This blog provides a detailed overview of the latest enhancements to the PLEXOS WECC Nodal Dataset v24.3, focusing on updates that improve energy modeling capabilities. Key enhancements in the v24.3 release include the introduction of new low and high hydro scenarios to better simulate variable hydrological conditions, expanded nodal contingency modeling to incorporate a greater number of potential system failures, and the integration of advanced analytics variables like spark spreads and implied market heat rate compatible with Cloud BI Analytics. Additionally, the blog discusses several long-term scenarios that address alternative asset retirement profiles, primarily concentrating on coal generators and CO2 emissions. These updates are designed to empower energy professionals with more precise tools for strategic planning and operational decision-making in the dynamic energy market.

The Western Interconnection

The Western Interconnection spans 1.8 million square miles covering full or partial regions of 14 U.S. states, the Canadian provinces of British Columbia and Alberta and Baja California, Mexico. It provides power to more than 80 million people (about twice the population of California) with approximately 136,000 miles of transmission lines. The WECC System features a mix of energy resources comprised of 36% natural gas generation, 25% hydroelectric power, and 25% renewable energy sources.

For more information about WECC visit: WECC Home Page

Significance of Hydroelectric Power in WECC

Hydrologic modeling plays a pivotal role in the Western Electricity Coordinating Council (WECC) system for many reasons. Primarily, the region boasts significant hydroelectric capacity, notably concentrated in the Pacific Northwest, home to expansive facilities such as those within the Columbia River Basin. Given this substantial water resource and power production infrastructure, precise modeling becomes imperative to forecast potential energy generation accurately, contingent upon fluctuating water flows influenced by seasonal variables like snowmelt and rainfall. These forecasts are indispensable for effectively managing energy supply fluctuations and ensuring balance between surpluses and deficits throughout the year. Furthermore, hydrologic modeling serves as a cornerstone for regulatory compliance and sustainable water resource management, particularly vital in regions where water rights and ecological impacts are rigorously regulated. The availability of hydroelectric power, a highly cost-effective energy source, plays a significant role in shaping electricity pricing and market operations. Effective hydro modeling enhances the accuracy of price forecasts, aiding utilities in making well-informed decisions about trading and risk management. Moreover, the ability of hydro plants to swiftly adjust output is paramount for supporting grid reliability and stability, particularly in balancing sudden demand shifts or supply disruptions. This underscores the indispensability of precise hydro modeling for operational planning.

As the WECC region progresses towards integrating more intermittent renewables like wind and solar, the flexibility and controllability of hydro resources become increasingly vital. Hydro modeling is essential in optimizing this integration, ensuring the grid is reliable in achieving performance along with economic and environmental aims. The most recent release of the PLEXOS WECC Nodal Dataset v24.3, presents enhanced tools designed to empower clients in analyzing hydro output with greater precision. This update introduces three important predefined hydro scenarios: 'Drought,' 'High Hydro,' and 'Average Hydro.' These scenarios are carefully crafted based on historical hydro conditions, providing users with the capability to simulate distinct hydrological circumstances. Moreover, the dataset now includes 22 years of unit-specific, historical monthly generation data. The extensive historical data in this latest release helps analysts to create hydro output simulations, allowing users and deciders to easily compare conditions and results from specific historical years to gain new insights and make thoughtful, evidence-based, defensible asset investment and operations decisions.

The figures below (Figure 1:Hydro Scenario Generation Comparison and Figure 2: Hydro Scenario Hourly Price Comparison) illustrate the impact of different hydro scenarios on generation and hourly electricity prices based on simulations from the latest WECC dataset. The "High Hydro" scenario, for instance, introduces an excess of low-cost hydroelectric power into the system, reducing reliance on more expensive generation sources such as natural gas and coal. Conversely, the "Drought Scenario" limits the availability of low-cost energy, requiring the use of higher-priced generators to meet demand. The price differential between "High Hydro" and "Drought" is particularly noticeable during the spring when hydro generation peaks.

Figure 1. Hydro Scenario Generation Comparison

Figure 1. Hydro Scenario Generation Comparison WECC Nodal

Figure 2. Hydro Scenario Hourly Price Comparison

Figure 2. Hydro Scenario Hourly Price Comparison WECC Nodal

Contingency Analysis

Contingency analysis is a vital part of nodal power flow modeling. It is essential for simulating potential system failures and assessing their effects on line flows, congestion rent, and locational marginal prices (LMP) throughout the network. Recognizing its importance, Energy Exemplar has greatly expanded the contingency data in the latest nodal dataset release, introducing 240 historical contingencies with 1,226 monitored lines and elements derived from the California Independent System Operator (CAISO) Day Ahead and Real Time Nomogram/Branch Shadow Price report. The data, covering the calendar years 2022 and 2023, has been analyzed and adapted to a compatible Power Flow/PLEXOS format. For enhanced clarity and easy cross-referencing, the names of the CAISO contingencies in PLEXOS are aligned with the ‘Constraint Cause’ names from the CAISO reports, easing straightforward reference to the original documents.

PLEXOS Cloud BI Analytics Compatibility

Launched in 2023, BI Analytics is a PLEXOS Cloud dynamic feature that simplifies dashboard and report creation directly within the platform. The latest update to the PLEXOS WECC Nodal Dataset includes historical data for benchmarking, custom columns and analytics variables that are optimized for integration with the BI Analytics feature, thus enhancing functionality and eliminating much of the need for post-simulation data processing.

BI Analytics enables the visualization of results from a single solution or the comparison of multiple solutions within the same study.

Figure 3: BI Analytics Report illustrates this capability, displaying historical prices alongside two different forecast simulations prepared using BI Analytics. This feature allows clients to directly plot and analyze simulation results, circumventing the cumbersome process of exporting data from PLEXOS, reformatting it for reporting purposes, and relying on third-party tools to create custom reports.

Figure 3. BI Analytics Report

Figure 3. BI Analytics Report WECC Nodal

New Analytic Variables

The dataset now includes new analytic variables for automatically calculating spark spreads and implied market heat rates, streamlining common forms of analysis in the energy sector. Spark spreads (Figure 4) are a metric used by energy market analysts to estimate the profitability of natural gas-fired CCGT power plants, enabling companies to make informed decisions about when to operate their gas-fired plants to maximize profitability.

The implied market heat rate, also referred to as the ‘break-even natural gas market heat rate,’ is another crucial metric in the power industry. It signifies the maximum heat rate to operate profitably, considering electricity prices and fuel costs while disregarding non-fuel expenses. It is calculated by dividing the electricity price by the fuel price. Only a natural gas generator operating below the implied heat rate value can profitably utilize natural gas for power generation, hence its alternative name. This metric plays a crucial role in business decision-making, including dispatch optimization of existing generators and general understanding of energy market conditions. By integrating these calculations directly into the dataset, the need for additional manual analysis is markedly reduced, enabling analysts to swiftly access critical insights into market dynamics and plant economics, facilitating more efficient and precise strategic planning and operational decisions.

Figure 4. Average CCCT Spark Spreads ($/MWh)

Figure 4. Average CCCT Spark Spreads ($/MWh) WECC Nodal

Figure 5. Implied Market Heat Rate (MMBtu/MWh)

Figure 5. Implied Market Heat Rate (MMBtu/MWh) WECC Nodal


Long-Term Planning and Scenarios

Long-term capacity expansion planning is crucial for maintaining reliable, efficient, and well-equipped electricity systems to meet future demands and comply with evolving regulatory requirements. It provides insight on forecasted future demands driven by population growth and economic development and provides a strategic roadmap for the expansion of power generation and transmission infrastructure. This planning process supports the best allocation of resources by finding the most effective mix of energy sources and technologies, balancing cost, reliability, and environmental impact. Additionally, it enables the structured integration of emerging technologies, ensuring the grid stays modern, flexible, and prepared for future advancements. Such planning supports utilities and grid operators in meeting stringent regulatory and policy mandates, such as renewable energy quotas and emissions reduction targets, helping to avoid penalties and contribute to national or regional environmental aims.

Capacity expansion plans are significantly influenced by decisions on generator retirements and the incorporation of renewable energy. The PLEXOS WECC Nodal Dataset v24.3 provides five distinct scenarios, allowing clients to select from preconfigured retirement scenarios or build upon the assumptions outlined in the dataset. These scenarios address varying assumptions about generator retirements and their implications for future capacity planning:

  • The "Base LTCE (Long-Term Capacity Expansion)" model includes planned retirements as reported by the EIA (Energy Information Administration) but excludes any fixed age or economic retirements.
  • The "Coal Retirements" model implements fixed-age retirements for coal plants, based on lifespans of either 50 or 85 years, depending on each generator’s current age. This reflects a structured phase-out of older, more polluting coal facilities as they reach the end of their operational life.
  • The "EIA Retirements" model determines retirements based on the operating life reported by the EIA, using a standardized lifespan benchmark for generators that offers a uniform approach to phasing out older equipment.
  • The "Historical Average Retirements" model factors in retirements based on the actual historical operating life of generators within the WECC, determined by service dates reported on the EIA-860. This scenario might indicate earlier retirements due to increased wear and tear on peaking units or strategic early retirements in transition to green energy.
  • The "Renewable/Zero Carbon" model, including the historical average retirements, mandates that only renewable or zero-carbon resources be built over the planning horizon. This scenario aggressively promotes the transition of the energy sector towards sustainable and environmentally friendly solutions.

Figure 6. LTCE Scenario Comparison (MW)

Figure 6. LTCE Scenario Comparison (MW) WECC Nodal

Carbon emissions analysis is a critical element of long-term energy planning. An extensive analysis of CO2 emissions was conducted for each model within the WECC system, considering the capacity additions as determined by each PLEXOS LTCE model. Projected emissions over the study horizon show notable differences, especially between the Renewable/Zero Carbon scenario and the other scenarios, which is largely attributed to the varying numbers of fossil fuel generators in each scenario.

In 2024, PLEXOS estimates that CO2 emissions will total 225 million metric tons (MMT) across the system. These emissions are projected to increase to 241 MMT and 254 MMT in the Historical Average and EIA Retirement scenarios, respectively, driven by an uptick in natural gas generation. Conversely, in the Base Model and Renewable/Zero Carbon scenarios, CO2 emissions are expected to decrease, falling to 216 MMT and 88 MMT by the end of the study period as renewable energy expansions gradually displace fossil fuel-based generation. Notably, emissions are forecast to steadily decline until 2032, when they are expected to stabilize due to increased operation of existing thermal generators to meet growing demand and new emissions from biogas generators.

Figure 7. Long Term Emissions Forecast (MMT)

Figure 7. Long Term Emissions Forecast (MMT) WECC Nodal  

The latest WECC Nodal Dataset v24.3 represents a significant advancement in energy modeling capabilities, reflecting Energy Exemplar's commitment to enhancing the precision and utility of our platform. By integrating detailed hydro scenarios, expanded contingency analysis, and advanced analytics variables like spark spreads and implied market heat rate, more accurate and nuanced energy market analyses are enabled.

These improvements support our clients in making informed decisions that are critical for operational success and strategic planning in the ever-evolving energy landscape. As we continue to innovate and respond to the needs of the industry, we invite feedback from our users to further refine and improve our offerings, ensuring that the PLEXOS WECC Nodal Dataset remains a vital resource for energy professionals worldwide.

Explore the full capabilities of the PLEXOS WECC Nodal Dataset v24.3 and see how it can enhance your energy modeling strategies - visit our Simulation Ready Datasets page now for detailed information.

Tiana Marmitt

Post by Tiana Marmitt

May 20, 2024

Tiana Marmitt is a seasoned energy industry professional with over a decade of experience in power market modeling, complemented by a background in economics that provides unique insights into energy markets. Currently, she serves as the Lead Energy Market Analyst at Energy Exemplar, where she provides functional leadership to a global team, develops simulation-ready datasets for North American energy markets, and engages in comprehensive market research to enhance energy market modeling accuracy. Before joining Energy Exemplar, Tiana had an extensive career as an Integrated Resources Planner for a utility located in the Western Interconnect. In this capacity, she performed production cost modeling and energy evaluations for long-term strategic planning and integrated resource plans (IRPs). She developed power supply budgets, Cash Flow at Risk (CFAR) models, maintained production cost model databases, and conducted cost/benefit analyses for various energy projects. Tiana also monitored regulatory and legislative changes, quantified their impact, and developed strategies and policy recommendations for regulatory compliance. Tiana's comprehensive expertise in energy market analytics, strategic planning, and regulatory compliance, combined with her educational background in economics, makes her a distinguished and knowledgeable professional in the energy sector.