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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 201))

Abstract

Today’s large scale utilization, faster depletion of fossil fuels and energy security encouraged the world towards renewable sources and smart power systems. The problem of Unit Commitment (UC) has in itself become increasingly complex with understandably growing system sizes and involvement of imperative reliability measures. Given the scenario, integration of non-conventional energy sources into the system exhibits challenges in both economic and secure system operations. The work here presents a technique for the secure commitment of thermal generating units in a power system integrated with a solar powered plant. A solar thermal plant (STP) is modeled employing concentrated solar power (CSP) technology of parabolic trough type collectors. Hourly output expected from STP is calculated based on forecasted solar insolation levels. The UC solution of conventional thermal units accounts for the intermittent nature of power output from STP present in the same grid. The problem is solved in two stages with the primary one attending to UC and the sub problem catering to the optimal dispatch of load among the units committed. The evolutionary technique of Particle Swarm Optimization (PSO) is used in solving the main UC problem. To account for the optimal and secure system operation system, the subroutine of optimal power flow (OPF) is run every interval (hour) of the UC planning period. The optimal dispatch cost obtained from this execution is submitted as a fitness value or criterion to the PSO routine. The effectiveness of the proposed technique is demonstrated on the standard IEEE 30 bus system.

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Correspondence to Ravikanth Reddy Gaddam .

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Gaddam, R.R., Jain, A., Belede, L. (2013). A PSO Based Smart Unit Commitment Strategy for Power Systems Including Solar Energy. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 201. Springer, India. https://doi.org/10.1007/978-81-322-1038-2_45

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  • DOI: https://doi.org/10.1007/978-81-322-1038-2_45

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