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Many controllers are available in the market for controlling the Permanent Magnet Synchronous Motor (PMSM) drive application, though the most preferably used one is Proportional Integral (PI), controller. However, it is found that the PI and other latest controllers have their own merits and demerits while analyzing their outputs via comparison. Thus it is decided to test the deed of hybrid controllers that can serve a lot better than standalone controllers for precise control applications. In this article, a conventional PI controller has been applied in closed-loop system in combination with recent controllers like Proportional Resonant Controller (PRC), Fractional order Proportional Integral Derivative (FOPID), Hysteresis Current Controller (HCC) and Fuzzy Logic Controller (FLC). The resultant hybrid controllers were (i) PI-FOPID (ii) PI-FLC (iii) FOPID-FLC (iv) HCC-FLC and (v) PRC-FLC. All these hybrid controllers are designed using MATLAB platform and the speed and torque responses are compared to allocate the better performance award to the hybrid controllers. The continuous and intermittent loads are considered while registering time-domain response of PMSM-Pump application. With the aid of time-domain response and THD, the topology to be tested in prototype is chosen and tested for resemblance of the speed response with the simulation output. PI-FLC hybrid controller tends to render optimum performance characteristics among all the other hybrid controllers and the same is validated in real time through hardware results.

Intelligent Transport System (ITS) intentions to attain traffic efficiency by diminishing traffic difficulties. It supplies information like traffic issues, real-time traveling information, parking availability, etc., in advance to the users who are connected with the smart cities that ensure travelers' safety and comfort. This ITS technique should merge with Electric Vehicles (EVs) because nowadays, EVs have become familiar in the last decade owing to the requirement to cut greenhouse gas emissions and fossil fuels. However, traffic jams caused by EVs driven to the charging stations (CSs) can result in the complex charging scheduling of EVs. Therefore, an effective algorithm is developed for optimal charging scheduling using the proposed Grey Sail Fish Optimization (GSFO). The proposed charging scheduling algorithm integrates Grey Wolf Optimizer (GWO) and Sail Fish Optimization (SFO). For each EV, the demand when charging is computed. The path used by the EV to travel to the charging station is determined by computing the path decision factor. In comparison to existing techniques, the proposed GSFO-based charging algorithm schedules EVs to charging stations based on the fitness function, and the performance was improved with a traffic density of 26.11 km, a distance of 0.0278 kW, and a power of 2.3377. To be more specific, the proposed GSFO improved when many vehicles were considered. WOS:000794281100001

In recent years, telecommunication is progressed due to the development of Long-Term Evolution (LTE) standards. This LTE network provides high-speed wireless communication for mobile devices and satisfies the requirements of customers from multi-cells. However, due to the congestion of mobile devices, each cell in the network may get overloaded. So, load balancing is the main challenge to the LTE network for reducing congestion or load in the cell. For load balancing, the optimal cell selection method is presented in this paper. Initially, the load factor of each cell is estimated. Then the load factor is compared with the predefined threshold load value. After the comparison, the heavy loaded cell handover the users to the optimal cell or cell with minimum load. This optimal cell is selected with the Opposition Based Artificial Flora (OAF) algorithm. Simulation results show that the proposed approach decreases 6% of the call blocking ratio (CBR) and 14% of Call Dropping Ratio (CDR) than the previous approaches.

Extraction of electrical energy through a photovoltaic (PV) system plays a vital role in this booming digital era. Maximum power tracking efficiency of PV becomes crucial on grid-connected power di...

In order to formulate the long-term and short-term development plans to meet the energy needs, there is a great demand for accurate energy forecasting. Energy autonomy helps to decompose a large-scale grid control into a small sized decisions to attain robustness and scalability through energy independence level of a country. Most of the existing energy demand forecasting models predict the amount of energy at a regional or national scale and failed to forecast the demand for power generation for small-scale decentralized energy systems, like micro grids, buildings, and energy communities. A novel model called Sailfish Whale Optimization-based Deep Long Short- Term memory (SWO-based Deep LSTM) to forecast electricity demand in the distribution systems is proposed. The proposed SWO is designed by integrating the Sailfish Optimizer (SO) with the Whale Optimization Algorithm (WOA). The Hilbert-Schmidt Independence Criterion (HSIC) is applied on the dataset, which is collected from the Central electricity authority, Government of India, for selecting the optimal features using the technical indicators. The proposed algorithm is implemented in MATLAB software package and the study was done using real-time data. The optimal features are trained using Deep LSTM model. The results of the proposed model in terms of install capacity prediction, village electrified prediction, length of R & D lines prediction, hydro, coal, diesel, nuclear prediction, etc. are compared with the existing models. The proposed model achieves percentage improvements of 10%, 9.5%,6%, 4% and 3% in terms of Mean Squared Error (MSE) and 26%, 21%, 16%, 12% and 6% in terms of Root Mean Square Error (RMSE) for Bootstrap-based Extreme Learning Machine approach (BELM), Direct Quantile Regression (DQR), Temporally Local Gaussian Process (TLGP), Deep Echo State Network (Deep ESN) and Deep LSTM respectively. The hybrid approach using the optimization algorithm with the deep learning model leads to faster convergence rate during the training process and enables the small-scale decentralized systems to address the challenges of distributed energy resources. The time series datasets of different utilities are trained using the hybrid model and the temporal dependencies in the sequence of data are predicted with point of interval as 5 years-head. Energy autonomy of the country till the year 2048 is assessed and compared.

Significant changes in society were emphasized as being required to achieve Sustainable Development Goals, a need which was further intensified with the emergence of the pandemic. The prospective society should be directed towards sustainable development, a process in which technology plays a crucial role. The proposed study discusses the technological potential for attaining the Sustainable Development Goals via disruptive technologies. This study further analyzes the outcome of disruptive technologies from the aspects of product development, health care transformation, a pandemic case study, nature-inclusive business models, smart cities and villages. These outcomes are mapped as a direct influence on Sustainable Development Goals 3, 8, 9 and 11. Various disruptive technologies and the ways in which the Sustainable Development Goals are influenced are elaborated. The investigation into the potential of disruptive technologies highlighted that Industry 5.0 and Society 5.0 are the most supportive development to underpin the efforts to achieve the Sustainable Development Goals. The study proposes the scenario where both Industry 5.0 and Society 5.0 are integrated to form smart cities and villages where the prospects of achieving Sustainable Development Goals are more favorable due to the integrated framework and Sustainable Development Goals’ interactions. Furthermore, the study proposes an integrated framework for including new age technologies to establish the concepts of Industry 5.0 and Society 5.0 integrated into smart cities and villages. The corresponding influence on the Sustainable Development Goals are also mapped. A SWOT analysis is performed to assess the proposed integrated approach to achieve Sustainable Development Goals. Ultimately, this study can assist the industrialist, policy makers and researchers in envisioning Sustainable Development Goals from technological perspectives.

Solar energy is the base for both photovoltaic (PV) power generation and plant growth. Inspired by this biological phenomenon, a novel crowded plant height optimisation (CPHO) algorithm was developed for solar PV maximum power point tracking (MPPT). This CPHO-tuned MPPT algorithm was developed with the aim of obtaining the optimal duty cycle (d) for DC-DC boost converter for maximum solar power extraction from PV panels with the help of a proportional-integral controller. Crowded plants regulate the growth of their stem height in relation to neighbouring plants, also known as height convergence. Using this CPHO-algorithm, the stable height of the plant found in a numerical value is taken as the optimal height of the plant. This optimal numerical value was converted into (d) for the converter. Under dynamic weather conditions, the (d) was optimally adjusted by the proposed algorithm to regulate the DC output of the converter. On the utility side, d–q vector control-based voltage source inverter was used for PV power integration into the grid. The performance of the converter control strategy of the proposed CPHO algorithm was compared with perturb and observe algorithm-based MPPT control, which was analysed on MATLAB/Simulink platform.

This article explores the numerical analysis of innovative heat exchanger to recover the heat energy from the diesel engines exhaust. The theoretical design and computational fluid dynamics(CFD) simulation of the Protracted Finned Counter Flow Heat Exchanger (PFCHE) was conducted in order to obtain an optimized design for fin protrusions. In this investigation, heat exchanger’s geometric parameters such as fin number and protrusions height have been varied and their subsequent effect on thermal effectiveness was compared. The results of various fin configurations were post-processed and compared in order to obtain better heat transfer rate from the exhaust gases. The contours of temperature distribution, pressure distribution and velocity distribution of exhaust gas with heat exchanger fins and inner pipe surfaces are discussed in detail for different cases of heat exchangers. From the numerical simulation, the PFCHE protrusion fin of 30 mm height revealed the maximum heat recovery rate and exhaust gas temperature reduction rate in comparison with the base heat exchanger model without fins. The simulation result also confirms that the PFCHE with 12 fins of 30mm height is the finest configuration to enhances heat recovery from the diesel engine exhaust gas.

Nowadays, extensive Electric Vehicle (EV) aggregator research has been carried out to design smart charging-discharging and scheduling of EVs. The EV aggregator plays a vital role to coordinate the energy management in between power grid and the EVs. This paper presents the optimal design of an EV aggregator in real-time for peak load shaving and valley filling. In practice, uncoordinated charging-discharging of grid-connected EVs may create serious issues to the power grid by introducing new peak demand and fluctuating load profile. In order to overcome the above issue, the EV aggregator's energy management system was optimized using Genetic Algorithm (GA) with constraints such as power balance and State-of-Charge (SOC). Furthermore, 24 hours dynamic load profile was considered in the optimization algorithm to manage the real-time power demand at each time instance. The deterministic optimization technique was tested in MATLAB-SIMULINK model for real-time application. The results showed that the proposed optimization algorithm can effectively reduce the peaks and fill valleys in compliance with various constraints in real-time.

The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action.

The rapid growth in the usage and development of renewable energy sources in the present day electrical grid mandates the exploitation of energy storage technologies to eradicate the dissimilarities of intermittent power. The energy storage technologies provide support by stabilizing the power production and energy demand. This is achieved by storing excessive or unused energy and supplying to the grid or customers whenever it is required. Further, in future electric grid, energy storage systems can be treated as the main electricity sources. Researchers and industrial experts have worked on various energy storage technologies by integrating different renewable energy resources into energy storage systems. Due to the wide range of developments in energy storage technologies, in this article, authors have considered various types of energy storage technologies, namely battery, thermochemical, thermal, pumped energy storage, compressed air, hydrogen, chemical, magnetic energy storage, and a few others. These energy storage technologies were critically reviewed; categorized and comparative studies have been performed to understand each energy storage system's features, limitations, and advantages. Further, different energy storage system frameworks have been suggested based on its application. Therefore, this paper acts as a guide to the new researchers who work in energy storage technologies. The future scope suggests that researchers shall develop innovative energy storage systems to face challenges in power system networks, to maintain reliability and power quality, as well as to meet the energy demand.

This work presents an economic analysis of a hybrid renewable energy source (HRES) integrated with an energy storage system (ESS) using batteries with a new proposed strategy. Here, the HRES system comprises wind turbines (WT) and a photovoltaic (PV) system. The hybrid WT, PV and energy storage system with battery offer several benefits, in particular, high wind generation utilization rate, and optimal generation for meeting supply-demand gaps. The real recorded data of various parameters of a 22 KV hybrid &lsquo, Regen&rsquo, feeder of 110/22 KV Vagarai Substation of TANTRANSCO in Palani of Tamilnadu in India was gathered, studied for the entire year of 2018, and utilized in this paper. The proposed strategy is the hybridization of two algorithms called Radial Basis Function Neural Network (RBFNN) and Oppositional Elephant Herding Optimization (OEHO) named the RBFNOEHO technique. With the help of RBFNN, the continuous load demand required for the HRES and be tracked. OEHO is used to optimize a perfect combination of HRES with the predicted load demand. The aim of the proposed hybrid RBFNOEHO is to study the cost comparison of the HRES system with the existing conventional base method, energy storage method (ESS) with batteries and with HOMER. The proposed Hybrid RBFNOEHO technique is evaluated by comparing it with the other techniques, it is found that the proposed method yields a more optimal solution than the other techniques.

The present research investigates the energy recovery capability of exhaust gases in Internal Combustion Engines (ICEs) in order to reap low grade waste heat energy. In order to achieve this aim, a double pipe, Protracted Finned Counter flow Heat Exchanger (PFCHE) was designed, analysed, fabricated and experimented with binary (water-ethanol) mixtures as working fluids. In the current work, as a first step, the theoretical design was completed and the computational simulation of PFCHE was executed. From the experimental investigation and analytical results, a positive notion was observed about the overall efficiency of the heat recovery system. It was proved that when the number of fins increased along with its height, then the heat transfer rate also got increased which further resulted in the improved performance of heat recovery system and increased Brake Thermal Efficiency from 32% to 39.6%. The developed heat recovery system was able to produce 0.35 kW–0.76 kW of power when the turbines were made to run at 1700 rpm to 4800 rpm respectively. Overall, the study concludes that the PFCHE increased the working fluid outlet temperature, effectiveness, heat transfer rate as well as the overall Brake Thermal Efficiency when compared with traditional heat exchangers that lack fins.

Charging–discharging coordination between electric vehicles and the power grid is gaining interest as a de-carbonization tool and provider of ancillary services. In electric vehicle applications, the aggregator acts as the intelligent mediator between the power grid and the vehicle. In recent years, researchers have introduced the concepts of aggregated energy management, centralized-decentralized planning, and ideal charging–discharging through improved technologies and integrated energy planning. These methods have the technical ability to adapt the distribution network according to load, aggregator-controlled optimal charging–discharging, demand management systems, strategic load assessments, and management. A comprehensive review suggests that large-scale electric vehicle charging technologies for controlled charging–discharging is becoming a pitfall within the grid and distribution network. This paper reviews several controlled charging–discharging issues with respect to system performance, such as overloading, deteriorating power quality, and power loss. Thus, it highlights a new approach in the form of multistage hierarchical controlled charging–discharging. The challenges and issues faced by electric vehicle applications are also discussed from the aggregator's point of view.

The availability of renewable energy sources along with the advancement of sensing and communication technologies has resulted in the sustainable operation of modern energy systems. An intelligent grid system is the integration of sensors and actuators, which enables the system to connect and exchange energy-related data from renewable sources to a computer system and end-users in a communication network. This data can be monitored in real-time with the help of the Internet of Things (IoT). However, several challenges exist in IoT, such as security, bandwidth management, interfacing interoperability, connectivity, packet loss, and data processing. In this paper, the key challenges and outstanding issues with the IoT when incorporated with energy systems are reviewed. The objective of this paper is to assess the suitability of different data transfer and communication protocols of IoT for deployment in the modern grid system. Moreover, several wireless IoT communication technologies are compared for their suitability in the multilayer network architecture and applications of energy systems.