Uncertainty Of Wind Speed Research Articles

On estimating long period wind speed return levels from annual maxima

The uniform risk engineering practices that are increasingly being adopted for structural design require estimates of the extreme wind loads with very low annual probabilities of exceedance, corresponding to return periods of up to 3000-years in some cases. These estimates are necessarily based on observational wind data that typically spans only a few decades. The estimates are therefore affected by both large sampling uncertainty and, potentially, non-negligible biases. Design practices that aim to meet mandated structural reliability criteria take the sampling uncertainty of long period wind speed or wind pressure estimates into account, but reliability could be compromised if estimates are also biased. In many circumstances, estimates are obtained by fitting an extreme value distribution to annual maximum wind speed observed over a few decades. A key assumption implicit in doing so is that wind speed annual maxima are max-stable. Departures from max-stability can exacerbate the uncertainty of long-period return level estimates by inducing systematic estimation bias as well. Observational records, however, are generally too short to assess max-stability. We therefore use wind speed data from a large (50-member) ensemble of CanRCM4 historical simulations over North America to assess whether wind speed annual maxima are max-stable. While results are generally reassuring at the continental scale, disquieting evidence of a lack of max-stability is often found in the central and southern parts of the continent. Results show that when annual maximum wind speeds are not max-stable, long period return level extreme wind speeds tend to be underestimated, which would compromise reliability if used to design infrastructure such as tall buildings and towers.

Bayesian estimation of copula parameters for wind speed models of dependence

Modelling the uncertainty of wind speed is essential in power flow analysis. Having abundant knowledge of the wind speed in an area is critical. A low volume of data can increase uncertainty in wind speed analysis. Spatial dependencies are often modelled before running probabilistic power flow and load flow analysis. Copulas are a popular way of capturing spatial dependence between multiple wind farms. Using NREL data from seven Northeastern United States wind farm sites, Bayesian inference will be used to determine the copula parameter uncertainty between weekly, daily, and hourly wind speed observations. This approach will be used on elliptical and single parameter Archimedean copulas. For each possible wind farm pair, an uninformative prior will be placed on the copula parameter. The resulting posterior will contain a distribution of copula parameter values based on the prior and the observed wind speed data. The posterior's credible interval is reviewed to determine the uncertainty in parameter estimation. The results show that using a data volume considerably more petite than 8760 hourly data points will result in more uncertainty in parameter estimation and inaccuracies in wind speed forecasting if using non-Bayesian methods for copula parameter estimation.

Application of machine learning for wind energy from design to energy-Water nexus: A Survey

The world’s dependency on fossil fuels is decreasing swiftly, and countries rely more on renewable energies. Among renewable energies, wind energy has become one of the most significant ones. Increasing its production and reducing energy and water costs has attracted many attentions. Moreover, due to the uncertainty of wind speed, power generation of wind farms is facing high volatility, which is affecting the electricity markets. Thus, in light of the rapid growth of wind energy technologies, new approaches based on advanced analytic are required. This paper presents a comprehensive review of artificial intelligence employed in wind energy systems, surveying the studies most applied in various applications and resulting from artificial neural networks (ANN) could be a sustainable approach instead of conventional methods in many cases. A large number of research studies associated with this topic are published since 2015, and based on the application could be categorized in five main groups: wind speed prediction, design optimization, fault detection, optimal control and maintenance planning. A statistical analysis of ANN application in these fields is carried out for the present time and future trends.

A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms

Wind energy has strong volatility and intermittent. Accurate wind speed prediction can not only improve the safety of the system, but also optimize dispatch and reduce economic losses. However, previous studies tend to ignore the influence of virtual components and lack effective identification of wind speed characteristics and a robust interval prediction scheme, resulting in poor results. To bridge these gaps, this paper proposes an energy theory method to solve the problem of modal over-decomposition. The study also combines effective modal recognition, uses different prediction methods according to modal characteristics and proposes a set of new optimization algorithms to improve nonlinear prediction capabilities. Finally, based on Monte Carlo theory, a set of interval prediction schemes that can adapt to different error characteristics are proposed. Under the verification of wind speed data in Changma, China and Sotavento, Spain. The mean absolute percentage error of wind speed deterministic prediction reaches 4.22% and 5.82%, respectively. The coverage rate of wind speed uncertainty prediction meets different confidence requirements, and the average interval width is still less than 2.5 m/s at 90% confidence. The results show that the forecasting system proposed in this paper is significantly better than all the comparative forecasting schemes, which can reduce the risk of fluctuations and improve the stability and safety of the wind power system.

Examination of the Chance Constrained Optimal WT Penetration Level in Distorted Distribution Network with Wind Speed and Load Uncertainties

Harmonic penetration can be problematic by the growing interconnection of Wind Turbines (WTs) in distribution networks. Since the active power outputs of WTs and loads in the distribution system have uncertainties, the optimal WT penetration level problem can be considered to have a stochastic nature. In this study, this problem is taken into account in the stochastic optimization method with the consideration of uncertainties in wind speed and distribution network load profile. Chance constraint programming is taken into account in the determination of optimal WT penetration levels by applying the Genetic Algorithm (GA) along with Monte Carlo Simulation (MCS). The harmonic power flow analysis based on the decoupled harmonic load flow approach is employed in the distorted distribution network. Chance constraints are considered for the harmonic issues such as the Total Harmonic Distortion of Voltage (VTHD), Individual Harmonic Distortion of Voltage (VIHDh), and Root Mean Square of Voltage (VRMS).

Impacts of joint operation of wind power with electric vehicles and demand response in electricity market

This paper investigates the impacts of joint operation of wind power producers (WPPs) with electric vehicles (EVs) and demand response (DR) resources as a virtual power plant (VPP) in electricity markets. In particular, this paper aims at answering the question of whether these resources have incentives to voluntarily form a VPP to participate in the electricity market. For this purposes, two stochastic multi-period game models of the day-ahead (DA) electricity market are proposed based on the oligopolistic competition theory. The first model considers when the WPP, EV aggregator (EVA), and DR aggregator (DRA) form a VPP and bid in the DA market in a cooperative way, and the Shapley value method is applied to allocate the VPP's profit among the WPP, EVA and DRA; while the second model focuses on when they bid in the DA market in a non-cooperative way. In both models, the scenario generation and reduction technique is employed to simulate wind speed uncertainties, and a deviation penalty mechanism is applied when the WPP's actual output deviate from its cleared quantity in the market. In addition, the conditional value-at-risk (CVaR) is used to measure financial risks of the WPP and analyze its risk preference. Numerical examples show that the VPP mode can help the WPP to reduce its deviation between its actual output and cleared quantity and improve its competitiveness in the market. In addition, individual profits of the WPP, EVA and DRA will all increase in the VPP mode, which means that the WPP, EVA and DRA have high incentives to voluntarily form a VPP and participate in the electricity market.

The robust recovery model of distribution network considering correlation between wind speed and load

In order to consider the correlation between distributed power output and load, this paper uses the value-at-risk theory to quantitatively calculate the uncertainty of wind speed and load demand, and establishes a wind speed-load joint probability distribution model using bivariate normal distribution, which will take into account the wind speed uncertainty. The robust decision-making problem of reliability and load participation is characterized as a mixed-integer linear programming problem, which takes the minimization of the equivalent load loss as the optimization goal, establishes the load recovery path as the optimization variable, and takes the wind speed and load as the disturbance variable to account for the wind speed-The load-dependent robust restoration model of the distribution network is solved by the Gurobi high-efficiency solver. Finally, the IEEE69-bus power distribution system with wind power was used to verify the effectiveness of this model and solution method.

Ultra-short-term wind-speed bi-forecasting system via artificial intelligence and a double-forecasting scheme

A key issue in the wind power industry is the accuracy and stability of wind-speed forecasting, which has an important impact on the power system management and market economy. Accurate prediction of wind speed points could guarantee regular work of smart grids, while interval prediction could evaluate the uncertainty of wind speed and further supply corresponding wind speed information. However, several studies concentrated on the deterministic point forecast of wind speed and seldom considered the interval forecast of wind speed, or performed the two separately, thereby making wind speed prediction inefficient and not comprehensive. To remedy this, we herein propose a wind speed bi-forecasting system that includes three parts: data preprocessing, combination forecasting, and evaluation, making up for the gaps in existing research and analysis. A decomposition algorithm is used to decompose the actual wind speed into a series of finite components as well as remove high-frequency noise components. The combination forecasting module adopts the weight combination mechanism based on a multi-objective multi-verse optimization algorithm to achieve a double output of prediction results. Finally, an evaluation module comprising hypothesis testing and evaluation standards was presented to comprehensively evaluate the system. The experimental results indicate that the system is excellent for predicting wind speed and analyzing the uncertainty of wind speed and thus could be practically applied as a wind power system programming technology.

Stochastic energy management of an electricity retailer with a novel plug-in electric vehicle-based demand response program and energy storage system: A linearized battery degradation cost model

This study investigates the stochastic optimal electricity procurement problem of an electricity retailer in a smart grid environment. Various energy provision means, including distributed generations (DGs), pool market (PM), forward contracts (FC), photovoltaic (PV) system, wind turbine (WT), and Battery energy storage system (BESS) are considered. A novel plug-in electric vehicle (PEV) based demand response program (DRP) is integrated into the problem considering economic incentives for PEV owners, which is computationally fast, and it is based on the uncertainties derived from PEVs, such as arrival/departure time, daily traveled miles and vehicle type. Moreover, the uncertainty of PM price, solar irradiation, wind speed, and conventional loads are taken into account by an artificial neural network (ANN)-based scenario generation method. Furthermore, an analytical linear BESS degradation cost model is integrated into the objective function to ensure fast global optimization. To confirm the functionality of the proposed method, different operation schemes, such as with and without DRP, with and without battery degradation cost, are thoroughly inspected. Additionally, the sensitivity of the problem for the number of PEVs is analyzed. The proposed two-stage stochastic mixed-integer linear problem (MILP) is solved by commercially available optimization software.

A novel approach to multi-horizon wind power forecasting based on deep neural architecture

In recent years, renewable energy sources have been installed in large numbers. Wind power in particular, a technology with very high potential, has become a significant source of energy in most power grids. However, wind power generation forecasting and scheduling remain very difficult tasks due to the uncertainty and stochastic behaviour of wind speed. This work provides a novel, powerful tool for wind power forecasting based on neural expansion analysis for time series forecasting (N-BEATS), a deep neural network approach. N-BEATS was designed as an easy-to-implement approach to solving non-linear stochastic time series forecasting problems. Additionally, a loss function is tailored to wind power forecasting to confront the issue of forecast bias. The results are compared with established models, such as statistical and machine learning approaches as well as hybrid models, using the real-world wind power data from 15 different European countries as input. Comprehensive and accurate results are obtained during this work, showing that this methodology can easily compete with other approaches and even outperform them in terms of accuracy in most cases. Additionally, the tailored loss function reduces the error significantly. The N-BEATS architecture is further customized to deliver decomposed components such as trend and seasonality, yielding interpretable outputs. These findings constitute considerable progress and provide support for decision makers.

Tropospheric Volcanic SO2 Mass and Flux Retrievals from Satellite. The Etna December 2018 Eruption

The presence of volcanic clouds in the atmosphere affects air quality, the environment, climate, human health and aviation safety. The importance of the detection and retrieval of volcanic SO2 lies with risk mitigation as well as with the possibility of providing insights into the mechanisms that cause eruptions. Due to their intrinsic characteristics, satellite measurements have become an essential tool for volcanic monitoring. In recent years, several sensors, with different spectral, spatial and temporal resolutions, have been launched into orbit, significantly increasing the effectiveness of the estimation of the various parameters related to the state of volcanic activity. In this work, the SO2 total masses and fluxes were obtained from several satellite sounders—the geostationary (GEO) MSG-SEVIRI and the polar (LEO) Aqua/Terra-MODIS, NPP/NOAA20-VIIRS, Sentinel5p-TROPOMI, MetopA/MetopB-IASI and Aqua-AIRS—and compared to one another. As a test case, the Christmas 2018 Etna eruption was considered. The characteristics of the eruption (tropospheric with low ash content), the large amount of (simultaneously) available data and the different instrument types and SO2 columnar abundance retrieval strategies make this cross-comparison particularly relevant. Results show the higher sensitivity of TROPOMI and IASI and a general good agreement between the SO2 total masses and fluxes obtained from all the satellite instruments. The differences found are either related to inherent instrumental sensitivity or the assumed and/or calculated SO2 cloud height considered as input for the satellite retrievals. Results indicate also that, despite their low revisit time, the LEO sensors are able to provide information on SO2 flux over large time intervals. Finally, a complete error assessment on SO2 flux retrievals using SEVIRI data was realized by considering uncertainties in wind speed and SO2 abundance.

A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

Due to the uncertainty and volatility of wind speed, its natural output power also exhibits strong volatility. In order to avoid the negative effects of the excessive fluctuation of the output power of wind turbines in the wind farm and the power grid, it is very important to accurately predict and reasonably plan and control the output power of the wind farm. Model predictive control (MPC) is a type of advanced control method that can deal with a multi-input multi-output nonlinear system. Compared to the traditional proportional integral derivative control, MPC is more suitable for the complex wind farm model and exhibits good control performance, and has been gradually applied to control the wind power in wind farms. In this article, we have summarized the application of the MPC technology in the prediction and control of wind power in a wind farm, analyze the application of the MPC technology, including MPC, multi-objective MPC, nonlinear MPC, and distributed MPC, in the wind farm power control with different optimization objectives. In addition, the optimization of the active and reactive power of wind farms has also been discussed in detail. Furthermore, some hot topics in the current research, such as the multi-objective optimization problem of coordinating the maximum power and reducing the fatigue load and power smoothing control problem, have been summarized. Finally, the existing problems of MPC applied to wind power systems have been discussed.

Determination of the emission rates of CO<sub>2</sub> point sources with airborne lidar

Abstract. Anthropogenic point sources, such as coal-fired power plants, produce a major share of global CO2 emissions. International climate agreements demand their independent monitoring. Due to the large number of point sources and their global spatial distribution, the implementation of a satellite-based observation system is convenient. Airborne active remote sensing measurements demonstrate that the deployment of lidar is promising in this respect. The integrated path differential absorption lidar CHARM-F is installed on board an aircraft in order to detect weighted column-integrated dry-air mixing ratios of CO2 below the aircraft along its flight track. During the Carbon Dioxide and Methane Mission (CoMet) in spring 2018, airborne greenhouse gas measurements were performed, focusing on the major European sources of anthropogenic CO2 emissions, i.e., large coal-fired power plants. The flights were designed to transect isolated exhaust plumes. From the resulting enhancement in the CO2 mixing ratios, emission rates can be derived via the cross-sectional flux method. On average, our results roughly correspond to reported annual emission rates, with wind speed uncertainties being the major source of error. We observe significant variations between individual overflights, ranging up to a factor of 2. We hypothesize that these variations are mostly driven by turbulence. This is confirmed by a high-resolution large eddy simulation that enables us to give a qualitative assessment of the influence of plume inhomogeneity on the cross-sectional flux method. Our findings suggest avoiding periods of strong turbulence, e.g., midday and afternoon. More favorable measurement conditions prevail during nighttime and morning. Since lidars are intrinsically independent of sunlight, they have a significant advantage in this regard.

Charging station Stochastic Programming for Hydrogen/Battery Electric Buses using Multi-Criteria Crow Search Algorithm

Today, the zero-emission transport system is a public will in megacities. A promising alternative among the countermeasures is to employ Battery Electric Buses (BEB) and Fuel-Cell Buses (FCB) at reasonable prices. This paper addresses Multi-product Charging Stations (MCS) in the selected bus terminals for refilling hydrogen and electricity. The core of MCS is constituted of a pair of Electrolyzer/Fuel Cell working back to back by Proton Exchange Membrane (PEM) technology. The innate flexibility in the operation of such a configuration enables for providing regulating services when the market price is soaring. The multi-criteria programming for the hourly schedule in different weather conditions is investigated using the Crow Search Algorithm. The objective is to enhance the daily profit of charging stations through the optimized multi-products/services selection. Uncertainty in wind speed, market price, and electric vehicles is considered in scenarios. The weightings are decided through the Analytic Hierarchy Process (AHP) and Criteria Importance through Inter-criteria Correlation (CRITIC) methods. The superiority of MC-CSA Stochastic Programming is showcased by simulations on a modified 33-bus IEEE distribution system using DK2 (east Denmark) data under subsidized and liberal markets. Regarding the daily profit outcomes, CRITIC was advantageous on windy days when the market prices vary with wind speed whilst AHP was preferred in low wind hours. The most lucrative operations are achievable when the products are fully accepted by regulating markets, BEBs, and FCBs on windy days. The results indicate that the additional remuneration on green hydrogen leaves a deeper impact rather than subsidies on electricity prices for EVs.

Probabilistic Assessment of DSTATCOM Operation in Distribution Systems Using Data Clustering Method

The importance of probabilistic assessment in distribution systems is very high due to the increasing penetration of renewable energy sources (RESs) with their fluctuating behavior. Also, there are some other uncertain variables in distribution systems such as loads fluctuations. More awareness of the system state by considering many uncertainties provides more certainty in decision making and causes better risk management. Moreover, the distribution static compensator (DSTATCOM) has been recently implemented due to its efficient abilities in the distribution systems operation especially when RESs are integrated into them. This article investigates the probabilistic assessment of DSTATCOM operation in a distribution system using the k-means-based data clustering method (DCM). The uncertainty of load demands, wind speed, and solar radiation are considered in this study. The performance of DCM is compared to the Monte Carlo simulation (MCS) and Latin Hypercube sampling (LHS) methods in terms of accuracy and computational burden. The efficiency of k-means-based DCM is investigated in the IEEE 69-node test system.

Day-ahead optimal bidding of microgrids considering uncertainties of price and renewable energy resources

Unpredictable faults always reduce the stability and reliability of the electrical system. The increasing use of renewable energy sources (RES) in recent decades has exacerbated power system problems. Microgrids (MG) participation in Ancillary Services (AS) market is a suitable solution for the optimal performance of power systems in these conditions. MGs can also maximize their profits by participating in the AS market. In this paper, the optimal stochastic bidding strategy in joint energy and AS (regulation up and regulation down, spinning reserve and non-spinning reserve) market is modeled. Uncertainties of wind speed and solar radiation are modeled using Weibull and Beta probability distribution functions (PDFs) and probability of call AS is computed for all available AS. Therefore, the risk of the bidding strategy is controlled using conditional value at risk (CVaR). ERCOT market simulation has been carried out in order to determine the participation of each generator in all of the mentioned markets for different prices of energy and also to present the bidding curve, based on real-world data.

Interval prediction algorithm and optimal scenario making model for wind power producers bidding strategy

Nowadays, renewable energies are important sources for supplying electric power demand and a key entity of future energy markets. Therefore, wind power producers (WPPs) in most of the power systems in the world have a key role. On the other hand, the wind speed uncertainty makes WPPs deferent power generators, which in turn causes adequate bidding strategies, that leads to market rules, and the functional abilities of the turbines to penetrate the market. In this paper, a new bidding strategy has been proposed based on optimal scenario making for WPPs in a competitive power market. As known, the WPP generation is uncertain, and different scenarios must be created for wind power production. Therefore, a prediction intervals method has been improved in making scenarios and increase the accuracy of the presence of WPPs in the balancing market. Besides, a new optimization algorithm has been proposed called the grasshopper optimization algorithm to simulate the optimal bidding problem of WPPs. A set of numerical examples, as well as a case-study based on real-world data, allows illustrating and discussing the properties of the proposed method.

Compensation optimization for a static Mars wind imaging Michelson interferometer

This work focuses on determining the optimal design for field widening, and thermal and achromatic compensating a static Mars wind imaging Michelson interferometer (MWIMI). The target lines of MWIMI are emission lines in the 1.27 µm band of the O2(1Δg) airglow. These emissions allow the dynamics of the Martian atmosphere from ~5 km to ~60 km to be observed. Two designs, a three-layer (consisting of two arm glasses and an air gap) and a four-layer (consisting of three arm glasses and an air gap) are examined for a path difference of 10 cm. All possible glass combinations from the SCHOTT glass catalog for these two designs are considered using a computer matching program to determine the optimal ones. The optimal glass combinations are listed and their properties are discussed. The error caused by the misalignment/tilt of optical surface, the homogeneity of glass, the variation of ambient temperature, the glass thickness error and the refractive index error is analyzed. The limits of the tilt angle of optical surface and the homogeneity for MWIMI are provided. The optical path difference varies by less than 0.1 wavelengths over the 5° angular range passing through the interferometer when the variation of ambient temperature and the thickness and refractive index uncertainties are considered. The wind velocity error, the primary factor for characterizing a field-widened Michelson interferometer, is analyzed. Calculations show that the wind speed uncertainty can be better than 1 m s − 1 for an altitude range of 0 – 90 km during daytime and is better than 6 m s − 1 for an altitude range of 0 – 75 km during nighttime.

The use of satellite products to assess spatial uncertainty and reduce life-time costs of offshore wind farms

Managers of offshore wind farms make strategic decisions based on information about site wind speeds and significant wave heights (SWH) available from numerical weather predictions (NWP) or local in-situ measurements. However, the coarse resolution with which such information are available, both in space and time, introduces a high degree of uncertainty into the decision process which in turn may result in higher costs during different stages of offshore wind farm life. The current work investigates how space-borne data describing wind speeds and SWH might be used to quantify spatial uncertainties and support decisions during the design and operation of offshore wind sites. Results have revealed that due to high spatial variance of wind speed, the estimated wind power can differ from that provided by an offshore met mast up to 11%. The methodology proposed for SWH has shown how data collected from distinct satellites can be efficiently interpolated (maximum absolute error observed around 1 m) to generate high-resolute spatial information of sea water surface, regardless of satellite trajectory distributions. The work has provided insights on how the propagation of measurement uncertainty through the wind farm area can affect both management costs and wind energy production over the plant life-cycle. • Use of SAR imagery to assess spatial uncertainty in wind speed provided by met mast. • New method to generate detailed 2D SWH maps by using along-track satellite data. • New products can improve costs during offshore wind farm lifecycles.

Capacity configuration optimization of standalone multi‐energy hub considering electricity, heat and hydrogen uncertainty

AbstractStandalone multi‐energy hub is the next frontier of electric grid modernization. It is vital to optimize the standalone multi‐energy hub capacity configuration to enhance the hub reliability, economic efficiency, and sustainability. Therefore, this paper proposes a novel multi‐objective capacity configuration model for standalone multi‐energy hub considering electricity, heat and hydrogen energy uncertainty. First, the standalone multi‐energy hub model with electricity, heat, and hydrogen energy is established. It takes into account photovoltaic generators, wind generation, combined heat and power units, power to gas, gas boiler and hydrogen storage tank to meet the electrical, thermal and hydrogen energy demands. At the same time, in order to solve the influence of uncertainty on hub capacity configuration, the typical source‐load scenarios are established considering the uncertainty of wind speed, solar radiation and energy demands. On this basis, the objective functions and constraints of the capacity configuration model are presented. The improved hybrid multi‐objective particle swarm optimization algorithm and fuzzy membership function are used to solve the model. Finally, case studies verified the effectiveness and rationality of the proposed model.