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Many projections of near-future electricity system foresee a constantly increasing necessity of power flexibility services. In particular, thanks to the growing presence of renewable generation and innovative load technologies, distribution resources are becoming attractive products in ancillary services markets. In order to open the market gates to distribution flexibility, constant interactions between transmission and distribution system operators are required and the European project SmartNet is investigating in detail the possible coordination schemes among these two actors. The paper describes the SmartNet simulator, one of the main tools developed within the project, which precisely estimates the impact of TSO-DSO coordination schemes from the bidding and market clearing perspective, taking into account the consequent effects on the network physics at both transmission and distribution level.

The present report is a public deliverable (Deliverable D3.2) of the MAGNITUDE H2020 funded European project.The objective of this deliverable is to identify, elaborate and evaluate future market schemes for multi-energy systems. Currently, electricity, gas and heat are traded in separated markets with very different trading principles (e.g., trade types, pricing methods) and different temporal specifications (i.e., temporal resolution, clearing horizon, gate closure times). As an example, the majority of gas is traded on a long term yearly basis, whereas more and more electricity is traded on a more short term hourly basis typically a day in advance or even closer to real time. Organised heat markets are currently non-existent and, in most situations, a fixed heat price applies. This report proposes possible multi-carrier market schemes which can better reflect the interaction and interdependencies between the different energy carriers, which eventually can lead to higher overall social welfare. The report specifically focuses on the design of day-ahead (DA) multi-carrier energy markets as this is seen as the first step

The two topics of this thesis are macroscopic railway timetabling and station platforming and routing. These problems are challenges that are faced by all railway infrastructure companies. This thesis was carried out in cooperation with Infrabel, the Belgian railway infrastructure manager. (1) Macroscopic Cyclic Timetabling of Entire Countries In the macroscopic Train Timetabling Problem (TTP), the goal is to obtain, for each train and for each station it visits, an arrival and a departure time. Sufficient time should be provided for trains to ride from one station to the next. In stations, trains that stop should be allowed a minimal stop time, so that passengers can board, transfer or leave. Of course trains cannot collide on the same track and should even be separated by typically 3 minutes for safety reasons. No commercial software tools exist yet that automatically solve this problem. Over the last decennia, research at universities has led to automatically constructed ’draft’ timetables for the Netherlands, but these timetables still lack robustness against small recurrent delays. A timetable for the 37 trains of the Berlin underground was also constructed automatically, but also lacks robustness. For Belgium, about 200 hourly trains have to be planned, but Infrabel additionally requires that the timetable implies minimal passenger travel time in practice. This implies it will be robust. This was a new modelling and computational challenge. This time, which has to be minimised, includes journey time and small recurrent delays. In our latest paper, we also include excess journey time, which is the sum of waiting time at departure and at arrival. We are able to generate a timetable for all 196 hourly Belgian passenger trains from the 2013 timetable in a computation time of about 2 hours. Assuming common delays, the reduction of expected passenger time is 3.8% and the average missed transfer probability goes down from more than 10% in the original timetable to less than 3% in our timetable. Additionally, we also optimised the Danish timetable with 88 trains in about one hour, with a reduction of 2.9% of expected passenger time and reduced percentages for the probability of missing a transfer that are similar to the Belgian case. (2) Station Train Platforming and Routing For the problem of station platforming and routing, the arrival and departure times of trains are considered as given by the timetable. The sequential planning of the timetable and then station platforming and routing corresponds to the process currently used at Infrabel and almost all other railway companies. The challenge is then, to assign, per station, a platform track to each train such that no two trains are using the same infrastructure element at any time. This Train Platforming Problem (TPP) has been intensively studied and solved during the last decennia. Computation times are of the order of seconds per station. However, realisation of these automated methods at railway companies or into commercial tools for the railway industry has been limited. We solve this by developing and integrating a platforming tool at Infrabel. This tool produces a picture showing the current - manually made - platforming plan and indicates any conflict between any train pairs in the station area. We then generate an assignment without conflicts, of as many trains to platforms as possible. Sometimes, not all trains can be assigned to platform tracks and only a partial solution is given. This means that in practice, some trains cannot be assigned or otherwise the arrival and/or departure times have to be adapted manually. In the last case the macroscopic timetable also will have to be changed again. We also produce a picture of our optimised assignment which consistently shows that indeed, in our solution, there is no single conflict left. We are able to automatically generate train to platforming assignments for all 530 Belgian stations in only 10 minutes of optimisation time for all 530 models together. Both our macroscopic timetabling tool and platforming and routing tool are integrated and useable at Infrabel. status: published

ispartof: Tijdschrift Vervoerswetenschap vol:51 issue:1 pages:49-66 status: published

With our Periodic Event Scheduling Problem (PESP) based timetabling method we are able to produce a passenger robust timetable for all 88 hourly passenger trains running on tracks managed by the Danish Infrastructure Manager Banedanmark. The objective function of our model is the total expected passenger journey time in practice and is minimised. The result of this is that the produced timetable reduces the expected journey time of all corresponding train passengers together by 2.9% compared to the original timetable dened by Banedanmark. Our simulations show that the average probability of missing a transfer is also reduced from 11.34% to 2.45%. The computation of this timetable takes only 65 minutes. The major innovations of our approach are the addition of a complete objective function to the PESP model and the addition of a particular cycle constraint set that reduces computation times. In this paper, we demonstrate that these combined innovations result in a method that quickly generates cyclic timetables for a train network spanning an entire country and that these timetables also reduce the expected passenger travel time in practice. no isbn ispartof: Proceedings of the Conference on Advanced Systems in Public Transport ispartof: Conference on Advanced Systems in Public Transport location:Rotterdam date:19 Jul - 23 Jul 2015 status: published

ispartof: pages:377-387 ispartof: Proceedings of the 3rd International Conference on Models and Technologies for Intelligent Transportation Systems 2013 vol:3 pages:377-387 ispartof: International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) location:Dresden, Germany date:2 Dec - 4 Dec 2013 status: published

When evaluating and optimizing the national railway timetable for all passenger trains in Belgium, we use the criterium of expected total passenger travel time in practice. During evaluation, we measure this, during optimization, we try to minimize this. The focus in this paper is to analytically derive the total passenger time as a stochastic function of the schedule. In a previous paper [9], we already derived the necessary local passenger flows from ticket sales data. These flows will serve as fixed weights in this new objective function. We start from measured delays in the current schedule and assume that they can be described well by a negative exponential distribution. In the case of constructing an optimized schedule, we assume delays will also occur in the new schedule according to the same distribution. We believe this is a conservative assumption, since the optimized schedule will be more robust against delays. We then decompose the global schedule of actions into local actions or pairs of subsequent actions, and derive probabilities and objective functions on the corresponding one or two local actions only. We sum these local functions to obtain a global objective function. This then becomes the schedules’ global quality criterium. We linearize this and use the result as the objective function of a linear programming based optimizer. With this objective function, we optimized the train schedule for the Belgian railways, which proves the method is scalable to large and complex real-life problems. ispartof: pages:1-20 ispartof: 5th International Seminar on Railway Operations Modelling and Analysis - RailCopenhagen pages:1-20 ispartof: 5th International Seminar on Railway Operations Modelling and Analysis - RailCopenhagen location:Copenhagen, Denmark date:13 May - 15 May 2013 status: published

A theoretically calculated station capacity very often appears to be based on an unrealistic train set. By considering the actual, current, as well as future train set scenarios with increasing trac, we determine the realistic station capacity of the railway stations in Belgium. Our module is able to deal with real-life station and train sets in a few minutes and is now fully integrated in the software of Infrabel, the Belgian Railway infrastructure Management Company. ispartof: pages:1-4 ispartof: 2nd International Conference on Models and Technologies for ITS pages:1-4 ispartof: International Conference on Models and Technologies for ITS location:Leuven date:22 Jun - 24 Jun 2011 status: published

In optimizing the national railway timetable for the Belgian infrastructure company Infrabel, we use the quality criterium of minimizing expected total passenger travel time. Optimizing the timing of all trains fairly over all passengers requires passenger flow information. Focus is usually on determining the train service offer side, being the train timing, without considering the demand side data, being the passenger flow information. In this paper, we focus on passenger flow derivation. We start from train ticket sales data and use Dijkstra’s modified shortest path algorithm on the Belgian passenger train graph to determine a route per passenger origin destination station pair. We sum route passenger flows over all these routes. We also show how, in optimization of a timetable, timing and flows should be treated as interdependent. This means that we can use a process of iteration over these two phases, which we then call reflowing and retiming. The infrastructure company did not have this passenger flow information available for the current schedule, but they need it to optimize many decisions. We determined these passenger flows for the current timetable. We also give some recommendations regarding the improvement of passenger flow related data recording procedures, which would significantly improve passenger service. ispartof: pages:1-20 ispartof: Proceedings of 4th International Seminar on Railway Operations Modelling and Analysis (IAROR): RailRome2011 pages:1-20 ispartof: 4th International Seminar on Railway Operations Modelling and Analysis (IAROR) RailRome2011 location:Rome date:16 Feb - 18 Feb 2011 status: published

In the last decade many studies have been devoted to railway timetables for passenger trains. In countries like the Netherlands and Switzerland some new concepts were developed to obtain a better performance. Belgium, an important transit nation and the first in continental Europe with a railway, cannot lack behind. In this spirit, a research project about robustness of a timetable is initiated in close cooperation with Infrabel, the manager of the Belgian railway infrastructure. In this paper we present the first results of this research; a generic definition of robustness of a railway timetable is presented. Although many research was devoted to robust railway planning, there is lack of a general definition of timetable robustness. We present a general definition and use this definition to measure how robust the current schedule is. This definition is the first step towards a new timetable that is significantly more robust from the passengers’ point of view. ispartof: pages:1-20 ispartof: Proceedings of 4th International Seminar on Railway Operations Modelling and Analysis (IAROR): RailRome2011 pages:1-20 ispartof: 4th International Seminar on Railway Operations Modelling and Analysis (IAROR) RailRome2011 location:Rome date:16 Feb - 18 Feb 2011 status: published

Many projections of near-future electricity system foresee a constantly increasing necessity of power flexibility services. In particular, thanks to the growing presence of renewable generation and innovative load technologies, distribution resources are becoming attractive products in ancillary services markets. In order to open the market gates to distribution flexibility, constant interactions between transmission and distribution system operators are required and the European project SmartNet is investigating in detail the possible coordination schemes among these two actors. The paper describes the SmartNet simulator, one of the main tools developed within the project, which precisely estimates the impact of TSO-DSO coordination schemes from the bidding and market clearing perspective, taking into account the consequent effects on the network physics at both transmission and distribution level.