Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China

We conducted 6-month deterministic and ensemble forecasts initialized from the ensemble mean analyses and 128-member ensemble analyses, respectively, on the first day of each month from 2016 to 2018 (i.e., 36 cases in total) to compare the forecast accuracies of the Kuroshio south of Japan. The results showed that the ensemble mean forecasts captured the Kuroshio paths better in general compared with the deterministic forecasts, especially during the Kuroshio large meander period after the summer of 2017. For 2016–2018, the predictability limit for the ensemble mean forecast is estimated to be 108 days on average, demonstrating for the first time that the ensemble mean forecasts achieve a significantly longer predictability limit than the 74-day limit of the deterministic forecasts for the Kuroshio. We also investigated the sensitivity of the forecast accuracy to the ensemble size using a resampling method. The results indicated that for the Kuroshio prediction, the ensemble mean forecasts are more accurate than the deterministic forecasts when the ensemble size exceeds 32 members. When the ensemble size is limited to 32 members or less, combining the deterministic and ensemble forecasts could improve the short-term Kuroshio forecasting. This study also identified substantial northward shift biases in the Kuroshio forecasts, suggesting that further system development is necessary to achieve more significant improvements in the forecast accuracy.

This study proposes an innovative framework for wind energy forecasting and power system scheduling. Leveraging advanced statistical and machine learning models, coupled with uncertainty modeling using Monte Carlo simulations, the approach aims to enhance accuracy in wind energy predictions. Optimization strategies, encompassing multi-objective decision-making and real-time scheduling policies, are introduced to address variability and uncertainty in power systems. Diversified wind farm layouts and energy storage integration further mitigate uncertainty. The paper emphasizes the significance of a robust monitoring system with feedback mechanisms. Case studies illustrate the framework's practical application, demonstrating its efficacy in optimizing power systems amidst uncertain wind conditions.

In the absence of convection permitting numerical weather prediction (NWP) ensembles, the most recent deterministic NWP precipitation forecast is usually addressed. However, the exact intensity, location and timing of a deterministic precipitation forecast is not always reliable because of the chaotic nature and complexity of precipitation formation mechanisms. This study examines a way to optimize the use of precipitation forecasts for deterministic NWP models. More specifically, it suggests using a spatially smoothed time‐lagged ensemble (TLE) to obtain more reliable precipitation forecasts. A global NWP model (integrated forecast system—IFS) and a regional convection permitting NWP model (COSMO) over the Eastern Mediterranean during the period 2016–2018 were used for the analysis. First, the paper defines light, light–moderate and moderate intensities for 6 hr accumulated precipitation (6hAP) and investigates the corresponding definitions for 1 hr accumulated precipitation (1hAP). Next, fractional skill score (FSS) are used to estimate the optimal spatial smoothing scale of a deterministic precipitation forecast for the three intensity categories for the 6hAP and 1hAP. The FSS is also used to compare COSMO and IFS deterministic precipitation forecasts, and to analyse the skill degradation with the forecast range. It is quantitatively shown that the useful scale of precipitation forecasts is smaller for larger accumulation time intervals. Finally, precipitation forecasts for TLE are formed from successive smoothed deterministic forecasts and compared with the most recent deterministic forecast. It is found that, on average, TLEs have better skills for both 6hAP and 1hAP. The reason for this improvement in skill is illustrated using a case study as an example.

[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAU

Recent literature on building energy performance simulation leans toward implementing uncertainty analysis (UA), instead of deterministic solutions, to handle ever-existing and pivotal uncertainties in building design decision-making process. Variations in weather temperature, degradation of building envelope material properties over time, and random behavior of occupants, among all, are the key sources of uncertainty in building energy consumption predictions. The UA couples to the sensitivity analysis (SA) to identify the most influential inputs on the uncertainties of the building energy consumption. This paper describes a newly-developed UA and SA predictive tool for building energy performance simulations. Energy performance simulations are based on a resistance-capacitance thermal model for the building. For a hypothetical residential building in College Station, Texas, USA, the present work describes and compares predicted probability distribution and sensitivity indexes produced by the UA-SA tool using a transient (dynamic) response analysis (TRA) and static response analysis (SRA). For brevity, the analysis considers uncertainty only for the exterior walls’ parameters including thickness, thermal conductivity, heat transfer coefficient, density, and heat capacity; i.e., a five-dimensional problem is solved. Compared to the TRA, predictions from the SRA underestimate the annual energy consumption up to 30%; however, SRA is significantly faster. Nonetheless, sensitivity indexes from the SRA and TRA closely match.

Wind as a renewable energy, is typical of clean energy, and wind power generation has good social and environmental benefits, which has developed rapidly in worldwide. In this paper, the problems of China's wind power industry and the world wind power industry experience are discussed. The distribution of resources for wind energy, wind energy resource assessment, monitoring and forecasting system, wind industry, policy influencing factors are detailed analysis, and based on China conditions for its development were discussed.

Probabilistic wind power forecasts that quantify the uncertainty in wind output have the potential to aid in the economic grid integration of wind power at large penetration levels. In this paper, a novel probabilistic wind forecasting approach based on pinball loss optimization is proposed, in conjunction with a multi-model machine learning based ensemble deterministic forecasting framework. By assuming the point-forecasted value as the mean at each point, one unknown parameter (i.e., standard deviation) of a predictive distribution at each forecasting point is determined by minimizing the pinball loss. A surrogate model is developed to represent the unknown distribution parameter as a function of deterministic forecasts. This surrogate model can be used together with deterministic forecasts to predict the unknown distribution parameter and thereby generate probabilistic forecasts. Numerical results of case studies show that the proposed method has improved the pinball loss by up to 35% compared to a baseline quantile regression forecasting model.

Wind Energy: An Introduction covers wind energy system types, operation, modeling, analysis, integration, and control. Beginning with a history of the development of wind energy, this comprehensive book: - Explains the aerodynamic theories that govern the operation of wind turbines - Presents wind energy statistics to address the stochastic nature of wind speed - Employs the statistical modeling of wind speed to evaluate sites for wind energy generation - Highlights the differences between the most common types of wind turbines - Analyzes the main power electronic circuits used in wind energy - Details the induction, synchronous, and permanent magnet generators from the basic principle of induced voltage to the steady-state and dynamic models - Explores the operation, stability, control, and protection of type 1, 2, 3, and 4 wind turbines - Discusses the main integration challenges of wind energy systems with electric utility systems - Features numerous models, illustrations, real-world examples, and exercise problems - Includes a solutions manual and figure slides with qualifying course adoption Wind Energy: An Introduction requires a basic knowledge of electric circuit theory, making it an ideal text for students at the senior-undergraduate and graduate levels. In addition, the book provides practicing engineers with a handy professional reference.

The increasing share of variable renewable energy (VRE) generation poses challenges to power systems. Possible challenges include adequacy of reserves, planning and operation of power systems, and interconnection expansion studies in future power systems with very different generation patterns compared to today. To meet these challenges, there is a need to develop models and tools to analyze the variability and uncertainty in VRE generation. To address the varied needs, the tools should be versatile and applicable to different geographical and temporal scales. Time series simulation tools can be used to model both today and future scenarios with varying VRE installations. Correlations in Renewable Energy Sources (CorRES) is a simulation tool developed at Technical University of Denmark, Department of Wind Energy capable of simulating both wind and solar generation. It uses a unique combination of meteorological time series and stochastic simulations to provide consistent VRE generation and forecast error time series with temporal resolution in the minute scale. Such simulated VRE time series can be used in addressing the challenges posed by the increasing share of VRE generation. These capabilities will be demonstrated through three case studies: one about the use of large‐scale VRE generation simulations in energy system analysis, and two about the use of the simulations in power system operation, planning, and analysis.This article is categorized under: Wind Power > Systems and Infrastructure Energy Infrastructure > Systems and Infrastructure Energy Systems Economics > Systems and Infrastructure

Renewable energy is intermittent by nature and to integrate this energy into the Grid while assuring safety and stability the accurate forecasting of there newable energy generation is critical. Wind Energy prediction is based on the ability to forecast wind. There are many methods for wind forecasting based on the statistical properties of the wind time series and in the integration of meteorological information, these methods are being used commercially around the world. But one family of new methods for wind power fore castingis surging based on Machine Learning Deep Learning techniques. This paper analyses the characteristics of the Wind Speed time series data and performs a literature review of recently published works of wind power forecasting using Machine Learning approaches (neural and deep learning networks), which have been published in the last few years.

According to the fact that the increase of wind power penetration rate causes the influence of wind power’s randomness and fluctuation on the power grid, the single short-term wind power point prediction often cannot meet the needs of power grid risk assessment and decision-making. In this paper, we first calculate the theoretical probability model of each wind power forecast box by the end function in MATLAB, and then use the exponential covariance function expression to determine the best covariance matrix corresponding to the dynamic scenarios, and determine the multivariate normal distribution model of wind farm output obedience at multiple connected moments; For each predicted moment of the wind power point prediction value of the wind belongs to the prediction box, we direct sample random vector which obey multivariate normal distribution to form the wind power dynamic scenario. After a simulation experiment on a real wind farm, the results show that the scenario set considering wind power fluctuation at different time scales can cover the measured wind power curve and the reliability of the method is proved.

In the coming years, ensuring the electricity supply will be one of the most important world challenges. Renewable energies, in particular wind energy, are an alternative to non-sustainable resources thanks to their almost unlimited supply. However, the chaotic nature and the variability of the wind represent a significant barrier to a large-scale development of this energy. Consequently, providing accurate wind power forecasts is a crucial challenge. This paper presents AMAWind, a multi-agent system dedicated to wind power forecasting based on a cooperative approach. Each agent corresponds to a turbine at a given hour, it starts from an initial production forecast and acts in a cooperative way with its neighbors to find an equilibrium on conflicting values. An assessment of this approach was carried out on data coming from a real wind farm.

The impact of European balancing rules on wind power economics and on short-term bidding strategies

Wind energy, the price of carbon allowances, and CO2 emissions: Evidence from Ireland

Evaluation of wind energy potential in province of Bushehr, Iran