A model-based parameter space in Monte Carlo simulations for European short-term adequacy assessments

Zhang, Menghan, Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714, Yang, Zhifang, Yu, Juan and Li, Wenyuan 2024. A model-based parameter space in Monte Carlo simulations for European short-term adequacy assessments. Presented at: 7th IEEE Conference on Energy Internet and Energy System Integration, Hangzhou, China, 15-18 December 2023. 2023 IEEE 7th Conference on Energy Internet and Energy System Integration (EI2). IEEE, pp. 4754-4760. 10.1109/EI259745.2023.10513254

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Abstract

The Short-Term Adequacy (STA) assessment is crucial for raising risk awareness among stakeholders and aiding European system operations in quickly identifying potential risks. To calculate cross-border exchange capacities in European STA assessments, the flow-based (FB) approach is used to define the parameter space in Monte Carlo simulations. However, Transmission System Operators (TSOs) currently rely on data-driven methods to define the FB domain. These methods necessitate extensive historical data on import-export capacities, which may not always align with Monte Carlo samples. Issues related to Energy Not Served (ENS) can arise even when Monte Carlo samples are aligned with the FB domain. To address these challenges, this paper introduces an analytical model based on parametric programming. This model maps the decision variables to zonal total loads, ensuring a more accurate parameter space in Monte Carlo simulations, and is free from ENS issues. Additionally, the model proactively adjusts the ENS index to reflect the parameter space under specific ENS conditions, thereby enhancing the generation of accurate Monte Carlo scenarios for subsequent STA assessments.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Additional Information:	© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher:	IEEE
ISBN:	9798350345094
Date of First Compliant Deposit:	13 June 2024
Date of Acceptance:	13 November 2023
Last Modified:	17 Jul 2024 00:07
URI:	https://orca.cardiff.ac.uk/id/eprint/169607

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