A Tale of Three Wind Parks: why digital tools matter in offshore wind operations

As offshore wind portfolios scale, they often inherit a mix of disparate monitoring and control solutions across different asset types. This rapid and often uncoordinated expansion, leads to three major issues:

1. Growing fleets: Expansion often prioritizes CAPEX savings over long-term efficiency, leading to fragmented monitoring setups sourced from different technologies.
   
   For example:
   • Geared or direct drive wind turbine generators as well as various substation components OEMs (e.g. from Europe or China), all which bring their own SCADA systems.
   • Pilot or innovation projects add more application types to installed fleets (e.g. co-locating with BESS or hydrogen production).
2. Increasing complexity: What started as a series of pioneering projects in a single country quickly turned into a "fleet jungle" where inconsistent systems and processes have created operational silos between technologies, parks, and partly even within an IPP’s O&M teams.
3. Compounding inefficiencies: Disjointed controls and incompatible systems lead to operational risks and rising O&M costs, including conflicting commands between platforms.

To future-proof operations, offshore wind players need a more integrated approach to fleet-wide monitoring and control.

The digital shift in O&M

While many offshore wind operators start with basic O&M strategies, digital transformation is key to long-term revenue optimization.

A shift from reactive maintenance to predictive and preventive O&M enables:

• Maximized asset lifespan through advanced analytics and automation
• Reduced downtime and maintenance costs with real-time diagnostics
• Increased revenue stability by proactively mitigating operational risks

As offshore wind matures, digitally enabled O&M will be the cornerstone of efficiency, sustainability, and profitability.

To illustrate how assets age, as well as how maintenance strategy, and service models impact cost and risk, let’s imagine an IPP operating in a single country (for example the UK).Within this context, I will use three hypothetical wind parks to illustrate offshore wind operations’ evolving challenges and cost structures..

Now that we’ve looked at the realities across three offshore wind scenarios, let’s break down why going digital matters, when to make the move, and how to put predictive asset health strategies in place.

In each case, recruiting talent is very hard and any cases of non-insured accidents cost $700 US per day. Therefore, for the sake of simplifying our example, we assume a total of 20 accident-related sick leave days per year for the country’s entire portfolio, amounting to approximately $14,000 per year.

The Why?

Some risks are too severe to quantify and too damaging to ignore. Catastrophic failures, such as staff injuries during turbine repairs caused by unexpected component temperatures or transformer oil leaks contaminating seawater, pose not just financial threats but reputational ones. For an IPP, these potential threats represent a worst-case scenario.

1. Costs of unplanned down-time

The annual cost of unplanned maintenance in OFS wind can be estimated at $100,000 per wind turbine generator (WTG) and per year for park 1, $40,000 for preventively-assessed park two and $10,000 for park three, including yield losses, spare part premium prices for express replacement and work hours for planning and executing unplanned service. So, for park one = 40 WTGs x $100,000 = $4m per year.

Park two = 100 WTGs x $40,000= $4m per year.

Currently park three doesn’t have unplanned downtime and, if so, would be financially covered by the WTG OEM.

2. Costs of preventive maintenance checking tours

Assuming each offshore wind maintenance cruise costs $10,000 per day (including equipment and fuel), and Park Two requires 20 such tours annually, the total preventive maintenance cost is approximately $2 million per year.

Next, the transactional and legal costs of non-acting WTG OEMs during full-service contracts with yield warranties. Even with advanced software, OEMs may not act promptly under full-service contracts with yield warranties. For example, a Danish operator had to run its own predictive analytics because OEM response was slow.

This applies for park three above = 200 WTGs x $10,000 = $2m per year.

Total cost per year for unplanned downtime and preventive vessel tours and disputes = $4m + $2m + $2m = $8m per year, this is the net optimizable amount per year of predictive asset health software minimizing above variables (but not the cost of minimally required O&M in wind, which is a multiple of above, but not affected by asset health software).

The When?

The ideal time to prepare the implementation of predictive asset health software is during the Front-End Engineering Design (FEED) stage, during project planning, even before the Final Investment Decision (FID).

• Optimization of the overall O&M concept before historical facts have grown in-house software complexity and internal arguments around if, why and from whom asset management software should be bought.
• Typically, predictive asset health software is procured as a package with SCADAs for set-up synergies and cost savings. If done correctly, this costs a fraction of procurement in multiple steps.
• At the latest, predictive asset health software should be procured after the WTG OEM pulls-back from full-service package or whenever a yield-based availability guarantee is no longer offered.

At the very latest, we see clients procure predictive asset health software after the first unexpected failures.

The How?

Ideally, a small concept study aligning prognostic asset health software architecture and feature requirements with other planned main software (e.g. SCADA, weather forecasts etc.) should be done before software procurement. The cost can be estimated at $50,000 per park above, i.e. for the above fleet example 50 x 4 = $200,000 one-time cost.

• A proof of concept for sample assets over six months of operation period ensures value-add, before predictive asset health software implementation at scale. Direct (software provider) and indirect (data correction, cleansing during 1-2 workshops) costs of $150,000 per park should be estimated, one-time, i.e. for above fleet example 4 x $150,000 = $600,000.
• Software deployment at scale, ensuring synergies with all installed temperature, acoustic, weather sensors, can be estimated at $300,000 per-park on average (less for small, more for large parks) one-time, i.e. for the above fleet example four x $300,000 = $1.2m.
• Software license fee for evergreen SaaS = 30% of upfront implementation = $400,000 per year.

In any case, we recommend insisting on software monitoring for both WTGs and substations.  The safety and environmental reputational costs of a burning switch or a transformer leaking oil are unquantifiably high in today’s labor and financial market.

Before we detail the numbers, here’s the essence:

Digitalizing offshore wind O&M isn’t just a technical upgrade—it’s a financial game-changer. By reducing downtime and optimizing maintenance, predictive asset health software turns operational risk into measurable savings. The following calculation shows how this translates into real value over a decade.

• Financial cost of a ten-year contract for all three parks = $600,000+ $1.2m + 10 x $400,000 = $5.8m over ten years, starting with a fractional payment only during proof of concept (PoC).
• Financial savings assuming unplanned downtime, preventive tours and settlements can be reduced by 30% through SW = 0.3 x $8m per year= $2.4m savings per year.

Resulting Return on investment is 2.42 years.

Financial net benefit over 10 years = Total benefit of 10 x $2.4m = $24m minus total cost of $5.8m= total benefit of $18.2m from three parks over a ten-year period.

A holistic, future-ready approach to renewables monitoring and control is key to sustainable growth. Proven top-level SCADA solutions enable real-time monitoring and control of wind farms, helping to analyze asset performance across OEMs and technologies, thus optimizing power output based on grid requirements and interfaces with energy markets. Hitachi Energy’s Network Manager GMS has been proven in 20+ GW of renewables assets to date and combined with advanced analytics, these tools lay the foundation for predictive, data-driven asset management.

Hitachi Energy’s Asset Performance Management enhances asset availability and reliability with advanced analytics. It includes an Asset Health module alongside Reliability and Optimization modules to improve maintenance strategies and business processes. We’ve written more about the benefits of insourcing O&M enabled by Asset Performance Management in wind here: Unleash your growth potential by insourcing wind O&M.

With our software suite, asset owners gain data-driven insights and decision support at the asset, system, and portfolio levels – ensuring smarter, more efficient operations.