A grid modeling expert has developed a novel approach to evaluating the best mix of generating resources to be added to a utility’s grid.
The approach enables comparison of hundreds of plausible resource portfolios, starting with a ranking of each portfolio’s annualized cost, as shown in the featured image above.
Derek Stenclik, founding partner of the consulting firm Telos Energy, said the approach offers many advantages over conventional resource planning, in a paper titled “Rethinking the Role of Capacity Expansion Modeling.”
Telos Energy plans to use the approach in supporting stakeholders as they participate in regulatory proceedings regarding a utility’s proposed integrated resource plan (IRP). Those stakeholders could include regulators, consumer advocates, and environmental groups.
Stenclik said “our vision is to work collaboratively with utilities during IRP proceedings” to implement the new approach and make the results available to all stakeholders.
The approach
Planning for how to reliably meet future energy demand is required in some states, and is also conducted by grid operators known as independent system operators.
Capacity expansion modeling has long been the first step in such planning. Initial capacity expansion modeling yields a small number of resource portfolios and is followed by portfolio testing to ensure that those portfolios are “reliable and operable,” Stenclik said.
Stenclik pointed to several drawbacks of capacity expansion modeling, saying that it produces “a narrow selection of portfolios” guiding decisions, and has limited transparency to non-utility stakeholders, such as regulators, customers, environmental advocates, and project developers.
Stenclik said his alternative method “skips the capacity expansion model completely” and instead uses a production cost model to evaluate hundreds of unique portfolios.
Demonstration grid
In his paper, Stenclik illustrated the approach with the example of a hypothetical power system with a peak load of 1000 MW.
To develop hundreds of unique portfolios that could potentially serve such a grid, Stenclik used a mathematical technique known as Monte Carlo sampling.
Then a production cost model (PCM) was initiated with a starting set of assumptions on operating costs and used to evaluate each portfolio in 8760-hour simulations across multiple weather years.
Any PCM could accomplish this task, Stenclik said, such as PLEXOS, Aurora, PROMOD, Encompass, SERVM, or open-source alternatives.
Under Stenclik’s approach, the total costs for each portfolio “are then calculated exogenously” based on the PCM dispatch results, “allowing a user to post-process operating costs with new assumptions.”
For an actual IRP prepared by a utility, “there’s typically a pathway to get the information needed for this type of analysis,” Stenclik said.
To visualize the resulting data, Stenclik used the CSV outputs from the production cost modeling as inputs to a program written in the Python language.
The Python program generates several bar graphs, starting with one that ranks the portfolios by annualized cost, as shown in the featured image above. For the 30 left-most bars of that graph, the program can also generate an image that illustrates the cost components, as shown nearby.
Those same 30 portfolios are shown in the same order in the second nearby image, which shows the mix of new resources added for each portfolio.
Stenclik said the user has the ability to sort, filter, and exclude portfolios based on their own preferences and objectives.
The method also enables rapid sensitivity analysis based on different cost assumptions, Stenclik said, “allowing users to make adjustments to key assumptions and immediately see the impact on the results.”
Because the 8760-hour simulations used by the production cost model directly incorporate challenging load periods and resource unavailability, Stenclik said the approach does not require planning reserve margins or effective load carrying capability values to determine the amount of resources to add for reliability. Rather, the approach identifies portfolios that show some level of unserved load.
User experience
Stenclik’s paper provides in its conclusion a link to the sample grid planning analysis that includes a video demonstration and a Python interface, enabling a user to learn more about the tool while highlighting “the important benefits of an efficient and collaborative stakeholder process.”
Telos Energy published the paper jointly with the nonprofit consultancy GridLab, an organization Stenclik credited with helping facilitate collaboration between stakeholders and grid experts.
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