3.6.7. Total Operation Costs

For each logistic solution, the total operation costs based on the operation duration *dop*, selected equipment, vessel fleet, port costs, and spare components (for O&M operations), are calculated as described in Equation (5).

$$\mathcal{C}\_{op} = d\_{op} \cdot (\mathcal{c}\_{parts} + \mathcal{c}\_{\text{resesels}} + \mathcal{c}\_{equip}) + \mathcal{C}\_{sparc} \tag{5}$$

Based on the total operation costs calculated for each logistic solution candidate, the optimal operation solution that minimizes total costs can be selected for each operation.

#### *3.7. Operation Calendarization*

For the installation and decommissioning project life-cycle phases, the operation calendarization functionality is responsible for scheduling the previously identified optimal operations on the project calendar, taking into consideration the project start date, operation net duration, expected weather delays in the considered month, as well as predefined operation sequence. For maintenance operations, the operation calendarization functionality schedules corrective maintenance operations in the aftermath of component failure, and preventive maintenance activities following the predefined preventive maintenance frequency. The periodicity of preventive maintenance operations, as well as the device shutdown requirement, which expresses whether device shutdown is assumed when carrying out the operation, are compiled in the maintenance catalog, as presented in Table 12. The preventive maintenance frequency values may be modified by the user to fit project specific requirements.

In case of component failure, or device shutdown requirement during preventive maintenance, resulting downtime per device is stored. The interrelationships between farm components and the ability of each device to produce and deliver its energy to the grid were represented in a hierarchy structure. This automatically generated tree-like structure, described in detail in [47], allows to evaluate the impacts of a given component failure (e.g., array cable) in the farm energy production (i.e., downtime of respective devices).


**Table 12.** Catalog of preventive maintenance operations, featuring operation name, annual periodicity, and device shutdown requirements.

#### **4. Case Study**

In order to demonstrate the functionalities of the Logistics and Marine Operations module, a case study was developed for the installation of a floating wave energy converter, inspired on Sandia's Reference Model 3 (RM3) [66]. Sandia's RM3 device consists of a 260 kW heaving point absorber. The overall design and dimensions are represented in Figure 6.

**Figure 6.** Sandia's RM3 floating wave energy reference model design and dimensions.

In the present case study, a deployment location in Europe was selected with similar characteristics (bathymetry and wave energy resource) to the RM3 reference site (Eureka, in Humboldt County, California). The mean reference site wave energy density is 33.5 kW/m. In Figure 7, the selected project site is depicted, as well as relevant nearby ports (stored in the terminal catalog) that were considered during the algorithm's port selection process. August 1st was specified as the installation starting date due to being the month with best weather conditions.

As a floating device, it was considered that the converter would be transported from port to site by wet towage. Drag-anchors were considered for station keeping. In order to export the generated power to shore, a 3.3 kV export cable with a total length of 6680 m, mostly buried at 0.5 m depth, was considered. The dimensions and characteristics of the subsystems were compiled in Table 13.

Based on the introduced list of components, the LMO module recognized that three operations would be required, in the recommended sequence: (i) installation of the mooring system, (ii) installation of the export cable, and (iii) installation of the device. It is suggested by the algorithm that the mooring system is pre-laid, an increasingly common practice in floating wind projects, followed by the cable installation operation to reduce the risks of cable damage during the mooring installation activities. Finally, the installation of the device consists of wet-towing the converter to site and connecting the pre-laid moorings and umbilical cable.

**Figure 7.** Project site in the North sea, including the farm deployment location (in red), relevant ports (blue circles), and optimal port identified by the port selection algorithm (blue star). Map generated in Python using Cartopy library [67].

**Table 13.** Dimensions and characteristics of the floating wave energy converter and sub-systems.


The results of the LMO module, featuring the selected vessels, durations, and costs for each operation, are shown in Table 14. Leveraging on the port terminal and vessel databases, as the algorithm identified the optimal port-fleet combination in respect on project costs. The *Ports Normands Associés*, in the north coast of France was selected for all three installation operations. An Anchor Handling Tug Support (AHTS) vessel was recommended for the mooring installation, a Cable Laying Vessel (CLV) for the cable installation (including cable burial), and two tugs for the device installation. Despite being the least energetic month, results suggest that the expected waiting on weather in August is not negligible, representing 41% and 27% of the total expected operation duration for the mooring and cable installation operations, respectively. It can be observed that for a single device, the total installation costs amounts to approximately 1.8 M€. Given that the installation of the cable and moorings are the largest contributors to the total project commissioning costs, significant economies of scale can be expected for projects with a higher number of devices, as multiple components would be installed per trip, avoiding unnecessary transits. The obtained installation cost figures showed good agreement with the RM3 installation cost breakdown, presented in Sandia's in-depth study [66]. It was found that differences in the results were mainly caused by the mobilization and demobilization assumptions in Sandia's study, which were not reproduced in LMO. A screenshot of the results page of the LMO module is shown in Figure 8.


**Table 14.** Results of the LMO module for the case study.


**Figure 8.** Screenshot of the Logistics and Marine Operations module: Installation results. Darker bars in the Gantt chart represent the estimated weather delays for each operation.

#### **5. Conclusions**

The present work describes the development of a novel methodology for designing the installation, maintenance, and decommissioning phases of ocean energy projects. Given the sensitivity of given marine operations to weather conditions and its impacts on project costs, a statistical weather window model was developed to estimate potential weather delays. Based on a database of vessels relevant to offshore renewable energy projects, simplified cost functions were produced for each vessel type to estimate the daily charter rates. Employing a systematic approach to infrastructure selection, and leveraging on comprehensive and user-modifiable databases of operations, vessels, port terminals, and equipment, the Logistics and Marine Operations module produces operation plans and optimal infrastructure solutions that satisfy project requirements and minimize total project costs. Tests performed for a case study based on a theoretical floating wave energy converter produced results in good agreement with the detailed study conclusions.

Given its open-source licensing and its community collaborative environment, continuous improvements of the Logistics and Marine Operations module are foreseen. Future research plans include improving functionalities and further demonstrating the developed methodology using data from real ocean energy projects, benchmarking against the outputs of other logistic support tools.

**Author Contributions:** Investigation and Methodology, F.X.C.d.F. and P.C.; software and coding, L.A. and F.X.C.d.F.; writing, F.X.C.d.F., review, F.X.C.d.F., P.C., and L.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work has been partially supported by European Union's Horizon 2020 research and innovation programme under grant agreement No 785921, project DTOceanPlus (Advanced Design Tools for Ocean Energy Systems Innovation, Development and Deployment).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data will be made available in Zenodo, a publicly accessible repository, at the end of the DTOceanPlus H2020 project. The authors may be contacted for further information.

**Acknowledgments:** In addition to EU funding and partner contributions, the present research was partially supported by Global Renewables Shipbrokers (GRS Offshore) by providing valuable insights and expertise in the topics of offshore vessel data modeling, charting cost estimates and vessel fuel consumption. A special thank you goes to Manuel Rentschler for his contribution to the coding of the maintenance algorithm, António Maximiano for his contribution in debugging the weather window tool, and António Sarmento to his review work.

**Conflicts of Interest:** The authors declare no conflicts of interest.

#### **Notes**


#### **References**

