Combined Economic And Emission Dispatch Problem Research Articles

Search and Rescue Optimization for Combined Economic Load and Emission Dispatch

The goal of combined economic and emission dispatch (CEED) in the power system is to solve the economics management of generators in order to achieve both minimum fuel prices and pollution levels while meeting load demands and operating limits. The Search and Rescue (SAR) optimization methodology is developed in this study to address the CEED problem, and the results gained are compared with the Evolutionary Programming and Flower Pollination Algorithm methods. Those analyses are able to evaluate the effectiveness as well as the rate of convergence of the methods under consideration. In general, the CEED problem is initially considered a bi-objective problem that has been turned into a single objective function by the use of the price penalty element in its solution. Both solutions were tested on an IEEE 10-Generator 39-Bus System, which has a valve point impact with transmission loss. MATLAB is additionally utilized to run modeling for the evaluated system, with each system subjected to three separate load demands. The results reveal that the SAR technique performs better because it generates resilient and effective solutions to the CEED problem with the lowest fuel price, greenhouse gas emissions, CEED price, and power loss.

Chaotic Butterfly Optimization Algorithm Applied to Multi-objective Economic and Emission Dispatch in Modern Power System

Aims : To optimize the economic and emission dispatch of the thermal power plant. Background: Considering both the economic and environmental aspects, a combined approach had made to attain a solution is known as the combined economic and emission dispatch problem. The CEED problem is a nonlinear bi-objective problem with conflicting behaviour with all the practical constraints. Objective: A new optimization method is improvised by applying the chaotic mapping to the butterfly optimization algorithm. This method is applied to the Combined Economic and Emission Dispatch (CEED) problem for optimizing consumed fuel cost and produced environment pollutant. Methods: Improved Chaotic Butterfly algorithm is applied to the optimization problem to optimize combined economic and emission dispatch. Result : The proposed technique is tested for four different test systems with various practical constraints like valve point loading, ramp rate limit and prohibited operating zones. The obtained results from the chaotic butterfly optimization algorithm (CBOA) are compared with other optimization techniques provide an optimum solution for CEED problem. Conclusion : Considering the environmental impact the novel metaheuristic swarm intelligence technique is applied with conflict of interest. Different test systems, with different practical operational constraints like valve-point loading, prohibited operating zones and ramp rate limits and emission dispatch have been analyzed to validate the implementation of the proposed algorithm in real life CEED problem situations.

An Efficient Chameleon Swarm Algorithm for Economic Load Dispatch Problem

Economic Load Dispatch (ELD) is a complicated and demanding problem for power engineers. ELD relates to the minimization of the economic cost of production, thereby allocating the produced power by each unit in the most possible economic manner. In recent years, emphasis has been laid on minimization of emissions, in addition to cost, resulting in the Combined Economic and Emission Dispatch (CEED) problem. The solutions of the ELD and CEED problems are mostly dominated by metaheuristics. The performance of the Chameleon Swarm Algorithm (CSA) for solving the ELD problem was tested in this work. CSA mimics the hunting and food searching mechanism of chameleons. This algorithm takes into account the dynamics of food hunting of the chameleon on trees, deserts, and near swamps. The performance of the aforementioned algorithm was compared with a number of advanced algorithms in solving the ELD and CEED problems, such as Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Earth Worm Algorithm (EWA). The simulated results established the efficacy of the proposed CSA algorithm. The power mismatch factor is the main item in ELD problems. The best value of this factor must tend to nearly zero. The CSA algorithm achieves the best power mismatch values of 3.16×10−13, 4.16×10−12 and 1.28×10−12 for demand loads of 700, 1000, and 1200 MW, respectively, of the ELD problem. The CSA algorithm achieves the best power mismatch values of 6.41×10−13 , 8.92×10−13 and 1.68×10−12 for demand loads of 700, 1000, and 1200 MW, respectively, of the CEED problem. Thus, the CSA algorithm was found to be superior to the algorithms compared in this work.

Solution of combined economic and emission dispatch problems of power systems without penalty

ABSTRACT In the present paper, the Optimization Without Penalty-based Optimization by Morphological Filter algorithm (OWP-based OMF) has been proposed to find the optimal solution for the Combined Economic and Emission Dispatch (CEED) problem. The objective function of the CEED problem is to minimize fuel cost and emission simultaneously while satisfying the load demand, equality and inequality constraints. The evaluation of OWP-based OMF performances is carried out on three test systems with 6, 10 and 40 generating units, with different constraints and various costs. The present paper shows that OWP-based OMF gives an accurate and effective solution of the CEED problem and outperforms the other tested algorithms.

A Developed Approach to Solve Economic and Emission Dispatch Problems Based on Moth-Flame Algorithm

The solution of Combined Economic and Emission dispatch (CEED) problems aims to minimize the operating fuel cost and emission levels simultaneously, while satisfying load demand and operational constraints. The CEED problem can be formulated as a nonlinear optimization problem with equality and inequality operating constraints. Moth-flame optimization algorithm (MFO) is one of the newest bio inspired optimization techniques. However, this paper proposes a modified moth-flame optimization algorithm (MMFO) to find the optimal solution of CEED problems. MMFO is mainly based on the concept of MFO with modification the path of moths in new spirals around the flame. The developed MMFO algorithm can be used to improve the convergence characteristics of CEED optimal solutions compared to other optimization algorithms. The performance of MMFO is compared with different optimization techniques. Valve point effect, environmental emissions, and transmission line losses are included in the CEED analysis to provide more practical results. MMFO is validated using different standard test systems. The simulated results show the effectiveness and the superiority of the developed MMFO against other well-known optimization techniques.

SOLVING COMBINED ECONOMIC AND EMISSION DISPATCH PROBLEM USING THE SLIME MOULD ALGORITHM

In this paper, a novel optimization method called Slime Mould Algorithm (SMA) for solving the ELD and CEED problem is presented. To investigate the effectiveness of the proposed algorithm, the 10 and 40 units considering the valve point loading effect test has been executed. The solving the Economic Load Dispatch (ELD) and Combined Economic and Emission Dispatch (CEED) are crucial task in modern power systems. The aim of ELD is assigning the best generation scheduling for minimum cost generation with satisfying the load demands while the CEED means assigning the best generation scheduling for cost and emission reduction simultaneously. The effectiveness of the proposed algorithm is compared with other algorithms. In this paper, a novel optimization method called Slime Mould Algorithm (SMA) for solving the ELD and CEED problem is presented. To investigate the effectiveness of the proposed algorithm, the 10 and 40 units considering the valve point loading effect test has been executed. The solving the Economic Load Dispatch (ELD) and Combined Economic and Emission Dispatch (CEED) are crucial task in modern power systems. The aim of ELD is assigning the best generation scheduling for minimum cost generation with satisfying the load demands while the CEED means assigning the best generation scheduling for cost and emission reduction simultaneously. The effectiveness of the proposed algorithm is compared with other algorithms.

Comparative study of the price penalty factors approaches for Bi-objective dispatch problem via PSO

One of the main objectives of electricity dispatch centers is to schedule the operation of available generating units to meet the required load demand at minimum operating cost with minimum emission level caused by fossil-based power plants. Finding the right balance between the fuel cost the green gasemissionsis reffered as Combined Economic and Emission Dispatch (CEED) problem which is one of the important optimization problems related the operationmodern power systems. The Particle Swarm Optimization algorithm (PSO) is a stochastic optimization technique which is inspired from the social learning of birds or fishes. It is exploited to solve CEED problem. This paper examines the impact of six penalty factors like "Min-Max", "Max-Max", "Min-Min", "Max-Min", "Average" and "Common" price penalty factors for solving CEED problem. The Price Penalty Factor for the CEED is the ratio of fuel cost to emission value. This bi-objective dispatch problem is investigated in the Real West Algeria power network consisting of 22 buses with 7 generators. Results prove capability of PSO in solving CEED problem with various penalty factors and it proves that Min-Max price penalty factor provides the best compromise solution in comparison to the other penalty factors.

Recurrent neural network for combined economic and emission dispatch

Recently, the combined economic and emission dispatch (CEED) problem, which aims to simultaneously decrease fuel cost and reduce environmental emissions of power systems, has been a widespread concern. To improve the utilization efficiency of primary energy, combined heat and power (CHP) units are likely to play an important role in the future. The goal of this study is to propose an approach to solve the CEED problems in a CHP system which consists of eight power generators (PGs), two CHP units and one heat only unit. Owing to the existence of power loss in power transmission line and the non-convex feasible region of CHP units, the proposed problem is a nonlinear, multi-constraints, non-convex multi-objectives (MO) optimization problem. To deal with it, a recurrent neural network (RNN) combined with a novel technique is developed. It means that the feasible region is separated into two convex regions by using two binary variables to search for different regions. In the frame of the neurodynamic optimization, existence and convergence of the dynamic model are analyzed. It shows that the convergence solution obtained by RNN is the optimal solution of CEED problem. Numerical simulation results show that the proposed algorithm can generate solutions efficiently.

A Novel Optimization Technique for Combined Economic and Emission Dispatch Problem considering Congestion Management

Background/Objective: In power systems, the dispatch of real power plays a pivotal role in mitigating the transmission congestion, yet at a very low and affordable cost. Thus, in this paper, the rescheduling of real power generation has been dealt with a view to manage the congestion in transmission. Methods/Statistical Analysis: In this paper, the congestion management in a deregulated electricity market has been endeavored for the problems of Combined Economic and Emission Dispatch (CEED) by employing a novel technique of optimization through Artificial Bee Colony (ABC) algorithm. In a real time transmission system, the congestion is an inevitable condition that occurs when the power flow in the line exceeds than that of its limit. In this work, the optimized solution for CEED problem has been obtained via ABC algorithm. An encoding scheme based on generating unit is used. Findings: By employing a robust and efficient methodology using ABC algorithm, an optimal technique on congestion management has been proposed, which takes into account of corrective re-dispatch of real power through the transmission system, under the CEED – environment on a restructured power system. The proposed method has been tested on an IEEE 30-bus system and their results have also been validated to show the efficacy of the proposed methodology, which throw light upon the fact that it has emerged out as an effective tool in handling the transmission congestion in a deregulated environment that results in the minimization of cost of re-dispatch on one hand and in a secured operating condition on the other. Application/Improvements: This work can be extended in the future by including reactive power support, which can further minimize the total operating cost of the system.

A Solution to the Combined Economic and Emission Dispatch Using Hybrid PSOGSA Algorithm

ABSTRACTThis article presents a new hybrid algorithm based on particle swarm optimization (PSO) and the gravitational search algorithm (GSA) for solving the combined economic and emission dispatch (CEED) problem in power systems. Performance of this approach for the CEED problem is studied and evaluated on three test systems with 3, 6, and 40 generating units, with various cost curve nature and different constraints. The results obtained are compared to those reported in the recent literature. Those results show that the proposed algorithm provides an effective and robust high-quality solution of the CEED problem.

Solving Combined Economic and Emission Dispatch Using Harvest Season Artificial Bee Colony Algorithm Considering Food Source Placements and Modified Rates

This paper presents performances of a harvest season artificial bee colony (HSABC) algorithm for obtaining the best solution of a combined economic and emission dispatch (CEED). Two strategies of placement are applied to IEEE-30 bus system for solving the CEED problem under some constraints. Modified rates and various distances are also used to demonstrate HSABC's performances. Simulation results show that two placement strategies have different implications for the CEED, application of various modified rates and distances affect to convergence speeds, minimum results are searched in different starting points at the first iteration and the total number of food sources gives effects to the HSABC's performances.

Spiral Optimization Algorithm for solving Combined Economic and Emission Dispatch

The Spiral Optimization Algorithm (SOA) is an optimization technique developed recently (2011) by K. Tamura and K. Yasuda at Tokyo Metropolitan University-Japan. SOA is a metaheuristic based on an analogy of spiral phenomena in nature and it is simple in concept, few in parameters and easy in implementation.In this paper, SOA is proposed for solving the Combined Economic and Emission Dispatch (CEED) problem. It is aimed, in the CEED problem, that scheduling of generators should operate with both minimum fuel costs and emission levels, simultaneously, while satisfying the load demand and operational constraints. The CEED problem is formulated as a multi-objective problem by considering the fuel cost and emission objectives of generating units. The bi-objective optimization problem is converted into a single objective function using a price penalty factor. The proposed algorithm has been implemented on three test systems with 3, 6, and 40 generating units, with different constraints and various cost curve nature. In order to see the effectiveness of the proposed algorithm, its results are compared to those reported in the recent literature. Those results are quite encouraging showing the good applicability of SOA for CEED problem.

Combined Economic and Emission Dispatch Using a Classical Technique

Abstract This paper presents a classical technique based on co-ordination equation to solve Combined Economic and Emission Dispatch (CEED) problem, considering nonlinear characteristics of the generator, such as ramp rate limits and prohibited operating zones. The multi-objective CEED problem is converted into a single objective problem using a modified price penalty factor approach. In this paper, incremental cost is heuristically updated iteratively, which makes the solution independent of the number of generator units. The approach includes the evaluation of trade-off curve between the fuel cost and the emission according to the multi-objective function. The performance of the proposed method is demonstrated using modified IEEE 14 and 30 bus systems with different loading conditions.

Sequential quadratic programming particle swarm optimization for wind power system operations considering emissions

In this paper, a computation framework for addressing combined economic and emission dispatch (CEED) problem with valve-point effects as well as stochastic wind power considering unit commitment (UC) using a hybrid approach connecting sequential quadratic programming (SQP) and particle swarm optimization (PSO) is proposed. The CEED problem aims to minimize the scheduling cost and greenhouse gases (GHGs) emission cost. Here the GHGs include carbon dioxide (CO2), nitrogen dioxide (NO2), and sulphur oxides (SOx). A dispatch model including both thermal generators and wind farms is developed. The probability of stochastic wind power based on the Weibull distribution is included in the CEED model. The model is tested on a standard system involving six thermal units and two wind farms. A set of numerical case studies are reported. The performance of the hybrid computational method is validated by comparing with other solvers on the test system.

Combined Economic And Emission Dispatch Using Artificial Neural Network

This paper proposes an Artificial Neural Network approach for solving the Combined Economic and Emission Dispatch (CEED) problem using Radial Basis Function (RBF) based neural network. The purpose of CEED is to minimize both the operating fuel cost and emission level simultaneously while satisfying load demand and operational constraints. This multi-objective CEED problem is converted into a single objective function using a modified price penalty factor approach.

A new ant algorithm solving the combined economic and emission dispatch problem

This paper presents a new approach to the Combined Economic and Emission Dispatch (CEED) problem, using the Continuous Ant System (C-ANT) algorithm. The CEED problem is a multi-objective nonlinear optimization problem with constraints. This problem is one of the fundamentals issues in power system operations. The paper focuses on the emission of nitrogen oxides (NO x ), because its control is significant in electric power planning. A new ant algorithm (C-ANT) is proposed for solving the CEED problem. The CEED problem is converted into a single optimization problem using a price penalty factor h. To show its efficiency and effectiveness, the proposed C-ANT algorithm is applied to a six-generator test system. The load demand of the system is 500 and 900 MW. Results show the applicability and efficiency of the proposed C-ANT algorithm. With respect to the applicability of the algorithm, the fact that it has been designed to accommodate continuous intervals in problem space distinguishes it from other similar algorithmic approaches.

Combined economic and emission dispatch solution using gravitational search algorithm

In this article, the Gravitational Search Algorithm (GSA) has been proposed to find the optimal solution for Combined Economic and Emission Dispatch (CEED) problems. It is aimed, in the CEED problem, that scheduling of generators should operate with both minimum fuel costs and emission levels, simultaneously, while satisfying the load demand and operational constraints. In this paper, the CEED problem is formulated as a multi-objective problem by considering the fuel cost and emission objectives of generating units. The bi-objective optimization problem is converted into a single objective function using a price penalty factor in order to solve it with GSA. The proposed algorithm has been implemented on four different test cases, having a valve point effect with transmission loss and having no valve point effect without transmission loss. In order to see the effectiveness of the proposed algorithm, it has been compared with other algorithms in the literature. Results show that the GSA is more powerful than other algorithms.

An Advanced Genetic Optimization Algorithm to Solve Combined Economic and Emission Dispatch Problem

The dispatch of electric load is one of the key functions in electrical power system operation, management and planning. The key intention of economic load dispatch is to reduce the total production cost of the generating system and at the same time the necessary equality and inequality constraints should also be fulfilled. In the present time, energy resources to generate mechanical power supplied to the rotor shaft of generating units are of fossil fuels. This leads to the emission of huge amount of carbon dioxide (CO2), sulfur dioxide (SO2) and nitrogen oxides (NOx) that results in atmospheric pollution. Reducing those pollutions resulted by usage of fossil-fired generating units has received great consideration. This provides wide field for the researchers to develop a better system to handle those needs. This leads to the development of Combined Economic and Emission Dispatch (CEED) techniques. There are various technique proposed by several researchers to solve CEED problem based on optimization techniques. The efficient optimization technique among the proposed work is Genetic Algorithm (GA). But still some problems like slower convergence and higher computational complexity exists in using GA for solving CEED problem. To overcome those difficulties, this paper uses Non- Dominated Ranked Genetic Algorithm (NRGA) which uses rank based Roulette Wheel selection algorithm with Pareto-based population ranking Algorithm. The simulation result shows that the proposed technique for solving combined economic and emission dispatch problem results in better convergence rate when compared to the existing techniques.

A Simplified Recursive Approach to Combined Economic Emission Dispatch

This article presents a simplified mathematical approach to find the optimal solution for combined economic and emission dispatch (CEED) problem. In this article, a CEED problem is formulated as a multi-objective problem by considering both economy and emission simultaneously. The bi-objective CEED problem is converted into single objective function using price penalty factor. A dynamic programming (DP) technique is presented in a simple form to solve the CEED problem. The validity of the proposed approach is tested on two sample systems and the results are compared to those reported in the recent literature. The study results have shown that the approach developed is efficient and suitable for real time applications.