Showing papers by "Vattenfall published in 2019"

Mathematical Optimization and Algorithms for Offshore Wind Farm Design: An Overview

Abstract: Wind energy is a fast evolving field that has attracted a lot of attention and investments in the last decades. Being an increasingly competitive market, it is very important to minimize establishment costs and increase production profits already at the design phase of new wind parks. This paper is based on many years of collaboration with Vattenfall, a leading wind energy developer and wind power operator, and aims at giving an overview of the experience of using Mathematical Optimization in the field. The paper illustrates some of the practical needs defined by energy companies, showing how optimization can help the designers to increase production and reduce costs in the design of offshore parks. In particular, the study gives an overview of the individual phases of designing an offshore wind farm, and some of the optimization problems involved. Finally it goes in depth with three of the most important optimization tasks: turbine location, electrical cable routing and foundation optimization. The paper is concluded with a discussion of future challenges.

Do we really need rotor equivalent wind speed

Abstract: The use of the rotor equivalent wind speed for determination of power curves and annual energy production for wind turbines is advocated in the second edition of the IEC 61400-12-1 standard. This requires the measurements of wind speeds at different heights, for which remote sensing equipment is recommended in addition to meteorological masts. In this paper, we present a theoretical analysis that shows that the relevance of the rotor equivalent wind speed method depends on turbine dimensions and wind shear regime. For situations where the ratio of rotor diameter and hub height is smaller than 1.8, the rotor equivalent wind speed method is not needed if the wind shear coefficient at the location of the wind turbine has a constant value between ?0.05 and 0.4: in these cases, the rotor equivalent wind speed and the wind speed at hub height are within 1%. For complex terrains with high wind shear deviations are larger. The effect of non-constant wind shear exponent, ie, different wind shear coefficients for lower and upper half of the rotor swept area especially at offshore conditions is limited to also about 1%.

Ultraviolet Absorption Cross Sections of KOH and KCl for Nonintrusive Species-Specific Quantitative Detection in Hot Flue Gases.

Abstract: An understanding of potassium chemistry in energy conversion processes supports the development of complex biomass utilization with high efficiency and low pollutant emissions. Potassium exists mainly as potassium hydroxide (KOH), potassium chloride (KCl), and atomic potassium (K) in combustion and related thermochemical processes. We report, for the first time, the measurement of the ultraviolet (UV) absorption cross sections of KOH and KCl at temperatures between 1300 K and 1800 K, using a newly developed method. Using the spectrally resolved UV absorption cross sections, the concentrations of KOH and KCl were measured simultaneously. In addition, we measured the concentrations of atomic K using tunable diode laser absorption spectroscopy, both at 404.4 and 769.9 nm. The 404.4 nm line was utilized to expand the measurement dynamic range to higher concentrations. A constant amount of KCl was seeded into premixed CH4/air flames with equivalence ratios varied from 0.67 to 1.32, and the concentrations of KOH, KCl, and atomic K in the hot flue gas were measured nonintrusively. The results indicate that these techniques can provide comprehensive data for quantitative understanding of the potassium chemistry in biomass combustion/gasification.

The role of stakeholders in creating societal value from coastal and ocean observations

Abstract: The importance of stakeholder engagement in ocean observation and in particular the realization of economic and societal benefits is discussed, introducing a number of overarching principles such as the convergence on common goals, effective communication, co-production of information and knowledge and the need for innovation. A series of case studies examine the role of coordinating frameworks such as the US’s Interagency Ocean Observing System (IOOS®), and the European Ocean Observing System (EOOS), public-private partnerships such as Project Azul and the Coastal Data Information Program (CDIP) and finally the role of the “third” or voluntary sector. The paper explores the value that stakeholder engagement can bring as well as making recommendations for the future.

Learning MILP Resolution Outcomes Before Reaching Time-Limit

Abstract: The resolution of some Mixed-Integer Linear Programming (MILP) problems still presents challenges for state-of-the-art optimization solvers and may require hours of computations, so that a time-limit to the resolution process is typically provided by a user. Nevertheless, it could be useful to get a sense of the optimization trends after only a fraction of the specified total time has passed, and ideally be able to tailor the use of the remaining resolution time accordingly, in a more strategic and flexible way. Looking at the evolution of a partial branch-and-bound tree for a MILP instance, developed up to a certain fraction of the time-limit, we aim to predict whether the problem will be solved to proven optimality before timing out. We exploit machine learning tools, and summarize the development and progress of a MILP resolution process to cast a prediction within a classification framework. Experiments on benchmark instances show that a valuable statistical pattern can indeed be learned during MILP resolution, with key predictive features reflecting the know-how and experience of field’s practitioners.

Effect of Landscape Elements on the Symmetry and Variance of the Spatial Distribution of Individual Birds within Foraging Flocks of Geese

Abstract: Behavioural instability is a newly coined term used for measuring asymmetry of bilateral behavioural traits as indicators of genetic or environmental stress. However, this concept might also be useful for other types of data than bilateral traits. In this study, behavioural instability indices of expected behaviour were evaluated as an indicator for environmental stress through the application of aerial photos of foraging flocks of geese. It was presumed that geese would increase anti-predator behaviour through the dilution effect when foraging near the following landscape elements: wind turbines, hedgerows, and roads. On this presumption, it was hypothesized that behavioural instability of spatial distribution in flocks of geese could be used as indicators of environmental stress. Asymmetry in spatial distribution was measured for difference in flock density across various distances to disturbing landscape elements through the following indices; behavioural instability of symmetry and behavioural instability of variance. The behavioural instability indices showed clear tendencies for changes in flock density and variance of flock density for geese foraging near wind turbines, hedgerows, and roads indicating increasing environmental stress levels. Thus, behavioural instability has proven to be a useful tool for monitoring environmental stress that does not need bilateral traits to estimate instability but can be applied for indices of expected behaviour.

Global Blockage Effects in Wind Farms

Abstract: An experimental and numerical study of wind-farm blockage has been performed to quantify the velocity reduction that the first row of a wind farm experiences due to other turbines downstream. In th ...

CFD Modelling of a Stepped Spillway with Various Step Layouts

Abstract: A traditional stepped spillway is prone to cavitation risks. To improve its hydraulic behaviors, distorted step faces and pool weirs are devised. By numerical modelling, comparative studies are con ...

The Role of Aleatory and Epistemic Uncertainties in a Stochastic Hosting Capacity Approach for Solar PV

Abstract: This paper introduces the terms aleatory and epistemic uncertainties for use in a stochastic hosting capacity method. The role these uncertainties play in the hosting capacity determination is illustrated. It is shown that distinction between aleatory (statistical) and epistemic (systematic) uncertainties is helpful to characterize the probability distributions correctly. For epistemic uncertainties, it is often challenging to obtain information on the probability distribution function. For aleatory uncertainties, a method for characterizing the probability distribution is presented. Aleatory uncertainties' data measurements are used to obtain a distribution best-fit. The background voltage measurement for a customer in a low-voltage distribution network is used to illustrate the method. Values were obtained for the distribution functions of the three-phase voltages. The used distribution functions are found to influence the resulting hosting capacity. This entails that there is need for measurements and data collection. A research challenge remaining concerns the stochastic model of epistemic uncertainty.

Forecasting cross-border power exchanges through an HVDC line using dynamic modelling

Abstract: As smart grids develop, power systems become more complex, and the role of data gain considerable importance for the reliability of power supply. Thus, data processing techniques have to be investigated and compared to increase the efficiency of asset management decisions. In this paper, we explore several black-box models in order to predict power exchanges through a high-voltage direct-current line between Sweden and Denmark, using publicly available data on loads and power prices. An auto-regressive moving average with external input model based on load data provides the most accurate forecasts according to mean square error and other selected criteria. This is the first step to build a more comprehensive model that will also include other technical data such as maintenance and unplanned outages, but also macroeconomic factors. The final goal is to provide network operators with a parsimonious sequential model composed of several modules giving accurate predictions that support efficient asset management decisions.