Essentially from the time of rural electrification until about the mid-1990s, forecasting was a relatively uncomplicated process – load was directly tied to economic growth and therefore basic econometric models were usually sufficient for producing reasonable forecasts. Then as conservation and energy efficiency efforts increased, load and economic activity began to decouple, especially in the residential sector. We began to see economic growth but flat or downward trends in household consumption as homes
In the distant past, electric utilities encouraged additional energy sales, especially o -peak energy sales, through programs designed to encourage fuel switching. Such programs were called strategic load building or valley filling programs. Such programs became disfavored at the turn of the century as climate concerns generated negative political pressure on associations of encouraging increased electricity consumption for the sake of growth. In recent years, the concept has returned, with modification, in the guise of beneficial electrification, in which the concept is to replace direct fossil fuel use with electricity in a way that reduces overall emissions or environmental impact. Beneficial electrification includes electric vehicle adoption, which will be discussed in more detail later, but also replacement of other appliances that might represent reduction in greenhouse gas emissions, such as replacement of gas appliances with electric. In such fuel switching instances, forecasters for both electric and gas utilities must consider electrification effects.
We’ve been trying to understand the impact that electric vehicles (EV) will have on forecasts and energy planning for some years now, so the sources a forecaster can rely on are more well-developed than the other issues forecasters are struggling with. However, EVs do still represent a challenge because for most utilities (some in California may be an exception), they are not well represented in the historical record that our forecast model rely upon. Therefore, forecasters are forced to perform post-modeling adjustments in which they independently project the specific impacts that EV will have on the system and then adjust the base case forecast.
With so many questions to answer, a forecaster might choose a simplified approach of trying to answer many of these questions with a simple assumption of “we’ll have 10% of vehicles within 20 years and a typical vehicle will require 400 kWh per month to charge”. Alternatively, a complex forecasting model that attempts to measure and answer all of the above questions can also be developed.
For many utilities, forecasting large commercial and industrial loads is so difficult that the traditional approach has been to hold such loads constant into the future only making adjustments for expansions, contractions, and new loads based on highly likely known changes. Of course, “highly likely” is a very loose term. Furthermore, the forecaster must taking into account that the demand and load factor assumptions a developer or customer gives to the utility for new load is often overstated. The forecaster relies upon key account representatives and information gleaned from regular discussions that utility management often has with such loads.
However, that is beginning to change in ways that could have drastic implications on planning at the utility and regional level. Entering the conversation are very large loads with high load factors: data warehouses, cryptocurrency mining, artificial intelligence centers, and indoor agricultural facilities. The expansion for these loads is seemingly exploding right now, ranging in size from just a few MW to upwards of 1,000+ MW at one site! Developers are looking for the best deal for power and canvasing entire regions of the country looking for deals from different utilities. The challenge for a forecaster at any one utility is, how to include such opportunities in a load forecast? After all, one successful contract could double or triple a rural cooperative’s peak demand. Just because a developer has feelers out or even is negotiating basic contract terms means the full load will come to fruition. Utilities, especially Investor Owned Utilities with large service territories, are taking a harder look at including something for these loads in a load forecast.
The onset of beneficial electrification, including electrification of North America’s vehicle fleet, and potential very large loads associated with data warehousing and data mining are the new difficult cases for load forecasters. There are methods that are being developed, deployed, and refined over time, so the industry will continue to evolve. It is important to know, though, that these changes in how we use power have profound implications not just on forecasting, but on planning too. Distribution grid impacts could be significant from an influx of electric vehicles or aggressive electrification efforts. Similarly, gas systems should try to understand the impact electrification efforts will have on their utilities. And a single very large industrial load can represent significant system planning impacts, no doubt. Importantly, though, given the size of such projects and the lack of clarity concerning whether they will interconnect and when creates very real difficulties for long-term integrated resource planning. So we all should work with our forecasters to develop the best methods we can to predict electricity and gas needs into the future under scenarios driven by evolving trends.