Senthil Krishnamurthy

Impact of price penalty factors on the solution of the combined economic emission dispatch problem using cubic criterion functions

Thermal power plants play a major role in power production. The impacts of the various pollutants such as sulphur dioxide (SO2), Nitrogen oxide (NOx) and carbon dioxide (CO2) affects the environmental issues. The fuel cost of the generator in an economic dispatch problem can be presented by any order polynomial. The literature review reported that most of the papers consider the single pollutant using the second order polynomial function. The paper formulates the dispatch problem using a cubic function for both fuel cost and emission values. The impacts of various pollutants and price penalty factors such as Max-Max, MinMax, Average, and Common are considered in the multi-objective dispatch problem. The dispatch problem is solved using Lagranges method and the simulation results are provided for IEEE 30 bus system. It concludes that Min-Max Price penalty factor provides minimum fuel cost and minimum emission values in comparison to the other price penalty factors.

Comparative analyses of Min-Max and Max-Max price penalty factor approaches for multi criteria power system dispatch problem with valve point effect loading using Lagrange's method

The objective of the economic dispatch problem of electrical power generation is to schedule the committed generating units output so as to meet the required load demand while satisfying the system equality and inequality constraints. This paper solves the combined economic emission dispatch problem by Min-Max and Max-Max price penalty factor approaches using Lagranges method. The price penalty factor for the combined economic emission dispatch (CEED) is the ratio of fuel cost to emission value. The literature papers reported that most of them use Max-Max Price penalty factor for the CEED problem. This paper analyses Min-Max and Max-Max price penalty factors for CEED problem. This problem is solved for a five generator system by considering the valve point effect for both fuel cost and emission function. The total fuel cost by considering valve point effect of the combined economic emission dispatch problem is less when using Min-Max price penalty factor approach in comparison to Max-Max price penalty factor.

Multi Objective Dispatch Problem with Valve Point Effect Loading of Fuel Cost and Emission Criterion

Cape Peninsula University of Technology and National research foundation (NRF) grant UID62364

Method for a parallel solution of the multi area economic dispatch problem

Abstract The power system simulation software tools are traditionally designed for serial codes and optimized using single-processor computers. They are inadequate in terms of computational efficiency and execution time for the ever-increasing complexity of the power grid. Due to the above-mentioned sequential computing demerits, this paper used MATLAB data parallelism message passing interface software to execute the Lagranges and Particle Swarm Optimization (PSO) algorithms in parallel with multiple processor units with different and large data sets for the solution of the Combined Economic Emission Dispatch (CEED) problem. The two important advantages of using parallel computing approach to solve the power system economic dispatch problem are 1) to increase the efficiency (solution quality) and 2) to reduce the execution time (speed-up) of the parallelization process for the CEED problem solution. The comparison between the Lagranges and PSO data-parallel solution quality and execution time is presented for the CEED problem for Institute of Electrical and Electronic Engineers (IEEE) 30 bus and IEEE 118 bus systems. The paper contributes to the on-line real-time market analyses of the deregulated power system, which need improved solution quality and a fast computation process to solve the power system energy management (CEED) problems for proper discussion and decision making at the control center level.

Investigation on the impact of the penalty factors over solution of the dispatch optimization problem

The objective of the economic dispatch problems of electrical power generation is to schedule the committed generating units output so as to meet the required load demand while satisfying the system equality and inequality constraints. This paper solves the combined economic emission dispatch problem by using various price penalty factor approaches and Lagranges method. The price penalty factor for the combined economic emission dispatch (CEED) problem is the ratio of fuel cost to emission value. Most of the existing papers use Max-Max Price penalty factor for the CEED problem. This paper analyses and compares the impact of four different penalty factors over the optimization solution and convergence of the calculation procedure for the CEED problem. The considered penalty factors are Min-Max, Max-Max, Min-Min and Max-Min price penalty factors. The CEED problem is solved for the IEEE 30 bus system and an Indian utility system with six generating units using various price penalty factors. The simulated results show that Min-Max price penalty factor gives the best optimization solution in comparison to other price penalty factors for the CEED problem.

Economic dispatch solution using different algorithms and softwares

This paper solves the Economic Dispatch (ED) problem using MATLAB, Power World Simulator (PWS) and RSCAD, the software environment of the Real Time Digital Simulator (RTDS). The solution for the real power of the generator is computed in all the three types of software environments for the criterion minimization of the operational (fuel) cost. Lagranges and Particle Swarm Optimization (PSO) algorithms are developed in MATLAB and Newton Raphson method in PWS and RSCAD software environments for solution of the economic dispatch problem. The IEEE eleven bus network given in [1], is considered as the case study in order to validate the results. It concludes that the economic dispatch problem solution using Lagranges algorithm in MATLAB software provides more reliable results in comparison to PWS and RTDS softwares.

Comparison of the Lagrange's and Particle Swarm Optimisation solutions of an Economic Emission Dispatch problem with transmission constraints

The power demand is increased rapidly and hence the power systems become more complex, so it is necessary to solve the dispatch problem with less computation time. This paper uses the optimization approach (Lagranges) and random variables selection approach Particle Swarm Optimisation (PSO) to solve the dispatch problem with transmission constraints and to compare the obtained solution and the time for its calculation. Formulation of a bi-criteria Combined Economic Emission Dispatch (CEED) problem is given. The application of Lagranges and PSO methods to the CEED problem is described and the algorithms for calculations are given. The computational time of the Lagranges algorithm depends on the selection of the initial values of the Lagranges variable (λ), and on the swarms, positions, and velocity selection in PSO algorithm. The IEEE 30 bus system is considered to validate the simulation results in MATLAB environment. It concludes that Lagranges algorithm provides better results for CEED problem in comparison to the PSO algorithm.

DECOMPOSITION METHOD FOR SOLUTION OF A MULTI-AREA POWER DISPATCH PROBLEM

Large interconnected power systems are decomposed into areas or zones based on the size of the electric power system, network topology and geographical location. Multi-area economic emission dispatch (MAEED) problem is an optimisation task in power system operation for allocating amount of generation to the committed units within these areas. Its objective is to minimise the fuel cost subject to the power balance, generators limits, transmission lines, and tie-line constraints. The solution of the MAEED problem in the conditions of deregulation is difficult, due to the model size, nonlinearity, and interconnections. It determines the amount of power that can be economically generated in the areas and transferred to other areas if it is needed without violating tie-line capacity constraints. High-performance computing (HPC) gives possibilities for reduction of the problem complexity and the time for calculation by the use of parallel processing for running advanced application programs efficiently, reliably and quickly.