Development of methodology for calculation of optimal distribution of electric power between power units of condensing power plant

Optimization methods are used to solve many problems in the field of energy. One of such tasks is the problem of optimal redistribution of power between power units in order to achieve minimum fuel consumption. This is especially important for powerful condensation power plants, where even relatively small fuel savings have significant economic effect. The article is devoted to description of developed method of such optimization, based on the application of differential evolution, which has many advantages over the "classical" methods of optimization. In particular, it was the global rather than the local extremum of the objective function that could be found; it was also easy and powerful to use with modern software. Differential evolution method is organized in the library SciPy of Python programming language, so calculation program was developed in this language to solve the problem. The work considers algorithm and structure of the developed program, as well as the procedure for preparing initial data and calculation process using example of a specific condensing power plant. Modules used in the program to populate the data arrays are mentioned, as well as to output the results in the form of high-quality graphs. With the help of the program, diagram of the optimal redistribution of capacities between power units for any total capacity of the power station is constructed. Also, for entire power range of the power plant, nominal fuel consumption and fuel economy are calculated when implementing the optimal redistribution of capacity in comparison with an even distribution. Obtained software product, available to everyone on the website of the authors, allows not only to study the practical application of differential evolution method, but also to create programs based on it to solve other optimization problems, some of which are mentioned in the article.

optimization, power redistribution, evolutionary methods, turbine power, differential evolution method, Python language