Daily Load Profile Research Articles

Modelling and Validation of Typical PV Mini-Grids in Kenya: Experience from RESILIENT Project

PV-based mini-grids are identified as a feasible and, often, only economically viable option for the electrification of Kenyan remote and scattered rural areas, where connection to the national grid is challenging, and the related costs are high, if not prohibitive. This paper presents the analysis of typical Kenyan PV mini-grids by using some results of the work in the project “Reliable, Efficient and Sustainable Mini-Grids for Rural Infrastructure Development in Kenya (RESILIENT)”. After presenting average annual and seasonal daily load profiles of residential and small commercial mini-grid customers identified from the measured demands, the paper introduces the main mini-grid components and their models, including a simplified, but reasonably accurate, model of a mini-grid battery storage system based on the manufacturer’s charge–discharge curves. All mini-grid components are assembled in a scalable and easily reconfigurable simulation model of an actual Kenyan PV mini-grid, and they are implemented for the evaluation of PV mini-grid performance and the potential for expansion and connection of additional residential and small commercial customers. During the validation of the developed simulation model using available measurement data, an empirical approach for adjusting the PV system output power is specified for a more accurate match with the measurements. The presented results indicate the importance of the information on the actual control algorithms and control settings of the mini-grid energy management systems, on the thermal dependencies and characteristics of both PV generation system and battery storage system, and on the availability of on-site measurements of temperature and input solar irradiance. The developed PV mini-grid model can be used for further analyses, such as to study the techno-economic performance of different mini-grid configurations, to identify the optimal sizing of mini-grid components, and to specify efficient control and operation schemes based on the locally available resources.

Off grid PV/Diesel/Wind/Batteries energy system options for the electrification of isolated regions of Chad

Access to reliable energy is fundamental for the development of any community. The electricity is produced in Chad solely from thermal plants that use fossil fuels, which are not environmentally friendly. In addition, the electrification rate of Chad is less than 11%. This work aims to propose some reliable electrification options for Chad, through hybrid energy systems. To achieve this objective, autonomous hybrid PV/Diesel/Wind/Batteries feasibility to meet the demand of electrical load in isolated regions of Chad is evaluated using HOMER software. The design is done considering three types of daily load profiles in each of the 16 regions that are not yet electrified in Chad; the low, medium and high community load profiles. From the simulation, it was observed that the optimal configurations were: PV/Battery, PV/Diesel/Battery and PV/Wind/Diesel/Battery for various consumers and sites. The COE was found to be in the range of 0.367 and 0.529 US$/kWh which shows that, the COE of some sites are less than the production cost of energy in Chad (0.400 US$/kWh) and therefore profitable. Using these hybrid systems, compared to single diesel generator will result in less CO2 emission per year (between 0 and 15670 kg/year). These results may guide investors and policies makers in the planning and implementation of various optimal feasible options that may be used to increase the electricity access rate of Chad, especially in remote areas.

Economic analysis of annual load loss due to voltage sags in industrial distribution networks with distributed PVs

In an industry, analysis of load disconnection indices is essential to determine the annual economic loss because of the high cost of load interruptions. With the interconnection of PVs in modern industrial networks, this analysis gets more complicated due to controls associated with PV inverters for reactive power support. Hence, the paper proposes a unique method for such analysis that relies on dynamic simulation to calculate load disconnections. The dynamic simulation allows the integration of the voltage tolerance curve of loads and controls associated with PVs making the scenarios more realistic. The indices are determined by employing statistical experiments which account for annual variability of daily solar and load profiles with the least possible runs of dynamic simulation. The proposed method is applied to calculate load disconnection indices for an IEEE 399-1997 industrial distribution system. A comparative analysis of the cost of disconnection under a variety of solar penetration and operation scenarios is presented. Network planners and reliability engineers can potentially utilize the proposed method to identify PV inverter controls, placement, and size effects on the annual load disconnection indices due to voltage sags.

Economic Feasibility of a Hybrid Microgrid System for a Distributed Substation

A hybrid microgrid system is modelled using HOMER-Pro software for real-time load data and available energy sources at Elapakkam village under Ramapuram substation, Kanchipuram, Tamil Nadu, India. Optimization approaches are applied for the selection of rating of the physical components, including solar PV systems, diesel generators, storage batteries, converters, inverters, and economic parameters such as system cost, fuel cost, and cash flow. The daily community load profile for the year 2018 was estimated based on data from TANGEDCO. Accordingly, the total load demand for the village represented 8760 lines of hourly load. The aim of this paper is to select an optimal-sized and reliable hybrid microgrid system to meet the load demands with available energy inputs. However, a comparison based on the cost of energy (COE) and the penetration of renewable energy is carried out for different case studies in the village with the economic-feasibility analysis of various countries. From this analysis, emissions cannot be completely avoided, they could be minimized by combining existing systems with renewable energy systems.

A Bi-objective Optimization Study Based on Elite Selection for Electric Vehicle Charging

Electric vehicle charging increases the grid-side load, and the safety and economy of grid operation are affected. First, we consider the influence of climatic factors on EV charging and simulate the daily load profile under disorderly charging using the Monte Carlo method. Then, the elite selection is proposed in the context of traditional algorithms to establish a dual-objective optimization model with the minimum peak-to-valley difference on the grid side and the minimum total cost on the customer side. Finally, a city grid is used as an example to verify that the model is effective. The results show that the establishment of a time-of-use tariff for electricity market supply and demand and the use of elite selection bi-objective optimization can reduce peak-to-valley differences and smooth out grid fluctuations, improving the safety and economy of grid operation.

The effect of time resolution on the modelling of domestic solar energy systems

This paper investigates the modelling accuracy of small-scale solar energy systems, in particular the effect of changing the time resolution. Accurate modelling of solar energy systems is important in designing cost-effective solutions and in research into micro-grid functionality. Currently, most modelling uses a resolution of one hour when simulating the energy in small scale renewable energy systems. Within each hour, these models take an average of the load demand and solar generation values. An hourly time resolution is ignorant of the large variations in a daily load profile that result from high power appliances and also misses short-term variations in solar generation. Few researchers using hourly modelling consider the implications in terms of the simulation accuracy. In this paper, 3 load profiles are modelled with grid connected solar systems, one with and one without a battery, totalling 6 models. Each model is simulated for 4 seasons of the year, modelled at hourly, 5 min and 1 min time resolution. The results show that the hourly model was a poor predictor of battery behaviour, over-estimating the battery state of charge (SoC) by up to 10%. It is also shown that, for systems without battery, the quantity of energy exported and self-consumed changed by an average of 5% and 8% respectively. The study concludes that increasing time resolution from hourly to 5-min resolution in energy models would be advantageous, increasing their accuracy in terms of battery behaviour and predicted self-consumption of solar energy. In particular, modelling at 5-min instead of hourly resolution shows the full benefits of installing battery storage with solar systems.

Resource Allocation and Impact Analysis of the System Parameters on Performance in Multi-Cell Massive MIMO Networks

Abstract This paper investigates the energy-efficient resource allocation algorithm for a massive multiple input multiple output (MIMO) system, in which each base station adapts the number of antennas to the daily load profile. Our paper examines the effect of two user location distribution (ULD) models, on the energy-efficiency (EE) of load adaptive masive MIMO system. We propose a resource allocation strategy to adapt the number of antennas based on tracking variations of ULD and cell loading maximizing the EE. We also evaluate impact of cell size, available bandwidth and output power level of the BS on EE at different cell loading.

A demand side response scheme for enhancing power system security in the presence of wind power

One of the major concerns associated with the wind power integration is its anti-peaking characteristic which increases the required ramping of the conventional generators and potentially threatens power system security. Recently, demand side response has become one of the likely successful solutions that can be employed in overcoming the negative impact of the anti-peaking characteristic of the daily net load curve. This paper presents a demand side response framework in which the load shifting strategy is effectively applied to improve the daily load profile of the system in the presence of wind power. The size of the load to be shifted is determined such that the system security in terms of voltage magnitudes and voltage stability margins is preserved for the normal state and prospective emergency states. To stimulate the demand side response candidates to shift their load and support the utilities to realize a particular daily load shape, an incentive reimbursement pattern based on the participants’ submitted sizes has been proposed. The mathematical formulation of the problem is stated as a nonlinear mixed integer programming problem. The problem is solved using Benders decomposition technique where the intact problem is split into a master problem and several smaller nonlinear problems solved individually by the conventional methods. The performance of proposed approach has been evaluated by its application on the IEEE 57- and 118-bus systems.

Differentially Private K-Means Clustering Applied to Meter Data Analysis and Synthesis

The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers. The sector is scrambling to define policies, such as the so called ‘15/15 rule’, to respond to the need. However, the current policies fail to adequately guarantee privacy. In this paper, we address the problem of allowing third parties to apply <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means clustering, obtaining customer labels and centroids for a set of load time series by applying the framework of differential privacy. We leverage the method to design an algorithm that generates differentially private synthetic load data consistent with the labeled data. We test our algorithm’s utility by answering summary statistics such as average daily load profiles for a 2-dimensional synthetic dataset and a real-world power load dataset.

A reduced-dimension feature extraction method to represent retail store electricity profiles

Characterising the inter-seasonal energy performance of buildings is a useful tool for a business to understand what is ‘normal’ for its portfolio of premises and to detect anomalous patterns of energy demand. When adding a new building to the portfolio, it will be useful to predict what will be the likely energy use as part of on-going monitoring of the site. For a large portfolio of buildings with, say, half-hourly energy use measurements (48 dimensions), analysis and prediction will require machine learning tools. Even so, it is advantageous to minimise the amount of data and number of dimensions and features required to find useful patterns in the measurement stream. Our aim is to devise a reduced feature set that can generate a statistically reasonable representation of daily electricity load profiles of retail stores and small supermarkets. We then test if our method is sufficiently accurate to predict and cluster measured patterns of demand. We propose an automatic method to extract features such as times and average demands from electricity load profiles. We used four regression models for prediction and six clustering methods to compare with the results obtained using all of the readings in the load profile. We found that the reduced feature set gave a good representation of the load profile, with only small prediction and clustering errors. The results are robust as prediction is supervised learning and clustering is unsupervised. This simplified feature set is a concise way to represent profiles without using small variances of the demand that do not add useful information to the overall picture. As modern sensor systems increase the volume, availability, and immediacy of data, using reduced dimensional datasets will be key to extracting useful information from high-resolution data streams.

Short-Term Load Forecasting with a Novel Wavelet-Based Ensemble Method

“Short-term load forecasting (STLF)” is increasingly significant because of the extensive use of distributed energy resources, the incorporation of intermitted RES, and the implementation of DSM. This paper provides a novel ensemble forecasting model with wavelet transform for the STLF depending on the decomposition principle of load profiles. The model can effectively capture the portion of daily load profiles caused by seasonal variations. The results indicate that it is possible to improve STLF accuracy with the proposed method. The proposed approach is tested with the data taken from Ontario’s electricity market in Canada. The results show that the proposed technique performs well in-terms of prediction when compared to existing traditional and cutting-edge methods. The performance of the model was validated with different datasets. Moreover, this approach can provide accurate load forecasting using ensemble models. Therefore, utilities and smart grid operators can use this approach as an additional decision-making tool to improve their real-time decisions.

Integrated Smart-Home Architecture for Supporting Monitoring and Scheduling Strategies in Residential Clusters

The monitoring of power consumption and the forecasting of load profiles for residential appliances are essential aspects of the control of energy savings/exchanges at multiple hierarchical levels: house, house cluster, neighborhood, and city. External environmental factors (weather conditions) and inhabitants’ behavior influence power consumption, and their usage as part of forecasting activity may lead to added value in the estimation of daily-load profiles. This paper proposes a distributed sensing infrastructure for supporting the following tasks: the monitoring of appliances’ power consumption, the monitoring of environmental parameters, the generation of records for a database that can be used for both identifying load models and testing load-scheduling algorithms, and the real-time acquisition of consumption data. The hardware/software codesign of an integrated architecture that can combine the typical distributed sensing and control networks present in modern buildings (targeting user comfort) with energy-monitoring and management systems is presented. Methods for generating simplified piecewise linear (PWL) representations of the load profiles based on these records are introduced and their benefits compared with classic averaged representations are demonstrated for the case of peak-shaving strategies. The proposed approach is validated through implementing and testing a smart-meter node with wireless communication and other wired/wireless embedded modules, enabling the tight integration of the energy-monitoring system into smart-home/building-automation systems. The ability of this node to process power measurements with a programable granularity level (seconds/minutes/hours) at the edge level and stream the processed measurement results at the selected granularity to the cloud is identified as a valuable feature for a large range of applications (model identification, power saving, prediction).

A novel seasonal segmentation approach for day-ahead load forecasting

Day-ahead load forecasting plays a crucial role in operation and management of power systems. Weather conditions have a significant impact on daily load profile, hence, it follows an almost similar pattern within a season. However, it varies markedly across the seasons. Existing literature on load forecasting adopts a very casual approach in considering the seasonality, based on either calendar month or some meteorological parameter, which is inconsistent and inaccurate, especially during transition periods, thus leading to high forecasting errors. This paper proposes a novel seasonal segmentation approach for day-ahead load forecasting that uses multiple bidirectional Long Short Term Memory (LSTM) networks. An index has been derived from various weather parameters that govern the selection of seasonal models for forecasting purposes. Weighted output from multiple seasonal models is also possible in special cases for better forecasting accuracy. The proposed seasonal segmentation approach avoids the need for frequent model retraining and results in a better forecast accuracy with a relatively simple LSTM structure. The performance of the proposed method has been validated and compared on the actual load data of Madhya Pradesh state (MP), India. The improved results suggest that the proposed approach can be applied reliably for load scheduling applications.

Optimized management of reactive power reserves of transmission grid-connected photovoltaic plants driven by an IoT solution

This paper presents a methodology for the analysis and simulation of the effect of operating large photovoltaic (PV) plants, in coordination, as static synchronous compensators (STATCOM). The goal is to improve voltage profiles at different load nodes and reduce power losses in transmission lines. The proposed approach takes into account the varying reactive power capacity in PV inverters, which depends on weather conditions.To implement the proposed method, proper Internet of Things (IoT) hardware and software solutions are required. In this context, the grid status and weather data need to be transmitted continuously, via wireless communication technology, to an edge computer. Based on the transmitted data, and using the system mathematical model, an optimization algorithm is then responsible for finding out the optimal reactive power setpoint for each plant in real time.The proposed method is implemented and tested successfully using MATLAB platform with the MATPOWER IEEE 30-bus test grid model. When only five 20 MW PV plants are connected to different locations in the grid with a penetration rate lower than 25 percent, the simulation shows the effectiveness of the optimal coordination of PV plants to deal with the effect on the transmission grid of instantaneous operation of multiple loads. In this context, a daily load profile of heat pumps, operating in winter scenario in multiple households, is approved. An improvement up to 68 percent in the global voltage profiles in the load buses for one-day scenario is achieved. Furthermore, total accumulated active and reactive power losses are reduced by 24.1 percent.

Hybrid energy storage design and dispatch strategy evaluation with sensitivity analysis:Techno‐economic‐environmentalassessment

AbstractInternational Energy Agency reported the net‐zero carbon target by 2050 in its recent annual report. The global energy landscape is expected to anticipate an energy transition with an increase in renewable energy share from 12% to 35% for electricity sector by 2050. Most of the countries in the world had pledged to be become net‐zero or carbon neutral countries by 2050. Solar photovoltaic (PV) and wind energy will contribute 50% of energy mix in this context. Solar PV and wind energy are intermittent resources that highly depending on the weather and climate conditions. The intermittency issues caused mismatch between electricity generation and demand profiles. This will greatly affect the requirement of meeting the daily load profile, satisfying the peak demand and resulting in generation of surplus, and so on. Energy storages play a vital role in this scenario by allowing this excess energy to be stored and used to meet the demand during low energy production. Previous literature evaluated the feasibility of utilizing battery energy storage in large‐scale solar PV plant in Malaysia using load following dispatch strategy, which can be further enhanced by utilizing a hybrid energy storage system. This study aims to develop an optimized hybrid energy storage system utilizing battery and supercapacitors to complement a large‐scale solar PV system. This study also evaluates two dispatch strategies that are load following and cycle charging for three different power system configurations. Subsequently, a market leading battery is modelled with parameters such as nominal voltage, capacity, round‐trip efficiency, minimum and maximum state of charge, and maximum and minimum charge and discharge current in four different scenarios utilizing two dispatch strategies. These studies are conducted using power system and energy storage modelling tools with localized energy data for the Malaysia context. The proposed hybrid energy storage system demonstrates an improvement of about 30% in annual throughput under the load following dispatch strategy. The hybrid power system also increases the renewable energy fraction by 5% in the system. This study creates an optimized framework in the development of energy storage for grid integration that permits high level of solar PV penetration in the transmission and distribution network.

Optimal Scheduling of Movable Electric Vehicle Loads Using Generation of Charging Event Matrices, Queuing Management, and Genetic Algorithm

The extensive adoption of electric vehicles (EVs) can introduce negative impacts on electric infrastructure in the form of sporadic and excessive charging demands, line overload, and voltage quality. Because EV loads can be movable around the system and time-dependent due to human daily activities, it is therefore proposed in this research to investigate the spatial effects of EV loads and their impacts on a power system. We developed a behavior-based charging profile simulation for daily load profiles of uncontrolled and controlled charging simulations. To mitigate the impact of increased peak demand, we proposed an optimal scheduling method by genetic algorithm (GA) using charging event matrices and EV queuing management. The charging event matrices are generated by capturing charging events and serve as an input of the GA-based scheduling, which optimally defines available charging slots while maximizing the system load factor while maintaining user satisfaction, depending on the weight coefficients prioritized by the system operator. The EV queuing management strategically selects EVs to be filled in the available slots based on two qualification indicators: previous charging duration and remaining state of charge (SoC). The proposed methodology was tested on a modified IEEE-14 bus system with 3 generators and 20 transmission lines. The simulation results show that the developed methodology can efficiently manage the peak demand while respecting the system’s operational constraints and the user satisfaction level.

Methodology for Generating Synthetic Load Profiles for Different Industry Types

To achieve its climate goals, the German industry has to undergo a transformation toward renewable energies. To analyze this transformation in energy system models, the industry’s electricity demands have to be provided in a high temporal and sectoral resolution, which, to date, is not the case due to a lack of open-source data. In this paper, a methodology for the generation of synthetic electricity load profiles is described; it was applied to 11 industry types. The modeling was based on the normalized daily load profiles for eight electrical end-use applications. The profiles were then further refined by using the mechanical processes of different branches. Finally, a fluctuation was applied to the profiles as a stochastic attribute. A quantitative RMSE comparison between real and synthetic load profiles showed that the developed method is especially accurate for the representation of loads from three-shift industrial plants. A procedure of how to apply the synthetic load profiles to a regional distribution of the industry sector completes the methodology.

Electric Vehicle Deployment and Integration in the Saudi Electric Power System

The demand for electricity in Saudi Arabia has grown in the last few years due to the growth in the economy and the population. The country has invested in many solutions such as promoting renewable energy and shifting to generation mix to respond to this growing demand. However, Electric Vehicles (EVs) are used as an important factor in achieving the Saudi Vision 2030 in its environmental and economical parts. This work gives an overview on the Saudi electrical energy system and then investigates the impact EVs technology in the electricity sector in Saudi Arabia and its relevant consequences. A statistical analysis is used to quantify the number of EVs, travelled distance and traffic congestions, and State of Charge (SOC). The data were used to implement a daily load profile for EVs for a large population of vehicles. The obtained results show that the EVs peak loads occur during the late evening and early morning at different means. Interestingly, the work shows that the peak periods of EVs occur during the off-peak times of the daily load curve. This means that a large population of EVs can offer more flexibility and improvement to the electric grid, and the summative EV load of a large population of vehicles has a smooth pattern and will not affect the national electric system.

Impact of COVID-19 on Ontario’s electricity market: Load, generation, emissions

The COVID-19 outbreak not only threatened global health, it has also –affected the energy markets around the world. This paper studies the impact of the pandemic on Ontario’s electricity market assessing the demand and supply balance over three distinct periods: pre-pandemic, start of the pandemic and during the period 2020–2021. The paper also evaluates the contribution of work-from-home and other mandates in reducing GHG emission. Furthermore, the impact of such rare events is studied on load forecasting. Our analysis shows that although demand dropped by 12% during the beginning of pandemic, it started rising to levels higher than the previous years. Consequently, due to the changes in the daily load profile, primarily due to the changes in consumers’ behavior, the emissions declined significantly during the lockdown and increased afterwards. Finally, this paper provides a short-term Feed Forward Neural Network (FFNN) model to predict future demand. The model performance was evaluated during the three distinct periods and showed high accuracy even in the initial stages of the pandemic: MAPE of 3.21% pre-pandemic, 13.86% beginning of pandemic and 4.23% during pandemic.

Meta-ANN – A dynamic artificial neural network refined by meta-learning for Short-Term Load Forecasting

In this paper a dynamic Artificial Neural Network (ANN) model called Meta-ANN is developed for forecasting the short-term grid load. The primary ingredient of the model is a base module which is an ANN trained over a large historical data set to learn the long-term trend and seasonality of grid load. To capture the nonstationary pattern of the grid load, an error-correction module based on the idea of meta-learning is integrated into the model. This module finetunes the base module according to most recent prediction errors. For each day of interest, Meta-ANN generates a new ANN model started from the base module by tracing the gradient of the prediction loss on recent observations weighted by learning rates with specific structures. The full Meta-ANN model is trained by jointly optimizing the base module and error-correction module via gradient descent algorithms. The implementation based on gradient descent algorithms is detailed with streamlined mathematical formulations. The proposed model is tested on the open-access data from Elia, a Belgian transmission system operator, for forecasting the daily mean load and load profile. The numerical study shows that Meta-ANN makes more accurate and robust prediction by effectively capturing the nonstationary pattern in grid loads.