@inproceedings{Celanovic:2009:Ee,
author = {Celanovic, Ivan and Haessig, Pierre and Carroll, Eric and Kati{\'c}, Vladimir and Celanovic, Nikola},
booktitle = {{Ee 2009, Novi Sad, Serbia}},
title = {{Real-Time Digital Simulation: Enabling Rapid Development of Power Electronics}},
year = {2009},
month = oct,
abstract = {In this paper, we present the Typhoon Real Time Digital Simulation (T-RTDS) platform for high-power electronic systems, the fastest simulation platform of its kind. T-RTDS is based on novel simulation algorithms and proprietary high-throughput low-latency processor architecture. This approach enables simulation with a 1μs time-step, including input/output (I/O) latency. As a case study, we present modeling, simulation and experimental results for a system comprising a rectifier, an inverter, an output filter and a load. In addition, we demonstrate the T-RTDS capabilities for testing and verification of control hardware and software for various operating conditions.},
groups = {NatC},
localfile = {RT emulation of Power Electronics/Celanovic 2009 Ee - RTDS.pdf},
location = {Novi Sad, Serbia},
url = {https://hal.archives-ouvertes.fr/hal-01095964}
}
@inproceedings{Poon:2010:CITRES,
author = {Poon, Jason and Haessig, Pierre and Hwang, J George and Celanovic, Ivan},
booktitle = {{IEEE CITRES 2010, Waltham, MA}},
title = {{High-speed hardware-in-the loop platform for rapid prototyping of power electronics systems}},
year = {2010},
month = sep,
abstract = {A hardware-in-the-loop (HIL) platform for power electronics is presented, which is based on a real-time circuit simulation algorithm and a high-throughput, low-latency processor architecture implemented in a field-programmable gate array (FPGA). By using a simulation sampling time of 1 μs, including input/output latency, the platform enables the high-fidelity, real-time simulation of power electronics systems. In addition, to realize real-time digital simulation of power electronics systems the HIL platform uses a flexible modeling environment and novel circuit compiler algorithms. The efficacy of the real-time HIL platform is demonstrated via a simulation of a variable speed induction motor drive. The results are compared with those from a real variable speed drive system under steady state operating conditions. The results demonstrate the feasibility of HIL simulation for power electronics energy conversion systems.},
doi = {10.1109/CITRES.2010.5619770},
groups = {IntC},
keywords = {circuit simulation; induction motor drives; power electronics; rapid prototyping (industrial); variable speed drives; field-programmable gate array; high-speed hardware-in-the loop platform; power electronics systems; rapid prototyping; real-time circuit simulation algorithm; variable speed induction motor drive; Computational modeling; Digital simulation; Integrated circuit modeling; Mathematical model; Power electronics; Real time systems; Testing},
localfile = {RT emulation of Power Electronics/Poon 2010 ITERES - HIL Platform.pdf}
}
@inproceedings{Kinsy:2011:PCIM,
author = {Kinsy, Michel and Majstorovic, Dusan and Haessig, Pierre and Poon, Jason and Celanovic, Nikola and Celanovic, Ivan and Devadas, Srinivas},
booktitle = {{PCIM 2011, Nuremberg, Germany}},
title = {{High-speed real-time digital emulation for hardware-in-the-loop testing of power electronics: A new paradigm in the field of electronic design automation (EDA) for power electronics systems}},
year = {2011},
month = may,
abstract = {This paper details the design and application of a new ultra-high speed real-time emulation for Hardware-in-the-Loop (HiL) testing and design of high-power power electronics systems. Our real-time hardware emulation for HiL system is based on a custom, heterogeneous, reconfigurable, multicore processor design that emulates power electronics, and includes a circuit compiler that translates graphic system models into processor executable machine code. We present the digital processor architecture, and describe the process of power electronic circuit compilation. This approach yields real-time execution on the order of 1μs simulation time step (including input/output latency) for a broad class of power electronics converters. We present HiL experimental results for three representative systems: a variable speed induction motor drive, a utility grid connected photovoltaic converter system, and a hybrid electric vehicle motor drive.},
groups = {IntC},
localfile = {RT emulation of Power Electronics/Kinsy 2011 PCIM - RT emulation of Power Electronics.pdf},
url = {https://hal.archives-ouvertes.fr/hal-01095956}
}
@article{Haessig:2015:WE,
author = {Haessig, Pierre and Multon, Bernard and {Ben Ahmed}, Hamid and Lascaud, St{\'e}phane and Bondon, Pascal},
journal = {Wind Energy},
title = {{Energy storage sizing for wind power: impact of the autocorrelation of day-ahead forecast errors}},
year = {2015},
month = jan,
note = {published online Oct 2013},
number = {1},
pages = {43--57},
volume = {18},
abstract = {The availability of day-ahead production forecast is an important step toward better dispatchability of wind power production. However, the stochastic nature of forecast errors prevents a wind farm operator from holding a firm production commitment. In order to mitigate the deviation from the commitment, an energy storage system connected to the wind farm is considered. One statistical characteristic of day-ahead forecast errors has a major impact on storage performance: errors are significantly correlated along several hours. We thus use a data-fitted autoregressive model that captures this correlation to quantify the impact of correlation on storage sizing. With a Monte Carlo approach, we study the behavior and the performance of an energy storage system using the autoregressive model as an input. The ability of the storage system to meet a production commitment is statistically assessed for a range of capacities, using a mean absolute deviation criterion. By parametrically varying the correlation level, we show that disregarding correlation can lead to an underestimation of a storage capacity by an order of magnitude. Finally, we compare the results obtained from the model and from field data to validate the model.},
doi = {10.1002/we.1680},
groups = {IntJ},
localfile = {Haessig 2015 WE - Autocorrelation of Forecast Errors - author.pdf},
url = {http://hal.archives-ouvertes.fr/hal-00863901}
}
@inproceedings{Haessig:2013:PTech,
author = {Haessig, Pierre and Multon, Bernard and {Ben Ahmed}, Hamid and Lascaud, St{\'e}phane and Jamy, Lionel},
booktitle = {{IEEE PowerTech 2013 Conference, Grenoble, France}},
title = {{Aging-aware NaS battery model in a stochastic wind-storage simulation framework}},
year = {2013},
month = jun,
doi = {10.1109/PTC.2013.6652505},
groups = {IntC},
keywords = {Autoregressive processes; energy storage sizing; power generation planning; production commitment; sodium sulfur battery; wind energy; wind power forecasting},
localfile = {Haessig 2013 PTech - stochastic NaS simulation - author.pdf},
url = {http://hal.archives-ouvertes.fr/hal-00863903}
}
@inproceedings{Haessig:2013:ESPy,
author = {Haessig, Pierre and Kovaltchouk, Thibaut and Multon, Bernard and {Ben Ahmed}, Hamid and Lascaud, St{\'e}phane},
booktitle = {{6th European Conference on Python in Science (EuroSciPy 2013), Brussels, Belgium}},
title = {{Computing an Optimal Control Policy for an Energy Storage}},
year = {2013},
month = aug,
pages = {51--58},
abstract = {We introduce StoDynProg, a small library created to solve Optimal Control problems arising in the management of Renewable Power Sources, in particular when coupled with an Energy Storage System. The library implements generic Stochastic Dynamic Programming (SDP) numerical methods which can solve a large class of Dynamic Optimization problems. We demonstrate the library capabilities with a prototype problem: smoothing the power of an Ocean Wave Energy Converter. First we use time series analysis to derive a stochastic Markovian model of this system since it is required by Dynamic Programming. Then, we briefly describe the ``policy iteration'' algorithm we have implemented and the numerical tools being used. We show how the API design of the library is generic enough to address Dynamic Optimization problems outside the field of Energy Management. Finally, we solve the power smoothing problem and compare the optimal control with a simpler heuristic control.},
groups = {IntC},
keywords = {Stochastic Dynamic Programming; Policy Iteration Algorithm; Autoregressive Models; Ocean Wave Energy; Power Smoothing},
localfile = {Haessig 2013 ESPy - Optimal control Python.pdf},
url = {http://arxiv.org/abs/1404.6389}
}
@inproceedings{Haessig:2014:SGE,
author = {Haessig, Pierre and Multon, Bernard and {Ben Ahmed}, Hamid and Lascaud, St{\'e}phane},
booktitle = {{SGE 2014, Cachan, France}},
title = {{Quelle importance du choix de la loi de gestion pour dimensionner un syst{\`e}me de stockage d'{\'e}nergie ?}},
year = {2014},
month = jul,
groups = {NatC},
keywords = {Dimensionnement de Stockage d'{\'E}nergie; Gestion d'{\'E}nergie; Optimisation Dynamique; Programmation Dynamique; Contr{\^o}le Stochastique; Couplage Dimensionnement-Gestion},
localfile = {Haessig 2014 SGE - choix loi gestion.pdf},
url = {https://hal.archives-ouvertes.fr/hal-01065158}
}
@inproceedings{Haessig:2014:SGE:EN,
author = {Haessig, Pierre and Multon, Bernard and {Ben Ahmed}, Hamid and Lascaud, St{\'e}phane},
booktitle = {{SGE 2014, Cachan, France}},
title = {{How important is the choice of the control policy when sizing an energy storage system?}},
year = {2014},
month = jul,
note = {in French},
groups = {NatC},
keywords = {Energy Storage Sizing; Energy Management; Dynamic Optimization; Dynamic Programming; Stochastic Control; Sizing-Control Coupling},
localfile = {Haessig 2014 SGE - choix loi gestion.pdf},
url = {https://hal.archives-ouvertes.fr/hal-01065158}
}
@mastersthesis{Haessig:2011:MSc,
author = {Haessig, Pierre},
school = {ENS Cachan - Sup{\'e}lec Laboratoire Signaux et Syst{\`e}mes},
title = {{Caract{\'e}risation de l{\rq}incertitude de production {\'e}olienne, rapport de stage de Master 2}},
year = {2011},
month = jul,
abstract = {En pr{\'e}lude {\`a} de futurs travaux sur le dimensionnement et la gestion d{\rq}un syst{\`e}me de stockage d{\rq}{\'e}nergie coupl{\'e} {\`a} un syst{\`e}me {\'e}olien de production d{\rq}{\'e}lectricit{\'e}, cette {\'e}tude se focalise sur un probl{\`e}me sous-jacent : la variabilit{\'e} de la production {\'e}olienne. Il s{\rq}agit de comprendre et de mod{\'e}liser ce caract{\`e}re incertain {\`a} partir de s{\'e}ries de donn{\'e}es de terrains. Les deux r{\'e}sultats principaux sont i) la mod{\'e}lisation de l{\rq}incertitude de la relation entre la puissance produite par une {\'e}olienne et la vitesse du vent, en mode de fonctionnement {\`a} vitesse variable et ii) la mod{\'e}lisation d{\rq}une erreur de pr{\'e}vision au moyen d{\rq}un mod{\`e}le conditionnellement h{\'e}t{\'e}rosc{\'e}dastique. Ces mod{\'e}lisations se sont faites en statique, sans prendre en compte l{\rq}aspect temporel. L{\rq}importance des ph{\'e}nom{\`e}nes temporels sugg{\`e}re de les {\'e}tudier prochainement.},
groups = {Thes},
localfile = {Haessig 2011 MSc - Caract{\'e}risation incertitude {\'e}olienne.pdf},
url = {https://hal.archives-ouvertes.fr/hal-01178663}
}
@phdthesis{Haessig:2014:PhD,
author = {Haessig, Pierre},
school = {ENS Cachan},
title = {{Dimensionnement \& gestion d'un stockage d'{\'e}nergie pour l'att{\'e}nuation des incertitudes de production {\'e}olienne}},
year = {2014},
month = jul,
abstract = {Le contexte de nos travaux de th{\`e}se est l'int{\'e}gration de l'{\'e}nergie {\'e}olienne sur les r{\'e}seaux insulaires. Ces travaux sont soutenus par EDF SEI, l'op{\'e}rateur {\'e}lectrique des \^{\i}les fran\c{c}aises. Nous {\'e}tudions un syst{\`e}me {\'e}olien-stockage o{\`u} un syst{\`e}me de stockage d'{\'e}nergie doit aider un producteur {\'e}olien {\`a} tenir, vis-{\`a}-vis du r{\'e}seau, un engagement de production pris un jour {\`a} l'avance. Dans ce contexte, nous proposons une d{\'e}marche pour l'optimisation du dimensionnement et du contr{\^o}le du syst{\`e}me de stockage (gestion d'{\'e}nergie). Comme les erreurs de pr{\'e}vision J+1 de production {\'e}olienne sont fortement incertaines, la gestion d'{\'e}nergie du stockage est un probl{\`e}me d'optimisation stochastique (contr{\^o}le optimal stochastique). Pour le r{\'e}soudre, nous {\'e}tudions tout d'abord la mod{\'e}lisation des composants du syst{\`e}me (mod{\'e}lisation {\'e}nerg{\'e}tique du stockage par batterie Li-ion ou Sodium-Soufre) ainsi que des entr{\'e}es (mod{\'e}lisation temporelle stochastique des entr{\'e}es incertaines). Nous discutons {\'e}galement de la mod{\'e}lisation du vieillissement du stockage, sous une forme adapt{\'e}e {\`a} l'optimisation de la gestion. Ces mod{\`e}les nous permettent d'optimiser la gestion de l'{\'e}nergie par la m{\'e}thode de la programmation dynamique stochastique (SDP). Nous discutons {\`a} la fois de l'algorithme et de ses r{\'e}sultats, en particulier de l'effet de la forme des p{\'e}nalisations sur la loi de gestion. Nous pr{\'e}sentons {\'e}galement l'application de la SDP sur des probl{\`e}mes compl{\'e}mentaires de gestion d'{\'e}nergie (lissage de la production d'un houlog{\'e}n{\'e}rateur, limitation des rampes de production {\'e}olienne). Cette {\'e}tude de l'optimisation de la gestion permet d'aborder l'optimisation du dimensionnement (choix de la capacit{\'e} {\'e}nerg{\'e}tique). Des simulations temporelles stochastiques mettent en {\'e}vidence le fort impact de la structure temporelle (autocorr{\'e}lation) des erreurs de pr{\'e}vision sur le besoin en capacit{\'e} de stockage pour atteindre un niveau de performance donn{\'e}. La prise en compte de param{\`e}tres de co{\^u}t permet ensuite l'optimisation du dimensionnement d'un point de vue {\'e}conomique, en consid{\'e}rant les co{\^u}ts de l'investissement, des pertes ainsi que du vieillissement. Nous {\'e}tudions {\'e}galement le dimensionnement du stockage lorsque la p{\'e}nalisation des {\'e}carts {\`a} l'engagement comporte un seuil de tol{\'e}rance. Nous terminons ce manuscrit en abordant la question structurelle de l'interaction entre l'optimisation du dimensionnement et celle du contr{\^o}le d'un syst{\`e}me de stockage, car ces deux probl{\`e}mes d'optimisation sont coupl{\'e}s.},
groups = {Thes},
localfile = {Haessig 2014 PhD.pdf},
url = {https://tel.archives-ouvertes.fr/tel-01126946}
}
@techreport{Benech:2015:Rep,
author = {Benech, Pierre and Haessig, Pierre},
institution = {IETR},
title = {{Estimation du nombre de personnes pr{\'e}sentes dans une pi{\`e}ce {\`a} partir de la concentration en dioxyde de carbone}},
year = {2015},
month = jul,
abstract = {Dans cette {\'e}tude, nous avons cherch{\'e} {\`a} estimer le nombre de personnes pr{\'e}sentes dans une salle de r{\'e}union {\`a} partir de la mesure de la concentration en CO2 dans cette pi{\`e}ce. Pour construire des estimateurs, nous avons utilis{\'e} une s{\'e}rie d'exp{\'e}riences o{\`u} les utilisateurs de la salle ont not{\'e} les horaires d{\rq}arriv{\'e}e, de d{\'e}part et le nombre de participants de la r{\'e}union. Trois estimateurs ont {\'e}t{\'e} con\c{c}us : - le premier est compos{\'e} d{\rq}un filtre du premier ordre (inversion de la dynamique du CO2) et d{\rq}une moyenne glissante pour lisser l'estimation - le second utilise un filtre de Kalman : son int{\'e}r{\^e}t est la distinction de la composante ext{\'e}rieure du CO2 - le troisi{\`e}me est bas{\'e} sur les cha{\^i}nes de Markov : plut{\^o}t que de mettre en place des seuils, on prend en compte le caract{\`e}re discret du nombre de personnes pr{\'e}sentes},
groups = {Rep},
localfile = {Misc/Benech 2015 Rep - CO2.pdf},
url = {https://hal.archives-ouvertes.fr/hal-01256686}
}
@inproceedings{Haessig:2015:PTech,
author = {Haessig, Pierre and Multon, Bernard and {Ben Ahmed}, Hamid},
booktitle = {{IEEE PowerTech 2015 Conference, Eindhoven, the Netherlands}},
title = {{Energy Storage Control with Aging Limitation}},
year = {2015},
month = jun,
doi = {10.1109/PTC.2015.7232683},
groups = {IntC},
localfile = {Haessig 2015 PTech - Aging control - author.pdf},
url = {https://hal.archives-ouvertes.fr/hal-01147369}
}
@incollection{Multon:2013:TIBE8100,
author = {Multon, Bernard and Aubry, Judica{\"e}l and Haessig, Pierre and {Ben Ahmed}, Hamid},
booktitle = {{Techniques de l{\rq}Ing{\'e}nieur}},
publisher = {{\'E}ditions T.I.},
title = {{Syst{\`e}mes de stockage d{\rq}{\'e}nergie {\'e}lectrique}},
year = {2013},
month = apr,
pages = {BE8100},
groups = {Other},
url = {http://www.techniques-ingenieur.fr/base-documentaire/energies-th4/stockage-de-l-energie-42638210/systemes-de-stockage-d-energie-electrique-be8100}
}
@inproceedings{Cosson:2016:CTDSG,
author = {Cosson, Marjorie and Gu{\'e}guen, Herv{\'e} and Haessig, Pierre and Dumur, Didier and {Stoica Maniu}, Cristina and Gabrion, V. and Malarange, G.},
booktitle = {{IFAC and CIGRE/CIRED Workshop on Control of Transmission and Distribution Smart Grids (CTDSG´16), Prague, Czech Republic}},
title = {{Stability Criterion for Voltage Stability - Study of Distributed Generators}},
year = {2016},
month = oct,
abstract = {This paper proposes a stability criterion for the study of distributed generators equipped with local reactive power regulations. Existing formal methods propose a memory- consuming analysis with some practical issues while dealing with large-scale systems. To cope with this, a novel analytic stability criterion is established in this work. Firstly, a necessary condition for system stability is demonstrated for a feeder hosting a single generator. The approach is illustrated on a real medium voltage feeder. Then, a conjecture is proposed to study the stability of feeders hosting several generators equipped with reactive power regulations. Conjecture validity is proved over several theoretical grids hosting up to four distributed generators thanks to simulations.},
groups = {IntC},
localfile = {Marjorie Cosson/Cosson 2016 CTDSG.pdf},
url = {https://hal.archives-ouvertes.fr/hal-01402205v1}
}
@inproceedings{Cosson:2016:SGE,
author = {Cosson, Marjorie and Gu{\'e}guen, Herv{\'e} and Haessig, Pierre and Dumur, Didier and {Stoica Maniu}, Cristina and Gabrion, V. and Malarange, G.},
booktitle = {{SGE 2016, Grenoble, France}},
title = {{Crit{\`e}re de stabilit{\'e} analytique pour les r{\'e}gulations locales de tension des producteurs d{\'e}centralis{\'e}s }},
year = {2016},
month = jun,
abstract = {Parmi les cons{\'e}quences du d{\'e}veloppement massif de la production raccord{\'e}e aux r{\'e}seaux de distribution, le maintien de la tension dans les bornes admissibles est un enjeu important. Afin de limiter les excursions de tension dues aux producteurs d{\'e}centralis{\'e}s, des r{\'e}gulations locales de la puissance r{\'e}active des producteurs sont envisag{\'e}es. Cependant, ces r{\'e}gulations peuvent mettre en danger la stabilit{\'e} des r{\'e}seaux. Ces travaux proposent d{\rq}{\'e}tablir un crit{\`e}re de stabilit{\'e} ajustant la rapidit{\'e} de la r{\'e}gulation aux param{\`e}tres du r{\'e}seau. Ce crit{\`e}re est tout d{\rq}abord {\'e}tabli formellement et de fa\c{c}on explicite dans le cas d{\rq}un r{\'e}seau accueillant une seule r{\'e}gulation Q(U). Puis le crit{\`e}re propos{\'e} est {\'e}tendu aux r{\'e}seaux accueillant plusieurs r{\'e}gulations. Ainsi, les gestionnaires de r{\'e}seau pourront proposer aux producteurs {\'e}quip{\'e}s de r{\'e}gulations Q(U) des r{\'e}glages assurant la stabilit{\'e} et {\'e}valuer la marge de stabilit{\'e} associ{\'e}e au r{\'e}glage.},
groups = {NatC},
localfile = {Marjorie Cosson/Cosson 2016 SGE - Crit{\`e}re stabilit{\'e} analytique.pdf},
url = {https://hal.archives-ouvertes.fr/hal-01361607}
}
@inproceedings{Haessig:2016:SGE,
author = {Haessig, Pierre and Multon, Bernard and {Ben Ahmed}, Hamid},
booktitle = {{SGE 2016, Grenoble, France}},
title = {{Effet du pas de temps sur la simulation d'un syst{\`e}me {\'e}olien-stockage}},
year = {2016},
month = jun,
groups = {NatC},
localfile = {Haessig 2016 SGE - timestep.pdf},
url = {https://hal.archives-ouvertes.fr/hal-01361555}
}
@inproceedings{Haessig:2016:SGE:EN,
author = {Haessig, Pierre and Multon, Bernard and {Ben Ahmed}, Hamid},
booktitle = {{SGE 2016, Grenoble, France}},
title = {{Effect of the timestep on the simulation of a wind-storage system}},
year = {2016},
month = jun,
note = {in French},
groups = {NatC},
localfile = {Haessig 2016 SGE - timestep.pdf},
url = {https://hal.archives-ouvertes.fr/hal-01361555}
}
@inproceedings{Zhao:2017:CIEEC,
author = {Zhao, Boyang and Wang, Xifan and Haessig, Pierre},
booktitle = {{2017 China International Electrical and Energy Conference (CIEEC), Beijing, China}},
title = {{A Novel SoC Feedback Control of ESS for Frequency Regulation of Fractional Frequency Transmission System with Offshore Wind Power}},
year = {2017},
month = oct,
note = {waiting final acceptance},
groups = {IntC},
localfile = {Misc/Zhao 2017 CIEEC - Wind-storage frequency regulation.pdf},
url = {https://hal.archives-ouvertes.fr/hal-01587183}
}
@unpublished{Chatel:2017:Rep,
author = {Chatel, Sylvain and Haessig, Pierre and Bourdais, Romain},
note = {internship report},
title = {{Security of the Distributed Model Predictive Control }},
month = sep,
year = {2017},
abstract = {In this paper, we explore the security of a distributed model predictive control (DMPC) scheme. A global system is decomposed into several local subsystems which share a common resource. For instance a heating problem among several rooms with a limited amount of power. Each subsystem owns a model predictive control (MPC) controller which cooperates and works iteratively with all the other controllers in order to reach the system's objective. Here, we focus on the security of such a system. Indeed, we report that a user could disturb the DMPC through several attacks in order to destabilize the resource distribution. Hence, examples of cheating protocols and counter-cheat ideas are presented and discussed.},
groups = {Rep},
institution = {CentraleSup{\'e}lec},
keywords = {distributed optimization; model predictive control; distributed model predictive control},
localfile = {Misc/Chatel 2017 Rep - DMPC Security.pdf},
url = {https://hal.archives-ouvertes.fr/hal-01587466}
}
@inproceedings{Faille:2017:POWID,
author = {Faille, Damien and Haessig, Pierre},
booktitle = {{60th Annual ISA Power Industry Division Symposium (POWID), Cleveland, USA}},
title = {{Heliostat aiming points optimization for Concentrated Solar Power plant}},
year = {2017},
month = jun,
abstract = {EDF is investigating the dynamic behavior of a tower Concentrated Solar Power plant which consists of a heliostat field, a receiver and a boiler. The paper presents a solution to optimize the aiming point for each heliostat. The method is based on a simplified Gaussian model that reproduces the flux distribution for each heliostat, as a function of sun position. This model is calibrated using flux data generated with Tonatiuh, a ray-tracing software. The total power for the heliostat field is computed neglecting the blocking and shading effect by summing up the contributions of the heliostats. A set of aiming points is obtained by different strategies to optimize the distribution of the flux on the receiver resulting from a compromise between flux spillage and flux uniformity.},
groups = {IntC},
localfile = {Misc/Faille 2017 POWID - heliostat aiming.pdf},
url = {https://hal.archives-ouvertes.fr/hal-01912767}
}
@inproceedings{Haessig:2018:SGE,
author = {Haessig, Pierre and {Prince Agbodjan}, Jesse James and Bourdais, Romain and Gu{\'e}guen, Herv{\'e}},
booktitle = {{SGE 2018, Nancy, France}},
title = {{Gestion d'{\'e}nergie avec entr{\'e}es incertaines : quel algorithme choisir ? Benchmark open source sur une maison solaire}},
year = {2018},
month = jul,
abstract = {Le pilotage optimal des systèmes énergétiques nécessite des stratégies de gestion basées sur des algorithmes d'optimisation. La palette d'outils est large, or chaque outil fait appel à des théories variées (optimisation convexe, dynamique, stochastique...) qui nécessitent chacune un temps d'appropriation allant de quelques jours à plusieurs mois. Il est donc difficile, pour le ou la praticien.ne novice en gestion d'énergie de saisir les principales caractéristiques de chaque approche pour pouvoir les comparer objectivement et finalement trouver la ou les méthodes les plus adaptées à un problème donné. Pour faciliter une comparaison objective et transparente, nous proposons un problème de gestion d'énergie emblématique et simple : une maison solaire avec production photovoltaïque et stockage. Après avoir justifié le dimensionnement du système, nous illustrons le benchmark par une première comparaison de quelques méthodes de gestion d'énergie (règle heuristique, MPC et optimisation anticipative). Nous soulignons en particulier l'effet de l'incertitude de la production solaire sur la performance. Ce benchmark, avec les méthodes de gestion décrites, est open source, accessible en ligne et multi-langage (Python, Julia et Matlab).},
groups = {NatC},
localfile = {Haessig 2018 SGE - Solarhome.pdf},
url = {https://hal.archives-ouvertes.fr/hal-02981871}
}
@inproceedings{Haessig:2018:SGE:EN,
author = {Haessig, Pierre and {Prince Agbodjan}, Jesse James and Bourdais, Romain and Gu{\'e}guen, Herv{\'e}},
booktitle = {{SGE 2018, Nancy, France}},
title = {{Energy management with uncertain inputs: which algorithms? Open source benchmark based on a solar home}},
year = {2018},
month = jul,
note = {in French},
groups = {NatC},
localfile = {Haessig 2018 SGE - Solarhome.pdf},
url = {https://hal.archives-ouvertes.fr/hal-02981871}
}
@software{Ausgrid_GitHub,
author = {Pierre Haessig},
groups = {Soft},
title = {{GitHub - pierre-haessig/ausgrid-solar-data: Analysis of the Solar home electricity dataset from Ausgrid.}},
url = {https://github.com/pierre-haessig/ausgrid-solar-data},
year = {2019}
}
@techreport{Kordonis:TAC,
author = {Kordonis, Ioannis and Charalampidis, Alexandros C. and Haessig, Pierre},
title = {{Optimal Control of MDPs over a Long Operation-Dependent Time Horizon and Application to Battery Energy Storage Systems}},
year = {2021},
note = {submitted to IEEE Transactions on Automatic Control},
file = {:Ioannis Kordonis/Kordonis TAC Submission 2021-05.pdf:PDF},
groups = {Rep},
url = {https://jkordonis.github.io/page/#publications}
}
@inproceedings{PrinceA_2020_CCTA,
author = {Jesse-James {Prince Agbodjan} and Pierre Haessig and Romain Bourdais and Hervé Guéguen},
booktitle = {2020 IEEE Conference on Control Technology and Applications (CCTA)},
title = {{Stochastic modelled grid outage effect on home Energy Management}},
year = {2020},
month = aug,
pages = {1080--1085},
abstract = {This paper focuses on improving the notion of resilience in existing controllers based on Model Predictive Control. The controller resulting that we have called resilient open-loop feedback controller takes into consideration a stochastic model of a default. The problem solved by the new controller is a deterministic approximate equivalent of the stochastic initial problem obtained throughout simple transformations. Compared to the state of the art controller, an important reduction of the number of variables needed to solve the problem is also observed. Two stochastic grid outages models are considered in order to test the performances of the controller.},
doi = {10.1109/CCTA41146.2020.9206157},
file = {:Jesse James Prince Agbodjan/PrinceA 2020 CCTA - Stochastic outage OLFC.pdf:PDF},
groups = {IntC},
keywords = {Optimization;Uncertainty;Resilience;Markov processes;Energy management;Adaptive control;Home Energy Management System (HEMS);Resilience;Model Predictive Control (MPC);multi objectives optimization;Open Loop Feedback Controller(OLFC)}
}
@article{PrinceA_2019_CSGRES,
author = {Prince Agbodjan, Jesse-James and Pierre Haessig and Romain Bourdais and Hervé Guéguen},
journal = {IFAC-PapersOnLine},
title = {Resilience in energy management system: A study case},
year = {2019},
issn = {2405-8963},
note = {IFAC Workshop on Control of Smart Grid and Renewable Energy Systems CSGRES 2019},
number = {4},
pages = {395 - 400},
volume = {52},
abstract = {This paper describes the process of introducing resilience in an existing controller based on Model Predictive control. The resulting controller called resilient controller aims at improving energy resilience during power outages by maintaining at least a minimum level of energy set by the user. The performances of the so-called resilient controller are compared with others controllers. The results show that the resilient controller is capable of satisfying a settled electricity need of an energetic system as long as possible during an outages, hence improving the resilience.},
doi = {https://doi.org/10.1016/j.ifacol.2019.08.242},
file = {:Jesse James Prince Agbodjan/PrinceA 2019 CSGRES - Resilient EMS.pdf:PDF},
groups = {IntC},
keywords = {Home Energy Management System (HEMS), Resilience, Model Predictive Control (MPC), multi objectives optimization},
url = {http://www.sciencedirect.com/science/article/pii/S2405896319305798}
}
@software{Haessig_2020_ZenCSL100,
author = {Pierre Haessig},
comment = {for conference paper},
doi = {10.5281/zenodo.4282693},
groups = {Soft},
month = nov,
publisher = {Zenodo},
title = {{pierre-haessig/convex-storage-loss: v1.0.0: initial release}},
url = {https://doi.org/10.5281/zenodo.4282693},
version = {v1.0.0},
year = {2020}
}
@inproceedings{Haessig_2021_PTech,
author = {Pierre Haessig},
booktitle = {{IEEE PowerTech 2021 Conference, Madrid, Spain}},
title = {{Convex Storage Loss Modeling for Optimal Energy Management}},
year = {2021},
month = jun,
abstract = {Models of energy storage systems used for optimal energy management commonly feature unsophisticated loss expressions (lossless or constant efficiency).
Preserving the tractability (i.e. convexity) of the optimization motivates this simpleness, at the expense of physical realism.
Relaxing the storage loss expression from an equality constraint to an inequality allows using more complex and physically realistic models while preserving convexity.
Although this relaxation has been used quite casually for a decade in varied energy management applications (power systems dispatch, electric vehicles), only few articles studied this technique for itself.
To increase the awareness of loss relaxation, along with its limitations, we depict a unified view of relaxed storage loss models.
Also, we propose the “convex monomial loss model”,
one continuously parametrized family of nonlinear convex expressions which contains all classical loss models and unlocks new possibilities for loss dependency in storage power and energy level.
This is one step to reunite piecewise linear and nonlinear models which are generally studied separately.
Finally, we compare the effect of these varied loss models on the optimal charge/discharge profile of a storage in an elementary energy management application.},
doi = {10.1109/PowerTech46648.2021.9494786},
file = {:Haessig 2021 PTech - Convex storage - untrimmed final.pdf:PDF},
groups = {IntC},
keywords = {convex optimization, energy management, energy storage, storage losses, storage models},
url = {https://hal.archives-ouvertes.fr/hal-03032241}
}
@article{Kordonis_2021_OCAM,
author = {Kordonis, Ioannis and Charalampidis, Alexandros C. and Haessig, Pierre},
journal = {Optimal Control Applications and Methods},
title = {Optimal operation of a grid-connected battery energy storage system over its lifetime},
year = {2021},
abstract = {This paper deals with the optimal control of grid-connected Battery Energy Storage Systems (BESSs) operating for energy arbitrage. An important issue is that BESSs degrade over time, according to their use, and thus they are usable only for a limited number of cycles. Therefore, the time horizon of the optimization depends on the actual operation of the BESS. We focus on Li-ion batteries and use an empirical model to describe battery degradation. The BESS model includes an equivalent circuit for the battery and a simplified model for the power converter. In order to model the energy price variations, we use a linear stochastic model that includes the effect of the time-of-the-day. The problem of maximizing the revenues obtained over the BESS lifetime is formulated as a stochastic optimal control problem with a long, operation-dependent time horizon. First, we divide this problem into a finite set of subproblems, such that for each one of them, the State of Health (SoH) of the battery is approximately constant. Next, we reformulate approximately every subproblem into the minimization of the ratio of two long-time average-cost criteria and use a value-iteration-type algorithm to derive the optimal policy. Finally, we present some numerical results and investigate the effects of the energy loss parameters, degradation parameters, and price dynamics on the optimal policy.},
doi = {10.1002/oca.2830},
file = {:Ioannis Kordonis/Kordonis 2021 OCAM - Battery operation over lifetime.pdf:PDF},
groups = {IntJ},
url = {https://hal.archives-ouvertes.fr/hal-03510877/}
}
@article{PrinceA_2021_IFACSC,
author = {Prince Agbodjan, Jesse-James A. and Pierre Haessig and Romain Bourdais and Hervé Guéguen},
journal = {{IFAC} Journal of Systems and Control},
title = {Integrating stochastic discrete constraints in {MPC}. Application to Home Energy Management System},
year = {2021},
month = jul,
pages = {100168},
abstract = {In this paper, in a Model Predictive Control problem, we tackle the integration of uncertain constraints that belong to a discrete set. We propose a controller that offers a solution to reformulate the discrete stochastic constraints to avoid the exponential growth of scenario tree experienced by existing controllers. The proposed controller’s efficiency is shown by benchmarking and comparing it with the Multistage Stochastic Programming algorithm in a context of Home Energy Management System. The comparison is made based on simulations using real-life data to manage the energy flow within a building connected to the electricity feeder, which random character involves its availability and nonavailability.},
doi = {10.1016/j.ifacsc.2021.100168},
file = {:Jesse James Prince Agbodjan/PrinceA 2021 IFACSC - Discrete Stochastic constraints MPC.pdf:PDF},
groups = {IntJ},
keywords = {Model Predictive Control, Open Loop Feedback Control, Multi Stage Stochastic Programming, Grid outage, Home Energy Management System},
publisher = {Elsevier {BV}},
url = {https://hal.archives-ouvertes.fr/hal-03367535}
}
@phdthesis{PrinceA_2021_PhD,
author = {Prince Agbodjan, Jesse-James A.},
school = {CentraleSupélec},
title = {{Design of resilient controllers for Buildings Energy Management}},
year = {2021},
month = may,
note = {NNT 2021CSUP0003},
file = {:Jesse James Prince Agbodjan/PrinceA 2021 PhD - Resilient controllers for Energy Management.pdf:PDF},
groups = {ThesO},
url = {https://www.theses.fr/2021CSUP0003}
}
@inproceedings{Haessig_2021_SGE,
author = {Haessig, Pierre and {Prince Agbodjan}, Jesse James and Bourdais, Romain and Gu{\'e}guen, Herv{\'e}},
booktitle = {{SGE 2021, Nantes, France}},
title = {Solar home 2020 : enrichissement du benchmark open source de gestion d'énergie avec entrées incertaines},
year = {2021},
month = jul,
abstract = {Nous présentons la version 2020 du banc de test Solar home. Cet outil open source permet de comparer de façon simple et objective différentes méthodes de gestion d'énergie en présence d'entrées incertaines. Ce cas test est basé sur les données réelles d'une maison solaire avec production photovoltaïque et stockage dont il faut piloter les flux d'énergie pour minimiser la facture énergétique. Par rapport à la version initiale de 2018, nous avons ajouté de nouvelles méthodes d'optimisation parmi les plus performantes: programmation dynamique stochastique et MPC (commande prédictive) stochastique du type OLFC. Nous discutons également de l'intérêt de la gestion par règles heuristiques optimisées, qui peut se révéler aussi performante que les méthodes ``optimales''. La conclusion sur le classement des méthodes est cependant remise en question par la sensibilité au scénario d'entrée qui reste largement à étudier.},
file = {:Haessig 2021 SGE - Solar home2.pdf:PDF},
groups = {NatC},
url = {https://hal.archives-ouvertes.fr/hal-03366746}
}
@inproceedings{Haessig_2021_SGE_EN,
author = {Haessig, Pierre and {Prince Agbodjan}, Jesse James and Bourdais, Romain and Gu{\'e}guen, Herv{\'e}},
booktitle = {{SGE 2021, Nantes, France}},
title = {Solar home 2020: expanding the open source energy management under uncertain inputs benchmark},
year = {2021},
month = jul,
note = {{in French}},
groups = {NatC},
url = {https://hal.archives-ouvertes.fr/hal-03366746}
}
@software{SolarHome_GitHub_92f794b,
author = {Haessig, Pierre and Prince Agbodjan, Jesse-James A.},
groups = {Soft},
title = {{GitHub - pierre-haessig/solarhome-control-bench: Open testbench for control and optimization methods for the energy management of a simple solar home.}},
url = {https://github.com/pierre-haessig/solarhome-control-bench/},
version = {92f794b},
year = {2021}
}
@software{SolIrr.jl_GitHub_03b97bf,
author = {Pierre Galloo-Beauvais and Pierre Haessig},
groups = {Soft},
title = {{GitHub - pierre-haessig/SolarIrradiance.jl: Solar radiation computation library, with applications in solar energy.}},
url = {https://github.com/pierre-haessig/SolarIrradiance.jl},
version = {03b97bf},
year = {2021}
}
@inproceedings{deGodoy_2022_ISGTEU,
author = {Evelise {de Godoy Antunes} and Pierre Haessig and Chaoyun Wang and Roberto {Chouhy Leborgne}},
booktitle = {ISGT Europe 2022, Novi Sad, Serbia},
title = {{Optimal Microgrid Sizing using Gradient-based Algorithms with Automatic Differentiation}},
year = {2022},
abstract = {Microgrid sizing optimization is often formulated as a black-box optimization problem. This allows modeling the microgrid with a realistic temporal simulation of the energy flows between components. Such models are usually optimized with gradient-free methods, because no analytical expression for gradient is available. However, the development of new Automatic Differentiation (AD) packages allows the efficient and exact computation of the gradient of black-box models. Thus, this work proposes to solve the optimal microgrid sizing using gradient-based algorithms with AD packages. However, physical realism of the model makes the objective function discontinuous which hinders the optimization convergence. After an appropriate smoothing, the objective is still nonconvex, but convergence is achieved for more that 90\% of the starting points. This suggest that a multi-start gradient-based algorithm can improve the state-of-the-art sizing methodologies.},
doi = {10.1109/ISGT-Europe54678.2022.9960498},
file = {:deGodoy 2022 ISGTEU - AD Microgrid sizing - author.pdf:PDF},
groups = {IntC},
url = {https://hal.archives-ouvertes.fr/hal-03370004}
}
@inproceedings{ElFeghali_2021_ModCon,
author = {Joy {El Feghali} and Guillaume Sandou and Herv{\'{e}} Gu{\'{e}}guen and Pierre Haessig and Damien Faille},
booktitle = {Proceedings of 14th Modelica Conference 2021, Linköping, Sweden, September 20-24, 2021},
title = {{Energy-based Method to Simplify Complex Multi-Energy Modelica Models}},
year = {2021},
month = sep,
publisher = {Linköping University Electronic Press},
abstract = {Energy production and consumption systems increasingly require more flexibility. The design of new control solutions can be a step, among others, towards flexibility. However, these control solutions often rely on the use of complex models, which are difficult to both manipulate and simulate. This paper presents a proof of concept of a method that reduces the complexity of multi-energy models modeled with Modelica language. This complexity-reducing method is based on simplifying the model’s components that contribute less to the total energy using an energy-based ranking technique. The proposed solution is successfully applied to a complex city district model. A property of the Modelica language further allows redeclaration of low-ranked components without being compelled to fully redesign the model. Criteria verifying the multi-energy reduced model’s precision, while respecting physical constraints, are also introduced.},
doi = {10.3384/ecp21181577},
file = {:Joy El Feghali/ElFeghali 2021 ModCon - Energy-based model simplication.pdf:PDF},
groups = {IntC},
url = {https://hal-centralesupelec.archives-ouvertes.fr/hal-03408376}
}
@inproceedings{ElSayegh_2022_JCGE,
author = {Elsy {El Sayegh} and Nabil Sadou and Pierre Haessig and Sarah Nasr and Julien Bruschi and Benoît Jacquet and Ali {El Akoum} and Hervé Guéguen},
booktitle = {Jeunes Chercheurs en Génie Électrique (JCGE22), Le Croisic},
title = {{Towards avoiding microgrid design failures arising from an unrealistic operating strategy: an anticipative White Box model versus a responsive Black Box model}},
year = {2022},
abstract = {This paper provides a comparison of two deterministic and stochastic approaches for microgrid sizing: an anticipative White Box (WB) model that optimizes both microgrid sizing and operation assuming a perfect forecasting of load and renewable energy production profiles, versus a responsive Black Box (BB) model that heuristically optimizes microgrid sizing with a realistic rule-based operation strategy. This work underlines the importance of optimizing microgrid design with the operating policy that will be implemented and assesses the techno-economic gaps compared to a sizing approach that assumes the perfect knowledge of the future. The case study results show that BB model is suitable for microgrid sizing with less computational requirements than the WB model.},
file = {:Elsy El Sayegh/El-Sayegh 2022 JCGE - Microgrid sizing White vs Black-box.pdf:PDF},
groups = {NatC},
url = {https://hal.archives-ouvertes.fr/hal-03702973}
}
@inproceedings{ElFeghali_2022_IWCSC,
author = {El Feghali, Joy and Sandou, Guillaume and Gu{\'e}guen, Herv{\'e} and Haessig, Pierre and Faille, Damien and Bouia, Hassan and Croteau, Dominique},
booktitle = {{IFAC Workshop on Control for Smart Cities, Sozopol, Bulgaria}},
title = {{Electrical Grid Flexibility via Heat Pump and Thermal Storage Control}},
year = {2022},
month = jun,
abstract = {District Heating Networks (DHN) that include heat pumps as a heating source can offer flexibility to electrical grids. These systems are now used as part of smart city development where the electrical consumption of heat pumps can be controlled. They respond to the Demand-Side Management (DSM) programs and change their consumption to satisfy the constraints on the electrical grid while respecting the end-user heating demand. When thermal storage is included in DHN, there is an increase in the flexibility offered and a shift to using less fossil fuel energy. This paper proposes a heat pump and thermal storage system for space and water heating of twelve tertiary buildings. The proposed DHN system is modeled using Dymola, and is controlled via MATLAB to respond to flexibility demands which are given at the electrical grid level by the Distribution System Operator (DSO). A Model Predictive Controller (MPC) regulates the electrical consumption of the heat pump to perform peak shaving or valley filling for the electrical grid, or to minimize the electrical energy consumed over a 24-hour time window. This paper also presents an optimization problem that responds to different programs using only the heat pump and the thermal storage to satisfy end-user demands. Results show that thermal storage is used more often to satisfy the heating demands of the buildings when the control is activated. The system should be notified early enough before the peak shaving demand so it would be possible to turn off the heat pump.},
doi = {10.1016/j.ifacol.2022.08.053},
groups = {IntC},
hal_id = {hal-03750044},
hal_version = {v1},
keywords = {Load Management ; Optimization ; Model Predictive Control ; Flexibility ; Electrical Grid ; District Heating Network ; Thermal Storage ; Heat Pump},
url = {https://hal-centralesupelec.archives-ouvertes.fr/hal-03750044}
}
@phdthesis{ElFeghali_2023_PhD,
author = {Joy {El Feghali}},
school = {Université Paris-Saclay},
title = {{Contribution à l'utilisation des modèles physiques en Modelica à des fins de commande de systèmes multi-énergies}},
year = {2023},
note = {NNT: 2023UPAST033},
file = {:Joy El Feghali/ElFeghali 2023 PhD.pdf:PDF},
groups = {ThesO},
url = {https://theses.fr/2023UPAST033}
}
@inproceedings{FayeBedrin_2023_PTech,
author = {Faye-Bédrin, Alexandre and Blavette, Anne and Haessig, Pierre and Bourguet, Salvy and Daminov, Ildar},
booktitle = {IEEE PowerTech 2023, Belgrade},
title = {{Stochastic Dynamic Programming for Energy Management of an Overplanted Offshore Wind Farm with Dynamic Thermal Rating and Storage}},
year = {2023},
month = jun,
publisher = {IEEE},
abstract = {We study the optimal energy management of an offshore wind farm which combines “overplanting” (more production than transmission capacity), “dynamic thermal rating” (DTR, transiently exporting more than the steady-state transmission capacity thanks to the large thermal inertia of the soil surrounding the export cable) and an energy storage (to mitigate both curtailements and forecast errors). This forward-looking setting, which aims at further reducing the Levelized Cost of Energy of offshore wind power, creates an optimization problem with both temporal couplings and uncertain inputs. The difficulty of this energy management problem comes from having time constants separated by several orders of magnitude due the thermal inertia of the cable surroundings. We propose an approximate solution based on large GPU implementation of Stochastic Dynamic Programming (SDP). In our performance comparisons, SDP outperforms simpler rule-based energy management schemes while we also explore the benefit of DTR in the context of overplanting.},
doi = {10.1109/powertech55446.2023.10202975},
file = {:Misc/FayeBédrin 2023 PTech - SDP Wind Farm with Dynamic Thermal Rating.pdf:PDF},
groups = {IntC},
url = {https://hal.science/hal-04212709v1}
}
@inproceedings{ElSayegh_2023_CIRED,
author = {Elsy {El Sayegh} and Pierre Haessig and Nabil Sadou and Julien Bruschi and Benoît Jacquet and Sarah Nasr and Hervé Guéguen},
booktitle = {27th International Conference on Electricity Distribution (CIRED 2023), Rome},
title = {Assessing the impact of uncertainties on the techno-economic performance of microgrids},
year = {2023},
note = {Paper n° 10762},
abstract = {This paper presents a framework for uncertainty management and treatment, an essential step toward achieving a robust microgrid design. The microgrid is first designed using a deterministic techno-economic optimization model. All uncertain inputs are identified and modeled properly (probabilistic framework for parameters and bootstrap for time series): this is called the Uncertainty Characterization step. An Uncertainty Analysis is then performed using Monte Carlo simulations to quantify the uncertainty impacts on the microgrid performance. Finally, a two-step Global Sensitivity Analysis is performed to identify and rank the uncertain factors that are mainly responsible for microgrid performance variations [1]. Morris’ method is used to discard unimportant factors with low computational cost. Sobol's variance-based method is applied to quantify the contribution of each uncertain input to the output variance. The case study results indicate that the proposed methodology provides a better understanding and handling of uncertainties during the microgrid design stage and acts as a decision-support tool when evaluating microgrids sizing decisions.},
file = {:Elsy El Sayegh/El-Sayegh 2023 CIRED - Impact of uncertainties.pdf:PDF},
groups = {IntC}
}
@inproceedings{Haessig_2023_SGE,
author = {Pierre Haessig and Elsy {El Sayegh} and Evelise {de Godoy Antunes} and Nabil Sadou},
booktitle = {{SGE 2023, Lille, France}},
title = {Modélisation des microréseaux : quelles architectures de code pour quelles fonctions ?},
year = {2023},
abstract = {Le dimensionnement des systèmes énergétiques tels que les microréseaux constitue un problème d'optimisation qui nécessite un programme informatique pour obtenir une solution numérique. Plus le problème de dimensionnement est scientifiquement riche (ex. : prise en compte des incertitudes), plus le programme pour le résoudre est informatiquement complexe. Cette complexité peut mettre en péril la validité et la maintenabilité d'un tel programme. La conception d'un outil de dimensionnement de microréseau est donc un (méta)-problème en soi. Cet article propose des pistes de conceptions logicielles modulaires qui permettent de traiter à la fois un problème de dimensionnement "de base" et plusieurs variantes scientifiques importantes. Cette approche qui vise à réutiliser un maximum de briques de base devrait permettre d'obtenir des programmes de meilleure qualité. Ces propositions s'accompagnent de la publication des premières versions de la famille d'outils de simulation et d'optimisation open source Microgrids.X.},
file = {:Haessig 2023 SGE - MicrogridsX architecture.pdf:PDF},
groups = {NatC},
url = {https://hal.science/hal-04277373}
}
@inproceedings{Haessig_2023_SGE_EN,
author = {Pierre Haessig and Elsy {El Sayegh} and Evelise {de Godoy Antunes} and Nabil Sadou},
booktitle = {{SGE 2023, Lille, France}},
title = {Microgrids modeling: which code architectures for which features?},
year = {2023},
note = {{in French}},
abstract = {Le dimensionnement des systèmes énergétiques tels que les microréseaux constitue un problème d'optimisation qui nécessite un programme informatique pour obtenir une solution numérique. Plus le problème de dimensionnement est scientifiquement riche (ex. : prise en compte des incertitudes), plus le programme pour le résoudre est informatiquement complexe. Cette complexité peut mettre en péril la validité et la maintenabilité d'un tel programme. La conception d'un outil de dimensionnement de microréseau est donc un (méta)-problème en soi. Cet article propose des pistes de conceptions logicielles modulaires qui permettent de traiter à la fois un problème de dimensionnement "de base" et plusieurs variantes scientifiques importantes. Cette approche qui vise à réutiliser un maximum de briques de base devrait permettre d'obtenir des programmes de meilleure qualité. Ces propositions s'accompagnent de la publication des premières versions de la famille d'outils de simulation et d'optimisation open source Microgrids.X.},
groups = {NatC},
url = {https://hal.science/hal-04277373}
}
@software{MgPy_GitHub_031,
author = {Pierre Haessig},
groups = {Soft},
title = {{GitHub - Microgrids-X/Microgrids.py: Operational \& economic simulation of Microgrid projects in Python.}},
url = {https://github.com/Microgrids-X/Microgrids.py},
version = {v0.3.1},
year = {2023}
}
@software{MgMat_GitHub,
author = {Pierre Haessig},
groups = {Soft},
title = {{GitHub - Microgrids-X/Microgrids.m: Operational \& economic simulation of Microgrid projects in Matlab.}},
url = {https://github.com/Microgrids-X/Microgrids.m},
year = {2023}
}
@software{SGEProcLight_GitHub,
author = {Pierre Haessig},
groups = {Soft},
title = {{GitHub - pierre-haessig/SGE-proceedings-light: Proceedings generator (light version) for SGE 2023 conference.}},
url = {https://github.com/pierre-haessig/SGE-proceedings-light},
year = {2023}
}
@inproceedings{Nikiema_2024_JCGE,
author = {Jean Fructhieu Nikiema and Pierre Haessig and Nabil Sadou and Hervé Guéguen},
booktitle = {Jeunes Chercheurs en Génie Électrique (JCGE24), Le Croisic},
title = {Optimisation du dimensionnement de micro-réseaux isolés avec stockage saisonnier d’hydrogène},
year = {2024},
file = {:Jean Nikiema/Nikiema 2024 JCGE - H2 dim gest règles.docx:Word 2007+},
groups = {NatC}
}
@techreport{Haessig_2024_SalvRep,
author = {Pierre Haessig},
institution = {HAL},
title = {Economic consistency of salvage value definitions},
year = {2024},
number = {hal-04097092},
abstract = {This short report analyzes the definition of the salvage value of a component within a wider project investment analysis. The analysis shows that the classical(*) definition is close but not exactly consistent from an economic point of view. For an exact economic consistency of the salvage value, we derive an alternate definition using a more complex formula, which we call the economically consistent salvage value. This formula is equivalent to the classical definition when the discount factor is zero. The key takeaway of our analysis is that using the classical definition creates an overestimation of the annualized project cost of up to 30\% in the worst cases (see section 3), even for a small discount factor! (*) classical definition in the field on energy system optimization at least One year after the 2023 version of this report, we found references from the Engineering Economics literature showing that the formula we derived was in fact already known under the term “implied” (Thuesen 1984) or “imputed” (Jones 1990) salvage value. Notice that we didn't rewrite the entire document to reflect the preexistence of these references, but comparisons are done in Appendix 6.2 to verify the equivalence between ours and literature definitions. Still, all the analyses (graphics, series development and sensitivity to interest rate and lifetime), presented in this document are, to the best of our knowledge, original. Another contribution is the generalization in the case of component replacements (section 4.1).},
file = {:Haessig 2024 Rep - Salvage value.pdf:PDF},
groups = {Rep},
url = {https://hal.science/hal-04097092}
}
@software{Mgjl_GitHub_0110,
author = {Pierre Haessig and Evelise {de Godoy Antunes} and Niels Thobie},
groups = {Soft},
title = {{GitHub - Microgrids-X/Microgrids.jl: Operational \& economic simulation of Microgrid projects in Julia.}},
url = {https://github.com/Microgrids-X/Microgrids.jl},
version = {v0.11.0},
year = {2024}
}
@software{MgJuMP_GitHub_7f792ac,
author = {Pierre Haessig},
groups = {Soft},
title = {{GitHub - pierre-haessig/Microgrids.JuMP: Microgrid sizing optimization using an algebraic formulation .}},
url = {https://github.com/pierre-haessig/Microgrids.JuMP},
version = {7f792ac},
year = {2025}
}
@phdthesis{ElSayegh_2024_PhD,
author = {Elsy {El Sayegh}},
school = {CentraleSupélec},
title = {{Methodology for dynamic sizing of microgrids under uncertainties}},
year = {2024},
month = mar,
note = {NNT 2024CSUP0004},
file = {:Elsy El Sayegh/Elsy 2024 PhD - dynamic microgrid sizing.pdf:PDF},
groups = {ThesO},
url = {https://theses.fr/2024CSUP0004}
}
@unpublished{Haessig_2025_CvxCost,
author = {Pierre Haessig and Elsy {El Sayegh} and Jean Fructhieu Nikiema and Nabil Sadou},
note = {First round of revision of journal publication},
title = {{Convexity of energy systems annualized cost with usage-dependent lifetime}},
year = {2025},
groups = {Rep},
url = {https://hal.science/hal-05052473}
}
@inproceedings{Nikiema_2025_PTech,
author = {Jean Fructhieu Nikiema and Pierre Haessig and Nabil Sadou and Hervé Guéguen},
booktitle = {{IEEE PowerTech 2025 Conference, Kiel, Germany}},
title = {{Microgrid optimal sizing integrating usage-dependent components lifetime into a MILP}},
year = {2025},
abstract = {Microgrids (MGs) sizing must be optimized to meet power demands while minimizing costs. The lifetime of the various MG components is variable and depends on their usage, affecting the overall cost. However, most studies that use MILP for MG design assume fixed lifetimes. A few account for variable lifetimes by adding ad hoc usage penalties to the cost function. . This paper proposes a MILP cost model for optimal MG sizing, which considers the impact of usage on component lifetime, while using the real LCOE as the optimization cost function. It also integrates operational constraints. We compare our approach with other approaches in the literature by applying it to an islanded MG. The results show that the proposed method achieved lower costs.},
doi = {10.1109/PowerTech59965.2025.11180567},
file = {:Jean Nikiema/Nikiema 2025 PTech - Microgrid usage-dependent MILP.pdf:PDF},
groups = {IntC},
url = {https://hal.science/hal-05163524}
}
@inproceedings{Sourisse_2025_PTech,
author = {Paul Sourisse and Pierre Haessig and Nabil Sadou},
booktitle = {{IEEE PowerTech 2025 Conference, Kiel, Germany}},
title = {{Convex Nonlinear Battery Aging Model for Microgrid Sizing Optimization}},
year = {2025},
abstract = {The sizing of microgrids with energy storage systems can be a complex optimization problem, so accurate models of battery degradation are often discarded to avoid intractable computational times. However, aging can downgrade battery ratings over time, perturb the system operation and eventually depreciate the microgrid design objectives. To better account for battery degradation in microgrid sizing, a C-rate dependent nonlinear cycling aging model from the literature is analyzed. The computational tractability is shown by proving the convexity of the resulting aging-aware optimization problem. Two aspects of the battery degradation model are investigated: 1) nonlinear component cost due to the degradation-dependent lifetime and 2) capacity fade along time. A simple microgrid design problem, expanded with this nonlinear C-rate dependent battery degradation model, is solved numerically. This shows the impact of the degradation model on the system sizing and cost, but also on the battery operation and computational time. Results are compared to those from the classical constant energy throughput model, which is not C-rate dependent.},
doi = {10.1109/PowerTech59965.2025.11180687},
file = {:Paul Sourisse/Sourisse 2025 PTech - Convex nonlinear battery sizing - author.pdf:PDF},
groups = {IntC},
url = {https://hal.science/hal-05056219}
}
@inproceedings{Haessig_2025_SGE,
author = {Pierre Haessig and Elsy {El Sayegh} and Jean Fructhieu Nikiema and Nabil Sadou},
booktitle = {{SGE 2025, Toulouse, France}},
title = {Convexité du coût annualisé des systèmes énergétiques avec une durée de vie dépendant de l'usage},
year = {2025},
abstract = {Le dimensionnement des systèmes énergétiques est un problème d'optimisation parfois complexe à résoudre. Pour avoir des garanties de convergence en un temps raisonnable, il est souhaitable que le problème soit convexe. Cependant, la prise en compte de l'impact du régime d'utilisation des composants d'un système énergétique sur leur durée de vie (en particulier cyclage batterie) aboutit à une expression non linéaire du coût annualisé avec taux d'actualisation. Nous prouvons ici que cette expression est convexe. Comparés à un modèle classique à durées de vie supposées fixes, les résultats avec dépendance à l'usage sont plus réalistes et réduisent le coût réel du système. De plus, le temps de résolution est du même ordre de grandeur grâce à une approximation linéaire par morceaux. Notre analyse permet également d'améliorer les formulations basées sur une pénalisation de l'usage.},
file = {:Haessig 2025 SGE - Convex cost.pdf:PDF},
groups = {NatC},
url = {https://hal.science/hal-05144507}
}
@inproceedings{Haessig_2025_SGE_EN,
author = {Pierre Haessig and Elsy {El Sayegh} and Jean Fructhieu Nikiema and Nabil Sadou},
booktitle = {{SGE 2025, Toulouse, France}},
title = {Convexity of energy systems annualized cost with usage-dependent lifetime},
year = {2025},
note = {{in French}},
abstract = {Le dimensionnement des systèmes énergétiques est un problème d'optimisation parfois complexe à résoudre. Pour avoir des garanties de convergence en un temps raisonnable, il est souhaitable que le problème soit convexe. Cependant, la prise en compte de l'impact du régime d'utilisation des composants d'un système énergétique sur leur durée de vie (en particulier cyclage batterie) aboutit à une expression non linéaire du coût annualisé avec taux d'actualisation. Nous prouvons ici que cette expression est convexe. Comparés à un modèle classique à durées de vie supposées fixes, les résultats avec dépendance à l'usage sont plus réalistes et réduisent le coût réel du système. De plus, le temps de résolution est du même ordre de grandeur grâce à une approximation linéaire par morceaux. Notre analyse permet également d'améliorer les formulations basées sur une pénalisation de l'usage.},
groups = {NatC},
url = {https://hal.science/hal-05144507}
}
@inproceedings{Villa_2025_SGE,
author = {{Luiz Fernando} {Lavado Villa} and Amalie Alchami and Arnaud Gaillard and Pierre Haessig and Mathieu Rossi and Jean Alinei and Clément Foucher},
booktitle = {{SGE 2025, Toulouse, France}},
title = {{HackuLoop - An open-science project to create open-source real-time Hardware-In-The-Loop micro-grid systems}},
year = {2025},
abstract = {Le dimensionnement des systèmes énergétiques est un problème d'optimisation parfois complexe à résoudre. Pour avoir des garanties de convergence en un temps raisonnable, il est souhaitable que le problème soit convexe. Cependant, la prise en compte de l'impact du régime d'utilisation des composants d'un système énergétique sur leur durée de vie (en particulier cyclage batterie) aboutit à une expression non linéaire du coût annualisé avec taux d'actualisation. Nous prouvons ici que cette expression est convexe. Comparés à un modèle classique à durées de vie supposées fixes, les résultats avec dépendance à l'usage sont plus réalistes et réduisent le coût réel du système. De plus, le temps de résolution est du même ordre de grandeur grâce à une approximation linéaire par morceaux. Notre analyse permet également d'améliorer les formulations basées sur une pénalisation de l'usage.},
file = {:Misc/Villa 2025 SGE - HackuLoop.pdf:PDF},
groups = {NatC},
url = {https://hal.science/hal-05222887}
}
@software{Haessig_2026_GH-nl-SDP,
author = {Pierre Haessig and {Jean Fructhieu} Nikiema},
groups = {Soft},
title = {{GitHub - pierre-haessig/netload-model-effect-SDP}},
url = {https://github.com/pierre-haessig/netload-model-effect-SDP},
year = {2026}
}
@unpublished{Nikiema_2026_Prep,
author = {Nikiema, Jean Fructhieu and Haessig, Pierre and Sadou, Nabil and Guéguen, Hervé},
note = {Submitted for journal publication},
title = {{MILP sizing of an islanded microgrid with hydrogen storage considering variable component lifetimes}},
year = {2026},
abstract = {The problem of microgrid sizing is finding the capacity of each generation and storage asset to obtain an optimal, efficient and reliable electricity supply system. Mixed-Integer Linear Programming (MILP) is one of the most used methods for this task. Unfortunately, to meet the linearity requirements of MILPs, component lifetimes are often assumed to be fixed. This assumption impacts the quality of the sizing and could lead to unscheduled costs. A com ponent usage penalty is sometimes applied to account for the impact of on lifetime while keeping the problem linear. However, this penalization method fails to take into account the discount rate and deviates from a proper an nualized cost according to microgrid economics. In this paper, we propose a method allowing for the accounting of usage impact on component lifetime, while keeping a proper annualized cost as optimization objective. The accu racy of our method and its impact on the sizing are demonstrated through a comparison with both the fixed lifetime model and the penalization.},
doi = {10.2139/ssrn.6992719},
groups = {Rep},
url = {https://hal.science/hal-05669563}
}
@inproceedings{Nikiema_2026_ISGT,
author = {Jean Fructhieu Nikiema and Paul Sourisse and Pierre Haessig and Nabil Sadou},
booktitle = {{IEEE PES ISGT Europe 2026, Budapest, Hungary}},
title = {{Impact of uncertainty modeling on Stochastic Dynamic Programming performance: the case of Microgrid energy management}},
year = {2026},
abstract = {The energy management of hybrid energy systems, which requires the coordination of multiple sources under uncertainty, remains a complex problem. In such systems, the production of variable renewable sources and demand are the main uncertain time series which complicate the reliable and optimal operation. Stochastic Dynamic Programming can handle such uncertain inputs and thus SDP is used to compute an optimal energy management policy of the system, for each of the 8760 hours of the year. However, the performance of that policy depends on the fitness of the uncertainty model used to represent uncertain inputs, so alternative models (Markov chains, autoregressive models) are compared. The impact of two specific model attributes, autocorrelation and normality, is scrutinized. Autocorrelation is shown to be the most impactful factor.},
file = {:Jean Nikiema/Nikiema 2026 ISGT - SDP uncertainty model.pdf:PDF},
groups = {IntC},
url = {https://hal.science/hal-05705022}
}
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