Abstract
A typical Virtual Power Plant (VPP) has a distributed architecture, composed by a central control system and decentralized control units, which coordinates and aggregates local resources. A key aspect of these distributed energy systems is the flexibility offered to the market. This flexibility is considered as the difference between the (partially shiftable) load requested to the system and the energy produced by the local available resources, and it is subject to the uncertainty of the renewable production. This work proposes robust day-ahead optimization models to analyze flexibility of different local resource configurations. For each configuration, we consider a Demand Side Management step to shift the requested load in predefined time windows, based on renewable production forecasts. Moreover, two different objective functions are considered: 1) the cost minimization for the use of available resources; 2) the minimization of the exchange with the external grid (i.e. the market). The models are implemented and tested using real data. We provide a comparative analysis on the expected flexibility and costs that can be exploited by a central system to provide value-added services to the market by synergistically managing different configurations of local resources of a VPP.
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This work has been partially supported by the VIRTUS Project (CCSEB00094).
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De Filippo, A., Lombardi, M., Milano, M. (2022). Robust Optimization Models For Local Flexibility Characterization of Virtual Power Plants. In: Bandini, S., Gasparini, F., Mascardi, V., Palmonari, M., Vizzari, G. (eds) AIxIA 2021 – Advances in Artificial Intelligence. AIxIA 2021. Lecture Notes in Computer Science(), vol 13196. Springer, Cham. https://doi.org/10.1007/978-3-031-08421-8_42
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