A stochastic operational decision framework for multiple local energy communities considering dependencies in uncertainty
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Accepted Version
Date
2025-10-06
Authors
Pandey, Vipin Chandra
Hayes, Barry Patrick
Journal Title
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Publisher
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
This paper presents a stochastic decision framework for multiple local energy communities (M-LECs) considering risk behaviour and dependencies in uncertainty. The proposed problem is presented in a bilevel stochastic framework comprising of M-LECs at the upper level and load-serving entity (LSE) at the lower level in the distribution system (DS). The M-LECs strategically optimizes their operating cost using a grid-informed price, distribution locational marginal price, cleared by LSE for peer-to-peer trading, and LECs to grid power transactions. Further, the uncertainty of various sources is modeled using a multi-variate copula distribution to consider their dependencies. The copula model generates correlated random samples to quantify the relationship among different uncertain variables. The problem is mathematically formulated using equilibrium problem with equilibrium constraints (EPEC). The simulation results are implemented on a modified IEEE-33 bus system to demonstrate the effectiveness of the proposed methodology.
Description
Keywords
Multiple local energy communities , Bi-level stochastic problem , Peer-to-peer trading , Copula , Risk optimization
Citation
Pandey, V. C. and Hayes, B. P. (2025) "A stochastic operational decision framework for multiple local energy communities considering dependencies in uncertainty', 2025 IEEE Kiel PowerTech, Kiel, Germany, 29 June - 3 July 2025, pp. 1-6. https://doi.org/10.1109/PowerTech59965.2025.11180306
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© 2025, 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.
