Search

In this paper, an autonomous hybrid power system with direct voltage and alternating voltage busbars with connected renewable sources and traditional sources that support renewable sources in the demand coverage during periods of power shortage in the system is proposed for power supply of remote locations. The issues of synthesis of the optimal structure of an autonomous system consisting of PV and WT generators with an integrated system of electric energy batteries (EEB) and diesel- generator station, at which the high reliability of power supply in remote areas is provided, are being studied. To optimally determine the installed capacity of the elements of the PV–WT–EEB–DG hybrid system, a machine learning algorithm (TLBO) is proposed, by means of which, based on the proposed multi-objective function, the most advantageous values of unit powers for PV and WT stations are determined, as well as the number of batteries and diesel generators that ensure the demand coverage in the hybrid system under various options for PV and WT generation shortage.In order to improve the efficiency of solving the optimization problem taking into account the variability of meteorological conditions, manifested in random and uncertain variability of PV and WT generation, as well as in the stochastic nature of the electricity consumption process, the studies on clustering these processes and determining typical scenarios for an array of hourly measurements of PV, WT and load generation during the year were carried out in the paper. Using the k–means approach, the numbers of clusters for PV, WT and load for typical geographic regions are determined. The results of the analysis of the optimal compositions of the hybrid AC-DS system in the PV, WT, BEE, DG in these regions are presented.

At present among renewable sources the wind and solar plants have the most significant portion of power generation. Randomly changing and intermittent nature of this power leads to the stochasticity of the power grid mode, estimation of parameters of which requires application of probabilistic modeling. In the paper it is proposed an advanced algorithm of probabilistic load flow based on the development of two-point estimation method, the efficiency of which is confirmed on the basis of computational experiments and comparative analysis of the Monte Carlo simulation results. Calculations and analysis of the modeling results were carried out on standard 14-nodal scheme of IEEE and real electrical network of “Azerenerji” Grid.