Understanding Coincident Peaks across all major ISOs and ERCOT 4CP

Coincident Peak- Understanding ERCOT 4CP, Arcus Power's CP call accuracy and importance of Peak Demand periods across all major ISO's in North America
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Managing energy demand is extremely important for utility companies and energy managers. One of the most important metrics is the coincident peak or CP. In this blog, we breakdown how Arcus Power was able to call with a 99.985% accuracy against ERCOT's CP call for the month of June and July 2024. We'll also dive into what coincident peak demand is, why it matters for managing the grid, and how different independent system operators (ISOs) figure it out.

Let's get started with the fantastic results from Arcus Power's recent estimated ERCOT CP calls against ERCOT's CP value.

Most of us in the energy industry and energy professionals are aware of the the importance of coincident peaks and curtailing operations during the CP events can help save large power users millions in energy cost savings (this is covered in detail below). At Arcus Power, we assist our customers in forecasting peak demand intervals, helping to minimize their exposure to demand charges, particularly in the context of ERCOT's 4CP

ERCOT 4CP are typically in the following months - June, July, August, and September. We ran an analysis of our CP calls for June and July, 2024 and the results are outstanding.

According to ERCOT operational data, a load peak was recorded at between 16:45 to 17:00 on June 27th, highlighting the coincident peak occurrence. But 'coincidentally' that was not the case. It's important to consider data like BLTs, SOESSs, NOIEs load most importantly Wholesale Storage Load- Essentially this is the energy that is independently metered from all other facilities to charge a technology capable of storing energy like Battery energy storage systems, flywheels, compressed air energy storage, pumped hydroelectric power, and electro chemical capacitors and releasing it later to generate electricity.
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Our Pwrstream product was still able to make the actual call precisely despite the irregularity in the ERCOT's CP call and thereby informed our customers accurately for operational decisions. Through our analysis we were able to identify the spike at 18:45 and that the actual 4CP call was in fact on Sunday as shown below.

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Subsequently, Arcus Power was able to make the estimated ERCOT call at the beginning of July with very high accuracy rates against ERCOT CP call making it once again a very important call to all our customers. You will see in the chart below from the Arcus Power dashboard that we recorded a peak interval between 16:30 and 16:45 at a 15 minute interval considering WSL and ESR at 81160 MW vs ERCOT actual at 81148 MW, with a difference of just -12 MW.

At Arcus Power, our CP philosophy is very clear - First priority is to NEVER miss a call, and our second priority is the accuracy of the call, and it's always great to see such high-accuracy calls making it compelling to be pitted against ERCOTs call accuracy. To learn more about how our analysis is compiled, be sure to schedule a free demo here.

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Now that we've established the accuracy of Arcus Power during the recent CP calls in ERCOT, let us dive deep into the fundamentals of coincident peak, and the various CP programs across all major ISOs.

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Understanding Coincident Peaks Across all Major ISOs
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What is Coincident Peak Demand?

Coincident peak demand refers to the highest point of energy consumption on a grid at any given time. It is typically measured in megawatts (MW) and occurs when the demand for electricity is at its peak, usually during extreme weather conditions or high-demand periods. This spike in energy usage can result in significant strain on the electrical grid and may lead to issues such as power outages or costly infrastructure upgrades.

Understanding CP demand is crucial for utility companies and ISOs as it helps them plan for future capacity needs, manage system reliability, and ensure adequate supply of electricity to meet customer demand.

Definition of Coincident Peak (CP) and Coincident Peak Demand

Coincident peak (CP) is the moment when the electrical demand of multiple users or areas reaches its highest point simultaneously. Coincident peak demand is the quantified level of consumption at this peak time. It's a key factor in determining the capacity needs and infrastructure investments required to maintain grid stability.

Why CP is Crucial for Grid Management and Planning?

CP is critical because it helps grid operators ensure they have enough capacity to meet the highest demand periods. Failing to manage CP effectively can lead to blackouts, equipment failures, and increased operational costs. By accurately forecasting and managing CP, utilities can optimize their resources and reduce unnecessary expenses.

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Difference Between Coincident Peak Demand and Non-Coincident Peak Demand

While coincident peak demand focuses on the highest simultaneous load across a region, non-coincident peak demand looks at the highest demand of individual users or areas, regardless of when it occurs. Understanding both metrics is essential for comprehensive grid management, as they offer different insights into consumption patterns and infrastructure needs.

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Importance of Coincident Peaks in Power Markets

Coincident peaks are not just a matter of technical interest; they have significant financial implications as well. From determining demand charges to influencing utility rates, CP plays a pivotal role in the economics of power markets.

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Role of CP in Determining Demand Charges and Utility Rates

Utilities often use CP to calculate demand charges, which are fees based on the highest level of power draw during peak times. These charges incentivize consumers to reduce their demand during peak periods, helping to maintain grid stability and lower operational costs.

Impacts of CP on Energy Costs for Large Consumers

For large consumers like data centers or industrial facilities, CP can significantly affect energy costs. High coincident peak demand can lead to higher demand charges, making it crucial for these consumers to manage their consumption during peak periods effectively.

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Overview of How Demand Response Programs Manage CP

Demand response programs are strategies used by utilities to reduce demand during peak periods. By encouraging consumers to shift their usage to off-peak times, these programs help flatten the demand curve, reduce the strain on the grid, and minimize the need for costly infrastructure investments. Learn more about how Arcus Power enables Demand Response Optimization through our strategic partnership with ENEL North America.

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Coincident Peak Demand in ERCOT

The Electric Reliability Council of Texas (ERCOT) has a unique approach to managing coincident peak demand, known as the Four Coincident Peaks (4CP) program. This system focuses on the hottest months when demand is highest.

Overview of ERCOT's 4CP Program

ERCOT's 4CP program identifies the highest demand periods during the summer months—June, July, August, and September. These peaks are used to determine transmission costs for large consumers, incentivizing them to reduce their consumption during these critical times.

Factors Influencing ERCOT CP

Several factors contribute to CP in ERCOT, including high summer temperatures that drive air conditioning use, industrial and commercial demand surges, and the variability of renewable energy sources like wind and solar.

Reducing CP Charges in Texas

In Austin, TX, for example, solar and storage solutions have been effective in reducing CP charges. By generating their own power and storing excess energy, businesses can avoid high demand charges during peak periods, leading to significant cost savings.

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Coincident Peak Demand in PJM

PJM Interconnection, serving the eastern United States, uses a system known as Peak Load Contribution (PLC) to manage coincident peak demand.

PJM's Peak Load Contribution (PLC)

PLC determines the share of peak load each consumer contributes to the grid. This metric is typically calculated during the summer months, focusing on the days with the highest demand.

Factors Influencing PJM CP

Population density, industrial activities, and weather patterns, particularly heat waves, play significant roles in determining CP in PJM. Demand-side management and demand response programs are crucial for mitigating these peak demands.

Predictive Approaches for PJM CP

Advanced technologies like machine learning and neural networks are increasingly used to forecast CP in PJM. These predictive models help grid operators anticipate peak periods and manage resources more efficiently.

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Coincident Peak Demand in Alberta (AESO)

The Alberta Electric System Operator (AESO) manages coincident peak demand through its Coincident Metered Demand (CMD) program, focusing on the winter months when heating demand spikes.

AESO’s Coincident Metered Demand (CMD)

CMD is used to measure the highest demand periods during December to February. These peaks are critical for managing capacity and ensuring grid reliability during the cold winter months.

Factors Affecting CP in Alberta

Extreme cold temperatures lead to heating demand spikes, and Alberta's energy-intensive industries also contribute to high CP. Natural gas supply and pricing can further influence these peak periods.

Grid Volatility and CP in Alberta

Alberta's reliance on natural gas makes the grid more susceptible to volatility. Effective CP management is essential to mitigate these risks and ensure a stable energy supply.

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Coincident Peak Demand in Ontario (IESO)

The Independent Electricity System Operator (IESO) in Ontario uses coincident peaks to determine Global Adjustment (GA) costs, affecting both summer and winter months.

IESO’s Global Adjustment (GA) and Coincident Peaks

GA costs are influenced by CP, with peaks typically occurring during summer heat waves and occasionally in winter. These peaks help determine the costs consumers pay for maintaining grid reliability.

Factors Influencing CP in Ontario

Weather-related demand, particularly during heat waves, and industrial activities significantly impact CP in Ontario. Effective management strategies are essential to mitigate these costs.

Strategies for Managing CP in Ontario

Demand response and energy efficiency programs are critical for managing CP charges in Ontario. By reducing consumption during peak periods, consumers can lower their GA costs and contribute to grid stability.

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Coincident Peak Demand in MISO

The Mid-continent Independent System Operator (MISO) calculates coincident peak demand during peak load seasons, often influenced by extreme weather events.

MISO’s Coincident Peak Day Determination

MISO uses historical data and predictive models to determine CP during peak load seasons. These peaks are crucial for planning and operational decisions.

Factors Influencing CP in MISO

High humidity and heat leading to increased cooling demand, along with agricultural and industrial energy consumption, significantly impact CP in MISO.

Impact of Weather Forecasting on CP in MISO

Accurate weather prediction is essential for managing CP in MISO. By anticipating extreme weather events, grid operators can better prepare and allocate resources to maintain stability.

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Coincident Peak Demand in SPP

The Southwest Power Pool (SPP) identifies coincident peak days often aligned with severe weather, primarily during the summer months.

SPP’s Approach to Coincident Peaks

SPP uses a combination of historical data and real-time monitoring to identify CP days. These peaks are critical for grid management and planning.

Factors Influencing CP in SPP

Tornadoes, storm-related outages, and high summer temperatures significantly impact CP in SPP. Effective management strategies are essential to mitigate these risks.

Grid Stability and CP in SPP

Maintaining grid stability during CP events is a top priority for SPP. By leveraging advanced technologies and demand response programs, SPP can better manage these peak periods.

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Coincident Peak Demand in NYISO

The New York Independent System Operator (NYISO) calculates coincident peak load in a highly urbanized region, focusing on the summer months.

NYISO’s Coincident Peak Load Calculation

NYISO uses a combination of real-time data and historical trends to determine CP, typically occurring in July and August. These peaks are crucial for ensuring grid reliability in a densely populated area.

Factors Affecting CP in NYISO

The urban heat island effect in New York City, along with large-scale commercial and residential demand, significantly impact CP in NYISO. Effective management strategies are essential to maintain grid stability.

Demand Response Programs in NYISO

NYISO leverages demand response programs to manage CP and prevent blackouts. By encouraging consumers to reduce their usage during peak periods, these programs help maintain grid reliability.

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Coincident Peak Demand in CAISO

The California Independent System Operator (CAISO) manages coincident peak demand with a focus on summer peaks, driven by heatwaves and renewable energy integration.

CAISO’s Coincident Peak Demand Strategy

CAISO uses a combination of historical data and real-time monitoring to identify CP during the summer months. These peaks are critical for ensuring grid reliability in a region heavily reliant on renewable energy.

Factors Influencing CP in CAISO

Heatwaves and their impact on air conditioning use, along with the growing role of renewable energy sources, significantly impact CP in CAISO. Effective management strategies are essential to mitigate these risks.

Challenges and Innovations in CAISO

CAISO faces unique challenges in managing CP due to its reliance on solar and storage solutions. Innovations in these areas are essential for maintaining grid stability and reducing peak demand charges.

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Coincident Peak Demand in ISO-NE

The Independent System Operator of New England (ISO-NE) manages coincident peak demand with a focus on winter heating and summer cooling demands.

ISO-NE’s Coincident Peak Management

ISO-NE uses a combination of real-time data and historical trends to determine CP, typically occurring during winter cold snaps and summer heatwaves. These peaks are crucial for ensuring grid reliability in a diverse climate.

Factors Influencing CP in ISO-NE

High energy demand during cold snaps and heatwaves, along with the role of fuel diversity, significantly impact CP in ISO-NE. Effective management strategies are essential to mitigate these risks.

ISO-NE’s Strategies for Managing CP

ISO-NE leverages advanced metering infrastructure (AMI) and real-time pricing to manage CP. By encouraging consumers to reduce their usage during peak periods, these strategies help maintain grid stability.

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Future Outlook on Coincident Peak Demand

The evolving role of renewable energy and storage solutions will play a significant part in CP management. Potential policy changes and advancements in technology will continue to shape the strategies used across different ISOs. By staying informed and proactive, energy professionals can better manage CP and contribute to a more reliable and efficient grid.

In conclusion, coincident peak demand is a complex but critical aspect of energy management. By understanding its implications and leveraging advanced strategies, stakeholders can optimize their operations and reduce costs.