D. M. Vinod Kumar

Fuzzy adaptive bacterial foraging congestion management using sensitivity based optimal active power re-scheduling of generators

This paper presents a new method of fuzzy adaptive bacterial foraging (FABF) based congestion management (CM) for the first time by optimal rescheduling of active powers of generators selected based on the generator sensitivity to the congested line. In the proposed method, generators are selected based on their sensitivity to the congested line to utilize the generators efficiently and optimal rescheduling of the active powers of the participating generators was attempted by FABF. The FABF algorithm is tested on IEEE 30-bus system and Practical Indian 75-bus system and the results are compared with the Simple Bacterial Foraging (SBF) and Particle Swarm Optimization (PSO) algorithms for robustness and effectiveness of congestion management. It is observed from the results that FABF is effectively minimizing the cost of generation in comparison with SBF and PSO for optimal rescheduling of generators to relieve congestion in the transmission line.

FAST APPROACH TO ARTIFICIAL NEURAL NETWORK TRAINING AND ITS APPLICATION TO ECONOMIC LOAD DISPATCH

ABSTRACT Artificial Neural Networks (ANN) are gaining popularity in various fields of engineering including electrical power systems due to their high computational rates and robustness.One of the ANN models extensively used for power system applications is the multilayer perceptron model based on back propagation algorithm. However,its training requires large number of input-output data sets which increases with system size and may become prohibitively large and time extensive. Moreover, the back propagation algorithm offers slow convergence with random initial weights. This paper presents a new approach to minimize the number of training patterns for ANN by using variable slope of the sigmoidal function for different test cases. In addition, the paper suggests the use of new functions for generating initial weights for training. The ANN models so developed have been tested to solve economic load dispatch (E.L.D.) problem on IEEE-14 bus test system and 89-bus Indian system. The proposed approach provides...

Generation bidding strategy in a pool based electricity market using Shuffled Frog Leaping Algorithm

Abstract In an electricity market generation companies need suitable bidding models to maximize their profits. Therefore, each supplier will bid strategically for choosing the bidding coefficients to counter the competitors bidding strategy. In this paper optimal bidding strategy problem is solved using a novel algorithm based on Shuffled Frog Leaping Algorithm (SFLA). It is memetic meta-heuristic that is designed to seek a global optimal solution by performing a heuristic search. It combines the benefits of the Genetic-based Memetic Algorithm (MA) and the social behavior-based Particle Swarm Optimization (PSO). Due to this it has better precise search which avoids premature convergence and selection of operators. Therefore, the proposed method overcomes the short comings of selection of operators and premature convergence of Genetic Algorithm (GA) and PSO method. Important merit of the proposed SFALA is that faster convergence. The proposed method is numerically verified through computer simulations on IEEE 30-bus system consist of 6 suppliers and practical 75-bus Indian system consist of 15 suppliers. The result shows that SFLA takes less computational time and producing higher profits compared to Fuzzy Adaptive PSO (FAPSO), PSO and GA.

Available transfer capability (ATC) determination using intelligent techniques

In this paper ATC has been computed for real time applications using three different intelligent techniques viz., i) back propagation algorithm (BPA) ii) radial basis function (RBF) neural network and iii) adaptive neuro fuzzy inference system (ANFIS). The ATC is to be made available on open access same time information system (OASIS), which is accessible to seller and buyer. The independent system operator (ISO) updates the ATC in real time. The three different methods are tested on IEEE 24-bus reliability test system (RTS) and compared with the conventional full AC load flow method for the base case, different transactions and line outage cases

Optimal rescheduling of real and reactive powers of generators for zonal congestion management based on FDR PSO

In a deregulated electricity market transmission congestion occurs when there is insufficient transmission capacity to simultaneously accommodate all requests for transmission service within a region. One of the most important tasks of Independent System Operator (ISO) is to manage congestion as it threatens system security and may cause rise in electricity price resulting in market inefficiency. In corrective action of congestion management schemes, it is crucial for ISO to select the most sensitive generators to reschedule their optimal real and reactive powers in congestion management. As the real and reactive power dispatches play a vital role to relieve the congestion at low congestion cost, in this paper, the reactive support of generators, in addition to the rescheduling of real power generation, has been considered to manage congestion. The re-dispatch of transactions for congestion management in a pool model is formulated as a Non-Linear Programming (NLP). The Fitness Distance Ratio Particle Swarm Optimization (FDRPSO) based Optimal Power Flow (OPF) is introduced for Congestion Management problem first time in this paper to solve the NLP. This paper has utilized the method of selection of generators from the most sensitive cluster/zone to re-dispatch the real and reactive powers simultaneously using two distribution factors, viz. Real and Reactive Power Transmission Congestion Distribution Factors (PTCDFs and QTCDFs). The proposed method has been tested on a practical 75-bus Indian System for single and multi line congestion cases. The results are compared with the Conventional Particle Swarm Optimization (CPSO), Real Coded Genetic Algorithm (RCGA) and Binary Coded Genetic Algorithm (BCGA) based OPFs.

Power system state forecasting using artificial neural networks

This paper presents a new method for power system state forecasting using artificial neural networks (ANN). The state forecasting problem has been solved in two steps: the filtering step and the forecasting step in an open loop configuration. Because under normal operating conditions the power system behaves in a quasi-static manner, a simplified model of the dynamic behavior of the power system states is considered. Two different ANN models have been used for these two steps of power system state forecasting problem. For the filtering step, a functional link network (FLN), and for the forecasting step, a time delay neural network (TDNN) have been used to simulate the dynamic behavior of the power system states. The proposed method has been tested on two IEEE test systems, and a practical Indian system and results have been compared with an extended Kalman filter (EKF) based technique [Leite da Silva et al., 1983].

Fuzzy PSO Congestion Management using Sensitivity-Based Optimal Active Power Rescheduling of Generators

This paper presents a new method of Fuzzy Particle Swarm Optimization (FPSO)-based Congestion Management (CM) by optimal rescheduling of active powers of generators. In the proposed method, generators are selected based on their sensitivity to the congested line for efficient utilization. The task of optimally rescheduling the active powers of the participating generators to reduce congestion in the transmission line is attempted by FPSO, Fitness Distance Ratio PSO (FDR-PSO), and conventional PSO. The FPSO and FDR-PSO algorithms are tested on the IEEE 30-bus and Practical Indian 75-bus systems, after which the results are compared with conventional PSO to determine the effectiveness of CM. Compared with FDR-PSO and PSO, FPSO can better perform the optimal rescheduling of generators to relieve congestion in the transmission line.

Swarm Intelligence Based Security Constrained Congestion Management using SSSC

The power system is said to be in a state of Congestion (iii) Operation of FACTS devices particularly series whenever the physical or operational constraints in a devices. transmission network become active. In a deregulated Non-cost-free means: environment, Congestion in the transmission lines can be relieved (i) Re-dispatch of generation in a manner different by one of the two congestion management methodologies viz. cost from the natural settling point of the market. free and non-cost free methods. In this paper, Congestion is Some generators back down while others relieved by using Cost Free method and is reduced by employing Static Synchronous Series Compensator (SSSC). Genetic increase their output. The effect of this is that Algorithm (GA) and Particle Swarm Optimization (PSO) generators no longer operate at equal incremental techniques were used to obtain the global optimal solution as the costs. objective function is nonlinear in Congestion Management and (ii) Curtailment of loads and the exercise of (not- these techniques were tested on IEEE 30-bus system. cost-free) load interruption options.

Optimal real and reactive power dispatch for zonal congestion management problem for multi congestion case using Adaptive Fuzzy PSO

In a deregulated electricity market one of the most important tasks of Independent System Operator (ISO) is to manage congestion as it threatens system security and may cause rise in electricity price resulting in market inefficiency. In corrective action of congestion management schemes, it is crucial for ISO to select the most sensitive generators to re-schedule their real and reactive powers optimally. As the reactive power plays a vital role to relieve the congestion at low congestion cost, in this paper, the reactive support of generators, in addition to the rescheduling of real power generation, has been considered. The optimal re-dispatch of transactions for congestion management in a pool model is formulated as a Non-Linear Programming (NLP). The Adaptive Fuzzy Particle Swarm Optimization based Optimal Power Flow (AFPSO-OPF) is introduced first time in this paper for Congestion Management problem or multi congestion case to solve the NLP. In this method, the inertia weight is dynamically adjusted using fuzzy IF/THEN rules to increase the balance between global and local searching abilities. To minimize the number of readjustments for the congestion management, this paper has used the method of selection of generators from the most sensitive cluster/zone using two distribution factors, viz. Real and Reactive Power Transmission Congestion Distribution Factors (PTCDFs and QTCDFs). The proposed method has been tested on a practical 75-bus Indian System for multi line congestion case and the results are compared with the Conventional Particle Swarm Optimization (CPSO), Real Coded Genetic Algorithm (RCGA) and Binary Coded Genetic Algorithm (BCGA) based OPFs.

Strategic bidding in deregulated market using particle swarm optimization

In this paper, particle swarm optimization method is proposed to determine the optimal bidding strategy in competitive electricity market. The market includes Generating companies (Gencos), large consumers who participate in demand side bidding, and small consumers whose demand is present in aggregate form. The effectiveness of the proposed method is tested with IEEE-30 bus system in which six generators and two large consumers are considered. Results are compared with the solutions obtained using the Genetic algorithm and Monte Carlo method.