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

Abstract

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.