The concept of smart grid was introduced a decade ago. Demand side management (DSM) is one of the crucial aspects of smart grid that provides users with the opportunity to optimize their load usage pattern to fill the gap between energy supply and demand and reduce the peak to average ratio (PAR), thus resulting in energy and economic efficiency ultimately. The application of DSM programs is lucrative for both utility and consumers. Utilities can implement DSM programs to improve the system power quality, power reliability, system efficiency, and energy efficiency, while consumers can experience energy savings, reduction in peak demand, and improvement of system load profile, and they can also maximize usage of renewable energy resources (RERs). In this paper, some of the strategies of DSM including peak shaving and load scheduling are highlighted. Furthermore, the implementation of numerous optimization techniques on DSM is reviewed.

Distributed generation connected with AC, DC, or hybrid loads and energy storage systems is known as a microgrid. Campus microgrids are an important load type. A university campus microgrids, usually, contains distributed generation resources, energy storage, and electric vehicles. The main aim of the microgrid is to provide sustainable, economical energy, and a reliable system. The advanced energy management system (AEMS) provides a smooth energy flow to the microgrid. Over the last few years, many studies were carried out to review various aspects such as energy sustainability, demand response strategies, control systems, energy management systems with different types of optimization techniques that are used to optimize the microgrid system. In this paper, a comprehensive review of the energy management system of campus microgrids is presented. In this survey, the existing literature review of different objective functions, renewable energy resources and solution tools are also reviewed. Furthermore, the research directions and related issues to be considered in future microgrid scheduling studies are also presented.

In this paper, a new low-cost small-scale three-phase brushless motor topology is proposed. This is a new topology for self-excitation of brushless wound rotor synchronous motor investigated initially for rated speed. For brushless excitation, the stator is designed to contain two windings, namely, three-phase armature winding and exciter winding. The rotor consists of two windings, i.e. harmonic winding and field winding, and a diode rectifier. An inverter feeds three-phase armature winding which is connected in series through an uncontrolled three-phase rectifier to a single exciter winding. When the current is supplied from the inverter, both the armature and exciter windings are energized eventually producing a flux in the air gap. Since the flux produced by the exciter winding is not synchronized with the field winding, it produces a harmonic to be intercepted by the harmonic winding on the rotor. Eventually, the voltage produced in the harmonic winding is used to feed dc current to the field winding for brushless operation. The results obtained by 2-D finite element analysis validate the brushless operation of the motor. The proposed motor is analyzed basically for topology verification with finite element analysis (FEA) results. Since the topology can be verified, further effort can be put to investigate such low-cost topologies for brushless wound rotor synchronous motor. It can be an alternative to a costly permanent magnet synchronous motor for specific applications.

The diesel generators sets (DGs) and battery storage systems (BSS) are the essential energy sources in a modern high-rise buildings. In this paper DG, BSS and Photovoltaic system (PV) has been considered to minimize the grid power injection using a centralized Energy Management System (EMS). Machine Learning (ML) techniques are used to predict the performance of various regression models by comparing grid power and load curves. It includes Artificial Neural Network (ANN), Wide Neural Network (WNN), Linear Regression (LR), Linear Regression Interaction (LR-I), Linear Regression Stepwise (LR-S), Regression Fine Tree (RF-T), Regression Coarse Tree (RC-T) and Gaussian Process Regression (GPR) based techniques. The Demand Side Management (DSM) techniques such as peak shaving and valley filling is integrated with ML technique in a Hybrid energy source (HS) system.The comparative analysis of results depicts the effective reshaping of the grid profile without scheduling or disconnecting the loads. Matlab simulation software is used to validate the results.

This paper presents a comparative performance analysis of 4-kW axial flux permanent magnet synchronous machines (AFPMSMs) with and without a rotor core. The present study is intended for low-speed applications; however, the investigated machines are designed using an improved diameter-to-length method and their comparative performance is assessed using comprehensive electromagnetic finite element analysis. The results of this analysis suggest that the proposed coreless topology has the advantages of higher output power, higher efficiency, and lower iron or core losses compared to the conventional iron core AFPMSM.

This paper presents performance improvement of dual stator axial-flux spoke type permanent magnet vernier machine (DSAFST-PMVM), which has the capability to generate high torque at a lower speed due to magnetic gearing effect. Flux focusing effect is created by means of dual stator single rotor topology with spoke type permanent magnets. It is best suitable for high-performance industrial applications such as servo motors, robot arms, wind power, electric vehicles, and elevator applications. A PM shape is proposed in this paper which has notches in such a way that it produces discrete skew effect which reduces the cogging torque and torque ripples. Optimization of magnet shape is done to make the optimized model more competitive than the proposed and basic models. Main parameters such as back emf, cogging torque, torque ripples, electromagnetic torque, VTHD, airgap flux densities, flux density distribution, power factor, and power of the machine are compared among the basic model, proposed model, and optimized models. The comparative analysis is done by using the time stepped 3D finite element method.