In order to fit in with the demands of the development of electricity market in China, a multi-objectiveoptimization mathematical model is presented to dispatch load within the units, taking economy, environmental pr...
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ISBN:
(纸本)9783037859728
In order to fit in with the demands of the development of electricity market in China, a multi-objectiveoptimization mathematical model is presented to dispatch load within the units, taking economy, environmental protection and quick responsiveness to dispatching commands into consideration at the same time. And take the minimal whole plant's power-supply coal cost rate, the minimal pollutant emissions and the minimal load adjusting time as these three objective functions respectively. The four constraint conditions are unit power balance constraint, load bound constraint, ramping constraint and pollution discharge standards constraint. An improved particleswarmoptimizationalgorithm is used to get the Pareto solution set. The optimal solution was obtained by using the method of multi-attribute decision making, through sequencing the solution set by comprehensive evaluation. A case study based on a power plant with 4x600MW units was carried out. The result shows that the method can solve the multi-objective optimal load distribution problem accurately and quickly, and get the good effect in energy conservation and emissions reduction.
In this paper, a joint redundancy and imperfect block opportunistic maintenance optimization model is formulated. The objective is to determine the wind farm redundancy level and the maintenance strategy which will si...
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In this paper, a joint redundancy and imperfect block opportunistic maintenance optimization model is formulated. The objective is to determine the wind farm redundancy level and the maintenance strategy which will simultaneously minimize the wind farm loss of load probability and life cycle cost. A new opportunistic maintenance approach is developed to take advantages of the maintenance opportunities. Different reliability thresholds are introduced for imperfect maintenance of failed turbines and working turbines and preventive dispatching of maintenance teams. In addition, a simulation method is developed to evaluate the performance measures of a wind farm system considering different types of wind turbine, maintenance activation delays and durations, and limited number of maintenance teams. Sensitivity analysis is conducted to discuss the influence of the different assumption and parameters of simulation model over the wind farm performance. Pareto optimal solutions are driven based on a multi-objective particle swarm optimization algorithm. Comparative study with the commonly used maintenance policy demonstrates the advantages of the proposed opportunistic maintenance strategy in significantly reducing maintenance cost and loss of load probability. (C) 2016 Elsevier Ltd. All rights reserved.
Through multi-particleswarmoptimizationalgorithm, this paper is aimed to solving the optimization problems of multi-production and multi-marketing strategy selection during the process of integrated marketing. In o...
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ISBN:
(纸本)9783642015090
Through multi-particleswarmoptimizationalgorithm, this paper is aimed to solving the optimization problems of multi-production and multi-marketing strategy selection during the process of integrated marketing. In order to achieve benefit maximization, the fittest marketing method shou Id be put in place into the marketing promotion of each product, which in fact is the problem of multi-objectiveoptimization decision. During the optimization process, first of all, convert discrete variable into continuous variable through the equivalent probability matrix, then update particleswarm and normalize particle position, and finally complete the selection of particle individual extremum and the Global extremum through decoding and fitness computing. The simulation results for the practical problem through this method show that the investment and rationalized distribution of marketing methods can obtain better expected benefits. The conclusion is that multi-objective particle swarm optimization algorithm is an effective method for solving the optimization allocation of products and marketing methods during the process of integrated marketing.
This paper presents a study of optimal control design for a nonlinear dynamical system with the multi-objectiveparticleswarmoptimization(MOPSO) algorithm. The multi-objective optimal design of the nonlinear control...
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This paper presents a study of optimal control design for a nonlinear dynamical system with the multi-objectiveparticleswarmoptimization(MOPSO) algorithm. The multi-objective optimal design of the nonlinear control involves 4 design parameters and 6 objective functions. The multiobjectiveparticleswarmoptimizationalgorithm finds the Pareto set and Pareto front efficiently. Numerical simulation and experiment validation are done on the rotary inverted pendulum system to verify this tuning technique. Numerical and experimental results show that the MOPSO tuning technique is quite effective.
Thermal modeling and optimal design of combined cooling, heating, and power generation system are presented in this article. Selecting the type and number of prime movers, their nominal power and operational strategy,...
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Thermal modeling and optimal design of combined cooling, heating, and power generation system are presented in this article. Selecting the type and number of prime movers, their nominal power and operational strategy, the heating capacity of backup boiler and storage tank, the cooling capacity of electrical and absorption chillers as well as electric cooling ratio (the ratio of electrical chiller capacity to the demand cooling capacity) were considered as nine design parameters. Three types of prime movers including gas turbine, diesel engine, and gas engine were studied in this article. multi-objective particle swarm optimization algorithm was applied to obtain the maximum actual annual benefit and exergy efficiency simultaneously. Actual annual benefit included the energy, economy, and environmental parameters, therefore with adding exergy parameters, 4 E analysis of combined cooling, heating, and power system was performed. The combined cooling, heating, and power system could run in two operation modes, named economical and electricity tracking modes. In the former case, it was allowed to sell the excess electricity to the network and in the latter, it was not allowed to sell the excess electricity to the grid. It was observed that actual annual benefit for the gas engine was higher than two other cases in economical mode. In electricity tracking mode, the gas engine and gas turbine were more profitable. In addition, the trends of optimum values of design parameters versus actual annual benefit and exergy efficiency in economical mode were investigated and the results were presented. Finally, the results of applying the assumptions of constant and variable running load of prime movers during a year were compared.
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