Post: Maximizing energy savings: how advanced forecasting and automation mitigates real-time price spikes in ERCOT

Maximizing energy savings: how advanced forecasting and automation mitigates real-time price spikes in ERCOT

Many large commercial and industrial facilities in ERCOT are on real-time indexed energy prices. This pricing structure can be highly beneficial when off-peak prices are low, resulting in significant cost savings for these facilities. However, the same structure can leave businesses vulnerable to sharp spikes in energy prices, particularly during periods of high demand or constrained supply. This volatility poses a financial risk for facilities that are not equipped to adjust their energy usage in response to real-time price fluctuations.

An example of this risk became evident on August 20 when a combination of factors led to extreme price spikes in the ERCOT market. During this time, the demand for electricity was exceptionally high, generation reserves were low, and there was less solar generation delivered than initially forecasted, leading to a shortfall. These conditions resulted in sustained price spikes nearing the system price cap of $5,000/MWh from 7:00PM to 8:30PM. For facilities exposed to real-time pricing, this sudden surge in costs could translate to a significant financial burden.

At GridBeyond, we utilize advanced machine learning forecasting to help our demand response clients prepare for these kinds of events. By analyzing both energy price trends and site-specific energy consumption patterns, we can predict when price spikes are likely to occur and recommend appropriate actions to mitigate the financial impact.

On August 20, our forecasting models indicated that a significant price spike was likely during the evening peak hours. As a result, we advised our clients to prepare for potential price increases and adjust their energy consumption accordingly. Specifically, we recommended that they curtail their energy usage from 7:00PM to 8:00PM, when we forecast the price spike to be most severe.

Taking one site we manage as an example, with ~1.0 MW of flexible load that we manage that was able to save over $8,000 in that one evening through our demand management programs.

At GridBeyond, we take a proactive approach to energy management, utilizing a combination of advanced forecasting, onsite edge controllers, and comprehensive monitoring and analytics to help clients better manage their energy consumption. Our machine learning models are constantly analyzing data from various sources, including weather patterns, market conditions, and site load profiles, to provide accurate forecasts of future energy prices. This enables us to offer clients timely recommendations on when and how to adjust their energy usage to avoid costly price spikes AND offer their flexibility as capacity into ancillary services like ERCOT’s Contingency Reserve Service (ECRS) and Responsive Reserve (RRS).

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