Predictive Management of Energy Resources in a Microgrid: a Simulation Case Study

Abstract : Nowadays, systems for decentralized production of electricity are booming. So, new management strategies are needed to integrate local electricity production into the grid. Indeed, depending on the grid load, injection of local production can impact positively or negatively the grid. Thus, sometimes, the grid could not accept all the decentralized production and an important risk of instability or blackout exists. The objective of the present work is to propose and validate, in simulation, a predictive strategy to manage the electricity consumption and production of multi-energy buildings. This approach is composed of a prediction tool, developed to forecast four variables: the electricity consumption of the house, the local production of electricity, the grid load and the price of electricity. The strategy uses the models of the local microgrid to manage efficiently the electricity. It decides if electricity has to be stored, unload or consumed according to the predictions. Different criteria are proposed and used to optimize this strategy. The aim is to promote the self-consuming energy and optimize the injection in the grid. The strategy is applied to a house with two local production systems (PV and wind mill) and an energy storage system (batteries). Systems for electricity production and the energy storage system have been modelled. TRNSYS software was used to model the thermal behaviour of the house including various occupancy scenarios. Based on these models and the forecasting of solar radiation and wind, a prediction of the local electricity production can be made. The load of the house and the load of the grid are forecasted using a prediction tool. The price of electricity is forecasted based on an identified model of the relationship with load of the grid. Simulation results show reductions of economics costs, a higher self-consumption and a global positive impact of injection into the grid.
Type de document :
Communication dans un congrès
Elsevier. Energy System Conference 2014, Jun 2014, Londres, United Kingdom
Liste complète des métadonnées

https://hal-univ-perp.archives-ouvertes.fr/hal-01264431
Contributeur : Julien Eynard <>
Soumis le : lundi 1 février 2016 - 19:26:55
Dernière modification le : jeudi 11 janvier 2018 - 06:22:15

Identifiants

  • HAL Id : hal-01264431, version 1

Collections

Citation

Aurélie Chabaud, Julien Eynard, Stéphane Grieu. Predictive Management of Energy Resources in a Microgrid: a Simulation Case Study. Elsevier. Energy System Conference 2014, Jun 2014, Londres, United Kingdom. 〈hal-01264431〉

Partager

Métriques

Consultations de la notice

101

Téléchargements de fichiers

171