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Modern society is becoming more dependent on secure and reliable power energy and increasing consumption power energy simultaneously. Rising fuel prices and growing environmental damage motivate Generation companies to use renewable energy. In this regard, Microgrids are being considered to build a future power network system and are being implemented widely due to its various advantages. It is made more promising by connecting it in parallel with nonrenewable distributed generators (e.g. Micro turbine and Fuel cell). Modern society needs more reliable, secure, and safe power. A Rising number of natural disasters that caused large blackouts put change of structure of the current power system. This paper investigates the impact of Microgrids to enhance the power system resiliency. The impact of networked Microgrids and distributed generators to enhance resilience and using theirs effectively during and after outages are also considered in this paper.

Aiming at the AC/DC hybrid micro-grid with power electronic transformer (PET) and considering the asymmetric operation mode of three-phase load in low voltage AC system, a multi-objective optimal scheduling model of AC/DC system based on PET is proposed in this paper. In view of the topology of PET establish its steady state model then analyzing the three-phase unbalance mitigation mechanism. Make full use of the flexibility of multi-port PET to coordinate the controlled DGs in AC/DC networks. Then a long-time scale optimal scheduling model was established to minimize the operating costs and suppression the three-phase voltage unbalance factor. The economy and reliability of the system have been improved. Finally through the simulation analysis of the constructed example, the results verify the correctness and effectiveness of the proposed model in this paper.

In this paper, the main aim is developing a product which will take automatic decisions according to the algorithm for selecting the source from all available energy sources depends on varying load conditions at the output. The different available energy sources will be at a common junction and one output from the developed system will drive the load. Two methods hybrid power train and demand side management will be demonstrated in the project. The objective are the fabrication and development of a hardware module, which will give the economically efficient load management system. The hardware is able to perform following functions: smart sensing (smart to sense the potential level of the batteries and take economical decision to switch energy sources without human intervention), load changing sensitivity (take switching sources decision with respect to load), auto-startup (startup of auxiliary generator without kick or key mechanism upon direction from controller), self-directed (a standalone system governed by controller).

The paper deals with designing and fabrication of a light weight (one man) electric vehicle prototype powered by switched reluctance machine SRM and controlled by half bridge low-voltage high-current converter. The machine will provide propulsion to drive light weight electric vehicle. The idea is to utilize the vehicle for disable persons by utilizing the methodology with advance design. The paper reveals the mechanical design specifications, control mechanism for SRM, different signals of power converter is analyzed using digital oscilloscope and discussion on the weight, speed and price/distance of prototype is given. The aim at The moment is to design, construct and perform an experimental run of a prototype in order to get a more comprehensive idea of the mechanical design and the driving circuitry.

Generation expansion planning (GEP) problem involves determining new power plants in terms of type, capacity and allocation for meeting future load demands. The GEP problem incorporates large number of constraints and it is a highly complex, large scale, non linear optimization problem. Large scale integration of Renewable Energy systems (RES) in GEP problem has always been averted by the high cost and poor availability rates of RES as compared to conventional fossil fuel fired power plants. However internalizing environmental externalities in GEP problem could help to promote large scale integration of RES. The proposed model in this paper examines the effect of internalizing externalities on the GEP problem. External cost values for China are evaluated using the unit damage costs of pollutants. The model has been implemented in MATLAB using Genetic Algorithm and is applied to a case study of one of the district power companies of China. The results show significant structural changes in favor of RES confirming that internalizing externalities can be used as a driving policy tool for large scale integration of RES.

With the development of Smart Grids worldwide, efforts have been focused on optimizing the role of Smart Grid in the present electrical system. The integration of renewable energy resources to the grid presents a great challenge since they are intermittent and vary constantly causing frequency fluctuations. Automatic Generation Control (AGC) solves this problem by keeping the frequency close to nominal value and maintaining the balance between generation and demand. AGC has been implemented in various softwares as an added functionality. Power Systems Analysis Toolbox (PSAT) is a MATLAB toolbox designed for power flow computations but it lacks AGC implementation. This paper presents AGC implementation on PSAT using Artificial Neural Networks (ANN) controller and PID controller. The goal is to optimize PSAT by adding extra functionalities to it. The AGC design is implemented on IEEE 14-bus system on PSAT. A comparison of ANN controller and PID controller is also presented. The results show successful addition of AGC to PSAT library. The AGC design helps to control the frequency, has high performance and can be extended to other power systems.

Risk of global climate change and confronting of tumbling carbon emissions necessitate new ways to collaboratively control and optimize energy production and load. Uncertainty in generation and demand for electric energy require elastic generation capacity and stochastic optimization of generation schedules. The introduction of time-variable energy tariffs is a loom to attract consumers to shift their energy loads into low demand periods. In this paper we will build a model to solve an economic dispatch problem with integration of stochastic demand side response so that the demand can be shifted from peak load hour to off-peak load hour. Unlike other models we will introduce the customer's willingness to shed their load by giving the price signal an hour ahead of time. In this paper we will also emphasis that neither the price signal should be given too high that the customer cut most of the load by transferring all to the off-peak hour nor it should be too low that the generation utility becomes unable to raise enough profit. The simulation shows the promising results of shedding the load on profitable price signals.