Load Profile Analysis Research Articles

Optimizing solar-wind hybrid energy systems for sustainable charging stations and commercial applications: A two-stage framework with ebola-inspired optimization

This paper presents a novel approach to designing and optimizing a Solar-Wind Hybrid Energy System (SWHS) for an Electric Vehicle Charging Station (EVCS) and a university shopping complex in India. Objectives include addressing sustainable energy challenges, ensuring reliability, and minimizing the Levelized Power Supply Price (LPSP). Key contributions encompass a two-stage framework for optimal SWHS configuration, consideration of uncertainties in renewable energy generation, and the use of the Ebola Optimization Search Algorithm (ESOA). The first stage models SWHS components, formulates an objective function, and addresses reliability to minimize LPSP. The second stage employs ESOA, inspired by Ebola spread, exploring both local and global search spaces. Simulation results and a load profile analysis validate the proposed framework and ESOA's performance, observing a significant improvement of 15% in LPSP and a 10% increase in system reliability. The study provides insights into optimal Distributed Generation (DG) sizing in SWHS, ensuring reliable and cost-effective power supply for EVCS and commercial applications, contributing to the renewable energy system design and optimization field. The analysis indicates an enhanced system reliability by 10 % and a notable reduction in LPSP by 15 %.

Load Profile Test Development and Analysis from Heavy Duty Truck Drive Cycles

On their way to their commercialisation, Fuel Cell Heavy Duty Trucks stakeholders need to demonstrate their cost effectiveness against conventional Diesel Trucks. Beyond the economies of scale of all relevant components that are employed in a Fuel Cell Electric Heavy Duty Truck, the durability of the Fuel Cell System and notably that of the Fuel Cell Stack plays an important role in the determination of a competitive cost of ownership.A key factor for durability improvement is the understanding of the degradation mechanisms and their mitigation. Degradation, however, can be very application specific, that is, different degradation mechanisms may be prevalent in heavy duty truck applications compared to passenger cars or stationary use. This is well known to research and industry.The European research project IMMORTAL is exactly aiming at exploring these mechanisms and their mitigation specifically for heavy duty truck application and developing and MEA that meets heavy duty requirements. Within this context, FPT Industrial, a brand of the Iveco Group, assists in defining the specific conditions met by Fuel Cell Stacks used in such powertrains.This presentation discusses how heavy duty truck load profiles can be extracted, how they may differ and how they may be assessed before they are used to define load profile tests (LPTs). It is also discussed how the results of such testing can be used to estimate degradation trends of the Fuel Cell and subsequently its durability and the cost of ownership of fuel cell trucks. The research leading to these results has received funding from the IMMORTAL project, which receives funding from the Fuel Cells and Hydrogen 2 Joint Undertaking (now Clean Hydrogen Partnership) under Grant Agreement No. 101006641. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme, Hydrogen Europe and Hydrogen Europe Research.

A Systematicity Review on Residential Electricity Load-Shifting at the Appliance Level

Load-shifting is a demand-side management (DSM) strategy to support the efficiency of the electricity grid during hours of peak demand. Load-shifting at the appliance level is an interesting topic to review, since appliance usage is one of the main inputs of the load-profile analysis. More literature reviews on load-shifting at the appliance level are required, as this is a specific issue in the body of literature on load-profile research, though only a limited number of studies are available at this time. It is also essential to focus on appliance usage patterns to improve our understanding of the impacts and characteristics of different appliances. Existing studies on load-shifting have used commonly structured literature reviews; our work addresses the transparency of each stage and substage in the selection of the final list of studies. The findings show that efficiency has been achieved in installed-capacity reductions; costs, including those of emission reductions; and peak consumption reductions. The most frequently used method in load-shifting at the appliance level is to develop load-shifting optimization algorithms. This work contributes by providing a transparent process of drawing up a systematicity literature review as a source of knowledge and grounded theory. It also contributes to specific research on load-shifting at the appliance level by highlighting and discussing the key findings for the reader. In particular, it contributes to improving energy efficiency by describing load-shifting methods at the appliance level and identifying both controllable and uncontrollable appliances. This detailed literature review at the appliance level can make valuable contributions in support of decision- and policymaking by illuminating new dynamic systems specifically in load-shifting and in demand-side management in general for energy efficiency purposes.

Toward environmental sustainability: data-driven analysis of energy use patterns and load profiles for urban electric vehicle fleets

The scale-up of urban electric vehicle (EV) fleets, driven by environmental benefits, is resulting in surging aggregate energy demands that may reshape a city's power supply. This paper establishes an integrated data-driven assessment model for investigating the energy use (kWh) patterns and charging load (kW) profiles of urban-scale EV fleets. To this end, urban EV operating and operational datasets are linked with climate data and vehicle specifications. Four vehicle fleet types are distinguished: private, taxi, rental, and business fleets. Statistical models regarding distribution analysis, spectrum analysis, and identical distribution tests are employed to analyze the patterns of driving distances, energy consumption, and shares of active charging EVs. The minute-level changes in charging EV numbers and aggregate charging power are examined to reflect the grid load impact. The results show that private light-duty EVs in Beijing consume an average of 9.1 kWh/day, with more charging activities on Fridays. The primary load peaks of light-duty EVs in Beijing usually occur between 11 p.m. and 1 a.m., attributable chiefly to the private fleet's midnight peak load estimated at 28 % of the total daily charging private EV count multiplied by 5.5 kW/EV. Secondary peaks occur between 8 a.m. and 10 a.m. on weekdays for private fleets, and at 4 p.m. for public fleets. Our work can be extensively used for analyses on transport emissions, urban power supply, infrastructure build-ups, and policymaking.

A Comparative Analysis of Electricity Load Profiles in Some European and Asian Countries. Part 2. Daily Load Profiles

A Comparative Analysis of Electricity Load Profiles in Some European and Asian Countries. Part 2. Daily Load Profiles

Load Profile and Load Flow Analysis for a Grid System with Electric Vehicles Using a Hybrid Optimization Algorithm

As they become more widespread, electric vehicles (EVs) will require more electricity to charge. It is expected that a range of grid transportation solutions that complement one another and considerable transmission infrastructure changes will be needed to achieve this goal. Strategic planning and control, including economic models and strategies to engage and reward users, can reduce energy loss on the power network. This would eliminate grid upgrades. Bidirectional charging of EVs can help transmission systems cope with EV allocation. Power loss and voltage instability are the transmission network’s biggest issues. Adding EV units to the transmission network usually solves these problems. Therefore, EVs need the right layout and proportions. This study determined where and how many radial transmission network EVs there should be before and after the adjustment. To discover the best EV position and size before and after the dial network modification, a hybrid genetic algorithm for particle swarm optimization (HGAIPSO) was utilized. Electric vehicles coordinated in an active transmission network reduce power losses, raise voltage profiles, and improve system stability. Electric vehicles are responsible for these benefits. The simulation showed that adding EVs to the testing system reduced power waste. The system’s minimum bus voltage likewise increased. The proposed technology reduced transmission system voltage fluctuations and power losses, according to the comparison analysis. The IEEE-30 bus test system reduced real power loss by 40.70%, 36.24%, and 42.94% for the type A, type B, and type C EV allocations, respectively. The IEEE-30 bus voltage reached 1.01 pu.

Validation and Benchmarking of Battery Management System

Abstract: Several benchmarking methods are developed for validation and comparability of Battery Management System (BMS) but due to varying methods and several parameters it is very hard to compare. State of Charge (SOC) validation is proposed in this paper, where based on several parameters a load cycle is developed. Frequency analysis is performed on varying driving loads and on certain timed points. This analysis of load profile is further used to create three validation profiles. On different temperatures the behaviour of the profile is observed. Long-term tests are preferred over one set-pair of temperature range. For estimation accuracy, transient behaviour, drift, failure, and temperature, many grading techniques are utilised. stability and residual charge estimation to evaluate the performance of different state estimations

Load Profile Analysis in Electrical Systems: The Impact of Electrical Signature and Monitoring Quality in the Energy Digitalization Process

Load Profile Analysis in Electrical Systems: The Impact of Electrical Signature and Monitoring Quality in the Energy Digitalization Process

An efficient of estimation the load profile analysis of photo voltaic system with different shading of local city

In this paper, we analysis the last technology of photovoltaic (PV) system and the main effective factors of operation in unique efficiency and optimize performance. the first of all we take the general view of physical aspects of sun light and motion of earth with respect of sun position, and analysis of light spectrum and its effect for each regen from spectrum on PV. We illustrated the results of variation of irradiance from deferent sites and show size system for same energy demand. We start by analysis of load-by-load profile working and go through to specify the economic azimuth degree and altitude degree depend on load profile and final put some technical suggestions to minimize the effect of shading and benefit from using a new technology by face shield (half cut cell). These calculations are proposed to make available well originated and equivalent important information so as to allow PV strategies to change quicker obsessed by arenas of application with different shading of local cityin AL-Mausaib Babylon, Iraq. The<em> </em>ratios of shading conditions (10%, 25%, 50%, 75%) and without shading were used as effectiveness restrictions. The outcomes confirmation that the performance of PV system is extensively reduced and the system can’t control the batteries, if the shading close 75% or additional for single panel.

A novel cluster-specific analysis framework for demand-side management and net metering using smart meter data

Demand-side management (DSM) and net metering (NM) plays a critical role in managing the energy imbalance by supervising the customer behavioural patterns. The availability of fine-grained energy consumption information from the smart meter empowers the energy policy-makers to design realistic DSM and NM plans with a detailed analysis of usage patterns, dynamics, and characteristics. This paper presents a novel cluster-specific analysis framework to examine trends in residential electricity consumption to understand their potential for participating in energy efficiency, demand response, and net metering programs. In this process, the proposed framework utilises the Hierarchical clustering on energy usage patterns of residential consumers of Kolkata, India to obtain the representative profiles. Further, six different load shape characteristics are proposed to semantically analyse the clustered profiles for understanding the underlying diversity in consumption behaviour. Moreover, a novel statistical approach is incorporated into the proposed framework to evaluate the electricity consumption of every customer based on the number of peaks in a day, solar size required, and consumption during solar generation hours. The findings emphasise the critical nature of examining consumption habits in order to execute rational and productive decisions for the residential sector The scalability experiment demonstrates the effectiveness of the proposed framework. The cluster-specific analysis thus recommends power utilities in categorising the groups to participate in the demand-side management, net metering, and hybrid programs based on their respective potential. This would also help utilities in better planning for future electricity needs with an increase in renewable energy sources. • A framework for determining the potential of customers for DSM & NM is proposed. • Practical analysis of load profiles using distinct characteristics is presented. • The number of peaks per day & usage during PV generation hours are key parameters. • The implications for DSM & NM are presented for customer recruitment process.

Study of Integrated Run off River Hydro and Solar PV Power Generation Scheme in Sikkim

Sikkim is naturally endowed with rich Hydro Power potential. The State’s generation capacity is contributed solely by the hydro stations. But recent years these stations are encountering increasing number of uncertain weathers, which resulted into shortfall of precipitation during monsoon and higher abnormal rainfall during pre-monsoon times. Though some of the rivers are perennial but these uncertainty are creating challenge for them. More over the state has a large number of Run off river (RoR) hydro systems, which has a limited storage capacity. This is practically impossible to store the amount of water resource required by the hydro power plant through the use of such small pondage system. Neither this practice is permitted. The uncertain whether creates threat to the power supply, in this backdrop the present work aims to integrate the existing RoR hydro power plant with a reasonable amount of solar PV to maintain the supply reliability throughout the year. The present work involves a pre-HOMER analysis of load profile, resources at the site and post-HOMER analysis and discusses issues that are likely to affect/influence the realization of the optimal solution.

A Novel Stochastic Model for Hourly Electricity Load Profile Analysis of Rural Districts in Fujian, China

Renewable energy is important for achieving carbon neutralization. However, power generation from renewable energy sources can be uncertain and uncontrollable. Therefore, understanding the features of energy demand is pivotal for integrating renewable energy sources and storage systems within the entire energy network. Traditional load profiles are depicted by fixed typical load curves, which cannot support detailed dynamic simulations of annual hourly electricity consumption. Here, a novel stochastic model for hourly electricity load profile analysis is proposed. A clustering-based model of hourly load was constructed to depict load profiles of typical days, while a non-linear regression method determined the temperature-related factors within annual daily load consumptions, based on which a stochastic simulation model was established for hourly electricity load profiles. The model’s performance was tested with electricity data of rural districts in Fujian Province, China. The proposed model achieved a coefficient of variation of the mean absolute error of 15.7%, which was significantly lower than that of the traditional model. Further, a simplified case was employed to analyze the application of the proposed stochastic model in the design of energy storage systems. The proposed method enables the optimal design of integrated energy networks with renewable energy sources and energy storage systems.

Analysis of electricity consumption behaviors based on principal component analysis and density peak clustering

SUMMARYAnalysis of electricity consumption behaviors lays the foundation for power grid planning, demand‐side response, electricity pricing, and energy efficiency improvement. In this study, we used the principal component analysis (PCA) to reduce the dimensionality of electricity load data and used the density peak clustering algorithm based on K‐nearest neighbors and shared nearest neighbor similarity (DPC‐KS) for cluster analysis of load profiles so as to obtain the electricity consumption behaviors of customers. In DPC‐KS, the local density was defined by integrating the idea of K‐nearest neighbors to find the density peaks, which promoted the accuracy of the cluster centers found. Also, a sample similarity measure criterion of shared nearest neighbor similarity was defined, a sample similarity matrix was built, and the samples were allocated accordingly, which enables a more accurate allocation of the remaining samples. Additionally, PCA and DPC‐KS were used to conduct cluster analysis for the electricity load data of 315 dedicated substation customers in a region, and four types of electricity consumption behaviors were obtained and analyzed. The experiments validated the effectiveness of DPC‐KS, and DPC‐KS provided technical support for intelligent decision‐making in the power grid.

Analysis of Load Profiles with Dynamic Loads, External Consumption and Added PV Generation

Analysis of Load Profiles with Dynamic Loads, External Consumption and Added PV Generation

Understanding Load Profiles of Mini-Grid Customers in Tanzania

Strategies for meeting Sustainable Development Goal 7 of providing access to electricity for all recognize the important role that off-grid solutions will need to play. Mini-grids will from part of this response, yet little data exists on household demand from these customers. Predicting demand accurately is a crucial part of planning financially viable mini-grid systems, so it is important to understand demand as fully as possible. This paper draws on metered data from two solar PV diesel hybrid mini-grid sites in Tanzania. It presents an analysis of load profiles from the different sites and categorizes households by demand characteristics. The paper then combines load profile data with household demographic and electrical asset ownership data to explore factors behind distinct load profile patterns of use. It concludes that load profiles are determined by a complex mix of appliance ownership, occupancy, and socio-economic status.

Impact of Revised Time of Use Tariff on Variable Renewable Energy Curtailment on Jeju Island

Jeju Island announced the “Carbon Free Island (CFI) Plan by 2030” in 2012. This plan aims to replace conventional generators with distributed energy resources (DERs) up to a level of 70% by 2030. Akin to Jeju Island, as DERs have been expanded in islanded power systems, variable renewable energy (VRE) has become a significant component of DERs. However, VRE curtailment can occur to meet power balance, and VRE curtailment generally causes energy waste and low efficiency, so it should be minimized. This paper first presents a systematic procedure for estimating the annual VRE curtailment for the stable operation of the islanded power systems. In this procedure, the VRE curtailment is estimated based on the power demand, the grid interconnection, the capacity factor of VRE, and conventional generators in the base year. Next, through the analysis of the hourly net load profile for the year in which the VRE curtailment is expected to occur, a procedure was proposed to find the season and hour when VRE curtailment occurs the most. It could be applied to revised Time-of-Use (ToU) tariff rates as the most cost-effective mitigation method of VRE curtailment on the retail market-side. Finally, price elasticity of electricity demand was presented for applying the revised ToU tariff rate scenarios in a specific season and hour, which found that VRE curtailment occurred the most. Considering self- and cross-price elasticity of electricity, revised ToU tariff rate scenarios were used in a case study on Jeju Island. Eventually, it was confirmed that VRE curtailment could be mitigated when the revised ToU tariff rates were applied, considering the price elasticity of demand.

An empirical analysis of domestic electricity load profiles: Who consumes how much and when?

With the increased share of renewables in power generation, end users play a key role in keeping the demand at levels that better match variable supply, maintaining lower overall system costs, and reducing carbon dioxide emissions. To increase the potential for demand-side flexibility, a deeper understanding of domestic electricity load profiles is needed. Informed by customer grouping based on similar consumption patterns and drivers, targeted interventions can be better designed to time-shift peak loads and reduce overall demand. Thus, the objectives of this study are (i) to segment households in relation to their electricity load patterns using K-means clustering and (ii) to investigate household characteristics that have an influence on electricity load patterns by employing multinomial probit regression. This study uses hourly electricity consumption for 2017, combined with population-based register data for a large sample of Danish households. The results indicate that four distinct Danish household groups are characterized by different timing and magnitudes of electricity consumption, which are influenced by specific sociodemographics and dwelling characteristics. Similarities between the groups emerge with respect to the evening peak consumption, seasonal variation in electricity demand, and weekend morning demand ramp-up. Challenges and opportunities for domestic load profiling in the power industry and policymaking are discussed.

Vehicle-to-Home Usage Scenarios for Self-Consumption Improvement of a Residential Prosumer With Photovoltaic Roof

This article proposes a procedure for the control of electric vehicle (EV) batteries, aiming to have an optimal matching between local renewable production, domestic loads, and EV consumption. The procedure starts with the analysis of historical photovoltaic (PV), EV, and domestic load profiles. Load and PV profiles are forecasted using statistical-based algorithms, while the expected patterns of EV usages are forecasted using a combination of statistics and clustering techniques. Then, the forecasted profiles are used to estimate future energy balances trough an optimization process. Finally, the real-time management corrects the forecasting logic and checks the parameters of the EV storage to guarantee its correct and safe operation. Three different EV usage profiles (obtained by the clustering of 215 real users) are shown and their impact on the energy balance of EV–PV–home systems is quantified. The results are finally compared with those obtained with a traditional rule-based logic working without forecasts, by also reporting a detailed analysis of the main aspects having an impact on the results.

An Approach of Electrical Load Profile Analysis Based on Time Series Data Mining

In the current electrical load profile analysis, considering the shortage of traditional methods on the typical load profile extraction of single consumers and the load profile feature extraction, this paper proposes an approach based on time series data mining. Firstly, this method reduces the dimension of the load profile of a single consumer based on the Piecewise Aggregate Approximation(PAA), and re-expresses the load profile of the consumer over a period based on the Symbolic Aggregate approXimation(SAX), representing the consumer’s load profile with a symbolic string to extract the typical load profile. Then, combined with the load characteristic indices and time series-based features, the typical load profiles of different consumers are clustered based on the K-means algorithm to analyze the power consumption behaviors. Finally, this paper performs a case analysis with a UCI test data set, and the results show that the proposed approach can excavate typical power consumption behaviors of consumers and improve the electrical load profile analysis efficiency and the clustering quality.

A Model of Customizing Electricity Retail Prices Based on Load Profile Clustering Analysis

The problem of customizing electricity retail prices using data mining techniques is studied in this paper. The density-based spatial clustering of applications with noise is first applied to load profile analysis, in order to explore end-users’ inherent electricity consumption patterns from their historical load data. Then, statistical analysis of end-users’ historical consumption is conducted to better capture their consumption regularity. After extracting these load features, a mixed integer nonlinear programming model for customizing electricity retail prices is proposed. In the proposed model, both the structure of time-of-use (TOU) retail price and the price level are optimized once given the number of price blocks. It is among the first that the optimization of TOU price structure is studied in electricity retail pricing research. The proposed model is mathematically reformulated and solved by online commercial solvers provided by the network-enabled optimization system server. Electricity usage data collected by the smart grid, smart city project in Australia is used to demonstrate the feasibility and efficiency of the developed models and algorithms.