Bi-Objective Generation Scheduling of Fixed Head Hydrothermal Power Systems through an Interactive Fuzzy Satisfying Method and Particle Swarm Optimization
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Mousumi Basu
This paper presents particle swarm optimization based interactive fuzzy satisfying method for bi-objective generation scheduling of fixed head hydro plants and thermal plants with nonsmooth fuel cost and emission level functions. The bi-objective problem is formulated considering two objectives (i) economy and (ii) emission. These two objectives are mutually conflicting and equally important. Assuming that the decision maker has imprecise or fuzzy goals for each of the objective functions, the bi-objective problem is transformed into a minimax problem, which is then handled by the particle swarm optimization technique. The solution methodology can offer a global or near-global noninferior solution for the decision maker. Numerical results for a sample test system have been presented to demonstrate the performance and applicability of the proposed method. The results obtained from the proposed method are compared to those found by interactive fuzzy satisfying method based on evolutionary programming technique.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- Application and Comparison of Metaheuristic Techniques to Generation Expansion Planning Incorporating Both Bilateral and Multilateral Transactions
- Bi-Objective Generation Scheduling of Fixed Head Hydrothermal Power Systems through an Interactive Fuzzy Satisfying Method and Particle Swarm Optimization
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Articles in the same Issue
- Article
- Application of Web Services in SCADA Systems
- Application and Comparison of Metaheuristic Techniques to Generation Expansion Planning Incorporating Both Bilateral and Multilateral Transactions
- Bi-Objective Generation Scheduling of Fixed Head Hydrothermal Power Systems through an Interactive Fuzzy Satisfying Method and Particle Swarm Optimization
- Hourly Emission Target Constrained Economic Dispatch - A Renewable Energy Approach
- Power System Events Classification using Pattern Recognition Approach
- A Neural Network Based Automatic Generation Controller Design through Reinforcement Learning
- IEC 60060-1 Requirements in Impulse Current Waveform Parameters
