Berkeley Lab Assesses Opportunities to Integrate Marginal Cost Data of the Cambium Model into Electric-Sector Decisions

November 9, 2021

NREL’s freely available Cambium tool generates forward-looking simulations of marginal wholesale electricity costs associated with NREL’s Standard Scenarios. The scenarios include growing shares of variable renewable energy (VRE, i.e. wind and solar), among other power sector assumptions, between 2018 and 2050. The tool’s primary output—hourly costs at more than 130 balancing areas—could serve as public and transparent data source that supports electric-sector decision-making processes across the U.S.

Berkeley Lab conducts a large range of analyses that use historical and forward-looking wholesale electricity prices to inform electric-sector decisions. In a new report, Berkeley Lab uses its expertise to evaluate the Cambium cost data. We compare Cambium data with historical wholesale prices for the year 2018 and other modeled prices for the year 2030. We then present eight case studies in which Berkeley Lab researchers use Cambium data to replicate previous analyses based on other price datasets. We describe where primary findings and underlying key price dynamics align or differ, and highlight possible novel insights from the Cambium data. Finally, we qualitatively evaluate the suitability of Cambium costs in ten additional Berkeley Lab studies, though a direct comparison with alternative price data was not feasible at this time. The goal is to inform how electric-sector decision-makers and DOE program offices may be able to use this cohesive dataset, and to highlight what improvements to Cambium may make it even more useful.

Overall, we find that insights from several previous Berkeley Lab studies that use other price data are reproduced with the Cambium data. While there are examples of significant differences in the magnitude of evaluated grid benefits, the directionality of findings are generally consistent. Cambium costs appear to be very promising for use in ongoing or planned studies at Berkeley Lab or for other DOE sponsored research.

Access to hourly data that reflect changing loads and marginal costs across days and seasons is highly useful for time-sensitive valuation research. Of great value is also the broad geographic scope of the data across the entire continental U.S. while providing granular balancing area-level results. Transparent long-term projections to 2050 with bi-annual resolution allow us to capture value streams across the entire life-cycle of a particular resource, accounting for the overall system evolution. The availability of multiple scenarios spanning a range of technology assumptions is very useful for sensitivity analyses. Inclusions of certain policy constraints such as renewable portfolio standards provide a realistic floor to future VRE expansion. We also value the availability of supplemental data such as technology-specific generation and marginal carbon emissions data.

Further improvement to some aspects of the Cambium data can make it even more useful. For example, Cambium underrepresents price variability, as shown in the figure below that compares empirical and modeled data of diurnal energy price profiles at major trading hubs across seasons for the year 2018.

Diurnal Energy Prices at Major Trading Hubs in 2018, Cambium vs. Historical Empirical Data. Shaded areas describe seasonal range of average price profiles.


Specifically, the model does not allow for negative prices (which commonly occur during times of high wind and solar), and it underrepresents price spikes during scarcity events. Cambium appears to not fully capture reductions in marginal costs in the middle of the day at high solar penetrations or at night with higher wind penetrations. It also does not reflect high prices that can result from market instruments such as ERCOT’s Operating Reserve Demand Curve (ORDC). The impact of underrepresenting price variability is that value of generation flexibility is biased low, compared to the value calculated based on recent empirical wholesale price patterns. This may cause battery storage, or other flexible generation operations, to be undervalued within Cambium. It may also cause estimates of solar and wind integration costs to be biased low. Relatedly the current weather representation underestimates uncommon but increasingly more frequent extreme weather events with important implications for reliability and price spikes. The figure below illustrates the general importance of price variability qualitatively with a number of case studies that are discussed in more detail in the report.

Impact of Price Variability on Consistency of Findings


In a few situations we found the Cambium prices data not suitable for our analyses. As explicitly acknowledged by NREL, the current representation of ancillary service (AS) prices is not in line with empirical market outcomes. A reliance on current Cambium AS price forecasts may impede cost-effectiveness assessments of technologies like PV+storage. We also found it infeasible to forecast power purchase budgets for utilities with the Cambium data as load-serving entities don’t typically rely exclusively on spot-market power purchases. Reporting average costs in addition to marginal costs may be useful for such research.

More generally, our evaluation suggests there are many promising applications of the Cambium data beyond existing Berkeley Lab projects. There is a great wealth in available data and to some extent we have only scratched the surface of possible research applications. The Cambium data seems very suitable for the use by multiple DOE program offices that can leverage this cohesive dataset themselves or in their sponsored research projects.

You can find a full technical report and a briefing slide deck of our assessment here.

A free webinar providing an overview of the Cambium tool and summarizing key findings of this report and will be held on November 30 at 10 AM Pacific/1 PM Eastern. Register for the webinar here:

For questions, please contact Jo Seel ( or Andrew Mills ( at Lawrence Berkeley National Laboratory.

We appreciate the funding support of Strategic Analysis of the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy in making this work possible.


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