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One of the major concerns of future power systems is to utilize EVs as effective flexible means of controlling load demand. In this paper, the main goal is to evaluate the effect of large-scale utilization of electric vehicles (EV) on the total system loss. In this context, first the EVs' charging demand will be obtained using two probabilistic parameters: 1) expected driven distance, 2) expected...
This paper presents a new approach for identifying the measurement error in the DC power flow state estimation problem. The proposed algorithm exploits the singularity of the impedance matrix and the sparsity of the error vector by posing the DC power flow problem as a sparse vector recovery problem that leverages the structure of the power system and uses l1-norm minimization for state estimation...
In this paper, we design and evaluate the feasibility of a system which minimizes residential electricity cost of individual homes by shifting demand over a daily forecast price cycle. Ideally, our system will accept use-time preferences from consumers and optimize their appliances' operation around those given patterns. However, using the system to recommend optimum use-times to consumers is also...
In recent years, Smart Grid was introduced to achieve an environmentally-friendly, adequate, secure and fossil fuel-independent power system. The large scale smart grid studies require accurate state estimation to obtain an acceptable adequacy level. There exist some challenges regarding anomalous power flow studies which motivate grid operators to utilize robust and accurate estimation methods. Therefore,...
Electric transportation is one of the key elements of the future power systems since conventional power networks are rapidly evolving towards smart grids. This transition creates the need for systematic utilization of electric vehicles (EV) in order to avoid unpredictable effects on the power systems. An accurate and efficient method for demand forecasting of EVs is needed to perform a feasible scheduling...
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