Wind Generation Forecast Research Articles

Impact of short-term wind forecast accuracy on the performance of decarbonising wholesale electricity markets

As the proportion of variable renewable generation capacity in our electricity systems rises, so too will the associated absolute forecast errors in their generation. This work quantifies the complex interactions between short-term renewable generation forecasts, short-term demand forecasts, generator unit commitment and economic dispatch in electricity systems with levels of variable renewable generation spanning 49–79% of annual electricity demand. When the short-term operational uncertainty caused by renewable generation becomes larger in absolute terms than that of demand, the accuracy of renewable generation forecasting has a critical influence on electricity market performance. That is, inaccurate short-term wind forecasts can increase hours of unserved energy and scarcity hours spent at market price cap, increase total system costs and prices, significantly increase returns to thermal generators and energy storage, reduce returns to wind generators, and increase greenhouse gas emissions slightly. For the generalised case study considered in this work, the value of improved short term wind generation forecasts looks to becomes significant once variable renewables generate more than roughly 60% of annual electricity demand. Reductions in average market prices of 5 $/MWh or more then appear plausible as acceptable levels of reliability are recovered. As such, improvements in short-term renewable generation forecasting are likely required in order to justify continued investment in variable renewables as the major means of achieving decarbonisation efficiently and reliably, and these could be achieved by any combination of improved forecasting, dispatch control or on-site firming.

Day-Ahead Parametric Probabilistic Forecasting of Wind and Solar Power Generation Using Bounded Probability Distributions and Hybrid Neural Networks

Day-Ahead Parametric Probabilistic Forecasting of Wind and Solar Power Generation Using Bounded Probability Distributions and Hybrid Neural Networks

Hierarchical model predictive control for islanded and grid-connected microgrids with wind generation and hydrogen energy storage systems

This paper presents a novel energy management strategy (EMS) to control a wind-hydrogen microgrid which includes a wind turbine paired with a hydrogen-based energy storage system (HESS), i.e., hydrogen production, storage and re-electrification facilities, and a local load. This complies with the mini-grid use case as per the IEA-HIA Task 24 Final Report, where three different use cases and configurations of wind farms paired with HESS are proposed in order to promote the integration of wind energy into the grid. Hydrogen production surpluses by wind generation are stored and used to provide a demand-side management solution for energy supply to the local and contractual loads, both in the grid-islanded and connected modes, with corresponding different control objectives. The EMS is based on a hierarchical model predictive control (MPC) in which long-term and short-term operations are addressed. The long-term operations are managed by a high-level MPC, in which power production by wind generation and load demand forecasts are considered in combination with day-ahead market participation. Accordingly, the hydrogen production and re-electrification are scheduled so as to jointly track the load demand, maximize the revenue through electricity market participation and minimize the HESS operating costs. Instead, the management of the short-term operations is entrusted to a low-level MPC, which compensates for any deviations of the actual conditions from the forecasts and refines the power production so as to address the real-time market participation and the short time-scale equipment dynamics and constraints. Both levels also take into account operation requirements and devices’ operating ranges through appropriate constraints. The mathematical modeling relies on the mixed-logic dynamic (MLD) framework so that the various logic states and corresponding continuous dynamics of the HESS are considered. This results in a mixed-integer linear program which is solved numerically. The effectiveness of the controller is analyzed by simulations which are carried out using wind forecasts and spot prices of a wind farm in center-south of Italy.

Scenario-Based Uncertainty Modeling for Power Management in Islanded Microgrid Using the Mixed-Integer Distributed Ant Colony Optimization

Reliable droop-controlled islanded microgrids are necessary to expand coverage and maximize renewables potential. Nonetheless, due to uncertainties surrounding renewable generation and load forecast, substantial power mismatch is expected at off-peak hours. Existing energy management systems such as storage and demand response are not equipped to handle a large power mismatch. Hence, utilizing dump loads to consume excess power is a promising solution to keep frequency and voltage within permissible limits during low-load hours. Considering the uncertainty in wind generation and demand forecast during off-peak hours, the dump load allocation problem was modeled within a scenario-based stochastic framework. The multi-objective optimization with uncertainty was formulated to minimize total microgrid cost, maximum voltage error, frequency deviation, and total energy loss. The mixed-integer distributed ant colony optimization was utilized in a massive parallelization framework for the first time in microgrids to solve the decomposed deterministic problem of the most probable scenarios. Moreover, a flexible and robust load-flow method called general backward/forward sweep was used to obtain the load-flow solution. The optimization problem was applied to the IEEE 69-bus and 118-bus systems. Furthermore, a cost benefit analysis was provided to highlight the proposed method’s advantage over battery-based power management solutions. Lastly, the obtained results further demonstrate the fundamental role of dump load as power management solution while minimizing costs and energy losses.

Machine learning based renewable energy generation and energy consumption forecasting

ABSTRACT Renewable energy generation sources are the need of the hour to mitigate the challenges of fossil fuels, environmental impacts, large transmission and distribution losses, and variable consumption pattern. Renewable energy such as solar PV generation or wind generation is most unpredicted generation and depends upon weather constraints. A promising prediction of generation is a suitable solution for this problem. A machine learning-based conventional neural network algorithm is proposed to forecast the generation and calculate the errors in generation forecast. The generated energy is supplied to consumers, but the consumers consume electricity by their choice and an energy management model is required at the consumer end to supply the available predicted generation to develop a self-sustainable microgrid. The energy consumption at consumer end is essential to predict, and based on the predicted consumption at hourly bases, the cost model for the consumers is developed. The consumption profile is forecasted using long short-term memory as it is a good tool for time-series forecasting using available sequential load consumption data. The demand is forecasted and analyzed for selected load categories (i.e. fixed, non-shift-able, and shift-able loads). The evaluation matrix mean square error (MSE), mean absolute error, and root mean square error (RMSE) are used for the error calculations. The RMSE for solar and wind power generation forecasting have been 2.472 and 3.034, respectively. The RMSE for fixed, non-shiftable, and shiftable demand forecasting have been 0.027, 0.067, and 0.21, respectively. The results show that the proposed methods for predictions of solar, wind power generation and energy consumption forecast achieve better accuracy. The presented methodology can be implemented with energy storage for renewable energy production and demand management for energy from sustainable energy resources.

Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors

Power generation forecasts for wind farms, especially with a short-term horizon, have been extensively researched due to the growing share of wind farms in total power generation. Detailed forecasts are necessary for the optimization of power systems of various sizes. This review and analytical paper is largely focused on a statistical analysis of forecasting errors based on more than one hundred papers on wind generation forecasts. Factors affecting the magnitude of forecasting errors are presented and discussed. Normalized root mean squared error (nRMSE) and normalized mean absolute error (nMAE) have been selected as the main error metrics considered here. A new and unique error dispersion factor (EDF) is proposed, being the ratio of nRMSE to nMAE. The variability of EDF depending on selected factors (size of wind farm, forecasting horizons, and class of forecasting method) has been examined. This is unique and original research, a novelty in studies on errors of power generation forecasts in wind farms. In addition, extensive quantitative and qualitative analyses have been conducted to assess the magnitude of forecasting error depending on selected factors (such as forecasting horizon, wind farm size, and a class of the forecasting method). Based on these analyses and a review of more than one hundred papers, a unique set of recommendations on the preferred content of papers addressing wind farm generation forecasts has been developed. These recommendations would make it possible to conduct very precise benchmarking meta-analyses of forecasting studies described in research papers and to develop valuable general conclusions concerning the analyzed phenomena.

Long-term wind and solar energy generation forecasts, and optimisation of Power Purchase Agreements

Due to more affordable solar and wind power, and the European Union regulations for decarbonisation of the economy, more than 40% of the Fortune 500 companies have targets related to green energy. This is one of the main reasons why multi-technology Power-Purchase Agreements (PPAs) are becoming increasingly important. However, there are risks associated with the uncertainty and variable generation patterns in wind speed and solar radiation. Moreover, there are challenges to predict intermittent wind and solar generation for the forecasting horizon required by PPAs, which is usually of several years. We propose a long-term wind and solar energy generation forecasts suitable for PPAs with cost optimisation in energy generation scenarios. We use Markov Chain Monte Carlo simulations with suitable models of wind and solar generation and optimise long-term energy contracts with purchase of renewable energy.

Power grid operation optimization and forecasting using a combined forecasting system

AbstractReliable photovoltaic and wind power generation forecasts are essential for efficient power systems operations. A combined forecasting system is developed, which integrates a data preprocessing method, a sub‐predictor selection rule, and a multi‐objective optimization to integrate various forecasting models. The proposed system effectively aggregates the advantages of all algorithms involved, facilitating greater prediction precision and stability. Experiments indicated that the proposed system can achieve higher quality point and interval forecasting performance relative to the comparative approaches.

AWS IoT analytics platform for microgrid operation management

Microgrid (MG) represents a promising opportunity for integrating renewable energy systems with the electric power grid. However, numerous complexities need to be addressed in the process. The electrical grid is complex, vulnerable, and centralized. Thus, the integration is challenging owing to the stochastic nature of renewable energy generation, which affects the possibility of reliable forecasting. The wastage due to poor estimation of clean energy generation discourages new investments in this area. However, recent advancements in big data technologies enable processing a large amount of data captured from multiple sources in real-time. It opens the possibility of improving the operational optimization of MGs and the performance of forecasting models. The overall MG problem is formulated using a two-stage stochastic mixed-integer linear programming problem with recourse. Amazon Web Services (AWS) IoT analytics platform inputs data in real-time and runs a sophisticated wind generation forecast analysis. The stochastic model is solved using the Sample Average Approximation (SAA) algorithm. The innovative methodology leads to significant improvements in the total average operating cost by integrating AWS IoT Analytics compared to traditional methods that use historical data. Computations are performed for different power-grid settings, including a power-outage, and different power generators units capacities with total operation average cost savings of 7.6% and 5.9%, respectively. Sensitivity analysis showed that the SSA algorithm could solve all the instances by providing high-quality solutions. The AWS IoT strategy outperformed at 7.7 % and 3.6% for optimality gap and CPU time, respectively. We examined an actual case in Peru for an agricultural application to assess the performance of a stochastic optimization model with a real-time IoT wind generation forecast strategy. The results revealed the following capabilities of our novel framework: (1) it can realize higher cost savings from the MG operating systems; (2) improve real-time renewable energy forecasting; (3) facilitate robust decision-making under conditions of uncertainty.

Short-term forecasting of wind and solar power generation

It is important to note that the generated power of renewable sources depends on the natural conditions at a particular geographic point, the level of wind flow speeds and solar radiation. The patterns characterizing these parameters depend on the time of year, locality and are purely probabilistic in nature. Taking into account the above-mentioned conditions for the effective implementation of “green” objects in the power supply system, the purpose of this work is to build forecasting models that are more likely to be able to determine what part of the load can be covered by the power supply system based on wind power and solar installations. This purpose was achieved by constructing and training artificial neural networks with data on the speed of wind flow and solar radiation obtained from real renewable energy facilities. The most significant result is the identification of the necessary forecasting horizon, taking into account the preservation of a relatively good quality of metrics, as well as understanding what additional data is required to improve this quality. The significance of the results obtained lies in the fact that they make it possible to determine what reserve capacity is required to be included in the project.

A novel ensemble model for long-term forecasting of wind and hydro power generation

Power generation scenario modelling has become an integral part of long-term planning in power system due to high penetration of variable renewable energy. It requires accurate estimates of power generation from different resources to find cost-optimal mix of generations. Predicting the generation of weather dependent renewables in long-term is not feasible but an adaptive long-term forecasting model based on univariate time-series analysis can provide the solution. Therefore, an effort has been made through this paper to provide accurate medium to long-term forecasts (a week-ahead to a year-ahead) for wind and hydro power generation using a novel ensemble forecasting model. The proposed model is devised in three phases; Phase-I develops a hybrid model using ARIMA (Auto Regressive Integrated Moving Average) and Bi-LSTM (Bidirectional Long Short Term Memory) predictions. Phase-II integrates the forecasts of seasonal and off-season generation periods obtained via a Diligent Search Algorithm (DSA). DSA is an innovative algorithm, designed to identify the hidden seasonalities that are responsible for the intermittent behaviour of wind and hydro power generation time-series. Finally, Phase-III facilitates amalgamation of prediction results of Phase-I and Phase-II to build the proposed forecasting model. Results show that MAE (Mean Absolute Error) for wind and hydro power are 1.97% to 5.52% and 2.3% to 6.42% while RMSE (Root Mean Square Error) varies from 2.79% to 7.8% and 2.63% to 8.4% respectively in a week-ahead to a year-ahead scenarios. Since this model is specifically designed for a year-ahead forecasting scenario, its performance can become unstable beyond this time horizon.

Solar and wind power generation forecasts using elastic net in time-varying forecast combinations

Precise renewable energy feed-in forecasts are essential for an effective and efficient integration of renewables into energy systems, and research contributions that help to reduce the uncertainty related to renewables are in high demand. This importance will increase in the future, as renewable energies are the world’s fastest growing electricity generation capacities. Forecast combinations have been empirically proven to outperform individual forecasting models in many disciplines. Our work uses an elastic net method, with cross-validation and rolling window estimation, in the context of renewable energy forecasts. Namely, the forecast combinations are obtained using regional data from Germany for both solar photovoltaic and wind feed-in during the period 2010–2018, with quarter-hourly frequency. The dynamic elastic net estimation, preceded by dynamic data pre-processing, improves forecasting accuracy for both photovoltaic and wind power feed-in forecasts. Moreover, our forecasting framework outperforms benchmarks such as simple average and individual forecasts. Our forecasting framework can be applied widely to estimate renewable power in other countries, systems, or individual power plants.

Multi-Objective Optimization and Dispatch of Distributed Energy Resources for Renewable Power Utilization Considering Time-of-Use Tariff

The intermittency of wind and solar power generation brings risks to the safety and stability of the power system. In order to maximize the utilization of renewables, optimal control and dispatch methods of the Distributed Energy Resources including the generators, energy storage and flexible demand are necessary to be researched. This paper proposes an optimization and dispatch model of an aggregation of Distributed Energy Resources in order to facilitate the integration of renewables while considering the benefits for dispatchable resources under time-of-use tariff. The model achieves multi-objective optimization based on the constraints of day-ahead demand forecast, wind and solar generation forecast, electric vehicles charging routines, energy storage and DC power flow. The operating cost, the renewable energy utilization and the revenues of storages and electric vehicles are considered and optimized simultaneously through the min–max unification method to achieve the multi-objective optimization. The proposed model was then applied to a modified IEEE-30 bus case, demonstrating that the model is able to reconcile all participants in the system. Sensitivity analysis was undertaken to study the impact of initial states of the storages on the revenues to the resource owners.

Ensemble forecasting for intraday electricity prices: Simulating trajectories

Recent studies concerning the point electricity price forecasting have shown evidence that the hourly German Intraday Continuous Market is weak-form efficient. Therefore, we take a novel, advanced approach to the problem. A probabilistic forecasting of the hourly intraday electricity prices is performed by simulating trajectories in every trading window to receive a realistic ensemble to allow for more efficient intraday trading and redispatch. A generalized additive model is fitted to the price differences with the assumption that they follow a zero-inflated distribution, precisely a mixture of the Dirac and the Student’s t-distributions. Moreover, the mixing term is estimated using a high-dimensional logistic regression with lasso penalty. We model the expected value and volatility of the series using i.a. autoregressive and no-trade effects or load, wind and solar generation forecasts and accounting for the non-linearities in e.g. time to maturity. Both the in-sample characteristics and forecasting performance are analysed using a rolling window forecasting study. Multiple versions of the model are compared to several benchmark models and evaluated using probabilistic forecasting measures and significance tests. The study aims to forecast the price distribution in the German Intraday Continuous Market in the last 3 h of trading, but the approach allows for application to other continuous markets, especially in Europe. The results prove superiority of the mixture model over the benchmarks gaining the most from the modelling of the volatility. They also indicate that the introduction of XBID reduced the market volatility.

Leveraging owners’ flexibility in smart charge/discharge scheduling of electric vehicles to support renewable energy integration

High integration of intermittent renewable energy sources (RES), predominantly wind power, has created complexities in power system operations. In addition, large fleets of Electric Vehicles (EVs) increase the uncertainty on electricity consumption significantly. Thus, an uncoordinated charging process will further complicate the grid scheduling. In this work, we propose a coordinated scheduling algorithm for charge/discharge of aggregated EV fleets to maximize the integration of wind generation and minimize the charging cost for EV owners in a vehicle-to-grid (V2G) setup. Challenges for people participation in V2G, such as battery degradation and insecurity about unexpected events, are also addressed.The real-time charge/discharge scheduling problem is formulated as a multi-objective mixed-integer quadratic programming problem that the aggregator takes advantage of real-time communication in smart grid to frequently update the EV schedule with updated real-time information about EV availabilities, wind generation forecast, and electricity price. Simulations demonstrate significant increase in wind utilization and reduction in charging cost and battery degradation compared to the uncontrolled charging scenario.

Comparative Analysis of Wind Energy Generation Forecasts in Poland and Portugal and Their Influence on the Electricity Exchange Prices

Currently, the privileged position of wind energy producers is being weakened by their enforced participation in the market on equal terms. This requires accurate production forecasting. The main aim of this study is to comparatively examine the wind generation forecasts in Poland and Portugal, as well as to verify their influence on the day-ahead market prices. The statistical analysis revealed significant deviations of the forecasted and actual wind production in both countries, which referred to the corresponding spot and balancing prices caused considerable financial losses by the wind energy suppliers. In this paper, the influence of the wind generation forecasts on the spot prices has been examined through developed the auto-regressive moving average (ARMA), ARMA with exogenous inputs (ARMAX) and non-linear auto-regressive neural network (NAR), NAR with exogenous inputs (NARX)artificial neural network (ANN) models. The results have shown that the usability of the information of forecasted wind generation is not unequivocal in models developed for spot prices in Poland, mainly because of the randomness and volatility of recorded wind generation forecasts. However, in the case of Portugal, the forecasted wind generation occurred to be a valuable input in spot prices models, which results in an improvement in the models’ accuracy.

Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend

To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method.

Combined Forecast Model for Wind Generation in Brazilian Monthly Dispatch Scheduling

The fast growing of wind generation in Brazil has brought benefits to the country's energy supply, especially under critical hydrological conditions, as observed between 2014 and 2017. However, deviations in wind generation predictions may lead to the depletion of reservoirs and expensive thermal generation. In order to improve the accuracy of wind generation forecasts for the monthly dispatch scheduling, different models based on Holt-Winters and SARIMA approach were proposed for Rio do Fogo wind farm, located in the Northeast of Brazil. The results show that the variance-weighted combination of a SARIMA (1,0,0) x (1,1,1)12 and a multiplicative Holt-Winters model provides 13.54% lower mean absolute percentage errors (MAPE) than the current forecast model (moving averages). Thus, it is possible to apply the SARIMA/Holt-Winters variance-weighted combination model with greater accuracy for forecasting the monthly generation of Rio do Fogo wind farm, during the dispatch planning carried out by the Brazilian independent system operator.

Analysis of the impacts of wind power forecast error on power system operation

With increasing penetration of renewable energy resources, the uncertain nature of these intermittent resources can introduce power dispatch problems resulting in serious risks to power system operational performance and reliability. Wind generation forecasts are crucial for power system scheduling and operation. In this study, a probabilistic method is proposed to assess the effects that wind generation forecast errors have on the dispatched power flow following short-term wind generation forecasts. Case studies have been conducted using a 29-bus reduced order model of the Great Britain power system under different scenarios. It is shown that the translation of wind plant forecast errors to errors in dispatched wind forecasts is non-linear at high penetration levels.

A novel composite neural network based method for wind and solar power forecasting in microgrids

Nowadays, wind and solar power generation have a major impact in many microgrid hybrid energy systems based on their cost and pollution. On the other hand, accurate forecasting of wind and solar power generation is very important for energy management in microgrids. Therefore, a novel prediction interval model, consisting of several sections (wavelet transform, hybrid feature selection, Group Method of Data Handling neural network, and modified multi-objective fruit fly optimization algorithm), has been developed to short-term predict wind speed and solar irradiation and to investigate the energy consumption of microgrids. The renewables prediction and the energy consumption analysis have been applied to the Favignana island microgrid, in the south of Italy, using the new proposed point forecast model (Group Method of Data Handling neural network and modified fruit fly optimization algorithm – GMDHMFOA) and a Pareto analysis. The results show that the proposed interval prediction model has a good performance in different confidence levels (95%, 90%, and 85%) to predict wind speed and solar irradiation than other already existing methods. In addition, the proposed point forecast model (GMDHMFOA) has an acceptable error and better performance than the other ones commonly used in predicting energy consumption. Lastly, the monthly energy consumption in different stations of the microgrid can be predicted by using the proposed model and provides suitable solutions for energy management of the microgrid.