YEQT XI: “Winterschool on Energy Systems”
Dec 11 - Dec 15
The 11th edition of the YEQT workshop, which will take place in December 2017, revolves around the theme “Stochastic Modeling and Analysis of Energy Networks”. This workshop aims to bring together young researchers (PhD students, postdocs, and recently appointed lecturers or assistant professors) and renowned scientists to share ideas, discuss research, and enable the next generation of researchers to obtain an overview of the emerging research field of energy networks and electricity markets.
Power grids are indispensable and critical infrastructures for modern-day society. Due to recent technological advances, power grids need to evolve and become more flexible and resilient systems to contrast the increasing uncertainties and volatility in power generation. Many challenging research questions and opportunities arise in this research field, with great need of stochastic enrichment. The increasing penetration of renewable energy sources and the advent of energy storage demand improved forecasting methods, which in turn are fundamentally changing the structure and the design of energy markets. More sustainable fossil-free, yet reliable, energy generation and dispatch require better forecasting techniques and new algorithms that fully account for intrinsic uncertainties.
This year’s YEQT has a different structure than usual, being structured as a winter school: the workshop consists of several tutorials presented by six renowned researchers, providing a broad overview of various topics in this research field. Next to the tutorials, there will be opportunities for young researchers to present their own research work with talks and/or at the poster session.
This workshop is linked to the upcoming semester “The mathematics of energy systems”, which will be held at Newton Institute (Cambridge, UK) in 2019.
NOTE: if your are interested in presenting a poster, please indicate this on your registration form!!!!
|Fiona Sloothaak||TU Eindhoven|
Onno Boxma, Bert Zwart
|Ana Busic||INRIA, Abstract|
|James Cruise||Heriot Watt University, Abstract|
|Pär Holmberg||Research Institute of Industrial Economics, Abstract|
|Pierre Pinson||Technical University of Denmark, Abstract|
|John Moriarty||Queen Mary University of London, Abstract|
|Kostya Turitsyn||MIT, Abstract|
Invited Speakers (confirmed)
|Angelos Aveklouris||TU Eindhoven, Abstract|
|Thomas Baroche||ENS Rennes, Abstract|
|Mario Blázquez de Paz||NTNU, Trondheim, Abstract|
|Martina Bucciarelli||University of Siena, Abstract|
|Hale Cetinay||TU Delft, Abstract|
|Henri Gerard||Ecole Nationale des Pont et Chaussées, Abstract|
|Marco Gerards||University of Twente, Abstract|
|Sándor Kolumbán||TU Eindhoven, Abstract|
|Angus Lewis||University of Adelaide, Abstract|
|Tommaso Nesti||CWI, Abstract|
|Phuong Nguyen||TU Eindhoven, Abstract|
|Lesia Mitridati||DTU, Abstract|
|Keith Ruddell||University of Auckland; IFN Stockholm, Abstract|
|Benjamin Sanderse||CWI, Abstract|
|Jure Vogrinc||Queen Mary University of London, Abstract|
Monday December 11
|09.30 – 10.00||Registration and welcome|
|10.00 – 10.45||Tutorial: PÄR HOLMBERG|
|10.45 – 11.30||Tutorial: ANA BUSIC|
|11.30 – 12.00||Break|
|12.00 – 12.30||Mario Blázquez de Paz|
|12.30 – 13.00||Benjamin Sanderse|
|13.00 – 14.15||Lunch (Voorhof Auditorium)|
|14.15 – 15.00||Tutorial: PIERRE PINSON|
|15.00 – 15.45||Tutorial: PÄR HOLMBERG|
|15.45 – 16.15||Break|
|16.15 – 17.00||Demonstration: Anna Kosek|
Tuesday December 12
|09.30 – 10.15||Tutorial: ANA BUSIC|
|10.15 – 11.00||Tutorial: PÄR HOLMBERG|
|11.00 – 11.30||Break|
|11.30 – 12.00||Hale Cetinay|
|12.00 – 12.30||Angus Lewis|
|12.30 – 14.00||Lunch (Lounge Eurandom)|
|14.00 – 14.45||Tutorial: JAMES CRUISE|
|14.45 – 15.30||Tutorial: PIERRE PINSON|
|15.30 – 16.00||Break|
|16.00 – 17.30||Poster session|
|18.30 –||Conference dinner in restaurant ‘De Bengel’|
Wednesday December 13
|10.00 – 10.45||Tutorial: PIERRE PINSON|
|10.45 – 11.30||Tutorial: JOHN MORIARTY|
|11.30 – 12.00||Break|
|12.00 – 12.30||Tommaso Nesti|
|12.30 – 13.00||Lesia Mitridati|
|12.30 – 14.00||Lunch (Voorhof Auditorium)|
|14.00 – 14.45||Tutorial: ANA BUSIC|
|14.45 – 15.00||Break|
|15.00 – 15.30||Martina Bucciarelli|
|15.30 – 16.00||Marco Gerards|
|16.00 – 16.15||Break|
|16.15 – 17.00||Tutorial: JAMES CRUISE|
Thursday December 14
|09.30 – 10.15||Tutorial: KOSTYA TURITSYN|
|10.15 – 11.00||Tutorial: JAMES CRUISE|
|11.00 – 11.30||Break|
|11.30 – 12.00||Angelos Aveklouris|
|12.00 – 12.30||Keith Ruddell|
|12.30 – 13.00||Jure Vogrinc|
|12.30 – 14.00||Lunch (Voorhof Auditorium)|
|14.00 – 14.45||Tutorial: JOHN MORIARTY|
|14.45 – 15.00||Break|
|15.00 – 15.30||Thomas Baroche|
|15.30 – 16.00||Phuong Nguyen|
|16.00 – 16.15||Break|
|16.15 – 17.00||Tutorial: KOSTYA TURITSYN|
Friday December 15
|09.30 – 10.15||Tutorial: JOHN MORIARTY|
|10.15 – 10.30||Break|
|10.30 – 11.00||Henri Gerard|
|11.00 – 11.30||Sándor Kolumbán|
|11.30 – 11.45||Break|
|11.45 – 12.30||Tutorial: KOSTYA TURITSYN|
|12.30 –||Closing and lunch (section D cafetaria Auditorium)|
Distributed control design for balancing the grid using flexible loads
Matching power supply and demand used to be relatively straightforward, with large and controllable power plants on the one hand, and demand that was relatively easy to predict on the other. In recent years, there has been a significant increase in renewable power generation. The drawback of the inexpensive energy from the wind and the sun is its uncontrollable intermittence and volatility, such as ramps with the setting sun or a gust of wind. Controllable generators manage supply-demand balance of power today, but this is becoming increasingly costly with increasing penetration of renewable energy, due to the need to compensate for the missed opportunity cost the power plants are facing while operating at a lower set-point to be able to ramp up and down more aggressively than in the past.
At the same time, the rapid development of “smart technologies” (e.g. Linky meter and the connected appliances) open new possibilities for innovation on the demand side. There is an enormous flexibility potential in the power consumption of the majority of electric loads (e.g. thermal loads such as water heaters, air-conditioners and refrigerators; electric vehicles etc.). Their power consumption can be shifted in time to some extent without any significant impact to the consumer needs.
It has been argued since the 1980s that consumers should be put in the loop: “demand response” will help to create needed supply-demand balance. However, consumers use power for a reason, and expect that the quality of service (QoS) they receive will lie within reasonable bounds. The major issue lies in the distributed nature of this flexibility resource: to pilot the flexible demand in real time requires a design of simple incentives for millions of devices. Moreover, the behavior of some consumers is unpredictable, while the grid operator requires predictable controllable resources to maintain reliability.
The goal of this tutorial is to describe an emerging science for distributed demand control to create virtual energy storage from flexible loads. By design, the grid-level services from flexible loads will be as controllable and predictable as a generator or a fleet of batteries, while maintaining strict bounds on QoS. The approach combines the techniques from the theory of controlled Markov processes, mean-field theory, and the automatic control.
Power systems and Queueing theory: Storage and Electric Vehicles
In the first two lectures of the tutorial we will consider models to understand the role of storage in mitigating increased variability and uncertainty in both generation and demand. We will mainly focus on the problem from an economic perspective, for example, considering the value of the store when used for arbitrage and the effect of competition.
In the third lecture we will move to consider the modelling of the charging of electric vehicles. Here we will consider and number of models and their relation to previously studied queueing systems.
Strategic bidding in electricity markets
The tutorial gives an introduction to market designs that are used in electricity markets, such as uniform pricing, discriminatory pricing, nodal pricing and zonal pricing. The tutorial will present techniques, such as the market-distribution function approach and the supply-function equilibrium (SFE), which can be used to determine the optimal bidding strategy of a producer and to predict the outcome of an auction. Moreover, we will analyse how a supply-function equilibrium is influenced by contracts, the pricing rule, transmission constraints and the information structure of the market.
Option contracts for power system balancing
There is an increasing number of battery storage systems distributed throughout the power grid. Their applications include renewable generation capture and backup power and, in the future, automated applications to grid support are anticipated. In this tutorial we will explore the use of optimal stopping theory to derive grid support strategies for battery storage. Beginning with a review of necessary theory, we will: discuss considerations for contract design; analyse a proposed contract inspired by financial options; and finally compare proofs and results using two different performance criteria.
Renewable energy forecasting: from basics to current high-dimensional problems
Renewable energy forecasts with lead times up to a few hours are important for system operators and utilities to maintain a balanced and reliable power system. Commonly, these forecasts are computed by time series models also using weather forecasts as input. More advanced models additionally employ data from surrounding sites or can adapt to changes in the weather regime or wind farm setup. The wealth of data generated by an increasing number of renewable power generation installations does not only provide possibilities for improvements but also challenges for common forecasting methodologies. First of all, we will discuss the basics of renewable energy forecasting, based on wind, solar and wave energy related examples, with focus on probabilistic forecasting. We will eventually introduce, apply and discuss some of the recent proposals for high-dimensional modelling and forecasting, mainly based on vector autoregression (VAR) and some generalization (for instance with regime-switching), as well as sparsification of coefficient matrices. This will first include the well-known Lasso VAR, as well as an online version of that estimator. Furthermore, alternative approaches to sparsification for VAR models will be presented and the balance between forecast accuracy and computational costs will be considered. Applications will be based on datasets with tens to hundreds of sites in Europe and Australia and perspectives for operational applications with thousands of sites will be discussed.
Stability and Security of modern power systems
Power system is the largest, and arguably the most complex machine ever built by humans. Due to inherent nature of power flows it lacks global stability and is naturally “fragile”. Large enough disturbances may cause the loss of stability and trigger the cascading failures resulting in major blackouts. Aggressive introduction of renewable generation increases the overall stress of the system, so the stability constraints will likely become the main barrier for transition to clean energy sources. Despite many decades of research, stability assessment is still the computational bottleneck in power grid operation process. The lecture will cover multiple aspects of power systems fragility, including voltage and transient stability as well as frequency control in the presence of intermittent renewables. A number of simple to understand illustrating examples will be discussed to explain the core stability challenges and typical solutions employed by system operators.
The second part of the lecture will focus on an overview of a number of new approaches to power system stability, security and emergency control developed by the author. Construction of inner approximations of solvability and feasibility sets is a classical problem introduced back in 80s that has attracted a lot of attention in a recent decade. A number of algorithms based on Banach and Brouwer fixed point theorems introduced recently will be briefly reviewed, and open questions discussed in the end. The Lyapunov Function Family approach provide a computationally tractable means for constructing approximation of operating point basin of attractions. This technique is shown to be applicable to a wide range of problems including synthesis of special protection systems and real-time network reconfiguration.
The talk will conclude with a discussion of a new set of dynamics and control problems arising in the area of low voltage power systems, specifically electrification of poorest communities in India and Africa. Ad hoc microgrids are an especially attractive technology as they can be deployed and operated without any specialist oversight and reconfigured based on the need of the community. However, ensuring stable operations without significant compromises in cost is a challenging problem. A number of advanced but under-utilized techniques like Brayton-Moser potentials were shown to be particularly useful in addressing this problem.
A Stochastic Resource-Sharing Network for Electric Vehicle Charging
We consider a distribution grid used to charge electric vehicles subject to voltage stability and various other constraints. We model this as a class of resource-sharing networks known as bandwidth-sharing networks in the communication network literature. Such networks have proved themselves to be an effective flow-level model of data traffic in wired and wireless networks. We focus on resource sharing networks that are driven by a class of greedy control rules that can be implemented in a decentralized fashion. For a large number of such control rules, we can characterize the performance of the system, subject to voltage stability constraints, by a fluid approximation. This leads to a set of dynamic equations that take into account the stochastic behavior of cars. We show that the invariant point of these equations is unique and can be computed by solving a specific ACOPF problem, which admits an exact convex relaxation. For the class of weighted proportional fairness control, we show additional appealing properties under the linearized Distflow model, such as fairness, and a product form property of the stochastic model.
Joint work with Maria Vlasiou (TU/e, CWI) and Bert Zwart (CWI, TU/e).
Grid Integration in a Stochastic Peer-to-Peer Market
The interest in a peer-to-peer approach as a future market has substantially increased over the last few years. Peer-to-peer market rely on multi-bilateral negotiation to match supply and demand. The objective is then to develop a peer-to-peer market taking network constraints into account to ensure the adequacy between power trades and grid limitations. In a first approach we will directly incorporate network constraints within P2P market optimization problem. And, in a second step, we will use incentive systems to encompass grid power flow limits.
Electricity auctions in the presence of transmission constraints and transmission costs
Electricity markets are moving through integration around the world. However, our understanding of those markets is still limited. I characterize the Bertrand equilibrium in a discriminatory-price electricity auction when suppliers submit a single offer price for their entire production capacity and they face transmission constraints and linear tariffs for the injection of electricity into the grid. With point of connection tariffs, which are used in the majority of the European countries, suppliers pay a tariff for the total electricity injected into the grid. In contrast, with transmission tariffs, suppliers only pay a tariff for the electricity sold in the other
market. Transmission tariffs outperform point of connection tariffs by maximizing consumers welfare and transmission efficiency. The consequences of an increase in transmission capacity differ considerably depending on the tariff. If the transmission tariffs are zero, an increase in transmission capacity is pro-competitive. In contrast, if the transmission tariffs are positive, an increase in transmission capacity is procompetitive only when the transmission capacity is low.
Optimal sizing of energy storage systems under uncertain demand and generation
Abstract: Energy storage systems have been recently recognized as an effective solution to tackle power imbalances and voltage violations faced by distribution system operators due to the increasing penetration of low carbon technologies. To fully exploit their benefits, optimal sizing of these devices is a key problem at the planning stage. This work considers the sizing problem of the energy storage systems installed in a low voltage network with the aim of preventing over- and undervoltages. In order to accommodate uncertainty on future realizations of demand and generation, the optimal sizing problem is formulated in a two-stage stochastic framework, where the second stage problem is a full AC optimal power flow providing the optimal storage control policy for given demand and generation profiles. By taking a scenario-based approach, the two-stage problem is approximated in the form of a multi-scenario, multi-period optimal power flow. Since the size of the latter problem rapidly becomes computationally intractable as the number of scenarios grows, we show how to derive a feasible solution for the multi-scenario problem by solving a single-scenario problem for each scenario. Moreover, when the objective is to minimize the total installed storage capacity, an iterative procedure to solve the sizing problem at the optimum is proposed. The whole procedure is tested on the topology of the IEEE 37-bus test network, considering scenarios of demand and generation which feature over- and undervoltages in the absence of storage devices. (joint work with Simone Paoletti and Antonio Vicino)
Markov Random Field for Wind Farm Planning
Over the last decades, our society has developed a more comprehensive understanding of the environmentally-friendly approaches to the energy generation, urging us to focus more on sustainable energy sources, such as wind energy. As a result, the integration of wind energy goals into their long-term policies has been the priority of many countries. This requires meticulous planning, which is challenging due to the uncertainty in wind profiles. In this work, we demonstrate a framework to discover those geographic areas that are well suited for building wind farms. We combine the key indicators of wind farm investment using fuzzy sets, and employ multiple-criteria decision analysis to obtain a coarse wind farm suitability value. We further demonstrate how this suitability value can be refined by a Markov Random Field (MRF) that takes the dependencies between adjacent areas into account. As a proof of concept, we take wind farm planning in Turkey, and demonstrate that our MRF modelling can accurately find promising areas for wind farms.
(joint work with T. Kekec, F. Kuipers, D. Tax)
On risk averse competitive equilibrium
Motivated by the management of electricity markets, we discuss risked competitive partial equilibrium in a setting in which agents are endowed with coherent risk measures. In this case, [A. Philpott, et al., 2016] have shown that it is possible to define a complete market for risk. Then a perfectly competitive partial equilibrium will be efficient, i.e. will also maximize risk-adjusted social welfare. If the market for risk is not complete, then equilibrium can be inefficient.
We make the following contributions:
– we show a reverse statement between risk averse equilibrium problems in complete markets adapting a result from [D. Ralph and Y. Smeers, 2015],
– in contrast to social planning models, we show by example that risked equilibria are not unique, even when agents’ objective functions are strictly concave,
– we also show that standard computational methods find only a subset of the equilibria, even with multiple starting points.
(joint work with Vincent Lecrere and Andy Philpott)
Planning of Flexibility in the Distribution Grid
In this talk we discuss how the stability in the distribution grid can be assured using the available flexibility in this grid. Although the current Dutch electricity grid is very robust and blackouts rarely occur, in the next two decades this stability is assumed to be at risk due to high synchronized peaks resulting from distributed generation and electrification of transportation and heating. Furthermore, these higher load peaks lead to higher losses, more asset wearing and power quality issues. The traditional way to solve such issues was to install more cables and transformers. However, a more sustainable and cost effective alternative is to use the available flexibility in the distribution grid. An often used approach to stimulate such flexibility is based on pricing schemes.
We show that the mentioned expected problems in the distribution grid cannot be prevented when price mechanisms based on uniform prices are used. As an alternative we present models that lead to nonlinear optimization problems which aim to make trade-offs between deploying flexibility “now” or preserving flexibility for later use. To make a proper trade-off, predictions are used. We show that for this not always predictions of detailed power profiles are needed, but that even some basic characteristics are sufficient for a robust deployment of the flexibility.
Optimal maintenance planning strategies for equipment with usage dependent degradation
The energy network infrastructure is a sensitive system with high availability requirements. Properly planned maintenance is crucial in order to meet these requirements and the planning should be carried out by considering all available information about the condition of the elements. As critical infrastructure, the elements of the energy network are closely monitored.
Our work focuses on optimal planning strategies for equipment under the assumption that the condition of the equipment is not directly observable. Only the usage of the equipment is observable (e.x. the load of a transformer) and a usage dependent degradation model is assumed. We devise optimal intervention strategies under these assumptions.
We propose a Markov modulated fluid model to capture the condition of the equipment and we propose a maintenance strategy that considers the observed usage process. The strategy builds on a simpler strategy that has no access to the usage. We conjecture that if the simpler strategy is optimal in the less informative setting then the proposed strategy is optimal among all strategies given the observation of the usage process. (Joint work with Stella Kapodistria and Martijn Gösgens)
Regime-switching models for South Australian wholesale electricity spot prices – estimation and application
Electricity spot prices are known to exhibit characteristics not often observed in other financial markets – seasonality on multiple scales, mean reversion, large price spikes, price drops and negative prices. These characteristics are largely attributed to the lack of effective storage options for electricity. The goal of my research is to capture the aforementioned characteristics using a statistical model. Such a model is useful to help market participants manage risk and for valuation of financial contracts and real options for investment. We use a regime-switching time-series model, which is an extension of a hidden Markov model, to model prices. The idea is that we can specify different regimes to model distinct behaviours, i.e., we may specify a base regime for `normal’ prices which display self-dependence and mean-reversion, and a spike regime for large upward price movements. A subtle but important part of our model is that we require each regime to be completely independent. This coupled with the fact that the regime sequence is not directly observable, makes parameter estimation difficult as the likelihood function is not computable for realistic datasets. In this talk I will introduce some regime-switching models for electricity prices, discuss parameter estimation using data-augmented MCMC and approximate maximum likelihood, and apply these methods to the South Australian electricity market. South Australia is a particularly interesting case study due to its relative isolation, high prices and huge price spikes (up to AUD$14000 compared to the average price of around AUD$80).
Power Systems Flexibility from District Heating Networks
The large-scale penetration of renewable energy sources has increased the need for flexibility in the power sector. In systems with a high share of Combined Heat and Power (CHP) plants and heat pumps, exploiting the potential synergies between heat and electricity can help tackle this issue. However, due to the strong linkage between heat and electricity outputs, the participation of CHPs and heat pumps in both systems can have negative impacts on the power sector. Thus, rather than focusing on flexibility in power systems alone, a more holistic approach to renewables integration relies on flexibility from coordinating heat and electricity.
In that context, we introduce a novel model for Combined Heat and Power Dispatch (CHPD), accounting for time delays and energy storage capacity in district heating networks. This approach provides operational flexibility to the energy system by temporally decoupling heat and electricity production. Moreover, in order to deal with non-convexities introduced by temperature dynamics in district heating networks, we propose a convex relaxation of heat flows based on outer approximation and McCormick envelopes. Finally, we assess the operational flexibility provided by the district heating network by comparing the proposed model to a conventional economic dispatch.
(Joined work with Pierre Pinson and Jalal Kazempour)
A Large deviation Approach to Cascading Failures in Power Grids Under Uncertainty
The advent of renewable energy has huge implications for the control of power grids. Due to the intermittent nature of renewable generation, traditional reliability constraints have to be replaced by probabilistic guarantees. Assuming a stochastic model for power injections, we use techniques from large deviations theory to investigate the probability of transmission line overloads in a small-noise regime, and we identify the most likely way for overloads to occur. Furthermore, we examine how line overloads propagate and potentially lead to cascading failures. We find that such cascading failures do not propagate in a nearest neighbor fashion, unlike epidemic models, and we illustrate this analytically in our model.
Distributed Intelligence for Smart Power routing and mATCHing (DISPATCH)
DISPATCH introduces an implicit interaction framework to overcome the uncertainty challenges introduced by the upcoming energy transition. At its core, the framework includes a local flexibility market that enables the mapping for both network ancillary services and electricity market participation to low levels in the grid. It provides a platform that allows trading actions for flexibility in a specific location given by grid conditions or balancing needs. Meanwhile, it is important to improve monitoring capabilities, allowing newly developed control applications to optimise network operation, and demand side management algorithms such as local energy markets to incorporate physical and geographical constraints and preferences of the network. By using input from demand side management, in combination with the above-mentioned monitoring, we are able to provide suitable set-points for local controllers, such as transformer tap changers, droop characteristics of inverter-based appliances, or state of charge of the batteries, but also demand side management applications and local energy markets, using the linearised sensitivity of the network. This talk will present most updated results of DISPATCH coming along with a specific use case in Amsterdam ArenA which is known as DISPATCH 2 project.
Supply function equilibrium with variable-volume obligations
Using a supply function equilibrium model, we examine the strategic influence of retail obligations and wind revenues on the real-time market. Electricity retailers who offer fixed-price contracts to their customers risk making large losses when both prices and loads are high. This is exacerbated by the positive correlation between loads and prices. Similarly, wind farm operators who receive spot prices suffer from a negative correlation between prices and their outputs. Integration with despatchable generation is a natural hedge against both these risks, but what are the effects of this vertical integration on market power?
When utilities have both uncertainty about their net production/consumption and market power, their offer strategies will reflect conditional distribution of their own net generation on the market demand for dispatchable generation. The degree of correlation has a strong effect on the shape of the equilibrium offer curve, and hence on the distribution of prices.
Uncertainty quantification techniques for fluid dynamics in energy applications
In this talk I will address uncertainty quantification (UQ) techniques for several complex fluid flow problems arising in energy applications. Applications areas that we consider are wind energy, liquefied natural gas (LNG), and conventional oil and gas transport problems, in which the focus is on reducing the cost of energy – an example is the uncertainty of wind power generation due to uncertainty in wind speed and direction. A common denominator in all these applications is the very high computational costs associated with solving the fluid dynamic problems, making quantification of uncertainties difficult. The main challenges in UQ of such systems are: (i) determine and parameterize the most important uncertainties, (ii) calibrate the mathematical-physical models based on measurement data or high-fidelity models, and (iii) determine how uncertainties propagate through the models and influence the quantity of interest. In particular, I will show two new techniques for tackling these challenges: (i) efficient model Bayesian calibration with surrogate models, and (ii) uncertainty quantification for high-dimensional, non-linear model responses based on machine learning techniques.
MCMC simulation of rare events and applications to the analysis of power line failures
In many models a rare event occurs when the realisation of a random vector lies outside some polyhedral region. In this talk I will discuss how to efficiently use MCMC (Markov chain Monte Carlo) methods to produce random samples distributed according to a given distribution but conditioned on taking values outside a given polyhedron. I will then present some applications in the context of power grids reliability, focusing on line failures due to stochastic load fluctuations.
Registration for the workshop is free, but compulsory: REGISTRATION FORM
Registration is CLOSED.
Eurandom, Mathematics and Computer Science Department, TU Eindhoven,
De Groene Loper 5, 5612 AE EINDHOVEN, The Netherlands
Eurandom is located on the campus of Eindhoven University of Technology, in the MetaForum building, 4th floor (more about the building). The university is located at 10 minutes walking distance from Eindhoven main railway station (take the exit north side and walk towards the tall building on the right with the sign TU/e).
Accessibility TU/e campus and map.
For invited tutorial speakers, we will take care of accommodation. Other attendees will have to make their own arrangements.
For hotels around the university, please see: Hotels (please note: prices listed are “best available”).
More hotel options can be found on Tourist Information Eindhoven.
For those arriving by plane, there is a convenient direct train connection between Amsterdam Schiphol airport and Eindhoven. This trip will take about one and a half hour. For more detailed information, please consult the NS travel information pages.
Many low cost carriers also fly to Eindhoven Airport. There is a bus connection to the Eindhoven central railway station from the airport. (Bus route number 401) For details on departure times consult Public Transport.
The University can be reached easily by car from the highways leading to Eindhoven. For details: Route and map TU/e campus.
● Conference facilities
Conference room, MetaForum Building “MF11 & 12”.
The meeting-room is equipped with a data projector, an overhead projector, a projection screen and a blackboard. Please note that speakers and participants giving an oral presentation are kindly requested to bring their own laptop or their presentation on a memory stick.
● Conference Secretariat
Upon arrival, participants should register with the workshop officer, and collect their name badges. The workshop officer will be present for the duration of the conference, taking care of the administrative aspects and the day-to-day running of the conference: registration, issuing certificates and receipts, etc.
Should you need to cancel your participation, please contact Chantal Reemers/Petra Rozema.
Secretariaat Stochastics, Chantal Reemers/Petra Rozema – e-mail email@example.com