• Title, Summary, Keyword: Transmission losses and Quadratic fuel cost function

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Multi-Area Economic Dispatch Considering Environmental Emission and Transmission Losses (연계 계통에서의 환경적 배출량과 손실을 고려한 최적 경제급전)

  • Choi, Seung-Jo;Rhee, Sang-Bong;Kim, Kyu-Ho;You, Seok-Ku
    • Proceedings of the KIEE Conference
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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.

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Secant Method for Economic Dispatch with Generator Constraints and Transmission Losses

  • Chandram, K.;Subrahmanyam, N.;Sydulu, M.
    • Journal of Electrical Engineering and Technology
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    • v.3 no.1
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    • pp.52-59
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    • 2008
  • This paper describes the secant method for solving the economic dispatch (ED) problem with generator constraints and transmission losses. The ED problem is an important optimization problem in the economic operation of a power system. The proposed algorithm involves selection of minimum and maximum incremental costs (lambda values) and then the evaluation of optimal lambda at required power demand is done by secant method. The proposed algorithm has been tested on a power system having 6, 15, and 40 generating units. Studies have been made on the proposed method to solve the ED problem by taking 120 and 200 units with generator constraints. Simulation results of the proposed approach were compared in terms of solution quality, convergence characteristics, and computation efficiency with conventional methods such as lambda iterative method, heuristic methods such as genetic algorithm, and meta-heuristic methods like particle swarm optimization. It is observed from different case studies that the proposed method provides qualitative solutions with less computational time compared to various methods available in the literature.