Centralised and Distributed Optimization for Aggregated Flexibility Services Provision

Pol Olivella-Rosell (Corresponding Author), Francesc Rullan, Pau Lloret-Gallego, Eduardo Prieto-Araujo, Ricard Ferrer-San-José, Sara Barja-Martinez, Sigurd Bjarghov, Venkatachalam Lakshmanan, Ari Hentunen, Juha Forsström, Stig Odegaard Øttesen, Roberto Villafafila-Robles, Andreas Sumper

Research output: Contribution to journalArticleScientificpeer-review

44 Citations (Scopus)


The recent deployment of distributed battery units in prosumer premises offer new opportunities for providing aggregated flexibility services to both distribution system operators and balance responsible parties. The optimization problem presented in this paper is formulated with an objective of cost minimization which includes energy and battery degradation cost to provide flexibility services. A decomposed solution approach with the alternating direction method of multipliers (ADMM) is used instead of commonly adopted centralised optimization to reduce the computational burden and time, and then reduce scalability limitations. In this work we apply a modified version of ADMM that includes two new features with respect to the original algorithm: first, the primal variables are updated concurrently, which reduces significantly the computational cost when we have a large number of involved prosumers; second, it includes a regularization term named Proximal Jacobian (PJ) that ensures the stability of the solution. A case study is presented for optimal battery operation of 100 prosumer sites with real-life data. The proposed method finds a solution which is equivalent to the centralised optimization problem and is computed between 5 and 12 times faster. Thus, aggregators or large-scale energy communities can use this scalable algorithm to provide flexibility services.
Original languageEnglish
Article number8978550
Pages (from-to)3257-3269
JournalIEEE Transactions on Smart Grid
Issue number4
Publication statusPublished - Jul 2020
MoE publication typeA1 Journal article-refereed


This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Program through INVADE H2020 Project (2017–2019) under Grant 731148, in part by the Ministerio de Ciencia, Innovación y Universidades under Project RTI2018-099540, and in part by InnoEnergy Ph.D. School. Paper no. TSG-00926-2019.


  • distributed optimization
  • Flexibility
  • smart grid


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