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Power utilities have shown great interest in utilising renewable energy resources for various environmental and economic reasons. However, the modelling and integration of conventional and renewable resources into electric microgrids will increase the complexity of microgrid reliability analyses. Therefore, an accurate reliability assessment of power distribution systems is expected to become a major challenge in the future. More extensive research must be conducted to improve system reliability assessment techniques and determine the impact of increased penetration of renewable resources. In this work, the reliability of wind-based electric microgrids was evaluated using a Markov model, taking the intermittent nature of wind speed into account. The effects of different wind speed modelling techniques based on the auto-regressive moving average method, Markov model, and probability distribution function on the reliability analysis of electric microgrids were assessed. The Roy Billinton test system was used to illustrate the analysis and evaluate the different load and system indices.

Due to the continuous increase in power demand and fast technological improvements and developments, the future of power systems is expected to be a challenging issue. The integration of new sources and technologies are needed to satisfy the utility requests and to decrease the use of fossil fuel sources. One of the main challenging issues in integrating wind power sources is how to forecast the long-term wind speed and study its impact on the reliability of the load. In this paper, the AutoRegression and Moving Average (ARMA) model is used to forecast the wind speed for Dhahran area in Saudi Arabia. Then, the wind power is simulated using the speed-power curve. Finally, the impact of the forecasted wind power on the reliability of residential, commercial, and industrial loads is assessed using Monte Carlo simulation.Index Terms--wind speed, forecasting, ARMA, wind power, reliability.

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