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

Journal

IEEE Transactions on Energy Markets, Policy and Regulation

Subject

Management Science and Operations

Authors / Editors

Bakhtiari H;Hesamzadeh M R;Bunn D

Biographies

Publication Year

2024

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.

Keywords

Stochastic inference-dual-based decomposition algorithm; Disjunctive programming; TSO-DSO-Retailer coordination

Available on ECCH

No


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