In Part I of this post, we discussed the concept of asset utilization — or load factor — and looked at recent trends for California’s investor-owned utilities (IOU). The trend over the past two decades for IOU load factors has been a decline from the 55%-60% range to the 50%-55% range. In Part II, we will explore the factors that affect load factor, whether there is an optimal load factor, and how California’s greenhouse gas policies may affect load factor.
Optimizing Load Factor
So, what is the optimal load factor for electric utilities in California? The short answer is that it depends. In a perfect world, we would design and operate our electric system to have a 100% load factor; that is, the average demand and peak demand are equal and we use all the capacity of the system all the time. Demand in this case would be flat – one level of demand every hour of every day of ever year. But, alas, we don’t live in that perfect world. Many factors contribute to load factor, including weather, customer type, and rate structures. As a result, there is no one-size-fits-all approach to determining the optimal load factor.
First, let’s take a look at weather as a driver of demand. As mentioned earlier, California’s electrical demand generally peaks in summer. During the hot summer months, demand tends to spike in the state. And to the extent that climate change brings higher average temperatures and more heat waves, as climate scientists expect for California (Gershunov & Guirguis 2012), load factor could be negatively affected by increased air conditioning loads in the hotter, inland areas and possibly increased loads along the coast as homeowners install air conditioners to beat the heat. And hot summer weather is typically not equally distributed in the state. California has 16 climate zones for energy planning purposes, and as a result temperatures in the SDG&E service territory could vary significantly from the temperatures experienced in the Southern California Edison territory.
Second, the types of customers that comprise an electric utility service territory also have an effect on load factor. Whether the area has high industrial loads or high residential loads matters. For example, in the San Diego Gas and Electric territory, the residential peak demand is highest by far, which has a significant influence on overall system demand. Because the residential sector tends to peak in the early evening, this has a strong influence on SDG&E’s overall demand curve. Figure 1 below shows load profiles for several sectors for June 23, 2015 as provided by SDG&E. Other utilities in the state may have a different load profile and therefore a different perspective on what is the optimal level.
Figure 1 Dynamic Load Profiles for SDG&E on 6/23/15
Third, electric rate structures also can play a role in determining the shape of the load curve and therefore the utilization rate of a given utility service territory. Taking the SDG&E service territory as an example, many medium and large commercial and industrial customers receive service on time-of-use rate structures. And many of those also have a separate demand charge component. At the residential level, most customers are on the DR rate, which charges average rates in a tiered rate structure with no time-of-use component. There are several arguments in support of tiered rates, including that they send a signal for high users to reduce their consumption. It is not entirely clear whether this is true but several studies (Ito 2014, Faruqui et al. 2015, see also Severin Borenstein’s recent blog post) have found little or no net reduction in energy use from tiered rates. Even if energy use dropped as a result of tiered rates, it is not clear what effect it would have on peak demand. Time-of-use on the other hand could have an effect on peak demand. A pilot study conducted by the Sacramento Municipal Utility District found an approximately 6%-12% average weekday load reduction due to time-of-use rates. It is important to realize that the discussion of rate structures is complicated and involves many considerations other than the effect on load factor.
Load Factor and California’s Greenhouse Gas Reduction Targets
Another consideration in contemplating the future of the electric system’s load factor in California is the role electricity will play in reaching long-term greenhouse gas reduction targets. Several studies have shown that reducing emissions to 80% below 1990 levels by 2050 would require mostly decarbonizing the electric supply, significantly electrifying the transportation systems with battery electric vehicles or electrically produced hydrogen, and converting many natural gas loads to electricity (see Williams et al. 2014, Greenblatt 2015). Such a shift toward electrification could affect peak demand and load factor, depending on how it is managed.
As we add significant load to the electric system in service of deep greenhouse gas reductions, it is important to consider how that additional load is managed and its potential effects on load factor. If done in a way that significantly increases peak demand and exacerbates the load factor, it could increase system costs. If we can add load in a way that raises average demand more than peak, we may be able to manage costs better. On the other hand, investing in the technology and communication systems necessary to efficiently and dynamically manage demand could also come with a hefty price tag, at least in the short run.
The Takeaways
Here are a few key takeaways from Part I and II.
- Asset utilization (or load factor) is an important consideration in energy policy that can affect overall operational costs and customer rates.
- Load factors for the three IOUs in California have been declining over the past two decades.
- Load factor is affected by many factors, including weather, customer composition, and rate structures.
- There is no one-size-fits-all approach to optimizing load factor.
- California’s greenhouse gas policies likely will add more electric load to the system, which will require careful management to avoid exacerbating already low load factors.
Incidentally, the overall load factor for the U.S. airline industry in 2014 was about 85% (IATA 2015), so we really don’t have half empty planes flying around. And we should take caution in comparing the electric and airline industries because there are many structural differences. Nonetheless, load factor and asset utilization are important concepts in the conversation about electric rates and deep greenhouse gas reductions.
The EPIC Energy Blog 
the asset utilization problem you discuss is an old one that dated back to the early days of the electric industry. Samuel Insull tried to solve this problem by serving a diversified customer base and convincing electric railroads, for example, to stop building dedicated facilities.
However you need to be careful when talking about asset utilization and load factor in the same sentence. Capacity factor is a better metric than load factor for measuring asset utilization, and those numbers are absolutely dismal – typically 50% or less.
This is a clear presentation.
It goes without saying, of course, that electric systems also require both physical equipment redundancies and reserves. With climate events increasing there will be an additional need to build-out redundancies throughout the system (building additional transmission lines to provide alternatives during and after wildfires; building new lines to where population will move due to lack of water and increasing heat; building to serve movements of climate migrants from the countries to the south; building or keeping in place additional “uneconomic” power plants to pick up the load when parts of normal generation go down due to flooding, excessive heat and fires, and solar drops out due to smoke and heavy clouding). Also, electric systems become stressed as heat increases. So, instead of building to meet anticipate load plus reserve, the reserve margin will need to be moved steadily upwards throughout and beyond the current planning horizon to maintain current levels of service. It is too late to prevent this need now; due to failure to act in the past, it is just built-in.
Second, and awkward because it is a social variable affecting the technical system, there is the secular trend since about 1970 for transfer of income from the poor, the middle class and the lower and middle upper class to the upper 1% of society by income. With this change from the more social democratic allocation of income (in, say, 1965) to the present, rationing (under other names) is a major factor occurring throughout American society. Will the electric system continue to serve all people, or will it provide different levels of service depending on income level?
In the past, electric companies had to plan to meet peak loads plus a reserve. This automatically created an electric supply that could meet the needs of nearly everyone year-around. As suggested by Jack Ellis, capacity factor is not a new concept. And the idea of lowering system costs by finding a way to smooth peaks has a very deep history. That is, it is not a “new energy vision” but a concept that load research analysts, load forecasters and system planners have worked with for decades. The difference now is that we have technologies that are actually changing when and at what levels systems peak (as demonstrated by the Duck Curve). These technologies are making some base load plants economic liabilities because they can under-price coal and nuclear during times of day when base load plant operation was previously essential. Yet, even as these technologies coupled with more advanced and deep demand-side management (net zero and near net zero) expand and some central generating stations become uneconomic, it is also likely that emergency situations will be experienced in which these stations and the command and control structure and quickly deployed resources that are generic to integrated utilities will be essential. Market structures can easily produce an illusion of resilience or perhaps a type of resilience up to a certain point when they fail. At that point we will need the “uneconomic” base load plants and the command and control structure and facilities management of integrated utilities (or something like them) to be able to respond on an emergency basis.
If we try to optimize a single variable such as capacity factor, these physical and social realities may drop from focus. A high capacity (but not too high) capacity factor is good from an engineering perspective and an economic perspective in normal times. But we are no longer in normal times. In any case, as Scott Anders notes, we will face meaningful cost increases to harden and build-out the system. This is required due to climate change, whatever the form of the system. In a time when parts of the system may drop out an markets may collapse, a lower capacity factor indicates redundancy and reserve capability. Redundancy and high reserve capacity are important survival factors, going forward.
I am surprised there is no mention of a possible role for storage in improving load factors. Without storage, rooftop solar will depress load factors, making the grid less efficient, but, combined with storage, solar energy stored and released during the 4 to 7 PM residential peak can shave that peak and improve the load factor. Of course, properly designed time-of-use rates are essential to provide the right incentive for customer investments in storage.
In summary, the combination of rooftop solar, on-site storage, and TOU rates is good for both the environment (GHG reducing) and the grid (efficiency enhancing).