Achieving an optimal trade-off between revenue and energy peak within a smart grid environment

We consider an energy provider whose goal is to simultaneously set revenue-maximizing prices and meet a peak load constraint. In our bilevel setting, the provider acts as a leader (upper level) that takes into account a smart grid (lower level) that minimizes the sum of users' disutilities. The latter bases its decisions on the hourly prices set by the leader, as well as the schedule preferences set by the users for each task. Considering both the monopolistic and competitive situations, we illustrate numerically the validity of the approach, which achieves an 'optimal' trade-off between three objectives: revenue, user cost, and peak demand

Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times

The quest for sustainability has arrived to the manufacturing world, with the emergence of a research field known as green scheduling. Traditional performance objectives now co-exist with energy-saving ones. In this work, we tackle a job shop scheduling problem with the double goal of minimising energy consumption during machine idle time and minimising the project’s makespan. We also consider uncertainty in processing times, modelled with fuzzy numbers. We present a multi-objective optimisation model of the problem and we propose a new enhanced memetic algorithm that combines a multiobjective evolutionary algorithm with three procedures that exploit the problem-specific available knowledge. Experimental results validate the proposed method with respect to hypervolume, -indicator and empirical attaintment functions

Bilevel Modelling of Energy Pricing Problem

International audienceCost minimization problem of a smart grid operator is integrated into the revenue optimization problem of an energy provider. Bilevel programming approach is applied to model the problem. The results of a classical exact method and two heuristic methods are compared

Genetic fuzzy schedules for charging electric vehicles

This work tackles the problem of scheduling the charging of electric vehicles in a real-world charging station subject to a set of physical constraints, with the goal of minimising the total tardiness with respect to a desired departure date given for each vehicle. We model a variant of the problem that incorporates uncertainty in the charging times using fuzzy numbers. As solving method, we propose a genetic algorithm with tailor-made operators, in particular, a new chromosome evaluation method based on generating schedules from a priority vector. Finally, an experimental study avails the proposed genetic algorithm both in terms of algorithm convergence and quality of the obtained solutions.Acknowledgements. This work was supported by the Spanish Government [Grant Nos.TIN2016-79190-R, MTM2014-55262-P]

Modèles de gestion du revenu et de régulation de la demande basés sur la programmation mathématique à deux niveaux dans un contexte de réseaux intelligents

Dans cette thèse nous étudions la problématique d’un fournisseur d’électricité qui souhaite à la fois réguler la demande et créer du revenu dans un environnement potentiellement compétitif (PRMDS). Nous proposons des modèles bi-niveaux pour représenter l’interaction hiérarchique entre le fournisseur d’électricité (le meneur) et ses clients (le suiveur). L’objectif du meneur est de maximiser son revenu en décroissant la valeur de pointe de la demande alors que l’objectif du suiveur est de minimiser la somme des coûts des clients. Nous supposons que les clients résidentiels sont inter-connectés entre eux via un réseau de communication bi-directionnel ce qui permet un pilotage de la demande par rapport aux prix par un agrégateur de réseau intelligent. Dans cette thèse nous avons proposé plusieurs modèles de programmation mathématique à deux niveaux bilinéaire bilinéaire pour le PRMDS. Ces modèles peuvent être reformulés sous forme de problèmes linéaire avec variables mixte (MIP) en utilisant les conditions de KKT. Ces modèles sont résolus de façon exacte sur des instances de taille moyenne via un logiciel commercial. Afin de résoudre des instances de plus grande taille, des heuristiques ont été proposées. Deux d’entre elles ont prouvé leur efficacité en terme de qualité de solution obtenue et de temps de calcul. Finalement nous avons considéré une version robuste du problème de programmation mathématique à deux niveaux. Des propriétés préliminaires ont été prouvées.This thesis is concerned with revenue optimization of an energy provider. A bilevel programming approach is proposed to model the relationship between the energy provider (leader) and power users (follower). The leader intends to achieve an optimal trade-off between revenue and peak load whereas the follower minimizes total cost of users to achieve system optimality. A smart grid structure that allows two-way communication is assumed to interconnect users and to schedule their demand regarding the prices. Day-ahead real-time prices are read by each customer's smart meter and the response is coordinated. In this thesis, we propose several bilinear bilevel programs that are presented and reformulated as single-level mixed integer problems using the KKT conditions of the follower's problem. These MIPs are solved to optimality for randomly generated instances using a commercial software. Different versions of the models are tested and compared. In order to solve large instances, several heuristics are developed. Two of these methods are shown to be efficient and solve large instances that cannot be solved within a reasonable time interval using exact method. Their outputs are compared to the exact solutions for small instances and their performances are evaluated. Finally, we address the robust bilevel optimization problem, discuss existing approaches, give illustrative examples, and propose avenues for future research

Vehicle routing problem with zone-based pricing

International audienc

Revenue management and demand side management in the energy field

International audiencePricing models for demand side management methods are traditional used to control electricity demand which became quite irregular recently and resulted in inefficiency in supply. We propose bilevel models to explore the relation and between energy suppliers and customers who are connected to a smart grid. This approach enables to integrate customer response into the optimization process of supplier who aims to maximize revenue or minimize capacity requirements. Numerical results are given

Bilevel Modelling of Energy Pricing Problem

International audienceCost minimization problem of a smart grid operator is integrated into the revenue optimization problem of an energy provider. Bilevel programming approach is applied to model the problem. The results of a classical exact method and two heuristic methods are compared

Bilevel Modelling of Energy Pricing Problem

International audienceCost minimization problem of a smart grid operator is integrated into the revenue optimization problem of an energy provider. Bilevel programming approach is applied to model the problem. The results of a classical exact method and two heuristic methods are compared

Energy Pricing Problems for Demand Sideand Revenue Management

International audienc