Abstract
The complexity of achieving optimal power flow in the presence of renewable resources decreases the accuracy and optimality levels of the power system due to the associated intermittency and uncertainty. The increased challenge of large-scale deployment of wind energy necessitates the proper modeling of wind impact on power system security and reliability levels . This article discusses a new reliable power flow optimization tool that accounts for wind power availability and uncertainty. An accurate wind forecast model is created to maintain power system security considering wind power variability. The error of the forecasting phase is included in the proposed model to accurately predict the available wind power. In this work, the scattered wind data is converted into informative frequency distribution considering the effect of averaging around integers, halves, and quarters. The proposed method maximizes the utilization level of wind energy without deteriorating the system security. The accuracy of the new proposed work is presented by comparing its results with other models discussed in the literature. A complete and integrated formulation of the objective function has been accomplished. The cost function includes transmission losses, generation operating costs, generation gas emissions, and valve-point effects. Reliable and efficient optimization algorithms are adopted to minimize the established cost function of the system—namely, teaching-learning-based optimization and symbiotic organisms search algorithms. The effectiveness of the proposed approach is validated using the IEEE 39-bus system.
Highlights
In light of the global warming alarms and the increased environmental concerns over using fossil fuels in power generation, wind power plants have become more attractive, on both economic and ecological levels, compared to fossil fuel power plants [1]−[5]
In [7], wind speed was presented in the optimal power flow (OPF) model using Weibull probability distribution function
If ei and ki are constants for the unit i, the valve-point effects are usually modeled in the cost function (FiV ) by a rectified sinusoid function added to the cost function in $, as
Summary
In light of the global warming alarms and the increased environmental concerns over using fossil fuels in power generation, wind power plants have become more attractive, on both economic and ecological levels, compared to fossil fuel power plants [1]−[5]. In [7], wind speed was presented in the optimal power flow (OPF) model using Weibull probability distribution function. The proposed model considers different periods and the accuracy is reflected in the wind speed forecast. Depending on the site of the power plant, its capacity, and the ramping capabilities of the system, the ramp-down limit is restricted to avoid the remainder unsafe region of forecast errors This problem is usually handled either by leaving some available wind power as a reserve for the plant as considered in [2] or by integrating energy storage systems into the plant. After wind speed is forecast using the proposed persistence model, the output power from the wind generator can be known and scheduled. The generation cost function of the thermal units used here is a combination of a nonlinear cost function, the valve-point effects, and the gas emission penalties
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