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.

Microgrids are being evolved as a potential alternative to reduce unrelenting dependency on central utility grids. Moreover, integrated multi-microgrid based clusters are forming in closed vicinities to enhance the benefits of microgrids. However, the power quality problem is one of the key issues to be solved in such systems, which is mainly caused by the rising penetration of nonlinear loads and interfacing of power electronic converters. To address this issue, this paper proposes a new control technique, named “adaptive neuro-fuzzy control strategy”. This controls the inverter of each microgrid in the cluster as well as the voltage source converter based distribution static compensator located at the point of common coupling between the cluster and the utility grid. This proposed control strategy uses the advantages of both fuzzy logic and artificial neural networks, thereby effectively controls the system. The proposed technique is modelled in MATLAB/Simulink software 2021a. For the analysis, various power quality indices such as voltage sag/swell, voltage unbalance, frequency deviations, power characteristics, total harmonic distortion, and neutral current compensation are measured. These indices of the proposed controller are compared with conventional PI and fuzzy logic-based controllers in view of various key IEEE/IEC standard tolerances. From these results, the proposed controller has proved its superiority.

The objective of this paper is to model a hybrid power system for buildings, which is technically feasible and economically optimal. With a view to promote renewable energy sources, photovoltaics and wind turbines are integrated with the grid connected building. The system is modeled and the optimal system configuration is estimated with the help of hybrid optimization model for electric renewables (HOMER). The logic is illustrated with a case study based on the practical data of a building located in southern India. This building is associated with 3.4 MWh/day priority load (peak load as 422 kW), as well as 3.3 MWh/day deferrable load (peak load as 500 kW). Sensitivity analysis is performed to deal with uncertainties such as the increase in electricity consumption and grid tariff, environmental changes, etc. From the simulation result, it is observed that the designed system is cost effective and environment friendly, which leads to 6.18 % annual cost savings and reduces CO₂ emissions by 38.3 %. Sensitivity results indicate that the system is optimal and adaptable in a certain range of unanticipated variances with respect to best estimated value. Finally, an energy management strategy is developed for the optimal system to ensure reliable power during contingency and disturbances. The green and hybrid power system designed can be adaptable to any critical and large consumers of urban buildings.

In modern urban energy communities, diverse natured loads (homes, schools, hospitals, malls, etc.) are situated in the same locality and have self-electricity generation/management facilities. The power systems of these individual buildings are called smart microgrids. Usually, their self-electricity generation is based on renewable energy sources, which are uncertain due to their environmental dependency. So, the consistency of self-energy generation throughout the day is not guaranteed; thus, the dependency on the central utility grid is continued. To solve this, researchers have recently started working on interoperable smart microgrids (ISMs) for urban communities. Here, a central monitoring and control station captures the energy generation/demand information of each microgrid and analyzes the availability/requirement, thereby executing the energy transactions among these ISMs. Such local energy exchanges among the ISMs reduce the issues with uncertain renewable energy and the dependency on the utility grid. To establish such useful ISMs, a well-established communication mechanism has to be adopted. In this view, this paper first reviews various state-of-the-art developments related to smart grids and then provides extensive insights into communication standards and technologies, issues/challenges, and future research perspectives for ISM implementation. Finally, a discussion is presented on advanced wireless technology, called LoRa (Long Range), and a modern architecture using the LoRa technology to establish a communication network for ISMs is proposed.

The smart home culture is rapidly increasing across the globe and driving smart home users toward utilizing smart appliances. Smart television (TV) is one such appliance that is embedded with smart technology. The users of smart TV have their interests in the programs. However, automatic recommendation of programs for user-to-user is still under-researched. Several papers discussed recommendation systems, but those are related to different applications. Even though there are some works on recommending programs to smart TV users (single-user and multi-user), they did not discuss the smart TV camera module to capture and validate the user image for recommending personalized programs. Hence, this paper proposes a convolutional neural network (CNN)-based personalized program recommendation system for smart TV users. To implement this proposed approach, the CNN algorithm is trained on the datasets ‘CelebFaces Attribute Dataset’ and ‘Labeled Faces in the Wild-People’ for feature extraction and to detect a human face. The trained CNN model is applied to the user image captured by using the smart TV camera module. Further, the captured image is matched with the user image in the ‘synthetic dataset’. Based on this matching, the hybrid filtering technique is proposed and applied; thereby the recommendation of the respective program is done. The proposed CNN algorithm has achieved approximately 95% training performance. Besides, the performance of hybrid filtering is approximately 85% from the single-user perspective and approximately 81% from the multi-user perspective. From this, it is observed that hybrid filtering outperformed conventional content-based filtering and collaborative filtering techniques.

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.