Search

With the advancement of distributed energy systems, energy sharing has emerged as a crucial trading mechanism on the demand-side, enabling participants to share self-generated energy with their neighbors through contractual agreements. Nevertheless, a comprehensive analysis is needed to balance the benefits among energy prosumers, given their distinct characteristics. This paper proposes a multi-energy sharing framework with flexible demand-side management based on full cooperation. We evaluate the economic and environmental performance of sharing participants, considering the impacts of different operation modes and diverse demand profiles. Cooperative game theory is employed to maximize the social welfare of all participants, with the different allocation schemes are used to distribute the cooperative surplus among stakeholders. The fairness of these schemes is assessed to ensure the feasibility and equity of the proposed framework. The results indicate that the centralized multi-energy sharing framework yields win-win outcomes for both individual and collective interests. Specifically, the total cost and carbon dioxide emissions of prosumers in the shared scenario are reduced by 13% and 16%, respectively, compared to individual operation scenarios. Moreover, thermal energy management is critically important for energy sharing. Furthermore, varying combinations of building types significantly affect cost savings and emission reductions, influencing energy sharing patterns and quantities. [ABSTRACT FROM AUTHOR]

In recent years, Internet of Things (IoT) applications have become across-the-board and are used by most smart device users. Wired Communication, Bluetooth, radio frequency (RF), RS485/Modbus, and zonal intercommunication global standard (ZigBee) can be used as IoT communication methods. The low delay times and ability to control homes from outside the building via the Internet are the main reasons wireless fidelity (Wi-Fi) communication is preferred. Commercially produced devices generally use their unique interfaces. The devices do not allow integration to form an intelligent home automation and demand-side energy management system. In addition, the high cost of most commercial products creates barriers for users. In this study, a local home automation server (LHAS) was created subject to low cost. Smart devices connected to the server through a Wi-Fi network were designed and implemented. The primary purpose of the design is to create an IoT network to form an LHAS. The IoT network will learn the energy consumption behavior of users for future Smart Grids. The designed intelligent devices can provide all the necessary measurements and control of houses. The open-source software Home Assistant (Hassio) was used to create the LHAS. Espressif systems (ESP) series microcontrollers (µCs) were chosen to design intelligent devices. ESP-01, NodeMCU, and ESP-32, the most widely used ESP models, were preferred. A convolutional neural network (CNN)/long short-term memory (LSTM) neural network was designed, and analysis was performed to learn the consumption behavior of residential users. [ABSTRACT FROM AUTHOR]

With the advancement of distributed energy systems, energy sharing has emerged as a crucial trading mechanism on the demand-side, enabling participants to share self-generated energy with their neighbors through contractual agreements. Nevertheless, a comprehensive analysis is needed to balance the benefits among energy prosumers, given their distinct characteristics. This paper proposes a multi-energy sharing framework with flexible demand-side management based on full cooperation. We evaluate the economic and environmental performance of sharing participants, considering the impacts of different operation modes and diverse demand profiles. Cooperative game theory is employed to maximize the social welfare of all participants, with the different allocation schemes are used to distribute the cooperative surplus among stakeholders. The fairness of these schemes is assessed to ensure the feasibility and equity of the proposed framework. The results indicate that the centralized multi-energy sharing framework yields win-win outcomes for both individual and collective interests. Specifically, the total cost and carbon dioxide emissions of prosumers in the shared scenario are reduced by 13% and 16%, respectively, compared to individual operation scenarios. Moreover, thermal energy management is critically important for energy sharing. Furthermore, varying combinations of building types significantly affect cost savings and emission reductions, influencing energy sharing patterns and quantities.

This research aims to optimize and compare the annual costs of energy services in buildings with critical loads and analyze case studies for hospitals and higher education institutions in the United States. Besides electricity and natural gas costs, the study considers all the infrastructure costs of capital amortization and maintenance. In addition, it studies energy resilience improvement due to distributed generation, including solar photovoltaic, solar thermal, internal combustion engine, and fuel cell sources. The optimization considers the electrical consumption, the heating and cooling demands, and the operational strategy of the energy storage systems. To simulate real scenarios, energy tariffs were modeled and considered, and final optimization results were produced. Some of the microgrid load was considered critical to model resilience benefits. The results show that if favorable energy tariffs are applied, the benefits of increasing energy resilience represent a novel market with high potential in facilities with significant critical loads. This methodology can be used in similar scenarios, adapting each particular load profile and critical load to provide a combined optimal solution regarding resilience and economic benefits. [ABSTRACT FROM AUTHOR]

The advancement of technology has significantly improved energy measurement systems. Recent investment in smart meters has enabled companies and researchers to access data with the highest possible temporal disaggregation, on a minute-by-minute basis. This research aimed to obtain data with the highest possible temporal and spatial disaggregation. This was achieved through a process of energy consumption measurements for six devices within seven houses, located in different communes (counties) of the Metropolitan Region of Chile. From this process, a data panel of energy consumption of six devices was constructed for each household, observed in two temporal windows: one quarterly (750,000+ observations) and another semi-annual (1,500,000+ observations). By applying a panel data econometric model with fixed effects, calendar-temporal patterns that help explain energy consumption in each of these two windows have been studied, obtaining explanations of over 80% in some cases, and very low in others. Sensitivity analyses show that the results are robust in a short-term temporal horizon and provide a practical methodology for analyzing energy consumption determinants and load profiles with panel data. Moreover, to the authors' knowledge, these are the first results obtained with data from Chile. Therefore, the findings provide key information for the planning of production, design of energy market mechanisms, tariff regulation, and other relevant energy policies, both at local and global levels. [ABSTRACT FROM AUTHOR]

The introduction of smart meters and time-use survey data is helping decision makers to understand the residential electricity consumption behaviour behind load profiles. However, it can be difficult to obtain the actual detailed consumption data due to privacy issues. Synthesising residential electricity consumption profiles may be an alternative way to develop synthetic load profiles that initially starts by reviewing the existing synthetic load profile methods. The purpose of this review is to identify the recent methods for synthesising residential electricity load profiles by conducting a rigorous standalone literature review. This review study has been applied and presented transparently and is replicable by other researchers. The review has answered the following research questions: the definition, concept and roles of residential electricity load profile and synthesised data; recent approaches and methods; research purposes; applicable simulations and validation methods of the final selected studies. The results show that the most applied approach in modelling residential electricity load profiles is the bottom-up approach. As it is detailed, it suitable to reflect the local residential behaviour in electricity consumption. Consequently, it is more complex to develop and calibrate the model as identified in the results. Bottom-up models are more powerful in analysing energy consumptions that focus on behavioural patterns, dwelling profiles and control strategies. [ABSTRACT FROM AUTHOR]

This research aims to optimize and compare the annual costs of energy services in buildings with critical loads and analyze case studies for hospitals and higher education institutions in the United States. Besides electricity and natural gas costs, the study considers all the infrastructure costs of capital amortization and maintenance. In addition, it studies energy resilience improvement due to distributed generation, including solar photovoltaic, solar thermal, internal combustion engine, and fuel cell sources. The optimization considers the electrical consumption, the heating and cooling demands, and the operational strategy of the energy storage systems. To simulate real scenarios, energy tariffs were modeled and considered, and final optimization results were produced. Some of the microgrid load was considered critical to model resilience benefits. The results show that if favorable energy tariffs are applied, the benefits of increasing energy resilience represent a novel market with high potential in facilities with significant critical loads. This methodology can be used in similar scenarios, adapting each particular load profile and critical load to provide a combined optimal solution regarding resilience and economic benefits.

Abstract Energy system modeling has been following the energy transition to investigate challenges and opportunities of future energy systems on all grid levels. Necessary input for sector-coupled energy system models are residential electricity and heat demand curves. The increasing importance of distribution grids and their modeling requires demand profiles in high spatial resolution.This paper presents a method to assign pre-generated electricity and heat demand curves to georeferenced residential buildings in Germany. We aim at overcoming fundamental shortcomings of the Standard Load Profiles and enable new possibilities for the modeling of distribution grids. Our approach provides a large variety in residential load profiles which spatially correspond to official socio-demographic data. All used input data sets as well as implemented methodology and the resulting profiles are publicly available under open source and open data licences to enable further use. Our results are validated on different aggregation levels as well as compared and discussed with the commonly used Standard Load Profiles.

The global lockdowns adopted by many countries to combat the outbreak of the COVID-19 pandemic led to a significant transformation in the teaching methods adopted in higher education institutions toward dependence on online learning systems. Although this pandemic has placed a technical and financial burden on academic institutions to facilitate the successful transition to online learning, it provides opportunities to understand the impacts of adopting new policies and strategies to improve the efficient utilization of resources and thus reduce operational costs. The detailed analyses of the changes in energy consumption can support assessing the potential savings in electricity bills with the wide-scale adoption of online learning methods in the future as a business as usual to improve and modernize the education systems. This paper provides a detailed analysis of the electrical energy consumption of the buildings within the campus of the University of Jordan. The diversity of building types on the campus (e.g., university hospital, humanities schools, scientific schools) supports the provision of more general and robust recommendations to extend the results to other institutions, particularly in developing countries. The Energy Use Intensity (EUI) per unit area and EUI per student are employed for the first time for benchmarking the energy usage in educational buildings in Jordan. Overall, the analyses show that the total electricity consumption in 2020 was significantly lower than in 2019, with a decrease of 20.8% from 27.7 GWh in 2019 to 21.9 GWh in 2020. It is also found that the most significant reduction occurred in the humanities buildings (i.e., a 39% drop in energy consumption). However, this volume of energy reduction is still relatively low, considering the absence of students. Furthermore, the hospital has an extremely high EUI value (161 kWh/m2/year) compared to the other categories (e.g., the EUI for humanities schools is 32.5 kWh//m2/year). To conclude, the electrical energy consumption data suggests that there may be significant opportunities for energy conservation in all building categories, especially in the hospital.

The available energy monitoring information in the dairy industry reveals significant gaps regarding the impact of operational factors on performance indicators and the generation of typical days from load profiles. This paper presents a method for predicting performance indicators and load profiles in the dairy industry based on multiple regression and clustering. The method is applied to a cheese dairy plant located in Tuscany, Italy, providing actionable insights for energy efficiency and renewable integration. With regard to performance indicators, predictions using multiple regression achieved accuracies within 8 % for electricity consumption and within 20 % for steam generation, mainly due to limited data availability. The combination of k-means clustering with multiple regression yielded an overall accuracy within approximately 10 % for electricity load profiles, enabling the labelling of clusters based on production and meteorological variables. The analysis of improved production planning and the compatibility of energy demand with solar resources showed potential reductions in the electrical performance indicator by 27 % and self-consumption rates between 14 % and 42 %, respectively. Validation with data from other dairy contexts confirms the accuracy of the method and the considerable potential for improvement, suggesting further implementation towards effective energy management in the dairy industry. [Display omitted] • The performance indicators and load profiles of a cheese dairy plant are predicted. • The method captures the influence of production and meteorological variables. • Regularisation of production volumes can yield up to 27 % electricity savings. • The potential for electricity self-consumption from solar energy is 14 %–42 %. • Application to other dairy contexts confirms the general validity of the method. [ABSTRACT FROM AUTHOR]

This document presents the modeling of load profile consumption for Low-and-Moderate-Income (LMI) communities in the Caribbean Islands, as well as an assessment of the solar-rooftop energy potential. In this work, real data, together with synthetic and electricity bill data, were collected to validate and improve the load profile models. The solar-rooftop energy potential was obtained through a National Renewable Energy Laboratory (NREL) software called the PVWatts calculator, and mathematical analysis. The analysis of rooftop solar energy potential was conducted to enable the minimum size of solar power systems to fit the energy demand in the community. The results obtained allow estimation of the capacity of the energy system for each house or an entire community. [ABSTRACT FROM AUTHOR]

This article presents an idea of the implementation of different real load profiles for energy storage system (ESS) operation. The considered approaches are based on real long-term measurements using energy meters, the adaptation of the standard profiles defined by the distribution system operator (DSO), as well as a mix of the level of contracted power and short-term measurements. All combinations are used as electricity demand to formulate an ESS operation plan that cooperates with the PV system and the electricity market. The GAMS solver is applied to obtain optimal operation tasks of the ESS to cover different real and pseudo-real load profiles of an industrial company. Obtained results are presented using a real case study of a metallurgy company with a 317 kWp photovoltaic installation and a 200 kW ESS. [ABSTRACT FROM AUTHOR]

This paper provides a standardised methodology to assess the suitability of using stand-alone solar photovoltaic (PV) systems for different productive uses of energy (PUE) at a country-wide level. The focus has been on the country of Tanzania, but the methodology is suitable for any location. This analysis has reviewed fourteen different PUE categories, with several sub-categories. The PUE were assessed for a wide range of factors, including cost of the system, potential income from the PUE, and potential market size. This report also highlights the PUE load profiles and energy requirements for comparison. Assessing a potential productive use is a complex process with multiple stakeholders and decision factors. The analysis here is not meant as a definitive answer, but to highlight the technical characteristics and economics of the potential productive uses for comparison. This methodology is a first pass method to assess the suitability of stand-alone PV systems for PUE applications at a country-wide level. For more detailed analysis of the PUE potential, the enabling environment, socio-cultural context and supporting services must be investigated in much greater detail. The quantitative methodology given here used computer-based simulation tools, including data processing (Excel & Python), geographical information systems (QGIS) and power system simulation (HOMER Pro). Input data from multiple sources, including in-country fieldwork, was used. This analysis has highlighted PUE with smaller energy requirements but low sensitivity to equipment or service cost to be the most suitable for more detailed analysis. Applying this methodology to Tanzania has shown stand-alone solar PV systems for barbershops, tailors, mobile carpenters, drip-feed irrigation systems and fishing lights as the lowest risk PUE for implementation. • Covers a standardised methodology for assessing and comparing productive uses of energy for remote rural applications. • Applies to remote stand-alone solar PV systems in Tanzania. • The methodology is a first pass assessment highlighting the most suitable productive uses of energy for future development. • Energy requirements, cost of system, potential market size and potential range of profit are compared for 25 use cases. • Drip-fed irrigations systems have the largest market potential and profitability in Tanzania. [ABSTRACT FROM AUTHOR]

Energy system modeling has been following the energy transition to investigate challenges and opportunities of future energy systems on all grid levels. Necessary input for sector-coupled energy system models are residential electricity and heat demand curves. The increasing importance of distribution grids and their modeling requires demand profiles in high spatial resolution.This paper presents a method to assign pre-generated electricity and heat demand curves to georeferenced residential buildings in Germany. We aim at overcoming fundamental shortcomings of the Standard Load Profiles and enable new possibilities for the modeling of distribution grids. Our approach provides a large variety in residential load profiles which spatially correspond to official socio-demographic data. All used input data sets as well as implemented methodology and the resulting profiles are publicly available under open source and open data licences to enable further use. Our results are validated on different aggregation levels as well as compared and discussed with the commonly used Standard Load Profiles. [ABSTRACT FROM AUTHOR]

As the energy transition is upon us, the replacement of combustion engines by electrical ones will imply a greater stress on the electrical grid of different countries. Therefore, it is of paramount importance to simulate a great number of hypothetical multi-variant scenarios to correctly plan the roll-out of new grids. In this paper, we deploy Generative Adversarial Networks (GANs) to swiftly reproduce the non-Gaussian and multimodal distribution of real energy-related samples, making GANs a valuable tool for data generation in the field. In particular, we propose an original dataset deriving from the aggregation of two European providers including hourly electric inland generation from several European countries. This dataset also comes along with the corresponding season, day of the week, hour of the day and macro-economic variables aiming at unequivocally describing the country’s energetic profile. Finally, we evaluate the performance of our model via dedicated metrics capable of grasping the non-Gaussian nature of the data and compare it with the state-of-the-art model for tabular data generation.

The COVID-19 pandemic has begun in early 2020 and still continues to strongly affect the entire world delivering a significant global, shock, but varying across countries and commodity sectors. The Government of the Republic of Serbia has been adopting different measures to slow down the dissemination of the coronavirus, specifically nationwide lockdown in March and April 2020. Business activities have been reduced. The pandemic situation has changed the lifestyle as people are mostly staying home and working from home. This paper provides a review of unprecedented impacts of COVID-19 pandemic, with restrictions and lockdown in Serbia, on electricity sector at this stage of the crisis. The outcomes offer a contribution to the body of literature because limited research has been conducted on these relationships in case of Serbia. Sets of statistical indicators are used to analyze changes the electricity sector has been facing. Data visualization is used to compare developments during the pandemic with those of previous years. Our research and data-driven analysis of these impacts should improve the understanding of the techno-economic effects of unforeseen events, such as a pandemic, on the power system, scrutinizing if effects could be relatively short-lived or longer-lasting. [ABSTRACT FROM AUTHOR]

Electrification of mobility is paving the way in decreasing emissions from the transport sector; nevertheless, to achieve a more sustainable and inclusive transport system, effective and long-term planning of electric vehicles charging infrastructure will be crucial. Developing an infrastructure that supports the substitution of the internal combustion engine and societal needs is no easy feat; different modes of transport and networks require specific analyses to match the requirements of the users and the capabilities of the power grid. In order to outline best practices and guidelines for a cost-effective and holistic charging infrastructure planning process, the authors have evaluated all the aspects and factors along the charging infrastructure planning cycle, analysing different methodological approaches from scientific literature over the last few years. The review starts with target identification (including transport networks, modes of transport, charging technologies implemented, and candidate sites), second, the data acquisition process (detailing data types sources and data processing), and finally, modelling, allocation, and sizing methodologies. The investigation results in a decision support tool to plan high-power charging infrastructure for electric vehicles, taking into account the interests of all the stakeholders involved in the infrastructure investment and the mobility value chain (distributed system operators, final users, and service providers). [ABSTRACT FROM AUTHOR]

The accurate prediction of heat load profiles with a daily resolution is required for a broad range of applications, such as potential studies, design, or predictive operation of heating systems. If the heat demand of a consumer mainly originates from (production) processes independent of the ambient temperature, existing load profile prediction methods fail. To close this gap, this study develops two ex post machine learning models for the prediction of the heat demand with a daily resolution. The selected input features are commonly available to each consumer connected to public natural gas and electricity grids or operating an energy monitoring system: Ambient temperature, weekday, electricity consumption, and heat consumption of the last seven days directly before the predicted day. The study's database covers electricity and natural gas consumption load profiles from 82 German consumers over a period of two years. Electricity and heat consumption correlate strongly with individual patterns for many consumers. Both shallow and deep learning algorithms from the Python libraries Scikit-Learn and Keras are evaluated. A Long Short-Term Memory (LSTM) model achieves the best results (the median of R2 is 0.94). The ex post model architecture makes the model suitable for anomaly detection in energy monitoring systems. [ABSTRACT FROM AUTHOR]

In this paper, a methodology for estimating end-use load shapes using the hourly whole-house metered load data, the household demographic survey data and the weather data (temperature) is presented. End use load shapes presents a method of generating realistic electricity load profile data for some of city of Tripoli domestic buildings. This method could help in predicting the daily load profile from individual flats to community. The results obtained show that the overall methodology provides an effective means for end-use load shape modeling and estimation.

This document presents the modeling of load profile consumption for Low-and-Moderate-Income (LMI) communities in the Caribbean Islands, as well as an assessment of the solar-rooftop energy potential. In this work, real data, together with synthetic and electricity bill data, were collected to validate and improve the load profile models. The solar-rooftop energy potential was obtained through a National Renewable Energy Laboratory (NREL) software called the PVWatts calculator, and mathematical analysis. The analysis of rooftop solar energy potential was conducted to enable the minimum size of solar power systems to fit the energy demand in the community. The results obtained allow estimation of the capacity of the energy system for each house or an entire community.

Energy efficiency is part of the goals set by governments around the world to reduce the energy footprint and provide sustainable development for all. The arrival of new technologies that allow the monitoring and self-control of electricity consumption within homes, such as smart meters, allow end users to integrate into the intelligent management systems of the electricity grid, by providing information on the flow of energy and prices. This work performs a classification of residential customers from the consumption data obtained from smart meters. For this, a methodology based on the simple K-means algorithm is used to identify patterns of behavior in the consumption of 1179 homes connected to a real low-voltage electricity distribution network in southern Chile equipped with smart meters, and the validation and refinement of the results using numerical measures belonging to the rough sets theory. Final groups are characterized based on their centroids, making possible to convert the large volumes of data collected into useful knowledge, which is beneficial for both the residential customer and the electric power distribution company. The results show that two clusters are the ones that best represent the set of clients. The opportunity to collect consumption data in real time through these devices offers perspectives to optimize public and private policies on electricity distribution. [ABSTRACT FROM AUTHOR]

Understanding how and when residential electricity is used throughout the day is integral to the successful implementation of potential residential demand management strategies. Our analysis characterizes the daily hourly load profiles of approximately 160,000 residential electricity customers across the Southern California Edison (SCE) service area during the period spanning 2015 to 2016 and 2018 to 2019 across weekends, weekdays, seasons, and climate zones. We find that total daily electricity usage was highest in the hottest months of the year compared to milder months, particularly for households located in the hottest climate zones. The most energy-consumptive hours occurred during the mid-afternoon during the hottest months, in contrast to early evening high consumption in cooler months. We find that customers with average daily consumption at or above the 80th percentile cumulatively consume over 40% of electricity during the hottest months of the year residential load, while the bottom half of customers cumulatively consume <25% of the total residential load. The disparities in electricity usage across SCE households are higher in the mid-day, especially in milder months across all regions, and in mild climate zones compared to hotter climate zones since loads are not as dependent on high HVAC loads. • Hourly smart-meter data of 160,000 homes were used to construct diurnal load profiles. • Disparities in hourly loads between households were measured using the Gini index. • The top 20% of households consumed about 40% of the daily electricity load. • The bottom half of consumers used <25% of the daily electricity load. • Customers in mild climates show less seasonal variability than those in hot climates. [ABSTRACT FROM AUTHOR]

Energy communities are emerging across Europe, and each country is currently in the process of forming regulations for their integration into the electricity grid. The efficacy of energy communities depends upon various factors, including member demographics, technological aspects, load profiles, solar irradiation, and spot prices within the community's geographical location. Notably, existing studies on energy communities predominantly focus on residential load profiles, with limited exploration into their impact on the distribution grid. This article aims to contribute to the existing literature by investigating the benefits of energy communities and their grid impact under diverse member configurations. Our approach involves the development of an optimisation model incorporating battery energy storage and shiftable loads, aimed at minimising the operational costs of energy communities over a one-year period. Case studies in Norway and Spain, with different load configurations: residential, commercial, and mixed load, are undertaken, utilising real hourly measurements to identify operational variations influenced by geographical location and seasonal fluctuations in load and photovoltaic (PV) generation. Additionally, we quantify the costs, CO 2 emissions, and self-consumption rates for energy communities. Furthermore, we assess the distribution grid impact in terms of import and export dynamics. The results underscore the substantial influence of load configurations on member benefits and distribution grid impacts, attributable to the inherent correlation between load and PV generation. In the context of energy community benefits, commercial loads demonstrate the best outcomes in Norway, whereas residential loads exhibit superior results in Spain. Conversely, concerning distribution grid impact, commercial loads prove most advantageous in Norway, while mixed loads yield the best results in Spain. Overall, our findings indicate that Spanish energy communities consistently achieve more substantial reductions in costs and CO 2 emissions compared to their Norwegian counterparts, irrespective of the load configuration. This study contributes valuable insights for policymakers, researchers, and industry stakeholders involved in the development and regulation of energy communities across Europe. • Grid impact of energy communities with different members: residential, commercial and mixed. • Interaction between PV generation, community battery and shiftable loads. • Comparing grid impact with energy community benefits. • Commercial loads in Norway and mixed loads in Spain prove to be grid-friendly options. • Spanish energy communities achieve highest cost and CO 2 emission reductions. [ABSTRACT FROM AUTHOR]

This data article refers to the paper ''Analysis of residential EV energy flexibility potential based on real-world charging reports and smart meter data'' [1]. The reported datasets deal with residential electric vehicle (EV) charging in apartment buildings. Several datasets are provided, with different levels of detail, aiming to serve various needs. The paper provides real-world EV charging reports describing 6,878 charging sessions registered by 97 user IDs, from December 2018 to January 2020. The charging reports include identifiers, plug-in time, plug-out time and charged energy for the sessions. Synthetic charging loads are provided with hourly resolution, assuming charging power 3.6 kW or 7.2 kW and immediate charging after plug-in. The non-charging idle time reflects the flexibility potential for the charging session, with synthetic idle capacity as the energy which could potentially have been charged during the idle times. Synthetic hourly charging loads and idle capacity are provided both for individual users, and aggregated for users with private or shared charge points. For a main garage with 33% of the charging sessions, smart meter data and synthetic charging loads are available, with aggregated values each hour. Finally, local hourly traffic density in 5 nearby traffic locations is provided, for further work related to the correlation with plug-in/plug-out times. Researchers, energy analysts, charge point operators, building owners and policy makers can benefit from the datasets and analyses, serving to increase the knowledge of residential EV charging. The data provides valuable insight into residential charging, useful for e.g. forecasting energy loads and flexibility, planning and modelling activities.

Load profiles of residential consumers are very diverse. This paper proposes the usage of a continuous wavelet transform and wavelet coherence to perform analysis of residential power consumer load profiles. The importance of load profiles in power engineering and common shapes of profiles along with the factors that cause them are described. The continuous wavelet transform and wavelet coherence has been presented. In contrast with other studies, this research has been conducted using detailed (not averaged) load profiles. Presented load profiles were measured separately on working day and weekend during winter in two urban households. Results of applying the continuous wavelet transform for load profiles analysis are presented as coloured scalograms. Moreover, the wavelet coherence was used to detect potential relationships between two consumers in power usage patterns. Results of coherence analysis are also presented in a colourful plots. The conducted studies show that the Morlet wavelet is slightly better suitable for load profiles analysis than the Meyer’s wavelet. Research of this type may be valuable for a power system operator and companies selling electricity in order to match their offer to customers better or for people managing electricity consumption in buildings.

Electricity supply has been identified as the key constraint to industrialization and economic development in Nigeria. The unbundling of the power sector was aimed at boosting electricity supply, this effort has yielded some appreciable results, but not very significant. As a result of the low power generation and distribution, Nigeria's federal government is working towards solving the prevailing problems of inadequate power in some key sectors by building power generating plants in some of the institutions of learning in the country. This paper looks at the determinants of electrical energy consumption and electrical energy audit, a case study of the University of Jos. The load profiles demand survey, load demand forecast and other important factors were investigated. The result revealed that there is available power of 22-23 hours from the national grid and the balance 1-2 hours of power is supplied by the generating sets, good savings in the cost of diesel and maintenance. An annual excess of 2,199,900 kWH is enjoyed by the university over the national per capita power consumption. [ABSTRACT FROM AUTHOR]

Load profiles of residential consumers are very diverse. This paper proposes the usage of a continuous wavelet transform and wavelet coherence to perform analysis of residential power consumer load profiles. The importance of load profiles in power engineering and common shapes of profiles along with the factors that cause them are described. The continuous wavelet transform and wavelet coherence has been presented. In contrast with other studies, this research has been conducted using detailed (not averaged) load profiles. Presented load profiles were measured separately on working day and weekend during winter in two urban households. Results of applying the continuous wavelet transform for load profiles analysis are presented as coloured scalograms. Moreover, the wavelet coherence was used to detect potential relationships between two consumers in power usage patterns. Results of coherence analysis are also presented in a colourful plots. The conducted studies show that the Morlet wavelet is slightly better suitable for load profiles analysis than the Meyer's wavelet. Research of this type may be valuable for a power system operator and companies selling electricity in order to match their offer to customers better or for people managing electricity consumption in buildings. [ABSTRACT FROM AUTHOR]

As residential PV generation penetrates in the low‐voltage distribution network (LVDN), the unbalance issue may be intensified due to the asymmetry of generations/loads in different phases. Phase‐reconfiguration device (PRD), which can reconfigure connected phases of residential customers, provides an effective method to address this issue. Noting that although the benefit brought by PRDs can vary if they are placed at different locations in the network, little literature has been reported on this topic. To bridge the research gap, this paper presents a novel method to optimally place PRDs in an LVDN aiming at minimizing the power unbalance running through the distribution transformer. The problem considers both installation, operational constraints, and boils down to a challenging mixed‐integer non‐convex programming problem, which is then reformulated as an efficient solvable mixed‐integer linear programming problem. Moreover, operational constraints are relaxed with slack variables penalized in the objective function, which makes sure a feasible solution is always available without or with minimal operational violations. Case studies based on a modified IEEE system and a practical system in Australia demonstrate that an efficient strategy can be provided to address the unbalance issue while improving the network's power supply qualities. [ABSTRACT FROM AUTHOR]

Integration of low carbon technologies poses a technical challenge on distribution transformers due to the dynamic loading and potentially frequent overloading scenarios. Transformer dynamic thermal rating is hence required, which is the most economical approach to tackle this challenge and ensure a safe operation. To reach the aim, it is important to enhance the accuracy of the dynamic thermal model, where the top-oil temperature is a key thermal parameter. In this study, a wide range of constant load temperature-rise tests were carried out on an 11/0.433 kV distribution transformer to study the dynamic thermal behaviour of the top-oil temperature. A model based on the IEC 60076-7 thermal model but with an improved oil time constant calibration was deduced for top-oil temperature modelling. The oil time constant calibration was inspired by IEEE C57.91 and verified by eight temperature-rise tests with load factors ranging from 0.7 to 1.4 pu. In addition, the improved top-oil temperature modelling was further verified in experiments under multiple load profiles. [ABSTRACT FROM AUTHOR]

Choosing suitable load indexes of load profiles is of vital importance for load profiles clustering, which has wide applications in load forecasting, power grid planning and electricity price designing. To obtain a set of load indexes with rigorous mathematical theory and clear physical meaning, this study proposed a singular value decomposition (SVD) based method to extract indexes. First, empirical rank‐l approximation derived from SVD is proposed to extract load indexes. The relationship between singular values and relative approximation error guarantees the indexes retain major characteristics of load profiles, while the invariance of right singular vectors over seasons and sample sizes endows the indexes with good generalisation ability. Then the right singular vectors are discretised to determine definition of indexes and reveal physical meanings of the indexes. Finally, the new set of indexes extracted by the proposed method are applied in indirectly clustering in the case study, which verify the validity of the indexes, the performance of the clustering method and the advantages of the new indexes over the existing load shape indexes. [ABSTRACT FROM AUTHOR]

In the modern world, many cities make use of state-of-the-art technologies for a diversity of applications. A field with very specific needs is the electric power system that deals with both large entities that govern themselves (grid operators) and the citizens. For both and all actors in between, there is an increased need for information. Steps to provide these data are always taken and several initiatives are ongoing across the world to equip residential users with last generation smart meters. However, a full deployment is still not possible. Considering this aspect, the authors propose KPIs for the specific situation when some information is available from the meters and other sources, but some is not. The study case is based on a residential area occupied mainly by university students and after an extensive measurement campaign the results have been studied and analysis methods proposed. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) of combined cooling, heat, and power (CCHP) has been gaining momentum in recent years as an efficient, secure alternative for meeting increasing power demands in the world. One of the most critical and emerging markets for DG-CCHP systems is commercial and institutional buildings. The present study focuses analysis on the main economic, energy-efficiency, and environmental impacts of the integration of three types of advanced DG technologies (high-temperature fuel cells, micro-turbines, and photovoltaic solar panels) into four types of representative generic commercial building templates (small office building, medium office building, hospital, and college/school) in southern California (e.g., mild climate), using eQUEST as energy simulation tool. Detailed load profiles for the four commercial building types during times of peak electric and peak gas consumption were analyzed and complementary strategies to further increase overall building energy efficiencies such as energy efficiency measures (e.g., day lighting, exterior shading, improved HVAC performance) and thermally activated absorption cooling were also investigated. Results show that the high-temperature fuel cell (HTFC) performance is best matched with the hospital energy loads, resulting in a 98% DG capacity factor, 85% DG heat recovery factor, and $860,000 in energy savings (6 years payback). The introduction of thermally driven double-effect absorption cooling (AC) in the college building with HTFC reduces significantly the building electricity-to-thermal load ratio and boosts the heat recovery factor from 37% to 97%. © 2007.

High-resolution residential smart meter data play a pivotal role in numerous applications, ranging from assessing hosting capacity in low-voltage grids to evaluating the economic viability of household-level solutions such as the integration of photovoltaic (PV) installations with battery energy storage systems (BESS). However, privacy concerns often restrict access to large-scale residential smart meter datasets, leaving only synthetic load profiles available to the public, which generally only represent averages of individual data. In response to this challenge, this paper proposes an innovative approach leveraging Generative Adversarial Networks (GANs) to generate synthetic residential load profiles. Our method operates in two distinct phases: First, we employ the DoppelGANger (DGAN) architecture to construct annual time series of daily consumption values. DGAN has been selected due to its demonstrated capacity for capturing interday and seasonal dynamics. Second, a wavelet-based decomposition-recombination technique is employed to create stochastic daily profiles with realistic intraday variations and peak demand behavior. Our DGAN-based approach demonstrates remarkable effectiveness, as evidenced by evaluation against a real-world dataset through a series of qualitative microbenchmarks. Moreover, the practical utility of the two-step methodology is explored in three downstream applications: (i) the integration of PV systems, (ii) PV-BESS systems increasing PV self-consumption, and (iii) PV-BESS systems boosting PV self-consumption while performing peak shaving. Our methodology is shown to accurately capture and model the intricate complexities inherent in real smart meter data. • Modeling high-frequency annual residential load profiles via two-step approach. • DGAN captures interday, weekly and seasonal long-term correlations. • Wavelet-based modeling at daily level assures realistic stochastic peak load behavior. • Benchmark GAN output via autocorrelation, annual consumption and profile variability. • Fidelity of synthetic data was validated for various PV and PV-BESS applications. [ABSTRACT FROM AUTHOR]

The development and the diffusion of the electromobility is crucial for reducing air pollution and increase sustainable transport. In particular, electrification of private mobility has a significantly role in the energy transition within urban areas, since the progressive substitution of conventional passenger cars by electric vehicles (EVs) leads to the decarbonisation of transport sector without direct emissions. However, increasing EV penetration in the market forces an expansion of the existing charging infrastructure with potential negative impacts on the distribution grid. In this context, a simplified approach is proposed to estimate the energy and power demand owing to the recharge of electric passenger cars within the city of Turin in Italy. This novel approach is based on the usage of floating car data (FCD) to identify the travel behaviour and parking habits of a non-EV passenger car in the city. Mobility data were then used to evaluate EVs energy consumption and charging needs considering different charging options (public or domestic) and range anxiety in different scenarios of EV diffusion. Aggregated load profiles and demand were finally evaluated both for the whole and for each zone of the city as possible resource for city planner or distribution system operators (DSO).

The design of reliable and sustainable rural electrification systems relies on accurate prediction of the electrical load. This paper evaluates the current methods for load estimation and proposes an improved approach for load estimation for off-grid unelectrified rural communities that yields more accurate estimates. Improved accuracy is mainly due to better modelling of the influence of customer habits and gender on the estimated current and future load using the Markov chain process. A program was developed using MATLAB software to generate load profiles. The results show that gender considerations have a significant impact on load profiles and that the Markov chain process can suitably be used to determine year-to-year load profiles by incorporating the effect of changes in customer habits on the estimated load. The results from the case study on energy consumption in rural community households showed an increase in average daily consumption when gender was considered during load estimation. The peak consumption when gender was considered was about 50% higher than the value for when gender was not considered.