A stochastic inference-dual-based decomposition algorithm for TSO-DSO-Retailer coordination

Bakhtiari, H, Hesamzadeh, M R and Bunn, D W (2024) A stochastic inference-dual-based decomposition algorithm for TSO-DSO-Retailer coordination. IEEE Transactions on Energy Markets, Policy and Regulation, 2 (1). pp. 13-29. ISSN 0885-8969

Abstract

The flexibility services available from embedded resources, being attractive to both the network operators and retailers, pose a problem of co-ordination and market design at the local level. This research considers how a Flexibility Market Operator (FMO) at the local level, analogous to market operators at the wholesale level, can improve the real-time operation of the power systems and efficiently manage the interests of the TSO, DSO, and Retailers. Using generalized disjunctive programming, a stochastic bilevel representation of the task is reformulated as a stochastic mixed-logical linear program (MLLP) with indicator constraints. An Inference-Dual-Based Decomposition (IDBD) Algorithm is developed with sub-problem relaxation to reduce the iterations. Using expected Shapley values, a new payoff mechanism is introduced to allocate the cost of service activations in a fair way. Finally, the performance and benefits of the proposed method are assessed via a case study application.

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Item Type:	Article
Subject Areas:	Management Science and Operations
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.
Date Deposited:	07 Aug 2023 09:45
Date of first compliant deposit:	01 Aug 2023
Last Modified:	13 Nov 2024 10:51
URI:	https://lbsresearch.london.edu/id/eprint/2965

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