Electricity Load Profiles Research Articles

Hourly electrical load estimates in a 100% renewable scenario in Italy

The study of the impact of the zero-emissions scenarios of several countries on the electrical demand is relevant to analyse the feasibility of the sustainable energy transition. This paper presents a structured method to assess the effect of the 100% renewable scenario on the hourly electrical load profile of a country taking Italy as reference case study. The hourly discretization is a fundamental approach to evaluate the contribution of the intermittent renewable sources during the day and the proposed methodology can be easily applied to several countries’ scenarios. Numerous decarbonization scenarios consider the adoption of electricity in several sectors. Consequently, the primary energy reduction (40% in the scenario considered) is usually accompanied by a relevant increase of the annual electricity demand (94%-124%). In this paper, each sector’s contribution is estimated separately, and the results show demand peaks above 100 GW and a baseload above 50 GW, which is more than double that of recent years. Electricity consumption is higher in the colder months and hydrogen and synthetic fuel production impacts significantly on the total electricity demand (26%-32%). The results of this work will be used to verify the feasibility of net zero scenarios with hourly discretization in further research analysis.

Effective and Local Constraint-Aware Load Shifting for Microgrid-Based Energy Communities

The rising energy demand, coupled with increased integration of distributed energy resources (DERs) and fluctuating renewable generation, underscores the need for effective load management within energy communities. This paper addresses these challenges by implementing effective, constraint-aware load shifting within microgrid-based energy communities. Specifically, the goal of this study is to flatten the electrical load profile of a High-Voltage (HV)/Medium-Voltage (MV) power transformer. The load of a central power transformer includes (a) the diverse, fluctuating electrical and thermal demands of buildings within the energy community and (b) the load of the area supplied by the substation excluding the energy community loads. To achieve a flattened load profile, we apply time shifting to both electrical and heating, ventilation, and air conditioning (HVAC) loads of the energy community, allowing for a redistribution of energy consumption over time. This approach entails shifting non-critical loads, particularly those related to HVAC and other building operations, to off-peak periods. The methodology considers critical operational constraints, such as maintaining occupant thermal comfort, ensuring compliance with building codes, and adhering to technical specifications of HVAC and electrical systems and microgrid organized energy communities. Detailed simulations were conducted to prove the effectiveness of this constraint-aware load-shifting approach.

Impact of Electric Vehicles Charging on Urban Residential Power Distribution Networks

Achieving transportation decarbonization and reducing carbon emissions are global initiatives that have attracted a lot of effort. The use of electric vehicles (EVs) has experienced a significant increase lately, which will have a considerable impact on current power systems. This study develops a framework to evaluate/mitigate the negative impact of increasing EV charging on urban power distribution systems. This framework includes data analytics of actual residential electrical load and EV charging profiles, and the development of optimal EV charging management and AC load flow models using an actual residential power distribution system in Saskatchewan, Canada. We use statistical methods to identify a statistically-extreme situation for a power system, which a power utility needs to prepare for. The philosophy is that if the power system can accommodate this situation, the power system will be stable 97.7% of the time. Simulation results show the house voltage and transformer loading at various EV penetration levels under this statistically-extreme situation. We also identify that the particular 22-house power distribution system can accommodate a maximum number of 11 EVs (representing 50% EV penetration) under this statistically-extreme situation. The results also show that the proposed optimal EV charging management model can reduce the peak demand by 43%. Since we use actual data for this study, it reflects the current real-world situation, which presents a useful reference for power utilities. The framework can also be used to evaluate/mitigate the impact of EV charging on power systems and optimize EV infrastructure development.

Generation of heat and electricity load profiles with high temporal resolution for Urban Energy Units using open geodata

Urban areas account for up to 87% of global energy consumption, with around a third of CO2 emissions from the building sector. Germany recently enacted a law targeting carbon neutrality in heating by 2045, requiring all municipalities to submit transformation plans for their heating infrastructure. Many are in early stages and need innovative methods to achieve these goals. This study proposes an automated GIS-based approach to generate heat and electricity load profiles for geographically referenced residential buildings and districts in Germany, using only open data. The methodology offers hourly temporal resolution and spatial detail from individual buildings to Urban Energy Units (UEUs), a concept introduced in prior studies. Nine distinct heating load profiles and nine electricity load profiles were identified. These profiles can adapt to different weather datasets and to three building refurbishment scenarios. The methodology and energy analysis were applied to a district in Oldenburg, Germany, demonstrating the model’s flexibility under varying boundary conditions. For this district, the analysis revealed a total heat demand of 9.9±7 GWh/a and an electricity demand of 2.3±0.126 GWh/a, with respective errors of 45% and 39% when compared to other local data, this demand is presented in both yearly and hourly resolutions. This methodology intends to support German municipalities by accelerating the initial phases of the municipal heating plans and deliver high-quality data on building heat and electricity demand.

Hybrid solar photovoltaic-wind turbine system for on-site hydrogen production: A techno-economic feasibility analysis of hydrogen refueling Station in South Korea's climatic conditions

The South Korean government has an ambitious policy to roll out hydrogen-powered vehicles across the country with 2000 hydrogen refueling stations (HRSs) infrastructure by 2050. However, the country currently lacks sufficient HRS infrastructure. In this context, this study proposes and investigates the technoeconomic feasibility and performance assessment of an optimal hybrid renewable energy system integrated with a vanadium redox flow battery for on-site hydrogen production. The system is designed to refuel a fleet of 20 fuel cell electric vehicles at seven South Korean locations with distinct climates. Based on the typical daily hydrogen and electric load profile of the HRS, the capital expenditure, net present cost, operation expenditure, levelized cost of hydrogen and levelized cost of energy were estimated to be, $5.95 - $13.2 M, $8.93 - $19.4 M, 82,670–202,184 $/yr, 8.77–19.1 $/kg and 2.1–4.58 $kWh, respectively.

Advancing Economical and Environmentally Conscious Electrification: A Comprehensive Framework for Microgrid Design in Off‐Grid Regions

AbstractThe design of renewable energy systems traditionally emphasizes life cycle costs, often focusing primarily on emissions rather than a comprehensive life cycle impact assessment. This research proposes a four‐tier methodology to balance cost‐effectiveness and sustainability in the electrification of remote areas. Tier 1 focuses on understanding the community context by analyzing electrical load profiles, meteorological data, and component specifications for microgrid design. Tier 2 evaluates the feasibility of various systems, optimizing them through cost analysis and Multi‐Criteria Decision‐Making (MCDM) to rank alternatives. Tier 3 assesses environmental impacts using life cycle assessment, ranking alternatives based on environmental criteria. Tier 4 integrates cost and environmental rankings to determine the most suitable energy configurations, followed by sensitivity analysis to ensure robust decision‐making. The methodology is validated through a case study of an unelectrified remote community, demonstrating that the PV‐Wind Turbine‐Biomass Generator‐Converter configuration is the most robust alternative, proving to be the optimal choice in 50% of the analyzed scenarios, achieving a Cost of Energy of 0.213 USD/kWh while minimizing environmental impact across all 18 criteria considered over a 25‐year life cycle. This novel framework offers a scalable approach to designing renewable energy systems, enhancing sustainable electrification efforts in developing regions.

A novel approach to energy management in the dairy industry using performance indicators and load profiles: Application to a cheese dairy plant in Tuscany, Italy

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.

Optimizing energy consumption in smart buildings: A model for efficient energy management and renewable integration

In the current landscape of sustainable energy, integrating photovoltaic (PV) generation and electric vehicles (EVs) into household energy management systems (EMSs) has become a pivotal strategy to enhance grid sustainability in residential areas and provide economic benefits to homeowners. This article introduces an innovative approach to an energy administration framework designed for intelligent households, to seamlessly integrate EVs, both with and without PV generation. The proposed plan meticulously optimizes residential electricity expenses. It standardizes the load gradient using Time-of-Use (TOU) cost, residential electricity use fluctuations, PV generation patterns, and specific variables of EVs, including entry and exit times and charging statuses. The management method is divided into two distinct phases based on charging constraints and initial charge status. Stage A comprises three operational states contingent upon the availability of PV generation, while Stage B involves five operational states predicted based on anticipated PV generation. Through this research, the advanced control strategy facilitates precise adjustment of energy absorption by EVs from the grid and/or PV generation, along with injected energy derived from EVs to the grid. It ensures minimal residential electricity expenses and synchronizes the electrical load profiles. The findings indicate a significant reduction in residential electricity expenses and a substantial normalization of power load characteristics. Furthermore, simulation results emphasize the superior performance of the proposed plan for intelligent households fitted with both EVs and PV generation, compared to households with EVs alone, in terms of lowering expenses and normalizing load scenarios.

Gaussian-based plug load profile prediction in non-residential buildings archetype

This paper presents an innovative approach to addressing the prevalent challenge of simulation uncertainty in urban building energy modeling (UBEM), focusing on accurately determining occupant-related input parameters. Traditional UBEM methods typically rely on standard schedules to create archetype models, which often fail to reflect the variability observed in real-world scenarios. To overcome this limitation, this research introduces a novel framework for generating electricity use profiles in institutional building archetypes across various climate zones. This framework integrates k-means clustering with Gaussian processes, effectively incorporating uncertainties into the prediction models. The evaluation of this stochastic model suggests that the methodology can give acceptable predictions on the electricity consumption of institutional buildings. The model demonstrates robust predictive capabilities, achieving a CVRMSE as low as 11% on weekdays and 8.7% on weekends, reflecting its strong predictive performance. However, its performance varies among different clusters and time periods, with specific clusters displaying more significant predictive inaccuracies at particular times. These results emphasize the importance of fine-tuning models and offer opportunities for improvement in predicting urban building energy consumption. This can be achieved by incorporating sensor-derived data to develop more detailed building profiles that include variable electricity usage patterns. This methodology has been integrated into a UBEM tool, enabling the generation of more realistic electricity load profiles.

Generating synthetic building electrical load profiles using machine learning based on the CRISP-ML(Q) framework

Abstract This study presents a machine learning application for generating synthetic building electrical load profiles. The implementation followed the Cross Industry Standard Process for the development of Machine Learning Applications with Quality assurance methodology, or CRISP-ML(Q) framework, to ensure a systematic machine learning development process. The model training performance was evaluated using the mean absolute error (MAE), root mean squared error (RSME), and coefficient of determination (R2) which were observed to be 0.0739, 0.1119, and 0.5728, respectively. These metrics remained consistent during the model testing phase, suggesting robust model performance. During the initial simulation experiment, the MAE and RMSE of the generated synthetic load profile were found to be 0.014 and 0.016, respectively, underscoring high model accuracy. Additional evaluation experiments showed that the developed machine learning application can generate realistic building load profiles using high-level parameters such as building type, average daily load, and peak demand. This study can aid in the development of demand-side management strategies and building energy management systems by providing realistic building electrical load profiles especially when real-world data is limited. For future work, researchers can consider integrating additional model features, refining data processing methods, and developing an agile version of the CRISP-ML(Q) framework.

Impact of Multi-Energy System and Different Control Strategies on a Generic Low-Voltage Distribution Grid

The rising electricity costs, cost of space heating, and domestic hot water end up driving consumers toward reducing expenses by generating their electricity through devices like photovoltaic systems and efficient combined heat and power plants. When coupled with thermal systems via an energy management system (EMS) in a Multi-Energy System (MES), this self-produced electricity can effectively lower electricity and heating bills. However, MESs with EMSs can serve various purposes beyond cost reduction via self-consumption, such as reacting to variable electricity prices, meeting special grid connection conditions, or minimizing CO2 emissions. These diverse strategies create unique prosumer profiles, deviating significantly from standard load profiles. The potential threat to the power grid arises as grid operators lack visibility into which consumers employ which control strategies. This paper investigates the impact of controlled MESs on the power grid compared to average households and answers whether new control strategies affect the planning strategies of low voltage grids. It proposes a comprehensive four-step toolchain for the detailed simulation of thermal–electrical load profiles, MES control strategies, and grid dynamics. It includes a new method for the grid impact analysis of extreme and average bulk values. As a result, this study identifies three primary factors influencing distribution power grids by MESs. Firstly, the presence and scale of photovoltaic (PV) systems significantly affect extreme values in the grid. Secondly, MESs incorporating combined heat and power (CHP) and heat pump (HP) units impact the overall grid performance, mainly reflected in bulk values. Thirdly, the placement of an MES with heating systems, especially when concentrated in one feeder, plays a crucial role in grid dynamics. Despite the three distinct factors identified as impactful on the power grid, this study reveals that the various control strategies, despite leading to vastly different grid profiles, do not exhibit divergent impacts on buses, lines, or transformers. Remarkably, the impact of MESs remains consistently similar across the range of control strategies studied. Therefore, different control strategies do not pose an additional challenge to the grid integration of MESs.

Constructing Australian Residential Electricity Load Profile for Supporting Future Network Studies

Constructing Australian Residential Electricity Load Profile for Supporting Future Network Studies

Comprehensive Dataset on Electrical Load Profiles for Energy Community in Ireland

This paper describes a comprehensive energy-related dataset, collected from residential electricity households within an energy community in Ireland, as part of StoreNet project. The data includes local weather parameters and per household power (W) and energy (Wh) components for various aspects such as active power consumption, PV generation, grid import and export, charging and discharging, and the state of charge of energy storage. Additionally, it provides weather data for the location at a 1-minute temporal resolution for the year 2020. The dataset has been validated by comparing measurements that should yield identical results to standard load profiles, with no significant inconsistencies discovered. Validation examples have also been given through the published articles where this dataset has been used to analyse peer-to-peer energy trading benefits for the energy community and decision-making support for aggregators. The dataset aids in understanding patterns in electrical load curves and the duty cycle of energy storage within an energy community. Furthermore, it can assist in comprehending the impact on distribution networks caused by distributed energy storage.

A machine learning-based framework for clustering residential electricity load profiles to enhance demand response programs

Load shapes derived from smart meter data are frequently employed to analyze daily energy consumption patterns, particularly in the context of applications like Demand Response (DR). Nevertheless, one of the most important challenges to this endeavor lies in identifying the most suitable consumer clusters with similar consumption behaviors. In this paper, we present a novel machine learning based framework in order to achieve optimal load profiling through a real case study, utilizing data from almost 5000 households in London. Four widely used clustering algorithms are applied specifically K-means, K-medoids, Hierarchical Agglomerative Clustering and Density-based Spatial Clustering. An empirical analysis as well as multiple evaluation metrics are leveraged to assess those algorithms. Following that, we redefine the problem as a probabilistic classification one, with the classifier emulating the behavior of a clustering algorithm, leveraging Explainable AI (xAI) to enhance the interpretability of our solution. According to the clustering algorithm analysis the optimal number of clusters for this case is seven. Despite that, our methodology shows that two of the clusters, almost 10% of the dataset, exhibit significant internal dissimilarity. As a result, these clusters have been excluded from consideration for DR programs. The scalability and versatility of our solution makes it an ideal choice for power utility companies aiming to segment their users for creating more targeted DR programs.

Techno-economic analysis on optimizing the value of photovoltaic electricity in a high-latitude location

This study performs a techno-economic analysis of different photovoltaic (PV) systems suitable for detached houses in high-latitude locations and quantifies the key economic indicators affecting their competitiveness. Residential PV systems providing different temporal power production profiles are compared, accounting for the electricity price variations in the day-ahead market and the possibility of self-consuming the produced electricity. The procedure allows novel and comprehensive case study analysis that captures PV production, self-consumption, and dynamic electricity pricing, ultimately revealing the value of the produced PV electricity. This procedure is applied to a hundred case studies representing single-family houses with different PV systems, electricity load profiles, and electricity contracts. Vertical East-West mounted bifacial panels (VBPV) reached superior performance, with 9.1% higher overall production (with 2019 weather data) and 7.4%–10.9% higher economic value (with 2019 and 2022 electricity price data) for the produced PV electricity compared with monofacial PV. Thus, VBPV is an excellent choice for households economically, but the availability of suitable installation sites in the urban environment limits the possibility of its utilization. The economic value of PV electricity strongly correlates with self-consumed electricity due to avoided transmission fees and taxes, and a significant drop in total production causes only a minor reduction in value if the amount of self-consumed electricity remains similar. Thus, for a small-scale producer who orients the panels toward the East and the West instead of the South with a 45° tilt, the economic loss is significantly smaller (even as low as 12.6%) than the production loss (23.2%).

A hybrid clustering approach for electrical load profiles considering weather conditions based on matrix-tensor decomposition

A hybrid clustering approach for electrical load profiles considering weather conditions based on matrix-tensor decomposition

Pattern-driven behaviour for demand-side management: An analysis of appliance use

Energy communities play a key role in the transition to sustainable energy, helping to inform and engage end-users so that they can become active energy consumers. In practice, trials and pilots often risk failure due to misplaced expectations and unforeseen behaviours when it comes to achieving flexible energy demand resources. In order to tackle these challenges, residential electricity load profile datasets and consumer survey results emerge as powerful tools for identifying controllable loads, energy consumption models, and tailored understanding of communities' energy contexts. This paper first outlines and analyses these datasets' capabilities to leverage data-driven decision-making for more efficient deployments of demand-side management (DSM) systems. A number of appliance behaviour patterns are extracted, based on high and flexible loads for shifting, being validated over three different use cases to support turn-key DSM in the presence and absence of renewable supply and bill saving. A genetic algorithm optimization is applied to underpin flexible demand reallocation and optimal community load profiles by combining time-variable tariff of use. Experiments demonstrate that controllable and shiftable appliances can reduce average peak load by up to 29% by increasing renewable self-consumption, leading to a valuable energy bill saving of 9%. Our findings also point to the current limitations of existing load/consumption datasets, which are hindering more efficient DSM design of flexibility and demand response programmes in energy communities.

Energy Household Forecast with ANN for Demand Response and Demand Side Management

This paper presents a short term load forecasting with artificial neural networks. Despite the great imprevisibility, it is possible to forecast the electricity consumption of a household with some accuracy, similarly to that the electricity utilities can do to an agglomerate of households. Nowadays, in an existing electric grid, it is important to understand and forecast household daily or hourly consumption with a reliable model for electric energy consumption and load profile. Demand response programs required this information to adequate the profile of energy load diagram to generation. In the short term load forecasting model, artificial neural networks were used, with a consumption records database. The results show that the artificial neural networks approach provides a reliable model for forecasting household electric energy consumption and load profile. To do so and using smart devices such as cyber-physical systems monitoring, gathering and computing in real time a database with weekdays and weekend, can improve forecasts results for the next hours, a strong tool for Demand Response and Demand Side Management.

Generation of Synthetic Electrical Load Profiles for Rural Communities in Developing Countries - Applied in Fiji

The electricity supply of developing countries undergoes massive changes. Due to the high dependency on fossil fuel imports, a lot of developing nations support the implementation of renewable generation schemes. To install an economical generation setup the generation capacity, as well as the consumption has to be balanced. Due to the often weak electricity infrastructure like the non-availability of a grid connection the systems have to operate most likely in the stand alone mode. Therefore, it is important to predict the consumption patterns of the supplied society. A program to create easily synthesized load profiles for a target community, by collecting simple information about the connected consumers and operation times, is proposed to help within the design process of a power system.

Validation of the Energy Demand Load Profile Estimator “PROFet” for Trondheim Non-residential Buildings

Accurate long-term forecasts of aggregate energy load profiles are crucial for effective energy system planning at regional and national scales. This study aims to validate PROFet, a flexible load profile modeling tool. PROFet forecasts weather-dependent heating and electrical load profiles at an hourly resolution for both residential and non-residential buildings connected to district heating systems. Given that the tool’s accuracy for residential buildings has been demonstrated in previous studies, further validation is needed for non-residential buildings. To achieve this, our study evaluates PROFet’s extrapolation performance using an out-of-sample test dataset from Trondheim municipality. The results show that PROFet accurately forecasts the hourly heating load during the space-heating season but is less accurate during periods when domestic hot water needs dominate. The tool exhibits nearly identical performance across the three cases, except for efficient kindergartens, where the estimation accuracy is lower.