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The Model

The Model

To determine whether it is financially and technically feasible for the City of Boulder to form a municipal electric utility, the city and consultants developed detailed models related to Boulder's electricity needs and the management of the local electric grid projected over 20 years.

Working groups comprised of community volunteers and industry experts reviewed massive amounts of information to verify the assumptions and inputs that went into these models.

Because resource costs are generally the highest single ongoing cost for utilities, the modeling illustrated resource packages for which the local electric utility might contract.

Risk and opportunity analysis was incorporated throughout. The models were stress-tested with additional risks to identify issues that could impact the city's ability to meet the tests required under the City Charter. More in-depth analysis examined the likelihood of each of risks and explored the actions a utility could take to mitigate them.

The models were vetted by a third-party reviewer to verify whether the materials prepared by staff and presented to council demonstrated that the city could form an electric power and light utility while meeting the conditions set by voters.

View a detailed flow chart with all the inputs included in the modeling.

The Five Stages in the Modeling Process

1. Understanding how Boulder uses energy, now and in the future

Staff and consultants projected how much electricity and capacity the city utility would need to purchase to meet customers' needs, looking at both current levels of investment in energy efficiency and local renewable energy, and if that investment were more than doubled.

2. Developing the Options to Model

To illustrate how the city utility might look under different conditions, five options were modeled that compared low- and high-cost resource mixes with different amounts of renewable energy. A sixth option looked at Xcel Energy's anticipated resource mix and costs, as reported by Xcel to the Colorado Public Utilities Commission. Local energy resources will be explored in subsequent phases, if council decides to move forward with the Municipalization exploration study (read the 2011 Localization Report for more information).

3. Identifying the Best Resource Options

HOMER Energy software used forecasted energy supply needs for the local electric utility, as well as estimated prices for different resources. It selected resource mixes based on factors like cost or carbon emissions.

4. Determining Overall Costs

A Microsoft Excel-based financial model received the load and energy resources and combined them with detailed data about operations and financing costs. The model was refined from the version used for the 2011 feasibility analysis to look at ranges of costs.

5. Identifying and Mitigating Current and Future Risks.

Because we're forecasting the future, things could go right or wrong. This step selected a few high-impact variables (like Stranded and Acquisition costs) and tested wide ranges of costs to determine whether it was possible, under a variety of conditions, for a local electric utility to show cost savings and better environmental performance compared to  Xcel Energy.

What kind of modeling did the city do, and why is modeling a valuable step in analyzing the possible creation of a local electric utility?

Models help lay out assumptions and predict results. The city’s modeling looked at possible future outcomes for Xcel Energy and for a local electric utility. The city used different models to address several key questions: 
  • What resources would be needed to meet Boulder’s demands over 20 years?
  • How much would an electric utility cost to run?
  • What is the likelihood that resource prices and market conditions could change due to a variety of factors?
The city’s modeling was reviewed by industry experts, including an independent third-party reviewer, and was determined to be sound.

How were the models measured?

The City Charter specified that a local electric utility’s performance needed to be measured against Xcel Energy’s. So, it was important for the city to determine what the future might look like for Xcel. This was a challenge, as the company doesn’t generally share any of its projections. But the exploration team did have access to data from Xcel’s publicly available filings at the Colorado Public Utilities Commission (PUC), the Federal Energy Regulatory Commission (FERC), and the Securities & Exchange Commission (SEC). In instances where the data was incomplete or unavailable, the city’s consultants used historic trends to make educated guesses about Xcel’s future.

This “Xcel Baseline” forecasted the revenues Xcel would need to collect from now until 2037 to cover power generation, transmission, customer service, operations and maintenance, capital investment, taxes, depreciation, profit and other operational requirements. A share of that amount was attributed to Boulder based on the community’s share of Xcel’s overall revenues. That cost was compared to the cost required to run a local electric utility that would serve the same customers.

What kind of information went into the models and where did the data come from?

The models provide a significant amount of very detailed information, ranging from assumptions about utility employee salaries, to transmission charges, to Boulder’s hourly energy needs. The data came from a host of sources: publicly available regulatory filings, information from peer utilities, white papers, indicative pricing (estimates provided for resources and other services), and recommendations from expert consultants. The city looked for data that was relevant to a utility operating in Colorado. Almost every data point related to the local electric utility was reviewed by a series of community working groups with diverse and deep expertise.

Several high-impact items were included:

  1. Acquisition Costs. Xcel said in 2011 that the city could owe as much as $150 million to purchase its assets to provide electric services within the city. The city believes this number is excessive, even with the subsequent addition of assets the city may seek to acquire, but staff used this number in all of its models to present a conservative case.
  2. Stranded Costs. Xcel officials informed the city in 2011 that Boulder could owe as much as $255 million in stranded costs in 2017. The city does not believe any stranded costs will be due because there is a market to purchase the electricity generated by Xcel that would have been used by city customers.  However, staff modeled several levels of stranded costs. These included $0 at one end and $255 million at the other end, plus quartiles in between to determine how much debt a local utility could take on while still meeting the community’s goals, particularly those related to rates and reliability. Based on a recent clarifying statement from the FERC, the city received confirmation that it could reduce its stranded cost obligation, if there is one, by continuing to purchase power some or all of its power from Xcel for some period of time. This means it would take longer to achieve the local electric utility’s environmental goals but it would allow for a path forward.
  3. Wind Prices. Although wind prices continue to decrease as technology improves, it is not clear whether the federal government will keep offering tax credits to wind developers. Because of this the city looked at wind prices with and without a tax credit. The modeled prices came from Xcel Energy’s resource plan and from research by Lawrence Berkeley National Laboratory.
  4. Natural Gas Prices. Natural gas prices can vary considerably, so the city modeled an extremely wide range of prices. While gas is not a renewable source of energy, it is considered a “bridge” fuel to maintain reliability and help the local electric utility move from a fossil fuel or coal baseload, which emit carbon, to a renewable energy baseload. City models used prices from Xcel Energy’s resource plan. These were then cross-checked with data from the Energy Information Administration, which is a statistical and analytical agency within the US Department of Energy that provides independent analyses for policymaking.
  5. Interest Rates. Unlike Xcel, which is required to pay tax on all of its debt, a local electric utility would have both taxable and non-taxable debt. The interest level paid on that debt can have a big impact and is a big uncertainty given today’s market conditions. The city sought recommendations from a financial advisor on interest rates to model and cross-checked those with Federal Reserve Board data. The city model incorporated a range of rates from as low as 4.1% to as high as 9.2% on a non-taxable load, with higher percentages on taxable debt.

How did the modeling work?

There were several steps to the modeling:
  1. Xcel Energy released estimates as to how much Boulder’s energy needs would grow over the next 20 years. This formed the basis for the city’s estimates about how much power it would need to purchase over time.
  2. Consultants used this forecast to develop a variety of 20-year resource mixes using HOMER software. They developed mixes from a variety of resources based on cost or carbon intensity. Those resource mixes had to meet Boulder’s hourly energy needs, plus a 15% reserve to meet federal requirements, while being low-cost and meeting or exceeding Xcel’s renewable energy mix. The resource mixes were then entered into the city’s financial model.
  3. The financial model has detailed information about what it would cost the local utility to operate for 20 years. This model took into account factors such as: an operations and maintenance budget, energy efficiency programs, payments to entities like Boulder Valley School District, bond payments, insurance and reserve funds, and more. A good financial model can test different conditions—such as fuel prices or capital costs—and report on how a local utility would perform. For example, the financial model shows whether the local utility could reduces greenhouse gas emissions compared to Xcel while maintaining operational costs that are lower or the same as Xcel’s over time.
  4. The last step involved analyzing whether some outcomes are more likely than others. For example, the local utility would face a different market in 2017 than today, so it could see very low interest rates or very high ones, depending on trends at that time. However, not every possibility the city considered is equally likely. The modeling “weights” some outcomes more highly than others based on feedback from industry specialists and community working groups.

The modeling produced a fine-grain picture of a local electric utility’s future and that of Xcel and gave the city the ability to identify both risks and opportunities that a local utility could mitigate or capitalize on if market conditions change.

So what did this effort show?

The bottom line is that the modeling shows that the city can meet the requirements defined in the Charter:
  • A local electric utility would have an annual operation and maintenance budget, a long-term capital plan, and resource reserves that would allow it to provide reliable service, meeting or exceeding both what Xcel offers and national electric reliability requirements.
     
  • A local electric utility could cut the carbon intensity of electricity Boulder receives, and the overall carbon emissions attributable to that electricity, in half when compared to Xcel—starting on the first day of operations and continuing over 20 years. It could also provide more than twice as much renewable energy than Xcel, including at least five percent of locally generated solar and hydroelectric power to start.
     
  • A local electric utility could perform better than Xcel financially on “Day 1” of operation. In addition to this, there are several options under which the utility could continue this financial advantage over 20 years.  Importantly, the financial performance of local electric utility depends on several factors; including how much it costs to acquire the equipment from Xcel, how “green” the community wants its electricity to be, and how markets change over time to make resources and the cost of borrowing more or less expensive. If wind prices continue to decline or carbon regulation goes into effect, the local electric utility is more likely to look better than Xcel over the long term. If wind prices skyrocket or coal prices plummet, Xcel could perform better over the long term. Resource mixes that prioritized low-cost generation (and included 30 to 50% wind) performed better financially than resource mixes that excluded coal or devoted a significant portion of the budget to local solar, but depending on future market conditions, those options could still be cost competitive.
     
  • This is where risk mitigation comes in. Through its modeling, the city identified what the financial risks could be for a local electric utility and ways these could be reduced or eliminated. The latest round of modeling included higher numbers for inputs like wind prices, but it still shows that there are possible paths where a local electric utility could be financially successful even under less favorable market conditions. By looking at higher prices, the city gained a more complete understanding about the point at which it might need to look at purchasing power from Xcel to reduce other potential costs. The important point is that this is a viable option—it just means it could take longer to meet the community’s clean energy goals.
     

How accurate can a model be?

While useful, no cost model is proof positive that a potential utility’s plans would be financially feasible. All cost models include estimates. The city’s Energy Future team used conservative estimates to ensure this model is as reliable as possible. The team then confirmed these estimates with working groups and numerous utility experts. By law, Xcel Energy is not required to provide detailed data regarding purchase of its system and other related expenses until legal proceedings are underway.

What kinds of things is the model not telling me?

There are some things the model just can’t show, because Xcel wouldn’t share information and it is too soon in the process to have certain kinds of detail. For example:

  • The model shows overall costs, not customer rates. Ratemaking is a complex endeavor that will require significant stakeholder involvement.
  • The model includes examples of possible resource mixes, not necessarily the exact resource package Boulder would have. (The actual resource mix would be determined through a public resource planning process that would include receiving competitive bids from power producers.) 
  • The model doesn’t allow for a local utility or Xcel Energy to “change” based on different market conditions. (In reality, it is fair to assume that, for example, a local utility might increase its innovation budget if power costs are low, and Xcel might build more transmission if the economy recovers and power demand soars.)

How open was the exploration team to challenges to its model?

Modeling can – and should – evolve as new information becomes available. As a result, the team conducted multiple rounds of modeling. The results shared here are the most conclusive available to date, but there were changes to inputs over the past several months. For example, the city hired an expert to review its forecast for Xcel’s future costs. This led to some changes in how the city projected the existing utility’s costs and attributed a share to Boulder that took into account a higher than typical proportion of commercial customers.

Other enhancements included:

  1. The city refined its capital replacement plan to incorporate maintenance of a local transmission loop and budget for undergrounding at least 50% of the system over 20 years.
  2. The city added nearly $1 million more than originally allocated energy efficiency rebates and incentives to ensure that the Boulder community continues to have access to the funding it has had with Xcel.
  3. The city refined gas prices based on a more recent (2013) forecast from Xcel, leading to a lower low price and a higher high price.
  4. The city refined  wind prices to make the primary price that was modeled higher—this reflects the possibility that federal wind subsidies would not continue to exist in 2017.

By making these adjustments, the city essentially “stress tested” its model.  In other words, Boulder layered in additional costs that intentionally made it more difficult for the local electric utility to meet the financial tests required under the Charter. This is valuable because it helps the community see what a worst-case scenario might look like and gives staff an opportunity to determine whether there are steps that could be taken to mitigate risk.

Did the city “stress test” Xcel’s baseline, too?

To some extent, but not nearly as much. The city looked at ranges of prices for  Xcel’s natural gas purchases,  new wind contracts, interest rates (and return on equity), and carbon exposure. The local utility was modeled with a range of debt coverage and operations and maintenance costs, but that wasn’t done for Xcel. Additionally, there are other factors for Xcel that could create risks, but there isn’t enough information to be sure. A good example is coal prices.  Historically, Xcel’s coal price projections have been lower than the actual increases. Other issues that Xcel has identified as risks to the Securities & Exchange Commission are increases in distributed solar generation, commercial and industrial customers going off-grid and environmental regulation. Interestingly, each of these “risks” for Xcel could be opportunities for a local electric utility.

Is the modeling complete?

Modeling is never 100% done, but the city believes it has modeled as much as is possible until Xcel provides more detailed information.

What debt service coverage ratio was used in the modeling and how would this be met?

The charter requires the operations budget to have a 1.25 coverage ratio. The model actually is set to have a higher level of 1.63, meaning that after the debt payment is made, an additional 63% of the debt amount is available for reserves.
 

Energy Future Modeling

Savings & Losses 20yr ($150 Million)Savings & Losses 20yr ($214 Million)Utility Cost for $150MUtility Cost for $214MCarbon intensity by optionCosts and OptionsRenewables by optionservice area

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