MCMC for Wind Power Simulation

Abstract

Abstract-This paper contributes a Markov chain Monte Carlo (MCMC) method for the direct generation of synthetic time series of wind power output. It is shown that obtaining a stochastic model directly in the wind power domain leads to reduced number of states and to lower order of the Markov chain at equal power data resolution. The estimation quality of the stochastic model is positively influenced since in the power domain, a lower number of independent parameters is estimated from a given amount of recorded data. The simulation results prove that this method offers excellent fit for both the probability density function and the autocorrelation function of the generated wind power time series. The method is a first step toward simple stochastic black-box models for wind generation.

Impact of Short-Term Variations in the Generation Output of Geographically Dispersed PV Systems

“…The distances between the pairs are distance 1.14, 2.4, and 3.7 deg, respectively. Note that close units (1,2) are well correlated at most of the scales, while more distant pairs (1,2) and (1,4) are negatively correlated at scales up to 80'.…”

“…Uniformly distributed random sampling of a transition from a current state to the next state is proposed in [2][3][4][5]. This study applies this random sampling to the proposed MCMC simulation that synthesizes rapid variations in the generation output of geographically distributed PV systems in minute intervals.…”

“…Rapid variations in short-term, typically minute-averaging resolution, PV generation result from transient cloudiness and weather disturbances in the atmosphere [10]. Since current PV generation affected by transient cloudiness depends only on previous generation, rapid variations in PV generation can be modeled by optimized Markov chain methods [2, 4,11]. Thus, to model the short-term intermittency of the transient cloudiness of the state, this study uses a Markov chain Monte Carlo (MCMC) simulation [7,10,12].…”

Risk-constrained optimal power flow with probabilistic guarantees

“…The forecasted wind power at the two buses is 50 and 40 MW, respectively. To generate wind power scenarios, we used a Markov Chain based model as described in [17], [7]. For the chance constrained optimization, we account for up to 2000 scenarios (depending on ε).…”

Probabilistic Modeling and Simulation of Transmission Line Temperatures Under Fluctuating Power Flows

“…The ramp rates of these generators are assumed to be sufficiently small to become neglected. The predicting time series for the fluctuating wind power generation are derived here by using a simple Markov chain model as described in [24]- [26], with the formalism given in Appendix B. However, owing to the generality of the proposed line temperature assessment methodology, alternative probabilistic models for short-term wind power forecasting could be readily used as well, such as the Markov-switching autoregressive model [27] or the ARIMA technique [17], [18], also depending on the specific meteorological conditions and data availability.…”