Using RAM in Offshore Wind Project Series - Concept Phase
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Offshore wind farms give access to abundant wind resources over onshore locations – at least 50% higher wind speeds according to studies1. Due to the higher wind speeds and larger available area, offshore wind farms have significant energy potential, allowing offshore turbines to generate more electricity per unit of installed capacity. They have higher capital costs than onshore wind farms, but this is offset to some extent by higher capacity factors. Ultimately, offshore wind farms will allow a much greater deployment of wind in the longer-term. In DNV, over the next five years, we expect to see significant technology development in both floating wind and bottom fixed turbines to reduce costs, scale, and increase applicability.
Offshore wind projects will have 1500 GW of installed capacity by 2050.[2]
DNV, Energy Transition Outlook 2023
To fully take up the potential, offshore wind needs to overcome its major challenges: Cost and Confidence. The industry needs to show that they can get the Levelized Cost of Electricity (LCOE) down as shown in Figure 1. This means getting the capital expenditure (CAPEX), operating expenditure (OPEX), cost of capital down and energy production up. But we also need to give confidence to the stakeholders. That is for the supply chain to give confidence to the developers, the developers to give confidence to the financiers and the regulators.
Figure 1 - World average levelized cost of wind energy.2
Building Confidence in Wind Farm Development
In the realm of project management, the importance of establishing the confidence during the concept stage cannot be overstated, especially when it comes to wind farm development. By scrutinizing factors such as site location, resource availability, regulatory requirements, and economic benefits amongst others, organizations can anticipate challenges and develop strategies to overcome them early on. This approach reduces chances of costly setbacks later and ensures efficient use of resources throughout the project. But how do you know exactly what site, concepts and technology are needed for your facility? If there is a huge financial benefit to it, how do you make sure your decisions are sound? Can you quantify the value and buy confidence in your decision-making process?
Without question, the answer today is yes. Innovations in analysis and modeling have provided the capability to deliver a comprehensive, accurate evaluation of your present risks and underlying expenditures (OPEX and CAPEX) at both equipment and system level — equipping you with all the necessary tools to proceed confidently, especially at the concept selection phase. Essentially, the industry can have confidence with the techno-economic analysis for multiple iterations of concepts and CAPEX analysis by identifying future production and asset risks.
Unexpected Allies to Overcome Challenges
DNV’s Reliability, Availability and Maintainability (RAM) tools Maros and Taro may not be the first tool you think of when trying to perform a techno-economic analysis in the concept stage for a wind farm project. However, the big-picture insights of a RAM study will define what’s possible with different concepts and highlight how changes in site, technology selection, amongst others can impact the overall risks in the investment. RAM analysis is a methodology used to predict asset performance based on reliability and maintainability. This methodology is well established and used in many domains such as the oil and gas and utility industries. For wind power applications, RAM methodology has a remarkably similar approach. Applying RAM analysis to renewable energy is an excellent opportunity which leverages decades of human investment into RAM simulation technologies and applies it to a progressive, sustainable industry. Think about what this means to investors and developers, for example.
Engineering and consulting firms are contracted to assess the viability and potential success of establishing a wind farm in a particular location. During this phase, various high-level concepts may be evaluated and options that determine the feasibility of the development will be defined. Although any RAM models developed in these early stages may lack the level of detail and certainty of later stages, it is during these early phases that RAM analysis, if applied correctly, can generate the greatest value. Analysts can potentially consider these parameters as part of the screening process for different offshore wind concepts:
Wind speed and forecasted power generation,
Different turbine capacity configurations,
Impact of different bathymetry profile on foundation design and failure modes,
Influence of wave height on failure modes, and
Investment on connecting wind farms to the grid.
Eliminate Guesstimates with Reliability Modeling
When performing a RAM study, a Block Flow Diagram (BFD) as shown in Figure 2 defines the connectivity of nodes and focuses on the production aspects of the system e.g. production profiles. Each node within the network will require its own Reliability Block Diagrams (RBDs) shown in Figure 3. These are used to identify the system’s components and their operating mode. Once we know what equipment items to be included in the model, we start looking into collecting reliability data. Typically, these data could be sourced from industry, vendor, or in-house data. Commonly used failure and repair distributions are supported in DNV’s Maros and Taro includes Exponential, Weibull, Normal, Log-Normal, Triangular, Rectangular etc.
Apart from component configuration and reliability, the maintainability and accessibility of offshore wind turbines plays a big part in determining the overall performance of the wind farm. The major difference between reliability and availability is the Operation and Maintenance (O&M) strategy of the system. A system can be very reliable: i.e. its failure frequency is extremely low, but when no maintenance or repair action is taken after a failure its availability becomes very poor. It is important that any planned maintenance activities or periodic events that can immediately affect production are accounted for. Most of the planned activities are for the purpose of preventive maintenance, but some periodic events can be operation or safety related. At this stage, the information about scheduled maintenance is very limited. As a rule, if these events can cause slowdown or shutdown of the production system, they should be included in the RAM analysis.
Figure 4 – Annual generation, annual production deferment and production availability.
RAM modeling applies Monte Carlo simulations of asset lifecycle, basically hundreds of “rolls of the dice” to determine the odds of various combinations of failure and availability patterns that predict the overall availability of your asset. The average (mean) production availability is often the most important performance indicator in a RAM analysis as shown in Figure 4. By ensuring assets spend more time fulfilling their roles, it minimizes costly downtime, resulting in heightened productivity and efficiency. Here’s what you can do with DNV’s RAM tools at this stage:
Risk Management: Identifying potential risks and vulnerabilities early in the concept stage allows project teams to incorporate mitigation strategies into the design and planning phases. By addressing risks upfront, project teams can reduce the likelihood of costly delays and design flaws.
Performance Evaluation: While actual performance data may not yet be available during the concept stage, establishing forecasted production availability metrics provides a baseline for evaluating the effectiveness of different design and operational scenarios. This allows project teams to make informed decisions and prioritize design features that optimize availability.
DNV’s RAM tools are designed to enable users to get insights into why a future scenario looks a certain way by getting insights into the future performance of the system resulting from a change in concept selection.
Finding the balance between performance and value
In addition, different analysis can also be used to explore several variables that will impact directly not only the uptime of the system but also expenditure and cash flows. Lifecycle Cost analysis (LCC) - which is typically used to evaluate the financial performance of different projects - seem to be even more important to wind farms when compared to the oil and gas industry. By integrating in the LCC the stochastic nature of failure using RAM analysis, the total cost of acquisition, ownership and maintenance can be more reliably estimated, even at the highest level. This helps owners to evaluate the performance of the network in operational and economic terms, and to make economically viable decisions at the earliest stages.
A technique commonly used to the compare the financial aspects of different projects is the Net Present Value (NPV). Net present value takes account of cash flows from the project and allows us to compare future projections to their present values by applying a discount factor. After taking this factor into account, projects become directly comparable. Should the value of the capital inflows exceed those of the outflows after the selected discount rate has been applied then the project will provide a positive cash flow, and the greater the value the better. However, if the NPV is negative, as shown in Figure 5 the returns from the project are less than the outflows and attempts should be made to minimize the NPV.
Figure 5 – Opportunity losses represented by negative Net Present Value (NPV).
Wrapping it up…
Performing RAM analysis could add significant value through identifying ways to increase production availability, hence revenues. Lost production would be very costly. Therefore, project budgets would be made available to maximize production availability, if the predicted increase in revenue could be demonstrated to outweigh any additional cost. Since the project is only in the early stages, it is likely the RAM analysis could be used to significantly influence the selected design and operating strategy by supporting critical decision making in the design process (e.g. equipment sparing, better reliability to relief corrective/planned maintenance).
Leveraging RAM and LCC analysis can be further evaluated to conduct comparison of different offshore wind design scenarios in the earliest phase shown in Figure 6.
Figure 6 – Comparing different concept alternatives.
It is important to point out that RAM is not a substitute for analytical models for structures and aerodynamics, arguably other tools are better placed to perform these detailed analyses. Nevertheless, the production risks and performance characteristics associated with the alternative concepts can be addressed using a RAM model.
How DNV can help
Grounded in our expertise in technical and regulatory aspects, DNV supports owners in realizing wind projects in a safe and cost-efficient manner. We have the technical expertise that enabled us to work on 97% of the world’s offshore wind projects. With a deep understanding of the entire offshore wind system, DNV helps stakeholders to mature offshore wind concepts from the idea to implementation. And the great thing is coupled with advisory insights, DNV’s RAM tools Maros and Taro can be applied to a new project or existing equipment to provide faster and more accurate decision-making. With technology that gives you an up-close look at the entire system and models the effects of changing conditions throughout, you can decide what adjustments to make with complete confidence and maximize your return on investment.
