A Systematic Decision Support Framework and Prioritization Method for Energy Projects in Industrial Organisations

Ocean energy is a relevant source of clean renewable energy, and as it is still facing challenges related to its above grid-parity costs, tariffs intended to support in a structured and coherent way are of great relevance and potential impact. The logistics and marine operations required for installing and maintaining these systems are major cost drivers of marine renewable energy projects. Planning the logistics of marine energy projects is a highly complex and intertwined process, and to date, limited advances have been made in the development of decision support tools suitable for ocean energy farm design. The present paper describes the methodology of a novel, opensource, logistic and marine operation planning tool, integrated within DTOceanPlus suite of design tools, and responsible for producing logistic solutions comprised of optimal selections of vessels, port terminals, equipment, as well as operation plans, for ocean energy projects. Infrastructure selection logistic functions were developed to select vessels, ports, and equipment for specific projects. A statistical weather window model was developed to estimate operation delays due to weather. A vessel charter rate modeling approach, based on an in-house vessel database and industry experience, is described in detail. The overall operation assumptions and underlying operating principles of the statistical weather window model, maritime infrastructure selection algorithms, and cost modeling strategies are presented. Tests performed for a case study based a theoretical floating wave energy converter produced results in good agreement with reality.

Download Full-text

Decision support framework for the regional facility location and development planning problem

Journal of Business Economics ◽

10.1007/s11573-021-01050-z ◽

2021 ◽

Author(s):

David Kik ◽

Matthias Gerhard Wichmann ◽

Thomas Stefan Spengler

Keyword(s):

Decision Support ◽

Facility Location ◽

Future Orientation ◽

Real Life ◽

Development Planning ◽

Major Influence ◽

Location Decisions ◽

Planning Problem ◽

Location Factor ◽

Decision Support Framework

AbstractLocation choice is a crucial planning task with major influence on a company’s future orientation and competitiveness. It is quite complex, since multiple location factors are usually of decision-relevance, incomparable, and sometimes conflictual. Further, ongoing urbanization is associated with locational dynamics posing major challenges for the regional location management of companies and municipalities. For example, respecting urban space as location factor, a scarcity growing over time leads to different assessment and requirements on a company’s behalf. For both companies and municipalities, there is a need for location development which implies an active change of location factor characteristics. Accordingly, considering locational dynamics is vital, as they may be decisive in the location decision-making. Although certain dynamics are considered within conventional Facility Location Problem (FLP) approaches, a systematic consideration of active location development is missing so far. Consequently, they may propagate long-term unfavorable location decisions, as major potentials associated with company-driven and municipal development measures are neglected. Therefore, this paper introduces a comprehensive decision support framework for the Regional Facility Location and Development planning Problem (RFLDP). It provides an operationalization of development measures, and thus anticipates dynamic adaptations to the environment. An established multi-criteria approach is extended to this new application. A complementary guideline ensures its meaningful applicability by practitioners. Based on a real-life case study, the decision support framework’s strength for practical application is demonstrated. Here, major advantages over conventional FLP approaches are highlighted. It is shown that the proposed methodology results in alternative location decisions which are structurally superior.

Download Full-text