Integrated Decision Support Tool for Planning and Design of Offshore Wind O&M Strategies

Transcription

1 Integrated Decision Support Tool for Planning and Design of Offshore Wind O&M Strategies Koopstra, H. (presenting author), Delft University of Technology Heijkoop, G. (co-author), Systems Navigator 1. Introduction Due to the offshore location and the uncertain weather conditions, wind farms at sea have different requirements, challenges and costs compared to onshore wind farms. This also holds for the Operations & Maintenance (O&M). Weather conditions have a substantial impact on the, cost, and planning of the O&M and require O&M strategies that minimize losses and O&M costs, while making sure scheduled maintenance tasks are completed in time. These differences and challenges results in substantially higher O&M costs compared to onshore wind farms; approximately twice as high [1]. According to [2], these O&M costs of offshore wind farms could be reduced by five till eight percentage points, due to technological potentials and increased efficiency. So there can be concluded that there is demand for an approach to make the O&M more effective. Previous research show a lack of an approach that is 1) integrated: including all the stakeholders requirements that are necessary to assess the effectiveness of the O&M and 2) generic: applicable for all wind farms. Therefore the goal of this thesis is to develop an integrated and generic approach that enable users to find the optimal strategy in the, costs and planning tradeoff triangle. The research followed the next main research question: What are the requirements for an integrated and generic offshore wind O&M decision support model and how could such an approach contribute to an effective O&M planning and process for offshore wind farms? 2. Methodology The research uses first a top-down requirements analysis. This identifies the requirements from the stakeholders, which aspects are already researched in previous studies and what the shortcomings of those studies are. Thereafter this article focusses on a bottom-up building of the decision support tool based on the identified requirements. The first part is performed by means of a literature study to obtain all the relevant aspects and requirements for the model and supplemented with interviews with experts in the field. The second part integrates all the requirements into an integrated tool that will represent the real offshore wind O&M process and can improve the O&M process and plan. To improve the O&M of offshore wind farms, a model of the real system is developed. The developed integrated O&M 1

2 decision support tool is specified as Discrete Event Simulation model 3. Decision Support Tool The developed O&M tool features four main capabilities: 1. The resource planning of scheduled maintenance The resource planning aspect in the decision support model displays the resource allocation for one replication without randomness (so no failures displayed). With the Gantt chart the user is able to see when which turbine will be maintained. The planning of preventive maintenance is not included in the previous studies examined during this research. Only [3] included a Gantt chart for the construction of an offshore wind farm. 2. The robustness of the preventive maintenance plan In the resource planning also targets dates can be included. The user gets insight whether or not the target is reached, based on the maintenance plan without randomness. Randomness is included in the target probabilities. The model shows the probability, based on multiple replications, the target will be reached (figure 1) and the target exceedance (figure 2). Figure 1: Preventive maintenance plan robustness Figure 2: Target date exceedance This requirement is not covered in previous studies or integrated tools. 3. Comparison and optimization of different strategies In a score card the user can specify the input parameters of multiple O&M strategies, consisting of i.a. maintenance activity requirements for both preventive and corrective maintenance, transport resource requirements, number of personnel per turbine, number of days per turbine and a preventive maintenance wind threshold. The developed tool enables the user to simulate the maintenance strategies and compare (the box plots of) the results or optimize on all the KPIs, including the variability, in order to find the optimum in the, cost and planning trade-offs. Furthermore, it is possible to optimize the O&M strategy by defining an objective for one or more KPIs using the Simio plugin OptQuest. OptQuest is able to use the minimum, maximum and increment values to find the best strategy combining the techniques Tabu search, scatter search, integer programming, and neural networks into a single, composite search algorithm [4]. The visual presentation of the output by means of box plots (see figure 3) and the optimization aspect are not covered by previous research. 2

3 Figure 3: Comparison and optimization of O&M strategies 4. Insight in the O&M process An animation (figure 4) of the maintenance process gives the user the possibility to verify the O&M process and provides the user insight in the process, both in the preventive maintenance as in the corrective maintenance process. During the animation, failures occur and the (jack-up) vessels are performing both types of maintenance. Figure 4: Visual insight in the O&M process Not any of the previous studies is able to provide insight in the process, they all only show numeric output neglecting the process. Validation of the Tool To validate the developed tool a comparison with another tool. With this technique, results of the simulation model being validated are compared to results of other (valid) models [5]. In this comparison, the developed tool will be compared with the ECN O&M Calculator, validated by a third party [6]. The two models are compared on the availabilities and the criteria. As input the same data are used as in the (fictitious) case by ECN [7], supplemented with assumptions where necessary. The ECN case study covers 130 turbines of 4.0MW, 120 km from the coast and with 30 hours of preventive maintenance per turbine per year. The costs and scheduling criteria are not used. The costs criteria are not relevant to include and compare, since the costs input parameters in the ECN model are unknown and thus assumed, except for the price per MWh. The planning robustness criteria are not included, since the ECN tool is not able to handle this aspect. There are no significant differences (table 2) and the developed O&M tool is considered comparable with the ECN O&M Calculator. Table 1: Validation model comparison results KPI Unit ECN O&M Developed O&M tool Calculator Mean Half width Energy-based availability Time-based % % availability Total MWh hour 1,761, ,751, , Lost MWh hour 192, , , Total revenues m Mean +- half width indicates 95% confidence interval For the face validation an interview is held with Willem van Dongen (Director Operation UK at Vattenfall). Together with this expert a demonstration of the tool with a sample case study (based on Vattenfall s Offshore Wind farm Egmond aan Zee (OWEZ) in the Netherlands) is performed including a comparison of different O&M strategies, the planning robustness assessment and the animation of the O&M process. According to Willem van Dongen [8], the tool presents realistic results for the OWEZ wind farm 3

4 based on the used inputs and shows the expected directional behavior for different strategies Furthermore, the tool is considered valuable for the sector in two phases of the lifespan of an offshore wind farm: 1. Prior to the construction of the wind farm, in order to get insight in the O&M costs of the (planned) wind farm. 2. During the operational phase of the wind farm. The O&M tool could be useful to test potential CAPEX investments. to plan the preventive maintenance of a wind farm and to execute that plan during the real maintenance campaign [8]. Such a pilot project will be planned in the short-term together with Vattenfall and Systems Navigator in order to validate the tool with data and results of an existing wind farm. 4. Conclusions The main result of this research is an integrated and generic decision support tool that is i.a. able to plan and asses the preventive maintenance planning, compare and optimize O&M strategies and to provide insight in the O&M process. The basis for the decision support tool, and the first part of the main research question, were the requirements set by actors within the offshore In the developed tool all the requirements set by actors in the offshore wind sector are included, apart for some spare parts logistics related ones (outside the scope of this version of the tool). The developed tool is validated by a comparison with the validated ECN tool and by a face validation. Recommendations and Future Research In the current tool assumptions are made on the spare parts logistics aspect which may be less realistic, however no significant impact is expected. Furthermore, it is recommended to performed a validation during the maintenance campaign project and compare and optimize real different O&M strategies, 4

5 References [1] L. W. M. M. Rademakers, H. Braam, M. B. Zaaijer, and G. J. W. Van Bussel, Assessment and Optimisation of Operation and Maintenance of Offshore Wind Turbines, [2] The German Offshore Wind Energy Foundation, Cost Reduction Potentials of Offshore Wind Power in Germany, [Online]. Available: Download EN_ShortVersion_CostRe ductionpotentialsofoffshorewindpow er.pdf. [Accessed: 25-Mar-2014]. [3] B. Scholz-Reiter, M. Lütjen, J. Heger, and A. Schweizer, Planning and control of logistics for offshore wind farms, in Proceedings of the 12th WSEAS international conference on Mathematical and computational methods in science and engineering, 2010, pp [6] ECN, Accurate estimation of maintenance costs offshore wind farms with ECN software tool, [Online]. Available: e-estimation-of-maintenance-costsoffshore-wind-farms-with-ecnsoftware-tool/. [Accessed: 24-Dec- 2014]. [7] R. P. Van de Pieterman, H. Braam, T. S. Obdam, L. W. M. M. Rademakers, and T. J. J. Van der Zee, Optimisation of maintenance strategies for offshore wind farms: A case study performed with the OMCE-Calculator, in The Offshore 2011 conference, [8] W. Van Dongen, Interview: O&M tool validation. (H. Koopstra, Interviewer). March 5th, [4] OptTek Systems, Tuning OptQuest for a particular problem. [Online]. Available: n/v66engine/optquest Engine Documentation/Default.htm#Tuning_ OptQuest_for_a_particular_problem. htm. [Accessed: 07-Apr-2015]. [5] R. G. Sargent, Verification and validation of simulation models, in Proceedings of the 37th conference on Winter simulation, 2005, pp