An Appropriate Wind Model for Wind Integrated Power Systems Reliability Evaluation Considering Wind Speed Correlations

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 windintegrated systems. OPEN ACCESSAppl. Sci. 2013, 3 108

Impacts of Energy Storage System on Power System Reliability: A Systematic Review

“…Hence, various techniques have been proposed to model wind energy, which is usually modelled using the auto-regressive moving average (ARMA) model or the Weibull function. The ARMA model generates random wind speed as an auto-regressive and moving average model [38]. Meanwhile, the Weibull distribution considers wind speed, shape factor, and scale factor [39].…”

Impact of Wind Geographical Correlation in Reliability Assessment Studies Using Sequential Monte Carlo Simulations

“…Practically, it should be noted that generated wind speed data need then to be transformed in the power domain using the power curves of the considered wind turbines. In order to sequentially sample wind speeds, AutoRegressive Moving Average (ARMA) models are commonly used [5][6][7] and geographical correlation between wind parks can be considered by means of Cholesky decomposition [6][7]. Practically, to the best authors' knowledge, the sensitivity of the computed reliability indices to the considered geographical correlation level between wind parks has not really been evaluated yet.…”

Reliability models of wind farms considering wind speed correlation and WTG outage

“…On one hand, some research works have been conducted to develop reliability models of wind farms incorporating wind speed correlation only, and different types of techniques such as Cholesky decomposition [8], genetic algorithm [2], time-shifting technique [9] and Copula method [10] are used for simulating correlated wind speed. On the other hand, some research works only focus on reliability models of wind farms considering WTG outage, and the apportioning method [3] or Markov chain method [4] is used to for modeling WTG outage.…”