Combined Economic Emission Dispatch Problem using Particle Swarm Optimization
28 Jul 2012-International Journal of Computer Applications (Foundation of Computer Science (FCS))-Vol. 49, Iss: 6, pp 1-6
TL;DR: In this article, particle swarm optimization (PSO) method was used to solve Combined Economic emission Dispatch Problem (CEEDP) of thermal units while satisfying the constraints such as generator capacity limits, power balance and line flow limits.
Abstract: This paper deals with particle swarm optimization (PSO) method to solve Combined Economic emission Dispatch Problem (CEEDP)of thermal units while satisfying the constraints such as generator capacity limits, power balance and line flow limits. PSO is a stochastic optimization process based on the movement and intelligence of swarms. The objective is to minimize the total fuel cost of generation and environmental pollution caused by fossil based thermal generating units. The bi-objective problem is converted into single objective problem by introducing price penalty factor to maintain an acceptable system performance in terms of limits on generator real power outputs, transmission losses with minimum emission dispatch. The proposed approach has been evaluated on an IEEE 30-bus test system with six generators. The results obtained with the proposed approach are compared with results of genetic algorithm and other technique. Keywords
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TL;DR: The power system design using smart grid architecture is developed to enhance the performance for verifying the various demand applications in power systems, integrate with available renewable energy sources, and enhance the storage capability and reliability in power system grid to deal with the suddenly change in the power system flow.
75 citations
TL;DR: In this paper, a particle swarm optimization (PSO) based algorithm is proposed to solve this highly nonlinear optimization problem with some constraints, namely; the Grashof's and free of the foregoing defects conditions.
Abstract: This paper presents the design of planar four-bar linkages free of order, branch and circuit defects, for the purpose of path generation, having clearances at one, two, three or all of its joints. Joint clearance is treated as a massless virtual link and its direction is known by the direction of the joint force. A Particle Swarm Optimization based algorithm is given here to solve this highly nonlinear optimization problem with some constraints, namely; the Grashof’s and free of the foregoing defects conditions. An example is included in which the optimal problem is solved for different cases; namely planar four-bar linkage having clearances at one, two, three, all of the joints and without clearance. For all the designs, the generated paths, the errors and the directions of the virtual links are plotted and are compared. Finally, we compare the optimal designs with reality.
56 citations
Cites background from "Combined Economic Emission Dispatch..."
...Moreover, PSO can generate an efficiently high quality solution with stable convergence characteristics [28, 43]....
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TL;DR: In this article , an improved Archimedes Optimization Algorithm (IAOA) is proposed to solve the Optimal Power Flow problem (OPF), which uses a different approach to build a neighborhood for each object in which neighbor data can be transferred between objects.
17 citations
01 Jan 2016
TL;DR: This book chapter focuses on the hybrid soft computing approaches in solving ELD problem and presents a concise and updated technical review of systems and approaches proposed by different research groups.
Abstract: 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.
17 citations
01 Oct 2017
TL;DR: In this paper, an economic operation considered renewable energy which is optimized using thunderstorm algorithm is presented, which is constrained by an emission standard and various technical limits implemented on the 62-bus system model.
Abstract: This paper presents an economic operation considered renewable energy which is optimized using thunderstorm algorithm. The problem is constrained by an emission standard and various technical limits implemented on the 62-bus system model. Simulations showed that the renewable energy inclusion penetrates to the unit commitment of generating units with strongly approach for the computational solution. This inclusion also affects to the individual power production in accordance to the fuel cost and pollutant discharge.
16 citations
Cites background or methods from "Combined Economic Emission Dispatch..."
...The SOF is targeted for determining the cheapest operating cost corresponded to the lowest emission [8], [11-12], [16]....
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...The fuel cost is approached using an economic load dispatch (ELD) whereas a pollutant discharge (PD) covers contaminant producers [2], [45], [8], [11-12], [14], [16]....
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...The EmiStd is used to measure the allowed emission from the burning of fossil fuels which is discharged in air [1-3], [11-14]....
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...These environmental requirements have been forced by the Clean Air Act Amendments of 1990 subjected to reduce an air contaminant [8], [11-15]....
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References
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TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed The relationships between particle swarm optimization and both artificial life and genetic algorithms are described
18,439 citations
TL;DR: This paper analyzes a particle's trajectory as it moves in discrete time, then progresses to the view of it in continuous time, leading to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies.
Abstract: The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. This paper analyzes a particle's trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A five-dimensional depiction is developed, which describes the system completely. These analyses lead to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the ability of the particle swarm to find optima of some well-studied test functions.
8,287 citations
TL;DR: A practical method is given for solving the power flow problem with control variables such as real and reactive power and transformer ratios automatically adjusted to minimize instantaneous costs or losses by Newton's method, a gradient adjustment algorithm for obtaining the minimum and penalty functions to account for dependent constraints.
Abstract: A practical method is given for solving the power flow problem with control variables such as real and reactive power and transformer ratios automatically adjusted to minimize instantaneous costs or losses. The solution is feasible with respect to constraints on control variables and dependent variables such as load voltages, reactive sources, and tie line power angles. The method is based on power flow solution by Newton's method, a gradient adjustment algorithm for obtaining the minimum and penalty functions to account for dependent constraints. A test program solves problems of 500 nodes. Only a small extension of the power flow program is required to implement the method.
1,575 citations
"Combined Economic Emission Dispatch..." refers background in this paper
...The rest position ever attained by each particle of the swarm is communicated to all other particles [4]....
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06 Jul 1999
TL;DR: A very simple particle swarm optimization iterative algorithm is presented, with just one equation and one social/confidence parameter, and the results are good enough so that it is certainly worthwhile trying the method on more complex problems.
Abstract: A very simple particle swarm optimization iterative algorithm is presented, with just one equation and one social/confidence parameter. We define a "no-hope" convergence criterion and a "rehope" method so that, from time to time, the swarm re-initializes its position, according to some gradient estimations of the objective function and to the previous re-initialization (it means it has a kind of very rudimentary memory). We then study two different cases, a quite "easy" one (the Alpine function) and a "difficult" one (the Banana function), but both just in dimension two. The process is improved by taking into account the swarm gravity center (the "queen") and the results are good enough so that it is certainly worthwhile trying the method on more complex problems.
1,550 citations
"Combined Economic Emission Dispatch..." refers methods in this paper
...The PSO algorithm searches in parallel using a group of individuals similar to other AI-based heuristic optimization techniques [12]....
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