**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**16

# economic dispatch Related Publications

##### 16 Upgraded Cuckoo Search Algorithm to Solve Optimisation Problems Using Gaussian Selection Operator and Neighbour Strategy Approach

**Authors:**
Mukesh Kumar Shah,
Tushar Gupta

**Abstract:**

An Upgraded Cuckoo Search Algorithm is proposed here to solve optimization problems based on the improvements made in the earlier versions of Cuckoo Search Algorithm. Short comings of the earlier versions like slow convergence, trap in local optima improved in the proposed version by random initialization of solution by suggesting an Improved Lambda Iteration Relaxation method, Random Gaussian Distribution Walk to improve local search and further proposing Greedy Selection to accelerate to optimized solution quickly and by “Study Nearby Strategy” to improve global search performance by avoiding trapping to local optima. It is further proposed to generate better solution by Crossover Operation. The proposed strategy used in algorithm shows superiority in terms of high convergence speed over several classical algorithms. Three standard algorithms were tested on a 6-generator standard test system and the results are presented which clearly demonstrate its superiority over other established algorithms. The algorithm is also capable of handling higher unit systems.

**Keywords:**
economic dispatch,
prohibited operating zones,
Ramp rate limits,
gaussian selection operator,
upgraded cuckoo search

##### 15 Performance Analysis of MATLAB Solvers in the Case of a Quadratic Programming Generation Scheduling Optimization Problem

**Authors:**
Dávid Csercsik,
Péter Kádár

**Abstract:**

**Keywords:**
Optimization,
MATLAB,
economic dispatch,
quadratic
programming

##### 14 Load Forecasting Using Neural Network Integrated with Economic Dispatch Problem

**Authors:**
Mariyam Arif,
Ye Liu,
Israr Ul Haq,
Ahsan Ashfaq

**Abstract:**

High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.

**Keywords:**
Artificial Neural Networks,
Linear Programming,
Demand-Side Management,
economic dispatch,
power generation dispatch

##### 13 An Efficient Stud Krill Herd Framework for Solving Non-Convex Economic Dispatch Problem

**Authors:**
Bachir Bentouati,
Lakhdar Chaib,
Saliha Chettih,
Gai-Ge Wang

**Abstract:**

The problem of economic dispatch (ED) is the basic problem of power framework, its main goal is to find the most favorable generation dispatch to generate each unit, reduce the whole power generation cost, and meet all system limitations. A heuristic algorithm, recently developed called Stud Krill Herd (SKH), has been employed in this paper to treat non-convex ED problems. The proposed KH has been modified using Stud selection and crossover (SSC) operator, to enhance the solution quality and avoid local optima. We are demonstrated SKH effects in two case study systems composed of 13-unit and 40-unit test systems to verify its performance and applicability in solving the ED problems. In the above systems, SKH can successfully obtain the best fuel generator and distribute the load requirements for the online generators. The results showed that the use of the proposed SKH method could reduce the total cost of generation and optimize the fulfillment of the load requirements.

**Keywords:**
economic dispatch,
crossover,
stud krill herd,
stud selection,
valve-point effect

##### 12 Comparative Performance of Artificial Bee Colony Based Algorithms for Wind-Thermal Unit Commitment

**Authors:**
P. K. Singhal,
R. Naresh,
V. Sharma

**Abstract:**

**Keywords:**
wind power,
economic dispatch,
Artificial Bee Colony Algorithm,
unit commitment

##### 11 Comparative Performance of Artificial Bee Colony Based Algorithms for Wind-Thermal Unit Commitment

**Authors:**
P. K. Singhal,
R. Naresh,
V. Sharma

**Abstract:**

**Keywords:**
wind power,
economic dispatch,
Artificial Bee Colony Algorithm,
unit commitment

##### 10 Optimal Placement of Capacitors for Achieve the Best Total Generation Cost by Genetic Algorithm

**Authors:**
Mohammad Reza Tabatabaei,
Mohammad Bagher Haddadi,
Mojtaba Saeedimoghadam,
Ali Vaseghi Ardekani

**Abstract:**

Economic Dispatch (ED) is one of the most challenging problems of power system since it is difficult to determine the optimum generation scheduling to meet the particular load demand with the minimum fuel costs while all constraints are satisfied. The objective of the Economic Dispatch Problems (EDPs) of electric power generation is to schedule the committed generating units outputs so as to meet the required load demand at minimum operating cost while satisfying all units and system equality and inequality constraints. In this paper, an efficient and practical steady-state genetic algorithm (SSGAs) has been proposed for solving the economic dispatch problem. The objective is to minimize the total generation fuel cost and keep the power flows within the security limits. To achieve that, the present work is developed to determine the optimal location and size of capacitors in transmission power system where, the Participation Factor Algorithm and the Steady State Genetic Algorithm are proposed to select the best locations for the capacitors and determine the optimal size for them.

**Keywords:**
Genetic Algorithm,
economic dispatch,
Lagrange,
Losses Reduction,
Capacitors
Placement

##### 9 Non-Smooth Economic Dispatch Solution by Using Enhanced Bat-Inspired Optimization Algorithm

**Authors:**
Farhad Namdari,
Reza Sedaghati

**Abstract:**

Economic dispatch (ED) has been considered to be one of the key functions in electric power system operation which can help to build up effective generating management plans. The practical ED problem has non-smooth cost function with nonlinear constraints which make it difficult to be effectively solved. This paper presents a novel heuristic and efficient optimization approach based on the new Bat algorithm (BA) to solve the practical non-smooth economic dispatch problem. The proposed algorithm easily takes care of different constraints. In addition, two newly introduced modifications method is developed to improve the variety of the bat population when increasing the convergence speed simultaneously. The simulation results obtained by the proposed algorithms are compared with the results obtained using other recently develop methods available in the literature.

**Keywords:**
economic dispatch,
non-smooth,
bat-inspired,
nonlinear practical constraints,
modified bat algorithm

##### 8 Q-Learning with Eligibility Traces to Solve Non-Convex Economic Dispatch Problems

**Authors:**
Mohammed I. Abouheaf,
Sofie Haesaert,
Wei-Jen Lee,
Frank L. Lewis

**Abstract:**

Economic Dispatch is one of the most important power system management tools. It is used to allocate an amount of power generation to the generating units to meet the load demand. The Economic Dispatch problem is a large scale nonlinear constrained optimization problem. In general, heuristic optimization techniques are used to solve non-convex Economic Dispatch problem. In this paper, ideas from Reinforcement Learning are proposed to solve the non-convex Economic Dispatch problem. Q-Learning is a reinforcement learning techniques where each generating unit learn the optimal schedule of the generated power that minimizes the generation cost function. The eligibility traces are used to speed up the Q-Learning process. Q-Learning with eligibility traces is used to solve Economic Dispatch problems with valve point loading effect, multiple fuel options, and power transmission losses.

**Keywords:**
economic dispatch,
q-learning,
Non-Convex Cost Functions,
Valve Point Loading Effect,
Eligibility Traces

##### 7 Transmission Expansion Planning with Economic Dispatch and N-1Constraints

**Authors:**
A. Charlangsut,
M. Boonthienthong,
N. Rugthaicharoencheep

**Abstract:**

This paper proposes a mathematical model for transmission expansion employing optimization method with scenario analysis approach. Economic transmission planning, on the other hand, seeks investment opportunities so that network expansions can generate more economic benefits than the costs. This approach can be used as a decision model for building new transmission lines added to the existing transmission system minimizing costs of the entire system subject to various system’s constraints and consider of loss value of transmission system and *N*-1 checking. The results show that the proposed model is efficient to be applied for the larger scale of power system topology.

**Keywords:**
Contingency,
economic dispatch,
scenario analysis,
transmission expansion planning

##### 6 Economic Dispatch Fuzzy Linear Regression and Optimization

**Authors:**
A. K. Al-Othman

**Abstract:**

**Keywords:**
economic dispatch,
Fuzzy Linear Regression (FLP)and Optimization

##### 5 Enhanced Particle Swarm Optimization Approach for Solving the Non-Convex Optimal Power Flow

**Authors:**
M. R. AlRashidi,
M. F. AlHajri,
M. E. El-Hawary

**Abstract:**

**Keywords:**
Particle Swarm Optimization,
economic dispatch,
optimal power flow

##### 4 Interactive Compromise Approach with Particle Swarm Optimization for Environmental/Economic Power Dispatch

**Authors:**
Ming-Tang Tsai,
Chih-Wei Yen

**Abstract:**

**Keywords:**
Particle Swarm Optimization,
Emission Control,
economic dispatch,
Interactive Compromise Approach

##### 3 Application of Soft Computing Methods for Economic Dispatch in Power Systems

**Authors:**
Jagabondhu Hazra,
Avinash Sinha

**Abstract:**

Economic dispatch problem is an optimization problem where objective function is highly non linear, non-convex, non-differentiable and may have multiple local minima. Therefore, classical optimization methods may not converge or get trapped to any local minima. This paper presents a comparative study of four different evolutionary algorithms i.e. genetic algorithm, bacteria foraging optimization, ant colony optimization and particle swarm optimization for solving the economic dispatch problem. All the methods are tested on IEEE 30 bus test system. Simulation results are presented to show the comparative performance of these methods.

**Keywords:**
Ant colony optimization,
Genetic Algorithm,
Particle Swarm Optimization,
evolutionary algorithm,
economic dispatch,
bacteria foraging optimization

##### 2 Two Area Power Systems Economic Dispatch Problem Solving Considering Transmission Capacity Constraints

**Authors:**
M. Zarei,
A. Roozegar,
R. Kazemzadeh,
J.M. Kauffmann

**Abstract:**

**Keywords:**
Power System Operation,
economic dispatch,
Direct
Search Method,
Transmission Capacity Constraint

##### 1 Application of Neural Networks in Power Systems; A Review

**Authors:**
M. Tarafdar Haque,
A.M. Kashtiban

**Abstract:**

The electric power industry is currently undergoing an unprecedented reform. One of the most exciting and potentially profitable recent developments is increasing usage of artificial intelligence techniques. The intention of this paper is to give an overview of using neural network (NN) techniques in power systems. According to the growth rate of NNs application in some power system subjects, this paper introduce a brief overview in fault diagnosis, security assessment, load forecasting, economic dispatch and harmonic analyzing. Advantages and disadvantages of using NNs in above mentioned subjects and the main challenges in these fields have been explained, too.

**Keywords:**
Power System,
Neural Network,
Fault diagnosis,
Load Forecasting,
economic dispatch,
security
assessment,
harmonic analyzing