scholarly journals A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 388
Author(s):  
Riccardo De Blasis ◽  
Giovanni Batista Masala ◽  
Filippo Petroni
Keyword(s):  
Markov Model ◽  
Electricity Market ◽  
Wind Farm ◽  
Central Italy ◽  
High Order ◽  
Distribution Model ◽  
Spot Price ◽  
Model Parameters ◽  
Price Series ◽  
Cross Correlations

The energy produced by a wind farm in a given location and its associated income depends both on the wind characteristics in that location—i.e., speed and direction—and the dynamics of the electricity spot price. Because of the evidence of cross-correlations between wind speed, direction and price series and their lagged series, we aim to assess the income of a hypothetical wind farm located in central Italy when all interactions are considered. To model these cross and auto-correlations efficiently, we apply a high-order multivariate Markov model which includes dependencies from each time series and from a certain level of past values. Besides this, we used the Raftery Mixture Transition Distribution model (MTD) to reduce the number of parameters to get a more parsimonious model. Using data from the MERRA-2 project and from the electricity market in Italy, we estimate the model parameters and validate them through a Monte Carlo simulation. The results show that the simulated income faithfully reproduces the empirical income and that the multivariate model also closely reproduces the cross-correlations between the variables. Therefore, the model can be used to predict the income generated by a wind farm.

A Probability Distribution Model to Simulate Wind Power Output Fluctuation

2014 ◽  
Vol 1070-1072 ◽  
pp. 171-176
Author(s):  
Chi Li ◽  
Chun Liu ◽  
Yue Hui Huang
Keyword(s):  
Probability Distribution ◽  
Wind Power ◽  
Power Output ◽  
Wind Farm ◽  
Probability Model ◽  
Distribution Model ◽  
Model Parameters ◽  
Stable Operation ◽  
Power Fluctuation ◽  
Short Time

It is of great significance for the safe and stable operation of power system to master the fluctuation characteristics of wind power output. On the basis of analyzing a large number of field measured data, a weighted mixed Gaussian probability model is proposed to simulate short-time wind power fluctuation characteristics of wind farm cluster, that evaluation indices to reflect the short-time maximum fluctuation of wind power output and maximum likelihood estimation algorithm based on Expectation Maximization (EM) to estimate model parameters are put forward. This model is compared with various other kinds of probability distribution model and the simulation results show that the weighted mixed Gaussian probability model possesses the highest precision, so as the effectiveness of the weighted mixed Gaussian probability model is verified.

Probabilistic Assessment of Wind Farm Active Power Based on Monte-Carlo Simulation

2013 ◽  
Vol 291-294 ◽  
pp. 536-540 ◽  
Author(s):  
Xin Wei Wang ◽  
Jian Hua Zhang ◽  
Cheng Jiang ◽  
Lei Yu
Keyword(s):  
Monte Carlo ◽  
Power Output ◽  
Wind Farm ◽  
Active Power ◽  
Assessment Methods ◽  
Probabilistic Methods ◽  
Model Parameters ◽  
Probability Assessment ◽  
Reliability Models ◽  
Reliability Parameters

The conventional deterministic methods have been unable to accurately assess the active power output of the wind farm being the random and intermittent of wind power, and the probabilistic methods commonly used to solve this problem. In this paper the multi-state fault model is built considering run, outage and derating state of wind turbine, and then the reliability model of the wind farm is established considering the randomness of the wind speed, the wind farm wake effects and turbine failure. The active wind farm output probability assessment methods and processes based on the Monte Carlo method. The related programs are written in MATLAB, and the probability assessment for active power output of a wind farm in carried out, the effectiveness and adaptability of built reliability models and assessment methods are illustrated by analysis of the effects of reliability parameters and model parameters on assessment results.

scholarly journals Economic Evaluation of Wind Power Projects in a Mix of Free and Regulated Market Environments in Brazil

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3325
Author(s):  
Vanderson Aparecido Delapedra-Silva ◽  
Paula Ferreira ◽  
Jorge Cunha ◽  
Herbert Kimura
Keyword(s):  
Electricity Market ◽  
Free Market ◽  
Market Price ◽  
Investment Project ◽  
Spot Price ◽  
Market Uncertainty ◽  
Discounted Cash Flow ◽  
Expected Return ◽  
Price Expectation ◽  
Free Market Price

The electricity market in Brazil is basically organized under two parts: the regulated market, where energy is traded through auctions, and the free market, where market participants freely negotiate the price and quantity of electricity. Although revenues obtained in the regulated market tend to be lower than in the free market, the auctions’ results show that investors still value the lesser degree of uncertainty associated with the regulated market. However, a growing interest in the free market by investors is recognized since the price of electricity tends to be higher. Therefore, this study investigates four free market price scenarios to assess the expected return for investors, using the traditional discounted cash flow approach complemented with Monte Carlo simulation to address market uncertainty. The study breaks new ground by capturing the weekly price fluctuations and including the price elasticity of demand of the free market. The results seem to indicate that the disclosure of the ceiling and floor price limits for the spot price can signal important information about the agents’ price expectation in the free market and can be used for investment project evaluation.

scholarly journals Analysis of a Stochastic Programming Model for Optimal Hydropower System Operation under a Deregulated Electricity Market by Considering Forecasting Uncertainty

Water ◽  
2018 ◽  
Vol 10 (7) ◽  
pp. 885 ◽  
Author(s):  
Bin Xu ◽  
Ping-An Zhong ◽  
Baoyi Du ◽  
Juan Chen ◽  
Weifeng Liu ◽  
...  
Keyword(s):  
Stochastic Programming ◽  
Electricity Market ◽  
Energy Production ◽  
Programming Model ◽  
Optimal Operation ◽  
Spot Price ◽  
Deregulated Electricity Market ◽  
Hydropower System ◽  
Stochastic Programming Model ◽  
Hydropower Operation

In a deregulated electricity market, optimal hydropower operation should be achieved through informed decisions to facilitate the delivery of energy production in forward markets and energy purchase level from other power producers within real-time markets. This study develops a stochastic programming model that considers the influence of uncertain streamflow on hydropower energy production and the effect of variable spot energy prices on the cost of energy purchase (energy shortfall). The proposed model is able to handle uncertainties expressed by both a probability distribution and discretized scenarios. Conflicting decisions are resolved by maximizing the expected value of net revenue, which jointly considers benefit and cost terms under uncertainty. Methodologies are verified using a case study of the Three Gorges cascade hydropower system. The results demonstrate that optimal operation policies are derived based upon systematic evaluations on the benefit and cost terms that are affected by multiple uncertainties. Moreover, near-optimal operation policy under the case of inaccurate spot price forecasts is also analyzed. The results also show that a proper policy for guiding hydropower operation seeks the best compromise between energy production and energy purchase levels, which explores their nonlinear tradeoffs over different time periods.

Permutation Min-Entropy and Statistical Complexity Analysis of Electricity Spot Price

2015 ◽  
Vol 14 (04) ◽  
pp. 1550040 ◽  
Author(s):  
Qingju Fan ◽  
Dan Li
Keyword(s):  
Temporal Dynamics ◽  
Complexity Analysis ◽  
Spot Price ◽  
Temporal Correlations ◽  
Price Series ◽  
Statistical Complexity ◽  
Electricity Spot Price ◽  
Insight Into ◽  
Refined Classification

In this study, we investigate the subtle temporal dynamics of California 1999–2000 spot price series based on permutation min-entropy (PME) and complexity-entropy causality plane. The dynamical transitions of price series are captured and the temporal correlations of price series are also discriminated by the recently introduced PME. Moreover, utilizing the CECP, we provide a refined classification of the monthly price dynamics and obtain an insight into the stochastic nature of price series. The results uncover that the spot price signal presents diverse temporal correlations and exhibits a higher stochastic behavior during the periods of crisis.

Remaining Useful Life Estimation Based on a Segmental Hidden Markov Model With Continuous Observations

2017 ◽  
Author(s):  
Zhen Chen ◽  
Tangbin Xia ◽  
Ershun Pan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Goodness Of Fit ◽  
Hidden Markov ◽  
Remaining Useful Life ◽  
Likelihood Method ◽  
Distribution Model ◽  
Unknown Parameters ◽  
Useful Life ◽  
Hidden States

In this paper, a segmental hidden Markov model (SHMM) with continuous observations, is developed to tackle the problem of remaining useful life (RUL) estimation. The proposed approach has the advantage of predicting the RUL and detecting the degradation states simultaneously. As the observation space is discretized into N segments corresponding to N hidden states, the explicit relationship between actual degradation paths and the hidden states can be depicted. The continuous observations are fitted by Gaussian, Gamma and Lognormal distribution, respectively. To select a more suitable distribution, model validation metrics are employed for evaluating the goodness-of-fit of the available models to the observed data. The unknown parameters of the SHMM can be estimated by the maximum likelihood method with the complete data. Then a recursive method is used for RUL estimation. Finally, an illustrate case is analyzed to demonstrate the accuracy and efficiency of the proposed method. The result also suggests that SHMM with observation probability distribution which is closer to the real data behavior may be more suitable for the prediction of RUL.

scholarly journals Decoding the Australian electricity market: New evidence from three-regime hidden semi-Markov model

2019 ◽  
Vol 78 ◽  
pp. 129-142 ◽  
Author(s):  
Nicholas Apergis ◽  
Giray Gozgor ◽  
Chi Keung Marco Lau ◽  
Shixuan Wang
Keyword(s):  
Markov Model ◽  
Electricity Market ◽  
New Evidence

scholarly journals Short-term earthquake forecasting experiment before and during the L’Aquila (central Italy) seismic sequence of April 2009

2015 ◽  
Vol 57 (6) ◽  
Author(s):  
Maura Murru ◽  
Jiancang Zhuang ◽  
Rodolfo Console ◽  
Giuseppe Falcone
Keyword(s):  
Central Italy ◽  
Aftershock Sequence ◽  
Earthquake Forecasting ◽  
Model Parameters ◽  
Short Term ◽  
Productivity Function ◽  
Epidemic Type Aftershock Sequence ◽  
Forecasting Performance ◽  
The One ◽  
Lower Magnitude

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>In this paper, we compare the forecasting performance of several statistical models, which are used to describe the occurrence process of earthquakes in forecasting the short-term earthquake probabilities during the L’Aquila earthquake sequence in central Italy in 2009. These models include the Proximity to Past Earthquakes (PPE) model and two versions of the Epidemic Type Aftershock Sequence (ETAS) model. We used the information gains corresponding to the Poisson and binomial scores to evaluate the performance of these models. It is shown that both ETAS models work better than the PPE model. However, in comparing the two types of ETAS models, the one with the same fixed exponent coefficient (<span>alpha)</span> = 2.3 for both the productivity function and the scaling factor in the spatial response function (ETAS I), performs better in forecasting the active aftershock sequence than the model with different exponent coefficients (ETAS II), when the Poisson score is adopted. ETAS II performs better when a lower magnitude threshold of 2.0 and the binomial score are used. The reason is found to be that the catalog does not have an event of similar magnitude to the L’Aquila mainshock (M<sub>w</sub> 6.3) in the training period (April 16, 2005 to March 15, 2009), and the (<span>alpha)</span>-value is underestimated, thus the forecast seismicity is underestimated when the productivity function is extrapolated to high magnitudes. We also investigate the effect of the inclusion of small events in forecasting larger events. These results suggest that the training catalog used for estimating the model parameters should include earthquakes of magnitudes similar to the mainshock when forecasting seismicity during an aftershock sequence.</p></div></div></div>

scholarly journals Calibration and validation of the Dynamic Wake Meandering model Part I: Bayesian estimation of model parameters using SpinnerLidar-derived wake characteristics

2021 ◽  
Author(s):  
Davide Conti ◽  
Nikolay Dimitrov ◽  
Alfredo Peña ◽  
Thomas Herges
Keyword(s):  
Wind Farm ◽  
Atmospheric Stability ◽  
Flow Characteristics ◽  
Uncertainty Assessment ◽  
Wake Flow ◽  
Model Parameters ◽  
Wake Field ◽  
Wind Speeds ◽  
Standard Lead ◽  
Estimation Of Model Parameters

Abstract. In this first part of a two-part work, we study the calibration of the Dynamic Wake Meandering (DWM) model using high spatial and temporal resolution SpinnerLidar measurements of the wake field collected at the Scaled Wind Farm Technology (SWiFT) facility located in Lubbock, Texas, U.S.A. We derive two-dimensional wake flow characteristics including wake deficit, wake turbulence and wake meandering from the lidar observations under different atmospheric stability conditions, inflow wind speeds and downstream distances up to five rotor diameters. We then apply Bayesian inference to obtain a probabilistic calibration of the DWM model, where the resulting joint distribution of parameters allows both for model implementation and uncertainty assessment. We validate the resulting fully-resolved wake field predictions against the lidar measurements and discuss the most critical sources of uncertainty. The results indicate that the DWM model can accurately predict the mean wind velocity and turbulence fields in the far wake region beyond four rotor diameters, as long as properly-calibrated parameters are used and wake meandering time series are accurately replicated. We demonstrate that the current DWM-model parameters in the IEC standard lead to conservative wake deficit predictions. Finally, we provide practical recommendations for reliable calibration procedures.

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