Customers & Industries: BC Hydro

BC Hydro uses @RISK to calculate uncertainty of electricity-conservation projects

  • Industry: Utilities
  • Product(s): @RISK
  • Application: Project Analysis

Summary

BC Hydro has used Palisade's risk analysis software @RISK for electricity load forecasting for many years; this use was expanded to the DSM program. @RISK was used to capture the level of uncertainty of the estimated savings for each individual DSM project. Around 60 projects were analysed on a case-by-case basis, and a probability distribution around the aggregate DSM savings forecast was developed.

There is a huge incentive for utilities such as BC Hydro to encourage energy conservation. But while the motivation is there, it is often difficult to know whether these ambitious targets can be achieved. Because @RISK was able communicate complex analyses in a transparent and meaningful way, BC Hydro has been able to set very aggressive energy conservation goals while feeling confident that the right balance between conservation and finding new supply side resources has been found.
Basil Stumborg, Decision Analysis Expert - Finance, BC Hydro

BC Hydro is the largest electricity supplier in British Columbia, Canada, providing around 90 percent of the province’s needs. It has annual revenues of around $5 billion. Its 1.7 million customers include residential, commercial and large industrial operations, and not-for-profit organisations such as universities and hospitals.

As a regulated public utility, BC Hydro is required to submit for approval a long-term energy procurement plans for every two to three years. The Long Term Acquisition Plan (LTAP) provides a 20-year view on how to meet customers' growing demand, and to procure electricity supply to fill any gap that exists between supply and demand. BC Hydro’s growing demand is caused by population growth, and the growth in usage per customer, which increasingly includes factors such as large-screen televisions.

BC Hydro’s electricity supply gap is growing, and its current plans are to meet its future supply needs preferentially through Demand Side Management (DSM), or energy conservation. Only after examining and implementing cost-effective DSM, can it turn to increasing supply to meet its needs.

Over the next 20 years BC Hydro intends to meet almost 75 percent of its incremental demand requirements through DSM. This is one of the most aggressive targets in North America.

@RISK analyses success of energy-saving

The challenge with any DSM program is that the factors driving electricity demand and DSM program implementation include considerable uncertainty.

BC Hydro’s overall DSM program consists of many activities such as: compact fluorescent light promotions; subsidies for energy efficient appliances; variable speed motor promotions (for home furnaces); and promotional activity aimed at motivating customers to use less energy. However, a key challenge for BC Hydro was ascertaining exactly how effective its DSM program would be. This is because the overall DSM program comprises many individual projects, and the electricity savings achieved would result from an aggregation of the individual project savings. In addition, the outcome of each project is in itself, uncertain.

BC Hydro has used Palisade's risk analysis software @RISK for electricity load forecasting for many years; this use was expanded to the DSM program. @RISK was used to capture the level of uncertainty of the estimated savings for each individual DSM project. Around 60 projects were analysed on a case-by-case basis, and a probability distribution around the aggregate DSM savings forecast was developed.

This has played a key role in BC Hydro's energy planning processes.

Winning over industry experts with @RISK

A key issue for BC Hydro is that the corporation is at the forefront of the energy conservation movement. Therefore there is limited historical data for BC Hydro to use as a guide for its DSM programs. As a result, it used estimates from experts in various industries to provide input to the @RISK model. However, according to research by Professor Mark Burgman (a director at the Australian Centre of Excellence for Risk Analysis), who cites a number of well-replicated examples, in estimation tasks, 'experts' have a tendency to be over-confident, biased and inaccurate. More specifically, 'expert' views tend to anchor on a mid-point (or expected) estimate, fail to take into account collected evidence, and give too much weight to recent or familiar cases.

BC Hydro therefore introduced its @RISK model to the DSM experts it worked with, and undertook analysis training with each of them. The first step was to request participants to provide an upper limit to their forecast. For example, they might be asked to put a value on the absolute maximum level of energy saving that could be expected in 2020. The next task was to query for more extreme outcomes. Using the same example, they would be asked to consider a situation in which there was a higher outcome than they had originally expected, and suggest what could have caused it, and what elements would need to align to give that result. From there, a probability value could be assigned for the possibility of seeing an outcome beyond the limit initially set. These steps were then repeated for the lowest limit to the each expert's forecast.

@RISK's extensive visual displays were used to create the appropriate distribution for each application. This was instrumental in assisting the experts to visualise how their predictions influenced the final outcome of the decision-making process.

This procedure was carried out for each energy-saving project being undertaken by BC Hydro. Examples included: encouraging consumers to use energy-saving light bulbs; promoting the replacement of electrical appliances such as cookers, dishwashers and tumble dryers for more energy-efficient models; and subsidising the cost of increased home-insulation.

@RISK identifies correlation between energy-saving projects

However, BC Hydro recognised that its projects did not operate in isolation from each other, so it set out to identify key commonalities between projects. In particular, interrelationships among two key uncertainties were explored: the participation rate for each DSM project and savings per participant for each project.

Traditionally it is difficult to assess these various correlations in a transparent and meaningful way. However, BC Hydro's use of @RISK and its Monte Carlo simulation capabilities ensured it was relatively straightforward to overcome this issue.

To illustrate, BC Hydro’s model made it clear that, for any given project, there was likely to be a strong positive relationship between the savings per participant, and the number of participants. Secondly, the model also posited that, rather than participation across projects being independent, it was was positively correlated. This fitted well with BC Hydro’s belief that a successful DSM programme would need to generate a 'conservation culture' that would lead people to save energy in many different areas of their lives, and resulted in BC Hydro using general public awareness campaigns around energy conservation. However, the model also highlighted that a failure to generate this culture will put a general downward pressure on energy savings across all projects (ie it increases the risk that they will not be successful).

Basil Stumborg, senior business strategy advisor at BC Hydro explains: “@RISK's graphical capability gave our industry experts the confidence to make predictions that, while outside their initial 'comfort zone', were ultimately more realistic. The strong visual element of the software helped them picture the correlation between key variables and draw a workable conclusion as to what will influence the success of DSM projects. Although parts of the study were still subjective because they were based on educated estimates rather than data, @RISK's sensitivity analysis was able to show that being exactly right was not necessary - the conclusions were robust to changes in these subjective estimates.”

Predicting the potential of a new electricity pricing structure with @RISK

The second element to the company's energy-saving programme looked at how changes to cost might affect consumption. For ten years, electricity prices in British Columbia had been held at a low, unvarying level. BC Hydro wanted to gain insight into whether introducing a two-tier electricity rate would result in consumers using less electricity. A two-tier rate introduces a second higher price level to consumers who use above a specific threshold amount of electricity per billing period. However, as with the DSM programs, little historical data was available, so the outcome was difficult to predict.

Once again, BC Hydro relied on previously-published material combined with expert opinion that was balanced with @RISK analysis. Three key uncertainties were highlighted: the potential timing of a rate restructuring, the level at which to set the higher price step, and the potential response of consumers to an electricity cost that was related to consumption. By using @RISK to capture these uncertainties, BC Hydro could communicate to its regulator and stakeholders not only how much energy it might save through these changes, but the level of certainty it had around its different rate structure options.

@RISK illustrates the potential of energy conservation

@RISK enabled BC Hydro to ascertain that DSM programs and rate restructuring were an attractive and cost-effective way to fill roughly 75 percent of British Columbia’s supply-demand gap. Energy conservation projects were low-cost compared to the supply-side options, and would not be impacted by fuel price risks. However, the work done exploring the uncertainties around energy conservation also highlighted that, at some point, the success of very large-scale energy conservation programs become too uncertain to depended on to the exclusion of all other options. Therefore, as it supply-demand gap grows, BC Hydro will also need to increase the supply of electricity in the province.

@RISK simplifies complex ideas but maintains accuracy

Stumborg concludes: “There is a huge incentive for utilities such as BC Hydro to encourage energy conservation. But while the motivation is there, it is often difficult to know whether these ambitious targets can be achieved. Because @RISK was able communicate complex analyses in a transparent and meaningful way, BC Hydro has been able to set very aggressive energy conservation goals while feeling confident that the right balance between conservation and finding new supply side resources has been found.”

Additional information

Distribution used: Tri-Gen distribution:

  • Simple form – signals to users and reviews a lack of specific knowledge of distribution
  • Cognitive burden for users (i.e. experts) is low (it requires 5 simple parameter inputs)
  • Fits in well with natural discussion of forecast outcomes (interview protocol questions)
  • Puts more weight in tails (compensates for overconfidence bias)

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