PARTNER CONTENT

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Insights in asset management

You’ ve worked with a wide range of project owners across all phases of the asset lifecycle. As portfolios grow, what are the most important lessons you’ ve learned about setting projects up for long-term success? Success at scale doesn’ t come from doing more- it comes from doing the right things earlier. As portfolios grow, so do the number of systems, contracts, stakeholders, and risks that need to be managed. We’ ve seen time and again that the owners who succeed long-term are the ones who take operations seriously from the beginning. That means realistic models( financial and technical), disciplined OPEX planning, centralized data systems, and a business assessment of internal priorities to determine what to insource vs outsource. It is critical to embed operational readiness into your early development process, not bolt it on later. Whether you’ re just stepping into long-term ownership or managing a multi-gigawatt fleet, the goal is the same- build a foundation that can actually scale.

What patterns have you seen separating the most successful project owners from those who struggle, especially as portfolios scale? Two of the biggest differentiators are realistic operating budgets and accurate performance models. We see organizations struggle because they don’ t budget adequately for asset management or allocate sufficient funds for O & M. Properly structured equipment warranties and guarantees can help, but do not cover everything. We are also well past the day when falling replacement costs could overcome a hardening of insurance markets. The most successful project owners are those who’ ve thought carefully about what it takes to operate their assets over the useful life. They understand the importance of proactive, centralized data management and build operational discipline during development. This theme of early-stage discipline ties directly to project success.

Common mistakes include reactive asset management, siloed systems, and a lack of data validation. When it comes to performance models, we see a significant portion of underperformance stemming from poor modeling. Prudent modeling includes subhourly calculations, multiple years of groundsource meteorological data, plus statistical review of the correlated history( rather than pure reliance on typical modeling year). For many projects, stress testing curtailment

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