The economic load dispatch (ELD) is one of the most complex optimization problems of electrical power system. Classically, it is to identify the optimal combination of generation level of all power generating units in order to minimize the total fuel cost while satisfying the loads and losses in power transmission system. In view of the sharply increasing nature of cost of fossil fuel, energy management has gained lot of significance nowadays. Herein lies the relevance of continued research on improving the solution of ELD problem. A lot of research work have been carried out on this problem using several optimization techniques including classical, linear, quadratic, and nonlinear programming methods. The objective function of the ELD problem being of highly nonlinear and non-convex nature, the classical optimization methods cannot guarantee convergence to the global optimal solution. Some soft computing techniques like Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Clonal Selection Algorithm (CSA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Genetic Algorithm (GA), etc. are now being applied to find even better solution to the ELD problem. An interesting trend in this area is application of hybrid approaches like GA-PSO, ABC-PSO, CSA-SA, etc. and the results are found to be highly competitive. In this book chapter, we focus on the hybrid soft computing approaches in solving ELD problem and present a concise and updated technical review of systems and approaches proposed by different research groups. To depict the differences in technique of the hybrid approaches over the basic soft computing methods, the individual methods are introduced first. While the basic working principle and case studies of each hybrid approach are described briefly, the achievements of the approaches are discussed separately. Finally, the challenges in the present problem and some of the most promising approaches are highlighted and the possible future direction of research is hinted.

https://www.springer.com/cda/content/document/cda_downloaddocument/9788132225430-c2.pdf?SGWID=0-0-45-1522874-p177560038

Study of Economic Load Dispatch by Various Hybrid Optimization Techniques

The power systems become operate closer to loadability limits; hence, the power systems static voltage stability assessment becomes an essential task in planning and operating for electric power systems to prevent voltage instability. In this paper, the improved moth flame optimization (IMFO) technique is applied for optimal location and size of (TCSC) with the aim of reducing load shedding, preventing voltage collapse, and enhancing the power system loadability. IMFO is developed to avoid the stagnating in local optima and improve the convergence characteristics of the conventional moth flame optimization. The loadability of the system is obtained using continuation power flow (CPF). The proposed approach is formulated by merging CPF with IMFO incorporated with TCSC. Multi-objective function is solved for minimization of loadability, load shedding, voltage stability index, and severity index. A contingency analysis is implemented on power system as two scenarios: The first scenario is outage of generator and the second scenario is outage line. Placement of TCSC has been determined by power flow analysis. The developed approach is tested on standard IEEE-30 bus system in normal operation, and contingency cases of generator and bus outage. The IMFO is compared to recent and well-known optimization techniques. The results reveal the efficiency of the proposed algorithm to reduce load shedding, continuous energy service to the customers and prevent occurrence of voltage collapse.

Enhancing power system loadability and optimal load shedding based on TCSC allocation using improved moth flame optimization algorithm

With increasing load requirements day by day, electric utilities have also grown up to meet the load requirements. The load flow gives us information on bus and branch voltages, active and reactive power, as well as losses in transformers, conductors, cables and the entire system of this substation. Short circuit studies are most needed in a power system in order to properly size the devices, so that they are able to handle short circuit currents. This data is also used to evaluate the correct sizing of protection relays and detection equipment. In this paper, we performed a simulation of 220/63/30 kV the high-voltage substation GHAZAOUET ETAP 16.0.0 using the advanced ETAP software, which solves various problems, such as overloading and live to ensure reliable energy.

Power flow and Short circuit of 220 kV Substation using ETAP

Technique to evaluate probability of occurrence of selected contingencies of 6 Bus Roy Billinton Test System (RBTS) and IEEE 9 Bus system by Markov modelling is presented in this paper. Obtained probabilities for selected contingencies are validated by comparing with binomial distribution based probabilities. Load flow analysis and performance index based contingency ranking of total elements of 6 bus RBTS and IEEE 9 Bus systems is simulated by Power System Simulation for Engineers (PSSE) software. Novel reliability evaluation technique which unify contingency ranking with Markov modeling is proposed for RBTS and extended to IEEE 9 Bus system. Accuracy, completeness and simple to implement are salient features of proposed novel reliability evaluation method. Reliability of RBTS and IEEE 9 Bus systems can be improved by incorporating Flexible Alternating Current Transmission Systems (FACTS) device. Improvement in the reliability and economic revenue of RBTS and IEEE 9 Bus systems due to Unified Power Flow Controller (UPFC) incorporation is evaluated.

/pdf/economic-profit-estimation-of-tranmission-system-using-novel-4mz59fz1ky.pdf

Economic profit estimation of tranmission system using novel contingency ranking with markov modelling

The objective of this paper is to predict the secure or the insecure state of the power system network using a hybrid technique which is a combination of Artificial Neural Network (ANN) and voltage stability indexes. Voltage collapse or an uncontrollable drop in voltage occurs in a system when there is a change in the condition of the system or a system is overloaded. A Transference Index (TI) which acts as a voltage stability indicator has been formulated from the equivalent two-bus network of a multi-bus power system network, which has been tested on a standard IEEE 30-bus system and the result is validated with a standard Fast Voltage Stability Index (FVSI). FACTS devices in the critical bus have been considered for the improvement of the voltage stability of the system. An ANN based supervised learning algorithm has been conferred in this paper alongside Contingency Analysis (CA) for the prediction of voltage security in an  IEEE 30 - bus power system network.

/pdf/voltage-stability-prediction-on-power-networks-using-14dozwm8z1.pdf

Voltage Stability Prediction on Power Networks using Artificial Neural Networks

An important factor in the operation of a power system is the desire to maintain system security. System security involves practices suitably designed to keep the system operating when the components fail. An operationally “secure” power system is the one with low probability of system blackout or equipment damage. A secured system is one which has the ability to undergo a set of disturbances without getting into an emergency condition. In other word a normal operating condition of a power system is considered to be secured if there is neither any occurrence of over loading of any neither equipment nor transient instability due to a set of credible contingency. The secured condition satisfies not only the loading and operating constraints but a third set of constraints known as the security constraints. In the modern days the power system is becoming wide and complex. Contingency Analysis (CA) is critical in power system analysis. This work reviews the techniques for contingency analysis based on line flow analysis. Contingency analysis is performed considering the line and generator outage contingencies, in order to identify the effect of an increase in loading to critical line and generator outages.

A study of power system security and contingency analysis

This paper presents a comparative study of classical method and soft computing optimization algorithm applied to solve Economic Load Dispatch problem with non-smooth fuel cost curves considering transmission losses, power balance and capacity constraints. The soft computing algorithm varies from the classical method in terms of the following basic factors. The genetic algorithm based approach produces significantly better solutions compared against those obtained using the standard economic dispatch approach. It also proves the robustness of this algorithm in solving this type of optimization problem. GA differs from Classical optimization techniques in that it works on a population of solutions and searching is on a bit string encoding of the real parameters rather than the parameters themselves. Also GA uses probabilistic transition rules. The performance of the soft computing algorithm is investigated and tested with a three generator power system. Simulation results are presented to show the comparative performance of these methods.

/pdf/comparison-of-classical-method-and-soft-computing-mmrmc32tnt.pdf

Anil K Jain

Papers

A study of power system security and contingency analysis

Comparison of Classical Method and Soft Computing Optimization Algorithm applied to Economic load Dispatch Problem