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Technological challenges to the widespread adoption of battery-powered devices contain substantial weight with a high cost and low power density. To bring an improvement in over-dependency on batteries, wireless power transfer is a ray of hope in energizing electric-driven devices. Moreover, for high voltage transmission lines, optimization of natural frequency plays an important role in efficient wireless power transfer (WPT) considering dc to load supply. In consideration of different aspects of wireless power transfer technology, a completely optimized method should be adapted for monitoring. In the present work, a model of an electric car vehicle has been developed based on WiTricity. This concept of wireless power transfer has been realized in this work as a small-scale simulated model, which can be used to charge batteries, mobile, door locks, and propeller clocks, Further, the evolving wireless power transfer technologies often face difficulty in asymmetrical variable-frequency pulse-width-modulated (WPT) systems. To deal with these multiple harmonics as inherently generated by variable frequency amplitude pulse width modulation (VFAPWM), a multiple harmonics analysis technique has been adopted in this work. Different parameters like loads and duty cycle have been varied with varying frequencies, to study the charging current harmonic distortion and voltage harmonic distortions. The difference in voltage observed was essentially nonexistent, with a 1.8 to 3 times variation in switching frequency. Moreover, the pattern of deviation has been noticed for output voltage when the load was varied from 20% to 100%. Additionally, a comparative study has also been performed in evaluating the charging current distortion pattern by the implementation of both MHA techniques and conventional first harmonic approximation (FMA).&nbsp

A hardware model of a solar tracker has been developed to explore different technical and environmental aspects to generate maximum power. The proposed model consists of a dual-axis structure, whose operation is based on maximum solar irradiance for a specific location. Three modes of operation differing in the degree of freedom of axes movement were tested using two distinct control strategies. Further, different tilt angles were considered for this study. The software platform used to operate the hardware model is Arduino Pro Mini. To analyse the effect of the above approaches on the output voltage and to further assess the implications on the all-day efficiency and cost, an in-house computer code written in the C programming language is used as the basic controller. The code interfaced with Arduino UNO. The Precise Position Algorithm was implemented to improve the utilization of solar rays further. The single-axis tracker harnessed 25% less power than the dual-axis tracker, while the fixed-axis tracker captured 51% less. Tilting the axis at an angle of 28o degrees provided the maximum power. The use of the Precise Position Algorithm resulted in 11% more all-day power and 40% more efficiency than the C programmingbased Arduino UNO.

In the present study, performance analyses of a solar module are made through the optimal variation of solar tilt angle, pertaining to the maximum generation of solar energy. The work has been carried out for a particular location at Tripura, in India, considering three different cases on an annual basis. An intelligent behavioural based algorithm, known as artificial bee algorithm (ABC), has been implemented for finding the optimal orientation of solar angle in analysing the performance. The result shows marginal differences are obtained in producing yearly maximum solar energy for different orientations of the PV module. It has been observed that the maximum average solar energy is obtained for the case where continuous adjustment is made by rotating the plane about the horizontal east-west axis within 20° to 30° tilt angle. The computed maximum and minimum of the monthly average efficiency is 10.9% and 8.7%, respectively. Further, a comparative study has been performed in generating average solar energy through optimal tilt angle by the implementation of Perturb & Observe method (P&O). The monthly average solar power computed by P&O method resulted better in a range of 2% to 15% in comparison to that obtained by ABC. While on the other hand, the efficiency computed by ABC algorithm was 15% to 19% better than that evaluated by P&O method for all the cases studied in the present work.

Drastic increases in oil prices and need to reduce global warming are issues that have raised huge public awareness, which in turn encouraged many developed and developing countries to adopt new energy policies. Renewable energy resources are gaining fast attention in solving power supply problems in almost every area to shield the environment. In solving power supply problem standalone microgrid systems including renewable energy sources plays a key role. For a stand-alone renewable power delivery system, battery is the crucial component which allows storing excess renewable energy and further using the energy as and when needed by the load. In order to achieve the best performance and utilization the state of charge of the batteries should be assessed. This paper presents heuristic approaches to evaluate the state of charge (SOC) of lead–acid battery through optimal power generation in a standalone hybrid wind-solar renewable power delivery system. The mathematical analysis is made by the application of modified heuristic techniques, i.e. tuned genetic algorithm (TGA) and improved colliding body optimization(ICBO), for a specific loading condition in India. More optimized power is generated by the renewable distributed energy sources when evaluated by ICBO than that achieved by TGA, for both winter and summer load demands. These optimized value of power generated by the application of ICBO, resulted in low average value of SOC. The low values of SOC further prevent the battery from malfunctioning for a longer period of time. In order, to explore more optimal generation pertaining to good value of SOC parametric studies of modified algorithms, i.e. TGA and ICBO have been carried out. A set of critical operational parameters have also been obtained through parametric study of TGO and ICBO for a specific real time load profiles. Thus the reliable and optimal values lead to a better prospect in utilizing standalone renewable power delivery system for Indian future power scenario. Moreover, a comparative study has also been performed in analysing the performances of conventional and non-conventional methods in this regard. It has been found that the heuristic techniques, viz ICBO and TGA delivered 13% and 17% better result in comparison to that obtained by linear programming method respectively. Thus it can be surmise that the heuristic techniques perform well in considering the uncertainties and approximations of real world problem of generation renewable power, pertaining to better values of SOC.

This work presents a hardware development of a solar tracking system using arduino pro mini and further analyzing the performance of dual axis solar tracking systems. The objective is to examine the performance of the parameters such as voltage, power, all day efficiency, cost; through different settings of solar tracker. The proposed work has been established on hardware and analyzed using software mode. The hardware has been set up using two servo motors, along with a light dependent resistor (LDRs), which senses the solar rays. Further the motions of the motors are performed by moving the solar panel towards point or location corresponding to the source of maximum light. The control portion was developed by using C programming as a software mode of operation. The control mechanism was targeted to the Arduino UNO for functioning of the hardware system. The result showed that the solar tracking system produces maximum all day power corresponding to dual axis tracker. It resulted in 25% more than that of a single axis tracker and 51% more than that of a fixed tilt angle. Moreover, a Therefore, a solar tracker can effectively capture the maximum sunlight source and can be used for any small applications in order to achieve more electrical power

With the ever increasing electricity demand in the twenty-first century, conventional power grids have to respond by adding new generation capacities. This, however, requires upgradation of transmission and distribution systems over utility structures in an environmentally sustainable manner, further expanding to new remote/rural areas. Thus, to retain techno-economical and environmental viability, the concept of decentralized power delivery systems has come up as a new alternative. Though the search for technological effectiveness of microgrids is important, the establishment of a standardized and competitive price structure in the electricity market is also required. Hence, nowadays, economic evaluation of decentralized power delivery systems are being dealt with proper attention. Most conventional methods are classical algorithms, which generally include replication and precision with a lengthy and extensive search process. These deterministic methods involve trial and error process, without any guidance, hint, adaptation and self-correction. In order to overcome such problems modern optimization techniques have emerged which do a random search on basis of some plausible hints and find solution by trial and error. Though these elementary soft-computing methods can attain possible solutions within a given finite time, they never assure to achieve the best solution. Thus to achieve better and more accurate solutions, artificial intelligence techniques with attributes such as partial truth, uncertainties, imprecise specification and approximation which are often present in problems of practical relevance in energy economics, will be explored. Further, considering the relative strength and weakness of different soft-computing techniques, hybrid algorithms will be discussed and assessed in terms of performance this chapter.

Optimal power generation (OPG) through reliable and clean technologies is nowadays of high concern. Meta-heuristic algorithms are attractive for robust and adaptable solutions to OPG problems. However, no algorithm can result in the most desirable solution to OPG problems. In the present work, two novel methods, namely improved colliding body optimization and tuned genetic algorithm have been proposed for electric power production through combination of renewable and non-conventional energy resources. Solar photovoltaic module and biomass gasification systems have been used to deliver power reliably for a real-time load data in an Indian scenario, with battery used for energy storage. Two case studies have been conducted considering different loading scenarios. To quantify the environmental impact, carbon footprint and carbon tax have been computed for the defined case studies. ICBO has been found to provide more consistent result, in generating optimal renewable power as well as computing carbon tax.

An economic estimation of an autonomous power delivery systemconstituting different types of non-conventional and renewable energyresources has been performed considering an Indian load demand sce -nario. The mathematical analysis was done by the application of a modi-fied differential evolution (MDE). The MDE-evaluated total annual costsfor the autonomous microgrid system utilizing three sets of distributedenergy resources (DERs) have been compared. Solar module and fuelcells as DERs form the first set of DERs; solar module and bio-mass gas -ifier unit as DERs is considered as second set; and fuel cell along withBattery Energy Storage System (BESS) constitute the third set of DERs.Different types of consumers together form the microgrid with optimalsupply of power from DERs. The optimal power generation conditionshave been obtained pertaining to minimum cost of microgrid system.For the same load demand in microgrid operation, the results, using ahybrid solar-biomass system are found to be most cost effective. A re-duction of 6.9 % in the annual cost is obtained using the combination ofsolar module and biomass gasifier unit from that obtained using a solarmodule fuel cell combination. The corresponding reduction is 10% fromthat utilizing only fuel cell.

An economic evaluation of a network of distributed energy resources (DERs) forming an autonomous power delivery system in an Indian scenario has been made. The mathematical analysis is based on the application of a real valued cultural algorithm (RVCA). The RVCA-evaluated total annual costs for the autonomous microgrid system utilizing both solar module and fuel cells as DERs and solar module and bio-mass gassifier unit as DERs have been compared. Different types of consumers together form a microgrid with the optimal supply of power from DERs. The optimal power generation conditions have been obtained pertaining to minimum cost of microgrid system. The results for different loading scenarios, using hybrid solar-biomass gassifier unit are found to be more cost competitive. A reduction of 8.1% in the annual cost is obtained using solar module-biomass gassifier unit to that using solar module-fuel cell for the same load demand in microgrid operation.

An economic evaluation of a network of distributed energy resources (DERs) comprising a microgrid structure of power delivery system in an Indian scenario has been made. The mathematical analysis is based on the application of tuned genetic algorithm (TGA). The analyses for optimal power operation pertaining to minimum cost have been made for two cases in Indian power delivery system. The first case deals with the consumers’ individual optimal operation of DERs, while in the second one, consumers altogether form a microgrid with the optimal supply of power from DERs. The total annual costs for these two cases are found to be economically competitive and encouraging. A reduction of approximately 5.7% in the annual cost has been obtained in the case of microgid system than that in the separately operating consumers’ system for a small locality of India. It is observed that the application of TGA results in a reduction of the minimum cost depicting an improved outcome in terms of energy economy.

Optimal reactive power dispatch (ORPD) is real power loss minimization aspect of power system which has gained several attentions from the researchers. Here by ORPD, real power loss of the network is attempted to be minimized and bus voltages are kept within stable voltage margin. ORPD is generally solved by controlling generator bus voltages, transformer tap settings and by placing shunt Var compensator in few buses. The optimal determination of the said variables are organized in this paper by applying a newly proposed nature inspired meta-heuristics technique i.e., Cuckoo search algorithm (CSA). As a test case IEEE 14-bus system is considered. The CSA based obtained results are compared with other reported evolutionary technique based results. The comparative study shows the effectiveness of the applied Cuckoo Search Algorithm technique.

Reactive power dispatch (RPD) is one of the major issues in the transmission and distribution domain of the power system. RPD being a fundamental part of optimal power flow problem, controls balanced voltage profile and minimises active power loss. As of its objective function, RPD problem is treated as mixed integer non linear function where metaheuristics algorithm may be fitted properly as solving tool. In this paper such an algorithm named particle swarm optimization (PSO) have been applied for solving reactive power dispatch problem. The special feature of PSO technique is its local as well as global optimization of the objective function. In this paper constriction factor is included in generalised PSO algorithm to enhance the velocity of the particle used in particle swarm optimization technique which is applied to the IEEE 14-bus system. In the test system control variable such as generator terminal voltages, transformer tap positions and the shunt capacitor placements are varied to solve the RPD problem. The obtained result is compared with previously used different optimization algorithm including generalised PSO. The comparison shows the effectiveness of the constriction factor and the practicability of the applied optimization technique in this paper.

Economic evaluation of a network of distributed energy resources (DERs), comprising a microgrid structure of power delivery system in an Indian scenario, has been made. The mathematical formulation of the economic estimation is based on a hybrid technique of Genetic Algorithm and simulated annealing (GASA). Analyses for optimal power operation pertaining to minimum cost for the two cases of Indian power delivery system have been considered. The first case deals with the consumers' individual optimal operation of DERs, without forming a microgrid, whereas the other one deals with the consumers' forming a microgrid with the optimal supply of power from DERs. The results relating to the total cost for different cases of loading scenario show that microgid provides an energy cost reduction of about 11% from that of separately operating consumers' system for a small locality of India ensuing to a future power system possibility.