Wind Power Forecast Uncertainty Research Articles

Joint Energy and Spinning Reserve Market Clearing Incorporating Wind Power and Load Forecast Uncertainties

This paper proposes an energy and spinning reserve market clearing (ESRMC) mechanism for wind-thermal power system, considering uncertainties in wind power and load forecasts. Two different market models for the ESRMC are proposed. One model includes reserve offers from the conventional thermal generators, and the other includes reserve offers from both thermal generators and demand/consumers. The stochastic behavior of wind speed and wind power is represented by the Weibull probability density function (pdf), and that of the load is represented by a normal pdf. This paper considers two objectives: total cost minimization and the system-risk-level minimization. The first objective includes the cost of energy provided by thermal and wind generators, and the cost of reserves provided by thermal generators and loads. It also includes costs due to overestimation and underestimation of available wind power and load demand. The system risk level is considered as another objective as wind power is highly uncertain. Multiobjective Strength Pareto Evolutionary Algorithm 2+ (SPEA 2+) has been used to solve the ESRMC problem. The results of the IEEE 30 bus system demonstrate the utility of the proposed approach.

Stochastic predictive control of battery energy storage for wind farm dispatching: Using probabilistic wind power forecasts

The limited dispatchability of wind energy poses a challenge to its increased penetration. One technically feasible solution to this challenge is to integrate a battery energy storage system (BESS) with a wind farm. This highlights the importance of a BESS control strategy. In view of this, a stochastic model predictive control scheme is proposed in this paper. Based on the forecasted wind power distributions, the proposed scheme ensures the optimal operation of BESS in the presence of practical system constraints, thus bringing the wind-battery combined power output to the desired dispatch levels. The salient feature of the proposed scheme is that it takes into account the non-Gaussian wind power uncertainties. In this scheme, a probabilistic wind power forecasting model is employed as the prediction model, which quantifies the non-Gaussian uncertainties in wind power forecasts. Using chance constraints, the quantified uncertainties are incorporated into the controller design, thus forming a chance constrained stochastic programming problem. Using warping function, this problem is recast as a convex quadratic optimization problem, which is tractable both theoretically and practically. This way, the proposed control scheme handles the non-Gaussian uncertainties in wind power forecasts. The simulation results on actual data demonstrate the effectiveness of the proposed scheme. The data used in the simulation are obtained in the real operation of a wind farm in China.

Generalized Sigma approach to unit commitment with uncertain wind power generation

Abstract Using stochastic unit commitment (SUC) to study the integration of uncertain wind power in transmission congested systems is computationally very intensive. This paper therefore presents an efficient deterministic alternative to SUC that generalizes the traditional three-sigma approach to transmission congested systems. In this Generalized Sigma or G-Sigma method, the bus residual demands and line power flows are treated as correlated random variables. Compared with SUC where wind power forecast uncertainty is represented through a large pool of scenarios, each with a distinct probability of occurrence, in the proposed G-Sigma method, a few representative scenarios are chosen on the perimeter of the elliptical confidence region corresponding to a certain level of security. The generation set points and reserve capacities are then scheduled to meet these few representative scenarios in addition to the expected error-free scenarios, thus guaranteeing a minimum level of security. The accuracy and performance of the proposed G-Sigma method were gauged and compared to a benchmark SUC via a three-region multi-unit system and by the IEEE 24-bus reliability test system with multiple units.

Operating Strategies for a GB Integrated Gas and Electricity Network Considering the Uncertainty in Wind Power Forecasts

In many power systems, in particular in Great Britain (GB), significant wind generation is anticipated and gas-fired generation will continue to play an important role. Gas-fired generating units act as a link between the gas and electricity networks. The variability of wind power is, therefore, transferred to the gas network by influencing the gas demand for electricity generation. Operation of a GB integrated gas and electricity network considering the uncertainty in wind power forecast was investigated using three operational planning methods: deterministic, two-stage stochastic programming, and multistage stochastic programming. These methods were benchmarked against a perfect foresight model which has no uncertainty associated with the wind power forecast. In all the methods, thermal generators were controlled through an integrated unit commitment and economic dispatch algorithm that used mixed integer programming. The nonlinear characteristics of the gas network, including the gas flow along pipes and the operation of compressors, were taken into account and the resultant nonlinear problem was solved using successive linear programming. The operational strategies determined by the stochastic programming methods showed reductions of the operational costs compared to the solution of the deterministic method by almost 1%. Also, using the stochastic programming methods to schedule the thermal units was shown to make a better use of pumped storage plants to mitigate the variability and uncertainty of the net demand.

Short-Term Spatio-Temporal Wind Power Forecast in Robust Look-ahead Power System Dispatch

We propose a novel statistical wind power forecast framework, which leverages the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind farms. Critical assessment of the performance of spatio-temporal wind power forecast is performed using realistic wind farm data from West Texas. It is shown that spatio-temporal wind forecast models are numerically efficient approaches to improving forecast quality. By reducing uncertainties in near-term wind power forecasts, the overall cost benefits on system dispatch can be quantified. We integrate the improved forecast with an advanced robust look-ahead dispatch framework. This integrated forecast and economic dispatch framework is tested in a modified IEEE RTS 24-bus system. Numerical simulation suggests that the overall generation cost can be reduced by up to 6% using a robust look-ahead dispatch coupled with spatio-temporal wind forecast as compared with persistent wind forecast models.

Variability and uncertainty of wind power in the California electric power system

ABSTRACTThe California generation fleet manages the existing variability and uncertainty in the demand for electric power (load). When wind power is added, the dispatchable generators manage the variability and uncertainty of the net load (load minus wind power). The variability and uncertainty of the load and the net load are compared when 8790 MW of wind power are added to the California power system, a level expected when California achieves its 33% renewable portfolio standard, using a data set of 26,296 h of synchronous historic load and modeled historic wind power output. Variability was calculated as the rate of change in power generated by wind farms or consumed by the load from 1 h to the next (MW/h). Uncertainty was calculated as the 1 h ahead forecast error [MW] of the wind power or of the load. The data show that wind power adds no additional variability than is already present in the load variability. However, wind power adds additional uncertainty through increased forecast errors in the net load compared with the load. Forecast errors in the net load increase 18.7% for negative forecast errors (actual less than forecast) and 5.4% for positive forecast errors (actual greater than forecast). The increase in negative forecast errors occurs only during the afternoon hours when negative load forecasts and positive wind forecasts are strongly correlated. Managing the integration of wind power in the California power system should focus on reducing wind power forecast uncertainty for wind ramp ups during the afternoon hours. Copyright © 2013 John Wiley & Sons, Ltd.

An effective method for modeling wind power forecast uncertainty

Wind forecasts are an important tool for electric system operators. Proper use of wind power forecasts to make operating decisions must account for the uncertainty associated with the forecast. Data from different regions in the USA with forecasts made by different vendors show the forecast error distribution is strongly dependent on the forecast level of wind power. For low wind power forecast, the forecasts tend to under-predict the actual wind power produced, whereas when the forecast is for high power, the forecast tends to over-predict the actual wind power. Most of the work in this field neglects the influence of wind forecast levels on wind forecast uncertainty and analyzes wind forecast errors as a whole. The few papers that account for this dependence, group wind forecasts by the value of the forecast and fit parametric distributions to actual wind power in each bin of data. In the latter case, different parameters and possibly different distributions are estimated for each data bin. We present a method to model wind power forecast uncertainty as a single closed-form solution using a logit transformation of historical wind power forecast and actual wind power data. Once transformed, the data become close to jointly normally distributed. We show the process of calculating confidence intervals of wind power forecast errors using the jointly normally distributed logit transformed data. This method has the advantage of fitting the entire dataset with five parameters while also providing the ability to make calculations conditioned on the value of the wind power forecast.

The influence of the new ECMWF Ensemble Prediction System resolution on wind power forecast accuracy and uncertainty estimation

Abstract. The importance of wind power forecasting (WPF) is nowadays commonly recognized because it represents a useful tool to reduce problems of grid integration and to facilitate energy trading. If on one side the prediction accuracy is fundamental to these scopes, on the other it has become also clear that a reliable estimation about their uncertainty is paramount. In fact prediction accuracy is unfortunately not constant and can depend on the location of a particular wind farm, on the forecast time and on the atmospheric situation. Previous studies indicated that the spread of power forecasts derived from the Ensemble Prediction System (EPS) in use at the European Centre for Medium-Range Weather Forecast (ECMWF) could be used as indicator of a three-hourly, three days ahead, wind power forecast's accuracy. In this paper a new application of the EPS, whose horizontal resolution was increased on January 2010 from T399/T255 (60 km) to T639/T319 (32 km), shows an improvement in the results implying that the power spread has actually enough correlation with the error calculated on the deterministic forecast in order to be used as an accuracy predictor. The periods for this comparison are from January 2008 until October 2008 (T399/T255) and from January 2011 until October 2011 (T639/T319). Moreover we have focused our attention on the influence of the new EPS configuration on the performance of a deterministic WPF conducted with the ensemble mean: the results show that increasing the EPS resolution yields a single-valued WPF whose performance is comparable with that of the new ECMWF deterministic high-resolution meteorological model, whose spatial resolution increased from T799 (25 km) to T1279 (15 km).

Application of probabilistic wind power forecasting in electricity markets

ABSTRACTThis paper discusses the potential use of probabilistic wind power forecasting in electricity markets, with focus on the scheduling and dispatch decisions of the system operator. We apply probabilistic kernel density forecasting with a quantile‐copula estimator to forecast the probability density function, from which forecasting quantiles and scenarios with temporal dependency of errors are derived. We show how the probabilistic forecasts can be used to schedule energy and operating reserves to accommodate the wind power forecast uncertainty. We simulate the operation of a two‐settlement electricity market with clearing of day‐ahead and real‐time markets for energy and operating reserves. At the day‐ahead stage, a deterministic point forecast is input to the commitment and dispatch procedure. Then a probabilistic forecast is used to adjust the commitment status of fast‐starting units closer to real time, on the basis of either dynamic operating reserves or stochastic unit commitment. Finally, the real‐time dispatch is based on the realized availability of wind power. To evaluate the model in a large‐scale real‐world setting, we take the power system in Illinois as a test case and compare different scheduling strategies. The results show better performance for dynamic compared with fixed operating reserve requirements. Furthermore, although there are differences in the detailed dispatch results, dynamic operating reserves and stochastic unit commitment give similar results in terms of cost. Overall, we find that probabilistic forecasts can contribute to improve the performance of the power system, both in terms of cost and reliability. Copyright © 2012 John Wiley & Sons, Ltd.

Operation and control of BESS using frequency‐linked pricing in real‐time market with high wind penetration

PurposeRecently, many countries have been pushing for a higher share of renewable energy sources, especially wind, in their generation mix. However, the intermittent and uncertain nature of wind power imposes a limit on the extent it can replace the conventional generation resources. In a high wind penetration scenario, the Battery Energy Storage System (BESS) offers a solution to the grid operation problems. The purpose of this paper is to evaluate the merits of price‐based operation of BESS in a real‐time market with high wind penetration using frequency‐linked pricing.Design/methodology/approachThe authors propose a real‐time market in which real‐time prices are based on the grid frequency. A model for real‐time price‐based operation of a conventional generator and a BESS is presented. Simulations for different wind penetration scenarios are carried out on an isolated area test system. Wind speed sequence is generated using composite wind speed model. A simplified model of wind speed to power conversion is adopted to observe the impact of increase in wind power generation on the grid frequency and the real‐time prices.FindingsThe result of simulations show that BESS not only helps in dealing with uncertainty in wind power forecasts, but also reduces the fluctuations in frequency due to wind power's intermittency. Price‐based operation of BESS results in higher operating revenues by discharging it at peak prices and reduces operating costs by charging it at minimum prices.Social implicationsThe study helps in achieving the societal goal of replacing fossil fuel generation by environment friendly generation and reducing green house gas emissions.Originality/valueThe novelty of this paper lies in the use of frequency‐linked pricing in real‐time market and proposing a control algorithm for operating BESS using these price signals.

Greenhouse Gas Emissions from Operating Reserves Used to Backup Large-Scale Wind Power

Wind farms provide electricity with no direct emissions. However, their output cannot be forecasted perfectly, even a short time ahead. Consequently, power systems with large amounts of wind power may need to keep extra fossil-fired generators turned on and ready to provide power if wind farm output drops unexpectedly. In this work, I introduce a new model for estimating the uncertainty in short-term wind power forecasts, and how this uncertainty varies as wind power is aggregated over larger regions. I then use this model to estimate the reserve requirements in order to compensate for wind forecast errors to a 99.999% level of reliability, and an upper limit on the amount of carbon dioxide that would be emitted if natural gas power plants are used for this purpose. I find that for regions larger than 500 km across, operating reserves will undo 6% or less of the greenhouse gas emission savings that would otherwise be expected from wind power.

A Probabilistic Method for Energy Storage Sizing Based on Wind Power Forecast Uncertainty

A novel method is proposed for designing an energy storage system (ESS) which is dedicated to the reduction of the uncertainty of short-term wind power forecasts up to 48 h. The investigation focuses on the statistical behavior of the forecast error and the state of charge (SOC) of the ESS. This approach gives an insight into the influence of the forecast conditions (horizon, quality) on the distribution of SOC. With this knowledge, an optimized sizing of the ESS can be done with a well-defined uncertainty limit. For this study, one-year time series of power output measurements and forecasts were available for two wind farms. As a reference, different forecast quality degrees are simulated based on a persistence approach. With the forecast data, empirical probability density functions (pdfs) are generated which are the basis of the proposed method. This approach can lead to a considerable reduction of the ESS and provides important information about the unserved energy. This unserved energy represents the remaining forecast uncertainty. As a consequence, the proposed probabilistic method permits the sizing of energy storage systems as a function of the desired remaining forecast uncertainty, reducing simultaneously power and energy capacity.

Review of Evaluation Criteria and Main Methods of Wind Power Forecasting

Wind power has characteristics of randomness and uncontrollability. China needs to learn from the successful experience of wind power prediction in European. Wind power forecasting (WPF) will be an important part of the power system construction in the future. Firstly, the classification of WPF is discussed according to the different classified methods. Secondly, the evaluation criteria about the uncertainty of WPF are summarized. Thirdly, we give a survey on the main methods of WPF. Finally, the development direction of WPF is proposed.

Probabilistic energy storage sizing for reducing wind power forecast uncertainty

A novel method is proposed for designing an energy storage system (ESS) which is dedicated to reducing the uncertainty of the short term wind power forecast. The investigation focuses on the statistical behaviour of the forecast error and the state of charge (SOC) of the ESS. This approach gives an insight into the influence of the forecast conditions on the distribution of SOC. With this knowledge, an optimised sizing of the ESS can be done with a well defined uncertainty limit. One-year power output data measurements and two types for forecast were used for this study. In addition, different forecast quality degrees are simulated based on the persistence approach. With the forecast data, empirical probability density functions (pdf's) of the SOC are generated which is the base of the proposed method. This approach can lead to a considerable reduction of the ESS and provides important information about the unserved energy. This unserved energy is the remaining forecast error or uncertainty. As a consequence, the proposed probabilistic method permits the sizing of the energy storage system as a function of the desired remaining forecast uncertainty.

Characterizing Wind Power Forecast Uncertainty with Numerical Weather Prediction Spatial Fields

Successful integration of wind power into power systems can be facilitated through better understanding of future uncertainty in wind power generation. This paper explores a new approach to characterizing this uncertainty using measures of the variability in the wind speeds predicted at multiple grid points in a Numerical Weather Prediction (NWP) system. This approach is compared to the traditional approach of using the spread from an NWP ensemble by using two measures of uncertainty; forecast errors in single time-series forecasts and observed temporal variability. Results show that the multiple grid point approach has a comparable skill level to NWP ensembles for predicting these uncertainty measures and in particular, demonstrates very good skill in predicting large forecast errors. These results also provide a positive evaluation of a terrain standardization method described in a companion paper. A possible extension of this work is to combine the multiple grid point approach with NWP ensembles to improve uncertainty characterization.

Minimizing regulation costs in multi‐area systems with uncertain wind power forecasts

AbstractIn a power system where there are many fast and/or large changes in generation or consumption, e.g. in a system with large amounts of wind power, it is more complicated to handle the frequency control efficiently. Minimizing regulation costs for the system operator in such a system requires the possibility to simulate the frequency control in the time range from minutes to a few hours. In this paper, it is shown how the frequency control during normal operation can be optimized using a multi‐area model, without exceeding frequency limits or transmission capacity. The model has also been expanded to include uncertainties in wind power forecasts, which may lead to an increase of the regulating costs. The optimization model is applied to numerical examples to show the impact of wind power on costs for regulating power and to show the value of better wind speed forecasts. No load forecast errors are taken into account. Copyright © 2007 John Wiley & Sons, Ltd.

Short‐term hydropower planning coordinated with wind power in areas with congestion problems

AbstractIn this paper, a day‐ahead planning algorithm for a multi‐reservoir hydropower system coordinated with wind power is developed. Coordination applies to real situations, where wind power and hydropower are owned by different utilities, sharing the same transmission lines, although hydropower has priority for transmission capacity. Coordination is thus necessary to minimize wind energy curtailments during congestion situations. The planning algorithm accounts for the uncertainty of wind power forecast. Only planning for the spot market is considered. Once the production bid is placed on the market, it cannot be changed. The solution of the stochastic optimization problem should, therefore, fulfill the transmission constraints for all wind power production scenarios. An evaluation algorithm is also developed to quantify the impact from the coordinated planning in the long run. The developed planning algorithm and the evaluation algorithm are applied in a case study. The results are compared with uncoordinated operation. The results of the case study show that coordination with wind power brings additional income to the hydropower utility and leads to significant reduction of wind energy curtailments. Copyright © 2007 John Wiley & Sons, Ltd.