Comptes Rendus
Stochastic control with state constraints via the Fokker–Planck equation. Application to renewable energy plants with batteries
[Contrôle stochastique avec contraintes d’état via l’équation de Fokker–Planck. Application aux centrales d’énergie renouvelable avec batteries]
Comptes Rendus. Mécanique, Volume 351 (2023) no. S1, pp. 89-110.

Bien que les énergies renouvelables permettent de réduire les émissions de carbone, leurs caractéristiques intermittentes peuvent entraîner des problèmes d’approvisionnement en électricité dans les réseaux de distribution. Le système de stockage d’énergie par batterie est généralement considéré comme un outil efficace pour y remédier. D’autre part, la modélisation mathématique, la simulation numérique, l’optimisation et la théorie du contrôle sont aujourd’hui d’une importance capitale pour traiter ce type de problèmes et les questions connexes. Dans cet article, nous présentons une méthodologie pour le développement de stratégies de soumission et de contrôle en temps réel pour les producteurs d’électricité sur un marché de l’électricité concurrentiel. Tout d’abord, un modèle stochastique d’une centrale éolienne avec stockage sur batterie est présenté dans le cadre des équations différentielles stochastiques (EDS). Ensuite, un problème de contrôle stochastique avec des contraintes d’état est introduit et les conditions d’optimalité correspondantes impliquant l’équation de Hamilton–Jacobi–Bellman sont déduites. À cette fin, on tire parti du fait que les problèmes de contrôle optimal pour les équations différentielles ordinaires stochastiques peuvent être formulés de manière équivalente comme des problèmes de contrôle optimal pour les équations aux dérivées partielles déterministes, à savoir l’équation de Fokker–Planck correspondante.

Although renewable energies are beneficial to reduce carbon emissions, its intermittent characteristics may result in power-supply issues in distribution grid. Battery energy storage system is generally regarded as an effective tool to deal with them. On the other hand mathematical modelling, numerical simulation, optimization and control theory are nowadays of paramount importance to handle this kind of problems and related issues. In this paper we present a methodology for the development of bidding strategies and real-time control for electricity producers in a competitive electricity marketplace. Firstly, a stochastic model of a wind power plant with battery storage is stated in the framework of stochastic differential equations (SDE). Then, a stochastic control problem with state constraints is introduced and the corresponding optimality conditions involving the Hamilton–Jacobi–Bellman equation are deduced. For this purpose, advantage is taken from the fact that optimal control problems for stochastic ordinary differential equations (SDE) can be equivalently formulated as optimal control problems for deterministic partial differential equations (PDE), namely, the corresponding Fokker–Planck equation.

Reçu le :
Accepté le :
Première publication :
Publié le :
DOI : 10.5802/crmeca.236
Keywords: Renewable energy plant, optimal energy biddings, stochastic control, Fokker–Planck equation, Hamilton–Jacobi–Bellman equation
Mot clés : Installations d’énergie renouvelable, offres d’énergie optimales, contrôle stochastique, équation de Fokker–Planck, équation de Hamilton–Jacobi–Bellman
Alfredo Bermúdez 1 ; Iago Padín 2

1 CITMAga and Universidade de Santiago de Compostela, Spain
2 Department of Applied Mathematics. Universidade de Santiago de Compostela. Spain
Licence : CC-BY 4.0
Droits d'auteur : Les auteurs conservent leurs droits
@article{CRMECA_2023__351_S1_89_0,
     author = {Alfredo Berm\'udez and Iago Pad{\'\i}n},
     title = {Stochastic control with state constraints via the {Fokker{\textendash}Planck} equation. {Application} to renewable energy plants with batteries},
     journal = {Comptes Rendus. M\'ecanique},
     pages = {89--110},
     publisher = {Acad\'emie des sciences, Paris},
     volume = {351},
     number = {S1},
     year = {2023},
     doi = {10.5802/crmeca.236},
     language = {en},
}
TY  - JOUR
AU  - Alfredo Bermúdez
AU  - Iago Padín
TI  - Stochastic control with state constraints via the Fokker–Planck equation. Application to renewable energy plants with batteries
JO  - Comptes Rendus. Mécanique
PY  - 2023
SP  - 89
EP  - 110
VL  - 351
IS  - S1
PB  - Académie des sciences, Paris
DO  - 10.5802/crmeca.236
LA  - en
ID  - CRMECA_2023__351_S1_89_0
ER  - 
%0 Journal Article
%A Alfredo Bermúdez
%A Iago Padín
%T Stochastic control with state constraints via the Fokker–Planck equation. Application to renewable energy plants with batteries
%J Comptes Rendus. Mécanique
%D 2023
%P 89-110
%V 351
%N S1
%I Académie des sciences, Paris
%R 10.5802/crmeca.236
%G en
%F CRMECA_2023__351_S1_89_0
Alfredo Bermúdez; Iago Padín. Stochastic control with state constraints via the Fokker–Planck equation. Application to renewable energy plants with batteries. Comptes Rendus. Mécanique, Volume 351 (2023) no. S1, pp. 89-110. doi : 10.5802/crmeca.236. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.236/

[1] World Energy Outlook 2022 (2022) (Technical report) | DOI

[2] J. Morales; A. Conejo; H. Madsen; P. Pinson; M. Zugno Integrating Renewables in Electricity Markets. Operational Problems, Springer, New York, 2014 | DOI

[3] J. García-González; R. Moraga; L. Matres; A. Mateo Stochastic Joint Optimization of Wind Generation and PumpedStorage Units in an Electricity Market, IEEE Transactions on Power Systems, Volume 23 (2008) no. 2, pp. 460-468 | DOI

[4] X. Li; D. Hui; X. Lai Battery Energy Storage Station (BESS)-Based Smoothing Control of Photovoltaic (PV) and Wind Power Generation Fluctuations, IEEE Transactions on Sustainable Energy, Volume 4 (2013) no. 2, pp. 464-473 | DOI

[5] H. Mehdipourpicha; R. Bo Optimal Bidding Strategy for Physical Market Participants With Virtual Bidding Capability in Day-Ahead Electricity Markets, IEEE Access, Volume 9 (2021), pp. 85392-85402 | DOI

[6] B. Hu; Y. Gong; C. Y. Chung; B. F. Noble; G. Poelzer Price-Maker Bidding and Offering Strategies for Networked Microgrids in Day-Ahead Electricity Markets, IEEE Trasaction on Smart Grid, Volume 12 (2021) no. 6, pp. 5201-5211 | DOI

[7] A. Bensoussan Stochastic Control of Partially Observable System, Cambridge University Press, Cambridge, 1992 | DOI

[8] M. Bardi; I. Capuzzo-Dolcetta Optimal Control and Viscosity Solutions of Hamilton–Jacobi–Bellman Equations, Birkhäuser, Boston, 2008

[9] M. Annunziato; A. Borzì Optimal Control of Probability Density Functions of Stochastic Processes, Mathematical Modelling and Analysis, Volume 15 (2010) no. 4, p. 393-40 | DOI

[10] M. Annunziato; A. Borzì; F. Nobile; R. Tempone On the Connection between the Hamilton–Jacobi–Bellman and the Fokker–Planck Control Frameworks, Applied Mathematics, Volume 5 (2014), pp. 2476-2484 | DOI

[11] E. Dupont; R. Koppelaarb; H. Jeanmarta Global available wind energy with physical and energy return on investment constraints, Appl. Energy, Volume 209 (2018) no. 1, pp. 322-338 | DOI

[12] P. Johnson; S. Howell; P. Duck Partial differential equation methods for stochastic dynamic optimization: an application to wind power generation with energy storage, Philos. Trans. R. Soc. Lond., Ser. A, Volume 375 (2017) no. 200, 20160301

[13] A. Lasota; M. C. Mackey Chaos, Fractals, and Noise: Stochastic Aspects of Dynamics, Springer, New York, 1994 | DOI

[14] H. Risken The Fokker–Planck Equation: Methods of Solution and Applications, Springer Series in Synergetics, 18, Springer, 1996 | DOI

[15] J.-L. Lions Contrôle optimal de systèmes gouvernés par des équations aux dérivés partielles, Dunod. Gauthier-Villars, Paris, 1968 (English translation: Optimal Control of Systems Governed by Partial Differential Equations, Springer, 1971)

[16] J. Han; A. Jentzen; E. Weinan Solving high-dimensional partial differential equations using deep learning, Proc. Natl. Acad. Sci. USA, Volume 115 (2018) no. 34, pp. 8505-8510 | DOI

[17] I. Ekeland; R. Temam Convex Analysis and Variational Problems, SIAM, Philadelphia, 1999 | DOI

[18] M. G. Crandall; H. Ishii; P.-L. Lions User’s guide to viscosity solutions of second order partial differential equations, Bull. Am. Math. Soc., Volume 27 (1992) no. 1, pp. 1-67 | DOI

[19] M. Fortin; R. Glowinski Augmented Lagrangian Methods: Applications to the Numerical Solution of Boundary-Value Problems, North-Holland, Amsterdam, 1983

[20] M. E. Gurtin Continuum Mechanics, Mathematics in Science and Engineering, 158, Academic Press Inc., 1981

[21] A. Bermúdez; C. Moreno Duality methods for solving variational inequalities, Comput. Math. Appl., Volume 7 (1981) no. 1, pp. 43-58 | DOI

[22] E. Pardoux; S. Peng Adapted solutions of a backward stochastic differential equation, Systems and Control Letters, Volume 14 (1990), pp. 55-61 | DOI

[23] A. Jentzen W. E Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations, Communications in Mathematics and Statistics, Volume 5 (2017), pp. 349-380

Cité par Sources :

Commentaires - Politique


Ces articles pourraient vous intéresser

Dynamic programming for mean-field type control

Mathieu Laurière; Olivier Pironneau

C. R. Math (2014)


On the planning problem for a class of Mean Field Games

Alessio Porretta

C. R. Math (2013)