The team started from a well-known reference design, the Volturn-US-S platform developed at the University of Maine, paired with the International Energy Agency’s 15 megawatt reference wind turbine. That turbine is a monster by any measure, with a rotor diameter of 240 metres and a hub height of 150 metres, and it rests on a steel platform built around a central column connected to three outer columns through pontoons and upper supports. The researchers defined six continuous design variables, including the main platform diameter, the diameters of the central and outer columns, and the height, width and thickness of the pontoons, and then let an optimization algorithm search that design space for the lightest structure that could still survive.

The search itself was conducted with a genetic algorithm, a computational technique that mimics biological evolution. A population of candidate platform designs, each described by its six geometric parameters, is evaluated for fitness, and the fittest individuals are combined through simulated binary crossover and polynomial mutation to produce offspring designs. Infeasible solutions that violate physical constraints are handled with a penalty scheme that lets the algorithm compare them purely on how badly they fail. Because the algorithm is inherently random, the team ran the entire optimization ten separate times, each starting from a population of one hundred individuals and running for up to one thousand generations, to gather statistics and confirm the results were not a fluke of a single lucky run.

Evaluating each candidate design required a physics model, and for that the researchers turned to RAFT, an open-source frequency-domain code that models the full floating wind turbine system as a rigid body with six degrees of freedom: surge, sway, heave, roll, pitch and yaw. Rather than simulating every turbulent gust and extreme wave in the time domain, which would be computationally prohibitive across thousands of design iterations, RAFT linearizes the dynamic equations around a mean steady state, computing how the platform responds to each excitation frequency under steady winds and stochastic sea states described by a JONSWAP wave spectrum. Aerodynamic loads on the rotor come from a steady-state blade-element momentum solver, while mooring forces are handled with a quasi-static approach. The code has been validated against higher-fidelity tools such as OpenFAST and showed good agreement, making it an ideal engine for rapid design iteration.

Crucially, the optimization was not allowed to produce a flimsy platform that would capsize or batter its turbine. The team imposed hard constraints on maximum platform offset, platform pitch, and nacelle acceleration at the tower top, along with limits on the fore-aft bending moment at the tower base and the static stress at the critical junction where the central column meets the pontoons. These quantities were computed for a set of representative load cases, including the turbine’s rated wind speed and harsh sea states typical of European waters, with the worst result across all cases checked against each limit. A static stress analysis using a free-body approach estimated bending stresses at the pontoon base from structure weight and buoyancy loads. The outcome was striking: primary steel mass fell from 3916 tonnes to 3273 tonnes, a reduction of roughly 16 percent, while every dynamic and structural constraint remained satisfied. The optimized design features slightly larger outer columns and pontoons but a smaller overall platform diameter.

With a leaner platform in hand, the researchers confronted a problem that engineering alone cannot solve: the price of steel. Commodity markets have been violently volatile in recent years, particularly after the global pandemic, and steel prices feed directly into the capital expenditure of any floating wind project, since primary steel accounts for around 83 percent of total platform cost. To bring order to this uncertainty, the team built a hybrid time series forecasting tool that blends two complementary models. The first is XGBoost, a gradient-boosted decision tree algorithm that excels at capturing short-term stochastic fluctuations in market data. The second is SARIMAX, a seasonal autoregressive statistical model that tracks longer-term trends and seasonal cycles while incorporating exogenous variables, external economic indicators such as rebar and copper prices, coal costs, and London Metal Exchange indices that act as leading indicators for steel.

The selection of those supporting variables was itself a rigorous filtering exercise. Candidate exogenous datasets were compared against the target hot-rolled coil steel prices using correlation metrics, monthly lag testing, and rolling stability analysis, with multiple transformations including log-differences applied to the data. Only indicators that passed thresholds for predictive strength, stability, sign consistency and non-redundancy made it into the final model. When tested against hindcast data covering the turbulent market period from 2022 to 2024, the hybrid approach clearly outperformed either model alone. Without supporting data, forecasts failed to revert to the mean after the 2020 price spike; with them, the hybrid model achieved a mean absolute error of 187.5, kept its average bias close to zero, and produced the most consistent performance across root mean square error and symmetric mean absolute percentage error metrics. Interestingly, the statistical SARIMAX model actually performed best in the earliest forecast months, while the machine learning model showed a tendency to overpredict and SARIMAX alone to underpredict, suggesting the two errors partially cancel in the blend.

The forecasting results were then translated into money. Monthly steel price predictions were aggregated into yearly values, converted to marine-grade steel prices, and multiplied by the platform mass to yield capital costs, with other components such as secondary steel, ballast and controls costed from published industry references. For a one gigawatt floating wind farm using a net capacity factor of 0.4925, the team calculated the levelized cost of energy over a thirty-year project lifetime with an eight percent discount rate. The numbers that emerged carry a sobering message for the industry: uncertainty in steel prices alone swings total capital expenditure by roughly 150 to 200 million euros at the gigawatt scale, a variation of 40 to 50 percent across the forecast scenarios. The XGBoost forecast, which sees prices rebounding 10 to 15 percent, represents a market recovery upper bound, while the SARIMAX forecast with its continued downward drift forms the lower bound, and the hybrid model provides a balanced baseline.

Against that backdrop of market turbulence, the engineering gains look meaningful but partial. The optimized platform achieved cost savings of approximately 10 to 15 percent across all scenarios, and structural optimization reduced the levelized cost of energy by around one euro per megawatt-hour or more in high-price scenarios. The overall LCoE variation across the steel price forecasts was more moderate, at roughly 3 to 5 euros per megawatt-hour, because the long project lifetime and high discount rate dampen the effect of upfront capital swings. The authors are careful to note the limits of their structural analysis, which uses a relatively simple static stress check rather than a full dynamic finite-element assessment, and they deliberately selected the least successful of their ten optimization runs to build in a safety margin. Still, the framework demonstrates that design improvements are possible for reference platforms while respecting real load constraints, and that hybrid forecasting can give developers and investors a defensible range of future costs.

What makes this study notable is its refusal to treat engineering and economics as separate silos. Floating offshore wind grew rapidly from its first full-scale prototype in 2009 to 88 megawatts of installed capacity by 2023, but progress has since stagnated as costs have risen in a difficult macroeconomic climate. To compete in renewable energy auctions, floating wind must cut costs across dimensions, materials and site-specific design, and the floating substructure has been identified as one of the highest-potential areas for savings. By showing that a genetic algorithm can carve 643 tonnes of steel out of a commercial-scale platform, and that machine learning can put credible bounds on the commodity price risk that dominates the remaining bill, the research offers the industry a template for making both the hardware and the financial case for floating wind more robust at the same time. The forecasting code, the genetic algorithm and the RAFT model are all openly available, inviting other teams to push the approach further.

Subject of Research: Structural and economic optimization of a 15 MW floating offshore wind platform using genetic algorithms and time series forecasting of steel prices

Article Title: Economic and design optimization of a 15 MW floating offshore wind platform using time series forecasting

Article References: White, C., Benifla, V., Cândido, J., & Gato, L. M. C. (2026). Economic and design optimization of a 15 MW floating offshore wind platform using time series forecasting. Wind Energy Science, 11(9), 3703-3717. https://doi.org/10.5194/wes-11-3703-2026

Image Credits: AI Generated

DOI: 10.5194/wes-11-3703-2026

Keywords: floating offshore wind, semi-submersible platform, genetic algorithm, time series forecasting, steel price volatility, levelized cost of energy, 15 MW wind turbine, structural optimization, XGBoost, SARIMAX, CAPEX, renewable energy economics

At the heart of the study lies a comprehensive methodological approach that blends quantitative cost modeling with detailed qualitative analysis. The researchers meticulously examined how specific technological advancements—from materials science to manufacturing processes, to legal and permitting innovations—have collectively driven down the costs of solar PV modules and their supporting balance-of-system (BOS) components. This hybrid analytical framework enabled them to penetrate layers of complexity where pure numerical data alone fall short, revealing nuanced pathways of innovation that have shaped the solar industry’s evolution.

One striking insight from the research is the recognition that pivotal innovations often emerged from domains seemingly unrelated to solar energy. For example, semiconductor fabrication processes, originally developed for microelectronics, directly influenced the precision and efficiency of silicon wafer production critical to PV cells. Metallurgical advances refined material purity and durability, while improvements in glass manufacturing enhanced the transmissivity and longevity of solar modules. Even sectors such as oil and gas drilling contributed by pioneering sophisticated construction techniques later adapted to PV system deployment.

The researchers identified a total of 81 unique technological and procedural innovations dating back to 1970 that cumulatively lowered PV system costs. These ranged from specialized developments like antireflective coated glass—which increases sunlight absorption by reducing surface reflection—to the digitization of permitting processes that expedite project approvals. By segmenting innovations between the physically mass-produced solar modules and the more locally tailored BOS elements—comprising mounting hardware, inverters, wiring, and related infrastructure—the study offers a granular view of how hardware and “soft” innovations have influenced cost trajectories.

In dissecting BOS costs, the study highlights that unlike PV modules, these costs significantly depend on regulatory, procedural, and software-driven factors. Delays on construction sites and inefficient permitting protocols impose financial burdens, which automated software tools for permit review and fast-tracking approvals are beginning to mitigate. Although the economic impacts of such software innovations have yet to be fully quantified, the research framework laid out in this study positions itself to analyze future developments that streamline deployment operations and reduce soft cost barriers.

The cross-pollination of knowledge across diverse industries is another key theme emphasized by the study. While the semiconductor, electronics, metallurgy, and petroleum sectors contributed directly to material and hardware improvements, BOS innovations drew heavily from fields like software engineering and utility management. Additionally, the policy and administrative realm played a significant role, with many BOS advancements originating from local governments, state agencies, and professional associations rather than traditional R&D institutions alone. This dynamic ecosystem of invention underscores how complementary sectors together accelerate technological progress within renewable energy.

One landmark example quantified through the research’s modeling effort is the introduction of wire sawing in the 1980s. This fabrication technique considerably enhanced wafer slicing precision, reducing silicon waste and raising throughput. The researchers estimate that wire sawing alone accounted for a system-wide cost reduction of approximately five dollars per watt, illustrating how targeted advances in manufacturing technology translate directly to economic gains in solar power generation.

Beyond reflecting on historical progress, the study offers forward-looking perspectives on the potential of emerging technologies to further drive down solar costs. Increased computational power enables new advancements such as automated engineering review systems and remote site assessment tools that could dramatically streamline BOS operations. Moreover, the team anticipates that automation, robotics, and AI-driven digital innovations will facilitate quality improvements and cost reductions, heralding a new wave of efficiency gains that build upon the fertile ground established in previous decades.

Importantly, the researchers underscore the value of their combined qualitative-quantitative methodology as a strategic tool. By understanding which innovations have yielded the most significant cost declines and identifying the industries that contributed key technologies, decision-makers in both the private and public sectors can make better-informed R&D investments and tailor policies that catalyze further breakthroughs. The approach demystifies the complex ecosystem of technological advancement, transforming what once seemed like an opaque “black box” into a transparent, analyzable phenomenon.

As solar energy continues its ascent as a cornerstone of global decarbonization efforts, insights from this study offer critical guidance on how to accelerate innovation cycles and reduce costs even further. By extending this analytical framework to other clean energy technologies, from wind turbines to advanced batteries, the researchers aim to uncover similarly rich networks of cross-sector innovation that underpin future sustainable infrastructure solutions. Equally, continued exploration into soft technology—encompassing digital tools, regulatory reforms, and deployment logistics—promises a fertile avenue for cost reduction outside of traditional hardware improvements.

The MIT study not only chronicles the remarkable journey that slashed solar PV costs by over 99 percent but also reveals the collaborative interplay of scientific discovery, engineering development, and policy innovation. The depth and breadth of knowledge exchange spanning multiple industries have been vital drivers, demonstrating that transformative energy technologies often thrive not in isolated silos but within vibrant networks of interdisciplinary progress. These lessons offer hope and practical direction for overcoming the remaining barriers to clean energy adoption worldwide, helping chart a path towards a more sustainable and affordable energy future.

Subject of Research: Technological innovations driving cost reductions in solar photovoltaic systems
Article Title: Not provided
News Publication Date: Not explicitly stated (article references “today,” suggesting the original release date is recent)
Web References: http://dx.doi.org/10.1371/journal.pone.0320676
References: PLOS One journal article
Image Credits: Not specified

Keywords

Renewable energy, Climate change, Energy, Batteries