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

Islanding Detection in Grid-Connected Urban Community Multi-Microgrid Clusters Using Decision-Tree-Based Fuzzy Logic Controller for Improved Transient Response

The development of renewable-energy-based microgrids is being considered as a potential solution to lessen the unrelenting burden on the centralized utility grid. Furthermore, recent studies reveal that integrated multi-microgrid cluster systems developed in urban communities maximize the effectiveness of microgrids and greatly decrease the utility grid dependence. However, due to the uncertain nature of renewable energy sources and frequent load variations, these systems face issues with unintentional islanding operations. This can create severe damage to the microgrid’s performance in its stable operating condition and lead to undesired transient responses. Hence, islanding must be identified rapidly to take preventive measures to address the issue. This requires the development of a suitable anti-islanding technique that is faster in terms of accuracy and timely detection. With this intention, this paper proposes a decision-tree-based fuzzy logic (DT-FL) controller for the rapid identification of islands in an urban community multi-microgrid cluster. The DT-FL controller’s operation includes two steps. First, the decision tree extracts the electrical parameters at the point of common coupling of the multi-microgrid system. Second, these extracted parameters are utilized for the online tuning of the fuzzy logic controller, for the fast detection of islanding. The multi-microgrid cluster under study, along with the proposed islanding technique, is implemented in the MATLAB-2021a software. The efficacy of the proposed DT-FL controller is validated by comparing its performance with that of the conventional fuzzy logic controller under different test scenarios. From the results, it is observed that the proposed DT-FL controller shows superior performance in terms of the islanding detection time as well as the transient response of the system when compared with the conventional controller

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