Matam Sailaja Kumari

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.

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.

Small signal stability of power system with SCIG, DFIG wind turbines

Voltage stability issue is a key problem which attracting worldwide attention because voltage stability may leads to voltage collapse. This paper investigates the impact of Squirrel Cage induction generator (SCIG) and Doubly Fed Induction Generator (DFIG) on the power system small signal stability. Here considered wind generators are SCIG which is fixed speed and DFIG is variable speed. Small signal stability study has been conducted on a modified IEEE 14 bus system with SCIG, DFIG wind turbine systems and their simulation results have been analyzed in this paper. Fixed speed wind generators are more simple to operate and reliable but they are limited as energy output of a wind turbine and stability issues are consider. Variable speed wind generators of similar rating improve stability of the system. All these modelings, simulations have been done in Power system Analysis Toolbox (PSAT), which is a MATLAB based toolbox to evaluate power system.

Improved Particle Swarm Algorithm Applied to Optimal Reactive Power Control

This paper presents an improved particle swarm optimization (IPSO) to solve the optimal reactive power control (ORPC) problem. In the new algorithm, particle not only studies from itself and the best one but also from other individuals. By this enhanced study behavior, the opportunity to find the global solution is guaranteed and also the speed of convergence can be improved. The feasibility of the proposed method is demonstrated and results are compared with linear programming, Fuzzy logic, genetic algorithm and classical PSO approaches. The developed IPSO has been tested on 6 bus Ward-Hale test system and modified IEEE 30 bus system for optimal reactive power and voltage control problem and results are found promising.

Unit commitment of thermal units in integration with wind and solar energy considering ancillary service management using priority list(IC) based genetic algorithm

Demand for electricity is gradually increasing in India and use of more thermal power generation is leading to increased emissions. The effect of emission of thermal power plant is adverse to the environment; we cannot largely depend on thermal power plant for power generation. So, this work considered the integration of thermal units with the renewable sources like solar and wind, while accounting for ancillary service management and renewable energy uncertainties. Unit Commitment (UC) and Economic load dispatch (ELD) have significant research applications in power systems and optimize the total production cost for the forecasted load demand of particular hour. UC decides the turn ON/ turn OFF decision of particular unit/units according to the forecasted load of particular hour optimally while satisfying all constraints of the unit like minimum up and down time, startup and shut down cost constraints. ELD optimizes the operation cost for all scheduled generating units with respect to the forecasted load demands of customers in particular hour. UC and ELD are performed to reduce the total production cost to as minimum as possible, so that customer will get electricity at minimum cost. This paper presents a Genetic algorithm and priority list approach for wind and solar integrated system having thermal generators, for solving UC problem. The dynamic programming approach is used for the UC and ELD and production cost of each hour is calculated for comparison purpose.

Priority list and particle swarm optimization based unit commitment of thermal units including renewable uncertainties

Utilizing thermal generation alone to meet the energy demand leads to adverse effects on environment. So, to minimize the environmental pollution, there is a need to enhance the renewable energy contribution in the grid. In this paper a hybrid Priority List and Particle Swarm Optimization (PL-PSO) approach to solve Unit Commitment (UC) and Economic Load Dispatch (ELD) of thermal units integrated with renewable sources like wind and solar power plant is presented. The on/off decision of thermal units is handled by PL technique, while Particle Swarm Optimization (PSO) solves ELD. Both programs are run simultaneously, fine tuning their solutions in search of a better solution. Wind and solar power output is modeled using MATLAB Simulink tool and coding respectively. Wind and Solar uncertainties are handled using scheduling of operating reserve Ancillary service. The problem formulation considers minimum up and down time, start up cost and spinning reserve constraints. The objective is to minimize the fuel cost associated with thermal units while satisfying constraints. Case studies are performed on 10 and 54 thermal unit systems including solar and wind generation and the results are encouraging.

LMP in single auction model of a restructured power system using bat algorithm

In restructured electricity markets, an effective transmission pricing method is required to address transmission issues and to generate correct economic signals. Transmission line constraints can result in variations in energy prices throughout the network. These prices depend on generator bids, load levels and transmission network limitations. A congestion charge is incurred when the system is constrained by physical limits. Locational Marginal Pricing (LMP) has become popular method in restructured power markets to address the congestion charges. This paper presents LMP computation with LP, GA and proposed Bat algorithm (BA) under three different loss cases in single auction model. In single auction model only suppliers submit the bids and the load is assumed to be inelastic. Fixed and Linear bids are considered for generators. All the methods are tested on IEEE 14 bus system, New England 39 bus system and Indian 75 bus system. Linear bids with BA show most optimal fuel cost compared to all other methods.

Transmission embedded cost allocation using proportional nucleolus based game theoretic approach in a restructured power market

In deregulated electricity markets there is a strong need for effective allocation of transmission embedded costs to market participants. The conventional usage based methods such as MW-Mile and ZCF methods which are currently employed in market scenario may fail to send right economic signals. Hence in this paper, cooperative game theory based approaches are demonstrated. The existing game theory based approaches like Nucleolus and Shapley Value methods are found to be inefficient for transmission embedded cost allocation, due to their own pros and cons. Therefore Proportional Nucleolus (P-N) method which is also a cooperative game theory approach is proposed in this paper to overcome the drawbacks of aforementioned methods. All the methods presented in this paper are tested on IEEE 14 bus system, and a comparative study was carried out with the obtained results.

Distribution of loss cost using proportional nucleolus method in competitive power markets

The present deregulated electricity industry has evolved into a distributed and competitive industry in which market forces drive the price of electricity and reduce the net cost of electricity. However the competitions in these markets require identification of the use of transmission networks, mainly the participation of utilities in losses caused in the transmission lines. This is because the consumers pay for their actual consumption where as generators are paid for their generation plus losses. Hence the loss cost allocation is of great importance as it should be allocated efficiently to both consumers and producers (both network users). Thus loss cost allocation in competitive electricity market requires a fair and efficient method with right economic signals. In this paper, loss cost allocation methodologies are presented in multilateral transaction frame work. Methodologies in cooperative game theory such as nucleolus, shapely and proportional nucleolus are presented. These are compared with conventional methods for loss allocation. Among all the methods presented, the proportional nucleolus is proved to be the efficient method with right economic signals. All the methods are implemented and results are compared for IEEE 14 bus, New England 39 bus and 75 bus Indian Power System.