This work studies a robust demand dispatch tool based on a stochastic unit commitment algorithm. Demand dispatch is formulated in the context of a small grid with partially flexible demand that can be shifted along a ...
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ISBN:
(纸本)9781467356688
This work studies a robust demand dispatch tool based on a stochastic unit commitment algorithm. Demand dispatch is formulated in the context of a small grid with partially flexible demand that can be shifted along a time horizon. It is assumed that the grid operator dispatches generation and flexible demand along the time horizon aiming at minimizing generation costs. The load not dispatched by the operator is not known with certainty, and is represented as a stochastic parameter in the optimization problem. Consumption restrictions associated with flexible demand are modeled by equality energy constraints. The performance of three evolutionary algorithms, the particle swarm optimization, the differential evolution algorithm and a hybrid algorithm derived from the previous, is presented.
Most supply chain programming problems are restricted to the deterministic situations or stochastic environments. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertai...
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Most supply chain programming problems are restricted to the deterministic situations or stochastic environments. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved Particle Swarm Optimization (PSO) algorithm based on the differentialevolution (DE) algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.
This work focuses on proposing a new algorithm, referred as HMA (Hybrid Metaheuristic algorithm) for the solution of the WTO (Wind Turbine Optimization) problem. It is well documented that turbines located behind one ...
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This work focuses on proposing a new algorithm, referred as HMA (Hybrid Metaheuristic algorithm) for the solution of the WTO (Wind Turbine Optimization) problem. It is well documented that turbines located behind one another face a power loss due to the obstruction of the wind due to wake loss. It is required to reduce this wake loss by the effective placement of turbines using a new HMA. This HMA is derived from the two basic algorithms i.e. DEA (differential evolution algorithm) and the FA (Firefly algorithm). The function of optimization is undertaken on the N.O. Jensen model. The blending of DEA and FA into HMA are discussed and the new algorithm HMA is implemented maximize power and minimize the cost in a WTO problem. The results by HMA have been compared with GA (Genetic algorithm) used in some previous studies. The successfully calculated total power produced and cost per unit turbine for a wind farm by using HMA and its comparison with past approaches using single algorithms have shown that there is a significant advantage of using the HMA as compared to the use of single algorithms. The first time implementation of a new algorithm by blending two single algorithms is a significant step towards learning the behavior of algorithms and their added advantages by using them together.
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