A stochastic programming approach for the optimal management of aggregated distributed energy resources

The paper focuses on the optimal management of distributed energy resources aggregated within a coalition. The problem is analyzed from the viewpoint of an aggregator, seen as an entity called to optimize the available resources so to satisfy the aggregated demand by eventually trading in the Day-Ah...

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Bibliographic Details
Published inComputers & operations research Vol. 96; pp. 200 - 212
Main Authors Beraldi, P., Violi, A., Carrozzino, G., Bruni, M.E.
Format Journal Article
LanguageEnglish
Published New York Elsevier Ltd 01.08.2018
Pergamon Press Inc
Subjects
Aggregator
Distributed energy resources
Distributed generation
Electricity consumption
Electricity pricing
Energy industry
Energy management
Energy market
Energy resources
Energy sources
Mathematical models
Operations research
Optimization
Parameter uncertainty
Resource management
Risk management
Stochastic models
Stochastic programming
Studies
Aggregator
Energy market
Risk management
Distributed energy resources
Stochastic programming
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Summary:The paper focuses on the optimal management of distributed energy resources aggregated within a coalition. The problem is analyzed from the viewpoint of an aggregator, seen as an entity called to optimize the available resources so to satisfy the aggregated demand by eventually trading in the Day-Ahead Electricity Market. Both a full and a residual perspective in the management of the integrated resources is investigated and compared. The inherent uncertainty affecting the optimal decision problem, mainly related to the demand profile, electricity prices and production from renewable sources, is dealt by adopting the two-stage stochastic programming paradigm. The proposed models (different for the full and residual case) present a bi-objective function, integrating the expected profit and a risk measure, the Conditional Value at Risk, to control undesirable effects caused by the random variations of the uncertain parameters. A broad numerical study has been carried out on real case study. The analysis of the results clearly shows the benefits deriving from the stochastic optimization approach and the effect of considering different levels of risk aversion.
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2017.12.018