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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.

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).

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.