A Framework for Real-Time Optimal Power Flow under Wind Energy Penetration

Abstract

Developing a suitable framework for real-time optimal power flow (RT-OPF) is of utmost importance for ensuring both optimality and feasibility in the operation of energy distribution networks (DNs) under intermittent wind energy penetration. The most challenging issue thereby is that a large-scale complex optimization problem has to be solved in real-time. Online simultaneous optimization of the wind power curtailments of wind stations and the discrete reference values of the slack bus voltage which leads to a mixed-integer nonlinear programming (MINLP) problem, in addition to considering variable reverse power flow, make the optimization problem even much more complicated. To address these difficulties, a two-phase solution approach to RT-OPF is proposed in this paper. In the prediction phase, a number of MINLP OPF problems corresponding to the most probable scenarios of the wind energy penetration in the prediction horizon, by taking its forecasted value and stochastic distribution into account, are solved in parallel. The solution provides a lookup table for optional control strategies for the current prediction horizon which is further divided into a certain number of short time intervals. In the realization phase, one of the control strategies is selected from the lookup table based on the actual wind power and realized to the grid in the current time interval, which will proceed from one interval to the next, till the end of the current prediction horizon. Then, the prediction phase for the next prediction horizon will be activated. A 41-bus medium-voltage DN is taken as a case study to demonstrate the proposed RT-OPF approach.

Chance Constrained Optimal Power Flow Using the Inner-Outer Approximation Approach

“…Although the inner-outer approximation is a general approach of solving chance constrained optimization problems for linear and nonlinear models, here we solve a DC (linear) OPF under uncertain penetration of wind power. The wind power is described by the Beta PDF [18,19]. The input data for the optimization is given in Table I and Fig.…”

“…during power system operations. Therefore, network operators have been facing numerous challenges dealing with such uncertainties to ensure not only optimal but also reliable [18,19] operation strategies.…”

Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

“…Wind power is currently one of the most important renewable energies in the world [1] in terms of penetration in the electric power system [2,3], economic impact and annual growth rate [4], both onshore [5] and offshore [6]. Electric power generation is usually carried out in large wind farms [7,8] far from urban centers [9,10], though, in the last few years, urban wind power generation is also gaining impulse [11], including its use in smart grids [12].…”

“…Note that the classification problems associated with WPREs are usually highly unbalanced, which makes it difficult to put into practice high-performance classification techniques without having to use specific over-sampling or similar techniques [43,44]. 3.…”

Interdependence of Transmission Branch Parameters on the Voltage Levels

“…For example, the algorithms introduced by authors in [2]- [4] need some verification in larger systems to identify the pros and cons of the solution. Another example is the concept of real-time optimal power flow in [5] that can be evaluated in numerous synthetic grids. Since Synthetic power networks are entirely fictitious but with the same characteristics as real networks, they can be freely published to the public to facilitate advancement of new technologies in power systems.…”