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This paper develops a pseudo-panel approach to examine household electricity demand behavior through the household life cycle and its response to income variations to help strengthen the energy policy-making process. Our empirical methodology is based on three rich independent microdata surveys (the National Housing Surveys), which are representative of the French housing sector. The resulted sample covers the 2006–2016 period. Using within estimations, this paper finds striking evidence that the income elasticity of French residential electricity demand is 0.22, averaged over our four cohorts of generations. In light of other works, our estimate stands in the lower range. The empirical results also show that residential electricity consumption follows an inverted U-shaped distribution as a function of the age of the household's head. Most notably, it appears that households at the mid-point of their life cycle are relatively the largest consumers of electricity. This outcome has important implications for policy-making. Any public policy aimed at reducing household energy consumption should consider this differentiation in consumption according to the position of households over the life cycle, and therefore target as priority households at the highest level of consumption. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has urged many governments in the world to enforce a strict lockdown where all nonessential businesses are closed and citizens are ordered to stay at home. One of the consequences of this policy is a significant change in electricity consumption patterns. Since load forecasting models rely on calendar or meteorological information and are trained on historical data, they fail to capture the significant break caused by the lockdown and have exhibited poor performances since the beginning of the pandemic. In this paper we introduce two methods to adapt generalized additive models, alleviating the aforementioned issue. Using Kalman filters and fine-tuning allows to adapt quickly to new electricity consumption patterns without requiring exogenous information. The proposed methods are applied to forecast the electricity demand during the French lockdown period, where they demonstrate their ability to significantly reduce prediction errors compared to traditional models. Finally, expert aggregation is used to leverage the specificities of each predictions and enhance results even further. [ABSTRACT FROM AUTHOR]

Waste heat from industry or urban facilities represents a largely underused and long disregarded energy source, while heating and cooling count for half the final energy demand in Europe. From the early 2010s onwards, waste heat recovery (WHR) is being recognized as a key challenge for energy transition and tends to be integrated into energy strategies at different levels. This paper provides an analysis of how WHR became a matter of public policy in Europe and in France. Based on a literature review, the analysis shows that WHR has been framed as a techno-economic problem, while some barriers (legal, organizational) to its development remain largely unaddressed. A study of European and French energy agendas illustrates how WHR progressively started to be recognized as an energy resource next to renewables. As a result, questions are raised as to further social science contributions to an extended research agenda addressing WHR. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the potential role of the electricity interconnectors in improving the security of supply in Great Britain (GB) in 2030. Real electricity demand and price data for GB and France in 2016 were used to understand the relationship between power exchange between the two countries and their wholesale electricity prices. A linear programming optimisation model was developed to find the economic power dispatch. Two interconnection links were considered; two‐way trade interconnector with a capacity of 5.4 GW and a 12.3 GW import‐only interconnector between GB and other states. The GB–France link transmits electricity from cheaper system to the more expensive one. The total electricity demand in 2030 will be 406 TWh. Gas‐fired power plants w/wo CCS will provide 83 TWh of the total electricity demand, whereas nuclear power plants will produce 74 TWh. In addition, wind farms and solar PVs are expected to deliver ~120 TWh electricity. CHP units will provide 88 TWh electricity in 2030. The electricity traded between GB and France in 2030 was found to be 33 TWh, which is 160% larger compared with 2016. The power import from France is about 27 TWh and occurs in 59% of the time. For 64% of the time, the interconnector with France is fully loaded. The electricity imported via the 12.3 GW interconnector in 2030 is 1 TWh and mainly occurs during winter‐time when the demand in GB is high. De‐rated capacity margin was calculated based on instantaneous electricity demand and varies between −2% and 139%. The impact of the price of the imported electricity via the 12.3 GW link was investigated. Increasing the price of the imported electricity via the 12.3 GW link results in a higher capacity factor for all the generation options except the 12.3 GW interconnector link. [ABSTRACT FROM AUTHOR]

Background, aim and scope: Electricity use or substitution is one of the key parameters with regard to life cycle assessment (LCA) results. At the same time, it is often used as an illustrative example to highlight the modelling differences between decision-oriented and descriptive LCA. Three basically different models exist in life cycle inventory analysis: the attributional, the consequential and the decisional model. This paper proposes criteria that help to classify typical LCA questions regarding real business cases and find the most appropriate life cycle inventory (LCI) model. The framework is applied to a case study of an LCA of electricity use and supply within the international operations of an environmental service company with headquarters in France. Main features: Individual decision with comparatively small consequences can be modelled under ceteris paribus (other things being equal) conditions. Decision situations with medium to large potential consequences should be modelled under the conditions of mutatis mutandis (the necessary changes being made). The key question is how to distinguish between small, medium and large consequences. We recommend using the relative economic size to classify objects of investigation and the LCA goals related to them into three groups to which the most appropriate LCI models are assigned. Results and discussion: The attributional approach is sensible for environmental reporting and product labelling and declaration where the relative economic size of the object of investigation is small. The decisional approach is sensible for LCAs of product and process development, as well as site and supplier evaluation carried out by private companies in case the relative economic size of the object of investigation is medium. The consequential approach is of relevance for policy support of governments and international organisations as well as for strategic decisions of companies, where the relative economic size of the object of investigation is large. The consequential approach is also sensible in product or service comparisons by companies, if they offer products or services that are in line or help to comply with large-scale government policy measures (like for instance promoting renewable fuels). The French attributional and decisional electricity supply mix causes greenhouse gas emissions of 98 and 225 g CO-eq./kWh, respectively, whereas the European attributional and decisional electricity supply mix causes greenhouse gas emissions of 554 and 473 g CO-eq./kWh, respectively. The volumes of high radioactive waste generated with the French and EU-27 electricity mixes amount to 11 and 3.5 mm/kWh for the attributional mixes as well as 3.8 and 0.034 mm/kWh for the decisional mixes. Conclusions: The criterion “relative economic size” helps to better decide on the appropriate LCI model to be applied in specific LCA case studies supporting any kind of decisions. Being quantitative, the “relative economic size” criterion is comparable to the criteria used to delimitate the product system (cut-off criteria mass, energy and environmental impact). The delimitation values proposed are still preliminary and still show a certain degree of ambiguity. Nevertheless, it proves to be both a practical and potentially relevant criterion. The case study of the French and European electricity mixes shows that a distinction of different decision contexts is required and feasible. Using official statistical information and published forecasts issued by the relevant industrial associations or governmental bodies significantly reduces the potential bias related to the determination of possible change-oriented electricity mixes. Recommendations and perspectives: The relative economic size of the object of investigation is a quantified criterion to decide on the most appropriate modelling approach in life cycle inventory. It is recommended to apply the criterion in the goal and scope phase of any LCA and to apply it on the production volume on the one hand and on purchase volumes on the other. Production and purchase volumes can be expressed in either economic or physical quantities and be related to the totals of economic sectors or political entities such as nations or international organisations. The current paper deals with the appropriate modelling approach illustrated with the electricity mix. The electricity sector is only one of many sectors where the choice of the modelling approach may reveal important differences in the overall environmental impacts. Thus, it is worthwhile to extend the concept presented in this paper to other economic sectors such as agriculture, mining or paper and pulp. This would help to better substantiate or to adjust the delimitation values and to gain more experience with the threshold criteria proposed. [ABSTRACT FROM AUTHOR]

To reach climate mitigation goals, the share of wind power in the electricity production is going to increase substantially in France. In winter, low wind days are challenging for the electricity system if compounded with cold days that are associated with peak electricity demand. The scope of this study is to characterize the evolution of compound low wind and cold events in winter over the 1950–2022 period in France. Compound events are identified at the daily scale using a bottom-up approach based on two indices that are relevant to the French energy sector, derived from temperature and wind observations. The frequency of compound events shows high interannual variability, with some winters having no event and others having up to 13, and a decrease over the 1950–2022 period. Based on a k-means unsupervised classification technique, four weather types are identified, highlighting the diversity of synoptic situations leading to the occurrence of compound events. The weather type associated with the highest frequency of compound events presents pronounced positive sea-level pressure anomalies over Iceland and negative anomalies west of Portugal, limiting the entrance of the westerlies and inducing a north-easterly flow bringing cold air over France and Europe generally. We further show that the atmospheric circulation and its internal variability are likely to play a role in the observed reduction in cold days, suggesting that this negative trend may not be entirely be driven by anthropogenic forcings. Despite this suggested role for cold days, the observed decrease in compound events does not seem to be strongly influenced by the regional atmospheric circulation. [ABSTRACT FROM AUTHOR]

This paper investigates the future role of cross-border electricity flow between Great Britain (GB) and its neighbors until 2030, considering high deployment of renewable energy sources (e.g., wind, solar, and biomass), enhanced interconnection capacity, and a partly electrified heating sector. It was assumed that two cross-border interconnectors links will connect GB's power system to its neighbors: (1) a one-way interconnector (IC1) that imports electricity to GB, and (2) a two-way one (IC2) between France and GB. The IC2 was allowed to transfer electricity from a cheaper power system to a more expensive one. The results show that at a fixed CO2 price, a change in power imported via IC1 will affect the power dispatch of the CO2 emitting power plants and biomass-fired power plants, and electricity trade via IC1 and IC2. At IC1 importing of £60/MWh, by raising the CO2 price from 60 to £70/ton, the share of CCGT power plants will reduce by 75%, and the power imported via IC1 link will face 19-times growth. With a constant IC1 import price, raising the CO2 tax will reduce the total quantity of electricity being exported to France via IC2. Moreover, increasing the CO2 tax will increase the emissions cost of gas and coal-fired generators, and the power required to meet the demand will be imported via IC1. With the IC1 electricity price set to £20/MWh and the CO2 tax set to £50/ton, there may be 595 periods out of 17,520 in which GB will be used as an electricity trade corridor. GB's total CO2 emissions should drop as the CO2 tax increases. [ABSTRACT FROM AUTHOR]

The purpose of the "Investment 85" model is to shed light on the evolution, up to 1985, of the optimal structure of electricity production equipment in France and to outline its geographical distribution by main regions. The model fits into the general pattern of theoretical studies which contribute to the preparation of investment decisions by Electricité de France. It was solved in 1965 and represents the status then current in the application of mathematical programming methods to investment decision problems faced by Electricité de France. A detailed description of the model including the production and consumption features is provided. The various components of the total cost and the constraints are discussed. A treatment of the problem under uncertainty is given and the main results of the model are summarized. In addition the paper contains a historical review of the main stages in the development of the model and its solution. [ABSTRACT FROM AUTHOR]

Recent economics literature emphasises the inextricable link between economic prosperity and efficient energy supply and consumption, arguing vehemently that much of today's prosperity is predicated on secure and reliable energy use and that modern industry would grind to a halt without the necessary energy infrastructure. France is an example of an advanced country with enormous energy infrastructure. The country's economic growth has been rather sluggish in recent years, despite its robust and efficient energy production and consumption. The current study analyses empirically whether France's current sustainable electricity use defies the theoretical and empirical literature. To achieve this objective, the standard tools of growth empirics were used over the period 1961–2015. The Zivot–Andrews trended and de-trended structural break tests, as well as Autoregressive Distributed lag (ARDL) bounds testing were employed to analyse the data. The findings of this study demonstrated that France's economic growth is stimulated by electricity usage, financial development, capital, import, and export. The study sheds light on the feedback impact between economic growth and power usage as well as capital and economic growth. With these findings, we argue that it is not electricity that has ceased to serve a functional purpose in France, rather there is a need for entrepreneurial innovative capacity to create factors that require an infinite horizon for the continued use of electricity, which seems to be impeding the country's economic growth potential, among other things. [ABSTRACT FROM AUTHOR]

Investments into wind power generation may be hampered by the uncertainty of future revenues caused by the natural variability of the wind resource, the impact of climate change on wind potential and future electricity prices, and the regulatory risks. We quantify the uncertainty of the economic value of wind farms in France, Germany, and Denmark, and evaluate the cost of support mechanisms needed to ensure the profitability of wind farms under present and future climates. To this end, we built a localised model for wind power output and a country-level model for electricity demand and prices. Our study reveals that support mechanisms are needed for current market conditions and the current climate, as well as under future climate conditions according to several scenarios for climate change and energy transition. The cost of support mechanisms during a 15-year period is evaluated to EUR 3.8 to EUR 11.5 billion per year in France, from EUR 15.5 to EUR 26.5 billion per year in Germany, and from EUR 1.2 to EUR 3.3 billion per year in Denmark, depending on the scenario considered and the level of penetration of wind energy. [ABSTRACT FROM AUTHOR]

Energy plays a vital role in promoting sustainable economic development in complex societies. This study has analyzed the impact of electricity consumption on three European Union member countries' economic growth, i.e., Portugal, France, and Finland, caring structural breaks in cointegration analyses. The empirical results indicate a positive impact of electric power consumption on economic growth in the long and short run in Finland and Portugal and in the long run in France. The findings also highlight the positive and significant role of the labor force in boosting economic growth in the long and short run in France and Finland. However, it shrinks economic growth in the long run in Portugal. The study discloses the positive role of capital in the long run in the case of Portugal. Similar results are found in all three countries in the short run. Moreover, the study diagnoses a bidirectional causal relationship between economic growth and electric power consumption in Finland in the long and short run and in France in the long run. A growth-promoting or electricity-led growth hypothesis is found in Portugal. By simulating the mean values of electric power consumption, economic growth follows an increasing trend in all the countries. Hence, electric power consumption has appeared an essential factor in elevating economic growth in all three selected countries. Based on these results, this study suggests that the provision of electricity supply ventures may be expanded in the selected EU member countries in order to enhance economic growth. The study also suggests that emphasis should be shifted from non-renewable energy sources to renewable energy sources to ensure the provision of clean energy to all the public under the umbrella of sustainable development goals of 2030. Hence, the present study contributes to achieving sustainable economic growth in the selected EU member countries. [ABSTRACT FROM AUTHOR]

Summary: This article studies a new architecture of a thermosyphon loop and its capacity to cool data center considering the climate of several cities in France. A parametric experimental study is carried out to show the effect of the refrigerant fill ratio, the input heat load, and the outside temperature. The optimal fill ratio is 16% and the maximum cooling capacity is 1900 W when the data center outside temperature is 20°C. A hydraulic and thermal analytical model is developed to simulate the thermosyphon loop, and it is validated with the experimental results. The cooling load of a typical data center with ten racks, each one dissipates 4.5 kW, is simulated by TRNSYS software for the eight chosen cities in France. The annual performance shows that the percentage of undissipated heat is 13.4% in Marseille that has the highest annual average outside temperature and 2.7% in Caen that has the lowest summer average outside temperature of the data center. The particularity of this work is that it gives the annual performance of a new architecture of a thermosyphon loop in several cities of France with different climates and without electricity consumption. [ABSTRACT FROM AUTHOR]

Abstract: Renewable energy sources play a role in providing energy services in a sustainable manner. In this paper, we review and estimate the renewable electricity potentials in France in a long term perspective, considering technical as well as social acceptability factors. Previous assessment studies are reviewed, and specific evaluations are conducted to estimate both the solar electricity potential on residential rooftops and the wind electricity potential in likely-developable areas. Potential effects on the grid power balance are examined for typical winter and summer days based on historical data sets. France has the resources to produce a large portion of its annual final electricity consumption with renewable energy. However, further analysis is needed to evaluate more in depth the influence of limiting factors such as the variability of certain resources, the distribution and transmission constraints, and the potential usage conflicts. [Copyright &y& Elsevier]

• A spatio-temporal analysis is proposed to simulate the household electricity demand related to electric appliances end-use. • A combination of a sample survey from 54 households in a urban context allowed for determining the influencing factors on possession of electric appliances. • The frequency of usage as well as the time of use of most of these appliances depend on the active occupancy of the household members. • The maximum energy demand is related to a middle-aged couple accommodated in a large dwelling. • The energy consumption per person is more efficient when various persons share the dwelling together. To analyse residential electricity consumption/demand, it is often broken down into five end-uses, namely lighting, hot water, space heating/cooling, cooking, and domestic appliances. Unlike other electricity end-uses, there is no specific method to determine the number of electric appliances in residential buildings, and it is difficult to estimate their pattern of usage due to household behaviour. The increasing demand of domestic appliance use in France in recent decades is attributed to advances in technology, increased dwelling size, and changes in lifestyle and comfort requirements, resulting in increased ownership and frequency of use of these appliances. Therefore, to gain a better understanding of the total demand, it is necessary to determine the variables impacting the ownership and utilisation of electric appliances. This paper provides a spatio-temporal analysis at a local scale, focusing on electric appliances in the residential sector. This analysis can be used to illustrate the areas where the application of energy efficiency measures can be improved. By identifying the socio-economic variables impacting electricity demand through micro- and macro-scale modelling techniques, we simulated the load profile of electric appliances, based on a survey of 54 households and national statistics regarding the daily activity of French people. The results demonstrate that size of dwelling, financial status, age of the reference person in the household, and household size and composition are the main variables determining the number of electric appliances owned by the household. These data are crucial for electricity demand forecasting, Demand Response and Demand-Side Management. [ABSTRACT FROM AUTHOR]

The world is facing major challenges related to global warming and emissions of greenhouse gases is a major causing factor. In 2017, energy industries accounted for 46% of all CO 2 emissions globally, which shows a large potential for reduction. This paper proposes a novel short-term CO 2 emissions forecast to enable intelligent scheduling of flexible electricity consumption to minimize the resulting CO 2 emissions. Two proposed time series decomposition methods are developed for short-term forecasting of the CO 2 emissions of electricity. These are in turn bench-marked against a set of state-of-the-art models. The result is a new forecasting method with a 48-hour horizon targeted the day-ahead electricity market. Forecasting benchmarks for France show that the new method has a mean absolute percentage error that is 25% lower than the best performing state-of-the-art model. Further, application of the forecast for scheduling flexible electricity consumption is studied for five European countries. Scheduling a flexible block of 4 h of electricity consumption in a 24 h interval can on average reduce the resulting CO 2 emissions by 25% in France, 17% in Germany, 69% in Norway, 20% in Denmark, and just 3% in Poland when compared to consuming at random intervals during the day. • Short-term forecasting of the CO 2 emissions of electricity. • Proposing two novel decomposition methods for CO 2 emissions forecasting. • 48-hour horizon forecast for bidding in the day-ahead electricity market. • Enables scheduling of flexible electricity consumption to minimize CO 2 emissions. [ABSTRACT FROM AUTHOR]

This article explores the effect of education and transportation infrastructure on foreign direct investment for the French economy over the period of 1965–2017. Economic growth, financial development and electricity consumption are also considered as additional determinants of foreign direct investment. In so doing, the SOR unit root test is applied in order to examine unit root properties of variables in the presence of sharp and smooth structural breaks in the series. To examine the presence of cointegration between the variables, the bootstrapping ARDL cointegration test is applied. The empirical results show the presence of cointegration between the variables. Education and transportation add to foreign direct investment. Financial development declines foreign direct investment. The relationship between electricity consumption (economic growth) and foreign direct investment is bidirectional. The nonlinear relationship between education (transportation infrastructure) and foreign direct investment is U‐shaped. [ABSTRACT FROM AUTHOR]

This article develops a quantile regression model to measure the magnitude of the direct rebound effect for residential electricity demand based on micro-level data from an original survey of a representative sample of 2356 French households. We explore the rebound effect for different groups of residents. Traditionally, debate has focused on decreased prices for energy services due to efficiency improvements and their effects on energy demand. The methodological innovation provided by this paper is that it implements two theoretical estimation strategies: (i) energy efficiency elasticity of the demand for energy services, and (ii) price elasticity of the demand for energy services. Our findings reject the hypothesis of a backfire effect in the context of residential electricity use. Specifically, based on energy efficiency elasticity, we provide evidence of a rebound effect of between 72% and 86% but with substantial heterogeneity among consumption quantiles. Our results show that the direct rebound effect has critical relevance for energy efficiency policy in the face of energy invisibility in France. • The rebound effect of residential electricity demand is estimated • The recent French PHEBUS survey and quantile regression are used • The results provide evidence of a rebound effect of between 38% and 86% • Findings reveal substantial heterogeneity among consumption quantile • The analysis suggests that the rebound effect has critical relevance for energy efficiency policymaking [ABSTRACT FROM AUTHOR]

Buildings play a central role in energy transition, as they were responsible for 67.8% of the total consumption of electricity in France in 2017. Because of that, detecting anomalies (outliers) is crucial in order to identify both potential opportunities to reduce energy consumption and malfunctioning of the metering system. This work aims to compare the performance of several outlier detection methods, such as classical statistical methods (as boxplots) applied to the actual measurements and to the difference between the measurements and their predictions, in the task of detecting outliers in the power consumption data of a tertiary building located in France. The results show that the combination of a regression method, such as random forest, and the adjusted boxplot outlier detection method have promising potential in detecting this type of data quality problem in electricity consumption. [ABSTRACT FROM AUTHOR]

Covid-19 affects the personal lives of millions and led to an economic crisis. Changed behavioral patterns and a reduction of industrial activity result in a reduction in power demand, and thus Covid-19 impacts the power systems around the world. Bottom-up mapping of the effect of Covid-19 on the energy demand is challenging, if not impossible. In order to analyze the impact of the pandemic on power demand, we instead propose a simplified approach based on an econometric analysis that quantifies the country-wide load reduction of Covid-19, using the number of active cases as well as the specific lockdown period as proxies. The time span covered is from 1 January 2016 to 31 August 2020. This long time span allows us to investigate the effect of Covid-19 on the power demand. We find that in Germany (DE) and Great Britain (GB) the power demand is reduced by about 1–1.7 MW per case, while in France the demand increased by 1 MW per case during times outside of the lockdown. On the other hand, in France the lockdown itself has a much higher load reduction effect in France than in GB and DE. Based on the elasticity of power demand regarding Covid-19 cases, we calculate the impact of Covid-19 on the power prices through reduced loads. We find that Covid-19 reduced power prices by 3 to 6 EUR per MWh. The effect of Covid-19 on carbon emissions in the power sector is likely to be small. In Germany, the country with the highest absolute level, emissions in the power sector were reduced by approximately 2% (4 Mio. t CO2). [ABSTRACT FROM AUTHOR]

Analyzes an experiment on real-time pricing of electricity for residential customers in France. Process of the real-time pricing; Details of the experiment made by a French state-owned electric utility; Estimation of the Frisch demand functions for daily electricity consumption; Evaluation of the responsiveness of customers to the pricing option.