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

Multi-objective optimization dispatching of a micro-grid considering uncertainty in wind power forecasting

Unlike other traditional energy resources, wind power outputs depend on natural wind resources that vary over space and time. Accurate wind power forecasting can reduce the burden of balancing energy equilibrium in electrical power systems. In this paper, we propose the short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method. The enhanced ensemble forecasting methods are grouped into two main categories: temporal ensemble and spatial ensemble forecasting. The temporal ensemble forecasting is implemented by autoregressive integrated moving average with explanatory variable model, polynomial regression with time-series data, and analog ensemble for a probabilistic approach. The spatial ensemble forecasting is implemented by geostatistical model and interpolation with geographical property data. In addition, the stochastic approach, analog ensemble is applied to reduce the uncertainty in wind power forecasting and use of Numerical Weather Prediction models for accurate wind power forecasting is considered. We conduct stochastic wind power forecasting using practical data of Jeju power system and evaluate the system reliability on wind power generation variations. As a result, the proposed model shows better performances than single models, while at the same time providing probabilistic forecasts. Based on these forecasts, the grid operators can identify critical operating time points to prepare for problems that can occur in the system due to wind power variations in advance.

The uncertainty of wind power generation imposes significant challenges to optimal operation and control of electricity networks with increasing wind power penetration. To effectively address the uncertainties in wind power forecasts, probabilistic forecasts that can quantify the associated probabilities of prediction errors provide an alternative yet effective solution. This article proposes a hybrid artificial neural network approach to generate prediction intervals of wind power. An extreme learning machine is applied to conduct point prediction of wind power and estimate model uncertainties via a bootstrap technique. Subsequently, the maximum likelihood estimation method is employed to construct a distinct neural network to estimate the noise variance of forecasting results. The proposed approach has been tested on multi-step forecasting of high-resolution (10-min) wind power using actual wind power data from Denmark. The numerical results demonstrate that the proposed hybrid artificial neural network approach is effective and efficient for probabilistic forecasting of wind power and has high potential in practical applications.

A multivariate time series wind power forecast model, dynamic factor model (DFM), is proposed to tackle a stochastic dynamic optimal power flow (OPF) problem integrated with wind energy and storage devices in this paper. The uncertainty of wind power forecasting is represented by a set of scenarios generated by the DFM. As opposed to other common methods of generating scenarios, such as Monto Carlo simulation, the DFM considers the spatial correlations among wind farms, which manifests a more accurate and real situation. DFM models the wind power as a multivariate stochastic process, which can be decomposed as the multiplication of fixed polynomial matrices and dynamic shocks. Such dynamic shocks are spatially and temporally uncorrelated white noises which in turn become the impulses to generate forecast scenarios. The stochastic optimization is to minimize the total expected generating cost over all scenarios in one day ahead. Storage devices are installed to compensate for the operation cost and power deficiency. The proposed forecast model is verified in both time and frequency domain, and stochastic optimization model is tested over modified IEEE 30-bus system.

Global wind power capacity has seen historic growth in the last decade and is anticipated to increase rapidly in the future. As such the ability to accurately forecast wind power, at all timescales, is important to both central network operators and individual wind power producers. This paper provides an overview of wind forecasting methodologies, focusing on their timescales of applicability, along with their performance, advantages, and shortcomings. This paper's contribution is in analysing, synthesising, and critiquing a large amount of wind power forecasting research, thereby clarifying the state of current knowledge and making it more accessible to researchers and decisions makers within the energy industry. To highlight rapidly developing areas within wind power forecasting, a novel distinction between fundamental and emerging forecasting methodologies is made. Fundamental methodologies include time series based and artificial intelligence methods; emerging methodologies, in turn, are built on fundamental methodologies and include spatial-temporal forecasting, probabilistic forecasting, combined forecasting methods, and the incorporation of exogenous atmospheric variables into forecasts. Furthermore, as very little research has been done on short-term forecasting specific to South Africa, the focus of the review is on statistical forecasting methods which are well suited to the short-term time horizon and are computationally inexpensive. It is anticipated that the insights provided by this structured review will contribute to the development of effective forecasting frameworks necessitated by the increase of wind power both globally and specific to the South African network.

Review of Evaluation Criteria and Main Methods of Wind Power Forecasting

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.

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.

This paper focuses on the basic framework of operating risk evaluation method considering two key uncertain factors: the wind power prediction and extreme weather. The output model of large scale wind farms including the uncertainty of wind power forecast is proposed in this paper. Then the system generation margin model considering the ramp rate of conventional generators is built up to study the operating risk of power system. At last, based on the forecasted and measured data of wind farms in Gansu province, a simulation on RBTS indicates that the fluctuation of wind power is the essential source of the risk for power system with large scale of wind farms. The outage loss of power system in extreme weather is much higher than the risk in normal weather but with a much lower probability, which is a “HILP” event.

The fluctuation and uncertainty of wind power generation bring severe challenges to secure and economic operation of power systems. Because wind power forecasting error is unavoidable, probabilistic forecasting becomes critical to accurately quantifying the uncertainty involved in traditional point forecasts of wind power and to providing meaningful information to conduct risk management in power system operation. This paper proposes a novel direct quantile regression approach to efficiently generate nonparametric probabilistic forecasting of wind power generation combining extreme learning machine and quantile regression. Quantiles with different proportions can be directly produced via an innovatively formulated linear programming optimization model, without dependency on point forecasts. Multistep probabilistic forecasting of 10-min wind power is newly carried out based on real wind farm data from Bornholm Island in Denmark. The superiority of the proposed approach is verified through comparisons with other well-established benchmarks. The proposed approach forms a new artificial neural network-based nonparametric forecasting framework for wind power with high efficiency, reliability, and flexibility, which can be beneficial to various decision-making activities in power systems.

The unit commitment of a generation system including a significant number of wind energy conversion systems will be based on wind power forecasts which will themselves be based on wind speed forecasts. Forecasts for periods of equal length with the startup/shutdown times of conventional units will be of great importance. The problem of getting good wind speed forecasts is investigated by fitting time series models to wind speed data. In this paper, the investigations are confined to projection times up to 3 and 6 h. In addition, the impact of hypothetical perfect wind power forecasts on the commitment of intermediate load units is indicated by calculations of the wind power variations within periods of up to 3 h.

A new multistage short-term wind power forecast model using decomposition and artificial intelligence methods

The rapid expansion of wind power gives rise to a number of challenges for power system operators and electricity market participants. The key operational challenge is to efficiently handle the uncertainty and variability of wind power when balancing supply and demand in ths system. In this report, we analyze how wind power forecasting can serve as an efficient tool toward this end. We discuss the current status of wind power forecasting in U.S. electricity markets and develop several methodologies and modeling tools for the use of wind power forecasting in operational decisions, from the perspectives of the system operator as well as the wind power producer. In particular, we focus on the use of probabilistic forecasts in operational decisions. Driven by increasing prices for fossil fuels and concerns about greenhouse gas (GHG) emissions, wind power, as a renewable and clean source of energy, is rapidly being introduced into the existing electricity supply portfolio in many parts of the world. The U.S. Department of Energy (DOE) has analyzed a scenario in which wind power meets 20% of the U.S. electricity demand by 2030, which means that the U.S. wind power capacity would have to reach more than 300 gigawatts (GW). The European Union is pursuing a target of 20/20/20, which aims to reduce greenhouse gas (GHG) emissions by 20%, increase the amount of renewable energy to 20% of the energy supply, and improve energy efficiency by 20% by 2020 as compared to 1990. Meanwhile, China is the leading country in terms of installed wind capacity, and had 45 GW of installed wind power capacity out of about 200 GW on a global level at the end of 2010. The rapid increase in the penetration of wind power into power systems introduces more variability and uncertainty in the electricity generation portfolio, and these factors are the key challenges when it comes to integrating wind power into the electric power grid. Wind power forecasting (WPF) is an important tool to help efficiently address this challenge, and significant efforts have been invested in developing more accurate wind power forecasts. In this report, we document our work on the use of wind power forecasting in operational decisions.

Wind power is undergoing a rapid development nationally as well as globally and in a number of countries covers an increasing part of the power supply. At the same time an ongoing liberalization of power markets is taking place and to an increasing extent the owners of wind power plants will themselves have to be responsible for trading the power at the spot market and financially handling the balancing. In the western part of Denmark (Jutland/Funen area), wind‐generated power from time to time covers almost 100% of total power consumption. Therefore some examples are chosen from this area to analyse in more detail how well large amounts of wind power in the short‐term are handled at the power spot market. It turns out that there is a tendency that more wind power in the system in the short run leads to relatively lower spot prices, while less wind power implies relatively higher spot prices, although, with the exception of December 2002, in general no strong relationship is found. A stronger relationship is found at the regulating market, where there is a fairly clear tendency that the more wind power produced, the higher is the need for down‐regulation, and, correspondingly, the less wind power produced, the higher is the need for up‐regulation. In general for the Jutland/Funen area the average cost of down‐regulation is calculated as 1·2 c€/kWh regulated for 2002, while the cost of up‐regulation amounts to 0·7 c€/kWh regulated. Copyright © 2003 John Wiley & Sons, Ltd.

This article suggests the application of multiresolution analysis by Wavelet Transform—WT and Echo State Networks—ESN for the development of tools capable of providing wind speed and power generation forecasting. The models were developed to forecast the hourly mean wind speeds, which are applied to the wind turbine’s power curve to obtain wind power forecasts with horizons ranging from 1 to 24 h ahead, for three different locations of the Brazilian Northeast. The average improvement of Normalized Mean Absolute Error—NMAE for the first six, twelve, eighteen and twenty-four hourly power generation forecasts obtained by using the models proposed in this article were 70.87%, 71.99%, 67.77% and 58.52%, respectively. These results of improvements in relation to the Persistence Model—PM are among the best published results to date for wind power forecasting. The adopted methodology was adequate, assuring statistically reliable forecasts. When comparing the performance of fully-connected feedforward Artificial Neural Networks—ANN and ESN, it was observed that both are powerful time series forecasting tools, but the ESN proved to be more suited for wind power forecasting.