Leuven, Belgium


Leuven, Belgium
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Verhelst J.,EnergyVille | Verhelst J.,Catholic University of Leuven | Ham G.V.,Catholic University of Leuven | Saelens D.,EnergyVille | And 3 more authors.
Energy and Buildings | Year: 2017

Sensor and actuator degradation occurs frequently in office buildings. These can have a large impact on HVAC performance, both on energy use and thermal (dis)comfort. The degree of impact depends on the faulty component(s), fault type(s) and fault severity and has a significant non-linear relation with the control strategy and comfort constraints. Concrete core activated (CCA) office buildings typically have a high thermal inertia, high comfort requirements and they are equipped with low exergy and low capacity production systems. This allows the inclusion of renewables, thermal storage and flexible load shifting, but this also augments the effects of small perturbations in control output. In this paper, the economic fault impact is investigated by dynamic simulations using an emulator model of a CCA office building in combination with four different control strategies. A virtual test-bed is developed, consisting of two emulated office zones and a temperature modulated concrete core activation HVAC system, augmented with persistent faults in temperature sensor and hydronic flow rate actuators. Both the fault free (FF) performance and the fault present (FP) performance are investigated and compared through the relevant, control-associated costs using an economic framework. This methodology is able to determine the fault sensitivity of different supervisory control strategies and assists with the selection of the most economical, fault-robust controller for a certain building type. Also, the most critical sensors and actuators are identified. The evaluated faults are shown to be detrimental for the control performance. The relative economic impact of simultaneous (realistic, randomly distributed and non-correlated) sensor and actuator faults, ranged from +7% to +1000%. By adhering to an appropriate commissioning frequency, this impact can be reduced. The optimal commissioning period for sensors and actuators was determined to be between 2.8 and 5.0 years (case study, controller and assumption dependent). The lowest financial impact due to degradation faults, for this case study and assumptions, is attained by the closed loop model predictive control (CL-MPC) supervisory algorithm, which incurred only a 15% relative increase of total present cost, as opposed to increases above +100% for the other investigated control strategies over the controller lifetime. This study highlights the relevance of taking faults into account when evaluating long term HVAC control performance and quantifies the economic impact of simultaneous persistent sensor and actuator faults on control performance. © 2017 Elsevier B.V.

Vandael S.,Catholic University of Leuven | Claessens B.,Energyville | Ernst D.,University of Liège | Holvoet T.,Catholic University of Leuven | And 2 more authors.
IEEE Transactions on Smart Grid | Year: 2015

This paper addresses the problem of defining a day-ahead consumption plan for charging a fleet of electric vehicles (EVs), and following this plan during operation. A challenge herein is the beforehand unknown charging flexibility of EVs, which depends on numerous details about each EV (e.g., plug-in times, power limitations, battery size, power curve, etc.). To cope with this challenge, EV charging is controlled during opertion by a heuristic scheme, and the resulting charging behavior of the EV fleet is learned by using batch mode reinforcement learning. Based on this learned behavior, a cost-effective day-ahead consumption plan can be defined. In simulation experiments, our approach is benchmarked against a multistage stochastic programming solution, which uses an exact model of each EVs charging flexibility. Results show that our approach is able to find a day-ahead consumption plan with comparable quality to the benchmark solution, without requiring an exact day-ahead model of each EVs charging flexibility. © 2010-2012 IEEE.

Aertgeerts A.,Catholic University of Leuven | Claessens B.,Energyville | De Coninck R.,Catholic University of Leuven | Helsen L.,Catholic University of Leuven
14th International Conference of IBPSA - Building Simulation 2015, BS 2015, Conference Proceedings | Year: 2015

The research of optimal control in residential building clusters is approached from different disciplines: building simulation and control engineering. Control engineers focus mainly on the research and development of sophisticated optimal control strategies combined with high-level simulation tools but less accurate building models for fast prototyping of new control strategies. On the other hand, building simulation experts develop detailed building models which provide realistic and accurate building representations, however often in a simulation environment which is less suited for control. This paper proposes several methodologies to extend building and cluster models in Modelica, an objectoriented modelling and simulation language, with a Python control layer in order to bridge the gap between both disciplines. Co-simulation tries to leverage the advantages of both approaches by enabling the combination of both in an integrated simulation and keeping the development of the building models and control strategies separate. Control algorithms developed in Python can then easily be tested on detailed models in Modelica. As such, the Modelica simulation model is used as an emulator or virtual test bed. These integrated co-simulations can provide new insights in the behavior of building clusters when using sophisticated control algorithms.

Reynders G.,Energyville | Reynders G.,Catholic University of Leuven | Reynders G.,Flemish Institute for Technological Research | Diriken J.,Energyville | And 3 more authors.
Energy and Buildings | Year: 2014

The integration of buildings in a Smart Grid, enabling demand-side management and thermal storage, requires robust reduced-order building models that allow for the development and evaluation of demand-side management control strategies. To develop such models for existing buildings, with often unknown the thermal properties, data-driven system identification methods are proposed. In this paper, system identification is carried out to identify suitable reduced-order models. Therefore, grey-box models of increasing complexity are identified on results from simulations with a detailed physical model, deployed in the integrated district energy assessment simulation (IDEAS) package in Modelica. Firstly, the robustness of identified grey-box models for day-ahead predictions and simulations of the thermal response of a dwelling, as well as the physical interpretation of the identified parameters, are analyzed. The influence of the identification dataset is quantified, comparing the added value of dedicated identification experiments against identification on data from in use buildings. Secondly, the influence of the data used for identification on model performance and the reliability of the parameter estimates is quantified. Both alternative measurements and the influence of noise on the data are considered. © 2014 Elsevier B.V.

Hermans M.,Catholic University of Leuven | Delarue E.,EnergyVille
International Conference on the European Energy Market, EEM | Year: 2016

Due to the increase of variable renewable energy sources (RES) such as wind and solar power, cycling of thermal power plants is gaining importance. The short-term costs of cycling are well understood and easily taken into account when making scheduling decisions. The long-term costs, which consist primarily of maintenance, however, are both difficult to accurately determine and account for at the scheduling stage. As these long-term costs are rising as a result of increasingly flexible operating regimes, operators should consider them in order to reduce total generation costs. Furthermore, the accrued damage and resulting maintenance costs due to cycling are greatly affected by the required flexibility in terms of the start-up mode (FAST or SLOW). In this paper, a unit commitment model that accounts for long-term maintenance costs is set up and used to assess the impact of slow and fast starts on the operational regime of combined-cycle gas turbines (CCGTs) and total system cost components in a case study. The simulation results show that the CCGT units adopt a high-cycling regime with increasing RES penetration in both the fast and the slow starting scenarios. Furthermore, the total system cost in the fast scenario is slightly higher, but the trend exhibits a turning point, which shows the interplay between flexibility and maintenance costs depending on the amount of RES. © 2016 IEEE.

De Ridder F.,EnergyVille | De Ridder F.,Flemish Institute for Technological Research | D'Hulst R.,EnergyVille | D'Hulst R.,Flemish Institute for Technological Research | And 2 more authors.
Procedia Computer Science | Year: 2013

We have explored to what extent charging electrical vehicles (EVs) can be exploited to stabilize smart grids. Firstly, we discuss the transition to a future with a lot of renewable energy resources. Next, a decentralized coordinated charging schedule for EVs is proposed, taking into account the comfort settings of the consumers and local and temporal flexibility. Based on the vehicle behavior information (trajectories, parking places and duration, etc.) the algorithm assures that all vehicles can follow their planned trajectories and that power constraints on each car park are always met. An advantage of this decentralized coordination algorithm is that the privacy of consumers, including their future trajectory planning, charging controllers, parking duration, etc. are all treated on local processors on board. As a consequence the responsibility for constructing the charging schedules is put only with the vehicle owner. On the other hand, the parking managers need only to be concerned with the network congestion issues. A first application focuses on controlling the power flows at these parking locations and on rescheduling the charging of the electrical vehicles, so that costs are minimized within the comfort settings and within the physical limitations of the charging stations. This coordinated charging is applied on a car fleet of 200 electrical vehicles and 56 parking locations. Trajectories are computed with an activity based model (FEATHERS). In a second application, the imbalance costs are taken into account as well. The main advantage is for the retailer, who can now actively use the flexibility of the charging process to lower his power trading costs. © 2013 The Authors. Published by Elsevier B.V.

De Ridder F.,EnergyVille | De Ridder F.,Flemish Institute for Technological Research | Claessens B.,EnergyVille | Claessens B.,Flemish Institute for Technological Research
International Transactions on Electrical Energy Systems | Year: 2014

SUMMARY In this paper, we propose an optimal bidding strategy for industrial combined heat and power (CHP) installations selling their power on multiple markets. This corresponds to the typical situation where power is traded on a day-ahead market (DAM) and a continuous intraday market (CIM). Each market has its own trading rules. The considered installations consist of a CHP, a conventional heating installation and a heat buffer. Each device has its own constraints, such as maximum and minimum deliverable heat and electrical power, and minimum and maximum buffer capacity. The objective is to determine the bidding strategy that will maximise the expected profit, while the future time evolution of both heat demand and market prices are unknown. To tackle this problem, we assume that the probability density functions (PDFs) of these variables are known or can be extracted from historical data. Then, by applying a tailored stochastic programming algorithm, the optimal bidding strategy can be constructed based on these PDFs and includes the different market rules and constraints on the installation. For a DAM, the bidding functions must be estimated in advance, which is a typical open-loop problem. On the other hand, the bidding functions for a CIM may be estimated almost in real time. This new scheme is exemplified for the Belgian market. Combining both markets can increase the expected profits significantly because risks due to uncertainties in heat demand are better controlled. © 2013 John Wiley & Sons, Ltd.

Reynders G.,Energyville | Reynders G.,Flemish Institute for Technological Research | Reynders G.,Catholic University of Leuven | Nuytten T.,Energyville | And 3 more authors.
Building and Environment | Year: 2013

In order to avoid grid instability and decreasing production efficiencies of large power plants due to a widespread integration of renewable electricity production, demand-side management (DSM) is proposed as a solution to overcome the possible mismatch between demand and supply. This research evaluates the potential to improve the balance between the electricity use for heating and local electricity production of a nearly zero energy building (nZEB), by active use of structural thermal storage capacity of the building.To quantify the DSM potential of structural thermal storage, the cover factors and peak electricity demand of a single family dwelling equipped with a photovoltaic (PV) system are chosen. Detailed representations of the PV system and the dwelling itself, heated by an air-water heat pump, are implemented in the modeling environment of Modelica and simulated for the heating-dominated climate of Belgium. The influence of the insulation level and the embedded thermal mass of the construction on the DSM potential is evaluated. The impact of the heat emission system is estimated by comparing a floor heating system with a radiator emission system.Results show that although the influence on the cover factors is limited, the use of the structural storage capacity for demand-side management shows strong potential to shift the peak electricity use for heating to off-peak hours. Furthermore, it is shown that not only the availability of the thermal mass, but also the interaction between the heating system and the thermal mass is of significant importance. © 2013 Elsevier Ltd.

Ramos A.,Catholic University of Leuven | De Jonghe C.,Catholic University of Leuven | Gomez V.,EnergyVille | Gomez V.,Flemish Institute for Technological Research | Belmans R.,Catholic University of Leuven
Utilities Policy | Year: 2016

The technical and economic layers of electricity markets are moving in opposite directions: on the technical side, corrective actions are often needed at a particular place in the distribution grid, while on the economic side, wholesale market solutions at the moment do not provide location-specific solutions. Thus far, system operators have had little involvement in market actions to balance the network or relieve congestion. The Distribution System Operator (DSO) is faced with a need for market tools to enable more active system management through the use of flexibility. This paper discusses market-design proposals that would enable access to flexibility contracting to solve network problems and aid in balancing actions at a specific location. Market design is studied in terms of temporal, spatial, contractual, and price-clearing dimensions. Three main approaches to contracting flexibility are analyzed. The first is the possibility to contract flexibility through the existing wholesale markets; the second is the creation of a separate flexibility platform; and the last is a reserve-type market approach. The choice of design depends on possible market power and entry-barrier issues. A semi-competitive reserve-type market approach is suitable for an emerging market and a competitive exchange is recommended for a more mature market. © 2016 Elsevier Ltd.

Scientists from imec (partner in Solliance and EnergyVille), Karlsruhe Institute of Technology (KIT), and Zentrum für Sonnenenergie- und Wasserstoff-Forschung Baden-Württemberg (Centre for Solar Energy and Hydrogen Research, ZSW), today announced that they have fabricated a thin-film solar module stack made up of perovskite and Copper Indium Gallium Selenide (CIGS) with a conversion efficiency of 17.8 percent. For the first time, this tandem module surpasses the highest efficiencies of separate perovskite and CIGS modules.

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