Power generation in today’s world is of utmost importance, due to which blockchain is used for the categorization and formation of decentralized structures. This paper has proposed decentralized energy generation using a nester, i.e., energy sharing without third-party intervention. Decentralized blockchain technology is applied to ensure power sharing between buyer and seller, and also to achieve efficient power transmission between prosumer and consumer. Energy management is associated with controlling and reducing energy consumption. Blockchain technology plays a major role in distributed power generation, for example, power-sharing (solar and wind energy), price fixation, energy transaction monitoring, and peer-to-peer power-sharing. These are operations performed by blockchain in renewable power generation. Solar power generation using blockchain technology can obtain an impact resting upon the power generation system. Distributed ledger is the key area of blockchain technology for recording and tracking each transaction in the distribution system to improve the efficiency of the overall transmission system. A smart contract is another important tool in the blockchain technology, which is issued to confirm an assent between buyer and seller before starting any energy transaction without external intervention and also to avoid time delay. Maximum power point tracking is conducted in PV cells using blockchain technology. Blockchain influences energy management systems to improve the utilization of energy, optimize energy usage, and also to reduce the cost.

Increased demand in renewable power move towards the balancing conventional dependent energy source lead the increase in technical development in reliable power generation. This model contains the wind speed variation, capacity, uncertainty, and production are considered in system evaluations to bring the wind power to more sustainable energy. More reliable VSC HVDC technology in power transmissions for longer distances is preferred to transmit the renewable generated energy to remote end areas. Authors introduced particle swarm optimization method into the wind integrated HVDC System. The system is developed in MATLAB/Simulink to evaluate the wind power generation performance and Transmission performance. The simulation results are correlated and validated with the existing VSC HVDC link parameters. The supremacy in wind energy with particle swarm optimization performance is validated and more viable to integrate wind energy system with VSC HVDC system to transmit the reliable power to remote areas.

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The study presented a suggested technique for determining the best operating point of a proportional-integral-derivative (PID) controller in the context of a wind turbine. Additionally, the strategy aimed to find the stability zones within the parameter space. The proposed approach uses a Modified Particle Swarm Optimization (MPSO) algorithm for optimum PID controller design. The modification is carried out by integrating most effectual Genetic Algorithm (GA) with PSO. The aforementioned intelligent algorithms are artificial learning mechanisms capable of identifying the ideal operating points. They were used to derive a function that represents the most favourable operating parameters, namely kp and ki, for each value of kd inside the stability area of the PID controller. In fact, GA has slow convergence process with local convergence issue. These issues are resolved by PSO algorithm while GA takes the responsibility for new population generation. The solutions under consideration were shown by simulations of a drive train model without time delay and a pitch control model including time delay. Simulations are carried out in MATLAB Simulink tool which is most effective is designing and validating power systems with VSC HVDC transmissions.

Data analytics using machine learning technologies when applied to the energy consumption data can provide valuable inputs for maintaining the perfect supply demand balance in a smart electrical grid system. In particular, the accurate predictions of energy consumption for future periods of time aids significantly in cost-cutting and energy saving for utility companies. Making use of the popular method of time-series forecasting and the Artificial Neural Networks (ANN) models, here in this paper, one of the variants of the Recurrent Neural Networks (RNN) model, the Long Short Term Memory (LSTM) model is applied for household electricity consumption forecasting. Real datasets from consumption building are used for experimenting the model and applied through Tensorflow platform with the keras functions in Python. The results obtained show significantly accurate values in predicting future consumption derived from models training with actual values of current consumption. Hence, this work provides yet another proof that the LSTM machine learning forecasting methods can be efficiently applied for household electricity forecasting.

Abstract Energy consumption in the field of transportation comes next to industrial consumption worldwide. If transportation is completely powered by renewable energy, the utilization of fossil fuels can be drastically reduced, which will result in a lesser amount of greenhouse gas emissions. Electric vehicles (EVs) can act as an alternative to make transportation pollution-free. Large-scale usage of EVs causes high electricity demand on the supply system. This problem can be overcome by utilizing renewable energy sources (RESs) for Electric Vehicle charging. Due to the unpredictability of RESs, coordinating EV charging with other loads and renewable generation is problematic. By using EVs as energy units, power fluctuations in the electric grid can be compensated. This paper presents a summary of recent research in the domain of integration of electric vehicles (EVs) to the smart grid. Electric vehicles-smart grid integrated systems face several issues related to communication, grid infrastructure and control in the future power system. Smart grid technologies are summarized in Section 2. The existing research articles in this area are classified into two based on the purpose: EVs integration into the electric grid and Vehicle to grid services. Finally, the research gaps and future scope of incorporating electric vehicles with renewable energy sources and the Smart grid are highlighted.

In many residential buildings the electrical wires of individual houses are laid in the same conduit pipe and some mistakes could be made in identifying similar coloured wires when they are laid in same conduit pipe. Most of the faults are caused by the neutral interconnection in the wiring system. Usually neutral wires are connected to neutral bus within the panel board or switchboard, and are "bonded" to earth ground. In our secondary distribution, tree system of supply is mostly utilized. The voltage of each phase to neutral will be maintained at rated value even during the unbalanced load conditions. If neutral wire connection is poor the voltage at each phase will be different from one another, such an isolated neutral point is called floating neutral and the voltage of the point is always changing. This is the reason for over voltage causing damage to appliance’s which should be protected. In this paper, a smart system that identifies power leakage and provides over voltage protection to the residential building is proposed.

Nowadays, Energy consumption in the field of transportation comes next to industrial consumption worldwide. If transportation is completely powered by renewable energy, the utilization of fossil fuels can be drastically reduced, which will result in a lesser amount of greenhouse gas emissions. Electric vehicles (EVs) can act as an alternative to make transportation pollution-free. Large-scale usage of EVs causes high electricity demand on the supply system. This problem can be overcome by utilizing renewable energy sources (RESs) for Electric Vehicle charging. Due to the unpredictability of RESs, coordinating EV charging with other loads and renewable generation is problematic. By using EVs as energy units, power fluctuations in the electric grid can be compensated. This paper presents a summary of recent research in the domain of integration of renewable energy sources with electric vehicles (EVs) under the smart grid environment. Electric vehicles-smart grid integrated systems face several issues related to communication, grid infrastructure and control in the future power system. Feasibility of integration of solar and wind energy systems with electric vehicles is discussed in section 2. The existing research articles in this area are classified into two based on the purpose: EVs integration into the electric grid and Vehicle to grid services. The function of V2G in the electricity market, as well as its management concerns, are investigated in section 3. Finally, the research gaps and future scope of incorporating electric vehicles with renewable energy sources and the Smart grid are highlighted.

In this paper, minimization of operating cost of DC microgrids is formulated. Utility grid, solar, wind and battery is associated with this formulation. In this optimization problem, both with and without losses are considered in the power flow model. Impact of renewable energy sources on reduction in operating cost is discussed. Based on the solar radiation and air density, power generation from solar and wind are calculated respectively. Heuristic method is used to solve this minimization problem. To analyze the operating cost, a six-bus customized system is used. To calculate the cost structure, three different cases are considered. The first case considers the system without any renewable energy sources or battery storage. In the second case, a solar and a Wind Energy Conversion system are added to the system and in the third case, the battery is included with the existing system. With the inclusion of renewable energy system, dynamic pricing and various load conditions, the proposed algorithm is minimizing the operating cost considerably.

This paper proposes a predictive techno-economic analysis in terms of voltage stability and cost using regression-based machine learning (ML) models and effectiveness of the analysis is validated. Predictive analysis of a power system is proposed to address the need for faster and accurate analyses that would aid in the operation and control of modern power system. Several methods of analyses including metaheuristic optimization algorithms, artificial intelligence techniques and machine learning algorithms are being developed and used. Predictive ML models for two modified IEEE 14-bus and IEEE-30 bus systems, integrated with renewable energy sources (solar and wind) and reactive power compensative device (STATCOM) are proposed and developed with features that include hour of the day, solar irradiation, wind velocity, dynamic grid price and system load. An hour-wise input database for the model development is generated from monthly average data and hour-wise daily curves with normally distributed standard deviations. The data feasibility tests and output database generation is performed using MATLAB. Linear and higher order polynomial regression models are developed for the 8760hr database using Python 3.0 in JupyterLab and a best-fit predictive ML model is identified by analysing the coefficients of determination. The voltage stability and cost predictive ML models were tested for a 24hr input profile. The results obtained and the comparison with the expected values are furnished. Prediction of the outputs for the test data validate the accuracy of the developed model.

The primary aim of this work is to feature the advantages of integrating natural source of energy from the solar and wind to the prevailing electric power systems. Two types of analysis are carried out in two test systems (standard and modified test systems) and the outcome of the test systems are compared. The two analyses are technical analysis and economic analysis. The stability of the voltage is analyzed under technical analysis and the price of energy consumed from the electric grid is calculated and analyzed under the economic analysis. Dynamic hourly load data, hourly solar radiation, hourly wind velocity, and dynamic electricity prices are considered for the standard IEEE system and modified test system (with the integration of RES). Voltage stability index (L-Index) and price of the electricity consumed from electric grid are found for standard test system and the outcome is compared with the outcome of modified test systems. MATLAB coding is done for techno-economic analysis for both test systems. It is inferred from the outcome that the integration of renewable energy sources fairly contributes to the economic benefit of the system by lowering the power purchased from the grid and enhance the stability of the system.

The selection of energy storage system is very crucial for electric vehicles. It should have good energy density, considerable power density and also it must be light weight. So a battery with considerably high energy density must be used in electric vehicles. Lithium ion batteries are very much preferred as electric vehicle batteries. They have high energy density, high life cycle and smooth operation. But the problem related with lithium batteries is they have high temperature sensitivity, and their operation will be affected by over current charging and over current discharging beyond their maximum rated values and also influenced by driving conditions and performance of motors used. So battery management, control and optimisation system is essential in electric vehicle energy storage battery packs. This paper is a review of the design of a novel battery management and control system for lithium ion batteries for performance improvement in electric vehicles.

The fascination about the smart grid technology worldwide depicts the deployment of smart management in grid monitoring and protection. A probe into a plethora of challenges about the smart organization of grid explored in this paper. A ground level review about the incooperation of distributed resources with smart protection in demand response and demand side management enhanced the performance of the grid infrastructure. The adequate support of the information technology provides incessant monitoring for grid control and protection. This survey comprehensively examined various research proposals on the environmental, technical and economical remuneration of distribution generation incorporation like stability, reliability, cost analysis and green energy optimization. These benefits result from the smart management of each renewable distribution generation elements. This paper also reviews the current technologies for the grid incorporation with distribution generation.

Increasing renewable energy penetration into integrated energy storage systems (ESS) requires more efficient methods to store the energy in an effective way. Possibly various energy storage system (ESS) technologies faces various problems such as charging and discharging, reliability, economy, compactness, and safety. This paper audits the diverse sorts of ESS innovations, structures, features, and classifications. Also gives the clear idea about applications, advantages, and limitations of all technologies in grid and transportation system. It also provides a general review of performance capabilities of Li-ion battery and also other advanced ESS for small satellite applications. A hybrid ESS which consists of a battery and a supercapacitor is used in pure electric vehicles.

The proposed system is very useful for the agricultural system. The converter topology used here is Multiplier Boost Converter which will enhance better voltage gained from the input source which is solar PV. Now the PV can able to produce 80V which is boosted upto 285V through the converter. The inverter design is quite easy such that it is possible to drive a PMSM motor. The controller is not necessary here as the voltage produced by the DC-DC converter is quite reliable. So this might find application in water pumping and electric vehicles.

In the modern era, most of the utility grid is connected with Renewable Energy resources (RERs). In addition to this, many power electronic converters and reactive power compensating devices are also incorporated into the existing grid. This makes the system complicated. Penetration of renewable energy resources affect many power system parameters like grid stability, quality of power, reactive power balance and Sufficient energy utilization. However, the Distributed Generation (DG) towards the power electronic interface creates some critical power quality events such as reactive power management, harmonics and voltage profile which makes the distributed system become a polluted one. This paper depicts the review of modelling and incorporation of various reactive power compensating devices like TCSC, SVC and STATCOM into RES. Power generation model of solar, wind and fuel farm is discussed in this paper. Reactive power compensating devices and its location and sizing are important for the stable and secure operation of the electric grid. Consequently, power quality issues, real-time interconnection issues and policies related to reactive power management are in this paper.

The performance of battery on different driving patterns is very important in the case electric vehicles energy storage system. Usually lithium ion batteries are used in electric vehicles due to its superior parameters and lightweight. According to the driving patterns the current drawn from the battery will change. Also the graph of current, load and speed versus time also changes according to driving patterns. An electric vehicle or automobile can be driven in several patterns. This can be varied according to road conditions and traffic. The different driving patterns are namely 1) city traffic (light & heavy) 2) suburban 3) state high way 4) national high way 5) express highway 6) local/country loads 7) small town 8) high range/hilly area. According to the above patterns vehicles must be controlled in different ways. This will cause power loss, heavy current drawing and continuous braking and starting which will increase the strain on the battery. So this will reduce or in simple term vary the discharge time. Also the cycle life of the battery will reduce. This paper is an analysis and comparison of different driving patterns and the change in lithium ion battery performance in Indian road conditions.

A theoretical analysis on the performance of (Bi2Te3-PbTe) hybrid thermoelectric generator (TEG) is presented in this paper. The effect of different performance parameters such as output voltage, output current, output power, maximum power output, open circuit voltage, Seebeck co-efficient, electrical resistance, thermal conductance, figure of merit, efficiency, heat absorbed and heat removed based on maximum conversion and power efficiency have been analyzed by varying the hot side temperature up to 350oC and by varying the cold side temperature from 30oC to 150oC. The results showed that a maximum power output of 21.7 W has been obtained with the use of one hybrid thermoelectric module for a temperature difference of 320oC between the hot and cold side of the thermoelectric generator at matched load resistance. The figure of merit was found to be around 1.28 which makes its usage possible in the intermediate temperature (250oC to 350oC) applications such as heating of Biomass waste, heat from Biomass cook stoves or waste heat recovery etc. It is also observed that the hybrid thermoelectric generator offers superior performance over 250oC of the hot side temperature, compared to standard Bi2Te3 modules

Batteries are used to store energy for a long period of time. It is one of the first forms of storing electrical energy. Electro chemical batteries such as Lithium-ion and Lithium-polymer batteries are used as energy storage systems in power systems and electric vehicles. This paper presents a study report of Lithium batteries on charging and discharging conditions. Here a Lithium-ion battery and Lithium-polymer battery is taken in to consideration. The batteries used here are rechargeable or secondary batteries.

Due to the increasing demand of electrical power and faster depletion of non-renewable energy resources, an alternate method to generate the required electrical power is the need of the hour. For small scale electrical power production, thermoelectric generators (TEG) evolve as a boon to the competitive world. In the present study, a hybrid TEG composed of both n-type Bismuth Telluride and p-type Lead Telluride semiconductor materials capable of operating up to 350°C is considered. Various performance parameters such as voltage, current, output power and load resistance are theoretically analyzed. The results showed that a maximum power output of 21.7W has been obtained with the use of one hybrid thermoelectric generator for a temperature difference of 320°C between the hot and cold side of the thermoelectric generator. It is also observed that the hybrid thermoelectric generator works best for the difference in temperature range varying from 220° C to 360°C between the hot and cold side of the TEG and offer superior performance over 260° C of the hot side temperature.