A socio-technical perspective to flexible design of energy infrastructure systems

2016 ◽  
Author(s):  
Yeshambel Melese ◽  
Rob Stikkelman ◽  
Paulien Herder
Keyword(s):  
Flexible Design ◽  
Energy Infrastructure ◽  
Infrastructure Systems

Prognostics and Health Management of Wind Energy Infrastructure Systems

2022 ◽  
Author(s):  
Celalettin Yuce ◽  
Ozhan Gecgel ◽  
Oguz Dogan ◽  
Shweta Dabetwar ◽  
Yasar Yanik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Data Augmentation ◽  
Health Management ◽  
Prognostics And Health Management ◽  
Quality Of Data ◽  
Energy Infrastructure ◽  
Infrastructure Systems ◽  
The Impact

Abstract The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute towards the Prognostics and Health Management (PHM) in this industry. These advancements are driven by the need to fully explore the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT). All these aspects need to be taken into consideration to perform an effective PHM of wind energy infrastructure. To address these aspects, four research questions were formulated. What is the role of uncertainty in machine learning (ML) in diagnostics and prognostics? What is the role of data augmentation and quality of data for ML? What is the role of PBML? What is the role of the DT in diagnostics and prognostics? The methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA). A total of 143 records, from the last five years, were analyzed. Each of the four questions was answered by discussion of literature, definitions, critical aspects, benefits and challenges, the role of aspect in PHM of wind energy infrastructure systems, and conclusion.

Sustainability Index of Community Energy Systems for Benchmarking and Multi-Criteria Decision Analysis

2016 ◽  
Author(s):  
Salim Moslehi ◽  
T. Agami Reddy
Keyword(s):  
Life Cycle ◽  
Sustainability Assessment ◽  
Energy Systems ◽  
Gas Production ◽  
Baseline Scenario ◽  
Solar Pv ◽  
Building Stock ◽  
Energy Infrastructure ◽  
Infrastructure Systems ◽  
Sustainability Index

Sustainable development of energy infrastructure systems is critical for a sustainable future. Planners are adopting a variety of tools from diverse domains to design, assess, operate and plan for sustainable energy systems. The purpose of this study is to propose a quantitative sustainability assessment framework, adopting a bottom-up approach suitable to energy infrastructure systems. We have extended many of the well-accepted processes and analysis methods applicable to individual buildings to a community involving numerous buildings and centralized energy systems. We propose a simple framework which allows determining a measure called SICES, Sustainability Index of Community Energy Systems, that recognizes the three important pillars of sustainability. Willingness to pay, captured by environmental externalities, is considered to be the social value which enables quantifying the impacts of energy systems on the environment, economy, and society. Well-developed tools and methods, such as building stock modeling, Life Cycle Assessment (LCA), and Life Cycle Costing (LCC), are used in conjunction with additional indices, i.e. Reliability, Robustness, and Resilience (RRR) to evaluate the sustainable performance of community energy systems. SICES explicitly considers environmental, health, and energy cost impacts of on-site energy systems (solar photovoltaics, combined heat and power systems, boilers, chillers, etc.) as well as those of the upstream systems, i.e. natural gas production and utility power generation facilities, accounting for location-specific and temporal variation in fuel mix. We present the results of a case study analysis of applying this framework to an actual campus with more than 280 buildings and numerous solar PV systems using year-long monitored data of hourly cooling, heating and electricity demands to compute the SICES index. How different energy mixes and application of energy conservation measures would alter the current SICES index of the campus is also illustrated and discussed. A new type of diagram called the “Sustainability Compass” is proposed which allows one to track the directional change and magnitude in SICES of different energy scenarios compared to a baseline scenario.

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