Solar and wind power have become increasingly cost-competitive over the past decade, prompting claims that they are now the cheapest sources of new electricity. Federal and state incentives have accelerated this transformation, leading to a massive expansion in U.S. renewable installations. According to the U.S. Energy Information Administration’s (EIA’s) most recent monthly report, more than 1,100 new wind and solar facilities are planned by 2030. The EIA further projects that wind and solar will account for 80% of all new electric generating capacity through 2035. These projections seem to validate the idea that renewables can economically meet rising electricity demand driven by economic growth, artificial intelligence (AI), and data centers. But this apparent success story may be masking a fundamental flaw in how energy sources are measured and compared.
Not All Megawatts Are Equal
The issue lies in how government data treats a megawatt of solar or wind power as equivalent to a megawatt from natural gas or coal. This comparison is misleading. Unlike traditional power plants that can generate electricity on demand, wind and solar only produce power when conditions are favorable—when the wind blows or the sun shines. This intermittency creates two major challenges that aren’t reflected in official projections.
First, grid operators can’t rely on wind and solar to consistently meet electricity demand during peak times. Second, the true costs of these resources are higher than reported because the grid requires backup power and additional infrastructure to manage variability.
Jonathan A. Lesser, PhD, president of Continental Economics and senior fellow at the National Center for Energy Analytics (NCEA), has argued that the EIA’s reporting methods need reform. “Although the EIA doesn’t make energy policy, the information that it publishes is used to evaluate policy options,” Lesser wrote in a July NCEA Issue Brief. “Unfortunately, the implicit bottom line on electricity adequacy that arises from EIA reports provides a misleading picture and distorts policymaking, even if it is an unintentional result of continuing a legacy data methodology appropriate for conventional power.”
Lesser contends that incomplete data makes wind and solar appear more reliable and cost-effective than they actually are. While these resources may have lower “sticker prices” per unit of energy, their intermittent nature means they deliver less practical value to the grid. This disconnect between reported capabilities and real-world performance could lead to inadequate planning for future electricity needs, especially as demand grows from data centers and AI applications.
“A key point I make is that you cannot simply compare costs; you need to compare value. They are not the same thing,” Lesser told POWER. “In my view, there are no ‘simple’ metrics that accurately convey differences in cost and value. Instead, one would prepare a comprehensive forecast of total costs and output, incorporating uncertainty about O&M [operations and maintenance] costs, lifetime, and annual output. The result would be a probability distribution of costs/MWh.”
In This Issue
Full issueOperating Model Deficiencies
Real-world conditions are already exposing the limitations of current models. A heat dome that enveloped large parts of the U.S. in June 2025 revealed vulnerabilities in the operational and trading strategies of utility-scale solar projects. According to Solargis, a solar data and software provider, aging assets and inaccurate models left U.S. solar projects exposed in merchant markets, where extreme heat drove real-time prices above $2,000/MWh.
“Elevated ambient temperatures in areas led to module temperature derating, reducing actual energy output,” Solargis reported. This effect was especially pronounced in older plants with outdated inverter technology. The company noted that conventional forecasting models often fail to capture the impact of extreme weather variability on solar performance, leaving operators exposed to financial risk.
“We’re working closely with traders and asset operators to improve the forecasting inputs used for day-ahead and real-time market participation,” Giridaran Srinivasan, Solargis’ CEO for the Americas, said in a statement issued to POWER. This includes refining project design evaluations by factoring in temperature anomalies and historical patterns of extreme weather at specific sites, he said.
Solargis’ enhanced models integrate real-world generation data and apply volatility-weighted adjustments to provide more accurate forecasts under uncertain conditions. These insights are increasingly used not just to optimize bids, but also to inform broader asset management strategies, particularly for older or underperforming plants.
The heat dome also raised questions about the future management of aging solar assets. As modules degrade and temperature sensitivities increase, operators must reassess both original design assumptions and real-time operational capabilities. Without accurate visibility into thermal losses and inverter behavior under heat stress, even established assets risk underperformance during high-revenue periods.
“Older assets with narrower MPPT [maximum power point tracking] voltage ranges are particularly vulnerable to output degradation, which is an operational reality that must now be factored into forward-looking forecasts,” Srinivasan noted. These operational challenges underscore Lesser’s broader point: without accurate metrics, planners may overestimate renewable reliability, risking shortfalls as demand surges from AI and data centers.
Fixing the Metrics
“If I had to use LCOE [levelized cost of energy], I would certainly not use EIA’s assumptions that all resources have 30-year lives,” Lesser told POWER. “For wind/solar, I would include the cost of backup needed to firm those resources. That would first require estimating the quantity of battery storage required to compensate fully for multiple cloudy days and multi-day wind droughts, and then adding that cost to the LCOEs of wind and solar.”
Clearly, these are important considerations. As the U.S. accelerates its renewable buildout, accurate comparisons between energy sources are more critical than ever. Policymakers must move beyond simplistic cost metrics and embrace models that reflect the true value and reliability of each resource—before the grid’s reliability is compromised.
—Aaron Larson is POWER’s executive editor.
