G. Kariniotakis, S. Camal, F. Sossan, B. Nouri, J. Lezaca, M. Lange, B. Alonzo, Q. Libois, P. Pinson, R. Bessa, C. Gonçalves
—Smart4RES is a European Horizon2020 project de-veloping next generation solutions for renewable energy forecast- ing. This paper presents highlight results obtained during the first year of the project. Data science is used throughout the proposed solutions in order to process the large amount of heterogeneous data available to forecasters, and derive model-free approaches of forecasting and decision-aid tasks. This paper presents a series of solutions addressing relevant for Photovoltaics (PV) and storage applications. High-resolution Numerical Weather Predictions and regional solar irradiance forecasting provide detailed information on local weather conditions and their variability. PV power forecasting benefits from such new data sources, but also the pro- posed collaborative data exchange. Finally, data-driven methods simplify decision-making for trading in short-term markets and for grid management.
{"title":"Data Science for Next Generation Renewable Energy Forecasting - Highlight Results from the Smart4RES Project","authors":"G. Kariniotakis, S. Camal, F. Sossan, B. Nouri, J. Lezaca, M. Lange, B. Alonzo, Q. Libois, P. Pinson, R. Bessa, C. Gonçalves","doi":"10.1049/icp.2021.2499","DOIUrl":"https://doi.org/10.1049/icp.2021.2499","url":null,"abstract":"—Smart4RES is a European Horizon2020 project de-veloping next generation solutions for renewable energy forecast- ing. This paper presents highlight results obtained during the first year of the project. Data science is used throughout the proposed solutions in order to process the large amount of heterogeneous data available to forecasters, and derive model-free approaches of forecasting and decision-aid tasks. This paper presents a series of solutions addressing relevant for Photovoltaics (PV) and storage applications. High-resolution Numerical Weather Predictions and regional solar irradiance forecasting provide detailed information on local weather conditions and their variability. PV power forecasting benefits from such new data sources, but also the pro- posed collaborative data exchange. Finally, data-driven methods simplify decision-making for trading in short-term markets and for grid management.","PeriodicalId":186086,"journal":{"name":"11th Solar & Storage Power System Integration Workshop (SIW 2021)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121360559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, battery energy storage (BES) has emerged as an economically viable technology to be adopted in large-scale photovoltaic (PV) and wind farms to facilitate their integration into the system and increase their economic value. This paper focuses on the determining a proper BES for such a system that will enable the system to respond to the power price variations and thus maximize the BES benefits. Additionally, this paper proposes a detailed dispatching scheme that can handle various operation constraints in order to maximize the BES benefits. This paper also takes into account the factors affecting the degradation of BES during its operation and shows that this is a critical factor in determining economic viability of the BES. A case study for a 300 MW solar power plant is given to illustrate the proposed method and assess the economic viability of the storage for this case. The results show the importance of adopting a detailed BES model to improve the accuracy of the estimated economic benefits.
{"title":"A multi-objective operational strategy for a utility-scale PV plus energy storage system","authors":"F. Alsaeed, M. Baran","doi":"10.1049/icp.2021.2489","DOIUrl":"https://doi.org/10.1049/icp.2021.2489","url":null,"abstract":"Recently, battery energy storage (BES) has emerged as an economically viable technology to be adopted in large-scale photovoltaic (PV) and wind farms to facilitate their integration into the system and increase their economic value. This paper focuses on the determining a proper BES for such a system that will enable the system to respond to the power price variations and thus maximize the BES benefits. Additionally, this paper proposes a detailed dispatching scheme that can handle various operation constraints in order to maximize the BES benefits. This paper also takes into account the factors affecting the degradation of BES during its operation and shows that this is a critical factor in determining economic viability of the BES. A case study for a 300 MW solar power plant is given to illustrate the proposed method and assess the economic viability of the storage for this case. The results show the importance of adopting a detailed BES model to improve the accuracy of the estimated economic benefits.","PeriodicalId":186086,"journal":{"name":"11th Solar & Storage Power System Integration Workshop (SIW 2021)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130659302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the context of the EU-founded project OSMOSE, the demonstration lead by REE focuses on the flexibility solution development with a high-level control that implements management strategies for the multi-component flexibility system (MCFS) flexibility services provision for the grid. The control is designed as Master Control (MC) that facilitates the integration of large amounts of renewable energy through an efficient use of the available resources, taking into consideration the characteristics of each device and optimizing the operating conditions and their State of Charge (SoC) to prevent from undesired reduction of their State of Health (SoH). The multi-component testing system is designed and modelled at simulation level to assess and optimize the response of the system and of each coordinated device at the same time.
{"title":"Multi services provided by the coordination control of different storage and FACTS devices","authors":"A. Kalms, F. Bouchotrouch, P. Pernaut, M. Estévez","doi":"10.1049/icp.2021.2496","DOIUrl":"https://doi.org/10.1049/icp.2021.2496","url":null,"abstract":"In the context of the EU-founded project OSMOSE, the demonstration lead by REE focuses on the flexibility solution development with a high-level control that implements management strategies for the multi-component flexibility system (MCFS) flexibility services provision for the grid. The control is designed as Master Control (MC) that facilitates the integration of large amounts of renewable energy through an efficient use of the available resources, taking into consideration the characteristics of each device and optimizing the operating conditions and their State of Charge (SoC) to prevent from undesired reduction of their State of Health (SoH). The multi-component testing system is designed and modelled at simulation level to assess and optimize the response of the system and of each coordinated device at the same time.","PeriodicalId":186086,"journal":{"name":"11th Solar & Storage Power System Integration Workshop (SIW 2021)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127523081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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