Advances in Smart Energy Systems

In this present work, the hybrid energy systems are used for replacing power grid electricity that is presently used for electrifying the lecture halls, in mechanical department of RLJIT, Doddaballapur, Bengaluru. There is lack of energy resources day by day and also increase in energy demand. This is where hybrid energy systems come into picture. A hybrid energy system is a combination of two or more renewable or non-renewable energy resources used for generation of electricity. The depletion of conventional energies leads to usage of hybrid systems to meet the growing energy needs and generation of electricity. In this study, a hybrid system of solar–wind–battery–diesel energy system is used as substitute for the grid power supply. The average monthly power required for lecture halls is calculated using respective methods. Solar irradiance and wind speed data have been collected from NASA’s POWER Data Access Viewer. The battery bank is used to store the energy produced from the renewable energy sources. Diesel generator is used for power generation when all other sources are at rest. This paper focuses on the usage of technique for order of preference by similarity to ideal solution (TOPSIS) method for getting the enhanced energy resource among the combination of these energy resources. Various combinations of hybrid energy systems are compared and analyzed with TOPSIS. Different criteria like capital cost, maintenance cost, availability, emission, etc., are also studied in this TOPSIS method, and also many alternatives are considered to get accurate results. The results show that the combination of solar–battery–diesel hybrid energy system is optimal combination for power supply to the lecture halls.

The Internet of Things (IoT) may be demarcated as any kind of network that is embedded with sensors, interlinked with software, and other technologies for communication or connectivity of devices over the Internet. IoT is a massive lively global network infrastructure and plays a chief role in smart grid growth and enhances intelligent grid information and communication. Smart grid (SG) is usually a data communication network that is consolidated with a power grid to collect data and information from the substation, consumers, and transmission lines. With IoT, it is like an upgraded version of the network. In this chapter, an attempt has been made to study various applications, communication infrastructures, protocols, and security services of IoT in SG. The importance of securities in the SG for secure communications and prevention of all possible failures or threats is to be discussed thoroughly. Moreover, an attempt has been made to give an idea of cyber security for the SG and its privacy is also to be addressed in this chapter.

With the increase in global population and global heating, energy demand is also constantly increasing. The uprising demand to be fulfilled by taking care of environmental conditions’ protection to keep global warming in check. Significant efforts have been put into designing, controlling, handling, planning, and managing energy systems. In this regard, bio-inspired or nature-inspired evolutionary optimization schemes in the existing energy systems and innovative energy sources are inducted into the available resources. On the other hand, quantum computing has changed the classical computational approach with speed and efficiency. The assurance of quantum computing in the optimization of energy systems also gained a research attraction. The optimization techniques employed with the quantum advantage by quantum computers supersede classical approaches. This study explores the viability of quantum computing in energy system optimization and various challenges to tackle. This work will help the readers to plan for applying this approach in sustainability energy harvesting, intelligent power and energy systems, distribution network, and renewable energy. Security of the smart grid, intelligent energy systems, evaluation of the energy production process, and other similar or related applications may also be explored.

Increased line losses in distribution networks is a result of rapid growth in load demand. Aside from that, maintaining voltage stability of the grid in a healthy state becomes a problem for the utility sectors due to fluctuating loads. This paper investigates optimal capacitor placement (OCP) in radial distribution system (RDS) using optimization techniques to solve the above issues. The current work solves the OCP problem using a simple and efficient symbiotic organisms search algorithm. The most desirable buses for the installation of the capacitor are discovered first using a sensitivity index study, minimizing the searching space for the optimization phase. The optimum size and position of the capacitor banks are then determined, with the goal of minimizing system losses and optimizing net annual profit. To demonstrate its effectiveness, the studied approach is applied on 69-, 85-, and 118 bus standard RDSs. Switchable capacitor banks are considered to deal with variable loading condition. Furthermore, the suggested method's performance under maximum load and variable load situations is compared to that achieved using existing cutting-edge methods to determine its utility.

An incredible attempt has been made to study natural fiber-based composites over the last two decades due to their rising demand in the industry as materials with good mechanical properties, lower cost, renewable, eco-friendly, and energy-efficient characteristics. Natural fiber composites are energy efficient and environmentally advanced than synthetic composites in most cases due to: (1) better energy efficiency due to the presence of lightweight natural fibers resulting in lower emissions and better fuel efficiency (energy-efficient automotive applications); (2) replacement of polluting base polymers with biodegradable natural fibers; (3) lower environmental shocks than synthetic composites; and (4) incineration of natural fibers composites upon disposal provides in recovered energy and carbon credits. Additionally, usage of industrial waste such as lime sludge in composite fabrications offers two-pronged advantages—(1) reuse of an industrial waste that otherwise causes pollution and (2) addition of a particulate reinforcement which would add to the properties of the composite. Hence, in this study, natural coir fiber and industrial lime sludge waste are used as reinforcement in an epoxy matrix to study the composites’ mechanical properties (tensile and flexural strength). However, it is paramount to know the optimum values of parameters (fiber length, fiber content, and lime sludge content) in order to produce the composite with the best mechanical parameters. Additionally, the influence of these three parameters on the mechanical properties of lime sludge-filled coir fiber-reinforced composites is also important. Hence, Taguchi’s L16 orthogonal array with three factors and four levels is used to determine the optimum process parameter values within the chosen levels in order to obtain the best mechanical properties. Moreover, analysis of variance (ANOVA) is also used to examine the effect of each process parameter on the end results. Regression models are also developed for each mechanical property studied as a function of the three input parameters. Overall, it was observed that increasing the fiber length plays the most dominant role in improving the mechanical properties of the energy-efficient lime sludge-filled coir fiber-reinforced polymeric composites.

This work illustrates about an extended state observer (ESO)-based optimal controller for controlling a two-link robotic manipulator. A full state observer was designed with the help of extending the states using control law. Minimizing the quadratic cost functions, control law was designed for the multiple input multiple output (MIMO) plant. An algorithm has been set for tuning the controller parameters whether the best optimized results were compared. This two-link robotic manipulator is very rigid and to design a perfect controller is complex task although in this work it has been tried to construct an effective linear quadratic regulator (LQR)-based extended full state observer. The unknown state was observed by the EFSO which was fed to the controller including an external disturbance. The disturbance was discarded by the controller observer pair, and a stable response was established.

Renewable technologies are plentiful, long lasting, and eco-friendly. Solar energy, which generates both heat and light, is the most abundant source of energy. Solar photovoltaic modules use solar radiations to generate electricity and thermal energy, while the remaining solar radiation content is lost to the environment. The first law of thermodynamics was used to perform an energy analysis on a solar photovoltaic module, and the second law of thermodynamics was used to perform an exergy analysis to determine energy losses and exergy efficiency during the photovoltaic conversion process. The operating parameters of a solar photovoltaic module are as follows: ambient temperature, photovoltaic module surface temperature, overall heat transfer coefficient, short circuit current, open circuit voltage, fill factor and solar radiation. These were achieved on a sunny day in the month of February at R.L.J.I.T, Doddaballapur. The experimental data are utilised to calculate the solar photovoltaic module’s energy and exergy efficiencies. The efficiency of the solar panel performance decreases as the temperature of the module rises. As a result, by reducing heat from the surface of the solar photovoltaic module, the module’s efficiency can be increased. Surface heat can be eliminated by delivering water or air as a medium to the solar photovoltaic module. Finally, ANN model was developed to determine the performance prediction models using multilayer perceptron neural network, and it reveals that the developed model with six neurons gives better performance with a confidence interval of 95%.

Radar sensing technology uses radio electromagnetic (EM) waves to provide 3D space localisation and 4D motion sensing. The mmWave radar shows advantages in low cost, low power, environment robustness and capability in material classification. In this paper, the capability of mmWave radar to perform industrial multi-material classification with obstruction is studied by measuring the reflected radar signal. The classified materials are common engineering materials which include metal, polymer, ceramic, composite and natural. The experiment is conducted using the IWR1443BOOST mmWave radar sensor. From a series of experiment results, the received radar signal is the unique material signature of a target object. The relative power measured by IWR1443BOOST is correlated to the target object’s relative permeability and permittivity. This indicated the mmWave radar can easily pick up unique material properties as well as the physical structure of target object with minor assistance from deep neural network model. Three models which are linear classifier, fully connected neural network (FCNN) and convolution neural network (CNN) are trained and inference on the radar signal. CNN shows the most robust performance even under noise, while linear classifier converges fastest. All models achieved satisfactory accuracy with minimum amount of training epochs. This is because the radar signals are having clear discriminative distribution as proven in standard deviation against mean plot. The models also perform under 16 mm thick obstruction and can classify less than 5 mm thin material. From the experiment, the mmWave radar provides highly accurate multi-material classification with deep neural network. Due to its’ capability in wall-penetration and environment robustness characteristics, mmWave radar is a new alternative solution for industrial automation and sensing application.

In this paper, the shell and tube heat exchanger is analyzed with ANSYS Fluid Flow (Fluent), where the shell contains of tubes inside it. The hot water flows through the tubes and cold water flows through the shell side. The heat transfer, velocity, temperature, pressure, and streamline flow of the fluid are analyzed in this study. The analysis is undertaken for (a) single shell and tube heat exchanger (b) single shell and tube heat exchanger using a corrugated tube. The experimental study shows that the temperature drop (\(\Delta T\)) and pressure drop (\(\Delta P\)) of the heat exchanger in the tube side for counterflow is more than parallel flow configuration in both plain and corrugated pipes. Moreover, the corrugated pipe has approximately 6 K more temperature drop in the tube side than the plain pipe heat exchanger model. Consequently, the pressure drop in corrugated heat exchanger is 37.5 times more than that of the plain pipe heat exchanger. The results suggest that with increase of mass flow rates, the temperature difference between the inlet and the outlet of the pipe decreases. The variation of temperature in the shell and tube sides of the heat exchanger is found to be higher in case of few baffles are used as compared to no baffle conditions.

The airfoil blades have significant impact on the aerodynamic efficiency of the wind turbine. In this regard, the current work focuses on the investigation of wind turbine-based airfoil blade profiles at different wind velocities and angle of attacks. In this paper, CFD analysis is carried out using Ansys Fluent to select the most suitable blade geometry for use in the wind rotors. For the current simulation, three NACA airfoils, namely NACA 2412, NACA 4412, and NACA 0012, are selected, and with this, the computed results are validated with the existing experimental results. With the validated CFD method, the different values of drag coefficients and lift coefficients have been calculated for the above-mentioned airfoils at various wind velocities and angles of attack. From the CFD analysis that is carried out, it can be concluded that NACA 4412 provides the maximum lift and drag making it suitable for the use in rotors as compared to the other blade profiles selected in our study.

Increased usages of non-renewable carbon-based energy sources are rapidly depleting its reserve and releases environmentally harmful greenhouse gases. Hence, it is essential to find alternative renewable, sustainable, and green energy solutions to support global and local energy generation. Extensive research on vertical axis wind turbines (VAWT) has shown the potential of harnessing wind energy at locations, where conventional horizontal axis wind turbines (HAWT) are not suitable, such as in urban areas of Malaysia. However, inconsistent wind speed throughout the year poses a problem in harnessing wind energy efficiently. An alternative way of extracting wind energy is possible from unnatural sources such as exhaust systems. The present research aims to conduct three-dimensional numerical investigations of a cooling tower exhaust air energy extractor using VAWT. The performance of the exhaust VAWT system has been measured through unsteady simulations, and its aerodynamics properties have been evaluated. Then, using flow guiding techniques, the overall performance of the air energy extractors has been analyzed using computational simulations. This investigation has shown a significant performance gain for the VAWT and provided a good understanding of VAWT aerodynamics and flow behavior under accelerated wind conditions to contribute to a green technology system for renewable energy generation.

The increase in global population has caused a higher demand for energy be it for the upsurge in industrialization or daily consumption. Nevertheless, the high levels of carbon emissions from major energy resources such as coal, petroleum and gas are alarming as repercussions through rising sea levels are posing a threat to modern civilization. Thus, renewable energy sources such as wind energy and solar energy are being ventured into as these natural resources cause minimal to no harm to the environment. Previous literature suggests that minimal studies are performed on the enhancement of the turbine component of the solar chimney power plant (SCPP) for power augmentation. This work proposes the study of the performance of the Savonius-style wind turbine (SSWT) in the SCPP. The sliding mesh approach is adopted for the SSWT model, whereas the radiation model is used for the SCPP model in ANSYS Fluent. The SSWT was validated against previous numerical and experimental results, while the SCPP model is validated using the outcomes obtained from the Manzanares plant. The numerical study is carried out at a tip speed ratio (TSR) of 1.0, where the velocity and pressure fluid flow profiles are examined at four different azimuth angles for its performance capabilities. The outcome of the study suggests that SSWT performs well in the SCPP, but is hindered by negative torque at certain azimuthal angles which reduces it overall power producing capacity. It is suggested that TSRs lower than TSR 1.0 may exhibit better drag formation which may lead to an improved turbine performance. Also, flow optimization solution is encouraged for an improved overall torque output, performance and power production by the SSWT in the SCPP.

This work explores automatic generation control learning under traditional situation for a three-area system: Sources in area-1 are thermal–solar thermal (ST); thermal–geothermal power plant (GPP) in area-2; and thermal-hydro in area-3. The work involves various assessments in the presence of constraints such as governor rate constraint, governor dead band, and time delay. An original endeavor has been set out to execute cascade controller with amalgamation of proportional-derivative and fractional order integral-derivative (FOID), hence named as PD-FOID. The performance of PD-FOID has been compared with varied controllers like integral (I), proportional-integral (PI), and proportional-integral-derivative (PID). Various investigation express excellency of PD-FOID controller over other controller from outlook regarding lessened level of peak_overshoot (P_O), peak_undershoot (P_U), settling_time (S_T). A swarm-based meta-heuristic cat swarm optimization (CSO) algorithm is applied to acquire the controller’s gains and parameters. Action in existence of redox flow battery is also examined which provides with noteworthy outcome. PD-FOID parameter values at nominal condition are appropriate for higher value of disturbance without the need for optimization.

Sophisticated technology developments and numerous ancillary services in the modern smart grid are taking part in electric vehicles (EVs). In the competitive electric market, EVs provide superior power management services. EV is a new distributed energy storage and can be used to compensate for power mismatch. This article discusses the significant impact of EVs on the unified control of voltage and frequency in a three-area thermal system including a wind turbine into all areas. A new cascade fuzzy PD and PID with filter coefficient (N) (CFPD-PIDN) controller, EVs and wind system are provided in all areas for combined voltage and frequency control under various investigations. Artificial flora algorithm is employed to obtain the CFPD-PIDN controller gains and other parameters under numerous scenarios. Various simulations are performed to validate the superiority of the proposed control strategy.

As part of the primary control of the autonomous microgrid (MG) voltage source converter (VSC) system, the inner loop voltage and current controller help to provide a fast transient response for frequency and voltage restoration. This paper proposed a fractional-order proportional plus integral (FOPI) controller for effective voltage and frequency management in autonomous MG VSC systems. Because of their fractional features, FO controllers make the VSC system more resilient than traditional PI controllers. Along with the conventional PI controller K_p and K_i gains, the FOPI controller has an extra edge of flexibility “λ”. The FO controllers parameters are tuned using the quasi-oppositional salp swarm algorithm (QSSA), a novel metaheuristic process. A droop controller that utilizes the dynamic change in the droop coefficients is also used to condense power transient and enhance the systems dynamic response while operating in the islanded mode. Furthermore, simulating the MG system in MATLAB Simulink, the dynamic performance of the proposed controllers is validated for the various abrupt change in system condition such as different initial load switching conditions for unequal ratings of distributed generation inverter and the effect of momentary fault (i.e., double line to ground fault). This paper compares the performance of conventional droop with PI controllers in inner voltage and current controllers and the suggested QSSA optimized FOPI controller with modified droop controller for autonomous MG systems. The simulation findings showed that the MG performance has improved using the proposed controller.

This article presents the conflated control pattern of voltage and frequency of the multi-area multi-source interconnected power system by the automatic voltage regulator (AVR) and automatic load frequency control (ALFC) systems. The three-area system has different plants as Area-1 comprises thermal-thermal-electric vehicle (EV fleet), Area-2 comprises thermal-thermal-geothermal, and Area-3 comprises thermal-thermal-wind turbine plant (WTP). As a new attempt for the conflated system, the fuzzy-based tilt-integral-derivative (FTID) controller is being implemented as a secondary controller of ALFC systems and core controller of AVR systems. The optimum controller parameters are achieved using a bio-inspired meta-heuristic algorithm named Harris hawks optimization (HHO) technique. The system performance is evaluated using the performance index called integral square error (ISE) by having 1% perturbation at Area-1. The supremacy of the proposed FTID controller is evaluated by comparing it to the PID and TID controllers using the obtained system dynamic responses and comparing them on the subject of all time-domain indices. The impact of FTID controller in the AVR system is compared to other controllers, and the consequences of the AVR system on the ALFC system are also analyzed. By modifying the generator parameters and changing the system loading conditions, the sensitivity study of the proposed controller reveals that the optimized controller parameters determined under nominal conditions are resilient enough and do not need to be modified. Eventually, the proposed study is investigated using a real-time hardware setup, namely the OPAL-RT OP4510, for real-time corroboration.