to be minimized (Chopra, Kumar, & Mehta, 2016). This is a complex problem to solve because of its large size, a nonlinear objective function and a wide number of restrictions (Bhattacharya & Chattopadhyay, 2010). Various evolutionary, heuristic and meta-heuristics optimization algorithms have been developed such as: Grey Wolf Optimization (GWO) (Chopra et al., 2016; Hong, MH, & Mohd Rusllim, 2014), non-dominated sorting genetic algorithm (NSGA-II) (Basu, 2008; Moraes et al., 2018), hybrid genetic algorithm (Thenmozhi & Mary, 2004), Tabu Search Algorithm (Li, Yang, Tseng, Wang, & Lim, 2018), Simulated annealing (Júnior, Nunes, Nascimento, Rodríguez, & Leite, 2017; Ziane, Benhamida, & Graa, 2017), Neural Networks (Deng, He, & Zeng, 2017), Harmony Search Algorithm (El Ela, El- Sehiemy, Shaheen, & Shalaby, 2017), particle swarm optimization (De et al., 2018), Differential Evolution (Jebaraj, Venkatesan, Soubache, & Rajan, 2017), **Ant** Colony Optimization (Zhou et al., 2017), Biogeography- Based Optimization (Ma, Yang, You, & Fei, 2017), genetic algorithm controlled by fuzzy logic (Song, Wang, Wang, & Johns, 1997).

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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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About one year ago, a new technique has been added to the meta-heuristic optimization approaches field, based on simulating of the hunting behaviour of antlions. This article proposes the use of **ant** **lion** optimization algorithm for solving the ORPD problem with an improved voltage stability index in power systems. The medium-scale, larger and large-scale test systems namely IEEE- 30, IEEE-118 and IEEE 300-bus are selected to demonstrate the per- formance of the proposed **approach**. The obtained results by **using** ALO are compared with other results of recent published algo- rithms. Therefore, the results prove the consistency and robustness

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Abstract: Environmental pollution is on the increase due to industrial advancement. Due to the environmental concerns that arise from the emissions produced by fossil-fueled electric power plants, the classical **economic** **dispatch**, which operates electric power systems so as to minimize only the total fuel cost, can no longer be considered alone. Thus, by environmental **dispatch**, emissions can be reduced by **dispatch** of power generation to minimize emissions. The **economic**- **emission** **load** **dispatch** problem has been most commonly solved **using** a deterministic **approach**. However, power generated, system loads, fuel cost and **emission** coefficients are subjected to inaccuracies and uncertainties in real-world situations.

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Abstract: Environmental pollution is on the increase due to industrial advancement. Due to the environmental concerns that arise from the emissions produced by fossil-fueled electric power plants, the classical **economic** **dispatch**, which operates electric power systems so as to minimize only the total fuel cost, can no longer be considered alone. Thus, by environmental **dispatch**, emissions can be reduced by **dispatch** of power generation to minimize emissions. The **economic**- **emission** **load** **dispatch** problem has been most commonly solved **using** a deterministic **approach**. However, power generated, system loads, fuel cost and **emission** coefficients are subjected to inaccuracies and uncertainties in real-world situations. This paper describe and Introduce a new nature Inspired Artificial Intelligence method called Firefly Algorithm(FA). The Firefly Algorithm is a stochastic Meta heuristic **approach** based on the idealized behavior of the flashing characteristics of fireflies. The aim is to minimize NOx **emission** and the generating unit’s combined fuel cost having quadratic cost characteristics subjected to limits on generator real power output & transmission losses. This paper presents an application of the FA to EELD for different Test Case system. The obtained solution quality and computation efficiency is compared to another artificial intelligence technique, called Genetic algorithm (GA) . The simulation results show that the proposed algorithm outperforms previous artificial intelligence method.

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The chief goal of EELD is to get optimum output of thermal generators in power system subjected to several constraints to diminish the operating costs. The thermal power plant operation is dependent upon incineration of fossil fuel which generates SOx, NOx and COx **emission**. The increasing pollution is a matter of environmental concern worldwide which has led to formation of international standards for emissions from industries and power plants. Different acts have been made which forces the industries to modify their principles to follow the environment-**emission** standards strictly. Therefore it is significant to consider **emission** constraint in **economic** **dispatch**. The **economic** & **emission** **dispatch** are contradictory in character and both must be considered together to find optimal **dispatch**. The problem is formulated as a multiobjective **economic** **emission** **load** **dispatch** (EELD) problem in which both the objectives (**emission** and economy) have to be minimized. Earlier traditional methods like Newton’s method, gradient **approach** and linear programming [1] were used for solving ELD problem. In the last years different techniques have been used for solving EELD. Nanda et.al [2] applied goal programming techniques for solving EELD. Song et.al [3] solved environmental/**economic** **dispatch** with genetic algorithm controlled by fuzzy logic. Abido [4] used genetic

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can no longer be considered alone. So Environmental **Economic** **dispatch** is a multi-objective problem. Well known long established techniques such as integer programming, dynamic programming and legarangian relaxation method[1-2] have been used to solve **economic** **load** **dispatch** problem. Nanda et al. solved **economic**-**emission** problem **using** goal programming technique for a system having six generator[3]. Nanda et al. also applied classical technique based on coordinated equation to obtain **economic** **emission** **load** **dispatch** for IEEE14 and 30 bus system[4].Dhillon et al.[5] applied weighted minimax technique and fuzzy set theory to find out solution. Recently other optimization method such as Genetic algorithm[7],Artificial Bee Colony Optimization[8], Modified **Ant** Colony Optimization[9] are applied for ceed solution,. Swarm intelligence algorithms [12 -16] is also applied by researchers. Niched parato genetic algorithm is also reported for optimum solution for ceed problem.

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In this paper, a genetic algorithm (GA) **approach** is presented for optimal solution of combined **economic** **emission** **load** **dispatch** (CEELD) problem. Fuel cost and **emission** are considered to formulate the multi-objective optimization problem. An optimal trade-off between fuel cost and **emission** is obtained **using** genetic algorithm. Two test systems are considered to show the effectiveness of the GA **approach**. An extensive analysis is done by presenting a short term thermal generation scheduling for the Test system-1.

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In this proposed research work, a new form of multi-area power system with a combination of thermal, hydro and PV sources. A new algorithm proposed for **load**-frequency control, which can both reduce control time and diminish the value of frequency deviation during the active operation of power systems. By developing of in- dustrial controllers, Proportional-Integral (PI) controllers is still one of the most popular controllers. A new ap- proach addressed for **load**-frequency control of interconnected three area power systems by **using** an **Ant** **Lion** **Optimizer** (ALO) algorithm in this paper. The algorithm applied to optimize the PI parameters. Also, to adjust the PI controller, the ITAE is used as a cost function. The ITAE criterion was chosen due to it can determine a healthy weight for error signal in terms of time. This event can reduce settling time in the lowest value and damp fluctuations, quickly [24]. In the proposed method, the Area Control Error (ACE) is also fixed and worked out by the feedback in each area; and therefore, the control action is done to set the ACE in zero value. As a result, frequency and tie power among areas are prevented in the stipulated values.

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[6] Mugdha Udgir, Hari Mohan Dubey, Manjaree Pandit , Gravitational Search Algorithm: A Novel Optimization **Approach** for **Economic** **Load** **Dispatch**” International Conference on Microelectronics, Communication and Renewable Energy (2013) [7] Xin-She Yang, Mehmet Karamanoglu, Xingshi He, Multi-objective Flower Algorithm for Optimization International Conference on Computational Science, ICCS (2013)

Earlier traditional methods like Newton’s method, gradient **approach** and linear programming [1] were used for solving **economic** **load** scheduling problem. In the last years different techniques have been used for solving EELD. Nanda et al [2] applied goal programming techniques to solve **economic** **emission** **load** **dispatch**. Song et.al [3] solved environmental/**economic** **dispatch** with genetic algorithm controlled by fuzzy logic. Abido [4] used genetic algorithm for the **economic** **emission** **load** **dispatch** (EELD) to find out pareto-optimal solutions. Ah King [5, 6] applied improved non-dominated sorting genetic algorithm (NSGA-II) for creating pareto-optimal front for EELD. Then mozhi [7] solved EELD **using** hybrid genetic algorithm. Perez [8] solved environmental/**economic** **dispatch** **using** differential evolution. Hong [9] applied immune genetic algorithm for EELD. Hazra [10] proposed bacteria foraging algorithm for **emission** constrained **economic** **dispatch**. Hemamalini [11] solved non convex EELD by applying particle swarm optimization. Sudhakaran applied refined genetic algorithm[12] and hybrid genetic algorithm[13] for solving EELD problem. Bhattacharya.et.al [14] presented a BBO technique to solve EELD of thermal generators with different **emission** substances (SOx, NOx, &COx).Niknam [15]proposed teaching learning based algorithm for dynamic EED. Abedinia [16] applied firefly algorithm (FFA) for EELD.

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With respect to constraint optimization problems, there are several approaches to handle constrained problem, one of the most popular **approach** often used is the penalty function [1]. The idea of penalty function is transform constraint optimization problem into an unconstrained problem by adding or subtracting value to the objective function this value called penalty term. There are many methods in penalty function **approach** to handle constraint problem such as: static penalty, dynamic penalty, adaptive penalty [2]. Each of this method follows general idea of penalty function **approach**, but the difference between them is the form of the penalty term.

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[4], dynamic programming [6], linear programming [7], quadratic programming [9], Lagrange relaxation method [8], Newton-based techniques [10], reported in the literature are used to solve such problems. Conventional methods have many draw back such as nonlinear programming has complex in nature. Linear programming **approach** is fast in operation but require linearization of objective function as well as constraints with non-negative variables. Quadratic programming is a special form of nonlinear programming which has some disadvantages associated with piecewise quadratic cost approximation. Newton-based method has a drawback of the convergence characteristics that are sensitive to initial conditions. The interior point method is computationally efficient but suffers from bad initial termination and optimality criteria. Recently, different heuristic approaches have been proved to be effective with promising performance, such as evolutionary programming **approach** [11], simulated annealing **approach** (SA) [12], Tabu search **approach** (TS) [13], pattern search (PS) [14], Genetic algorithm (GA) [15], Differential evolution (DE) [16], **Ant** colony optimization [17], Neural network [18], particle swarm optimization (PSO) [19], [20], [21], modified particle swarm optimization MPSO [24],SHOPSO [22], WIPSO [28],MOPSO [29]. Although the heuristic methods do not always guarantee discovering globally optimal solutions in a limited time, but also provide practical solution. EP is rather slow converging to a near optimum for some problems. SA is very time consuming, and cannot be utilized easily to tune the control parameters of the annealing schedule. TS difficult in defining effective memory structures and

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sweep is a modern technique which widely utilized in recent years. Because of its accuracy, and simplicity, it is used in this paper to calculate power flow analysis of any sophisticated system **using** the following algebraic formulas supported with simple diagram as Fig. 1 Power flow solution is used in the planning and design stages as well as during the operating stages. Two matrices are developed to obtain **load** flow solution.

In 1992, Marco Dorigo introduced a probabilistic algo- rithm known as **Ant** Colony Optimization (ACO) tech- nique. In his PhD thesis, he described that ACO resem- bles the natural behavior of a colony of **ant** during their random expedition to find the best path between their nest and food source. The **ant** will deposit a chemical trace known as pheromone. The pheromone will act as the stimulant to attract more ants to utilize on the same path. Any less-traveled paths will be forgotten since their pheromone traces has been evaporated. Marco Dorigo employed this behavior into his research to solve the travelling salesmen problem (TSP) [1]. Since, ACO has attracted many researchers to employ the algorithm into their research. Mohd Rozeli Kalil et. al successfully im- plements ACO to gain maximum loadability in voltage control study [2]. Ashish Ahuja and Anil Pahwa [3] stated in their research that ACO significantly minimized the loss in a distribution system. Moreover, D. Nualhong et. al utilizes ACO in his research to solve the unit com- mitment problems [4]. However, further research on the algorithm indicates that ACO suffers from several short-

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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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Generating the correct amount of electric supply to the consumers is crucial among energy providers. By doing so, the utilities will enjoy maximized profits at a feasible operating cost and consumers will receive satisfying amount of energy. The utilities mainly use **economic** **load** **dispatch** to strategize their energy dispatching program [1]. **Economic** **load** **dispatch** (ELD) aims for producing the correct amount of energy among the available generating units to serve the **load** demand at the most feasible cost [2], [3] and presented as a quadratic mathematic equation. In order to feasibly generate the correct amount of electrical energy, several considerations is taken into account when solving ELD problem, including the prohibited operating zones, valve-point effect, ramp-rate limits, and **emission** constraints [4] – [7]. Neglecting these considerations will cause inaccurate ELD. Nonetheless, these considerations will cause ELD to become complex in terms of mathematical modelling and challenging to solve [8]. For example, prohibited operating zones will cause the fuel-cost curve to separate into several segments and form multiple decision spaces [9]. Valve-point effect will cause ripples to the fuel-cost curve and increase the non-linearity of ELD problem [10]. Ramp-rate is the rate of a generator changing its output. It requires a dynamic process of **economic** **dispatch** that varies with time and causes the curve to become non-convex curve [11], [12].

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The **economic** **load** **dispatch** for the first test case with the corresponding loads is given as 585 MW, 700 MW and 800 MW, respectively [25]. The proposed PSO method is applied to obtain the minimum generation cost. Table 4.2 provides the results of optimal scheduling of generators obtained by Classical PSO method for three thermal unit system losses are neglected.

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In Dynamic **Economic** **Load** **Dispatch** (DELD), optimization and evolution computation become a major part with the strategy for solving the issues. From various algorithms Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms are used to encode in a vector form and in sharing information and both approaches are based on the master-apprentice mechanism for the Dual Evolution Strategy. In order to overcome the challenges like the clustering of PSO, optimiza- tion problems and maximum and minimum searching, a new **approach** is developed with the im- provement of searching and efficient process. In this paper, an Enhanced Hybrid Differential Evo- lution and Particle Swarm Optimization (EHDE-PSO) is proposed with Dynamic Sigmoid Weight us- ing parallel procedures. A hybrid form of the proposed **approach** combines the optimizing algorithm of Enhanced PSO with the Differential Evolution (DE) for the improvement of computation **using** pa- rallel process. The implementation and the parallel process are analyzed and discussed to gather relevant data to show the performance enhancement which is better than the existing algorithm.

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