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Markov models are memoryless and are used to study systems whose future probabilistic behavior is uniquely determined by their present state. This property is extremely useful in the analysis of many complex engineering systems. It is proposed to employ this type of model to study and assess the performance of an Integrated Renewable Energy System (IRES). An IRES utilizes two or more renewable energy resources, conversion technologies, and end-use technologies to supply a variety of energy and other needs in remote areas. There will be multiple inputs and outputs in different forms. Hierarchical Modeling Technique in which states are established based on primary failures, secondary failures etc. will be used. This model I will be helpful in assessing the performance of IRES in terms state probabilities and residence times.