**1. Introduction**

The decarbonization of the energy sector is urgent, requiring global action to achieve our long-term climate goals and to mitigate the impacts of climate change [1]. To meet our ambitious emission cuts, innovation in low-carbon technologies and mass deployment of renewable energy generation will be fundamental [2,3].

As a renewable energy resource, ocean energy is clean, abundant, and powerful. Wave and tidal energy are attractive sources of renewable energy, as they have low variability when compared to wind, can be accurately forecast, and are fit to respond to the electricity demand during night-time [4]. Additionally, the production profile of wave and tidal energy systems is complementary to wind and solar, smoothing the otherwise peaking nature of renewables in the production mix [5]. It is estimated that about 100 GW of wave and tidal energy capacity can be deployed in Europe by 2050 [6], creating significant carbon emission reductions as well as economic growth opportunities. Europe's seas and oceans could therefore play a fundamental role in the decarbonization of the energy sector, contributing to the transition from a power system based on imported fossil fuels, to a flexible and interconnected system based on clean, renewable, and infinite domestic resources [7]. However, the ocean energy sector is still facing challenges related to performance, reliability, and survivability, which ultimately translates into above grid-parity costs.

Logistics and marine operations are major cost drivers of marine renewable energy projects. Even though researchers have made significant progress over the last years

**Citation:** Correia da Fonseca, F.X.; Amaral, L; Chainho, P. A Decision Support Tool for Long-Term Planning of Marine Operations in Ocean Energy Projects. *J. Mar. Sci. Eng.* **2021**, *9*, 810. https://doi.org/10.3390/ jmse9080810

Academic Editors: Eugen Rusu, Kostas Belibassakis and George Lavidas

Received: 9 June 2021 Accepted: 22 July 2021 Published: 27 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

in what concerns the installation and operation and maintenance (O&M) planning of offshore wind farms [8–11], advances for ocean energy farms have been more modest, attesting for the lower maturity of the sector [12]. For offshore wind, the installation costs typically represent one fifth to one third of the project's CapEX [13–15], while O&M activities represent about one-fourth to one-half of the total lifetime costs of the project [16]. However, for less mature sectors such as wave and tidal energy, slightly larger percentages may be expected [17]. These costs are typically amplified when deploying projects in further offshore waters, as the marine operations related to the construction, installation, maintenance, and decommissioning of such farms become increasingly challenging. Even though deploying farms further offshore is expected to improve resource availability and consequently increase the expected power output of the farm (while also minimizing competition for space and visual disturbance [18]), more severe weather climates and larger distances to shore translate into lower farm accessibility, higher risks of work delays, and ultimately larger project costs. As a significant fraction of the marine operation costs can be attributed to vessel charter (according to Dalgic et al. [19], approximately 73% of the total O&M costs are related to vessel hiring [19]), even modest reductions in operation duration may result in significant cost-reductions [20].

Planning the logistics of offshore renewable projects is a highly intertwined process, with multiple conflicting objectives and alternatives, and a large optimization potential. Given the complexity associated with planning such logistics, computational tools have been developed to support decision-making at different project stages. Computational tools can be distinguished according to (i) decision-making time-scale (long-term strategic planning based on historical weather data, or short-term daily operational planning based on weather forecasts), (ii) project phase (installation, O&M, and decommissioning), (iii) target sector (offshore wind, ocean energy, or both), (iv) licensing type (open-source, private, or commercial), and (v) software functionalities (e.g., weather window modeling, operation planning, infrastructure selection, failure/degradation modeling, revenue modeling, and techno-economic assessments).

Table 1 shows a list of the main logistic support tools developed to date with the goal of supporting offshore projects. It can be seen that most development efforts have focused on producing O&M simulation tools to estimate the OPEX of offshore wind projects. Some of these tools were developed to simulate the degradation of farm components and the occurrence of failures, replicating real-world decisions in respect to the scheduling of preventive and corrective maintenance activities [21]. This is the case of ECN O&M Tool, the ECN O&M Calculator (formerly OMCE) and the O2M model of DNV-GL [22]. As most operations carried out at sea are significantly weather dependent, computational tools generally include weather window models to estimate the potential waiting on weather contingencies. Some commercial and sector-agnostic tools, such as Mermaid [23] and ForeCoast Marine [24] (marked as "Agnostic" in Table 1), have focused almost exclusively on this type of service, quantifying weather risks for different operation types and target sectors. Another similar commercial product worth mentioning is StormGeo [25], which also provides short-term decision support based on near-future weather forecasts.

 *Mar. Sci. Eng.* **2021**, *9*, 810

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However, it is possible to observe that very limited advances have been made in the development of logistic support tools suitable for ocean energy farm design. Existing computational tools either focus exclusively on the O&M phase (e.g., WES O&M Tool [38]) or are limited in functionality. A reduced number of tools has been developed to address vessel selection in offshore wind projects (e.g., NOWICOB [30] and StrathOW-OM [35]). Still, these tools do not consider the selection of ports and equipment (nor their impacts on optimal vessel selection), and most importantly, are not easily adaptable to ocean energy projects [39]. Moreover, most existent tools are notably user input-intensive, and thus unsuitable for project and technology developers at early development stages where uncertainties and unknowns are large. Finally, despite the growing number of open-source initiatives, which have been found to contribute significantly to sector innovations and to the European Union's economy [40,41], most computational tools were developed under private or commercial licenses with limited published research. As such, these tools miss out on the key benefits of open-source projects, related to higher transparency, robustness and scrutiny, as well as continuous improvements through community collaborations.

At a project development phase, integrating preliminary plans for the installation, maintenance, and decommissioning in the design process has the potential to reveal unexpected impacts of certain component design decisions on logistic costs. This is a particularly important step for ocean energy projects as less mature technologies have higher cost-optimization potential, frequently achievable with simple concept adjustments. In order to address the identified research gaps and market needs, the Logistics and Marine Operations (LMO) module was developed and integrated within DTOceanPlus software, an open-source suite of design tools for ocean energy projects [42]. The LMO module is responsible for designing and planning the project life-cycle phases (i.e., installation, maintenance, and decommissioning) of ocean energy projects. Reflecting the most recent experiences and best practices of the offshore wind sector, the LMO module produces logistic solutions comprised of optimal selections of vessels, port terminals, equipment, as well as operation plans, for ocean energy projects. An innovative methodology to optimize the selection of vessels, port terminals, and equipment was developed. A novel vessel cost modeling methodology was implemented in order to take into consideration the impacts of vessel capabilities on charter price, and reveal cost reduction pathways. Comprehensive, purpose-built databases of offshore operations, vessels, ports, and equipment were generated to support the main functionalities of the tool, even when unknowns are large and data availability is limited. These databases will be made freely available upon the final release of the DTOceanPlus software. Leveraging on its main functionalities, the Logistics and Marine Operations module proposes optimal logistic solutions that minimize total project costs, guiding project design and strategic investment decisions in ocean energy projects at different stages of technological and project maturity.

The present paper describes in detail the novel Logistics and Marine Operations tool, one of the seven design modules of the DTOceanPlus software. In Section 2, the DTOcean-Plus software is briefly presented. The underlying operating principle, main functionalities, and methodology of the Logistic and Marine Operations tool are described in detail in Section 3. A brief test case showcasing the functionalities of the DTOceanPlus Logistics module is described in Section 4. The most important outcomes of the work are summarized in Section 5.

#### **2. DTOceanPlus Suite of Design Tools**

DTOceanPlus is an open source, integrated suite of design tools, developed to support the selection, development, deployment, and assessment of ocean energy systems at different stages and levels of aggregation (component, sub-system, and array). Building on the results from the original DTOcean project [43–45], at the time of writing, DTOceanplus software is being developed within a 3-years long EU-funded H2020 project with the same name [42], aimed at accelerating the commercialization of the ocean energy sector.

As illustrated in Figure 1, DTOceanPlus was developed in a modular fashion, with a set of independent but integrated tools:

	- **–** *Site Characterization (SC)*, to characterize the deployment site in respect to environmental (e.g., met-ocean) and geotechnical conditions;
	- **–** *Machine Characterization (MC)*, to characterize the device's prime mover;
	- **–** *Energy Capture (EC)*, to design and optimize the device hydrodynamics at an array level;
	- **–** *Energy Transformation (ET)*, to design the Power Take-off (PTO) unit and control strategies;
	- **–** *Energy Delivery (ED)*, to design the electrical power transmission system of the farm;
	- **–** *Station Keeping (SK)*, to produce foundations and mooring designs;
	- **–** *Logistics and Marine Operations (LMO)*, to design logistical solutions related to the installation, operation, maintenance, and dismantling operations.
	- **–** *System Performance and Energy Yield (SPEY)*, to assess projects in respect to their energy performance;
	- **–** *System Lifetime Costs (SLC)*, to assess projects from the economic and financial investment perspectives;
	- **–** *System Reliability, Availability, Maintainability, Survivability (RAMS)*, to evaluate different reliability aspects of ocean energy projects;
	- **–** *Environmental and Social Acceptance (ESA)*, to evaluate ocean energy projects in respect to their environmental and social impacts.

**Figure 1.** Representation of DTOceanPlus tools.
