Abstract— **Economic** **Load** **Dispatch** (ELD) proves to be a vital **optimization** process in electric power system for allocating generation amongst various units to compute the cost of generation, the cost of emission involving global warming gases like sulphur dioxide, nitrous oxide and carbon monoxide etc. In this dissertation, we emphasize **ramp** **rate** **constriction** **factor** **based** **particle** **swarm** **optimization** (RRCPSO) for analyzing various performance objectives, namely cost of generation, cost of emission, and a **dual** **objective** function involving both these objectives through the experimental simulated results. A 6-unit 30 bus IEEE test case system has been utilized for simulating the results involving improved weight **factor** advanced **ramp** **rate** limit constraints for optimizing total cost of generation and emission. This method increases the tendency of particles to venture into the solution space to ameliorate their convergence rates. Earlier works through dispersed PSO (DPSO) and **constriction** **factor** **based** PSO (CPSO) give rise to comparatively higher computational time and less good optimal solution at par with current dissertation. This paper deals with **ramp** **rate** and **constriction** **factor** **based** well defined **ramp** **rate** PSO to compute various objectives namely cost, emission and total **objective** etc. and compares the result with DPSO and weight improved PSO (WIPSO) techniques illustrating lesser computational time and better optimal solution.

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The ELD problem assumes that the amount of power to be supplied by a given set of units is constants for a given interval of time and attempts to minimize cost of supplying this energy subject to constraints of the generating units. Therefore it is concerned with the minimization of total cost incurred in the system and constraints over the entire **dispatch** period. Here the **economic** **load** **dispatch** problem was solved for six units generating station for a total **load** demand of 1263 MW without considering complexity, **ramp** **rate** units and prohibited operating zones and without losses. The problem was solved by Natural exponent inertia weight strategy i.e. e1-PSO and e2-PSO with MATLAB 7.10 environment above mentioned two strategies has been successfully applied to determine the optimal generation schedule of the six unit test system. Detailed conclusions of the results are given below:

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Optimal scheduling of generation is an important task in the power plants operation which aims to allocate power generations to match **load** demand at minimal possible cost while satisfying all the power units and system constraints [1]. The complexity of the problem is due to the nonlinear and non-smooth characteristics of the input-output curves of the generators, because of valve-point effect, **ramp** **rate** limits and prohibited operating zones. The mathematical programming **based** **optimization** methods such as lambda iteration, base point participation method, Gradient and Newton’s methods can solve successfully the optimal scheduling problems [2]. But unfortunately, these methods are ineffective to handle the complex optimal scheduling problems with non-differentiable characteristics due to high complexity. Dynamic programming can solve such type of problem, but it suffers from curse of dimensionality. Hence for optimal solution this problem needs a fast, robust and accurate solution methodology. Now days heuristic search methods such as simulated annealing (SA)[3] genetic algorithm (GA) [4]-[5], evolutionary programming (EP) [6], **particle** **swarm** **optimization** (PSO) [7]-[10], Bacteria foraging **optimization** (BFO) [11], differential evolution (DE) [12] and chaotic ant **swarm** **optimization** [13] are employed to solve the optimal scheduling problems All the approaches have achieved success to a certain extent. New **optimization** algorithms[18-24] can be used for DG placement and **economic** **load** **dispatch** problems.

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In this paper, a hybrid method incorporating PSO with TVAC and EP, called TVAC-EPSO has been proposed to solve nonconvex ED problems. The performance of the proposed method has been tested on three different test systems which consider the practical generator constraints such as valve point effects, POZ and **ramp** **rate** limits. The results obtained by TVAC-EPSO method were compared with other methods reported in literatures. The comparison study was done **based** on the optimum generation cost and execution time for every test system. The proposed method is able to reduce the generation cost significantly especially for a large system (15-unit system). Also, this method is able to reach an optimum solution within less computation time. From this study, it can be concluded that the proposed TVAC-EPSO method can be an alternative approach in finding a better optimum solution for nonconvex ELD problems.

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In practice, real input–output characteristics present higher order nonlinearities and discontinuities due to valve-point loading effects caused by the sharp increase in losses when steam admission valves are first opened [2]. However, generating units may have prohibited operating zones due to faults in the machines themselves or the associated auxiliaries, such as boilers, feed pumps, leading to instabilities in certain ranges of the unit loading [3]. In addition, many generating units need the cost function to be modelled as piecewise function, due to their capability of operating with multi-fuel sources (coal, nature gas, or oil), leading to the problem of determining the most **economic** fuel to burn [4]. Furthermore, due to the fact that unit generation output cannot be changed instantaneously, the unit in the actual operating processes is restricted by its **ramp** **rate** limits [5]. Also, for security and reliability considerations of power systems, spinning reserve capacity must be sufficient to absorb source contingencies and major **load** forecast errors without **load** shedding [1, 6].

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In these models, the **ramp** **rate** limits and the denied zones of the units were considered. Out of available methods three methods utilized for solving the CEED with Generator Constraints. Firstly the problem is solved by PSO and compared with ordinary Lambda iterative method and a Binary coded Genetic Algorithm (GA). At individually sample system, under a similar assessment task and the individual definition, 50 preliminaries were made utilizing the PSO method and the best outcome is presented. A realistic misfortune coefficients grid of the influence framework organize was utilized to inducement the transmission line misfortune and delight the transmission limit choking influences. Programmed in MATLAB and executed on a PC @1.5 GHz. In spite of the fact that the PSO technique seems, by all accounts, to be a touchy to the tuning of certain confinements, as per the encounters of numerous examinations, the accompanying PSO and GA parameters can be utilized.1. GA Method

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The **economic** **load** **dispatch** (ELD) of power generating units has always occupied an important position in the electric power industry. The primary **objective** of ELD is to schedule the committed generating units output so as to meet the required **load** demand at minimum cost satisfying all unit and system operational constraints. The optimal ELD should meet **load** demand, generation limit, **ramp** **rate**, prohibited operating zone,[1,2] etc. For solving **economic** **load** **dispatch** various conventional methods like bundle method[3], non linear programming , mixed integer linear programming [4- 7], dynamic programming[5], quadratic programming [6] , Lagrange relaxation method [8], network flow method [9], direct search method [10] are used to solve such problems. When compared with the conventional (classical) techniques[10-11], modern heuristic **optimization** techniques **based** on operational research and artificial intelligence concepts, such as evolutionary algorithms [12-13], simulated annealing[14,15], artificial neural networks[16-18], and taboo search [19,20] have been given attention by many researchers due to their ability to find an almost global optimal solution for ELD problems with operation constraints. ELD problem is non linear, non convex type with multiple local optimal point due to the inclusion of valve point loading effect, multiple fuel options with diverse equality and inequality constraints. Dynamic programming method is one of the approaches to solve the non-linear and discontinuous ELD problem, but it suffers from the problem of “curse of dimensionality” or local optimality. Thus the conventional methods have failed to solve such problems as they are sensitive to initial estimates and converge into local optimal solution and computational complexity. Modern heuristic **optimization** techniques **based** on operational research and artificial intelligence concepts, such as simulated annealing[14-15], evolutionary programming [13] , genetic algorithm, tabu search [19-20], neural network, **particle** **swarm** **optimization** provides better solution.

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Unfortunately, for generating units with non-linear characteristics, such as prohibited operating zones, **ramp** **rate** limits, and non-convex cost functions, the conventional methods can hardly to obtain the optimal solution. Furthermore, for a large-scale mixed-generating system, the conventional method has oscillatory problem resulting in a local minimum solution or a longer solution time [4].

In the traditional **economic** **dispatch** problem, the cost function for each generator has been approximately represented by a single quadratic function and is solved **using** mathematical programming **based** on the **optimization** techniques [8]. Lagrange Relaxation (LR) method is commonly used to solve large scaled unit commitment problems. LR has been successfully applied to the complex unit commit problem including various hard constraints (**ramp** **rate** constraints, minimum up and down time, etc.). Unit commitment is a nonlinear mixed integer **optimization** problem. It schedules the operation of the generating units as minimum operating cost satisfying the demand and other constraints. [9]

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Different methods have been developed to solve ED problem in past few decade. Traditional algorithm such as lambda iteration, gradient method, Newton method, linear programming [3] and quadratic programming [4] has been implemented to solve ED problems. However, most of these methods are not capable of solving ED problem with non- convex and highly nonlinear solution space [5]. The practical ED problem with valve point effect, prohibited operating zone, multifuel options and **ramp** **rate** limit represent non- smooth or non-convex **optimization** problem (with equality and inequality constraints). This makes the finding of global optimum difficult and cannot be solve by traditional methods.

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Under the new deregulated electricity industry, power utilities try to achieve high operating efficiency to produce cheap electricity. High operating efficiency minimizes the cost of a kilowatt-hour to a consumer and the cost to the company delivering a kilowatt-hour in the face of constantly rising prices for fuel, labor, supplies, and maintenance. Operational economics involving power generation and delivery can be subdivided into two parts. **Economic** **dispatch** (ED), as one part is called, has the distribution of generated power at lowest cost as its main **objective**. Minimum-loss, as the second part is called, deals with minimum-loss delivery of the generated power to the loads. The ED of power generating units has always occupied an important position in the electric power industry. ED is a computational process where the total required generation is distributed among the generation units in operation, by minimizing the selected cost criterion, subject to **load** and operational constraints. For any specified **load** condition, ED determines the power output of each plant (and each generating unit within the plant) which will minimize the overall cost of fuel needed to serve the system **load** (Wood and Wollenberg 1996). ED is used in real-time energy

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Abstract: This paper proposes a modified **particle** **swarm** **optimization** considering time-varying acceleration coeﬃcients for the **economic**-emission **load** **dispatch** (EELD) problem. The new adaptive parameter is introduced to update the **particle** movements through the modification of the velocity equation of the classical **particle** **swarm** **optimization** (PSO) algorithm. The idea is to enhance the performance and robustness of classical PSO. The price penalty **factor** method is used to transform the multiobjective EELD problem into a single-**objective** problem. Then the weighted sum method is applied for finding the Pareto front solution. The best compromise solution for this problem is determined **based** on the fuzzy ranking approach. The IEEE 30-bus system has been used to validate the eﬀectiveness of the proposed algorithm. It was found that the proposed algorithm can provide better results in terms of best fuel cost, best emissions, convergence characteristics, and robustness compared to the reported results **using** other **optimization** algorithms.

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local and global explorations. Thus, hybrid PSO-GWO algorithm is proposed by combining the two algorithms. To prove the efficiency of the proposed algorithm, two different cases of IEEE standard generator system with and without VPL effects are considered and the proposed algorithm is compared with GWO and PSO. The main conclusions are: (i) the hybridization of PSO and GWO enhances the local search exploration capability of the algorithm, (ii) The convergence **rate** of the algorithm is higher for hybrid PSO-GWO in comparison to GWO and PSO, (iii) hybrid PSO- GWO is well efficient in solving the ELD problem, since in all cases; the minimum total cost of generation is obtained from this algorithm, (iv) PSO algorithm is found to be the worst performer in comparison to others, and (v) the performance of hybrid PSO-GWO is not affected from the change in test systems.

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Power system stability is the tendency of a power system to develop restoring forces equal to or greater than the disturbing forces to maintain the state of equilibrium. In this paper, improved QEMA **based** **particle** **swarm** **optimization** which is one of the computational algorithm technique is applied successfully to solve the problem of **economic** **load** **dispatch**. The **objective** function considered here is minimization of fuel cost of generators for IEEE 30 bus 6- generating systems used in thermal power plant.

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Abstract— The main goal of **economic** **load** **dispatch** (ELD) problem is to find an optimal operating condition for the committed generating units in order to minimize total operational cost while satisfying the constraints. The ELD problem becomes more complicated and non-convex when valve point effects of the generator are considered. The penalty function approach (PFA) is widely used to handle the constraints in ELD problem due to simple implementation. However, it requires a proper penalty **factor** tuning and provides inconsistent result. This paper investigates the performances of modification of infeasible **particle** (MIP) method **based** on **particle** **swarm** **optimization** (PSO) for solving ELD problem. The performances of MIP and PFA methods have been compared in terms of optimal result, convergence characteristic and robustness. The proposed MIP and PFA have been tested on three standard test systems (consists of 3, 6 and 40 generating units) to validate their effectiveness. The simulation result confirmed that MIP has better convergence characteristic and more robust compared to PFA. Therefore, the MIP approach can be applied in any **optimization** algorithm for solving constraint ELD problem effectively.

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Abstract- At the present time, the extensive use of fossil **based** fuels in power generation units requires the concern of the environmental pollution. The traditional **economic** power **dispatch** cannot meet the environmental safety requirements, since it focus only on minimizing the total fuel cost of the system. The multi-**objective** **optimization** in electric power systems treats **economic** and emission act as competing objectives, to reach an optimal solution some reasonable trade off among objectives are require. Therefore, in this paper, we investigated the environmental/**economic** power **dispatch** problem by employing a **particle** **swarm** **optimization** algorithm. The power **dispatch** is formulated into a bi-**objective** **optimization** problem, which is to minimize the fuel cost as well as pollutant emission simultaneously. Two objectives are merged in to a single **objective** by **using** the weighted sum method. Best cost and best emission solution are obtained for different loading conditions for the standard IEEE 118-bus, 14 generating units system.

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The Combined **Economic** emission **load** **dispatch** problem(CEEDP) is one of the fundamental issue in power systemoperation.In recent years the **economic** **dispatch** problem has become increasingly concerned with environment matters due to emission of several contaminants such as sulphur dioxide (SO2) ,oxides of Nitrogen (NOx) into atmosphere from fossil fuels generating units. The CEEDP has been usually considered as the minimization of an **objective** function representing the generation cost and/or the transmission loss. The constraints involved are the physical laws governing the power generation-transmission systems and the operating limitations of the equipment.

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Abstract—This paper presents grey wolf **optimization** method for solving multi-**objective** **economic** emission **load** **dispatch** (EELD) problem in diverse test power systems. Grey Wolf **Optimization** (GWO) is a new meta-heuristic motivated from grey wolf. Diverse emission gases considered for the case studies are SOx, NOx and COx. GWO is applied on diverse test cases for finding EELD solution. Comparison of the obtained results is carried out with other techniques stated in literature which shows that GWO is effective to solve EELD.

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Abstract: In the previous decade, numerous endeavors have been made to tackle the ELD issue, and different sorts of requirements or targets have been joined through different scientific arranging and **optimization** systems. Customary techniques incorporate the Newton-Raphson strategy, the Lambda iterative technique, the Base Point and Partitionation **Factor** strategies, the Gradient strategy, and so forth. Be that as it may, these exemplary scheduling algorithms require gradual cost bends to be monotonically expanding or fragment by-section direct. Considering the profoundly nonlinear nature of the component requires a very strong algorithm to abstain from adhering to neighborhood ideal. Since the issue is non-direct, procedures dependent on established analytics can't take care of these kinds of issues. The **objective** of the research paper is to design and simulate quantum computing **based** modified **particle** **swarm** **optimization** for multi **objective** emission and **economic** **dispatch** problem. The algorithm has been tested on multiple test systems with valve point loading cost function as well. Results have been compared with contemporary research and found to be efficient in comparative assessment on same test system.

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important plan here is to reduce the production cost while maintaining the varying **load** demand at any time of the day. **Economic** **Load** **Dispatch** focus upon the coordination of the production cost by proper scheduling of all power plants operating on the system. The power generation must vary according to the **load** demand, which may vary with season. It is therefore illogical to suppose that the same level of power must be generated at all time. Therefore the **economic** operation gave the explanation of the **load** condition at all times. There is a need of method which calculates the total generating cost of all the units which supplying a **load**. Conventional methods are like linear programming, newton method, gradient method, mixed –integer programming ,quadratic programming , newton flow programming developed to evaluate the ED problems. These methods fails due to their convergence characteristics. So, Intelligence search and non-quantity methods such as neural network, evolutionary algorithms, tabu search, **particle** **swarm** **optimization**, fuzzy set applications , analytic hierarchical process are used to solve ED problems. Many of these methods are inspired by **swarm** behaviour in nature. **Optimization** problems requires high-dimensional search space, the conventional **optimization** algorithms do not give a appropriate solution because the search space increases exponentially as according to the size of problem, therefore solving these problems by **using** intelligence **swarm** behaviour. The gravitational search algorithm is a novel heuristic **optimization** method **based** on mass interaction and law of gravity. This algorithm has good ability to search the global best solution , but it suffer the slow searching speed in the last iterations. The goal is to find the global best outcome among all the possible inputs. Two main characteristics are required for global best outcome exploration and exploitation. Exploration is the skill of an algorithm to search whole parts of problem space whereas exploitation is the ability to give best solution. So , there is a new hybrid model presents which combines the PSO and GSA algorithms named as PSOGSA. Some standard test functions are used to compare the hybrid algorithm with some other techniques.

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