Probabilistic power flow of correlated hybrid wind‐photovoltaic power systems

Abstract

As a matter of course, the unprecedented ascending penetration of distributed energy resources, mainly harvesting renewable energies such as wind and solar, is concomitant with environmentally friendly concerns. This type of energy resources are innately uncertain and bring about more uncertainties in the power system context, consequently, necessitates probabilistic analysis of the system performance. Moreover, the uncertain parameters may have a considerable level of correlation to each other, in addition to their uncertainties. The two point estimation method (2PEM) is recognised as an appropriate probabilistic method. This study proposes a new methodology for probabilistic power flow studies for such a problem by modifying the 2PEM. The original 2PEM cannot handle correlated uncertain variables, but the proposed method has been equipped with this ability. To justify the impressiveness of the method, two case studies namely the Wood & Woollenberg 6-bus and the IEEE118-bus test systems are examined using the proposed method, then the obtained results are compared against the Monte Carlo simulation results. Comparison of the results justifies the effectiveness of the method in the respected area with regards to both accuracy and execution time criteria.

Probabilistic optimal power flow in correlated hybrid wind-PV power systems: A review and a new approach

“…Generally, this matter is stated through the covariance or correlation coefficient matrix. The correlation coefficients can be obtained from (27).…”

“…The Two Point Estimation Method (2PEM) was proposed for P-OPF study in [25]. In [26,27], two distinct mathematical approaches for handling correlated variables by 2PEM are proposed. It is claimed that the 2PEM does not perform well if there are too many number of uncertain variables in large systems [25].…”

Heuristic probabilistic power flow algorithm for microgrids operation and planning

“…Wind speed changes in every minute, hour, day and year, which highlights the importance of a probability model. Based on report [29], the Weibull distribution is suitable to represent the distribution for the wind speed for long-term planning purposes. Weibull PDF is as…”

Advanced probabilistic power flow methodology for power systems with renewable resources

“…Its accuracy mostly depends on the number of samples. Conversely, point estimate methods [4][5][6] use an approximation technique, in which input variables are decomposed into a series of pairs of values and weights, and then the moments of the output variables are calculated as a function of the inputs. In a different way, the analytical approach applies an arithmetic algorithm (e.g., using a convolution technique [7] or cumulant method [8][9][10][11]) with probability density functions (PDFs) and/or cumulative distribution functions (CDFs) of input random variables so that PDFs and CDFs of output random variables can be obtained.…”