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The use of photovoltaic (PV) sources is becoming very popular in smart gridfor their ecological benefits, with higher scalability and utilization forlocal generation and delivery. PV can also potentially avoid the energy lossesthat are normally associated with long-range grid distribution. The increasedpenetration of solar panels, however, has introduced a need for solar energymodels that are capable of producing realistic synthetic data with small errormargins. Such models, for instance, can be used to design the appropriate sizeof energy storage devices or to determine the maximum charging rate of aPV-powered electric vehicle (EV) charging station. In this regard, this paperproposes a stochastic model for solar generation using a Markov chain approach.Based on real data, it is first shown that the solar states areinter-dependent, and thus suitable for modeling using a Markov model. Then, theprobabilities of transition between states are shown to be heterogeneous overdifferent time segments. A model is proposed that captures the inter temporaldependency of solar irradiance through segmentation of the Markov chain acrossdifferent times of the day. In the studied model, different state transitionmatrices are constructed for different time segments, which the proposedalgorithm then uses to generate the solar states for different times of theday. Numerical examples are provided to show the effectiveness of the proposedsynthetic generator.

Synthetic Generation of Solar States for Smart Grid: A Multiple Segment Markov Chain Apptoach