Abstract
This paper presents a novel probabilistic optimization algorithm for simultaneous active and reactive power dispatch in power systems with significant wind power integration. Two types of load and wind-speed uncertainties have been assumed that follow normal and Weibull distributions, respectively. A PV bus model for wind turbines and the wake effect for correlated wind speed are used to achieve accurate AC power flow analysis. The power dispatch algorithm for a wind-power integrated system is modeled as a probabilistic optimal power flow (P-OPF) problem, which is operated through fixed power factor control to supply reactive power. The proposed P-OPF framework also considers emission information, which clearly reflects the impact of the energy source on the environment. The P-OPF was tested on a modified IEEE 118-bus system with two wind farms. The results show that the proposed technique provides better system operation performance evaluation, which is helpful in making decisions about power system optimal dispatch under conditions of uncertainty.
Highlights
In recent years, renewable energy has become a significant source of electric power
This paper has explored probabilistic optimal power flow (P-optimal power flow (OPF)) to determine optimal active and reactive power dispatch in power systems with significant wind power, considering the wake effect to increase accuracy
A correlated wind speed model was applied assuming that the wind farms were located relatively close to each other to facilitate forecasting the total wind power generation from wind farms
Summary
Renewable energy has become a significant source of electric power. Renewable sources behave much differently than traditional sources due to their stochastic nature. Ruiz-Rodriguez et al [6] presents a probabilistic analysis of the impact of wind speed uncertainty on optimal power flow These studies have two main drawbacks: inadequate modeling of wind speed and wind turbines, and consideration only of network snapshots instead of time series. In short a correlated wind speed model considering wake effect will be useful in forecasting the overall power generation of multiple wind farms for each time period, regardless of the wind speed forecasting technique, whether that be based on historical data, meteorological data, or a combination of the two. The proposed probabilistic power flow in this paper includes the following: uncertainty modeling of load and wind speed correlated wind speed and wake effect.
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