• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1498-1506
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• 1999

In this paper, a hybridization of Evolutionary Strategy (ES) and a Two-Phase Neural Network(TPNN) is applied to the optimal environmental and economic operation. As the evolutionary computation, ES is to search for the global optimum based on natural selection and genetics but it shows a defect of reducing the convergence rate in the latter part of search, and often does not search the exact solution. Also, neural network theory as a local search technique can be used to search a more exact solution. But it also has the defect that a solution frequently sticks to the local region. So, new algorithm is presented as hybrid methods by combining merits of two methods. The hybrid algorithm has been tested on Emission Constrained Economic Dispatch (ECED) problem and Weighted Emission Economic Dispatch (WEED) problem for optimal environmental and economic operation. The result indicated that the hybrid approach can outperform the other computational efficiency and accuracy.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1100-1102
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• 1998

This paper presents the Two-Phase Neural Network(TPNN) to slove the Optimal Economic Environmental Dispatch problem of thermal generating units in electric power system. The TPNN, Compared with other Neural Networks, is very accurate and it takes smaller computer time for a optimization problem to converge. In this work, in order to provide useful information to the system operator, we are used the total environmental weight and relative weighting of individual insults(e.g., $SO_2$, $NO_X$ and $CO_2$) also, presented the simulation results of the dispatch changes according to the weights. The Two-Phase Neural Network is tested on a 11-unit 3-pollutant system to prove of effectiveness and applicability.

• Journal of Energy Engineering
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• v.6 no.2
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• pp.129-136
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• 1997

This paper presents a new economic load dispatch considering atmospheric emissions such as NOx and SO$_2$caused by the operation of fossil-fueled thermal generation in power systems. The proposed method is described for scheduling their output of thermal power units so as to comply with total emission constraint, area emission constraint and the both of those constraints. Also, by using a trade-off curve, representing all dispatch alternatives and conflict between the emission and the fuel cost, the sensitivity analysis of the emission and the fuel cost is applied to this algorithm. By the way, this proposed method is analyzed how dispatch changes as a function of the total environmental cost, and as a function of the relative weighting of individual environmental insults, e.g, NOx and SO$_2$. By applying the proposed method to the system, the usefulness of this method is verified.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.574-577
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• 1995

The proposed method is described for scheduling their output of thermal power units so as to comply with total emission constraint, area emission constraint and the both of those constraints. Also, by using a trade-off curve, representing all dispatch alternatives and conflict between the emission and the fuel cost, the sensitivity analysis of the emission and the fuel cost is applied to this algorithm. By the way, this proposed method is analyzed how dispatch changes as a function of the total environmental cost, and as a function of the relative weighting of individual environmental insults, e.g, NOx and $SO_{2}$. By applying the proposed method to the system, the usefulness of this method is verified.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.3
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• pp.395-402
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• 2009

This paper presents the newly developed Pareto-based multi-objective Bees Algorithm with weighted sum technique for solving a power system multi-objective nonlinear optimization problem. Specifically, the Pareto-based Bees Algorithm with memorized zone has been developed to alleviate both difficulties from classical techniques and intelligent techniques for multi-objective problems (MOP) and successfully applied to an Environmental/Economic (electric power) dispatch (EED) problem. This multi-objective Bees Algorithm has been examined and applied to the standard IEEE 30-bus six-generator test system. Simulation results have been compared to those obtained using other approaches. The comparison shows the potential and effectiveness of the proposed Bees Algorithm for solving the multi-objective EED problem.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.3
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• pp.403-410
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• 2009

The aim of this research is to develop the Bees Algorithm named 'the dynamic allocated Bees Algorithm' for multi-objective problem, especially in order to be suit for Pareto optimality. In addition two new neighbourhood search methods have been developed to produce enhanced solutions for a multi-objective problem named 'random selection neighbourhood search' and 'weighted sum neighbourhood search' and they were compared with the basic neighbourhood search in the dynamic allocated Bees Algorithm. They were successfully applied to an Environmental/Economic (electric power) dispatch (EED) problem and simulation results presented for the standard IEEE 30-bus system and they were compared to those obtained using other approaches. The comparison shows the superiority of the proposed dynamic allocated Bees Algorithms and confirms its suitability for solving the multi-objective EED problem.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.341-343
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• 2000

Traditionally electric power system are operated in such a way that the total fuel cost is minimized regardless of accounting for tie-lines transmission constraint and emissions produced. But tie-lines transmission and emissions constraint are very important issues in the operation and planning of electric power system. This paper presents the Two-Phase Neural Network(TPNN) to solve the Economic Load Dispatch (ELD) problem with tie-lines transmission and emissions constraint considering transmission losses. The transmission losses are obtained from the B-coefficient which approximate the system losses as s quadratic function of the real power generation. By applying the proposed algorithm to the test system, the usefulness of this algorithm is verified.

• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.55-65
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• 2009

Due to the rapid increase of electricity demand, consideration of environmental constraints in optimal power flow (OPF) problems is increasingly important. In Algeria, up to 90% of electricity is produced by thermal generators (vapor, gas). In order to keep the emission of gaseous pollutants like sulfur dioxide (SO2) and Nitrogen (NO2) under the admissible ecological limits, many conventional and global optimization methods have been proposed to study the trade-off relation between fuel cost and emissions. This paper presents an efficient decomposed Parallel GA to solve the multi-objective environmental/economic dispatch problem. At the decomposed stage the length of the original chromosome is reduced successively and adapted to the topology of the new partition. Two subproblems are proposed: the first subproblem is related to the active power planning to minimize the total fuel cost, and the second subproblem is a reactive power planning design based in practical rules to make fine corrections to the voltage deviation and reactive power violation using a specified number of shunt dynamic compensators named Static Var Compensators (SVC). To validate the robustness of the proposed approach, the algorithm proposed was tested on the Algerian 59-bus network test and compared with conventional methods and with global optimization methods (GA, FGA, and ACO). The results show that the approach proposed can converge to the near solution and obtain a competitive solution at a critical situation and within a reasonable time.

• Journal of information and communication convergence engineering
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• v.1 no.2
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• pp.93-97
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• 2003

This paper proposes a new generation scheduling algorithm which can consider total quantity of contamination and energy limits based on Lagrangian Relaxation. First, we formulate a Lagrangian function which consider quantity of contamination including substitution of fuel, and quantity of fuel. Second, we developed Multi-state S.U.D.P. which can treat multiple fuels. Third, we propose an effective generation scheduling algorithm including Multi-state S.U.D.P.. As a results, unit commitment and generating power can be obtained to meet the environmental and fuel constraints with fuel substitution. By applying it to the test system, effectiveness of the method is verified.

• Journal of Energy Engineering
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• v.13 no.2
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• pp.128-132
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• 2004

This paper presents a new methodology to get pareto-optimal solution for generation planning. First, we apply dynamic programming, and we can get an optimal economic dispatch considering total quantity of contamination for the specified term. Second, we developed a method which can get pareto-optimal solution. This solution is consisted of a set of optimal generation planning. As a result, decision maker can get pareto-optimal solutions, and can choose a solution. We applied this method to the test system, and showed the usefulness.