Abstract

Adverse environmental impacts of carbon emissions are causing increasing concerns to the general public throughout the world. Electric energy generation from conventional energy sources is considered to be a major contributor to these harmful emissions. High emphasis is therefore being given to green alternatives of energy, such as wind and solar. Wind energy is being perceived as a promising alternative. This source of energy technology and its applications have undergone significant research and development over the past decade. As a result, many modern power systems include a significant portion of power generation from wind energy sources. The impact of wind generation on the overall system performance increases substantially as wind penetration in power systems continues to increase to relatively high levels. It becomes increasingly important to accurately model the wind behavior, the interaction with other wind sources and conventional sources, and incorporate the characteristics of the energy demand in order to carry out a realistic evaluation of system reliability. Power systems with high wind penetrations are often connected to multiple wind farms at different geographic locations. Wind speed correlations between the different wind farms largely affect the total wind power generation characteristics of such systems, and therefore should be an important parameter in the wind modeling process. This paper evaluates the effect of the correlation between multiple wind farms on the adequacy indices of wind-integrated systems. The paper also proposes a simple and appropriate probabilistic analytical model that incorporates wind correlations, and can be used for adequacy evaluation of multiple wind-integrated systems.

Highlights

  • Fossil fuel is presently the major source for electricity production, and is believed to be a major contributor to greenhouse gas emissions

  • More and more electric power systems will be connected to multiple wind farms at different geographic locations

  • This paper presented studies to model wind behavior with reasonable accuracy while incorporating the correlation between wind farms in order to evaluate the reliability of wind integrated power systems

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Summary

Introduction

Fossil fuel is presently the major source for electricity production, and is believed to be a major contributor to greenhouse gas emissions. 6000 MW of additional wind capacity is expected to come into operation before 2015 [1], increasing the penetration level to about 5% It is a trend in Canada but all around the world. Different techniques have been used to model wind generation and integrate them to evaluate the reliability of wind-integrated power systems (WIPS). Reference [6] used auto regressive and moving average (ARMA) model to generate wind speeds of correlated wind farms by using correlated random number seeds and studied the impact of correlations on the adequacy indices of a WIPS using sequential Monte Carlo Simulation (MCS). Presented a genetic algorithm to obtain optimum random number seeds to generate wind speeds of correlated wind farms and used MCS to study the effect of wind penetration and correlation on system risk. This paper focuses on simplifying the wind models incorporating the wind correlations and wind penetration levels

Wind Data Modeling
Wind Power Modeling
Impact of Wind Penetration and Correlation
Appropriate Wind Capacity Model Considering Wind Correlation and Penetration
Findings
Conclusions
Full Text
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