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Leuven, Belgium

Ruelens F.,Catholic University of Leuven | Iacovella S.,Catholic University of Leuven | Claessens B.J.,EnergyVille | Claessens B.J.,Flemish Institute for Technological Research | Belmans R.,Catholic University of Leuven

The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant during the day. This constant temperature set point ensures that the heat pump operates in its more efficient heat-pump mode and minimizes the risk of activating the less efficient auxiliary heating element. As an alternative to a constant set-point strategy, this paper proposes a learning agent for a thermostat with a set-back strategy. This set-back strategy relaxes the set-point temperature during convenient moments, e.g., when the occupants are not at home. Finding an optimal set-back strategy requires solving a sequential decision-making process under uncertainty, which presents two challenges. The first challenge is that for most residential buildings, a description of the thermal characteristics of the building is unavailable and challenging to obtain. The second challenge is that the relevant information on the state, i.e., the building envelope, cannot be measured by the learning agent. In order to overcome these two challenges, our paper proposes an auto-encoder coupled with a batch reinforcement learning technique. The proposed approach is validated for two building types with different thermal characteristics for heating in the winter and cooling in the summer. The simulation results indicate that the proposed learning agent can reduce the energy consumption by 4%-9% during 100 winter days and by 9%-11% during 80 summer days compared to the conventional constant set-point strategy. © 2015 by the authors. Source

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

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. Source

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

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. Source

Vandael S.,Catholic University of Leuven | Claessens B.,EnergyVille | Ernst D.,University of Liege | Holvoet T.,Catholic University of Leuven | And 2 more authors.
IEEE Transactions on Smart Grid

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. Source

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

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. Source

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