Economy, reliability and environmental friendly are primary goals when modeling modern unitcommitmentproblems. In this study, we establish a multi-objectiveunitcommitment model considering the above objectives. In...
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ISBN:
(纸本)9781538604854
Economy, reliability and environmental friendly are primary goals when modeling modern unitcommitmentproblems. In this study, we establish a multi-objectiveunitcommitment model considering the above objectives. In particular, the pricing support for ultra-low emissions is addressed together with startup/shutdown, generation and environment concerns when calculating the operation cost of thermal units, which conforms the present situation of power markets, especially in China. To solve the complicated nonlinear model, a multi-objective particle swarm optimization algorithm is developed. Finally, a series of experiments were performed on a modified 26-thermal-unit test system, which demonstrates the superiority of this research.
Restructuring of power system stresses the need for economic and reliable generation of power. Therefore generating units should be committed considering fuel cost and reliability level of the system. This necessitate...
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Restructuring of power system stresses the need for economic and reliable generation of power. Therefore generating units should be committed considering fuel cost and reliability level of the system. This necessitates the need for multi-objectives to be met in a unitcommitmentproblem (UCP). Since the above objectives are conflicting in nature, a novel methodology employing optimal deviation based firefly algorithm tuned fuzzy membership function is applied to multi-objective unit commitment problem (MOUCP). The ON/OFF status of the generating units is obtained by binary coded FF whereas the sub-problem economic dispatch (ED) is obtained by real coded FF. Here the conflicting functions are formulated as a single objective function using fuzzy weighted optimal deviation. The fuzzy membership design variables are tuned using real coded FF;thereby the requirement of expertise for setting these variables are eliminated. The proposed methodology is validated on 100-unit system, IEEE RTS 24-bus system, IEEE 118-bus system and a practical Taiwan Power (Taipower) 38-unit system over a 24-h period. Effective strategy on scheduling spinning reserve is demonstrated by comparing its performance with other methods reported in the literature.
This article proposes a hybrid cuckoo search algorithm (CSA) integrated with fuzzy system for solving multi-objective unit commitment problem (MOUCP). The power system stresses the need for economic, non-polluting and...
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This article proposes a hybrid cuckoo search algorithm (CSA) integrated with fuzzy system for solving multi-objective unit commitment problem (MOUCP). The power system stresses the need for economic, non-polluting and reliable operation. Hence three conflicting functions such as fuel cost, emission and reliability level of the system are considered. CSA mimics the breeding behavior of cuckoos, where each individual searches the most suitable nest to lay an egg (compromise solution) in order to maximize the egg's survival rate and achieve the best habitat society. Fuzzy set theory is used to create the fuzzy membership search domain where it consists of all possible compromise solutions. CSA searches the best compromise solution within the fuzzy search domain simultaneously tuning the fuzzy design boundary variables. Tuning of fuzzy design variables eliminate the requirement of expertise needed for setting these variables. On solving MOUCP, the proposed binary coded CSA finds the ON/OFF status of the generating units while the real coded CSA solves economic dispatch problem (EDP) and also tunes the fuzzy design boundary variables. The proposed methodology is tested and validated for both the single and multi-objective optimization problems. The effectiveness of the proposed technique is demonstrated on 6, 10, 26 and 40 unit test systems by comparing its performance with other methods reported in the literature. (C) 2012 Elsevier B.V. All rights reserved.
Restructuring of power system has drastically changed the mechanism of reliability management in solving the unitcommitmentproblem (UCP). In general, operating reserve capacity is predetermined either by dispatch ru...
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Restructuring of power system has drastically changed the mechanism of reliability management in solving the unitcommitmentproblem (UCP). In general, operating reserve capacity is predetermined either by dispatch rules or predefined by reliability index. This paper considers that operating reserve capacity in a power system is bendable which is based on the costreliability issues. In UCP, these two competing objectives such as fuel cost and reliability level of the system are optimized simultaneously as a multiobjectiveunitcommitmentproblem (MOUCP) using the proposed fuzzy adapted firefly (FF) algorithm. The ON/OFF status of the generating units is obtained by binary coded FF, where as the economic dispatch is obtained by Lambda iteration method. The fuzzy design boundary variables are tuned, using real coded FF, thereby the requirement of expertise for setting these variables are eliminated. The proposed methodology is validated on IEEE RTS 26 unit test system. The effectiveness of the proposed technique is demonstrated by comparing its performance with other methods reported in the literature.
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