. Cette-ferme,-située-au-nord-de-la-réunion, appartient à Quadrant , ex-Aérowatt (comme celle de Grand Maison en Guadeloupe). D'une puissance nominale de 10,2 MW, elle est composée de 37 turbines Verget, 2005.

?. Power and . Data, Wind-Power-Forecasting-Data.aspx). Fichiers mensuels avec un pas de temps de 1 heure

=. Soe_traj, E_rated p_sto_traj = sto.record

E. St-o, k + 1) = E st o (k ) + P st o (k )? t P mis (k + 1) = ?P mis (k ) + w (k )

P. Aaaaaa and V. Mmmmmm, Markov-switching autoregressive models for wind time series, Environmental Modelling & Software, vol.30, issue.0, pp.92-101

J. C. Aaaaa, M. Dd, and . Llll, Chance-Constrained and Stochastic Viable Management of an Hydroelectric Dam, CLAIO-SBPO, Rio de Janeiro, pp.24-28, 2012.

J. Aaa, The spectrum of power from wind turbines, Journal of Power Sources, vol.169, issue.2, pp.369-374, 2007.

J. A. , H. Bbb-aaaaa, and B. Mmm, Sizing Optimization Methodology of a Surface Permanent Magnet Machine-Converter System Over a Torque-Speed Operating Proole : Application to a Wave Energy Converter, IEEE Trans. Industrial Electronics, vol.59, issue.5, pp.2116-2125, 2012.

J. A. , P. Bbbbb, B. Mmm, H. Bbb-aaaaa, and B. Bbbbbbbbb, Energy Storage System Sizing for Smoothing Power Generation of Direct Wave Energy Converters, 3rd International Conference on Ocean Energy, 2010.

G. K. Bbbbb, N. H. Cccc, and W. P. , The approximation of long-memory processes by an ARMA model, Journal of Forecasting, vol.20, issue.6, pp.367-389, 2001.

R. Bbbbbbb, Dynamic Programming, 1957.

D. P. Bbbbbbbbb, Dynamic Programming and Optimal Control, Athena Scientiic, 2005.

E. Bbbbb, P. Bbbbbbb, I. Eeeeeeeeeeoo, A. Rrrrr, S. Llll et al., Optimal Energy Management Strategy of an Improved Elevator With Energy Storage Capacity Based on Dynamic Programming, IEEE Trans. Industry Applications, vol.50, issue.2, pp.1233-1244, 2014.

H. Bbbbbbb, T. Cccccc, P. Llllll, J. F. Mmmmmmm, U. Aaaaa et al., Lifetime Modelling of Lead Acid Batteries, 2005.

A. Bbb, Overview of the sodium-sulfur battery for the IEEE Stationary Battery Committee, IEEE Power Engineering Society General Meeting, pp.1232-1235, 2005.

S. , B. H. Bbbbbbbbbbb, J. Ddddddddddnn, and A. Llllllll, Modèle de vieillissement instantané des batteries Li-ion, Rapport de stage de Master 1 Mécatronique Master's thesis, ENS Cachan -antenne de Bretagne Statistical Analysis of Wind Power Forecast Error, IEEE Trans. Power Syst, vol.14, issue.233, pp.983-991, 2008.

O. Bbbbbb, J. K. , and D. U. Ss, Ageing behaviour of electrochemical double layer capacitors : Part II. Lifetime simulation model for dynamic applications, Journal of Power Sources, vol.173, issue.1, pp.626-632, 2007.

A. Bbbbb, Simulation of Short-term Wind Speed Forecast Errors using a Multi-variate ARMA(1,1) Time-series Model, 2005.

H. Bbbbbb, M. A. Rr, and D. , Optimization-based power management of a wind farm with battery storage, Wind Energy, vol.16, issue.8, pp.1197-1211

A. Bbbbb, Caractérisation et prédiction probabiliste des variations brusques et importantes de la production éolienne, Mines ParisTech -Centre Énergétique et Procédés, 2012.

A. Bbbbb, R. Gggggg, and G. Kkkkkkk, Forecasting ramps of wind power production with numerical weather prediction ensembles, Wind Energy, vol.16, issue.1, pp.51-63

T. Bbbbbbbb and S. Ssss, Quantifying Short-Term Wind Power Variability Using the Conditional Range Metric, IEEE Trans. Sustain. Energy, vol.3, issue.3, pp.369-378

P. J. Bbbbbbbbb and R. A. Dd, Time Series : Theory and Methods. Springer Series in Statistics, 1991.

S. P. Bbbbbb and G. O. Rrrrrrr, Convergence assessment techniques for Markov chain Monte Carlo, Statistics and Computing, vol.8, issue.4, pp.319-335, 1998.

M. Bbbbbbbb, P. Bbbbbbb, F. Bbbbbbbb, P. Bbbbbbbbb, S. Hhhhhhhh et al., Main aging mechanisms in Li ion batteries, Journal of Power Sources, vol.146, issue.12, pp.90-96, 2005.

P. Cc, Batteries de véhicule électrique : en route pour une seconde vie stationnaire ?, 2011.

S. Cc, W. Hhhhh, M. Fffff, and D. Hhhhhh, On-line fuzzy energy management for hybrid fuel cell systems, International Journal of Hydrogen Energy, vol.35, issue.5, pp.2134-2143, 2010.

C. J. Ccc, A. N. Hhhhh, M. I. Hhhhhh, M. C. Ssss, and R. T. Jjjjjjjj, Managing Renewable Power Generation

L. M. Cccc, Scheduling of Power System Cells Integrating Stochastic Power Generation, 2008.

P. E. Mmmmm, N. Ll, and Y. Mmmmmm, An optimized autoregressive forecast error generator for wind and load uncertainty study, Wind Energy, vol.14, issue.8, pp.967-976, 2011.

L. S. Ddd and P. A. Lllll, A new Laplace second-order autoregressive time-series model?NLAR, IEEE Trans. Information Theory, issue.25, pp.31-645, 1985.

S. D. Dd and D. F. Sssss, Simple rainnow counting algorithms, International Journal of Fatigue, vol.4, issue.1, pp.31-40, 1982.

M. Eeeee, J. B. Ggggggggg, J. V. , S. Kkkkkk, F. Hhhh et al., Development of a Lifetime Prediction Model for Lithium-Ion Batteries based on Extended Accelerated Aging Test Data, Journal of Power Sources, issue.0, pp.215-248, 2012.

E. Sei, Bilan Prévisionnel de l'Equilibre OOre/Demande d'électricité ? Guadeloupe

E. Sei, Bilan Prévisionnel de l'Equilibre OOre/Demande d'électricité ? La Réunion

E. Eee, V. Ggg, P. Fffffff, Y. C. Zzzzz, M. Sssss et al., Active Power Controls from Wind Power : Bridging the Gaps, 2014.

B. T. Eeeee, J. B. Kkkkk, and J. L. Sssssssss, Time series analysis of wind speed with timevarying turbulence, Environmetrics, vol.17, issue.2, pp.119-127, 2006.

J. Eeee and G. Ccccc, Energy Storage for the Electricity Grid : Beneets and Market Potential Assessment Guide, 2010.

H. K. Ff, J. A. Rrrrr, and A. Uuuu, On the coupling between the plant and controller optimization problems, Proceedings of the 2001 American Control Conference, pp.1864-1869, 2001.

Y. Gg, S. Cc, and P. Lllll, Energy Management for an Electric Vehicle Based on Combinatorial Modeling, Proceedings of the International Conference on Industrial Engineering and Systems Management, p.9, 2013.

O. Cachan, Modélisation énergétique et optimisation économique d'un système de production éolien et photovoltaïque, 2002.

G. Gggggg, R. Bbb, G. N. Kkkkkkk, M. Ddddddd, and C. Ddddd, The state-ofthe-art in short-term prediction of wind power : A literature overview, 2011.

G. K. Ggggg, R. J. Hhhhhhh, L. Ttttttt, and R. L. Ttttttt, Theory & Methods : Non- Gaussian Conditional Linear AR(1) Models, Australian & New Zealand Journal of Statistics, vol.42, issue.4, pp.479-495, 2000.

P. Hhhhhhh, Caractérisation de l'incertitude de production éolienne, 2011.

P. Hhhhhhh, T. K. , B. Mmm, H. Bbb-aaaaa, and S. Lllll, Computing an Optimal Control Policy for an Energy Storage, 6th European Conference on Python in Science, pp.51-58, 2013.

P. Hhhhhhh, B. Mmm, H. Bbb-aaaaa, and S. Lllll, Quelle importance du choix de la loi de gestion pour dimensionner un système de stockage d'énergie ?, 2014.

P. Hhhhhhh, B. Mmm, H. Bbb-aaaaa, S. Lllll, and P. Bbbbbb, Energy storage sizing for wind power : impact of the autocorrelation of day-ahead forecast errors

P. Hhhhhhh, B. Mmm, H. Bbb-aaaaa, S. Lllll, and L. Jjjj, Aging-aware NaS battery model in a stochastic wind-storage simulation framework, IEEE PowerTech 2013 Conference, 2013.

B. Hhhhhhhh and A. Ddd, Cooperation of a Grid-Connected Wind Farm and an Energy Storage Unit ? Demonstration of a Simulation Tool, IEEE Trans. Sustain. Energy, vol.3, issue.1, pp.49-56, 2012.

H. Eeeeeeee-cccc, Invitation for Low Cost Renewable Energy Projects on Oahu Through Request for Waiver from Competitive Bidding. Attachment 1 : Generator Performance Requirements, 2013.

T. Hhhh, P. Pppppp, and S. Fff, Global Energy Forecasting Competition, International Journal of Forecasting, vol.30, issue.2, pp.357-363, 2012.

J. R. Hhhhhhh, Modeling persistence in hydrological time series using fractional differencing, Water Resources Research, vol.20, issue.12, pp.1898-1908, 1984.

Z. F. Hhhhhhh, L. W. Ccccc, M. Ssss, and A. Iiiiii, Modeling of Sodium Sulfur Battery for Power System Applications, Elektrika, vol.9, issue.2, pp.66-72, 2007.

W. Kk, E. Ffffff, and J. Aaa, The variability of interconnected wind plants, Energy Policy, vol.38, issue.8, pp.4400-4410, 2010.

N. Kk, Y. Iiiiii, Y. Ssssssss, M. Ffffffff, K. Ooo et al., Development and eld experiences of stabilization system using 34MW NAS batteries for a 51MW wind farm, Industrial Electronics (ISIE) IEEE International Symposium on, pp.2371-2376, 2010.

M. J. Kkk and H. Pppp, Power management and design optimization of fuel cell/battery hybrid vehicles, Journal of Power Sources, vol.165, issue.2, pp.819-832, 2007.

R. Kkkkkkk, Thermal properties of sodium-sulphur cells, Journal of Applied Electrochemistry, vol.14, issue.1, pp.39-46, 1984.

R. K. and K. Hhhhhhh, Quantile Regression, The Journal of Economic Perspectives, vol.15, issue.4, pp.143-156, 2001.

M. K. , T. Bbbbbbb, A. Uuuuu, and G. Aaaaaaaaa, Deening a degradation cost function for optimal control of a battery energy storage system, PowerTech (POWERTECH), 2013 IEEE Grenoble, pp.1-6

T. K. , B. Mmm, H. Bbb-aaaaa, J. A. , and P. Vvvvv, Enhanced Aging Model for Supercapacitors taking into account Power Cycling : Application to the Sizing of an Energy Storage System in a Direct Wave Energy Converter, 9th Ecological Vehicles and Renewable Energies (EVER) 2014 conference, 2014.

M. Lllll, On the Uncertainty of Wind Power Predictions?Analysis of the Forecast Accuracy and Statistical Distribution of Errors, Journal of Solar Energy Engineering, vol.127, issue.2, pp.177-184, 2005.

C. C. Lll, H. Pppp, and J. Ggggggg, A stochastic control strategy for hybrid electric vehicles, Proceedings of the 2004 American Control Conference, pp.4710-4715, 2004.

M. Lllllll, C. Jjjjjjjjj, S. Lllll, and P. Rrrr, Making the Sun Reliable with Li-Ion Energy Storage : Solar PV Energy Management for Large PV Power Plants on Isolated Islands, 25th European Photovoltaic Solar Energy Conference and Exhibition, pp.3812-3820, 2010.

B. B. Mmmmmmmmm, A Fast Fractional Gaussian Noise Generator, Water Resources Research, vol.7, issue.3, pp.543-553, 1971.

J. Mmmmmmm, A. Rrrrrr, G. Hh, C. A. , J. Mmgg et al., Hybrid2 -A Hybrid System Simulation Model : Theory Manual, 2006.

M. Mm and T. Nnnnnnnnn, Mersenne Twister : A 623-dimensionally Equidistributed Uniform Pseudo-random Number Generator, ACM Transactions on Modeling and Computer Simulation, vol.8, issue.1, pp.3-30, 1998.

M. Mm, Island breezes, IEEE Power Energy Mag, vol.7, issue.6, pp.59-64, 2009.

A. Mmmmmmmmm, A. Bbbbb, G. Kkkkkkk, R. C. Gggggg, D. Nnnnnn et al., Impact of PV forecasts uncertainty in batteries management in microgrids Large band simulation of the wind speed for real time wind turbine simulators, IEEE PowerTech 2013 Conference, pp.1-6, 2002.

H. A. Nnnnnnn, H. Mmmmmm, and T. S. Nnnnnnn, Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts, Wind Energy, vol.9, issue.12, pp.95-108, 2006.

T. Oooooo, M. Kkkkk, and A. Ooooo, Development of Sodium-Sulfur Batteries, International Journal of Applied Ceramic Technology, vol.1, issue.3, pp.269-276, 2004.

G. P. and B. Kkkkkk, MCMC for Wind Power Simulation, IEEE Trans. Energy Convers, vol.23, issue.1, pp.234-240, 2008.

P. and C. Eeeeeeeeee, DirectiveCE du Parlement européen et du Conseil du 23 avril 2009 relative à la promotion de l'utilisation de l'énergie produite à partir de sources renouvelables, pp.16-62, 2009.

M. Ppppppp and L. Pppp, Multi-stage stochastic optimization applied to energy planning, Mathematical Programming, pp.359-375, 1991.

F. Ppppp and B. E. Ggggggg, IPython : a System for Interactive Scientiic Computing, Computing in Science and Engineering, vol.9, issue.3, pp.21-29, 2007.

P. Pppppp and H. Mmmmmm, Adaptive modelling and forecasting of ooshore wind power uctuations with Markov-switching autoregressive models, Journal of Forecasting, vol.31, issue.4, pp.281-313

P. Pppppp, H. Mmmmmm, H. A. Nnnnnnn, G. P. , and B. Kkkkkk, From probabilistic forecasts to statistical scenarios of short-term wind power production, Wind Energy, vol.12, issue.1, pp.51-62, 2009.

P. Pppppp, G. P. , B. Kkkkkk, and J. Vvvvvvvvv, Dynamic sizing of energy storage for hedging wind power forecast uncertainty, In Power Energy Society General Meeting, pp.1-8, 2009.

V. P. , H. J. Bbbbbbbb, J. H. Oo, P. P. Vvvv, D. Rrrrrrr et al., Modeling Battery Behavior for Accurate State-of-Charge Indication, Journal of The Electrochemical Society, issue.11, pp.153-2013, 2006.

R. Ddddddddddd and C. Tttt, R : A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2009.

Y. Rrrrrrrrr, S. Bb, F. Bbbbbbb, and S. Ppppp, Optimal Power Flow Management for Grid Connected PV Systems With Batteries, IEEE Trans. Sustain. Energy, vol.2, issue.3, pp.309-320, 2011.

R. Rrrrmmmmmmm, B. Ssssss, and X. Rrrr, Optimization methodologies for the energy management and sizing of a microgrid with storage, SGE 2014, 2014.

D. U. Ss and H. Wwwww, Comparison of diierent approaches for lifetime prediction of electrochemical systems?Using lead-acid batteries as example, Journal of Power Sources, vol.176, issue.2, pp.534-546, 2008.

L. Sssss, Integration study of small amounts of wind power in the power system, 1994.

L. Sssss, Simulation of wind speed forecast errors for operation planning of multiarea power systems, International Conference on Probabilistic Methods Applied to Power Systems, pp.723-728, 2004.

P. Ssssssss, P. Pppppp, N. Ccccccccc, H. Mmmmmm, L. Jjjjjj et al., Power uctuations from large wind farms - Final report, Risø DTU, 2009.

Y. Cachan, Optimisation des prools de consommation pour minimiser les coûts économique et énergétique sur cycle de vie des systèmes photovoltaïques autonomes et hybrides -Evaluation de la technologie Li-ion, 2010.

R. S. Ttt, Model Checking via Parametric Bootstraps in Time Series Analysis, Journal of the Royal Statistical Society. Series C (Applied Statistics), vol.41, issue.1, pp.1-15, 1992.

B. Uuuuuu, M. Ggggggg, E. Ppppppp, W. Kkkkk, and A. Bbbbb, Impacts of Wind Power on Thermal Generation Unit Commitment and Dispatch, IEEE Trans. Energy Convers, vol.22, issue.1, pp.44-51, 2007.

S. W. , S. C. Ccccccc, and G. , The NumPy Array : A Structure for EEcient Numerical Computation, Computing in Science and Engineering, vol.13, issue.2, pp.22-30, 2011.

J. Vvvvvv, P. Nn, M. W. , C. Vvvv, K. C. Mmmmmm et al., Ageing mechanisms in lithium-ion batteries, Journal of Power Sources, vol.147, issue.12, pp.269-281, 2005.

K. Y. , T. Nnnnnnnn, and G. K. , New Control Method for Regulating Stateof-Charge of a Battery in Hybrid Wind Power/Battery Energy Storage System, Power Systems Conference and Exposition, pp.1244-1251, 2006.

K. Y. , T. Nnnnnnnn, and G. , Analysis of data obtained in demonstration test about battery energy storage system to mitigate output uctuation of wind farm, Integration of Wide-Scale Renewable Resources Into the Power Delivery System CIGRE/IEEE PES Joint Symposium, pp.1-1, 2009.

E. Ces-travaux-sont-soutenus-par and . Sei, opérateur électrique des îles françaises Nous étudions un système éolien-stockage où un système de stockage d'énergie doit aider un producteur éolien à tenir, vis-à-vis du réseau, un engagement de production pris un jour à l'avance. Dans ce contexte, nous proposons une démarche pour l'optimisation du dimensionnement et du contrôle du système de stockage (gestion d'énergie ) Comme les erreurs de prévision J+1 de production éolienne sont fortement incertaines

. Pour-le-résoudre, système (modélisation énergétique du stockage par batterie Li-ion ou Sodium-Soufre) ainsi que des entrées (modélisation temporelle stochastique des entrées incertaines) Nous discutons également de la modélisation du vieillissement du stockage, sous une forme adaptée à l'optimisation de la gestion. Ces modèles nous permettent d'optimiser la gestion de l'énergie par la méthode de la programmation dynamique stochastique (SDP) Nous discutons à la fois de l'algorithme et de ses résultats, en particulier de l'eeet de la forme des pénalisations sur la loi de gestion. Nous présentons également l

. Cette-Étude-de, Des simulations temporelles stochastiques mettent en évidence le fort impact de la structure temporelle (autocorrélation) des erreurs de prévision sur le besoin en capacité de stockage pour atteindre un niveau de performance donné. La prise en compte de paramètres de coût permet ensuite l'optimisation du dimensionnement d'un point de vue économique, en considérant les coûts de l'investissement, des pertes ainsi que du vieillissement. Nous étudions également le dimensionnement du stockage lorsque la pénalisation des écarts à l'engagement comporte un seuil de tolérance. Nous terminons ce manuscrit en abordant la question structurelle de l'interaction entre l'optimisation du dimensionnement et celle du contrôle d'un système de stockage, car ces deux problèmes d'optimisation sont couplés. ? Production Réseau Gestion d'énergie Dimensionnement Stockage Mots-clés : Énergie éolienne, Contrôle optimal stochastique