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Conference programme 

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Poster session

Lead Session Chair:
Stephan Barth, Managing Director, ForWind - Center for Wind Energy Research, Germany
Lars Nonås MARINTEK, Norway
Co-authors:
Lars Magne Nonås (1) F P Elin Halvorsen-Weare (1) Magnus Stålhane (1)
(1) MARINTEK, Bergen, Norway

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Poster
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Presenter's biography

Biographies are supplied directly by presenters at OFFSHORE 2015 and are published here unedited

Dr. Lars Magnus Nonås – Senior research scientist Has participated in several offshore wind projects and is currently leading the work on "Integrated Logistics" within an EU-funded research project called LEANWIND. Dr Nonås has had a central role in building the competence field of vessel fleet optimization in maritime transport systems (offshore wind, offshore O&G, shipping) within MARINTEK through both industry and R&D projects. His work is focused around operations research, optimization, logistics and supply chain management. Prior to joining MARINTEK, Dr Nonås worked as a university lecturer at NHH and taught courses on production economics and supply chain management.

Abstract

Finding cost-optimal solutions for the maritime logistic challenges for maintenance operations at offshore wind farms

Introduction

In order for offshore wind to be a profitable future energy resource it is vital to reduce the cost of energy significantly. One obvious way to do this is to learn from other more mature industries. A research field called operations research is heavily used for example in aviation and land based transportation system in order to support planning experts to make better and more informed decisions based on quantitative advanced decision support tools. This can be seen as a contrast to the ad-hoc process that is widely used in today's young and immature offshore wind industry.

Approach

Common for many decision support tools are the use of some sort of simulation algorithm to evaluate the effects of a given solution. We are adapting a different approach where we utilize mathematical models and optimization techniques to select the cost-optimal vessel fleet among the options that are available for a given planning horizon. This means that while the simulation model merely calculate the cost of a given input scenario from a domain expert, the optimization model automatically searches among all possible scenarios before it provides the cost of the optimal one to be validated by the domain expert.

Main body of abstract

In order to determine a cost-optimal vessel fleet and corresponding infrastructure the underlying optimization model needs to correctly evaluate other relevant aspects of the logistic system as well. We have to consider which vessels should be used to support which maintenance activities at what time. To decide when a vessel can operate, we use the weather limitations of the vessel classes in conjunction with weather data from the wind farm's location. Weather data and electricity prices are used to determine the downtime cost. In total, output from the model is not only the cost-optimal vessel fleet and infrastructure, but also a wide range of statistics: Cost break-down, the vessel fleet's accessibility, number of working shifts per vessel, and availability of the wind farm,
Since the model also includes detailed information on other aspects of the logistic system, it can be adapted to analyse and provide insights to other interesting features. This creates new and innovative ways for a decision maker to get valuable information regarding the logistic system for maintenance operations at offshore wind farms. This can be e.g. to calculate the cost of increasing the availability of the wind farm, or to evaluate when it is best to execute preventive maintenance activities from a minimum cost logistic perspective. The model can be adapted to instead of taking weather limitations for a vessel class as an input parameter, evaluate which limitations that a vessel and access system should ideally have to reduce cost and increase availability of the wind farm.

Conclusion

We will in this presentation provide an overview of what mathematical models and optimization techniques can bring to decision support tools for the maritime logistics challenges for offshore wind farms. Our ongoing research for instance within LEANWIND and NOWITECH is creating new and innovative methods that can help decision makers to take make the right decisions and hence reduce the cost-of-energy.


Learning objectives
The main objective is to inform the industry of the possibility and capability of operations research and optimization based decision support in particular. We will illustrate our recent research with examples of how different aspects of the maritime logistic challenges can be analysed based on real life problems of our industry partners. We will also provide examples of how this competence field is successfully integrated into the logistics system in other more mature industries.