Machine Learning-Enhanced Benders Decomposition Approach for the Multi-Stage Stochastic Transmission Expansion Planning Problem

Article


Borozan, S., Giannelos, S., Falugi, P., Moreira, A. and Strbac, G. 2024. Machine Learning-Enhanced Benders Decomposition Approach for the Multi-Stage Stochastic Transmission Expansion Planning Problem. Electric Power Systems Research. 237, p. Art. 110985. https://doi.org/10.1016/j.epsr.2024.110985
AuthorsBorozan, S., Giannelos, S., Falugi, P., Moreira, A. and Strbac, G.
Abstract

The necessary decarbonization efforts in energy sectors entail integrating flexible assets and increased levels of uncertainty for the planning and operation of power systems. To cope with this in a cost-effective manner, transmission expansion planning (TEP) models need to incorporate progressively more details to represent potential long-term system developments and the operation of power grids with intermittent renewable generation. However, the increased modeling complexities of TEP exercises can easily lead to computationally intractable optimization problems. Currently, most techniques that address computational intractability alter the original problem, thus neglecting critical modeling aspects or affecting the structure of the optimal solution. In this paper, we propose an alternative approach to significantly alleviate the computational burden of large-scale TEP problems. Our approach integrates machine learning (ML) with the well-established Benders decomposition to manage the problem size while preserving solution quality. The proposed ML-enhanced Multicut Benders Decomposition algorithm improves computational efficiency by identifying effective and ineffective optimality cuts via supervised learning techniques. We illustrate the benefits of the proposed methodology by solving multi-stage TEP problems of different sizes based on the IEEE24 and IEEE118 test systems, while also considering energy storage investment options.

JournalElectric Power Systems Research
Journal citation237, p. Art. 110985
ISSN0378-7796
Year2024
PublisherElsevier
Accepted author manuscript
File Access Level
Repository staff only
Publisher's version
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.1016/j.epsr.2024.110985
Publication dates
Online25 Aug 2024
Publication process dates
Accepted12 Aug 2024
Deposited24 Sep 2024
Copyright holder© 2024 The Authors
Permalink -

https://repository.uel.ac.uk/item/8y405

Download files


Publisher's version
1-s2.0-S0378779624008708-main.pdf
License: CC BY 4.0
File access level: Anyone

  • 16
    total views
  • 4
    total downloads
  • 2
    views this month
  • 1
    downloads this month

Export as

Related outputs

An integrated planning framework for optimal power generation portfolio including frequency and reserve requirements
Ayo, O, Falugi, P. and Strbac, G 2024. An integrated planning framework for optimal power generation portfolio including frequency and reserve requirements. IET Energy Systems Integration. In Press.
Automatic scenario generation for efficient solution of robust optimal control problems
Zagorowska, M., Falugi, P., O'Dwyer, E. and Kerrigan E. C. 2024. Automatic scenario generation for efficient solution of robust optimal control problems. International Journal of Robust and Nonlinear Control. 34 (2), pp. 1370-1396. https://doi.org/10.1002/rnc.7038
Automatic Scenario Generation for Robust Optimal Control Problems
Zagorowska, M., Falugi, P., O'Dwyer, E. and Kerrigan, E. C. 2023. Automatic Scenario Generation for Robust Optimal Control Problems. IFAC 2023: 22nd World Congress of the International Federation of Automatic Control. Yokohama, Japan 09 - 14 Jul 2023 Elsevier for the International Federation of Automatic Control. https://doi.org/10.1016/j.ifacol.2023.10.1743
Automating the data-driven predictive control design process for building thermal management
Falugi, P., O'Dwyer, E., Shah, N. and Kerrigan, E. C. 2022. Automating the data-driven predictive control design process for building thermal management. ECOS 2022 35th International Conference. Copenhagen, Denmark 03 - 07 Jul 2022 Danmarks Tekniske Universitet (DTU). https://doi.org/10.11581/dtu.00000267
A Modelling Workflow for Predictive Control in Residential Buildings
O’Dwyer, E., Atam, E., Falugi, P., Kerrigan, E. C., Zagorowska, M. A. and Shah, N. 2022. A Modelling Workflow for Predictive Control in Residential Buildings. in: Vahidinasab, V. and Mohammadi-Ivatloo, B. (ed.) Active Building Energy Systems: Operation and Control Springer, Cham. pp. 99-128
MPC and Optimal Design of Residential Buildings with Seasonal Storage: A Case Study
Falugi, P., O’Dwyer, E., Zagorowska, M. A., Atam, E., Kerrigan, E. C., Strbac, G. and Shah, N. 2022. MPC and Optimal Design of Residential Buildings with Seasonal Storage: A Case Study. in: Vahidinasab, V. and Mohammadi-Ivatloo, B. (ed.) Active Building Energy Systems: Operation and Control Springer, Cham. pp. 129-160
Fast and accurate method for computing non-smooth solutions to constrained control problems
Nita, L., Vila, E. M. G., Zagorowska, M. A., Kerrigan, E. C., Nie, Y., McInerney, I. and Falugi, P. 2022. Fast and accurate method for computing non-smooth solutions to constrained control problems. European Control Conference (ECC) 2022. London, UK 12 - 15 Jul 2022 IEEE. https://doi.org/10.23919/ECC55457.2022.9838569
Data-Driven Predictive Control With Improved Performance Using Segmented Trajectories
O’Dwyer, E., Kerrigan, E. C., Falugi, P., Zagorowska, M. and Shah, N. 2022. Data-Driven Predictive Control With Improved Performance Using Segmented Trajectories. IEEE Transactions on Control Systems Technology . 31 (3), pp. 1355 - 1365. https://doi.org/10.1109/TCST.2022.3224330
Predictive control co-design for enhancing flexibility in residential housing with battery degradation
Falugi, P., O’Dwyer, E., Kerrigan, E. C., Atam, E., Zagorowska, M. A., Strbac, G. and Shah, N. 2021. Predictive control co-design for enhancing flexibility in residential housing with battery degradation. 7th IFAC Conference on Nonlinear Model Predictive Control NMPC 2021. Bratislava, Slovakia 11 - 14 Jul 2021 Elsevier for the International Federation of Automatic Control. https://doi.org/10.1016/j.ifacol.2021.08.517
Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty
Falugi, P., Giannelos S, Jain A., Borozan S., Moreira A., Bhakar R., Mathur J. and Strbac G. 2021. Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty. Energies. 14 (22), p. 7813. https://doi.org/10.3390/en14227813
Robust and automatic data cleansing method for short-term load forecasting of distribution feeders
Huyghues-Beaufond, N., Tindemans, S., Falugi, P., Sun, M. and Strbac, G. 2020. Robust and automatic data cleansing method for short-term load forecasting of distribution feeders. Applied Energy. 261 (Art. 114405). https://doi.org/10.1016/j.apenergy.2019.114405