Daily Load Profile Research Articles

Multi-objective-based economic and emission dispatch with integration of wind energy sources using different optimization algorithms

In this work, a study of economic and emission dispatch issues based on the multi-objective optimization is solved, and generation costs and emissions are reduced by utilizing multi-objective optimization techniques. This optimization is carried out in an IEEE-30 bus system, with and without the integration of wind energy sources, with equality and inequality constraints. The equality constraints are the power balance constraints, stipulating that to have an optimal solution, the generated power must be adequate to satisfy the load demand plus losses. The inequality constraints are a collection of limitations for active power generation, reactive power generation, generator bus voltage, and load bus voltage. To track the hourly load demand, a daily load profile is established using the IEEE-30 bus system. The generation costs and emissions in the system are optimized using multi-objective particle swarm optimization and multi-objective Ant–Lion Optimization approaches. In order to determine the goals’ minimum values, a fuzzy min–max technique is applied. The values that have been minimized are then compared to determine how well wind energy integration has reduced the generation costs and emissions. Two case studies are performed in this work. For Case 1, the total generation costs and emissions using MOPSO are less, with a difference of $42.763, while MOALO has lower emissions, with a difference of 157.337 tons. For Case 2, with the implementation of wind energy, MOPSO has lower total generation costs, with a difference of $51.678, and lower emissions, with a difference of 459.446 tons.

Characterizing residential sector load curves from smart meter datasets

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.

Forecasting urban electric vehicle charging power demand based on travel trajectory simulation in the realistic urban street network

This paper presents a spatial-temporal urban charging power demand forecasting method based on EV travel trajectory simulation in the integrated urban street network and functional zones. Each EV trip is simulated via a travel trajectory based on the EV user’s travel behavior, which refers to the user’s decision-making processes regarding the choices of trip purpose, departure time, destination, trip route, parking time, and the time, location, and mode of EV charging. EV’s daily travel trajectory is built by merging the travel trajectories of the EV’s daily trips. The spatial-temporal slow and fast urban charging power demands are predicted using the simulated daily travel trajectories of available EVs in the urban area. The proposed simulation method is applied in the urban area of Campinas, Brazil. The results indicate that EVs’ daily travel trajectories are realistically produced to predict the daily charging load profiles at urban activity locations and functional zones.

Design of daily load profiles in environmentally friendly commercial and industrial microgrids

The purpose of the study is to solve the problem of design of daily load profiles for optimal control of an environmentally friendly commercial and industrial microgrid (CIM) (power is generated only by renewable energy sources) connected to the power system by a power transmission line. This goal is achieved by adjusting the planned daily load profiles of consumers located on the territory of the CIM. The adjustment means shifting power consumption to another time of the day in question (power consumption is delayed). The problem of the delayed power allocation is represented as an optimization multiple knapsack problem that adapts to the problem-solving process. The proposed algorithm was tested on a 6-node system according to the scenario that involved adjustment of the load profile in the CIM to ensure that the power flow from the power system remains within specified limits. Compliance with the limits guarantees uninterrupted power supply from the power system, which is a fundamental requirement when developing load profiles. Experiments were carried out to evaluate the delivery of uninterrupted power supply to CIM consumers depending on the initial data. The findings indicate that the disruptions in power supply to CIM consumers are completely eliminated if the load can be divided to shift it to other hours of the day and when the load of 0.151 MW is disconnected in operating state 7. The total load should be divided into at least three parts. Disconnection of 0.151 MW is performed to prevent disconnection of the commercial and industrial microgrid from the power system, which would result in a power deficit of 4.652 MW.

A Novel Statistical Framework for Optimal Sizing of Grid-Connected Photovoltaic–Battery Systems for Peak Demand Reduction to Flatten Daily Load Profiles

Integrating photovoltaic (PV) systems plays a pivotal role in the global shift toward renewable energy, offering significant environmental benefits. However, the PV installation should provide financial benefits for the utilities. Considering that the utility companies often incur costs for both energy and peak demand, PV installations should aim to reduce both energy and peak demand charges. Although PV systems can reduce energy needs during the day, their effectiveness in reducing peak demand, particularly in the early morning and late evening, is limited, as PV generation is zero or negligible at those times. To address this limitation, battery storage systems are utilized for storing energy during off-peak hours and releasing it during peak times. However, finding the optimal size of PV and the accompanying battery remains a challenge. While valuable optimization models have been developed to determine the optimal size of PV–battery systems, a certain gap remains where peak demand reduction has not been sufficiently addressed in the optimization process. Recognizing this gap, this study proposes a novel statistical model to optimize PV–battery system size for peak demand reduction. The model aims to flatten 95% of daily peak demands up to a certain demand threshold, ensuring consistent energy supply and financial benefit for utility companies. A straightforward and effective search methodology is employed to determine the optimal system sizes. Additionally, the model’s effectiveness is rigorously tested through a modified Monte Carlo simulation coupled with time series clustering to generate various scenarios to assess performance under different conditions. The results indicate that the optimal PV–battery system successfully flattens 95% of daily peak demand with a selected threshold of 2000 kW, yielding a financial benefit of USD 812,648 over 20 years.

Five-Port Isolated Bidirectional DC-DC Converter for Interfacing a Hybrid Photovoltaic–Fuel Cell–Battery System with Bipolar DC Microgrids

This paper introduces a novel five-port, three-input, dual-output isolated bidirectional dc-dc converter (FPIBC) topology with an effective controller for power-sharing and voltage-balancing in bipolar dc microgrids (BPDCMGs). The proposed converter acts as the interface for the integration of a hybrid generation system comprising a solid oxide fuel cell (SOFC), a photovoltaic (PV) system, and a battery into BPDCMGs. It employs a reduced number of circuit elements compared with similar multiport converter topologies suggested for BPDCMG applications. Symmetrical bipolar output voltages are ensured by a voltage-balancing circuit composed of a fully controlled switch and four diodes. The FPIBC is equipped with different controllers for output voltage regulation and balancing, power sharing, maximum power point tracking of the PV, the optimum operating region of the SOFC, and constant-current, constant-voltage charging of the battery. To verify the viability and effectiveness of the proposed system, a simulation model was developed with a 4.2 kW SOFC, a 3.7 kW PV, and a 140 V 10.8 Ah battery in MATLAB/Simulink. The performance of the FPIBC was evaluated through extensive case studies with different operational modes, including battery charge/discharge states and SOFC and PV parameter changes under varying load conditions. In addition, the proposed system was examined using a daily dynamic load profile. According to the simulation results, a peak efficiency of 97.28% is achieved and the voltage imbalance between the output ports is maintained below 0.5%. It is shown that the FPIBC has advantages over previous converters in terms of the number of ports, number of circuit elements, bipolar output voltage, bidirectional power flow, and efficiency.

Appliance-wise residential monthly average daily load profile simulation for 2022 and 2030 in Auroville, India

Appliance-wise residential monthly average daily load profile simulation for 2022 and 2030 in Auroville, India

Design of daily load profiles in commercial and industrial microgrids based on renewable energy sources

The commercial and industrial microgrid (CIM) based on renewable energy sources is a solution to the environmental problems. This paper examines CIM based on renewable energy sources connected to the power system by one power transmission line. The power flow from the power system and the limits of its deviations are determined in advance. Compliance with the limits is a basic condition when design of daily load profiles in CIM, because this guarantees uninterrupted power supply from the power system at normal prices. The research is aimed at solving the problem of generating daily load profiles for CIM. The problem is solved using the resources of active consumers registered in the demand management program based on the “load shifting” technique and considering the resources of energy storage systems. An algorithm for generating daily load profiles is developed, which allows the use of various methods for calculating the optimal allocation of shifted load. The proposed algorithm was tested on a 6-node circuit. The resulting daily load profiles satisfy the specified conditions.

Intelligent categorization and interactive mechanism for smart demand side management of residential consumers

Power Grids are becoming resilient to the erupting complications of imbalanced supply and demand through intelligent technologies that analyze and allow communication between utility and consumers. This paper proposes a strategy for smart energy management among residential consumers using machine learning algorithm that intelligently classifies high power consumers contributing to peak demand in every quarter of a day as Category A, Category B, Category C and Category D. Classification is performed on daily load profile data of 29,546 residential consumers using different supervised learning techniques. The raw data of consumers are preprocessed with statistical analysis to label peak consumers in different time slots and highest testing accuracy of 92.23% has been achieved with Random Forest classifier. The peak loads of classified consumers are then individually clipped to curtail the load of every consumer within baseline power of every quarter. Adoption of this methodology will facilitate to provide recommendations to every classified consumer of different categories for load curtailment and imposition of penalty based on the baseline limit. The power deficits during different quarters of a day estimated as 0.968%, 0.740%, 0.993% and 0.164% of load have been effectively adjusted by the classified consumers with per capita load adjustment of 3.641 kW, 1.164 kW, 1.628 kW and 0.984 kW respectively. Thus, overall power deficit, 0.73% of daily total load (12,265.7 kW) has been mitigated through intelligent distribution of load with minimal stress on consumers, which proves the efficiency of proposed Demand Side Management algorithm in sustaining growing demands within limited resources.

Optimal peak-shaving for dynamic demand response in smart Malaysian commercial buildings utilizing an efficient PV-BES system

Reducing peak demand on the utility grid benefits both grid operators and consumers. However, achieving this goal while maintaining human comfort presents a significant challenge. This study proposes the deployment of an energy-efficient grid-connected solar photovoltaic (PV) and battery energy storage (BES) system to perform peak shaving. We employ an optimal rule-based control strategy that considers day-ahead PV power and load demand predictions, along with a daily load profile limit, specifically designed for the C1 tariff in Malaysian Commercial Buildings. The peak shaving control strategy proactively determines optimal schedules for battery charging and discharging, aiming to effectively minimize peak demand. To regulate the daily demand accordingly, we employ a root-finding algorithm to determine the optimal demand limit in conjunction with the formulated rule-based peak-shaving control strategy. The required optimal inputs for the rule-based control strategy are determined using a genetic algorithm, which minimizes peak grid power. We verify the effectiveness of the rule-based peak-shaving method using MATLAB. The results demonstrate significant reductions in both energy consumption and peak demand for different load profiles. Specifically, we achieve a reduction of 19.02%, 20.9%, 4.72%, and 0% in energy consumption, and 42.46%, 49.82%, 40.44%, and 33.1% in peak demand for case studies (CS) 1, 2, 3, and 4, respectively. Furthermore, we observe that the application of the rule-based peak shaving method maintains a state-of-charge (SOC) of the battery at 50% by the end of the day, allowing for day-to-day management flexibility.

Comparative techno-economic assessment of electric vehicle charging preferences: A quantitative study for sustainable EV policy in Pakistan

Sustainable induction of Electric vehicles (EV) adoption in developing countries requires a comprehensive policy and planning. Among several challenges, one of the biggest challenges is a sustainable charging station infrastructure integration to the existing weak distribution power network. Based on technological advancements, EV owners enjoy better milage in one charge. This has given a liberty to charge EVs as per the desire of user. There are two kinds of charging preferences, one is home charging and the other is fast charging. Both charging choices have distinct economic and technical aspects that need to be quantified in line with the available power infrastructure so as to develop a policy regulation. This study aims to quantitatively assess home charging, level 3 fast charging and their techno-economic aspects using social demographics of car owners. To analyze the demographics of local vehicle mobility practices in Pakistan, a comprehensive survey comprising online questionnaire was conducted. >100 car owners were surveyed about their daily driving practices to understand the driving distances and their preference of charging vehicles. The driving owners from distinct occupations ranging from cab drivers, job holders and businessmen participated in the survey. The survey facilitated to identify the demographics of the car owners which helped in conducting simulations and quantification analysis. Monte Carlo simulation was used for 1000 cars samples to estimate the level 1 and level 3 charging profiles in Python. In second step, HOMER Grid software was used to economically assess the impact of home charging and fast charging. It was revealed that charging choices have significant impact on the daily load profile of EVs. While having home charging, 59.9 % higher units are purchased from the grid. Whereas 65.5 NPC is increased for the project. On the other hand, the daily profile of preferred home charging is 61.4 % higher compared to the fast charging due to much concentrated charging impact during the limited time of the day. Results highlighted that Solar PV plays an important role for fast and home charging. Without solar PV charging stations will consume 60 % more grid units and 58 % higher Net present cost. The study concludes with a set of policy recommendations that must be taken into account to support the weak grid and lessen the EV charging impact on main utility.

Hosting capacity of distribution networks for controlled and uncontrolled residential EV charging with static and dynamic thermal ratings of network components

AbstractThe ongoing electrification of road transportation sector, which is expected to continue to strongly increase over the next years, will result in the connection of a significant number of electric vehicle (EV) chargers in LV and MV distribution networks, particularly in residential applications with on‐board (“slow”) EV chargers. In order to evaluate loading limits of existing distribution networks for the maximum number of EV chargers that can be safely connected (commonly denoted as a network EV “hosting capacity”, HC), this paper introduces a general approach to determine one commonly used network design parameter (after‐diversity maximum demand, ADMD) and one new parameter (maximum daily energy demand, MDED), which are both obtained from the load profiles of maximum per‐hour demands for uncontrolled residential EV charging. The presented approach uses actual EV charging data from the UK as the inputs in Monte Carlo simulations to generate daily EV charging profiles for arbitrary numbers of EVs, enabling to identify related ADMD, MDED and per‐hour maximum demand values, as well as their seasonal variations. The assessed ADMD, MDED and hourly maximum EV charging demands for uncontrolled EV charging are then combined with available UK residential daily load profiles before the EVs are connected (“pre‐EV demands”), where their combined coincidental and noncoincidental maximum demands are evaluated against the static thermal rating (STR) and dynamic thermal rating (DTR) loading limits of network components (transformers and overhead lines), taking into account relevant weather/ambient conditions. This is denoted as a network HC for uncontrolled EV charging. Finally, evaluating the resulting per‐hour maximum demand values against the STR and DTR loading limits and MDED values allows to select one particular scheduling method for controlled EV charging, which gives the absolute maximum number of EVs that can be safely connected in the considered network, that is, maximum network HC for fully controlled EV charging. The presented approach is illustrated on the example of the IEEE 33‐bus test network (modelled using typical UK network components), for the pre‐EV residential demands taken from the recordings at a UK MV substation, and for ambient data taken from a UK Met Office weather station. Obtained results allow to evaluate the range of network EV HC values for uncontrolled and controlled EV charging, that is, lower and upper HC limits, which can be correlated with the commonly used allocations of the firm and non‐firm network HC, respectively.

Investigation on Energy Flow Performance of a Photovoltaic/Battery-Based Isolated Charging Station for Battery-Powered Electric Vehicles

Researchers are paying attention to the current trend of electric vehicles (EVs), but there are not many places to charge them and serve as electric vehicle charging stations (EVCSs). In order to prevent the lack of EVCS for battery EVs (BEVs) on any road with all modes of operation, this paper proposed the design of a novel configuration and control structures for the isolated charging station (ICS) based on the solar PV system for efficient energy utilization to charge battery EVs (BEVs) with flexible double function cascaded converter (MPPT and DC-link voltage regulation). This was done with the aid of a battery energy storage system (BESS) as a backup/auxiliary source. The PV system is reliant on solar radiation and the daily load profile. Therefore, the BESS is employed to provide continuous power to BEVs and overcome PV’s intermittent nature. The cascaded converter is used to achieve the MPP and to regulate the DC-link voltage to its reference value (100 V), while the buck-boost matching bidirectional DC-DC voltage converters (MBDVCs) are used for charging and discharging mode/buck and boost mode, respectively, of BEVs and BESSs. Additionally, the MBDVCs are used as an interface between the ICS sources with the load through the developed control systems. The outcomes of various operating modes show that the system functions effectively. To verify the system performance, the MATLAB/Simulink application builds the system setup.

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

Stochastic generation of residential load profiles with realistic variability based on wavelet-decomposed smart meter data

Residential smart meter data with high time resolution are integral to many data-driven applications, ranging from hosting capacity studies to R&D activities of private enterprises. However, privacy legislation restricts public availability of large-scale datasets. Furthermore, existing datasets may suffer from imbalances in terms of underrepresented classes. To address these concerns, this study presents a novel decomposition–recombination approach for generating synthetic load profiles that exhibit realistic variability and demand peaks. High-frequency load profiles are decomposed into a low-frequency base load and high-frequency variability at the daily level through a discrete wavelet transformation. Components from different households are subsequently rescaled, shifted and recombined in a stochastic load profile generator to obtain new daily load profiles with high-fidelity behavior. The performance of this generator is evaluated through benchmarking, resulting in a mean average error of 0.09 kW on an average value of less than 3 kW for the daily peaks, whilst preserving their seasonality. The introduced load profile generator is validated as an alternative to privacy-sensitive residential smart meter data in a hosting capacity case study. The analysis focuses on the voltage drop caused by residential electric vehicle charging, considering both real and synthetic data. The synthetic data demonstrated voltage drops with a mean average error less than 0.2 V for the 10th and 90th percentile when benchmarked with respect to the real voltage level distribution. The introduced decomposition–recombination method is shown to accurately capture the high-frequency variability and peak behavior, and is suitable for practical applications at the daily level.

Changes in energy use profiles derived from electricity smart meter readings of residential buildings in Milan before, during and after the COVID-19 main lockdown

The COVID-19 pandemic had a profound impact on society, causing changes in various aspects of people's lives, including their energy use habits. This has prompted a need for checking and updating standard energy use profiles, particularly for residential electricity use. To address this topic, a study was conducted on 24 multifamily buildings in Milan, using clustering to extract patterns from a database of quarter-hourly electricity use data from 2019 to 2020. This study found an increase in electricity usage during the COVID-19 lockdown period for residential buildings, likely associated with the imposed restrictions. The research also highlighted a shift in energy usage from the morning peak to the central hours of the day during the working days of the lockdown period, while a gradual increase in electricity usage throughout the day and no morning peak was observed during the Autumn (post-COVID) period. The findings can assist regulators and businesses in weighing the benefits and drawbacks of remote working and provide modellers with a complete set of daily load profiles for an Italian residential case study.

Reactive power control of solar photovoltaic inverters for grid code compliance support

The compensation of reactive power in smart inverters is one solution to address the issue of voltage violations in the distribution network due to the penetration of solar photovoltaic power generation. However, options for reactive power control are limited during variations in irradiation and daily load on the feeder. This study aims to investigate the performance difference between four reactive power control techniques including Q(V) control, Q(P) control, fixed Q-Var, and fixed power factor (PF) available in smart inverters to reduce voltage violations due to PV integration and comply with the grid-code. Three-phase balanced power flow was simulated in a medium voltage distribution network (MVDN) considering the reactive power control mode of the inverter under variations in solar radiation and daily load. The results showed that the Q(V) control was more effective in improving distribution feeder voltage than other techniques and showed its compliance with the grid-code. The limiting setting point for var injection or power factor limit should be proportional to the daily grid load profile.

Personalized retail pricing design for smart metering consumers in electricity market

In the current deregulated electricity market, flexible consumers are more active in participating in market activities via the representation of electricity retailers. However, without an effective communication infrastructure, the connection between retailers and the consumers they serve is incomplete. Nowadays, smart meters are being rolled out worldwide to enhance the connection and data exchanges between retailers and consumers. Specifically, smart meters enable retailers to provide customers with detailed information about retail tariffs and their energy usage at different times of the day, which in turn enables customers to manage their energy use more proactively. This paper drops this assumption and makes use of data acquired from smart meters to design a personalized retail pricing scheme for different types of consumers. To formulate this problem, a bi-level optimization model is proposed, with the upper-level problem representing the pricing decision made by the retailer and two lower-level problems representing the demand response of consumers and the wholesale market clearing process, respectively. Afterward, we convert this bi-level optimization model into a single-level mathematical program with equilibrium constraints by using its Karush Kuhn Tucker optimality conditions and complementary conditions. The scope of the examined case studies is fourfold. First, consumers are classified based on their daily load profiles using the advanced clustering method. Second, the physical benefit of fully exploring the consumer’s demand flexibility as well as the economic benefits of increasing retailers’ profitability and reducing consumers’ energy bills are evaluated with respect to the traditional uniform retail pricing scheme. Third, the impacts of consumers’ demand flexibility on electricity market outcomes and business cases are investigated. Finally, the proposed personalized retail pricing scheme is verified to relieve the strategic retailer’s market power reduction caused by the flexibility of demand, which is beneficial to the retailer’s profitability.

Performance Evaluation Method of Day-Ahead Load Prediction Models in a District Heating and Cooling System: A Case Study

Many researchers are devoted to improving the prediction accuracy of daily load profiles, so as to optimize day-ahead operation strategies to achieve the most efficient operation of district heating and cooling (DHC) systems; however, studies on load prediction and operation strategy optimization are generally isolated, which leaves the following question: what day-head load prediction performance should be paid attention to in the operation optimization of DHC systems? In order to explain this issue, and taking an actual DHC system as a case study, this paper proposes an evaluation method for the prediction of daily cooling load profiles by considering the impact of inaccurate prediction on the operation of a DHC system. The evaluation results show the following: (1) When prediction models for daily load profiles are developed, the prediction accuracy of the daily mean load should be emphasized, and there is no need to painstakingly increase the accuracy of load profile shapes. (2) CV and RMSE are the most suitable deviation measures (compared to others, e.g., MAPE, MAE, etc.) for the evaluation of load prediction models. A prediction model with 27.8% deviation (CV) only causes a 3.74% deviation in operation costs; thus, the prediction performance is enough to meet the engineering requirements for the DHC system in this paper.

A PT-DA-Based electric taxi charging load prediction method considering environmental factors

The electric taxis (ETs) charging load is of great importance for the normal and the stable operation of power systems. A prediction model for the ET spatio-temporal charging load is proposed, which considers the drivers’ subjective decision-making and the ET travel patterns. First, an improved Gaussian mixture model with a genetic algorithm is developed based on the real ET data. Besides, a prospect theory based on the Dijkstra algorithm for choosing charging stations is put forward. Then, considering the ambient temperature, a calculation method for the remaining energy in the ET battery is utilized. Based on the refined travel patterns and the Monte Carlo method, the charging load in three scenarios are analyzed, which studies the influence of temperature. ET charging loads of different scales are obtained and the influence of charging load on the typical daily load profile is discussed. The simulation results show that the prediction models can combine the drivers’ psychology with the objective traveling time, and the average error between the real load and the predicted load is less than 1%.