Synergies of Wind Power and Electrified Space Heating: Case Study for Beijing

2014 ◽  
Vol 48 (3) ◽  
pp. 2016-2024 ◽  
Author(s):  
Xinyu Chen ◽  
Xi Lu ◽  
Michael B. McElroy ◽  
Chris P. Nielsen ◽  
Chongqing Kang
Keyword(s):  
Wind Power ◽  
Space Heating

Wind power prefeasibility study for agricultural applications in Kerman, Iran: a case study

2014 ◽  
Vol 5 (1) ◽  
pp. 25 ◽  
Author(s):  
Ighball Baniasad Askari ◽  
Lina Baniasad Askari ◽  
Mehran Ameri
Keyword(s):  
Wind Power ◽  
Agricultural Applications

Wind power resource assessment and wind-hydrogen generation potential: a case study

2022 ◽  
pp. 1-17
Author(s):  
Rakesh Narayana Sarma ◽  
Vineeth Kumar ◽  
Suresh Lal S R ◽  
Minu Reghunath ◽  
Arya Jayan ◽  
...  
Keyword(s):  
Wind Power ◽  
Hydrogen Generation ◽  
Resource Assessment ◽  
Power Resource ◽  
Hydrogen Generation Potential

Feasibility study about using a stand-alone wind power driven heat pump for space heating

2018 ◽  
Vol 228 ◽  
pp. 1486-1498 ◽  
Author(s):  
Hailong Li ◽  
Pietro Elia Campana ◽  
Yuting Tan ◽  
Jinyue Yan
Keyword(s):  
Wind Power ◽  
Heat Pump ◽  
Feasibility Study ◽  
Space Heating

scholarly journals Probabilistic forecasting of wind power production losses in cold climates: a case study

2018 ◽  
Vol 3 (2) ◽  
pp. 667-680 ◽  
Author(s):  
Jennie Molinder ◽  
Heiner Körnich ◽  
Esbjörn Olsson ◽  
Hans Bergström ◽  
Anna Sjöblom
Keyword(s):  
Wind Power ◽  
Energy Production ◽  
Spatial Representation ◽  
Forecast Skill ◽  
Cold Climates ◽  
Probabilistic Forecasting ◽  
Probabilistic Forecasts ◽  
Forecasting System ◽  
Production Losses

Abstract. The problem of icing on wind turbines in cold climates is addressed using probabilistic forecasting to improve next-day forecasts of icing and related production losses. A case study of probabilistic forecasts was generated for a 2-week period. Uncertainties in initial and boundary conditions are represented with an ensemble forecasting system, while uncertainties in the spatial representation are included with a neighbourhood method. Using probabilistic forecasting instead of one single forecast was shown to improve the forecast skill of the ice-related production loss forecasts and hence the icing forecasts. The spread of the multiple forecasts can be used as an estimate of the forecast uncertainty and of the likelihood for icing and severe production losses. Best results, both in terms of forecast skill and forecasted uncertainty, were achieved using both the ensemble forecast and the neighbourhood method combined. This demonstrates that the application of probabilistic forecasting for wind power in cold climates can be valuable when planning next-day energy production, in the usage of de-icing systems and for site safety.

scholarly journals Probabilistic forecasting of wind power production losses in cold climates: A case study

2017 ◽  
Author(s):  
Jennie P. Söderman ◽  
Heiner Körnich ◽  
Esbjörn Olsson ◽  
Hans Bergström ◽  
Anna Sjöblom
Keyword(s):  
Wind Power ◽  
Energy Production ◽  
Cold Climate ◽  
Forecast Skill ◽  
Cold Climates ◽  
Probabilistic Forecasting ◽  
Probabilistic Forecasts ◽  
Forecasting System ◽  
Production Losses

Abstract. The problem of icing on wind turbines in cold climates is addressed using probabilistic forecasting to improve next- day forecasts of icing and related production losses. A case study of probabilistic forecasts was generated for a two- week period. Uncertainties in initial and boundary conditions are represented with an ensemble forecasting system, while uncertainties in the spatial representation are included with a neighbourhood method. Using probabilistic forecasting instead of one single forecast was shown to improve the forecast skill of the ice-related production loss forecasts and hence the icing forecasts. The spread of the multiple forecasts can be used as an estimate of the forecast uncertainty and of the likelihood for icing and severe production losses. Best results, both in terms of forecast skill and forecasted uncertainty, were achieved using both the ensemble forecast and the neighbourhood method combined. This demonstrates that the application of probabilistic forecasting for wind power in cold climate can be valuable when planning next-day energy production, in the usage of de-icing systems, and for site safety.

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