Congestion Management in Deregulated Power Systems using Genetic Algorithm

2020 ◽  
Author(s):  
P. Sharmila ◽  
J. Baskaran ◽  
C. Nayanatara ◽  
Sai Ganesh CS ◽  
Haariharan N C
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Congestion Management ◽  
Deregulated Power Systems

scholarly journals Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. Vijayakumar
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Electricity Market ◽  
Optimization Methods ◽  
Evolutionary Programming ◽  
Congestion Management ◽  
Transmission Congestion ◽  
Nsga Ii ◽  
Line Overload ◽  
Nondominated Sorting

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.

scholarly journals ATC using FACTS Devices in Deregulated Power Systems

2017 ◽  
Vol 7 (2 (S)) ◽  
pp. 214
Author(s):  
Prakash Burade ◽  
Rajendra Sadafale ◽  
Anand Satpute
Keyword(s):  
Genetic Algorithm ◽  
Power Generation ◽  
Power Systems ◽  
Ant Colony Optimization ◽  
Power Flow ◽  
Reactive Power ◽  
Integer Optimization ◽  
Thermal Limits ◽  
Facts Devices ◽  
Deregulated Power Systems

A Repeated Power Flow with FACTS devices including ATC is used to evaluate the feasible ATC value within real and reactive power generation limits, line thermal limits, voltage limits and FACTS operation limits. An IEEE-30 bus system is used to demonstrate the effectiveness of the algorithm as an optimization tool to enhance ATC. A Genetic Algorithm technique is used for validation purposes. Introduction of FACTS devices in a right combination of location and parameters could enhance ATC and Ant Colony optimization can be efficiently used for this kind of nonlinear integer optimization.

scholarly journals Optimal TCSC placement for congestion management in deregulated power systems using antlion optimization algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 77
Author(s):  
Majid Moazzami ◽  
Hossein Shahinzadeh ◽  
Gevork B. Gharehpetian ◽  
Abolfazl Shafiei
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Congestion Management ◽  
Test System ◽  
Production Costs ◽  
High Price ◽  
Facts Devices ◽  
Antlion Optimization ◽  
Deregulated Power Systems

Congestion management is one of the important issues in the deregulated power systems. There are several methods to eliminate congestion. Utilizing FACTS devices is an appropriate option for large-scale and quick control of flows of transmission lines. FACTS devices such as Thyristor Controlled Series Capacitor (TCSC) can help to mitigate the transmitting flow of power in the congested lines, which leads to an increase in the network loading ability as well as reduction of both losses and production costs. Due to the considerably high price of FACTS devices, it is important to determine their optimum location on the network. Accordingly, in this paper, the Antlion optimization algorithm (ALO) has been employed to conduct a congestion management analysis to determine the optimal location for the installation of TCSC, which is simulated on an IEEE 14-bus test system subject to satisfy the constraints of the market environment.

Multi-Objective Differential Evolution for Voltage Security Constrained Optimal Power Flow in Deregulated Power Systems

2013 ◽  
Vol 14 (6) ◽  
pp. 591-607 ◽  
Author(s):  
J. Preetha Roselyn ◽  
D. Devaraj ◽  
Subhransu Sekhar Dash
Keyword(s):  
Power Systems ◽  
Differential Evolution ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Voltage Stability ◽  
Congestion Management ◽  
Load Shedding ◽  
Voltage Security ◽  
Optimal Power ◽  
Deregulated Power Systems

Abstract Voltage stability is an important issue in the planning and operation of deregulated power systems. The voltage stability problems is a most challenging one for the system operators in deregulated power systems because of the intense use of transmission line capabilities and poor regulation in market environment. This article addresses the congestion management problem avoiding offline transmission capacity limits related to voltage stability by considering Voltage Security Constrained Optimal Power Flow (VSCOPF) problem in deregulated environment. This article presents the application of Multi Objective Differential Evolution (MODE) algorithm to solve the VSCOPF problem in new competitive power systems. The maximum of L-index of the load buses is taken as the indicator of voltage stability and is incorporated in the Optimal Power Flow (OPF) problem. The proposed method in hybrid power market which also gives solutions to voltage stability problems by considering the generation rescheduling cost and load shedding cost which relieves the congestion problem in deregulated environment. The buses for load shedding are selected based on the minimum eigen value of Jacobian with respect to the load shed. In the proposed approach, real power settings of generators in base case and contingency cases, generator bus voltage magnitudes, real and reactive power demands of selected load buses using sensitivity analysis are taken as the control variables and are represented as the combination of floating point numbers and integers. DE/randSF/1/bin strategy scheme of differential evolution with self-tuned parameter which employs binomial crossover and difference vector based mutation is used for the VSCOPF problem. A fuzzy based mechanism is employed to get the best compromise solution from the pareto front to aid the decision maker. The proposed VSCOPF planning model is implemented on IEEE 30-bus system, IEEE 57 bus practical system and IEEE 118 bus system. The pareto optimal front obtained from MODE is compared with reference pareto front and the best compromise solution for all the cases are obtained from fuzzy decision making strategy. The performance measures of proposed MODE in two test systems are calculated using suitable performance metrices. The simulation results show that the proposed approach provides considerable improvement in the congestion management by generation rescheduling and load shedding while enhancing the voltage stability in deregulated power system.

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