Structural changes and dynamic behaviour of vanadium oxide-based catalysts for gas-phase selective oxidations

Selective oxidation is one of the simplest functionalization methods and essentially all monomers used in manufacturing artificial fibers and plastics are obtained by catalytic oxidation processes. Formally, oxidation is considered as an increase in the oxidation number of the carbon atoms, then reactions such as dehydrogenation, ammoxidation, cyclization or chlorination are all oxidation reactions. In this field, most of processes for the synthesis of important chemicals used vanadium oxide-based catalysts. These catalytic systems are used either in the form of multicomponent mixed oxides and oxysalts, e.g., in the oxidation of n-butane (V/P/O) and of benzene (supported V/Mo/O) to maleic anhydride, or in the form of supported metal oxide, e.g., in the manufacture of phthalic anhydride by o-xylene oxidation, of sulphuric acid by oxidation of SO2, in the reduction of NOx with ammonia and in the ammoxidation of alkyl aromatics. In addition, supported vanadia catalysts have also been investigated for the oxidative dehydrogenation of alkanes to olefins , oxidation of pentane to maleic anhydride and the selective oxidation of methanol to formaldehyde or methyl formate [1]. During my PhD I focused my work on two gas phase selective oxidation reactions. The work was done at the Department of Industrial Chemistry and Materials (University of Bologna) in collaboration with Polynt SpA. Polynt is a leader company in the development, production and marketing of catalysts for gas-phase oxidation. In particular, I studied the catalytic system for n-butane oxidation to maleic anhydride (fluid bed technology) and for o-xylene oxidation to phthalic anhydride. Both reactions are catalyzed by systems based on vanadium, but catalysts are completely different. Part A is dedicated to the study of V/P/O catalyst for n-butane selective oxidation, while in the Part B the results of an investigation on TiO2-supported V2O5, catalyst for o-xylene oxidation are showed. In Part A, a general introduction about the importance of maleic anhydride, its uses, the industrial processes and the catalytic system are reported. The reaction is the only industrial direct oxidation of paraffins to a chemical intermediate. It is produced by n-butane oxidation either using fixed bed and fluid bed technology; in both cases the catalyst is the vanadyl pyrophosphate (VPP). Notwithstanding the good performances, the yield value didn’t exceed 60% and the system is continuously studied to improve activity and selectivity. The main open problem is the understanding of the real active phase working under reaction conditions. Several articles deal with the role of different crystalline and/or amorphous vanadium/phosphorous (VPO) compounds. In all cases, bulk VPP is assumed to constitute the core of the active phase, while two different hypotheses have been formulated concerning the catalytic surface. In one case the development of surface amorphous layers that play a direct role in the reaction is described, in the second case specific planes of crystalline VPP are assumed to contribute to the reaction pattern, and the redox process occurs reversibly between VPP and VOPO4. Both hypotheses are supported also by in-situ characterization techniques, but the experiments were performed with different catalysts and probably under slightly different working conditions. Due to complexity of the system, these differences could be the cause of the contradictions present in literature. Supposing that a key role could be played by P/V ratio, I prepared, characterized and tested two samples with different P/V ratio. Transformation occurring on catalytic surfaces under different conditions of temperature and gas-phase composition were studied by means of in-situ Raman spectroscopy, trying to investigate the changes that VPP undergoes during reaction. The goal is to understand which kind of compound constituting the catalyst surface is the most active and selective for butane oxidation reaction, and also which features the catalyst should possess to ensure the development of this surface (e.g. catalyst composition). On the basis of results from this study, it could be possible to project a new catalyst more active and selective with respect to the present ones. In fact, the second topic investigated is the possibility to reproduce the surface active layer of VPP onto a support. In general, supportation is a way to improve mechanical features of the catalysts and to overcome problems such as possible development of local hot spot temperatures, which could cause a decrease of selectivity at high conversion, and high costs of catalyst. In literature it is possible to find different works dealing with the development of supported catalysts, but in general intrinsic characteristics of VPP are worsened due to the chemical interaction between active phase and support. Moreover all these works deal with the supportation of VPP; on the contrary, my work is an attempt to build-up a V/P/O active layer on the surface of a zirconia support by thermal treatment of a precursor obtained by impregnation of a V5+ salt and of H3PO4. In-situ Raman analysis during the thermal treatment, as well as reactivity tests are used to investigate the parameters that may influence the generation of the active phase. Part B is devoted to the study of o-xylene oxidation of phthalic anhydride; industrially, the reaction is carried out in gas-phase using as catalysts a supported system formed by V2O5 on TiO2. The V/Ti/O system is quite complex; different vanadium species could be present on the titania surface, as a function of the vanadium content and of the titania surface area: (i) V species which is chemically bound to the support via oxo bridges (isolated V in octahedral or tetrahedral coordination, depending on the hydration degree), (ii) a polymeric species spread over titania, and (iii) bulk vanadium oxide, either amorphous or crystalline. The different species could have different catalytic properties therefore changing the relative amount of V species can be a way to optimize the catalytic performances of the system. For this reason, samples containing increasing amount of vanadium were prepared and tested in the oxidation of o-xylene, with the aim of find a correlations between V/Ti/O catalytic activity and the amount of the different vanadium species. The second part deals with the role of a gas-phase promoter. Catalytic surface can change under working conditions; the high temperatures and a different gas-phase composition could have an effect also on the formation of different V species. Furthermore, in the industrial practice, the vanadium oxide-based catalysts need the addition of gas-phase promoters in the feed stream, that although do not have a direct role in the reaction stoichiometry, when present leads to considerable improvement of catalytic performance. Starting point of my investigation is the possibility that steam, a component always present in oxidation reactions environment, could cause changes in the nature of catalytic surface under reaction conditions. For this reason, the dynamic phenomena occurring at the surface of a 7wt% V2O5 on TiO2 catalyst in the presence of steam is investigated by means of Raman spectroscopy. Moreover a correlation between the amount of the different vanadium species and catalytic performances have been searched. Finally, the role of dopants has been studied. The industrial V/Ti/O system contains several dopants; the nature and the relative amount of promoters may vary depending on catalyst supplier and on the technology employed for the process, either a single-bed or a multi-layer catalytic fixed-bed. Promoters have a quite remarkable effect on both activity and selectivity to phthalic anhydride. Their role is crucial, and the proper control of the relative amount of each component is fundamental for the process performance. Furthermore, it can not be excluded that the same promoter may play different role depending on reaction conditions (T, composition of gas phase..). The reaction network of phthalic anhydride formation is very complex and includes several parallel and consecutive reactions; for this reason a proper understanding of the role of each dopant cannot be separated from the analysis of the reaction scheme. One of the most important promoters at industrial level, which is always present in the catalytic formulations is Cs. It is known that Cs plays an important role on selectivity to phthalic anhydride, but the reasons of this phenomenon are not really clear. Therefore the effect of Cs on the reaction scheme has been investigated at two different temperature with the aim of evidencing in which step of the reaction network this promoter plays its role.

Ultra-low power WSNs: distributed signal processing and dynamic resource management

This thesis presents several data processing and compression techniques capable of addressing the strict requirements of wireless sensor networks. After introducing a general overview of sensor networks, the energy problem is introduced, dividing the different energy reduction approaches according to the different subsystem they try to optimize. To manage the complexity brought by these techniques, a quick overview of the most common middlewares for WSNs is given, describing in detail SPINE2, a framework for data processing in the node environment. The focus is then shifted on the in-network aggregation techniques, used to reduce data sent by the network nodes trying to prolong the network lifetime as long as possible. Among the several techniques, the most promising approach is the Compressive Sensing (CS). To investigate this technique, a practical implementation of the algorithm is compared against a simpler aggregation scheme, deriving a mixed algorithm able to successfully reduce the power consumption. The analysis moves from compression implemented on single nodes to CS for signal ensembles, trying to exploit the correlations among sensors and nodes to improve compression and reconstruction quality. The two main techniques for signal ensembles, Distributed CS (DCS) and Kronecker CS (KCS), are introduced and compared against a common set of data gathered by real deployments. The best trade-off between reconstruction quality and power consumption is then investigated. The usage of CS is also addressed when the signal of interest is sampled at a Sub-Nyquist rate, evaluating the reconstruction performance. Finally the group sparsity CS (GS-CS) is compared to another well-known technique for reconstruction of signals from an highly sub-sampled version. These two frameworks are compared again against a real data-set and an insightful analysis of the trade-off between reconstruction quality and lifetime is given.

Empirical Analysis of Credit Risk Regime Switching and Temporal Conditional Default Correlation in Credit Default Swap Valuation: The Market liquidity effect

In this paper, we extend the debate concerning Credit Default Swap valuation to include time varying correlation and co-variances. Traditional multi-variate techniques treat the correlations between covariates as constant over time; however, this view is not supported by the data. Secondly, since financial data does not follow a normal distribution because of its heavy tails, modeling the data using a Generalized Linear model (GLM) incorporating copulas emerge as a more robust technique over traditional approaches. This paper also includes an empirical analysis of the regime switching dynamics of credit risk in the presence of liquidity by following the general practice of assuming that credit and market risk follow a Markov process. The study was based on Credit Default Swap data obtained from Bloomberg that spanned the period January 1st 2004 to August 08th 2006. The empirical examination of the regime switching tendencies provided quantitative support to the anecdotal view that liquidity decreases as credit quality deteriorates. The analysis also examined the joint probability distribution of the credit risk determinants across credit quality through the use of a copula function which disaggregates the behavior embedded in the marginal gamma distributions, so as to isolate the level of dependence which is captured in the copula function. The results suggest that the time varying joint correlation matrix performed far superior as compared to the constant correlation matrix; the centerpiece of linear regression models.

Dynamic Effects of Currency Depreciation on Stock Market Returns during the Asian Financial Crisis

The current international integration of financial markets provides a channel for currency depreciation to affect stock prices. Moreover, the recent financial crisis in Asia with its accompanying exchange rate volatility affords a case study to examine that channel. This paper applies a bivariate GARCH-M model of the reduced form of stock market returns to investigate empirically the effects of daily currency depreciation on stock market returns for five newly emerging East Asian stock markets during the Asian financial crisis. The evidence shows that the conditional variances of stock market returns and depreciation rates exhibit time-varying characteristics for all countries. Domestic currency depreciation and its uncertainty adversely affects stock market returns across countries. The significant effects of foreign exchange market events on stock market returns suggest that international fund managers who invest in the newly emerging East Asian stock markets must evaluate the value and stability of the domestic currency as a part of their stock market investment decisions.

Conditional parallelization of nonStrict independence. Procedures and assessmen

This paper presents a conditional parallelization process for and-parallelism based on the notion of non-strict independence, a more relaxed notion than the traditional of strict independence. By using this notion, a parallelism annotator can extract more parallelism from programs. On the other hand, the intrinsic complexity of non-strict independence poses new challenges to this task. We report here on the implementation we have accomplished of an annotator for non-strict independence, capable of producing both static and dynamic execution graphs. This implementation, along with the also implemented independence checker and their integration in our system, have resulted what is, to the best of our knowledge, the first parallelizing compiler based on nonstrict independence which produces dynamic execution graphs. The paper also presents a preliminary assessment of the implemented tools, comparing them with the existing ones for strict independence, which shows encouraging results.

Hybrid wind-diesel power plant valuation and optimization using real option theory and dynamic programming

El sistema de energía eólica-diesel híbrido tiene un gran potencial en la prestación de suministro de energía a comunidades remotas. En comparación con los sistemas tradicionales de diesel, las plantas de energía híbridas ofrecen grandes ventajas tales como el suministro de capacidad de energía extra para "microgrids", reducción de los contaminantes y emisiones de gases de efecto invernadero, y la cobertura del riesgo de aumento inesperado del precio del combustible. El principal objetivo de la presente tesis es proporcionar nuevos conocimientos para la evaluación y optimización de los sistemas de energía híbrido eólico-diesel considerando las incertidumbres. Dado que la energía eólica es una variable estocástica, ésta no puede ser controlada ni predecirse con exactitud. La naturaleza incierta del viento como fuente de energía produce serios problemas tanto para la operación como para la evaluación del valor del sistema de energía eólica-diesel híbrido. Por un lado, la regulación de la potencia inyectada desde las turbinas de viento es una difícil tarea cuando opera el sistema híbrido. Por otro lado, el bene.cio económico de un sistema eólico-diesel híbrido se logra directamente a través de la energía entregada a la red de alimentación de la energía eólica. Consecuentemente, la incertidumbre de los recursos eólicos incrementa la dificultad de estimar los beneficios globales en la etapa de planificación. La principal preocupación del modelo tradicional determinista es no tener en cuenta la incertidumbre futura a la hora de tomar la decisión de operación. Con lo cual, no se prevé las acciones operativas flexibles en respuesta a los escenarios futuros. El análisis del rendimiento y simulación por ordenador en el Proyecto Eólico San Cristóbal demuestra que la incertidumbre sobre la energía eólica, las estrategias de control, almacenamiento de energía, y la curva de potencia de aerogeneradores tienen un impacto significativo sobre el rendimiento del sistema. En la presente tesis, se analiza la relación entre la teoría de valoración de opciones y el proceso de toma de decisiones. La opción real se desarrolla con un modelo y se presenta a través de ejemplos prácticos para evaluar el valor de los sistemas de energía eólica-diesel híbridos. Los resultados muestran que las opciones operacionales pueden aportar un valor adicional para el sistema de energía híbrida, cuando esta flexibilidad operativa se utiliza correctamente. Este marco se puede aplicar en la optimización de la operación a corto plazo teniendo en cuenta la naturaleza dependiente de la trayectoria de la política óptima de despacho, dadas las plausibles futuras realizaciones de la producción de energía eólica. En comparación con los métodos de valoración y optimización existentes, el resultado del caso de estudio numérico muestra que la política de operación resultante del modelo de optimización propuesto presenta una notable actuación en la reducción del con- sumo total de combustible del sistema eólico-diesel. Con el .n de tomar decisiones óptimas, los operadores de plantas de energía y los gestores de éstas no deben centrarse sólo en el resultado directo de cada acción operativa, tampoco deberían tomar decisiones deterministas. La forma correcta es gestionar dinámicamente el sistema de energía teniendo en cuenta el valor futuro condicionado en cada opción frente a la incertidumbre. ABSTRACT Hybrid wind-diesel power systems have a great potential in providing energy supply to remote communities. Compared with the traditional diesel systems, hybrid power plants are providing many advantages such as providing extra energy capacity to the micro-grid, reducing pollution and greenhouse-gas emissions, and hedging the risk of unexpected fuel price increases. This dissertation aims at providing novel insights for assessing and optimizing hybrid wind-diesel power systems considering the related uncertainties. Since wind power can neither be controlled nor accurately predicted, the energy harvested from a wind turbine may be considered a stochastic variable. This uncertain nature of wind energy source results in serious problems for both the operation and value assessment of the hybrid wind-diesel power system. On the one hand, regulating the uncertain power injected from wind turbines is a difficult task when operating the hybrid system. On the other hand, the economic profit of a hybrid wind-diesel system is achieved directly through the energy delivered to the power grid from the wind energy. Therefore, the uncertainty of wind resources has increased the difficulty in estimating the total benefits in the planning stage. The main concern of the traditional deterministic model is that it does not consider the future uncertainty when making the dispatch decision. Thus, it does not provide flexible operational actions in response to the uncertain future scenarios. Performance analysis and computer simulation on the San Cristobal Wind Project demonstrate that the wind power uncertainty, control strategies, energy storage, and the wind turbine power curve have a significant impact on the performance of the system. In this dissertation, the relationship between option pricing theory and decision making process is discussed. A real option model is developed and presented through practical examples for assessing the value of hybrid wind-diesel power systems. Results show that operational options can provide additional value to the hybrid power system when this operational flexibility is correctly utilized. This framework can be applied in optimizing short term dispatch decisions considering the path-dependent nature of the optimal dispatch policy, given the plausible future realizations of the wind power production. Comparing with the existing valuation and optimization methods, result from numerical example shows that the dispatch policy resulting from the proposed optimization model exhibits a remarkable performance in minimizing the total fuel consumption of the wind-diesel system. In order to make optimal decisions, power plant operators and managers should not just focus on the direct outcome of each operational action; neither should they make deterministic decisions. The correct way is to dynamically manage the power system by taking into consideration the conditional future value in each option in response to the uncertainty.

Interlevel correlations of temperature and density, surface to 60 km. /

"Meteorology Division. Project 6670."

Macroeconomic Dynamics of Market Risk Capital Requirements and Credit Supply Interdependence

Thesis (Ph.D.)--University of Washington, 2016-06

Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.

A comparison of variational and Markov chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational Gaussian Process Smoother with an exact solution computed using a Hybrid Monte Carlo approach to path sampling, applied to a stochastic double well potential model. It is demonstrated that the variational smoother provides us a very accurate estimate of mean path while conditional variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother. © 2008 Springer Science + Business Media LLC.

Supporting decision-making for self-adaptive systems:from goal models to dynamic decision networks

Context/Motivation - Different modeling techniques have been used to model requirements and decision-making of self-adaptive systems (SASs). Specifically, goal models have been prolific in supporting decision-making depending on partial and total fulfilment of functional (goals) and non-functional requirements (softgoals). Different goalrealization strategies can have different effects on softgoals which are specified with weighted contribution-links. The final decision about what strategy to use is based, among other reasons, on a utility function that takes into account the weighted sum of the different effects on softgoals. Questions/Problems - One of the main challenges about decisionmaking in self-adaptive systems is to deal with uncertainty during runtime. New techniques are needed to systematically revise the current model when empirical evidence becomes available from the deployment. Principal ideas/results - In this paper we enrich the decision-making supported by goal models by using Dynamic Decision Networks (DDNs). Goal realization strategies and their impact on softgoals have a correspondence with decision alternatives and conditional probabilities and expected utilities in the DDNs respectively. Our novel approach allows the specification of preferences over the softgoals and supports reasoning about partial satisfaction of softgoals using probabilities. We report results of the application of the approach on two different cases. Our early results suggest the decision-making process of SASs can be improved by using DDNs. © 2013 Springer-Verlag.

Dynamic models of exchange rate dependence using option prices and historical returns

Models for the conditional joint distribution of the U.S. Dollar/Japanese Yen and Euro/Japanese Yen exchange rates, from November 2001 until June 2007, are evaluated and compared. The conditional dependency is allowed to vary across time, as a function of either historical returns or a combination of past return data and option-implied dependence estimates. Using prices of currency options that are available in the public domain, risk-neutral dependency expectations are extracted through a copula repre- sentation of the bivariate risk-neutral density. For this purpose, we employ either the one-parameter \Normal" or a two-parameter \Gumbel Mixture" specification. The latter provides forward-looking information regarding the overall degree of covariation, as well as, the level and direction of asymmetric dependence. Specifications that include option-based measures in their information set are found to outperform, in-sample and out-of-sample, models that rely solely on historical returns.

Between Principle and Practicality: A Dynamic Realist Examination of Independence in the Canadian Justice System

This work examines independence in the Canadian justice system using an approach adapted from new legal realist scholarship called ‘dynamic realism’. This approach proposes that issues in law must be considered in relation to their recursive and simultaneous development with historic, social and political events. Such events describe ‘law in action’ and more holistically demonstrate principles like independence, rule of law and access to justice. My dynamic realist analysis of independence in the justice system employs a range methodological tools and approaches from the social sciences, including: historical and historiographical study; public administrative; policy and institutional analysis; an empirical component; as well as constitutional, statutory interpretation and jurisprudential analysis. In my view, principles like independence represent aspirational ideals in law which can be better understood by examining how they manifest in legal culture and in the legal system. This examination focuses on the principle and practice of independence for both lawyers and judges in the justice system, but highlights the independence of the Bar. It considers the inter-relation between lawyer independence and the ongoing refinement of judicial independence in Canadian law. It also considers both independence of the Bar and the Judiciary in the context of the administration of justice, and practically illustrates the interaction between these principles through a case study of a specific aspect of the court system. This work also focuses on recent developments in the principle of Bar independence and its relation to an emerging school of professionalism scholarship in Canada. The work concludes by describing the principle of independence as both conditional and dynamic, but rooted in a unitary concept for both lawyers and judges. In short, independence can be defined as impartiality, neutrality and autonomy of legal decision-makers in the justice system to apply, protect and improve the law for what has become its primary normative purpose: facilitating access to justice. While both independence of the Bar and the Judiciary are required to support access to independent courts, some recent developments suggest the practical interactions between independence and access need to be the subject of further research, to better account for both the principles and the practicalities of the Canadian justice system.

Measuring, Modeling, and Forecasting Volatility and Correlations from High-Frequency Data

This dissertation contains four essays that all share a common purpose: developing new methodologies to exploit the potential of high-frequency data for the measurement, modeling and forecasting of financial assets volatility and correlations. The first two chapters provide useful tools for univariate applications while the last two chapters develop multivariate methodologies. In chapter 1, we introduce a new class of univariate volatility models named FloGARCH models. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures, and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of the models in a realistic numerical study and on the basis of a data set composed of 65 equities. Using more than 10 years of high-frequency transactions, we document significant statistical gains related to the FloGARCH models in terms of in-sample fit, out-of-sample fit and forecasting accuracy compared to classical and Realized GARCH models. In chapter 2, using 12 years of high-frequency transactions for 55 U.S. stocks, we argue that combining low-frequency exogenous economic indicators with high-frequency financial data improves the ability of conditionally heteroskedastic models to forecast the volatility of returns, their full multi-step ahead conditional distribution and the multi-period Value-at-Risk. Using a refined version of the Realized LGARCH model allowing for time-varying intercept and implemented with realized kernels, we document that nominal corporate profits and term spreads have strong long-run predictive ability and generate accurate risk measures forecasts over long-horizon. The results are based on several loss functions and tests, including the Model Confidence Set. Chapter 3 is a joint work with David Veredas. We study the class of disentangled realized estimators for the integrated covariance matrix of Brownian semimartingales with finite activity jumps. These estimators separate correlations and volatilities. We analyze different combinations of quantile- and median-based realized volatilities, and four estimators of realized correlations with three synchronization schemes. Their finite sample properties are studied under four data generating processes, in presence, or not, of microstructure noise, and under synchronous and asynchronous trading. The main finding is that the pre-averaged version of disentangled estimators based on Gaussian ranks (for the correlations) and median deviations (for the volatilities) provide a precise, computationally efficient, and easy alternative to measure integrated covariances on the basis of noisy and asynchronous prices. Along these lines, a minimum variance portfolio application shows the superiority of this disentangled realized estimator in terms of numerous performance metrics. Chapter 4 is co-authored with Niels S. Hansen, Asger Lunde and Kasper V. Olesen, all affiliated with CREATES at Aarhus University. We propose to use the Realized Beta GARCH model to exploit the potential of high-frequency data in commodity markets. The model produces high quality forecasts of pairwise correlations between commodities which can be used to construct a composite covariance matrix. We evaluate the quality of this matrix in a portfolio context and compare it to models used in the industry. We demonstrate significant economic gains in a realistic setting including short selling constraints and transaction costs.

Dynamic model development of adiabatic compressed air energy storage

The share of variable renewable energy in electricity generation has seen exponential growth during the recent decades, and due to the heightened pursuit of environmental targets, the trend is to continue with increased pace. The two most important resources, wind and insolation both bear the burden of intermittency, creating a need for regulation and posing a threat to grid stability. One possibility to deal with the imbalance between demand and generation is to store electricity temporarily, which was addressed in this thesis by implementing a dynamic model of adiabatic compressed air energy storage (CAES) with Apros dynamic simulation software. Based on literature review, the existing models due to their simplifications were found insufficient for studying transient situations, and despite of its importance, the investigation of part load operation has not yet been possible with satisfactory precision. As a key result of the thesis, the cycle efficiency at design point was simulated to be 58.7%, which correlated well with literature information, and was validated through analytical calculations. The performance at part load was validated against models shown in literature, showing good correlation. By introducing wind resource and electricity demand data to the model, grid operation of CAES was studied. In order to enable the dynamic operation, start-up and shutdown sequences were approximated in dynamic environment, as far as is known, the first time, and a user component for compressor variable guide vanes (VGV) was implemented. Even in the current state, the modularly designed model offers a framework for numerous studies. The validity of the model is limited by the accuracy of VGV correlations at part load, and in addition the implementation of heat losses to the thermal energy storage is necessary to enable longer simulations. More extended use of forecasts is one of the important targets of development, if the system operation is to be optimised in future.