Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).The modern-day urban energy sector possesses the integrated operation of various microgrids located in a vicinity, named cluster microgrids, which helps to reduce the utility grid burden. However, these cluster microgrids require a precise electric load projection to manage the operations, as the integrated operation of multiple microgrids leads to dynamic load demand. Thus, load forecasting is a complicated operation that requires more than statistical methods. There are different machine learning methods available in the literature that are applied to single microgrid cases. In this line, the cluster microgrids concept is a new application, which is very limitedly discussed in the literature. Thus, to identify the best load forecasting method in cluster microgrids, this article implements a variety of machine learning algorithms, including linear regression (quadratic), support vector machines, long short-term memory, and artificial neural networks (ANN) to forecast the load demand in the short term. The effectiveness of these methods is analyzed by computing various factors such as root mean square error, R-square, mean square error, mean absolute error, mean absolute percentage error, and time of computation. From this, it is observed that the ANN provides effective forecasting results. In addition, three distinct optimization techniques are used to find the optimum ANN training algorithm: Levenberg−Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The effectiveness of these optimization algorithms is verified in terms of training, test, validation, and error analysis. The proposed system simulation is carried out using the MATLAB/Simulink-2021a® software. From the results, it is found that the Levenberg−Marquardt optimization algorithm-based ANN model gives the best electrical load forecasting results.Peer reviewe

Adaptive Type-1 Fuzzy Controller for Lag-Dominant First and Second Order Nonlinear Systems

Most of the current day industries are suffering from nonlinear processes. Thus, both the stability and the process performance of high-degree nonlinear systems with dominating delay might be difficult to achieve. Adaptive and intelligent fuzzy classifiers and controllers have been more popular in recent years as a means of overcoming a significant number of difficulties faced by the industrial sector. A large number of dynamic process plants with a variety of orders and kinds have been represented heuristically and recognized. Fuzzy structures have also been employed for these interactive systems by making use of fuzzy and linguistic techniques. In view of all these initiatives, the purpose of this paper is to conduct an experimental investigation into the performance of a LabVIEW-based Type-1 Adaptive Mamdani Fuzzy Controller (AMFC) that has been designed and applied over a lag-dominant and a second-order nonlinear Dual Input Tank System (DITS) and Single Input Tank System (SITS). Compared to other type-1 approaches that were previously experimented with and are now in existence, the adaptability of AMFC demonstrates that it is quite effective. Performance indices such as the Integrated/Summated Absolute Error (IAE) and the Integrated/Summated Squared Error (ISE) are also computed for several variable set point profiles of DITS. These indices measure errors in integrated absolute value and integrated squared value, respectively. Adaptive Type-1 Intelligent Fuzzy Controller’s response and error reduction efficiency have been found for several flow configurations of DITS, namely Multiple Input Multiple Output (MIMO) and Single Input Single Output (SISO). From the results, it can be concluded that the proposed experimental validation may be used for a wide variety of process challenges that are experienced in industrial systems to achieve robust and low error controller performances

Critical Performance Analysis of Four-Wheel Drive Hybrid Electric Vehicles Subjected to Dynamic Operating Conditions

Hybrid electric vehicle technology (HEVT) is emerging as a reliable alternative to reduce the constraints of battery-only driven pure electric vehicles (EVs). HVET utilizes an electric motor as well as an internal combustion engine for its operation. These components would work on battery power and fossil fuels, respectively, as a source of energy for vehicle mobility. The power is delivered either from battery or fuel or both sources based on user requirements, road conditions, etc. HEVT uses three major propelling systems, namely, front-wheel drive (FWD), rear-wheel drive (RWD), and four-wheel drive (4WD). In these propelling systems, the 4WD model provides torque to all four wheels at the same time. It uses all four wheels to propel thereby offering better driving capability, better traction, and a strong grip on the surface. The 4WD-based HEVs comprise four architectures, namely, series, parallel, series-parallel, and complex. The literature focuses primarily on any one type of architecture for analysis in the context of component optimization, fuel reduction, and energy management. However, a focus on dynamic analysis that gives a real performance insight was not conducted, which is the main motivation for this paper. The proposed work provides an extensive critical performance analysis of all four 4WD architectures subjected to various dynamic operating conditions (continuous, pulse, and step-up accelerations). Under these conditions, various performance parameters such as speed (of vehicle, engine, and motor), power (of engine and battery), battery electrical losses, charge patterns, and fuel consumption are measured and compared. Further, the 4WD architecture performance is validated with FWD and RWD architectures. From MATLAB/Simulink-based evaluation, 4WD HEV architectures have shown superior performance in most of the cases when compared to FWD type and RWD type HEVs. Moreover, 4WD parallel HEV architecture has shown superior performance compared to 4WD series, 4WD series-parallel, and 4WD complex architectures

Power Quality Improvement in Renewable-Energy-Based Microgrid Clusters Using Fuzzy Space Vector PWM Controlled Inverter

An increased electricity demand and dynamic load changes are creating a huge burden on the modern utility grid, thereby affecting supply reliability and quality. It is thus crucial for modern power system researchers to focus on these aspects to reduce grid outages. High-quality power is always desired to run various businesses smoothly, but power-electronic-converter-based renewable energy integrated into the utility grid is the major source of power quality issues. Many solutions are constantly being invented, yet a continuous effort and new optimized solutions are encouraged to address these issues by adhering to various global standards (IEC, IEEE, EN, etc.). This paper therefore proposes a concept of establishing a renewable-energy-based microgrid cluster by integrating various buildings located in an urban community. This enhances power supply reliability by managing the available energy in the cluster without depending on the utility grid. Further, a “fuzzy space vector pulse width modulation” (FSV-PWM) technique is proposed to control the inverter, which improves the power supply quality. This work uniquely optimized the dq reference currents using fuzzy logic theory, which were used to plot the space vectors with effective sector selection to generate accurate PWM signals for inverter control. The modeling/simulation of the microgrid cluster involving the FSV-PWM-based inverter was carried out using MATLAB/Simulink®. The efficacy of the proposed FSV-PWM over the conventional ST-PWM was verified by plotting voltage, frequency, real/reactive power, and harmonic distortion characteristics. Various power quality indices were calculated under different disturbance conditions. The results showed that the use of the proposed FSV-PWM-based inverter adhered to all the key standard requirements, while the conventional system failed in most of the indices