A Biased Random Key Genetic Algorithm Approach for Unit Commitment Problem

A Biased Random Key Genetic Algorithm (BRKGA) is proposed to find solutions for the unit commitment problem. In this problem, one wishes to schedule energy production on a given set of thermal generation units in order to meet energy demands at minimum cost, while satisfying a set of technological and spinning reserve constraints. In the BRKGA, solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval [0, 1]. The GA proposed is a variant of the random key genetic algorithm, since bias is introduced in the parent selection procedure, as well as in the crossover strategy. Tests have been performed on benchmark large-scale power systems of up to 100 units for a 24 hours period. The results obtained have shown the proposed methodology to be an effective and efficient tool for finding solutions to large-scale unit commitment problems. Furthermore, from the comparisons made it can be concluded that the results produced improve upon some of the best known solutions

Structured genetic algorithm technique for unit commitment problem

This paper presents and identifies alternative strategies with the advantages of Genetic Algorithm for solving the Thermal Unit Commitment(UC) problem. A Parallel Structure has been developed to handle the infeasibility problem in astructured and improved Genetic Algorithm (GA) which provides an effective search and therefore greater economy. In addition, this proposed method lead us to obtain better performance by using both computational methods and classification of unit characteristics. Typical constraints such as system power balance, minimum up and down times, start up and shut-down ramps have been considered. A number of effective parameters related to UC problem have been identified. This method is developed and tested by using C# program. Tests have been performed on 10 and 20 units systems over a scheduling period of 24 hours. The final results are compared with those obtained genetic schemes in other same research

Unit commitment solution using an optimized genetic system

This paper presents an investigation into the application of an optimized Genetic Algorithm (GA) to solve the Thermal Unit Commitment (UC) problem. A Parallel structure was first developed to handle the infeasibility problem in a structured and improved GA which provides an effective search process and therefore greater economy. The proposed methodology resulted in a better performance with faster operation by using both computational methods and classification of unit characteristics. Typical constraints such as system power balance, minimum up and down times, start-up and shut-down ramps, have also been considered. A number of important parameters (standard and new parameters) of the UC problem have been identified. The proposed method is implemented and tested using a C# program. The tests are carried out using two systems including 10 and 20 units during a scheduling period of 24 h. The results are finally compared with those obtained from genetic schemes in other similar investigations through which the effectiveness of the proposed scheme is affirmed

Transfer of human proinsulin gene into Cucumber (Cucumis sativus L.) via agrobacterium method

Nowadays, approximately 5.8% in adult population around the world are suffering by diabetes. It can be caused by an increase in risk factors such as being overweight. Also it has been estimated that the number of patients will be doubled in near future and the demands for insulin hormone will be growing up by 3 to 4 % annually. Therefore, it’s necessary to develop new methods for hormone production with high rate of capacity in future. By advanced technology of transgenic DNA, the transgenic plants are introduced as an attractive system for expression and production of many kinds of pharmaceutical proteins. In this study, we investigated transfer of Human Proinsulin Gene into the Cucumber (Cucumissativus L.). Transgenic cucumber could be a great prospect for future source of eatable insulin pharmaceutical drugs to be taken by patients.Agrobacterium tumefaciensstrain LBA4404 carrying proinsulin genes with CaMV 35S promoter was used for the transformation purpose. The transgenic plants were analyzed by PCR, RT-PCR, SDS-PAGE, Dot blot and Electrochemiluminescence techniques. Production of proinsulin in cucumber could be a great prospect in molecular farming of human proinsulin