbeid med Bergen og Oslo kommune og partnerne Standard Norge, FutureBuilt og Norwegian Green Building Council. Bydelene Ådland i Bergen og Furuset i Oslo er pilotområder i prosjektet. Prosjektet er delt inn i to arbeidspakker (WP), hvor WP1 tar utgangspunkt i utviklingsprosjekter ( bottom-up ), mens WP2 tar utgangspunkt i kommuneplanlegging ( top-down ). Det er videre 4 aktiviteter i hver av arbeidspakkene (tasks). Denne rapporten avslutter Task 1.2. Rapporten redegjør for et sett verktøy som skal testes ut i Task 1.3. Dette arbeidet har hatt fokus på en indikatorbasert verktøykasse som kan dekke behov påvist i Task 1.1 og Task 2.1. Målet er at verktøykassen vil være nyttig for områder med fokus på energieffektiv og smart byutvikling, gjennom at disse, på en enklere måte, kan velge samt følge opp gode mål og nøkkelindikatorer. Basert på relevante indikatorer (KPIer) samlet fra litteraturen ble en sluttliste på over 21 hovedindikatorer generert gjennom en strukturert utvelgelsesprosess. Indikatorer er fordelt på underkategorier og sektorer. Målene som er definert av de involverte byene og pilotområdene har blitt samlet og strukturert. Målene er kategorisert i fem hovedkategorier: 1. CO 2 reduksjon 2. Økt bruk av fornybar energi 3. Økt energieffektivitet 4. Økt bruk av lokale energikilder 5. Grønn mobilitet Rapporten redegjør for metodikk for valg og utvikling av egnede mål og indikatorer og hvordan disse verktøyene kan tilpasses pilotområdene Furuset og Zero Village Bergen. Metodikken baserer seg på Multi Attributt Beslutningstaking (MADM), for å gjøre objektive valg basert på all tilgjengelig informasjon. Gjennom den utviklede prosessen er det utarbeidet en foreløpig verktøykasse med 21 hovedindikatorer delt i underkategorier og sektorer. For å forenkle bruken av indikatorene og knytte dem til måloppnåelse, er det foreslått et indikatorbasert planleggingsverktøy for områder. Verktøyets hovedformål er å knytte spesifikke tiltak til grad av måloppnåelse via beregning av tiltakenes påvirkning på valgte indikatorer. Rapporten går også i gjennom andre verktøy som kan være relevante for bærekraftig byutvikling i pilotområdene i kapittel, samt redegjør for det juridiske rammeverket som ligger til grunn for ulike måledata. En rapport tilknyttet Task 2.2 er utviklet av NTNU parallelt med denne rapporten. I task 2.2 så presenteres et planleggingshjul. Hjulet illustrerer mulige verktøy som kan bidra til at energiplanlegging integreres i kommunal planlegging. I tillegg til planleggingshjulet så presenteres en rekke planleggingsverktøy brukt i internasjonale Smart City eksempler. Disse verktøyene kan bistå planleggingsprosessen, og vil bli testet i task 2. 3, sammen med anbefalingene fra denne rapporten.publishedVersio

Lessons learned from innovative energy solutions to enable zero emissions area

In line with the EU's vision of "local energy communities", Campus Evenstad, a Norwegian university campus, takes energy actions that contribute to the clean energy transition. The campus has been developed over several years, demonstrating a number of innovative and sustainable technologies and energy solutions, for example, vehicle-to-grid (V2G), biomass-based combined heat and power (CHP), solar energy, energy storage, energy efficiency measures, and a zero-emission building. The aim of this paper is to share experiences from operations of the energy solutions. The lessons drawn from Campus Evenstad demonstrate that, despite the difficulties associated with being an early adopter, there are valuable learnings and positive outcomes gained from putting solutions into action. The project yields significant insights and practical knowledge that have benefited the property owner, the wider construction industry, and the scientific community at large. Dissemination of the project's experiential knowledge has far-reaching implications for future property concepts and decision-making processes across the industry. Campus Evenstad is a pilot in The Research Centre on Zero Emission Neighbourhoods in Smart Cities (FME ZEN). This paper summarizes the lessons learned from implementing innovative energy solutions, in the process of transforming an existing university campus into a zero-emission neighbourhood. A lot of operational experience has been gained, both on individual technologies and the interaction between these, and the interaction with the national energy system. Dedicated professionals, from the property developer, operating staff, and campus management to researchers, have been central to the realization of the solutions.publishedVersio

Energy measurements at Skarpnes zero energy homes in Southern Norway: Do the loads match up with the on-site energy production?

Five houses are designed as zero-energy homes in Skarpnes, Norway. The energy goal is to achieve net zero-energy balance on an annual basis. The houses have heat pumps and solar cells (PV). Energy use and delivered energy have been monitored from June 2015. Variations between calculations and measurements are explained by technical and non-technical reasons. For the first year, higher than expected energy loads result in a solar energy cover factor of 65–87% of delivered electricity. The PV generation performs satisfactorily, hence, it may be possible to achieve energy goals during later years provided technical adjustments or behavioral changes.publishedVersionnivå

ZEB Pilot house Larvik (Multicomfort) AS-built

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