Kravchyshyn V. Intellectualization of management of the complex system of electric energy generation

In the dissertation work the solution of a scientific problem is presented, intellectualization of the system for management power dynamic processes of the wind power station which contains energy storage by using the developed decision support system which make decisions about wind turbines set advisable to use taking into account the consumers needs, wind energy potential and technical characteristics of batteries. A model of management of energy-dynamic processes of a wind power station based on the production rules, which consider the battery capacity and provides an opportunity to effectively manage the existing generation capabilities to meet the consumers’ needs, is developed. Production rules are constructed taking into account the changes of such parameters: wind speed, load of consumers, battery power capacity. For correct implementation of the algorithm in a specific geographic region, a method of statistical analysis of wind speed is developed, and it provides the ability to determine the probabilistic wind speed range and to estimate the ratio between power of wind power station and battery capacity. Method for determining the active composition of a wind power station is developed. Method based on the modification of the dynamic programming method, expert estimates of parameters and uses generalized wind turbine efficiency criteria, available battery capacity and provides the opportunity to make real time decisions. This method adds an opportunity to minimize the deviation of the total power of wind power station from the consumers load and to maximize the efficiency of the wind power station. The developed method is used to format the algorithm of the intelligent control block of the wind power station. It provides determination of the active composition of the wind power station, uses an intelligent system built on product rules, to formulate additive tips to the dispatcher about managing of the energy flow, to ensure the consumers load, the efficient use of battery and the effective operation of the wind power station and provides the reflection of the results in intuitively understandable mode and provides for the construction of situational maps of the features that gives an opportunity to monitor system status in real time. This method adds an opportunity to minimize the deviation of the total power of wind power station from the consumers load and to maximize the efficiency of the wind power station. Situational maps of features based on artificial neural networks, which provide visualization of the output parameters in the real time, and allow the operator to react promptly to the critical values of the consumers load and wind power station power are developed.