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Wind Energy Benchmarking
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James Ingham Sciemus, United Kingdom WIND ENERGY BENCHMARKING Abstract ID: 434 Poster code: PO.070 | Download poster: ![]() |
Presenter's biography
Biographies are supplied directly by presenters at WindEurope 2016 and are published here uneditedJames is a director of the newly launched Wind Energy Benchmarking Services; a Joint Venture between ORE Catapult and Sciemus. James joined Sciemus in 2009 and was part of the team that delivered the Wind Risk Assessment Tool (WindRAT) model to market. As Head of Renewables at Sciemus, James specialises in the modelling of performance and failure data for renewable energy projects and delivering insight to Owner/Operators, Investors and Insurers for power generation portfolios in Europe and the USA. James has a BSc (Hons) in Business Enterprise from De Montfort University and an MSc in Knowledge Management from Cranfield University.
Abstract
Wind Energy Benchmarking
Introduction
Benchmarking is the process of comparing performance metrics and processes of one business to others in the same industry with the aim of determining relative performance. This information is used to identify “best in class” performance and quantify the benefit of implementing best practice. Furthermore, when sufficient data is available, historical industry performance data can give a strong insight into future expected performance. In the context of the wind industry, benchmarking provides a vehicle to help reduce O&M costs through optimisation of O&M strategies.
With ageing wind assets, decreasing subsidies and tightening margins, benchmarking data is vital to understanding performance and directing investment in wind farms. To date, benchmarking in the wind energy industry has been completed within a select population of data. To gain a valuable insight into the industry’s performance, international collaboration is necessary along with a thorough understanding of data acquisition and processing such that the benchmarking is accurate and meets the requirements of its participants. This presentation aims to outline the methodology proposed to facilitate industry collaboration and, subsequently, benchmark effectively across the wind industry.
Approach
There are a various challenges associated with building an effective benchmarking system that offers value to each participant. Within each of those challenges, the advantages and disadvantages of the solutions will be evaluated.
• Establishing a Common Component Breakdown of Wind Farms
o Single Taxonomy vs multiple Taxonomies
• Data Acquisition and Processing of Input Metrics
o SCADA
o Business Intelligence System
o Work Order System
o Hybrid Approach
• Providing Valuable Output Metrics
o Categorising metrics suitably: Availability, Energy Production, Operations, Reliability
o Defining clear and comprehensive metrics within these modules
• Delivery of Output Metrics
o Web browser environment
o CSV (spreadsheet format)
o Report
• Quality Assurance and Dissemination of Best Practice
o Ensuring the data accurately reflects wind farm performance and operations
o Working Groups
Different examples of benchmarking programmes will then be discussed from the Offshore Wind, Onshore Wind, Thermal Power and Oil & Gas sectors.
Main body of abstract
Work has been completed on establishing the best method to develop a benchmarking system for the wind energy industry. A standardised approach is necessary to ensure relevancy to the participants, therefore a common taxonomy must be implemented. To address the challenge of accurate data acquisition, a combination of data sources should be used to collect, validate and process data including SCADA, Business Intelligence systems and Work Order Management systems. The benchmark output needs to be digestible by a wide range of audiences therefore clear categorisation of metrics is critical along with definitive individual metrics for each category. Furthermore, the presentation of this output should be easily accessible by all participants and formatted such that it can be subject to further processing or presented in key decision making scenarios. Lastly, it is critical that the data is representative of the performance for each participant. The data gathering, processing and uploading should be optimised and shared via working groups to establish a best practice which all participants can adopt.
Conclusion
Evidence presented has shown that benchmarking across industry peers can drive collaboration and help share best practices. It helps to identify high performance and quantifies the benefit of adopting new and improved practices.
Relevancy and accuracy of data is key to deliver value to the industry and this is achieved through a standardised approach by participants.
Collaboration with industry peers can help to drive down the cost associated with O&M through strategy optimisation.
Learning objectives
1. Understand why benchmarking is important for the wind industry;
2. Understand the challenges faced in completing a benchmarking exercise;
3. Appreciate the advantages and disadvantages of the solutions to these challenges;
4. Identify the most appropriate solutions to build the overall benchmarking system;
5. Understand how this has been approached across the Power industry.
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