A novel hybrid algorithm for rescheduling-based congestion management scheme in power system
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ORIGINAL PAPER
A novel hybrid algorithm for rescheduling-based congestion management scheme in power system Jyoti Srivastava1,2 · Naresh Kumar Yadav1 · Arvind Kumar Sharma2 Received: 26 February 2019 / Accepted: 20 March 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Secure and continuous power flow in the transmission line is one of the critical issues that must be rectified. In fact, rescheduling-based congestion management is considered to be one of the promising solutions for this aspect. Still, the model faces issues on the basis of rescheduling costs. More research works have been addressed so far to solve the problems of congestion management. Optimization algorithms also play a vital role in solving this problem. Under this scenario, this paper introduces a new rescheduling-based congestion management model that incorporates a new algorithm, refractor update-based ROA (RU-ROA) that optimizes the generating power of added generators with the bus system. The proposed RU-ROA algorithm is the hybridization of two algorithms, namely rider optimization algorithm (ROA) and water wave optimization (WWO), that aims to manage the congestion with the reduced cost of rescheduling. Further, the proposed model compares its performance over other conventional models like particle swarm optimization, FireFly, grey wolf optimization, traditional ROA and traditional WWO-based rescheduling strategy with respect to cost analysis and convergence analysis, and proves the efficiency of proposed work over others. Keywords Congestion management · Cost function · Optimization algorithm · ROA · Rescheduling strategy
List of symbols GSF GLCM TLBO WWO ALO ABC RU-ROA PSODAC FF MOPSO
B
Generator sensitivity factor Generation and load shedding cost minimization Teaching–learning-based optimization Water wave optimization Ant lion optimizer Artificial bee colony Refractor update-based ROA PSO with distributed acceleration constant FireFly Multi-objective particle swarm optimization
Jyoti Srivastava [email protected]
1
Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, Haryana, India
2
KIET Group of Institutions, Muradnagar, Ghaziabad, U.P., India
GWO ROA PSHU DRPs LSM BSF RED PSO SOs CM GENCOs Pp Pp COtotal PDE PLO , L 0p and L 00 L qpmin Vi , Vimax ∂imin , ∂imax Pp (n − 1)
Grey wolf optimization Rider optimization algorithm Pumped storage hydro unit Demand response programs Load served maximization Bus sensitivity factor Relative electrical distance Particle swarm optimization System operators Congestion management Generating companies Generation quantity in MW Previously generated power quantity Total cost to modify the active power output ($/N) Total power demand Transmission loss Loss coefficients Voltage limits Angle limits Generated active power at (n − 1)
123
Electrical Engineering
Pp (n)
Power generated at the current hour Limit of maximum power flow in Mimax j MVA Change in active power Pp Penalty cost forced on violating E constraints the constrai
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