Issues related to global warming and weather changes have forced people start to pay attention to energy saving. We expect that Smart Home Energy Management (SHEM) would be an important development over the next decade. In some environments cost is important, in other environments living quality is important and in other environments a tradeoff between cost and living quality is important. SHEM means being able to manage electrical loads so as to meet different purposes in homes. In this thesis, we develop a SHEM to curtail some electrical loads at peak time to meet predefined circuit level demand limits while minimizing the effect on users¡¦ living quality. The core of our SHEM is an electrical control loop which is developed based on heuristic modifications through lots of case studies and trials. To this end, we study several utilization characteristics of household loads including air conditioning, water heaters clothes dryers, and electric vehicles and model their behaviors through computer simulations. Finally, we implement the whole ideal of our SHEM in LabVIEW (Laboratory Virtual Instrument Engineering Workbench). Several simulations are conducted to verify the robustness and efficiency of our SHEM. keyword : Quick Charge,Load Priority,Convience Preference,Severity Indices,Duration Indices.

The first part of this dissertation presents an innovative method for the assessment of generation system reliability. In this method, genetic algorithm (GA) is used as a search tool to truncate the probability state space and to track the most probable failure states. GA stores system states, in which there is generation deficiency to supply system maximum load, in a state array. The given load pattern is then convoluted with the state array to obtain adequacy indices. In the second part of the dissertation, a GA based method for state sampling of composite generation-transmission power systems is introduced. Binary encoded GA is used as a state sampling tool for the composite power system network states. A linearized optimization load flow model is used for evaluation of sampled states. The developed approach has been extended to evaluate adequacy indices of composite power systems while considering chronological load at buses. Hourly load is represented by cluster load vectors using the k-means clustering technique. Two different approaches have been developed which are GA parallel sampling and GA sampling for maximum cluster load vector with series state revaluation. The developed GA based method is used for the assessment of annual frequency and duration indices of composite system. The conditional probability based method is used to calculate the contribution of sampled failure states to system failure frequency using different component transition rates. The developed GA based method is also used for evaluating reliability worth indices of composite power systems. The developed GA approach has been generalized to recognize multi-state components such as generation units with derated states. It also considers common mode failure for transmission lines. Finally, a new method for composite system state evaluation using real numbers encoded GA is developed. The objective of GA is to minimize load curtailment for each sampled state. Minimization is based on the dc load flow model. System constraints are represented by fuzzy membership functions. The GA fitness function is a combination of these membership values. The proposed method has the advantage of allowing sophisticated load curtailment strategies, which lead to more realistic load point indices.