Electrical Load Research Articles

Design and Practical Implementation of Microgrid Inverter Control Using TMS320F28335 Microcontroller with Improvement in Electrical Power Quality

Nowadays, the proliferation of distributed renewable energy sources is a fact. A microgrid is a good solution to self-manage the energy generation and consumption of electrical loads and sources from the point of view of the consumer as well as the power system operator. To make a microgrid as versatile as necessary to carry that out, a flexible inverter is necessary. In this paper, an algorithm is presented to control an inverter and make it complete and versatile to work in grid-connected and in isolated modes, injecting or receiving power from the grid and always compensating the harmonics generated by the loads in the microgrid. With this inverter, the microgrid can work while optimizing its energy consumption or according to the power system operator instructions. The inverter proposed is tested in a designed Matlab/Simulink simulation platform. After that, an experimental platform designed and built ad hoc, including a DC source, AC linear and non-linear loads, and a Semikron power inverter, is used to test the proposed control strategies. The results corroborate the good system performance. The replicability of the system is guaranteed by the use of low-cost devices in the implementation of the control.

A Robust Linear Active Disturbance Rejection Control for Renewable Energy Grid-Connected System Based on APF

With the energy transition and changes in electrical load structures, harmonic distortion in power systems is increasingly threatening grid stability, especially during the integration of renewable energy sources. Active power filters (APFs) are commonly used to improve power quality, but current fluctuations and voltage instability caused by DC-side capacitor charging and discharging hinder effective harmonic compensation. To address this, this paper presents a method combining a variable-step-size adaptive linear predictor with linear active disturbance rejection control (VALP-LADRC). By integrating an adaptive linear predictor (ALP) with LADRC, the proposed method effectively reduces the impact of disturbances on control, improving voltage regulation and harmonic compensation, and enhancing renewable energy grid integration. To address delays during the adjustment of initial parameters in the adaptive predictor, we combine it with a variable-step-size algorithm, significantly improving disturbance rejection and tracking accuracy, while solving voltage drop issues. The simulation results in a photovoltaic grid-connected system show that VALP-LADRC effectively mitigates disturbances, ensures voltage stability, and enhances power quality. This approach offers a promising solution for supporting sustainable development through better renewable energy integration and improved power quality.

Short-term Load Forecasting Based on K-medoids Clustering and XGBTide Model

Introduction: Electric power load is significantly influenced by weather conditions, making accurate load prediction under varying weather scenarios essential for effective planning and stable operation of the power system. This paper introduces a short-term load forecasting method that combines k-Medoids clustering and XGB-TiDE. Initially, k-Medoids clusters the original load data into categories such as sunny, high temperature, and rain/snow days. Subsequently, XGBoost identifies critical features within these subsequences. The combined forecast model, XGB-TiDE, is then tailored for each subsequence. Here, the TiDE model's predictions are refined point-by-point using the XGBoost results to derive the final short-term load forecasts. An empirical analysis using real power load data from a specific region demonstrates that our proposed model achieves superior accuracy, especially under extreme weather conditions such as high temperatures and precipitation. Background: Electric power load is significantly influenced by weather conditions, making accurate load prediction under varying weather scenarios essential for effective planning and stable operation of the power system. Objective: Addressing the limitations of short-term load forecasting under extreme weather conditions, this paper introduces a novel approach that leverages k-Medoids clustering and the XGBTiDE model to enhance forecasting accuracy. This method strategically segments power load data into meaningful clusters before applying the robust predictive capabilities of XGB-TiDE, aiming for a significant improvement in forecast precision. Method: Initially, k-Medoids clusters the original load data into categories such as sunny, high temperature, and rain/snow days. Subsequently, XGBoost identifies critical features within these subsequences. The combined forecast model, XGB-TiDE, is then tailored for each subsequence. Here, the TiDE model's predictions are refined point-by-point using the XGBoost results to derive the final short-term load forecasts. Results: The model presented in this study demonstrates outstanding performance across all weather conditions, consistently achieving lower mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) compared to other models. For instance, on a sunny day, this model records an MAE of 8.49, RMSE of 10.29, and MAPE of 2.55%, markedly surpassing the Autoformer, which shows an MAE of 18.29, RMSE of 22.33, and MAPE of 5.50%. These results underscore the superior accuracy of our proposed forecasting approach. Conclusion: An empirical analysis using real power load data from a specific region demonstrates that our proposed model achieves superior accuracy, especially under extreme weather conditions such as high temperatures and precipitation.

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.

Redesign of Virtual Power Plant-led Electricity Demand Response Program

As the scale of renewable energy on the demand side continues to grow, a new demand response program (DRP), the virtual power plant (VPP), pays a rebate to the consumer and purchases his rooftop solar electricity. This electricity is dispatched to areas of electricity shortages, which is vital in balancing electricity loads and avoiding energy waste. However, the new DRP differs from the traditional DRP in terms of paying rebates. The traditional DRP requires consumers to respond above a specified baseline to receive a rebate. In contrast, in the new DRP, consumers can receive a rebate even if their response is small (i.e., no baseline). There is no research that addresses this divergence. Therefore, we construct a supply chain consisting of a consumer and a VPP for this novel DRP and analyze the strategic interactions between them in two situations where a baseline is not set/set. Our results show room for improvement in the DRP that dispatches electricity generated from rooftop solar. If the consumer generates more electricity from rooftop solar, the baseline, rather than frustrating the consumer, motivates the consumer to be more responsive. While the baseline also discourages the demand response process, it reduces the emissions generated from the consumer’s over-responsiveness. Additionally, our study suggests that a baseline, whether it facilitates or hinders the DRP, could benefit the VPP, depending on the business model of the VPP. In addition, we add several extensions to demonstrate the robustness of our model.

Forecasting the electric power load based on a novel prediction model coupled with accumulative time-delay effects and periodic fluctuation characteristics

Forecasting the electric power load based on a novel prediction model coupled with accumulative time-delay effects and periodic fluctuation characteristics

Modeling method for simulating electrical load system of civil aircraft based on CPU-FPGA

Modeling method for simulating electrical load system of civil aircraft based on CPU-FPGA

Leveraging temporal dependency in probabilistic electric load forecasting

Leveraging temporal dependency in probabilistic electric load forecasting

Electric load simulation method of aircraft electrical energy distribution based on FPGA

Electric load simulation method of aircraft electrical energy distribution based on FPGA

The impact of inhabitant traits and house types on the electricity load forecasting of private households

The impact of inhabitant traits and house types on the electricity load forecasting of private households

Advanced machine learning approach with dynamic kernel weighting for accurate electrical load forecasting

Advanced machine learning approach with dynamic kernel weighting for accurate electrical load forecasting

Residential Electrical Load Forecasting Based on a Real-Time Evidential Time Series Prediction Method

Residential Electrical Load Forecasting Based on a Real-Time Evidential Time Series Prediction Method

Probabilistic adoption scenarios of distributed energy resources in residential buildings for a geospatial projection of the electrical load

Probabilistic adoption scenarios of distributed energy resources in residential buildings for a geospatial projection of the electrical load

Using crafted features and polar bear optimization algorithm for short-term electric load forecast system

Using crafted features and polar bear optimization algorithm for short-term electric load forecast system

The impact of increased information content on electricity load forecasting

The impact of increased information content on electricity load forecasting

Capturing complex electricity load patterns: A hybrid deep learning approach with proposed external-convolution attention

Capturing complex electricity load patterns: A hybrid deep learning approach with proposed external-convolution attention

Feature Modelling for Electricity Load Forecasting Using Hybrid EEMD-BiLSTM with Spatially Correlated Weather Data

Feature Modelling for Electricity Load Forecasting Using Hybrid EEMD-BiLSTM with Spatially Correlated Weather Data

System Design of a Customized Solar Photovoltaic Power System for a Microcontroller-based Weather Station in Minna, Nigeria

In atmospheric and meteorological studies, missing values in acquired research data possess serious challenge on the integrity of research output emanating from their use. Missing values or readings may arise due to temporal or permanent failure in power supply to the measuring weather station. In this work, effort was made to address the fundamental issue of remote power at a standalone solar powered weather stations by professionally applying solar photovoltaic power system installation technique and the requisite knowledge of local parameters of the installed location to design a customize power supply pack for the weather station. The protocol involves an elaborate electrical load assessment and analysis of the weather station, followed by an independent component wise design formulation leading to selection of suitable solar module, battery, charge controller and voltage regulator along with their outdoor installation design. It was recommended that the design be validated using solar photovoltaic software followed by physical implementation which should be done in line with the system and installation design.

Development of an Optimized Neural Network Model using Hyperparameters Optimization for Electrical Load Prediction

Electrical load prediction has become essential to the efficient operation, control and management of modern electric power systems. Various machine learning prediction models have been developed for electrical load prediction, this includes Support Vector Regression, Fuzzy Logic and Neural Network (NN) modelling approaches. However, incorrect selection of model hyperparameters, which are parameters that affect the output of the prediction models, could result in low prediction accuracy of machine learning models. Hence, the development of an optimized NN model for electrical load prediction was presented in this study. Historical daily data of temperature, rainfall, relative humidity and windspeed for Osogbo, Nigeria was obtained from the National Aeronautics and Space Administration website; while electrical load data for the same location was collected from the Transmission Company of Nigeria. The data captured a period of five years (2017 to 2021). The NN models were developed with MATLAB R2022a software, and two hyperparameters, hidden layers and neuron counts, were optimized using the Bayesian optimization technique to enhance the quality of the models. The models were evaluated using mean absolute error (MAE), and root mean square error (RMSE). The MAE and RMSE for the non-optimized NN model were 6.5247 and 8.2725 respectively. Meanwhile, for the optimized NN model, the MAE and RMSE were 5.6571 and 7.4289 respectively. The obtained results show that the optimized NN performed better than the non-optimized NN models. Therefore, for more accurate load prediction, the method developed of this research is suggested for use by utility providers.

The Application of BMS in Electrical Energy Management and Carbon Emission Reduction

This article presents the application of Building Management System (BMS) in electrical load management. The BMS system in this article structure includes DDC, IOT 2050 Siemens, sensors, inverters and loads. Implementing this system in buildings saves energy, reduces operating costs and reduces CO2 emissions. A comparative analysis was conducted between projects with and without BMS implementation. The investment in the BMS incurs an additional capital expenditure, with a payback period of 7.3 years and a system lifespan of 20 years, providing a return of more than 13% on the investment. The results show significant energy savings with total consumption reduced by 13%. These findings highlight the effectiveness of BMS in optimizing energy use across different areas of a building, providing significant financial and environmental benefits. This research contributes to understanding the economic and environmental implications of integrating BMS into infrastructure development, creating favorable conditions for stakeholders in the construction and energy sectors.