Stéphane Ploix

Optimal Power Flow Management for Grid Connected PV Systems With Batteries

This paper presents an optimal power management mechanism for grid connected photovoltaic (PV) systems with storage. The objective is to help intensive penetration of PV production into the grid by proposing peak shaving service at the lowest cost. The structure of a power supervisor based on an optimal predictive power scheduling algorithm is proposed. Optimization is performed using Dynamic Programming and is compared with a simple ruled-based management. The particularity of this study remains first in the consideration of batteries ageing into the optimization process and second in the “day-ahead” approach of power management. Simulations and real conditions application are carried out over one exemplary day. In simulation, it points out that peak shaving is realized with the minimal cost, but especially that power fluctuations on the grid are reduced which matches with the initial objective of helping PV penetration into the grid. In real conditions, efficiency of the predictive schedule depends on accuracy of the forecasts, which leads to future works about optimal reactive power management.

Tabu search for the optimization of household energy consumption

This paper focuses on demand-side load management applied to the residential sector. A home automation system controlling household energy is proposed. It is decomposed into three layers: anticipation, reactive and device layers. This paper deals with an anticipation layer that allocates energy by taking into account predicted events. It consists in computing both the starting times of some services and in determining set points of others while satisfying the maximal power constraint. A constraint satisfaction problem formulation has been proposed. Because the complexity is NP-hard, a tabu search is used to solve the problem. It maximizes user comfort and minimizes energy cost. An application example is presented

Parameter uncertainty computation in static linear models

Static linear models characterized by bounded uncertainties on both the equation error and the parameters are studied. The additive equation error is assumed to belong to an interval while the parameters fluctuate inside a time-invariant bounded domain. An algorithm is proposed for evaluating different bounded domains. The algorithm can be extended to cope with the determination of the central value of the domain containing the parameters. Contrary to most of traditional estimators, the resulting estimator takes into account the distribution of the uncertainties of a model.

Brief Causal fault detection and isolation based on a set-membership approach

This paper presents a diagnostic methodology relying on a set-membership approach for fault detection and on a causal model for fault isolation. Set-membership methods are a promising approach to fault detection because they take into account a priori knowledge of model uncertainties and measurement errors. Every uncertain model parameter and/or measurement is represented by a bounded variable. In this paper, detection consists of verifying the membership of measurements to an interval. First order discrete time models are used and their output is explicitly computed with interval arithmetic. Fault isolation relies on a causal analysis and the exoneration principle, which allows focusing the consistency tests on simple local models. The isolation strategy consists of two steps: performing minimal tests found with the causal graph and determining on line additional relevant tests that reduce the final diagnosis. An application for a nuclear process is used in order to illustrate the methods efficiency.

A Multi-agent Home Automation System for Power Management

This paper presents the principles of a Home Automation system dedicated to power management that adapts power consumption to available power ressources according to user comfort and cost criteria. The system relies on a multi-agent paradigm. Each agent is embedded into a power resource or an equipment, which may be an environment (thermal-air, thermal-water, ventilation, luminous) or a service (washing, cooking), and cooperates and coordinates its action with others in order to find acceptable near-optimal solution. The control algorithm is decomposed into two complementary mechanisms: an emergency mechanism, which protects from constraint violations, and an anticipation mechanism, which computes the best future set-points according to predicted consumptions and productions and to user criteria. The paper details a negotiation protocol used by the both mechanisms and presents some preliminary simulation results.

An Anticipation Mechanism for Power Management in a Smart Home using Multi-Agent Systems

This paper presents the principles of a home automation system dedicated to power management that adapts power consumption to available power ressources according to inhabitant comfort and cost criteria. The system relies on a multi-agent paradigm. Each agent supports a service achieved by several devices, it cooperates and coordinates its action with others in order to find an acceptable near-optimal solution. The control algorithm is decomposed into two complementary mechanisms: a reactive mechanism, which protects from constraint violations, and an anticipation mechanism, which computes a plan for global consumption according to predicted productions and consumptions and to inhabitant criteria. The paper shows how to compute a global consumption plan relying on Tabu Searchs algorithm and how to reduce the problem complexity by dividing the whole problem into independent sub-problems.

Brief paper: Parity relations for linear uncertain dynamic systems

A new approach for the design of parity relations for linear dynamic systems with additive and multiplicative uncertainties is presented. Instead of cancelling uncertainties following the example of the so-called robust approaches, uncertain parity relations take uncertainties into account as bounded variables. The method is based on the analysis of zonotopes representing the uncertainties. It leads both to Boolean detection results and to an indicator representing the distance to the opposite decision.

User behavior prediction in energy consumption in housing using Bayesian networks

This paper deals with the problem of the user behavior prediction in a home automation system. Anticipating the needed energy for a service is based on the available prediction (like user requests) which contains the uncertainties. When the future users requests are not available in a home automation system thanks to programmatic, it is interesting to predict it to anticipate the energy needed in order to avoid some problems like peak consumption. A general method to predict users requests for services in energy consumption is proposed. The method relies on Bayesian networks to predict and diagnose users behavior in housing. Some results and perspectives are presented in this paper.

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.

AUTOMATIC DESIGN OF DETECTION TESTS IN COMPLEX DYNAMIC SYSTEMS

Abstract In complex industrial plants, there are usually lots of sensors and the modelling of the plant leads to lots of mathematical relations. Before using classical tools for fault detection, the first problem to solve is: what sensors and mathematical relations have to be selected for the design of a detection test such as a state observer or a parity space based detection algorithms. This paper presents a general method for automatically selecting relevant sensors and relations that may be used for the design of the different detection tests. This method, which is based on a structural analysis of the process, provides all the testable subsystems and permits the selection of the most interesting detection tests regarding detectability and diagnosticabillity criteria.