Quantifying the probabilistic distribution of power flows

Background. Active power flow capacities are used to monitor the compliance with power system stability requirements while configuring and running the system. The grid is modeled mathematically for such design. The projected daily load curve and its associated magnitudes are calculated per rules, i.e. set specifically for specific time intervals. However, actual loads are not constant and can generally be represented by an interval that contains the actual values. Grid parameters are also seasonal, as they are affected by accidents, weather, etc. In other words, a grid model uses a number of assumptions. This paper proposes Monto-Carlo simulation to simulate the parameters of a power system as affected by changing the initial loads at grid nodes as well as the line resistance values per Gaussian distribution. The method can find how these values affect power flows in grid branches as well as determine the margin of static stability. Materials and methods: Monte Carlo Method (method of statistical testing). Results. This methodology has been applied to a base-generator/base-load two-node system as well as to a base-load-generator three-node system; besides, it has been applied to the model of Radkino-Arlansky District, Republic of Bashkortosan’s power system. For these models, the team calculated the active and reactive power flows for each successfully computed operating mode that would not compromise static stability. The obtained values were used to plot distributions and to find the range of their variation. Then the static stability factors were calculated and their dependences on the interval length of initial grid parameters changing were constructed. Conclusions. Changing the range of power flows in the branches of the analyzed grid helped researchers find which of them had the greatest fluctuation range and which nodes would affect the flows the most if altered in capacity. Thus, the capacities of these nodes are subject to more accurate adjustment.