Main content area

Energy management by generator rescheduling in congestive deregulated power system

Nesamalar, J. Jeslin Drusila, Venkatesh, P., Raja, S. Charles
Applied energy 2016 v.171 pp. 357-371
algorithms, electric power, electricity, energy, energy conservation, energy costs, fossil fuels, renewable energy sources, system optimization, India
Optimal energy delivery and energy consumption is vital in electric power systems as large amount of electricity cannot be stored in its electrical form. As part of upgradation, power systems are undergoing deregulation. One among the key issues of the deregulated power system is overload on a transmission line, also referred as congestion. Congestion is not acceptable as it increases the energy price and threatens system reliability and security. In this paper, a method of energy management is presented to remove congestion on transmission lines by rescheduling generators with the objective of minimizing energy rescheduling cost on day-ahead and hour-ahead basis. Usually, optimization methods are useful to achieve maximum gain. The Cuckoo Search Algorithm is employed in this article in order to get the optimized result. Numerical analysis of modified IEEE 30-bus system and real time application for TamilNadu (TN) 106-bus system is presented to provide evidence of the performance of the energy management measure. The realistic cases of base load, peak load, bilateral and multilateral power transactions, generation failure, and transmission line outages are considered and their corresponding energy generation, energy consumption and energy savings are obtained and are compared with the results of Particle Swarm Optimization. The discussed results show that the presented approach of energy management can reduce energy rescheduling cost and energy generation cost. In addition to that, the rescheduling of generators based on Renewable Energy Sources (RES) can further reduce the congestion cost, system energy loss and the usage of fossil fuels. The presented algorithm takes less computational time to achieve their optimal energy rescheduling cost when compared with Particle Swarm Optimization.