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The energy transition towards sustainable resources is more urgent than ever given the environmental and geopolitical challenges. Being one of the major energy users, cities need to understand their energy sector to accomplish its transition, by means of data. However, data are not easily accessible and have their own challenges. This paper presents a joint effort between researchers, city representatives and industry to provide an urban system service that supports research, accelerates urban innovation, and involves the community. An energy data portal, "NRGYHUB", has been developed, where hourly data from thousands of energy meters are available. These meters were collected from neighborhoods in the city of Västerås, Sweden, and they measure electrical and heating energy. In addition, the data are complemented by geometrical and non-geometrical information of the buildings, as well as demographic statistics of the areas. The paper describes the process of data collection, preprocessing, and visualization, in addition to the main challenges and limitations of the project. This dataset can be used for energy use benchmarking, prediction, and analysis. • The creation and implementation of an energy data portal is detailed and made available on nrgyhub.mdu.se. • Legal and licensing issues in data sharing are explored, offering insights for those interested in Sweden and beyond • By showcasing real-world problems, the impact and role of this data sharing initiative are demonstrated. [ABSTRACT FROM AUTHOR]

This article analyzes energy poverty in Latin America and the Caribbean and the importance of energy efficiency to address this issue. The paper also touches upon energy subsidies in Mexico and their negative environmental and social consequences. Then, the article studies the potentially detrimental billing practices of Mexico's state electricity company, which are provoked by the enterprise's growing past-due portfolio and losses, and the possible infringement of electricity consumers' rights. The authors propose three courses of action for consumers to protest the company's possibly perjudicial billing. Finally, the authors suggest that a distributed electricity system in Mexico could combat energy poverty and strengthen electricity users' rights. [ABSTRACT FROM AUTHOR]

Abstract Most of the sustainable scheduling research deals with some environmental issues such as electricity consumption or carbon emissions although there are many other environmental and social indicators. Whereas, more and more manufactures are executing many solutions not only via implementation of environmentally-oriented but also socially responsible economically sound and management program. This proposed research has an objective to get a comprehensive view of the state and progress of sustainable scheduling by considering more sustainable indicators to assist many manufacturers to apply environmentally sound technological solutions. To achieve this objective, we, first, create a pool of sustainable manufacturing indicators that refers to some previous research that focus on applicable-proven indicators. Then, we employ an iterative process that is adapted from the general guidelines for literature review. We conceptualize, search, evaluate, analyze, and synthesize all 50 relevant papers resulting a completely new research framework to characterize the research. We use mathematical model components, i.e. manufacturing model, the system of objectives, objective function, constraints, model type, and optimization method, as the dimensions to classify the papers that were included in our analysis. We also apply the triple bottom line pillars – economic, environmental, and social to specify attributes and categories for the objective function and constraints. In literature classification step, the framework seems very sensitive since it can identify 49 different sustainable scheduling configurations from 50 reviewed papers. It means the framework can easily grasp the contributions of each paper. Then, in the analyses phase, we use 'sustainable link' to indicate if one research can be classified as sustainable scheduling research. The results show that energy cost and greenhouse gas indicators become the most frequently used indicators in sustainable scheduling. On the other hand, the mapping shows that some links have been rarely or never included, indicating potential areas for further research. We also list four main directions for future research which are: implementing other optimization methods; adding sustainability indicators; extending the model to a larger scale of manufacturing system; and loosening some assumptions. [ABSTRACT FROM AUTHOR]

This paper presents the outcome of an extensive research that ultimately proposes a dynamic quasi comprehensive model to assess the carbon footprint of fossil fuel electrical energy. The key parameters and their respective effects have been fully considered. Such key parameters include plant type, fuel type, fuel transportation, emissions for fuel extraction and processing, within-facility power consumption at the power plant, degradation rate, site ambient conditions, and losses related to transmission and distribution. The proposed model has two significant features. The first feature considers a framework that generates a dynamic model. The time span in the dynamic framework is flexible and can vary from 1 s to the entire functional life of a power plant. The second feature of the model (representing its comprehensiveness) considers the aforementioned nine key parameters, holistically and simultaneously. Applying the proposed model across a wide range of circumstances leads to the estimation of the quantitative effect of each parameter. This shows the priority of order that the parameters should be given when designing a low-emission fossil-fuel power plant. Among the different standard cases of plant designs discussed in this paper, combined-cycle power plants running on natural gas have the minimum carbon footprint. Hence, the power plant and fuel types have the greatest effect on carbon footprint per unit of generated electrical power. Moreover, other mentioned parameters demonstrate significant effect on the results in this research work. The variation of parameters from least to most can result in a four-fold increase in carbon footprint. [ABSTRACT FROM AUTHOR]

Electricity consumption and material wastage of manufacturing industries have directly or indirectly influenced the environment. Many manufacturing enterprises are committed to improving energy and material efficiencies. To achieve this goal, one of the high-efficiency ways is to optimize the scheduling. In this study, a multi-object optimization model is constructed for a particular kind of batch processing scheduling problem existed in flexible flow shops, where the objects include makespan, electricity consumption and material wastage. First, the models for electricity consumption and material wastage are built, based on which a mathematic model for scheduling problem is built afterwards. Finally, a hybrid non-dominated sorting genetic algorithm II (NSGA-II) method is employed to solve the multi-object optimization scheduling model. A case study based on a real-world paper mill industry is presented to demonstrate the effectiveness of the method and its application in practice. Moreover, a real-life scheduling problem is investigated, and the result shows that the proposed method is more efficient than manual scheduling in energy and material saving. In fact, many tissue paper mills or other manufacturing industries schedule by manual labor in China, indicating that the proposed method has a significant potential in improving energy and material efficiencies in the studied flexible flow shop, and in energy and material saving in similar industries. [ABSTRACT FROM AUTHOR]

Electricity consumption in cities is steadily increasing. Using a life cycle approach and fuel mix data from power companies’ sustainability reports, this paper analyzes greenhouse gas (GHG) emissions from electricity consumption in Hong Kong. The results show that coal contributed on an average 74.3 percent, liquefied natural gas 25.1 percent, and oil 0.6 percent of the thermal energy consumed for generating electricity in Hong Kong between 2002 and 2015. Besides, Hong Kong imported an average of 7.96 billion kWh per year net electricity from a nuclear power plant in Shenzhen. During this period, GHG emissions from annual electricity consumption ranged between 27.0 and 34.1 million tons (MT) and the emission factor ranged between 702 and 792 g CO 2 -eq/kWh. Hong Kong’s gross domestic product (GDP) increased steadily from USD 176 billion in 2002 to USD 297 billion by 2015. As a result, Hong Kong’s electricity productivity increased from 4.62 to 6.75 USD/kWh while GHG emission from electricity consumption per GDP decreased from 0.153 to 0.104 MT CO 2 -eq/USD billion. Hong Kong’s annual electricity consumption was predicted over the short-term period (2016–2020) and medium term (up to year 2030). Electricity consumption is likely to increase to 44.63 billion kWh by 2030 while GHG emissions from electricity consumption would increase to 33.92 MT of CO 2 -eq. The implications are discussed at the end of the paper. [ABSTRACT FROM AUTHOR]

The sustainability transition of electricity sectors is a matter of competition between multiple emerging renewable systems and dominant, established conventional systems. These transitions are multi-dimensional and are featured with non-linear and causal interactions between social, technical, economic and political components. Understanding the dynamic of transitions, i.e. how transitions unfold, can inform effective policy interventions. This paper aims to present a narrative-modelling approach to improve the understanding and description of transition dynamics in electricity sectors. The central ideas of the paper are: (1) the use of qualitative transition narratives helps to capture the co-evolving nature of society and technology which are simplified in modelling approaches; (2) narratives, with concepts from the sustainability transitions field, also guide the development of a model structure; and (3) computational models, in return, reproduce the complexity of transition dynamics, i.e. feedback loops, non-linearity, and time delays—the features which are impracticable to analyse with transition narratives alone. We use the historical transition of India's electricity sector to demonstrate the implementation of our proposed approach. First, an overview of the transition narratives is presented and the model structure, developed based on the narratives, is explained. Then, it is argued how the coupling of the narratives and model improves our understating of the positive impacts as well as the side-effects of stable feed-in tariffs and accelerated depreciation on the penetration levels of wind and solar electricity. [ABSTRACT FROM AUTHOR]

The seven papers in this issue of The Electricity Journal (Volume 36, Issues 2–3) tackle questions related to planning for electricity system resilience under uncertainty related to extreme weather events, the emerging experiences from transactive energy and low-income energy efficiency projects, and the ratemaking tools available to regulators to guide utility investments in a way that continues to effectively balance utility and customer needs even in the face of rapidly changing operating environment. The collection of these papers points to three critical needs that are emerging in the electricity system: 1. Integration, 2. Coordination, and 3. Regulatory resources. [ABSTRACT FROM AUTHOR]

Under the trend of global low-carbon development, reasonable and accurate prediction of electricity consumption plays an essential role in vigorously adjusting power system structure, promoting electrification, and other energy-saving and emission reduction measures. Considering the development trend of energy consumption, this paper introduces the Logistic model of energy structure into the system structure, and establishes a novel grey prediction model with system structure. According to the division of energy factors with similar attributes, this model seeks the internal relationship of the development of electricity consumption and describes the interaction between related factors and multiple main factors in the form of equations, which makes the model have better applicability and stability. In the validation part, the ten types of energy are divided according to their attributes, and the main factor group and the related factor group are distinguished. The model proposed in this paper is used for simulation and prediction, and is compared with the three types of models (six models). In the two cases, the simulation error of the new model is as low as 3.9790%, and the prediction error is 0.5645%. Compared with other models, the new model has shown good performance in the case of electricity consumption forecasting in China. The effectiveness of the optimization of the model in structure, background, and application is verified. At the same time, based on the analysis and prediction of China's consumption data, this paper gives relevant policy suggestions for developing China's power structure. • A novel model based on energy background (EPMGM(1,M,N)) is established. • The relationship of factors in the system are characterized by the equations. • A Logistic model based on energy structure is introduced to the new model. • China's electricity consumption is effectively predicted by the model. [ABSTRACT FROM AUTHOR]

Home energy consumption is increasing, and renewable energy sources are being deployed. The use of renewable energy sources as an alternative to fossil fuels and nuclear energy is a significant opportunity for reducing CO 2 emissions. However, carbon emissions from electricity generation are dependent on the type of renewable energy source and the quantity of the electricity that is produced. With the emergence of the smart grid, residents are able to reduce their electricity consumption by shedding the power usage in their homes. The objective of this study is to smooth power peak demand and diminish CO 2 emissions. This paper describes the power shedding benefits of reducing energy consumption and CO 2 emissions. This reduction of CO 2 emissions eliminates the need for auxiliary power plants in the grid in the case of peak demand points and the need to switch to renewable energy sources. In this study, we prove that emerging information and communication technology can reduce energy use and CO 2 emissions. Wireless communication architecture is introduced in this study to increase grid flexibility and rapidity in the data flow among all of the components in the proposed system. Our proposed system with local renewable generation can reduce CO 2 emissions nearly 72% of all CO 2 emissions when all appliances are supplied with the grid. With our load shedding algorithm, the CO 2 emissions decrease nearly 91%. The yearly estimation shows that our system can reduce 62% of CO 2 emissions and 37% of consumed power. In this paper, we introduce an efficient architecture of a smart home energy management system and propose a shedding algorithm for home energy usage. This system is based on wireless communication among home appliances, a home management and control system, a grid management and control system and domestic renewable energy sources. Our proposed system, which employs wireless communication, domestic renewable generation and a power shedding algorithm, can operate in conjunction with future smart grids by applying real-time data, such as peak demand points. Numerous real data sets permitted the development of some of the statistics in the system results. [ABSTRACT FROM AUTHOR]

Since the prices of oil and gasoline increase, the wind power will become increasingly competitive. So, one should present the safe conclusions with respect to the economic viability in terms of investment and operation of the wind turbines at a given location. Egypt enjoys fully a high potentiality of wind energy and its applications, nevertheless the study of this renewable source is still below the required level. Technical assessment of a two wind farms of 300 MW installed capacity has been presented on the proposed locations, where the correct roughness factor was estimated and the rate of wind power density potential per month at 100 m altitude has been derived. Frequency distributions, monthly capacity factor and annual full load hours have been investigated to choose the best suitable wind turbines over there. Annual capacity factors are high ranged between 44% and 42%, respectively, that represents 3855 h of annual operation hours in Kharga station, while 3680 of operative yearly hours were at Dakhla South station. The considered wind farms could generate 1130 GWh of electricity per year. Average cost per kWh is around 2 € cent. In terms of practical economic analysis and considerations given in this paper, Kharga and Dakhla South sites are global areas for wind power plants and have the ability to generate electricity with low price. Also, it is lower than the local electricity tariff in Egypt, which was generated by other ways through the fiscal year 2017/2018, that is equivalent to 5.4 US cent per kilowatt-hour. As a result, this great amount of electricity produced from this venture is relatively more than the needs of Egypt and can be exported to Southern European countries on the Mediterranean Sea, that will reduce the domestic fossil fuel consumption and create a clean regional climate by preventing thousands of tons of carbon emissions per year. • Proposed wind farms will generate energy 1,130 GWh/year. • Annual capacity factors are high ranged between 44% and 42%. • Economical wind applications are possible in the two sites. • The expected cost varies in the range of 1.96–2.09 € cents/kWh. [ABSTRACT FROM AUTHOR]

Abstract Various processing parameters in selective laser melting (SLM) affect power profile and scanning time, which directly relates to electrical energy consumption. These processing parameters also control the microstructure of materials, which further influence the mechanical properties of the fabricated parts. In this paper, we investigate the correlation between electrical energy consumption and mechanical properties, and study whether electrical energy can be effectively reduced without significantly compromising mechanical properties by optimizing processing parameters. 316 L stainless steel was used as powder materials. Two key parameters, laser power and exposure time, were selected, and several mechanical properties, including density, hardness, wear resistance, tensile strength, flexural strength, and torsional strength, were tested. The results of electrical energy consumption and mechanical properties were jointly analyzed using growth rate comparison. It was found that the improvement of various mechanical properties with increased electrical energy consumption differs greatly. Density can be effectively increased without significantly increasing the electrical energy, but the electrical energy needs to be greatly increased in order to achieve a high flexural strength. Growth rate three-dimensional maps of mechanical properties and electrical energy consumption are presented as a reference for processing parameter optimization. Highlights • Electrical energy and mechanical properties of SLMed parts were jointly studied. • Growth rate comparison is used to jointly analyze the results. • Acceptable density can be achieved without greatly increasing electrical energy. • Achieving higher mechanical strength requires more electrical energy. • The electrical energy can be effectively consumed for hardness and wear resistance. [ABSTRACT FROM AUTHOR]

Highlights • Parameters of 60 electricity-saving measures and 16 abatement measures are compiled. • Synergies of electricity use in China's chemical sector at grid level are assessed. • Efficient use of power can cut 154 Mt CO 2eq , 295 kt SO 2 , 322 kt NO x and 64 kt PM 2.5. • North and Northwest grids have the largest economic benefits on emissions reduction. • Grid with high electricity price is more sensitive to price change than other grids. Abstract Industry is a large electricity user. China's chemical industry (globally the largest based on sales) contributes 7% to China's GDP, while it consumes 11% of the total electricity consumption in industry and is responsible for 40% of total CO 2eq , 40% of SO 2 , 59% of NO x and 18% of PM-emissions of the chemical industry emissions. The heterogeneity of GHG and air pollutant emissions across electricity grids (within a country) is rarely included in analyses. In this paper, electricity conservation supply curves are developed (distinguishing the grids) to estimate the cost-effective and technical potentials of electricity conservation in China's chemical industry. The emission factors per grid for GHG (i.e. CO 2 , CH 4 and N 2 O) and air pollutants (i.e. SO 2 , NO x and PM 2.5) are calculated and used to quantify the emissions mitigation achieved by electricity saving technologies in the chemical industry for the period 2012–2035. Results show that significant multiple benefits can be obtained by implementing electricity efficiency measures. There are large differences among the six grids in terms electricity savings and emissions abatement of GHG and air pollutants. 83% of the total electricity saving potential is contributed by the North, Northwest and Central grids, equal to 32% of baseline electricity consumption in 2035. In 2035, 129 Mt of CO 2 , 33 kt CO 2eq of CH 4 , 571 kt CO 2eq of N 2 O, 235 kt of SO 2 , 275 kt of NO x and 52 kt of PM 2.5 in these three grids can be avoided as a result of electricity savings (a reduction of 31–33% compared to baseline emissions). When decision-makers set targets for energy saving and emission reduction, the multiple benefits and grid heterogeneity should not be ignored. [ABSTRACT FROM AUTHOR]

A pre-requisite to reducing the energy intensity of the building sector is to assess and understand current drivers of energy consumption. Drivers include building design characteristics, outdoor environmental conditions, as well as the operation patterns of building systems by facility managers and occupants. In the United Arab Emirates (UAE), where buildings consume more than 70% of the total electricity generated, a comprehensive analysis of the drivers of building energy performance has not yet been conducted. This paper proposes an empirical top-down statistical framework to comprehensively evaluate drivers of energy use for a stock of buildings. The approach is unique in its ability to capture the combined effects of technical, as well as, operation-related building parameters. A combination of data analysis techniques, including CHAID decision trees and regression models, is proposed and applied to data gathered from 713 mixed-use buildings in downtown Abu Dhabi. Results indicate that in addition to physical building parameters such as window tinting, operational parameters, including AC cleanliness, chiller condition, and thermostat temperature setting have shown significant impacts on energy consumption levels. The findings shed light on the crucial – yet often overlooked – role of facility managers and occupants in ensuring efficient energy performance in the building sector. [ABSTRACT FROM AUTHOR]

In the US, building sector consumes approximately 41% of all U.S. primary energy and 70% of all generated electricity. Office building uses the largest percentage of primary and derived energy in the commercial buildings sector. Therefore, energy saving is the primary target of building renovation and equipment retrofit. To estimate energy savings after a renovation or equipment retrofit, measurement and verification (M&V) must be conducted based on energy use models or benchmark tools to track and assess savings. Although some methods and modeling tools are applied more often, there is no well-accepted model formulation methodology and some methods have high uncertainty. This paper discusses a method to formulate the energy use model as an inverse regression model via an advanced and robust high-dimensional data analysis method. According to the inverse regression model, independent variables determined to be most influential to the energy consumption should be metered prior to building retrofit or audit. To select the most influential variables, this research considers a comprehensive set of potential key variables to avoid underspecified model issue. The models established by this method are observed to have great power in predicting the building energy consumption. More importantly, through the variable selection procedure, the method identifies key variables that should be monitored to continuously improve the building energy performance. A simulation study and a case study are presented to show the effectiveness of the new approach. [ABSTRACT FROM AUTHOR]

Intermittent generation is challenging for the existing power systems, which have difficulties in following varying electricity production. To help the generation and demand be in balance, demand response (DR) provides one promising approach. Among many DR applications, residential micro-CHP systems can be advantageous, as these systems typically comprise storage in addition to a controllable cogeneration unit. Current state-of-the-art control strategies for residential micro-CHP systems utilize time-varying electricity price to optimally operate the system. Thus, such systems have the ability to export or import electricity when either one is beneficial. In this paper, we are interested in the imported electricity. The presence of the intermittent generation in a power system occasionally causes surplus electricity production, which the system should cope with. In such a case, it may be profitable to use electricity for heat production in a micro-CHP system. Therefore, this paper examines the potential overall benefit of integrating a resistor for storage heating in the system, which can act as a power sink when needed. A cost-minimizing control strategy is represented for the coordination of a residential micro-CHP system. Furthermore, its operation is tested in simulations, and the effect of the resistor is analyzed from the prosumer and power system point of view. [ABSTRACT FROM AUTHOR]

This paper uses a one-dimensional, physics-based model of a valve-regulated lead-acid (VRLA) battery to examine the impact of demand response on uninterruptible power supply (UPS) availability in a datacenter. Datacenters are facilities that provide services such as cloud computing, web search, etc. They are also large electricity consumers. An energy-efficient 15 MW datacenter, for instance, may pay $1 m per month for electricity. Datacenters often utilize VRLA batteries to ensure high reliability in serving their computational demand. This motivates the paper's central question: to what extent does the use of datacenter UPS batteries for demand response affect their availability for their primary purpose (namely, emergency power)? We address this question using a physics-based model of the coupled diffusion-reaction dynamics of VRLA batteries. We discretize this model using finite differences, and simulate it for different datacenter battery pack sizes. The results show that for a typical datacenter power demand profile, a VRLA battery pack sized for UPS functionality can provide demand response with only a minimal loss of UPS availability. [ABSTRACT FROM AUTHOR]

The light-to-electricity conversion efficiency is of critical interest for the laser power beaming technology. The coupled interaction between electricity conversion and heat generation determines the actual light-to-electricity conversion efficiency. In this paper, a coupled model is established to describe such coupled interaction and the equilibrium point of laser illuminated Photovoltaic (PV) cells. A simplified equation is developed to solve the equilibrium temperature and the electricity conversion efficiency of a thin-film PV cell evenly illuminated by a laser. Numerical examples are provided for typical conversion conditions and a preliminary parametric study with sensitivity analysis is carried out for future system design. [ABSTRACT FROM AUTHOR]

This paper compares the work by Mark Jacobson et al. of 100% renewables to the rigors of long-run utility system planning. This comparison to integrated resource planning (IRP) allows comparison between assumptions used by Jacobson to results from real-world planning studies. Seven criteria are proposed for designing such a study. [ABSTRACT FROM AUTHOR]

Severe environmental problems are resulted from the rapid growth of electricity consumption in China, since thermal power plant occupies the predominant position in power generation and releases large amount of pollutants and greenhouse gas. Electricity consumption of residential sector grows even faster than that of industrial sector in recent years. Residential sector is increasingly believed to have huge potential of energy saving and efficiency improvement. This paper aims at discovering the regional difference of household electricity consumption (HEC). First, a research framework is provided to identify the characteristics of HEC from the following three dimensions, consumption volatility, pattern similarity and temperature sensitivity. Households are divided into different groups in each city. The major household groups that account for the largest proportion are identified and then regarded as representatives of different cities. The fluctuation characteristics of their typical load profiles are compared and discussed in detail. Moreover, we design an Empty City Index (ECI) to further discuss the relationship between HEC and the population flow in special time intervals, for example, Chinese Spring Festival. The empirical study demonstrates that population outflow during Spring Festival is more obvious in wealthier city. Through k-means clustering, typical household groups as well as their typical load profiles are identified in each city. And the results show that under the current tiered pricing for household electricity (TPHE), nearly 10% households in each city still have potential for load reduction and load shifting. For the entire Jiangsu province, such households account for 12.5%. Policy implications and the applicability of this study are suggested and discussed respectively to government, household customers and electric power companies. [ABSTRACT FROM AUTHOR]

With the penetration of smart grid into factories, energy-efficient production scheduling has emerged as a promising method for industrial demand response. It shifts flexible production loads to lower-priced periods to reduce energy cost for the same production task. However, the existing methods only focus on integrating energy awareness to conventional production scheduling models. They ignore the labor cost which is shift-based and follows an opposite trend of energy cost. For instance, the energy cost is lower during nights while the labor cost is higher. Therefore, this paper proposes a method for energy-efficient and labor-aware production scheduling at the unit process level. This integrated scheduling model is mathematically formulated. Besides the state-based energy model and genetic algorithm-based optimization, a continuous-time shift accumulation heuristic is proposed to synchronize power states and labor shifts. In a case study of a Belgian plastic bottle manufacturer, a set of empirical sensitivity analyses were performed to investigate the impact of energy and labor awareness, as well as the production-related factors that influence the economic performance of a schedule. Furthermore, the demonstration was performed in 9 large-scale test instances, which encompass the cases where energy cost is minor, moderate, and major compared to the joint energy and labor cost. The results have proven that the ignorance of labor in existing energy-efficient production scheduling studies increases the joint energy and labor cost, although the energy cost can be minimized. To achieve effective production cost reduction, energy and labor awareness are recommended to be jointly considered in production scheduling. [ABSTRACT FROM AUTHOR]

This paper presents the results of a long-term, detailed electricity end-use analysis of hotel guest rooms in order to provide a better understanding of the electricity use patterns in guest rooms and their explanatory factors. To accomplish this, a one-year of sub-hourly electricity measurements for an entire floor and major electricity end uses of fourteen guest rooms in a case-study hotel in Washington, D.C. were analyzed along with coincident weather and occupancy data collection. A regression analysis was performed on the monthly and daily end-use electricity data to identify key variables affecting different electricity end uses of the guest rooms. As a result, it was found that a regression model with a single temperature variable did not fully describe the electricity use behaviors of hotel guest rooms. The addition of the occupancy variable significantly improved the model fit for both monthly and daily total floor electricity use and was statistically significant. A review of the electricity end uses showed strong correlations between outdoor temperature and the electricity end uses for space conditioning, especially the DOAS unit and the VRF outdoor unit. However, for other end uses such as plug loads, lighting, and the VRF indoor units, the observed correlations to outdoor temperature were not significant but were moderately correlated to occupancy. [ABSTRACT FROM AUTHOR]

Distributed energy resources present opportunities to serve grid and customer need through two-way power flow. New values, methods, and planning are required to efficiently interconnect and utilize DERs. This paper analyzes New York’s and California’s DER proceedings to provide a comparison of the similarities and differences between these and other states that will result in different levels of deployments and valuations of DERs. [ABSTRACT FROM AUTHOR]

The methods applied in the attempt to minimize electricity consumption waste as result of consumer's consumption patterns is of crucial concern for government, investors and even consumers. There is a need to identify the potential factors influencing electricity consumption pattern in order to encourage sustainable practices. To examine the electricity consumption pattern and their influencing factors, this study applied Fuzzy-DEMATEL model to examine the sustainable lifestyle factors of electricity consumption pattern in Nigeria. The originality of this paper lies in the proposed approach that integrates fuzzy logic and DEMATEL to evaluate sustainable lifestyles factors that influence industries electric consumption patterns for the first time in Nigeria. Firstly, 24 sustainable lifestyle factors were sourced from literature sources and grouped into 4 clusters of economic, political, environmental and socio-cultural criteria. Two sets of questionnaires were distributed to 30 experts who comprise of managers and senior technicians in Nigerian Power Industry. The results show that the critical factors which influence industries electric consumption patterns are corruption, inadequate planning and design, electricity theft, literacy level, population demography and lifestyle attitude. The implication for decision makers is that in improving power consumption pattern in the Nigerian industries; emphasis should be placed on the critical sustainable lifestyle factors. Thus, corruption should be minimized, planning and design adequately implemented, theft of electricity curbed while improving literacy level and lifestyles of the masses to improve electric consumption patterns. [ABSTRACT FROM AUTHOR]

• Earlier phase-out of coal is not the best answer to achieve energy transition. • The 2050 plan and historical RES investments maxima are not enough to reach climate neutrality. • What is the effect of regional RES limits over Nationwide RES limits. • Where will be the states in Germany having major RES deployment. • Myopic optimization reveals new challenges for energy policy. This paper will introduce the open-source model MyPyPSA-Ger, a myopic optimization model developed to represent the German energy system with a detailed mapping of the electricity sector, on a highly disaggregated level, spatially and temporally, with regional differences and investment limitations. Furthermore, this paper will give new outlooks on the German federal government 2050 emissions goals of the electricity sector to become greenhouse gas neutral by proposing new CO 2 allowance strategies. Moreover, the regional differences in Germany will be discussed, their role and impact on the energy transition, and which regions and states will drive the renewable energy utilization forward. Following a scenario-based analysis, the results point out the major keystones of the energy transition path from 2020 to 2050. Solar, onshore wind, and gas-fired power plants will play a fundamental role in the future electricity systems. Biomass, run of river, and offshore wind technologies will be utilized in the system as base-load generation technologies. Solar and onshore wind will be installed almost everywhere in Germany. However, due to the nature of Germany's weather and geographical features, the southern and northern regions will play a more important role in the energy transition. Higher CO 2 allowance costs will help achieve the 1.5-degree-target of the electricity system and will allow for a rapid transition. Moreover, the more expensive, and the earlier the CO 2 tax is applied to the system, the less it will cost for the energy transition, and the more emissions will be saved throughout the transition period. An earlier phase-out of coal power plants is not necessary with high CO 2 taxes, due to the change in power plant's unit commitment, as they prioritize gas before coal power plants. Having moderate to low CO 2 allowance cost or no clear transition policy will be more expensive and the CO 2 budget will be exceeded. Nonetheless, even with no policy, renewables still dominate the energy mix of the future. However, maintaining the maximum historical installation rates of both national and regional levels, with the current emissions reduction strategy, will not be enough to reach the level of climate-neutral electricity system. Therefore, national and regional installation requirements to achieve the federal government emission reduction goals are determined. Energy strategies and decision makers will have to resolve great challenges in order to stay in line with the 1.5-degree-target. [ABSTRACT FROM AUTHOR]

This paper investigates factors associated with variation in daily total (electricity and gas) energy consumption in domestic buildings using linked pre-COVID-19 smart meter, weather, building thermal characteristics, and socio-technical survey data covering appliance ownership, demographics, behaviours, and attitudes for two nested sub-samples of 1418 and 682 British households selected from the Smart Energy Research Laboratory (SERL) Observatory panel. Linear mixed effects modelling resulted in adjusted R2 between 63% and 80% depending on sample size and combinations of contextual data used. Increased daily energy consumption was significantly associated (p -value < 0.05, VIF < 5) with: households living in buildings with more rooms and bedrooms, that are older, more detached, have air-conditioning, and experience colder (more heating degree days) or less sunny weather; households with more adult occupants, more children, older adult occupants, higher heating temperature setpoints, and that do not try to save energy. The results demonstrate the value of smart meter data linked with contextual data for improving understanding of energy demand in British housing. Accredited UK researchers are invited to apply to access the data, which has recently been updated to include over 13,000 households from across Great Britain. This paper provides guidance on appropriate methods to use when analysing the data. [ABSTRACT FROM AUTHOR]

[Display omitted] • Easy-to-use machine learning model for hourly renewable production and load profile modeling. • Novel variance-correction method to improve the reliability of the modeled profiles. • Probabilistic energy supply simulation based on 42 years of meteorological data. • Detailed evaluation and novel categorization of Dunkelflaute events. • Uncertainty analysis of the share of photovoltaic, wind and nuclear electricity. The increasing penetration of weather-dependent renewable energy generation calls for high-resolution modeling of the possible future energy mixes to support the energy strategy and policy decisions. Simulations relying on the data of only a few years, however, are not only unreliable but also unable to quantify the uncertainty resulting from the year-to-year variability of the weather conditions. This paper presents a new method based on artificial neural networks that map the relationship between the weather data from atmospheric reanalysis and the photovoltaic and wind power generation and the electric load. The regression models are trained based on the data of the last 3 to 6 years, and then they are used to generate synthetic hourly renewable power production and load profiles for 42 years as an ensemble representation of possible outcomes in the future. The modeled profiles are post-processed by a novel variance-correction method that ensures the statistical similarity of the modeled and real data and thus the reliability of the simulation based on these profiles. The probabilistic modeling enabled by the proposed approach is demonstrated in two practical applications for the Hungarian electricity system. First, the so-called Dunkelflaute (dark doldrum) events, are analyzed and categorized. The results reveal that Dunkelflaute events most frequently happen on summer nights, and their typical duration is less than 12 h, even though events ranging through multiple days are also possible. Second, the renewable energy supply is modeled for different photovoltaic and wind turbine installed capacities. Based on our calculations, the share of the annual power consumption that weather-dependent renewable generation can directly cover is up to 60% in Hungary, even with very high installed capacities and overproduction, and higher carbon-free electricity share targets can only be achieved with an energy mix containing nuclear power and renewable sources. The proposed method can easily be extended to other countries and used in more detailed electricity market simulations in the future. [ABSTRACT FROM AUTHOR]

Herein, a solar-driven, CO 2 -fed rechargeable molten salt carbon battery is reported, which has the characteristics of capacity-tunable and noble metal catalyst-free, and continuously captures CO 2 and converts it into fuel (CO). The battery uses mixed molten carbonate as the electrolyte, which has excellent reversible electrode kinetics and superior ionic conductivity, providing the rechargeable molten salt battery excellent storage capacity and power capability. The battery is capable of continuously absorbing CO 2 from the atmosphere and converting it into carbon and carbon monoxide when it is charged. The carbon accumulates at the cathode and is used to discharge, thus producing carbon monoxide, constituting a battery capable of continuously converting carbon dioxide into carbon monoxide and storing electrical energy. The battery is perfectly matched to solar thermal and solar electricity and can be used as a novel method of storing solar energy. This paper investigates the battery performance under four electrolyte conditions. The results show that the battery has the most superior discharge performance when using a Li-Na-K-Cs-Li 2 O mixed electrolyte, maintaining ultra-high faradaic efficiency (100%) for 20 cycles. This work provides a new horizon for CO 2 conversion and solar energy utilization. [Display omitted] • A solar battery complex is novelty built to yield and store fuel and electricity. • The battery is fed by solar energy and CO 2. • The battery can adjust the production of CO and electricity. • The battery achieved high process efficiency in multiple cycles. [ABSTRACT FROM AUTHOR]

The increasing population living in cities is leading to higher resource utilization, which strains the cities’ ability to focus on sustainability. Adoption of different technologies can transform cities into “smart cities” that utilize energy in a more efficiently. This paper presents results from a technology assessment tool developed together with six mid-sized European cities. The main areas of focus have been evaluated based on the cities’ priorities: transportation (both public and private) and consumers’ perspectives on the use of smart electricity meters. The use of electric vehicles in Sweden, and a techno-economic evaluation of biogas-derived biomethane and electricity use in public transportation have been analyzed. The main conclusions show an overall higher efficiency for biogas-derived electricity use in electric buses; a need for higher consumer engagement through more detailed information provision for both increasing EV market penetration and electricity savings; and a need to establish detailed technology assessments for successful technology adoption in cities. [ABSTRACT FROM AUTHOR]

A rapid growth in interest in self-consumption of electricity generated by rooftop photovoltaic (PV) systems has been observed in recent years. This is fueled by decreasing levelized cost of electricity and feed-in tariffs for PV-systems as well as by increasing electricity prices, especially in the residential sector. Besides PV-battery systems, electrical heat pumps are a promising measure to increase self-consumption of electricity generated by distributed generation technologies in residential dwellings. However, little scientific research on the ability of heat pump systems to increase self-consumption of PV-electricity has been carried out so far. Therefore in this paper a mixed integer linear programing model for the optimal operation, system configuration and sizing for heat pump based house energy systems is developed and applied under different scenario conditions. The results show that the dimensioning of the heat pump is hardly influenced by the scenario assumptions, whereas the optimal sizing of PV is strongly dependent on the scenarios and in particular on the electrical load profiles. Over all scenario variations, the sizing of electrical and thermal storages is demand-driven and hardly any interdependencies with respect to optimal sizing can be observed between the different storage technologies. The availability of feed-in-tariffs generally yields large PV systems with high levels of self-sufficiency but low levels of self-consumption. Stricter feed-in-limitations lead to a reduction of the optimal PV systems size, but not to an increase in the optimal size of storage technologies. Over a wide range of base scenarios and scenario variations PV systems as well as additional flexibility options are part of the optimal system configuration, rendering it a promising and robust measure to decrease the operating cost of heat pump systems in the future. The results obtained in this study can provide valuable guidelines for manufacturers, installers and end-customers for the design of cost-effective self-consumption driven heat pump systems in the future. [ABSTRACT FROM AUTHOR]

A thermoelectric generator is one of the key candidates for the renewable energy devices, which directly converts waste heat into electricity. The wide applications of the device are suppressed because of the device low efficiency. In this paper, a new innovative design of the thermoelectric generator incorporating the extended pin with segmented pin configuration is introduced. The new design allows the device operating at two different cold junction temperatures. The maximum efficiency and the output power for the innovative design of thermoelectric device are formulated. The performance of the thermoelectric device is evaluated using the operating parameters such as the hot and cold junction temperatures in terms of temperature ratio, and external load resistance ratio. The reveals that the innovative design improves the maximum efficiency and output power of the thermoelectric generator. Increasing the cold junction temperature difference increases the device maximum efficiency by 3.5–6.2%. The maximum device output power and maximum thermal efficiency occur at different values of external load parameter. However, the reduction in the efficiency is considerably small for the external load parameter maximizing the device output work. [ABSTRACT FROM AUTHOR]

With the increasing negative effects of fossil fuel combustion, many countries have paid more attention on supporting environmentally friendly energy generation, particularly renewable and sustainable energy sources (RES). However, renewables still cannot economically compete with fossil-fuels and are facing new challenges. Appropriate electricity pricing mechanism plays a vital role on mitigating the stress of limited fossil fuels and can promote renewable energy consumption. As one major type of renewable energy, wind power has been globally promoted. In order to promote the application of wind power, appropriate wind power prices (WPP) should be established by considering the local conditions. This paper targets such a field and conducts a bibliometric and network analysis based on the data from Scopus. The results show that the numbers of total related publications are gradually increasing, with the US as the leading country. European countries also have outstanding achievements. Moreover, both the most cited articles and keywords distribution offer future research directions. In general, this study provides valuable insights to both wind power researchers and practitioners. [ABSTRACT FROM AUTHOR]

Coal, a major source for electricity generation in many regions, is expected to keep its position among global energy sources in the next two decades. There are significant life cycle impacts of coal used for electricity generation such as heavy metal emissions and acidification. Impacts related to the combustion phase have so far been the focus of attention and are better documented than other phases of the life cycle. Relatively less attention has been paid to impacts of mining and coal preparation processes at mining sites due to generation of large quantities of wastewater, particulate matter and heavy metal emissions, and the use of heavy machinery which is energy-intensive. To this date, there are only a few studies specifically addressing impacts associated with the mining and coal preparation phases in the open literature. To further advance our understanding of the scale of impacts during these phases, this paper presents a life cycle assessment of lignite from extraction phase to the delivery to the power plant based on analysis of high quality data from twelve lignite mining sites that serve major lignite power plants in Turkey. For impacts on land use, four indicators which include erosion resistance, mechanical filtration, groundwater replenishment loss and biotic production, are estimated. Following life cycle impact assessment, two environmental performance improvement alternatives that can be easily implemented in mining sites are evaluated to quantify the potential degree of improvement. These alternatives include dewatering of the slurry waste and recovery of coal, and using biodiesel mixed fuel and watering roads to reduce dust formation. The analyses show that 40% improvement can be realized for ecotoxicity potential to water category through simple changes in mining practices. [ABSTRACT FROM AUTHOR]

This paper addresses an energy-conscious single machine scheduling problem under time-of-use (TOU) or time-dependent electricity tariffs, in which electricity prices may vary from hour to hour throughout a day. The key issue is to assign a set of jobs to available time periods with different electricity prices so as to minimize the total electricity cost required for processing the jobs. The main contribution of this work is two-fold. First, a new continuous-time mixed-integer linear programming (MILP) model is proposed for the problem. Second, an efficient greedy insertion heuristic is developed. In the proposed heuristic, the jobs are inserted into the available time periods one after another in non-increasing order of their electricity consumption rates and each job is inserted into the time period(s) with minimum electricity cost. A real-life case study from a Chinese company reveals that the total electricity cost can be reduced by about 30% with the proposed algorithm. Computational experiment on randomly generated instances also demonstrates that our algorithm can yield high-quality solutions with low electricity costs within dozens of seconds for large-scale single machine scheduling problems with 5000 jobs. The algorithm can be applied by production managers to scheduling jobs on a single machine under TOU electricity tariffs to save electricity costs. [ABSTRACT FROM AUTHOR]

Electric and hydrogen vehicles can help decarbonize heavy duty vehicles (HDV). Few studies examine how to meet energy requirements of decarbonized HDVs, and all assume electricity will come from centralized systems. However, decarbonized HDVs could significantly increase energy demands in areas with limited transmission access, potentially favoring deployment of distributed energy resources (DERs). In this paper, we develop an optimization-based techno-economic model that minimizes costs of meeting HDV energy demands by optimizing investments in and operations of DERs, investments in transmission interconnections, and wholesale electricity purchases. We apply it to a modeled U.S. dataset of electric HDV charging demands to quantify the deployment and value potential of three DERs — solar, batteries, and nuclear small modular reactors (SMRs) in the year 2040. For fleets of 100% electric HDVs to 60% electric and 40% hydrogen HDVs, DERs are deployed at 78% to 95% of all charging stations and meet between 24% to 30% of total HDV energy demand. Investments in DERs reduce annual costs by $647 million to $1.9 billion across all stations, while individual stations can save $20 million to over $100 million annually. SMRs make up over 99% of total deployed DER capacity, indicating significant potential for SMR deployment in this emerging market. Widespread DER deployment is robust to capital cost uncertainty in SMRs and transmission lines, wholesale electricity prices, and other factors. [Display omitted] • We create a model to quantify the value of distributed energy resources for meeting decarbonized heavy duty vehicle energy demand. • Distributed energy resources are deployed at 78% to 95% of all charging stations. • Annual cost savings from distributed energy resources investments can be as high as $1.9 billion nation-wide, and $104 million at an individual station. • Small modular microreactors supply approximately between 24% and 30% of heavy duty vehicle energy demand annually. [ABSTRACT FROM AUTHOR]

Russia is frequently referred to as a country with substantial energy efficiency and renewable energy potential. In 2000–2008 energy-gross domestic product (GDP) ratios were improved by 35%, however, the contribution of technological progress accounts for only 1% of the energy-GDP ratio reduction. At the same time, although new policy mechanisms to stimulate renewable energy development have been recently introduced, renewable technology deployment has not yet taken off. Economic theory suggests that there is no better incentive for industry development than cost signals. This paper adapts the levelised cost of energy methodology to examine the cost structures associated with electricity generation by conventional and new technology types for a Russian region (Moscow). The model, run for two fuel price scenarios, allowed us to conclude that the regional energy supply system is heavily dependent on the natural gas price and that the diversification provided by technology development will be beneficial for the energy security of the region. We conclude that new and renewable technologies become cost-effective for electricity generation as domestic natural gas prices reach parity with export prices. However, strong political and financial support is needed to boost technological development and renewables application in Russia. [ABSTRACT FROM AUTHOR]

Energy-intensive enterprises (EIEs) are typical kinds of industrial loads. They consume large amounts of electricity, and are very sensitive to electricity prices. Moreover, they have very good schedulability: they own various adjustable devices and dispatchable self-owned generation units, and have great flexibility in making production decisions. The characteristics of EIEs make them potentially ideal for coordinating with power systems and gaining a win–win situation, especially when the renewable energy penetration rate is high. However, problems still remain as to how to organize this coordination. In this paper, we design a decomposed coordinative scheduling (DCS) approach in which independent EIEs and the system exchange information iteratively to achieve final settlements. Based on a general modeling of EIEs, we introduce a mathematical formulation for DCS. The corresponding algorithm is also provided. We compare DCS to other scheduling approaches in case studies. It shows that DCS can significantly improve the benefits of the two sides without harming the privacy of EIEs. [ABSTRACT FROM AUTHOR]

In response to growing concerns on the environment and the need to save energy, working machine manufacturers have started to consider hybridization of their products. This paper presents a systematic modelling approach to identify the most promising hybrid configurations for existing skid loaders. This approach involves accurate modelling of the existing version (fitted with a conventional power train), model validation by matching the simulation results with experimental tests, design by modelling of different hybrid configurations and corresponding cost/benefit analysis. [ABSTRACT FROM AUTHOR]

Time-of-use (TOU) pricing serves as a cost-effective way to realize electricity demand response, which aims at relieving peak demand. Customer participation is critical to the success of TOU pricing programs. To fulfill the potential of such programs, customers must be able to access electricity tariffs and understand their terms. This paper reports a survey of 43 TOU pricing programs targeting industrial customers offered by U.S. utilities. This work is inspired by and complements the Federal Energy Regulatory Commission survey of demand response in the electric power industry, highlighting the interpretation of key pricing components and specific characteristics of TOU tariff sheets collected from public sources. The case studies examine various industrial scenarios to predict electricity cost savings when customers are facing the transition from flat rates to TOU pricing. The analysis results show that the cost savings vary enormously, ranging from −72.0% to +82.6%, depending on specific utility programs and switching strategies involved. Such information is useful for customers to determine whether to participate in a TOU pricing program. Key findings and implications for industrial customers, utilities, and regulatory agencies are also discussed. [ABSTRACT FROM AUTHOR]

Environmental performance (EP) uncertainties span a number of energy technology options, and pose planning risk when the energy system is subject to environmental constraints. This paper presents two approaches to integrating EP uncertainty into the long-term energy planning framework. The methodologies consider stochastic EP metrics across multiple energy technology options, and produce a development strategy that hedges against the risk of exceeding environmental targets. Both methods are compared within a case study of emission-constrained electricity generation planning in British Columbia, Canada. The analysis provides important insight into model formulation and the interactions with concurrent environmental policy uncertainties. EP risk is found to be particularly important in situations where environmental constraints become increasingly stringent. Model results indicate allocation of a modest risk premium in these situations can provide valuable hedging against EP risk. [ABSTRACT FROM AUTHOR]

Approximately 27% of the European energy consumption is caused by the domestic sector, where 19% of the end use energy demand is caused by electric devices. To investigate the factors at play, a stochastic bottom-up model for the generation of electric load profiles is introduced in this paper. The model is designed for investigating the effects of occupant behaviour, appliance stock and efficiency on the electric load profile of an individual household. For each activity of a person in the household, an electric appliance is used, and its electricity consumption is linked to measured electric load traces with a time resolution of 10 s. Probability distributions are incorporated for when and how often an appliance is operated. Duration of operation is given as probability density conditional on the start time. Shared use of an appliance by multiple persons is included in the model. Seasonal effects are considered by using changing probability sets during the course of the year. For validation, seven subgroups, which reflect typical household configurations, were formed and tested against measured field data from 430 households in 9 different cities across Germany. The results showed an accuracy of 91% and a correlation of up to 0.98. [ABSTRACT FROM AUTHOR]

Electric bills consist of a cost related to the consumed energy and a cost related to the maximum demanded power. This latter part usually accounts for approximately 25–40% of the bill. Demanded power by big consumers is measured in real time and electric companies highly penalise them if the maximum demanded power (along the billing period) exceeds the contracted power by the consumer. In this paper we propose a new method that, given the demanded power of close consumers for a time window (power profile), power costs are reduced by re-allocating the demanded power among consumers in order to keep all of them below or equal to their contracted power. We also propose and analyse some strategies to set a preference when not all power profiles can be kept below the contracted power. We tested this method using real-based simulated power profiles of eight different business buildings located in Girona and the power cost reduction achieved reached approximately 20%. [ABSTRACT FROM AUTHOR]

Life cycle environmental performance of lignocellulosic ethanol produced through different production pathways and having different co-products has rarely been reported in the literature, with most studies focusing on a single pre-treatment and single co-product (electricity). The aim of this paper is to understand the life cycle energy use and greenhouse gas (GHG) emissions implications of alternative pre-treatment technologies (dilute acid hydrolysis, ammonia fiber expansion and autohydrolysis) and co-products (electricity, pellet, protein and xylitol) through developing a consistent life cycle framework for ethanol production from corn stover. Results show that the choices of pre-treatment technology and co-product(s) can impact ethanol yield, life cycle energy use and GHG emissions. Dilute acid pathways generally exhibit higher ethanol yields (20-25%) and lower net total energy use (15-25%) than the autohydrolysis and ammonia fiber expansion pathways. Similar GHG emissions are found for the pre-treatment technologies when producing the same co-product. Xylitol co-production diverts xylose from ethanol production and results in the lowest ethanol yield (200 L per dry t of stover). Compared to producing only electricity as a co-product, the co-production of pellets and xylitol decreases life cycle GHG emissions associated with the ethanol, while protein production increases emissions. The life cycle GHG emissions of blended ethanol fuel (85% denatured ethanol by volume) range from -38.5-37.2 g CO2 eq/MJ of fuel produced, reducing emissions by 61-141% relative to gasoline. All ethanol pathways result in major reductions of fossil energy use relative to gasoline, at least by 47%. [ABSTRACT FROM AUTHOR]

Recent papers argue that the energy return on energy invested (EROI) for renewable electricity technologies and systems may be so low that the transition from fossil fuelled to renewable electricity may displace investment in other important economic sectors. For the case of large-scale electricity supply, we draw upon insights from Net Energy Analysis and renewable energy engineering to examine critically some assumptions, data and arguments in these papers, focussing on regions in which wind and solar can provide the majority of electricity. We show that the above claim is based on outdated data on EROIs, on failing to consider the energy efficiency advantages of transitioning away from fuel combustion and on overestimates of storage requirements. EROIs of wind and solar photovoltaics, which can provide the vast majority of electricity and indeed of all energy in the future, are generally high (≥ 10) and increasing. The impact of storage on EROI depends on the quantities and types of storage adopted and their operational strategies. In the regions considered in this paper, the quantity of storage required to maintain generation reliability is relatively small. • EROIs of wind and solar technologies are generally high and increasing. • Impact of storage on system EROI depends on quantity, types and use of storage. • Power systems with high solar and wind penetrations need small storage capacities. • Energy conversion efficiency greatly increases EROI of macro-economic system. [ABSTRACT FROM AUTHOR]

• Bottom-up estimation tool to develop electric public transport (EPT) charging loads. • Uses GTFS public data and can be scaled to hundreds of cities/states globally. • EPT could add 17% to critical peak demand on some distribution zone substations. Battery electrified public transit (BEPT), including buses and passenger ferries, is a promising solution to transport-related climate emissions and urban air pollution, but introduces potentially challenging large, coincident demand in the low-voltage distribution network. This paper presents a tool for estimating the energy and charging demand of electrified public transit using public data that is available in over 150 cities/states globally and demonstrates it in two case studies. The tool applies heuristic vehicle scheduling to existing public General Transit Feed Specification data to model the charging profiles and electricity consumption of BEPT for various charging regimes across different geographical scales. For the case study of New South Wales, Australia, the impacts of BEPT are most significant at the low-voltage network, where adding a battery-electric bus depot was found to increase the annual critical peak demand at the local zone substation in Summer by up to 17% while exacerbating demand during the daily evening peak period by 20–30%. Across the entire state of NSW, a full transition to BEPT increases annual electricity consumption by 1.28–1.34% and peak daily demand by 1–3%. [ABSTRACT FROM AUTHOR]

• Temporal variation of environmental impacts related to buildings electricity consumption is modelled. • Short term (e.g. peak demand) and long term aspects (e.g. variation of production capacities) are accounted for. • The method was applied in a case study, addressing uncertainties. • Consequences of electrification in the building sector can be evaluated. • Electrification does not necessarily reduce greenhouse gases emissions. The building stock is a major contributor to energy consumption and greenhouse gases emissions (GHG), which can be evaluated using life cycle assessment (LCA). Electrification of buildings, e.g. replacing fuel and gas boilers with heat pumps, in order to reduce these emissions is often seen as an option, but this will have short term effects by increasing peak demand, and long term effects by requiring more electricity production capacities. In this paper, a methodology to account for such interaction in LCA is presented. It connects three models addressing: market allocation on a national scale over a long term period, short term variation (i.e. seasonal, daily and hourly) of the electricity mix also on a national scale, and building energy simulation at the scale of one building. This methodology has been applied to a case study including a sample of buildings in the French context, but it can be used in other countries. Six buildings have been studied over 100 years considering 50 energy transition scenarios. Results show that the environmental impacts vary more depending on the scenarios than on the types of the building. Marginal mixes considered in consequential LCA are mainly composed of coal, gas, nuclear and peak technology production which explains the highest values of the different impacts compared to average mixes used in attributional LCA. This approach allows to address uncertainties related to electricity production. [ABSTRACT FROM AUTHOR]

• End-use of electricity consumption in residential buildings in Gulf Cooperation Council countries was presented. • Discrepancies in certain indicators have been observed by the end-use of electricity consumption. • Improving the efficiency of air conditioners and thermal insulation of the walls are the best measures to conserve electricity in residential buildings. • The results can be used in areas with a similar climate, living standard, buildings types, and the inhabitants' behavior. Energy conservation and improvement in energy efficiency yield multiple technical, economic, environmental, and social benefits and realize comfort and sustainability of residential buildings (RBs). This review paper presents the end-use of electricity consumption in RBs and the results of economic analysis. In addition, a description and discussion of studies on energy conservation measures, Net Zero Energy Building, and renewable energy in RBs are discussed. The percentage of end-used energy consumption for different purposes obtained in many studies are often not consistent. Moreover, discrepancies in certain indicators have been observed, necessitating the need to clarify the factors in the consumption of electricity in RBs. Further, a few studies used the term energy consumption in buildings (total or electrical); however, specifying the type of buildings to identify and use the necessary indicators of energy consumption in each type of building are desirable. Furthermore, most studies emphasized improving the efficiency of air conditioners and thermal insulation of the walls as these are the best measures to conserve electricity. [ABSTRACT FROM AUTHOR]

Solar photovoltaic systems have been growing in popularity in prosumer households as a means of increasing the share of renewable energy and decreasing electricity import. The available self-consumption is, however, limited by a temporal supply–demand imbalance. In this paper, options for improving the self-consumption of a prosumer household are studied by using three-year data sets of electricity import and export data from two distinct, real-life cases from Finland. Two separate approaches are analysed: the use of energy storages, physical or monetary, and changing of the electricity metering method. A switch of the electricity metering method from instant phasewise to hourly net metering was found to increase the self-sufficiency by about 3 to 5 percentage points and have an annual monetary benefit of a few tens of euros when a network storage was used. Considering the energy storage methods under study, the network energy storage was found to be more economically feasible than a physical or a virtual battery energy storage, even though a physical battery storage could increase the self-sufficiency as much as by 30 percentage points with a storage capacity of 20 kWh. The studied virtual battery concept was found to limit the profitable solar photovoltaic plant size if high enough storage capacity was not provided. When a physical battery energy storage is used, switching to hourly net metering does not add value to the system. A significant decrease in the system cost is required for a physical battery energy storage to be economically competitive in northern climate conditions. • Measured electricity flow is used to study energy storage concepts in Finland. • Change of electricity metering method can increase self-sufficiency up to 5 p.p. • Physical battery with 20 kWh capacity can increase self-sufficiency up to 30 p.p. • Network storage is currently the most profitable concept from the studied options. • The physical battery cycle rate will remain low even with small battery sizes. [ABSTRACT FROM AUTHOR]

Prosuming combines energy production and consumption on a decentralised level and can contribute to expanding renewable energies and integrating intermittent energy in a decarbonised energy system. This is only one way that conventional electricity consumers can become active agents in electricity systems, but the current literature lacks a comprehensive typology of this field. Furthermore, the literature on prosuming focuses on households and largely neglects organisations. Addressing these two gaps in the literature, this paper first suggests a typology of eight archetypes that encompass electricity production, demand management, and storage as dimensions. Second, it maps the decision-making process for prosuming infrastructure in small and medium-sized organisations based on 14 in-depth interviews from Germany. To better understand the implementation processes of prosuming infrastructure and to identify critical success factors for organisations to become active electricity agents, it outlines a comprehensive collection of motives, drivers and barriers that are shaped by the organisational context and play a significant role when organisations become energy prosumers. [ABSTRACT FROM AUTHOR]

• A hybrid model with a combination of existing wired, wireless, and cellular technologies to guarantee real-time monitoring at low cost. • Minimizing the time delay at minimum installation cost and to determine the total energy consumption associated with 5G technology. • Investigate the performance of both linear and direct link network architectures to support the future SG. • Find the optimal location for placement of cellular enabled transmission towers and show the relationship between time delay and total energy consumption by feasible regions. [Display omitted] Smart Grid (SG) faces several challenges to efficiently transfer the power generated to power consumers. So, a robust monitoring tool is required to monitor the transmission lines in order to ensure security of the resource. This power transmission monitoring is a good example of ultra reliable low latency application of 5G with the aim to provide quality of service and quality of experience. The primary objective of this study is to design a wireless network for real-time monitoring of transmission lines to take the preventive measures. In this paper, we present an Internet of Things enabled real-time transmission line monitoring system comprising of wireless, wired, and cellular technologies. The objective is to minimize the time delay at minimum installation cost of the network. In our proposed model, all the sensors are powered by Renewable Energy Resources (RES) like wind and solar energy, etc. The placement problem is formulated to determine the location of cellular enabled transmission towers. Moreover, feasible regions are also calculated to show the relationship between time delay and energy consumption. Results show that proposed model provides efficient solutions and takes less time for data transmission and is more energy efficient. [ABSTRACT FROM AUTHOR]