M. Sydulu

Particle Swarm Optimization Based Capacitor Placement on Radial Distribution Systems

This paper presents a novel approach that determines the optimal location and size of capacitors on radial distribution systems to improve voltage profile and reduce the active power loss. Capacitor placement & sizing are done by loss sensitivity factors and particle swarm optimization respectively. The concept of loss sensitivity factors and can be considered as the new contribution in the area of distribution systems. Loss sensitivity factors offer the important information about the sequence of potential nodes for capacitor placement. These factors are determined using single base case load flow study. particle swarm optimization is well applied and found to be very effective in radial distribution systems. The proposed method is tested on 10,15, 34, 69 and 85 bus distribution systems.

Congestion Management in Deregulated Power System by Optimal Choice and Allocation of FACTS Controllers Using Multi-Objective Genetic Algorithm

Congestion management is one of the technical challenges in power system deregulation. This paper presents single objective and multi-objective optimization approaches for optimal choice, location and size of Static Var Compensators (SVC) and Thyristor Controlled Series Capacitors (TCSC) in deregulated power system to improve branch loading (minimize congestion), improve voltage stability and reduce line losses. Though FACTS controllers offer many advantages, their installation cost is very high. Hence Independent System Operator (ISO) has to locate them optimally to satisfy a desired objective. Genetic Algorithms (GA) are best suitable for solution of combinatorial optimization and multi-objective optimization problems. This paper presents optimal location of FACTS controllers considering branch loading (BL), voltage stability (VS) and loss minimization (LM) as objectives at once using GA. It is observed that the locations that are most favorable with respect to one objective are not suitable locations with respect to other two objectives. Later these competing objectives are optimized simultaneously considering two and three objectives at a time using multi-objective Strength Pareto Evolutionary Algorithms (SPEA). The developed algorithms are tested on IEEE 30 bus system. Various cases like i) uniform line loading ii) line outage iii) bilateral and multilateral transactions between source and sink nodes have been considered to create congestion in the system. The developed algorithms show effective locations for all the cases considered for both single and multi-objective optimization studies.

New approach with muller method for profit based unit commitment

This paper presents a new approach with Muller method for solving profit based unit commitment (PBUC). In deregulated environment, the generation companies (GENCOs) schedule their generators to maximize their profit rather than satisfying the power demand. While solving the PBUC problem, the information of forecasted price at the given predicted power demand is known. The PBUC problem is solved by the proposed approach in two stages. Initially, committed units table obtains information of the committed units and finally the non linear programming sub problem of economic dispatch is solved by Muller method. The proposed approach has been tested on a power system with 3 and 10 generating units. Simulation results of the proposed approach have been compared with existing methods and also with the traditional unit commitment. It is observed from the simulation results that the proposed algorithm provides maximum profit with less computational time compare to existing methods.

2Index and GA based Optimal Location and Sizing of Distribution System Capacitors

Shunt capacitors can be installed in a distribution system to a required level of reactive power support to reduce energy and peak power losses. The amount of compensation to be provided is linked with the desirable objectives subject to the operational constraints. Thus optimal capacitor placement problem aims at determination of capacitor locations and their respective sizes. This paper presents a power loss based approach to determine suitable capacitor locations and an index and genetic algorithm based approach for optimal capacitor sizing. Highest suitability of nodes for capacitor placement and the corresponding sizes are determined. The proposed method has been tested on 15,31,34,69 and 85-bus distribution networks and the results are found to be very promising with considerable reduction in active power loss and improvement in voltage profile. Results for an 85-bus radial distribution system are presented.

Improved Pre-prepared Power Demand Table and Muller's Method to Solve the Profit Based Unit Commitment Problem

This paper presents the Improved Pre-prepared Power Demand (IPPD) table and Muller’s method as a means of solving the Profit Based Unit Commitment (PBUC) problem. In a deregulated environment, generation companies (GENCOs) schedule their generators to maximize profits rather than to satisfy power demand. The PBUC problem is solved by the proposed approach in two stages. Initially, information concerning committed units is obtained by the IPPD table and then the subproblem of Economic Dispatch (ED) is solved using Muller’s method. The proposed approach has been tested on a power system with 3 and 10 generating units. Simulation results of the proposed approach have been compared with existing methods and also with traditional unit commitment. It is observed from the simulation results that the proposed algorithm provides maximum profit with less computational time compared to existing methods.

Congestion management in deregulated power system by optimal choice and allocation of FACTS controllers using multi-objective genetic algorithm

Congestion management is one of the technical challenges in power system deregulation. This paper presents single objective and multi-objective optimization approaches for optimal choice, location and size of Static Var Compensators (SVC) and Thyristor Controlled Series Capacitors (TCSC) in deregulated power system to improve branch loading (minimize congestion), improve voltage stability and reduce line losses. Though FACTS controllers offer many advantages, their installation cost is very high. Hence Independent System Operator (ISO) has to locate them optimally to satisfy a desired objective. Genetic Algorithms (GA) are best suitable for solution of combinatorial optimization and multi-objective optimization problems. This paper presents optimal location of FACTS controllers considering branch loading (BL), voltage stability (VS) and loss minimization (LM) as objectives at once using GA. It is observed that the locations that are most favorable with respect to one objective are not suitable locations with respect to other two objectives. Later these competing objectives are optimized simultaneously considering two and three objectives at a time using multi-objective Strength Pareto Evolutionary Algorithms (SPEA). The developed algorithms are tested on IEEE 30 bus system. Various cases like i) uniform line loading ii) line outage iii) bilateral and multilateral transactions between source and sink nodes have been considered to create congestion in the system. The developed algorithms show effective locations for all the cases considered for both single and multi-objective optimization studies.

Comparison of Genetic Algorithms and Particle Swarm Optimization for Optimal Power Flow Including FACTS devices

This paper describes the performance of two population based search algorithms (Genetic Algorithms and Particle Swarm Optimization) when applied to Optimal Power Flow (OPF) including Static VAR Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) devices. The OPF optimizes a power system operating objective function, while satisfying a set of system operating constraints. The basic OPF solution is obtained with fuel cost minimization as the objective function and the optimal settings of the power system are determined. OPF can also be formulated for reactive power optimization, as minimization of system active power losses and improving the voltage stability in the system. In the present paper different objective functions that reflect Fuel cost minimization, System power loss minimization, Voltage Stability Enhancement (L-index minimization), Power loss minimization with SVC device and Power loss minimization with combined application of SVC and TCSC devices have been considered. To monitor and improve voltage stability in power system, minimization of sum of squared L-indices of all the load buses is considered as objective function in OPF. This index also guides the optimal location for VAR compensation. During normal operating conditions a planning engineer requires that all line flows and voltages are within limits while minimizing investment (including losses). While during outage conditions, line loading and voltages are again desired within limits while minimizing investment. It is important to obtain feasible solutions with in a minimal amount of engineering time.

New approach with Muller method for Solving Profit Based Unit Commitment

This paper presents a new approach with Muller method for solving profit based unit commitment (PBUC). In deregulated environment, the generation companies (GENCOs) schedule their generators to maximize their profit rather than satisfying the power demand. The PBUC problem is solved by the proposed approach in two stages. Initially, the information of committed units is obtained by a simple approach and finally non linear programming sub problem of economic dispatch is solved by Muller method. The proposed approach has been tested on a power system with 3 and 10 generating units. Simulation results of the proposed approach have been compared with existing methods and also with traditional unit commitment. It is observed from the simulation results that the proposed algorithm provides maximum profit with less computational time compared to existing methods.

A detailed literature review on wind forecasting

There are several forecasting methods available to estimate the uncertainty of the wind. Wind behavior is chaotic in nature. These forecasting methods are used to predict wind power generation capacity for the grid. With the introduction of smart grid has created enough space for integrating renewable (wind power) in to the grid. Several methods have been proposed by researchers to estimate the wind speed. In present days there is a lot of research is going on to estimate the wind speed by using mathematical, biologically inspired computing methods to minimize the prediction error. This paper presents a review of several forecasting techniques which are using presently. This paper will be helpful for the new researchers who are going to work in this area. This paper will also be helpful to the wind farm operators to know about the present wind estimation model capabilities and will give an idea to estimate the wind speed at their particular wind farms.

A directional search genetic algorithm to the economic dispatch problem with prohibited operating zones

This paper outlines a directional search genetic algorithm (DSGA) for solving the Economic dispatch (ED) problem with prohibited operating zones. Some of the nonlinear characteristics of the generator, such as prohibited operating zones are considered using the proposed method in practical generator operation. In this paper, two executions of directional search genetic algorithm are used. In the first execution, it determines the dispatch of generators without considering Prohibited-operating zones. If there is no violation, the optimal dispatch is obtained. If there is a violation in units, set their limits, and then it uses an efficient approach to determine the most advantageous space using the average power of prohibited operating region. In the second execution, it obtains the optimal dispatch for the remaining units. The proposed method is very efficient for both small and large size of generating units with prohibited operating zones and it remarkably reduces the computation time.