ERCOT Summer 2024 and the Grid Stress Index

Overview

What’s the best way to model power market dynamics that drive energy prices? In this blog post, we review traditional models via generation stacks, introduce the Grid Stress Index as a way to modernize traditional approaches for renewable growth and contemporary market realities, and finally show how well this model works out of sample by comparing summer 2024 electricity prices in Texas (ERCOT) to pre-season Sunairio distribution estimates.

The Traditional Approach: Grid Dispatch and the Generation Stack

Regional power grids are in balance between supply (generation and imports) and demand (customer load and exports). To minimize unnecessary costs, regional grid operators continually dispatch the least-cost mix of generation that meets demand while accounting for both physical constraints (e.g. transmission line capacity) and techno-economic constraints (e.g. minimum unit run times, minimum reserve requirements). 

Reflecting this least-cost mandate, regional energy prices are traditionally modeled via an idealized generation stack supply curve that assumes that all resources are dispatchable and ignores congestion and losses.

In Figure 1, for example, a demand of 4,000 MW would correspond to a least-cost generation mix that first dispatches all wind, solar, and hydrothermal generation and then dispatches a portion of combined-cycle gas generation. The marginal energy price would correspond to the marginal cost of combined-cycle gas generation ($25).¹

Unfortunately, not all grid capacity is available at any point in time. Power plants have outages and maintenance; wind and solar generation potential is weather-dependent. The widths of the blocks in Figure 1 (and thus the capacity vs price relationship) is constantly in flux.

A Modern Approach to Modeling the Power Grid Supply/Demand Balance via the Grid Stress Index

To modernize the traditional formulation of price as a function of demand, we first remove non-dispatchable renewables from the supply curve and instead net them against demand. The resulting measure of effective demand, net demand, is then the amount of energy that needs to be met with dispatchable capacity.

Net demand = Demand - (Non-dispatchable Renewables)

To normalize across periods with different demand levels and available capacity, we then transition from raw energy space to percentage space. A net demand of 7,000 MW when the grid has 10,000 MW of dispatchable capacity, for example, is modeled as 70% utilization of dispatchable resources.

Net Demand / Dispatchable Capacity

Next, we adjust the dispatchable capacity by the amount of generation outages to yield available dispatchable capacity, giving us

Net Demand / Available Dispatchable Capacity

or

Grid Stress Index = (Demand - (Non-dispatchable Renewables)) / (Dispatchable Capacity - Outages)

which is a single metric ranging from 0% to 100%, that describes the tightness of the power grid.

Finally, we map this metric to delivered spark spread prices instead of raw marginal prices, allowing us to normalize for the effect of changing fuel prices. We assume a standard heat rate of 6.5 MMBtu / MWh

Spark Spread = LMP - Heat Rate * Delivered Gas Price

The Relationship Between the Grid Stress Index and Market Prices

Moving from theory to practice, Figure 2 plots our Grid Stress Index metric against delivered spark spreads for all hours June-August 2023. 

The relationship looks very similar to the idealized marginal supply curve (Figure 1). We can clearly see a slow rise in marginal cost followed by an inflection point around 75% available dispatchable capacity utilization, after which prices rise swiftly. Interestingly, the extremes of this plot are higher than those modeled by the traditional approach, as prices are often well above physical fuel costs (reflecting scarcity-seeking behavior from market participants). Putting this together, we’ve effectively learned the true ERCOT marginal supply curve without actually modeling the individual unit generation stack. 

Using Sunairio Simulations to Model Grid Stress Index 

Now that we’ve established the fundamental relationship between our measure of the Grid Stress Index and energy prices, we note that realistic simulation of this measure requires correctly producing inputs that are probabilistic (replicating extreme events at the correct likelihood) and fully correlated (replicating the correct joint distribution of load, renewables, and generation outages). The Sunairio simulation suite is purpose-built for this task.

In order to present an up-to-date view of energy resources, we update installed capacity projections monthly and retrain/re-simulate energy models weekly. 

As we can see from Table 1, there are significant trends in both installed generation capacity and demand, making the downstream effect on the hourly Grid Stress Index complicated to anticipate.

What Did Sunairio Say About ERCOT Summer 2024 Grid Risks?

To benchmark Sunairio ERCOT simulations against historical realizations, we compared our pre-summer simulated distribution of Grid Stress Index to the actual distribution from June through August 2024. ²

Figure 3 (left) presents the distribution of forward-looking Sunairio simulations from May 3, 2024 (blue line) against actual distribution (green histogram). The fit is quite close, implying that Sunairio simulations from early May accurately reflected the various risks to the hourly Grid Stress Index (alternatively, one can interpret this fit as an indication that ERCOT June-August 2024 hourly grid balances were distributed close to expectations).

However, comparing the 2023 and 2024 historical distributions of the Grid Stress Index (Figure 3, right), we see that the ERCOT market was much tighter in 2023 than in 2024. In particular, 2023, had far fewer hours in the high-priced regime above 75% available dispatchable generation utilization–a shift that we see ultimately reflected in realized prices too (Figure 4): 

Looking at Table 2, we note that 2024 realized prices were not only much lower than their 2023 counterparts but also much lower than pre-season market forward prices.  Sunairio pre-season ERCOT simulations at that time, however, were much closer. In other words, Sunairio simulations are excellent predictors of 1-3 month-forward grid balances and are also effective market signals of significant downside to energy prices relative to the same period from 2023.

Conclusions

1. The Grid Stress Index is an improved metric for modeling power grid supply-demand balances that inherently accounts for renewables variability and unit outages.
   
2. The hourly distribution of the Grid Stress Index showed that the June-August 2024 period in ERCOT had significantly less risk of high-priced hours compared to the same period in 2023.
   
3. Sunairio simulations of summer ERCOT grid balances–generated in May 2024–were an extremely accurate forecast of actual grid conditions.
   
4. Sunairio simulations from May 2024 were an accurate signal of downside price risk in ERCOT for the June-August 2024 period relative to the same period from 2023. 

1. Battery storage will also appear in the supply stack at varying marginal costs depending on their cost of charging and their efficiency. ↩︎
2. The conventional power market definition of summer is limited to July-August, though we add June as well in this analysis to increase sample size. ↩︎