A1 Journal article (refereed), original research

Decentralized cooperative scheduling of prosumer flexibility under forecast uncertainties


Open Access hybrid publication

Publication Details
Authors: Mashlakov Aleksei, Pournaras Evangelos, Nardelli Pedro H.J., Honkapuro Samuli
Publisher: Elsevier
Publication year: 2021
Language: English
Related Journal or Series Information: Applied Energy
Volume number: 290
ISSN: 0306-2619
JUFO-Level of this publication: 3
Open Access: Open Access hybrid publication
Location of the parallel saved publication: http://urn.fi/URN:NBN:fi-fe202103127227

Abstract

Scheduling of prosumer
flexibility is challenging in finding an optimal allocation of energy
resources for heterogeneous prosumer goals under various forecast
uncertainties and operation constraints. This study addresses this
challenge by introducing a bottom-up framework for cooperative
flexibility scheduling that relies on a decentralized network of
scheduling agents to perform a coordinated decision-making and select
subset of households’ net load schedules that fulfills the
techno-socio-economic prosumer objectives in the resource operation
modes and ensures the reliability of the grid. The resource flexibility
in terms of alternative operation schedules is mathematically modeled
with multiobjective optimization
that attains economic, environmental, and energy self-sufficiency
prosumer goals with respect to their relative importance. The
coordination is achieved with a privacy-preserving collective learning
algorithm that aims to reduce the aggregated peak demand of the
households considering prosumers’ willingness to cooperate and accept a
less preferred resource schedule. By utilizing the framework and
real-world data, the novel case study is demonstrated for prosumers
equipped with solar battery systems in a community microgrid. The
findings show that the flexibility scheduling with an optimal prosumer
cooperation level decreases the global costs of collective peak shaving
by 83% while increasing the local prosumer costs by 28% in comparison
with noncooperative scheduling. However, the forecast uncertainty in net
load and parameters of the frequency containment reserve causes
imbalances in the planned schedules. It is suggested that the imbalances
can be decreased if the flexibility modeling takes into account
variable specific levels of forecast uncertainty.


Last updated on 2021-18-03 at 08:48