{"id":6544,"date":"2020-11-23T10:27:08","date_gmt":"2020-11-23T09:27:08","guid":{"rendered":"https:\/\/l2ep.univ-lille.fr\/?p=6544"},"modified":"2020-12-01T10:37:32","modified_gmt":"2020-12-01T09:37:32","slug":"soutenance-de-these-xin-wen-7-dec-2020","status":"publish","type":"post","link":"https:\/\/l2ep.univ-lille.fr\/en\/soutenance-de-these-xin-wen-7-dec-2020\/","title":{"rendered":"Soutenance de Th\u00e8se, Xin WEN, 7 D\u00e9c. 2020"},"content":{"rendered":"

Optimisation stochastique pour la planification de la production d\u2019\u00e9lectricit\u00e9 dans une communaut\u00e9 \u00e9nerg\u00e9tique locale en situation d’incertitude li\u00e9e aux \u00e9nergies renouvelables<\/span><\/strong><\/p>\n

lundi 07 d\u00e9cembre 2020 \u00e0 13h30,<\/p>\n

Lieu : Centrale Lille, Institut Cit\u00e9 Scientifique, 59650, Villeneuve d’Ascq, France,\u00a0Salle : Amphi BODA<\/p>\n

Visio conference : Cliquez ici pour participer \u00e0 la r\u00e9union<\/a><\/p>\n

Mots-cl\u00e9s<\/span><\/strong>\u00a0optimisation stochastique,incertitude,gestion de l’\u00e9nergie,puissance de r\u00e9serve,\u00c9nergie renouvelable,micro r\u00e9seau<\/p>\n

R\u00e9sum\u00e9<\/span><\/strong><\/p>\n

Dans un syst\u00e8me \u00e9lectrique, l’engagement des g\u00e9n\u00e9rateurs (UC) et leur r\u00e9f\u00e9rence de puissance doivent \u00eatre planifi\u00e9es pour satisfaire la demande tout en respectant les contraintes d’exploitation du syst\u00e8me. De nos jours, des communaut\u00e9s \u00e9nerg\u00e9tiques ont \u00e9merg\u00e9 avec des besoins \u00e9nerg\u00e9tiques individuels et une augmentation de la production distribu\u00e9es. La forte p\u00e9n\u00e9tration des sources d\u2019\u00e9nergies renouvelables augmente l’incertitude sur leur pr\u00e9vision tandis que les exigences en mati\u00e8re de fiabilit\u00e9 du syst\u00e8me \u00e9lectrique augmentent. Par cons\u00e9quent, les approches d\u00e9terministes traditionnelles des UC doivent \u00e9voluer vers une optimisation stochastique. L\u2019objectif est de proposer une m\u00e9thodologie d’optimisation probabiliste et stochastique pour des d\u00e9cisions optimales de planification de la production et des r\u00e9serves de puissance (OR) dans un micro r\u00e9seau urbain avec le souhait de r\u00e9duire au minimum les co\u00fbts d’exploitation et les \u00e9missions. La fourniture de puissance et de r\u00e9serve doit tenir compte de l’incertitude des SER et de la demande tout en consid\u00e9rant le compromis entre s\u00e9curit\u00e9 et fonctionnement \u00e9conomique. Un syst\u00e8me de contr\u00f4le, de supervision et d’acquisition de donn\u00e9es (SCADA) convivial est d\u00e9velopp\u00e9 avec l’interface graphique Matlab pour int\u00e9grer et visualiser la gestion \u00e9nerg\u00e9tique de l’\u00e9nergie.<\/p>\n

Stochastic Optimization for Generation Scheduling in a Local Energy Community under Renewable Energy Uncertainty<\/span><\/strong><\/p>\n

Keywords<\/span><\/strong>\u00a0Stochastic optimization, uncertainty, unit commitment (generation scheduling), energy management system, power reserve, renewable energy, microgrid<\/p>\n

Abstract<\/span><\/strong><\/p>\n

In electrical systems, the unit commitment (UC) and power scheduling plans the operating of generating units in order to satisfy the load demand under system operating constraints. Nowadays, energy communities have emerged with individual community energy requirements and increasing capacity deployment of distributed energy resources. The high penetration of renewable energy sources (RES) and load demand increases locally the power system uncertainty. Hence, traditional deterministic approaches for one day ahead UC should evolve to stochastic optimization methods. The main goal of this thesis is to propose a probability-based and stochastic optimization methodology for optimal generation and operating reserve (OR) scheduling decisions in an urban microgrid with the objective of addressing the minimization of operating costs and emissions.<\/p>\n

Based on an uncertainty modelling with forecasting error distributions, a LOLP-based risk assessment method is used to determine an appropriate amount of operating reserve (OR) for each time step of the next day. Then, in a first stage, a deterministic optimization within a mixed-integer linear programming (MILP) method generates the unit commitment of controllable generators with the day-ahead PV and load demand prediction. In a second stage, a set of scenario is built to model future and probable uncertainties. It is integrated into a stochastic optimization of the operational planning. Issues of the second stage are the commitment of enough flexible and fast generators to handle unexpected deviations from predictions. An extension is proposed through the scheduling and planning of local storage systems for power reserve provision. The significance of the proposed methodology is illustrated with results obtained from a studied urban microgrid system. A user-friendly Supervisory Control And Data Acquisition (SCADA) system is developed with the Matlab GUI to integrate and visualize the energy management operation.<\/p>\n","protected":false},"excerpt":{"rendered":"

Optimisation stochastique pour la planification de la production d\u2019\u00e9lectricit\u00e9 dans une communaut\u00e9 \u00e9nerg\u00e9tique locale en situation d’incertitude li\u00e9e aux \u00e9nergies renouvelables lundi 07 d\u00e9cembre 2020 \u00e0 13h30, Lieu : Centrale Lille, Institut Cit\u00e9 Scientifique, 59650, Villeneuve d’Ascq, France,\u00a0Salle : Amphi BODA Visio conference : Cliquez ici pour participer \u00e0 la r\u00e9union Mots-cl\u00e9s\u00a0optimisation stochastique,incertitude,gestion de l’\u00e9nergie,puissance […]<\/p>\n","protected":false},"author":8,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[18,3],"tags":[],"_links":{"self":[{"href":"https:\/\/l2ep.univ-lille.fr\/en\/wp-json\/wp\/v2\/posts\/6544"}],"collection":[{"href":"https:\/\/l2ep.univ-lille.fr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/l2ep.univ-lille.fr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/l2ep.univ-lille.fr\/en\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/l2ep.univ-lille.fr\/en\/wp-json\/wp\/v2\/comments?post=6544"}],"version-history":[{"count":4,"href":"https:\/\/l2ep.univ-lille.fr\/en\/wp-json\/wp\/v2\/posts\/6544\/revisions"}],"predecessor-version":[{"id":6552,"href":"https:\/\/l2ep.univ-lille.fr\/en\/wp-json\/wp\/v2\/posts\/6544\/revisions\/6552"}],"wp:attachment":[{"href":"https:\/\/l2ep.univ-lille.fr\/en\/wp-json\/wp\/v2\/media?parent=6544"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/l2ep.univ-lille.fr\/en\/wp-json\/wp\/v2\/categories?post=6544"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/l2ep.univ-lille.fr\/en\/wp-json\/wp\/v2\/tags?post=6544"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}