Duy Long Ha

A HOME AUTOMATION SYSTEM TO IMPROVE HOUSEHOLD ENERGY CONTROL

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-Fords algorithm. This control mechanism makes it possible to have a more flexible control of the overall power consumption in housing in exploiting natural thermal energy accumulation.

An approach for home load Energy Management problem in uncertain context

This paper focuses on energy management problem applied to residential sector. The optimizing procedure of energy management is based on the available prediction (weather prediction, user¿s habit etc) which contains the uncertainty and imperfect knowledge. This uncertainty can be modelled as interval model. An approach composing three steps is proposed. Multi-parametric programming is used to calculate the family of an optimal solution. Two examples are proposed to illustrate this approach.

Realtimes dynamic optimization for demand-side load management

Abstract This paper focuses on Demand-Side load Management applied to residential sector. A multi-scale optimization mechanism for demand-side Load management is proposed. It composes the Load Management Layer, which carries out the distribution of the energy in housing. A dynamical limit for power consumption will be applied to each household. The home automation system integrated in each household plays the role of controlling the whole energy consumption in the housing by using service and appliances flexibilities, thus the energy consumption has the possibilities to be modified and controlled. Thanks to the feedback of the satisfaction of the client by the home automation system, the Load Management Layer can modify the limit of power consumption of the household again. This control mechanism takes dynamically into account the comfort of the users and satisfies the constraint from the energy production capabilities. A simulation of 400 housings points out the performance of this control mechanism.

Optimal power control for smart homes

Abstract This paper depicts a formulation of the global home electricity management problem, which consists in adjusting the electric energy consumption to the cost and availability variations of the power supply. The available flexibilities of the services to inhabitants are used to compute optimal plans for appliances control. Two optimization criteria are addressed: the users comfort and the economic cost. A global multi-layer solving approach is depicted that includes first the computation of consumption/production coordination plans and then the dynamic matching to the actual consumption/production data. The paper focuses on the computation of the anticipative plan through a MILP formulation of the energy management problem. Application examples are given.

Home Energy Management Problem: towards an Optimal and Robust Solution

A home automation system basically consists of household appliances linked via a communication network allowing interactions for control purposes (Palensky & Posta, 1997). Thanks to this network, a load management mechanism can be carried out: it is called distributed control in (Wacks, 1993). Load management allows inhabitants to adjust power consumption according to expected comfort, energy price variation and CO2 equivalent rejection. For instance, during the consumption peak periods when power plants rejecting higher quantities of CO2 are used and when energy price is high, it could be possible to decide to delay some services, to reduce some heater set points or to run requested services even so according to weather forecasts and inhabitant requests. Load management is all the more interesting that local storage and production means exist. Indeed, battery, photovoltaic panels or wind mills provide additional flexibilities. Combining all these elements lead to systems with many degrees of freedom that are very complex to manage by users. The objective of this study is to setup a general mathematical formulation that makes it possible to design optimized building electric energy management systems able to determine the best energy assignment plan, according to given criteria. A building energy management system consists in two aspects: the load management and the local energy production management. (House & Smith, 1995) and (Zhou & Krarti, 2005) have proposed optimal control strategies for HVAC (Home Ventilation and Air Conditioning) system taking into account the natural thermal storage capacity of buildings that shift the HVAC consumption from peakperiod to off-peak period. Zhou & Krarti (2005) has shown that this control strategy can save up to 10% of the electricity cost of a building. However, these approaches do not take into account the energy resource constraints, which generally depend on the autonomy needs of off-grid systems (Muselli et al., 2000) or on the total power production limits of the suppliers in grid connected systems. The household load management problem can be formulated as a assignment problem where energy is considered as a resource shared by appliances, and tasks are energy consumptions of appliances. Ha et al. (2006a) presents a three-layers household energy control system that is both able to satisfy the maximum available electrical power constraint and to maximize user satisfaction criteria. This approach carries out more reactivity to adapt consumption to the energy provider requirements. Ha et al. (2006b) proposes a global solution for the household load management problem. In order to adapt the consumption to the available energy, the home automation system controls the appliances in housing by determining the 5

Optimal scheduling for coordination renewable energy and electric vehicles consumption

The massive penetration of electric vehicles may lead to strong grid impacts, especially on the distribution grid. The Solar Mobility concept aims to resolve this situation using photovoltaic systems for recharging the vehicles. Key element for solar mobility is the energy management system, which minimizes the grid impact and maximizes the solar coverage during charging process. This paper presents the practical experience achieved with the operation of ten electrified vehicles and solar station during two years. The optimal load scheduling problem of electric vehicles will be presented. It is shown how the Energy Management System provides significant improvement of solar coverage and grid impact thought the demonstration period.