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RESEARCH PRODUCT

A Methodology for Exploiting Smart Prosumers’ Flexibility in a Bottom-Up Aggregation Process

Diego ArnoneMichele CacioppoMariano Giuseppe IppolitoMarzia MamminaLiliana MineoRossano MuscaGaetano Zizzo

subject

Fluid Flow and Transfer ProcessesTechnologyprosumerQH301-705.5Process Chemistry and TechnologyTPhysicsQC1-999bottom-up aggregationGeneral EngineeringEngineering (General). Civil engineering (General)Computer Science ApplicationsSettore ING-IND/33 - Sistemi Elettrici Per L'Energiademand response; bottom-up aggregation; prosumer; VAEChemistryBottom-up aggregation Demand response Prosumer VAEdemand responseVAEGeneral Materials ScienceTA1-2040Biology (General)InstrumentationQD1-999

description

The electrical power system is evolving in a way that requires new measures for ensuring its secure and reliable operation. Demand-side aggregation represents one of the more interesting ways to provide ancillary services by the coordinated management of a multitude of different distributed resources. In this framework, aggregators play the main role in ensuring the effectiveness of the coordinated action of the distributed resources, usually becoming mediators in the relation between distribution system operators and smart prosumers. The research project DEMAND recently introduced a new concept in demand-side aggregation by proposing a scheme without a central aggregator where prosumers can share and combine their flexibility with a collaboration–competition mechanism in a platform called Virtual Aggregation Environment (VAE). This paper, after a brief introduction to the DEMAND project, presents the algorithm for the day-ahead estimation of prosumers’ flexibility and the cooperative–competitive algorithm for the bottom-up aggregation. The first algorithm evaluates various couples of power variation and desired remuneration to be sent to the VAE for further elaborations and, for showing its potentiality, is applied to two different case studies: a passive user with only controllable loads and prosumers with controllable loads, photovoltaics and a storage system. The aggregation algorithm is instead discussed in detail, and its performance is evaluated for different population sizes.

10.3390/app12010430https://dx.doi.org/10.3390/app12010430