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zirich","previews":{"preview":{"ref":"f0673517-0919-4ca2-8aca-537fa826a1f6","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/5785d332-7626-44a6-a869-0fd3d5453e5a/4b3c9b85-7eec-44d8-8e0a-2b3757bf8855/presentation-f0673517-0919-4ca2-8aca-537fa826a1f6.jpg"}}},{"index":1,"time":208280,"duration":87480,"relevance":-1,"hit":false,"text":"Representative days RD vs representative hours RH The most common way to solve the storage representation problem Keep daily chronology of the clustered time steps representative days RD For long er term storage: Superpose daily storage levels with the storage levels of the RD However, Clustering RD requires many representative time steps to approximate the input data well! Representative hours RH require fewer time steps for the same approximation quality ETH iirich Kotzr.., Markowitz, Robins, Stolten, Time series aggregation for energy system design: Modeling seasonal storage. Applied Energy 2018 Storage level RD Daily storage level is repeated Why are RH storage representation methods underrepresented Until now, the storage level cannot be reconstructed accurately and efficiently","previews":{"preview":{"ref":"f0673517-0919-4ca2-8aca-537fa826a1f6","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/5785d332-7626-44a6-a869-0fd3d5453e5a/0de2e4d9-e787-4737-9a77-d0bf996f0e5f/presentation-f0673517-0919-4ca2-8aca-537fa826a1f6.jpg"}}},{"index":2,"time":295760,"duration":111480,"relevance":-1,"hit":false,"text":"How to reduce the number of storage level variables and constraints without losing any information Let take generic input time series with 20 time steps EMH zirich","previews":{"preview":{"ref":"f0673517-0919-4ca2-8aca-537fa826a1f6","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/5785d332-7626-44a6-a869-0fd3d5453e5a/4da057a5-a121-4544-a12d-1a9235a9ed27/presentation-f0673517-0919-4ca2-8aca-537fa826a1f6.jpg"}}},{"index":3,"time":407240,"duration":140280,"relevance":-1,"hit":false,"text":"Mathematical description of reduced RH storage representation with self- discharge Constant net charging for RH New indices: Representative time steps: AHst AHsr- AHs. 10 AHsi Storage level time steps: Storage level equations for andt 10 Lst Lsta7 AH, sta sit- Ps AHs ing We always add Lse- Lse- 4Hs, the same AH, Storage level represpftation reduced Lst 10 Lse- Qs AHsi- LL Lsgato Usear 95 Msins 15 Capacity Modeled storage time steps Repeated storage time steps ETH zirich","previews":{"preview":{"ref":"f0673517-0919-4ca2-8aca-537fa826a1f6","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/5785d332-7626-44a6-a869-0fd3d5453e5a/b0be9abb-ba7f-4f00-af6b-4f6207a135d5/presentation-f0673517-0919-4ca2-8aca-537fa826a1f6.jpg"}}},{"index":4,"time":547520,"duration":407800,"relevance":-1,"hit":false,"text":"Conclusions Contribution: RH- based storage level representation method with reduced storage level index Clustering accuracy of using Storage level representation to ensure representative hours computational tractability Conclusions: RH represent the input data time series more accurately than RD with fewer representative time steps The proposed method shows strong reduction in solving time while retaining good accuracy The method can reproduce the operation of short- term and long- term storage well laa 100 to 500 representative hours are good trade- off between accuracy and computational complexity XS for large- scale multi- storage energy systems ETH zirich Za SS eo","previews":{"preview":{"ref":"f0673517-0919-4ca2-8aca-537fa826a1f6","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/5785d332-7626-44a6-a869-0fd3d5453e5a/818a475d-2ccd-4c96-a970-cce440bb5fc1/presentation-f0673517-0919-4ca2-8aca-537fa826a1f6.jpg"}}}]}}}}</article></body></html>

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zirich","previews":{"preview":{"ref":"f0673517-0919-4ca2-8aca-537fa826a1f6","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/5785d332-7626-44a6-a869-0fd3d5453e5a/4b3c9b85-7eec-44d8-8e0a-2b3757bf8855/presentation-f0673517-0919-4ca2-8aca-537fa826a1f6.jpg"}}},{"index":1,"time":208280,"duration":87480,"relevance":-1,"hit":false,"text":"Representative days RD vs representative hours RH The most common way to solve the storage representation problem Keep daily chronology of the clustered time steps representative days RD For long er term storage: Superpose daily storage levels with the storage levels of the RD However, Clustering RD requires many representative time steps to approximate the input data well! Representative hours RH require fewer time steps for the same approximation quality ETH iirich Kotzr.., Markowitz, Robins, Stolten, Time series aggregation for energy system design: Modeling seasonal storage. Applied Energy 2018 Storage level RD Daily storage level is repeated Why are RH storage representation methods underrepresented Until now, the storage level cannot be reconstructed accurately and efficiently","previews":{"preview":{"ref":"f0673517-0919-4ca2-8aca-537fa826a1f6","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/5785d332-7626-44a6-a869-0fd3d5453e5a/0de2e4d9-e787-4737-9a77-d0bf996f0e5f/presentation-f0673517-0919-4ca2-8aca-537fa826a1f6.jpg"}}},{"index":2,"time":295760,"duration":111480,"relevance":-1,"hit":false,"text":"How to reduce the number of storage level variables and constraints without losing any information Let take generic input time series with 20 time steps EMH zirich","previews":{"preview":{"ref":"f0673517-0919-4ca2-8aca-537fa826a1f6","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/5785d332-7626-44a6-a869-0fd3d5453e5a/4da057a5-a121-4544-a12d-1a9235a9ed27/presentation-f0673517-0919-4ca2-8aca-537fa826a1f6.jpg"}}},{"index":3,"time":407240,"duration":140280,"relevance":-1,"hit":false,"text":"Mathematical description of reduced RH storage representation with self- discharge Constant net charging for RH New indices: Representative time steps: AHst AHsr- AHs. 10 AHsi Storage level time steps: Storage level equations for andt 10 Lst Lsta7 AH, sta sit- Ps AHs ing We always add Lse- Lse- 4Hs, the same AH, Storage level represpftation reduced Lst 10 Lse- Qs AHsi- LL Lsgato Usear 95 Msins 15 Capacity Modeled storage time steps Repeated storage time steps ETH zirich","previews":{"preview":{"ref":"f0673517-0919-4ca2-8aca-537fa826a1f6","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/5785d332-7626-44a6-a869-0fd3d5453e5a/b0be9abb-ba7f-4f00-af6b-4f6207a135d5/presentation-f0673517-0919-4ca2-8aca-537fa826a1f6.jpg"}}},{"index":4,"time":547520,"duration":407800,"relevance":-1,"hit":false,"text":"Conclusions Contribution: RH- based storage level representation method with reduced storage level index Clustering accuracy of using Storage level representation to ensure representative hours computational tractability Conclusions: RH represent the input data time series more accurately than RD with fewer representative time steps The proposed method shows strong reduction in solving time while retaining good accuracy The method can reproduce the operation of short- term and long- term storage well laa 100 to 500 representative hours are good trade- off between accuracy and computational complexity XS for large- scale multi- storage energy systems ETH zirich Za SS eo","previews":{"preview":{"ref":"f0673517-0919-4ca2-8aca-537fa826a1f6","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/5785d332-7626-44a6-a869-0fd3d5453e5a/818a475d-2ccd-4c96-a970-cce440bb5fc1/presentation-f0673517-0919-4ca2-8aca-537fa826a1f6.jpg"}}}]}}}}</article></body></html>