Abstract
This paper presents a new three-layer household energy control system capable both to satisfy the maximum available electrical energy constraint and to maximize user comfort criteria. These layers are composed by: an equipment layer, with local and fast control mechanism, a protection layer, which is triggered when energy constraints are violated, and an anticipation mechanism, which adjusts future set-points of equipments in order to tackle energy events that can be forseen. The scheduling problem of the set-points of equipments is modelled by a graph and solved by the Bellman-Ford's algorithm. This control mechanism makes it possible to have a more exible control of the overall power consumption in housing in exploiting natural thermal energy accumulation.Copyright c 2006 IFAC
Keywords: Energy control system, home automation, scheduling, dynamic
programming
1. INTRODUCTION
Demand-Side load Management (DSM) (G. Thomas, 2000) are methods that coordinate the activities of energy consumers and energy providers in order to best t energy production capabilities with consumer needs and, in this way, avoiding energy demand peaks, which generally have adverse environmental impacts and increase energy production costs (Wacks, 1991). In residential sector, the development of Home Automation (HA) systems bring the possibility for energy consumers to participate in DSM systems by automatically adapting their consumption to production needs (Wacks, 1993).
(Wacks, 1991) presents basic kinds of DSM control:
- Direct control that actually forces shift of the customer electricity demand by directlyinterrupting the high power-consuming appliances.
- Local control that consists in setting up a policy where energy prices encourage consumption at o-peak periods when the total consumption is low.
However, these kinds of control are not very reactive and does not take into account user comfort and cannot make distinction between the dierent appliances. A home automation system (Peter and Ratko, 1997) basically consists of appliances linked via a communication network allowing appliances to communicate one each other. These home automation systems can carry out a new load management mechanism called distributed control (Wacks, 1993). This DSM control allows energy providers to charge user for the actual energy production cost in a very precise way, and it also allows the user to adjust his power consumption according to energy price variation. In the peak period, the domestic customer would be able to decide whether to wait and save money or to use appliances even so. The demonstration in (Boivin, 1995) shows that the HA systems can oer savings of up to 15% or even 20% on electric bill by more eciently managing household demand. This strategy is more reactive than DSM control but more complex to control by user. Because the user comfort is not taken into account automatically.
Energy management problem can be formulated as scheduling problem where energy is considered as a resource shared by appliances, and device energy demands are considered as tasks. Generally speaking, these approaches coordinate consumption activities in scheduling all tasks as soon as possible in order to reduce the overall consumption while satisfying maximum energy resource constraint. These approaches do not manage dierences between predictions and eective values. (Penya, 2003) proposes a solution based on one-day user consumption predictions. A parallel and distributed genetic algorithm optimizes the consumption of buildings in order to adjust the energy provider needs and user's demands.
In (Duy Long et al. , 2005), an adaptation of the static Resource Constraint Project Scheduling
Problems (RCPSP) is presented to improve the management of electric heating systems. This approach is able to coordinate the controls of electric heaters while satisfying the resource constraint.
Nevertheless, the problem requires precise predictive models and, moreover, it is NP-hard. This paper presents a new three-layer household energy control system capable both to satisfy the maximum available electrical energy constraint and to maximize user satisfaction criteria. This approach brings more reactivity for tting the energy provider needs. The proposed control strategy mainly relies on interactions between calculators embedded into domestic appliances. Thermal air environments equipped with electric heaters are used to illustrate the capability of the control mechanism. Natural thermal accumulation is used to adjust power consumption in real time because they are the main consumption factor during winter in continental regions. However, the approach can be easily extrapolated to other situations such as Heat, Ventilation and Air Conditioning (HVAC) systems.
The rest of paper is organized as follow: section 2 deals with problem modeling, including thermal and comfort modeling. In section 3, a problem formulation is proposed. Section 4 depicted the anticipation layer and section 5 the protection layer. Results are presented in section 6.
Duy Long Ha Stephane Ploix Eric Zamai
Mireille Jacomino
frstname.nameg@inpg.fr
LAG (Laboratoire d'Automatique de Grenoble)
B.P 46, 38402 St-Martin d'Heres CEDEX
France
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