An Interior Point Optimization Method for Stochastic SCUC in the Presence of PEVs

What is it about?

By increasing the penetration level of renewable energies on the generation side and the emergence of new variable load on the demand side, stochastic analysis of the conventional security constrained unit commitment problem has become more important for the secure optimal operation of the electricity market. Today, the increasing utilization of plug-in electric vehicles, which consume electricity rather than fossil fuel for driving, offers new opportunities and challenges to the operation of electric power system. By appropriate managing and day-ahead scheduling of these types of vehicles, challenges can be replaced by opportunities for the power system operation and planning. In this study, a new method is proposed for stochastic security-constrained unit commitment problem in the presence of wind power generations and plug-in electric vehicles. The method enjoys the advantages of conventional scenario-based approaches and mitigates their barriers by using interior point optimization techniques. The proposed algorithm is implemented on two standard networks: A 6-bus test system and a large-scale 118-bus system. This study demonstrate the accuracy and efficiency of the proposed method, especially in large-scale power systems with different types of uncertainties. Conclusion: By increasing the speed of simulation, more uncertainties can be modeled and therefore, more realistic and accurate results can be obtained.

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Why is it important?

 Proposing a new algorithm for stochastic security-constrained unit commitment problem using interior point optimization.  Different uncertainties such as variable loads, volatile wind power generations, and plug-in electric vehicles are modeled.  Efficiency and capability of the proposed method are investigated.  A large number of uncertainties are considered in order to evaluate the proposed method in a large-scale system with multiple uncertainties.