Exploiting the flexibility potential of water distribution networks: A pilot project in Belgium
peer reviewed ; Flexibility, and in particular, energy storage is expected to assume a key role in the efficient and secure operation of the power system, and thus, in the transition towards a carbonfree electricity sector. In this paper, we propose a methodology for exploiting the flexibility existing in water distribution systems from water storage in reservoirs. The methodology relies first on a modelling approach, from which an optimization problem is defined. The resolution of this optimization problem leads to an operating pattern for the pumps. The methodology assumes that all the elect... Mehr ...
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Dokumenttyp: | journal article |
Erscheinungsdatum: | 2023 |
Verlag/Hrsg.: |
Institute of Electrical and Electronics Engineers
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Schlagwörter: | flexibility / water distribution network / model predictive control / Engineering / computing & technology / Energy / Computer science / Ingénierie / informatique & technologie / Energie / Sciences informatiques |
Sprache: | Englisch |
Permalink: | https://search.fid-benelux.de/Record/base-26985269 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://orbi.uliege.be/handle/2268/302546 |
peer reviewed ; Flexibility, and in particular, energy storage is expected to assume a key role in the efficient and secure operation of the power system, and thus, in the transition towards a carbonfree electricity sector. In this paper, we propose a methodology for exploiting the flexibility existing in water distribution systems from water storage in reservoirs. The methodology relies first on a modelling approach, from which an optimization problem is defined. The resolution of this optimization problem leads to an operating pattern for the pumps. The methodology assumes that all the electricity is bought on the day-ahead market, where the bids are placed by constructing and solving an optimization problem. The uncertain water consumption and the electricity market prices are predicted using machine learning techniques. The methodology is tested on a real-life water distribution network in Belgium and the results. ; 7. Affordable and clean energy