L.J. De Vries

Bio: L.J. De Vries is an academic researcher. The author has contributed to research in topics: Electricity market & Capital cost. The author has an hindex of 1, co-authored 1 publications receiving 145 citations.

Securing the public interest in electricity generation markets. The myths of the invisible hand and the copper plate

Abstract: Two aspects of investment in generation capacity in liberalized electricity markets are investigated: the question of whether investment will be sufficient to meet society's goals for the reliability of electricity supply (generation adequacy) and the question of how to coordinate investment in electricity generation capacity in a competitive market while bound by the physical requirements and limitations of the electricity networks. The study focuses on the situation in European electricity markets. A number of factors discourage generating companies from investing in a level of generation capacity that is optimal for society as a whole. Due to the limited possibilities for the storage of electricity and the low price-elasticity of demand, electricity prices are highly volatile. This, in addition to the lack of historical trend data (due to the short history of liberalized electricity markets), insufficient transparency and high capital costs, causes investment risk to be high. Investment risk is increased by several sources of regulatory uncertainty. Given these circumstances, it is rational for investors to be cautious. A number of policy options for improving investment incentives and for stabilizing the volume of generation capacity, called capacity mechanisms, are described and analyzed. A policy framework is introduced for evaluating them and deciding on the best policy options for different circumstances. With respect to the issue of coordinating investment in electricity generation capacity with the networks, the consequences of the choice for fixed transmission tariffs in most European countries were investigated. While fixed transmission tariffs are intended to make the market simple and transparent, paradoxically they create the need for several additional measures to compensate for their external effects. Among these, the implementation of a congestion management method ranks among the most necessary measures. The options for congestion management, given the choice for fixed transmission tariffs, are analyzed and compared.

Demand Response With Micro-CHP Systems

Abstract: With the increasing application of distributed energy resources and novel information technologies in the electricity infrastructure, innovative possibilities to incorporate the demand side more actively in power system operation are enabled. A promising, controllable, residential distributed generation technology is a microcombined heat and power system (micro-CHP). Micro-CHP is an energy-efficient technology that simultaneously provides heat and electricity to households. In this paper, we investigate to what extent domestic energy costs could be reduced with intelligent, price-based control concepts (demand response). Hereby, first the performance of a standard, so-called heat-led micro-CHP system is analyzed. Then, a model-predictive control (MPC) strategy aimed at demand response is proposed for more intelligent control of micro-CHP systems. Simulation studies illustrate the added value of the proposed intelligent control approach over the standard approach in terms of reduced variable energy costs. Demand response with micro-CHP lowers variable costs for households by about 1%-14%. The cost reductions are highest with the most strongly fluctuating real-time pricing scheme.

Improving real-time train dispatching: Models, algorithms and applications

Abstract: Traffic controllers monitor railway traffic sequencing train movements and setting routes with the aim of ensuring smooth train behaviour and limiting as much as existing delays. Due to the strict time limit available for computing a new timetable during operations, which so far is rather infeasible by using existing tools, railway traffic controllers usually restrict themselves mostly to a few manual timetable modifications and the chosen traffic control actions may be often sub-optimal. This PhD thesis is principally concerned with the design, implementation and evaluation of an advanced and robust laboratory tool for supporting railway traffic controllers in the everyday task of managing timetable disturbances. This dynamic traffic control system co-ordinates the speed of successive trains on open track, solves expected route conflicts and provides dynamic use of platform tracks in stations or alternative paths in a corridor between stations. Blocking time theory for modeling track occupation and signaling constraints is combined with alternative graphs for solving dynamic traffic control problems with the aim of increasing the punctuality and the use of infrastructure capacity at a network scale. The feasibility of the dispatching options is verified in a very short computation time by dynamic updating of the corresponding headways, train speeds and blocking time graphs, while the costs of the alternative dispatching options are measured in terms of maximum and average delays between consecutive trains at stations and other relevant points within the investigated network. To this end, the following achievements are included: (i) An innovative model for railway traffic optimization is presented to predict accurately train traffic flows and to enable the computation of optimal network schedules, i.e., all trains are managed simultaneously in a railway network for a given time period. (ii) The development of fast and effective scheduling algorithms based on the proposed model for the real-time management of a complex railway network is addressed. The objectives are to predict the evolution of train traffic within short computation times and to improve the punctuality by pro-actively detecting and solving train conflicts. (iii) A better use of rail capacity and a further improvement of punctuality are achieved by an iterative adjustment of train orders and routes in case of disturbances. Novel problem dedicated algorithms highlight the potential use of rerouting instead of only rescheduling the trains in order to limit the delay propagation as much as possible. (iv) Constructive algorithms for the dynamic modification of running times are provided that satisfy the timetable constraints of train orders and routes and guarantee the real-time feasibility of the running times, while respecting the signaling and safety systems in use. (v) A temporal decomposition method is introduced for the short-term traffic planning and control over a time period of up to several hours. This approach is of interest for traffic controllers since delays between running trains propagate considerably in time and space during heavily perturbed operations. (vi) A large set of computational studies on real-world instances proves that the automated decision support tool provides better solutions in terms of delay minimization compared to dispatching rules adopted by traffic controllers. Test beds are the hourly timetables of the Schiphol railway bottleneck and of the Utrecht - Den Bosch dispatching area. We study practical size instances and different types of disturbances, including multiple delayed trains, dwell time perturbations and blockage of some tracks.