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The authors compare the energy consumption and CO2 emissions from vehicles using internal combustion engines (ICE), battery electric vehicles (BEV), fuel cell electric vehicles (FCEV), and two types of hybrid vehicles, BEV-ICE hybrid and BEV-FCEV hybrid. This paper considers several scenarios for four countries' electricity production from primary energy sources to estimate total CO2 release. Energy consumption of the vehicle per 100 km, emissions during manufacturing, battery production, and lifecycle of the vehicle are considered in the total amount evaluation of CO2 released. The results show that with current technologies for battery manufacturing, and a significant proportion of national grid electricity delivered by fossil fuels, BEV is the best choice to reduce carbon emissions for shorter driving ranges. In the case of electricity generation mainly by low-carbon sources, FCEV and BEV-FCEV hybrid vehicles end up with lower carbon dioxide emissions. In contrast, with electricity mainly generated from fossil fuels, electric vehicles do not reduce CO2 emissions compared to combustion cars. [ABSTRACT FROM AUTHOR]

Our goal is to examine the efficiency of different intraday electricity markets and if any of their price prediction models are more accurate than others. This paper includes a comprehensive review of Germany, France, and Norway's (NOR1) day-ahead and intraday electricity market prices. These markets represent different energy mixes which would allow us to analyze the impact of the energy mix on the efficiencies of these markets. To draw conclusions about extreme market conditions, (i) we reviewed the market data linked to COVID-19. We expected higher volatility in the lockdowns than before and therefore decrease in the efficiency of the prediction models. With our analysis, (ii) we want to draw conclusions as to whether a mix based mainly on renewable energies such as that in Norway implies lower volatilities even in times of crisis. This would answer (iii) whether a market with an energy mix like Norway is more efficient in highly volatile phases. For the analysis, we use data visualization and statistical models as well as sample and out-of-sample data. Our finding was that while the different price and volatility levels occurred, the direction of the market was similar. We could find evidence that our expectations (i–iii) were met. [ABSTRACT FROM AUTHOR]

This paper presents a statistical model for predicting the time-averaged total power consumption of an indoor swimming facility. The model can be a powerful tool for continuous supervision of the facility's energy performance that can quickly disclose possible operational disruptions/irregularities and thus minimize annual energy use. Multiple linear regression analysis is used to analyze data collected in a swimming facility in Norway. The resolution of the original training dataset was in 1 min time steps and during the investigation was transposed both by time-averaging the data, and by treating part of the dataset exclusively. The statistically significant independent variables were found to be the outdoor dry-bulb temperature and the relative pool usage factor. The model accurately predicted the power consumption in the validation process, and also succeeded in disclosing all the critical operational disruptions in the validation dataset correctly. The model can therefore be applied as a dynamic energy benchmark for fault detection in swimming facilities. The final energy prediction model is relatively simple and can be deployed either in a spreadsheet or in the building automation reporting system, thus the method can contribute instantly to keep the operation of any swimming facility within the optimal individual energy performance range. [ABSTRACT FROM AUTHOR]

A significant part of energy consumption in Northern countries goes to heating. There is no consensus about the most efficient source of renewable heat there. This paper presents a field study for a 7.8 m2 vacuum tube solar collector facility that is conservatively located in the cloudy and cold climate conditions of western Norway. We analyse a year-long operation by examining the rig's statistics. We show that in Nordic latitudes with rainy climate conditions, a domestic solar hot water system can produce 2200 kWh t h /y at a thermal efficiency of up to 72%. The average amount of heat produced by the collectors was up to 14.7 kWh t h /d. This was enough to sustain the domestic hot water demand in an average Norwegian household for 6 months with a short period of auxiliary heating. In conclusion, we calculated that a 3× upscaled area facility would deliver over 25 kWh t h covering six months of total heat consumption. The payback period for the facility is 12 years. [ABSTRACT FROM AUTHOR]

Increasing awareness of climate issues and environmental policy has been a key factor behind the continuous transition of energy systems at different levels. This paper aimed to analyse the energy system of Viken county, located in Norway. An energy system model using the energy system analysis tool EnergyPLAN was developed. Based on the reference year 2018, two future scenarios which focus on changes in the transport sector for the year 2030 and 2050 were designed. Besides changes in the transport sector, the integration of renewable energy sources is considered in the future scenarios. The results of the analysis regarding the future scenario shows substantial CO 2 reduction as a result of energy-carrier switching within the transport sector. The integration of new energy sources, in this case, solar PV and wind power, helps to offset increased import dependency. The primary energy supply decrease and the share of renewable energy increases significantly. [ABSTRACT FROM AUTHOR]

Increasing awareness of climate issues and environmental policy has been a key factor behind the continuous transition of energy systems at different levels. This paper aimed to analyse the energy system of Viken county, located in Norway. An energy system model using the energy system analysis tool EnergyPLAN was developed. Based on the reference year 2018, two future scenarios which focus on changes in the transport sector for the year 2030 and 2050 were designed. Besides changes in the transport sector, the integration of renewable energy sources is considered in the future scenarios. The results of the analysis regarding the future scenario shows substantial CO 2 reduction as a result of energy-carrier switching within the transport sector. The integration of new energy sources, in this case, solar PV and wind power, helps to offset increased import dependency. The primary energy supply decrease and the share of renewable energy increases significantly. [ABSTRACT FROM AUTHOR]

Municipal energy systems in the northern regions of Finland, Norway, and Sweden face multiple challenges: large-scale industries, cold climate, and a high share of electric heating characterize energy consumption and cause significant peak electricity demand. Local authorities are committed in contributing to national goals on CO2 emission reductions by improving energy efficiency and investing in local renewable electricity generation, while considering their own objectives for economic development, increased energy self-sufficiency, and affordable energy costs. This paper formulates a multi-objective optimization problem about these goals that is solved by interfacing the energy systems simulation tool EnergyPLAN with a multi-objective evolutionary algorithm implemented in Matlab. A sensitivity analysis on some key economic parameters is also performed. In this way, optimal alternatives are identified for the integrated electricity and heating sectors and valuable insights are offered to decision-makers in local authorities. Piteå (Norrbotten, Sweden) is used as a case study that is representative of Nordic municipalities, and results show that CO2 emissions can be reduced by 60% without a considerable increase in total costs and that peak electricity import can be reduced by a maximum of 38%. [ABSTRACT FROM AUTHOR]