Advanced Insights into Power System Modeling for Long-Term Resource Planning

Introduction

In an era where the electrical grid is undergoing rapid transformation, understanding the results from power system models becomes critical for stakeholders engaged in long-term resource planning. This guide, developed by the National Renewable Energy Laboratory (NREL), extends beyond the basics to equip utility commission staff, state energy office personnel, and interested stakeholders with the knowledge to better evaluate Integrated Resource Plans (IRPs).

Building on the foundational Beginner’s Guide to Understanding Power System Model Results, this advanced edition delves deeper into key modeling themes, specifically:

• Electricity Demand Evolution
• Demand-Side Resources
• Resource Adequacy
  

Additional insights into clean energy mandates, environmental justice, resilience, and integrated planning further position readers to assess the robustness of energy strategies in a decarbonizing grid.

Evolving Electricity Demand

Drivers of Demand Growth

Historically, electricity demand has grown modestly. However, with widespread electrification—especially in transportation (electric vehicles) and heating (heat pumps)—demand profiles are expected to shift dramatically. This trend is magnified by policies like the Inflation Reduction Act that incentivize electrification.

Modeling Demand Shifts

Demand isn’t simply about more electricity—it's about when and where that electricity is used. This requires:

• Bottom-up modeling of utility service territories
• Inclusion of weather-dependent load variations
• Estimations of adoption for technologies like EVs and smart thermostats
  

The modeling must account for peak shifts, such as the transition from summer afternoon peaks to winter morning peaks due to electric heating. As shown in Figure 1 on page 3, the modeled growth scenarios highlight the need for flexible and resilient grid planning.

Demand-Side Resources: Unlocking Flexible Load

Categories and Modeling Approaches

Demand-side resources (DSRs) include any intervention that alters the shape or magnitude of electricity consumption. They may be:

• Prespecified (Exogenous): Modeled based on external forecasts
• Selectable (Endogenous): Chosen by the planning model based on cost-effectiveness

Examples include:

• Distributed Generation (e.g., rooftop PV)
• Energy Efficiency (e.g., insulation)
• Demand Response (e.g., shifting load)
• Demand Flexibility (e.g., EV charging)
• Time-based Pricing mechanisms

Challenges in Inclusion

DSRs are difficult to quantify due to:

• Heterogeneity of building stock and consumer behavior
• Difficulty in forecasting adoption and cost
• Lack of robust performance data compared to traditional generation
  

However, when modeled correctly, demand-side interventions can defer infrastructure upgrades, reduce emissions, and improve system efficiency.

Resource Adequacy: Ensuring Grid Reliability

Definition and Importance

NERC defines resource adequacy as the grid’s ability to meet load at all times, accounting for outages and unexpected events.

Events like:

• California’s 2020 heatwave
• Winter Storm Uri (2021)
• Winter Storm Elliott (2022)

These have underscored the stakes in planning for rare but impactful disruptions.

RESOURCE ADEQUACY - Figure 2 on page 6 shows that weather-driven unplanned outages can far exceed expected norms, demanding new modeling approaches that address this volatility.

Modeling Metrics

Common metrics include (Attached table coming from page 7):

• Planning Reserve Margin (PRM)
• Loss-of-Load Expectation (LOLE)
• Loss-of-Load Hours (LOLH)
• Expected Unserved Energy (EUE)

Each has trade-offs between interpretability and accuracy. LOLE (e.g., 0.1 events/year) is widely used, but lacks granularity on event size or cost implications.

Capacity Credit and ELCC

To value individual resources' contribution to adequacy, models often use:

• Capacity Credit (% of nameplate capacity usable at peak)
• Effective Load Carrying Capability (ELCC)

These require rigorous data, especially as grids integrate more variable renewable energy and storage. Advanced models simulate thousands of scenarios, considering outages, weather, and load correlations, though this is computationally intensive.

Clean Energy Standards and Planning Implications

As more jurisdictions adopt 100% clean energy mandates, modeling must adapt to:

• Technology eligibility (e.g., CCS-equipped gas, nuclear)
• Treatment of imports/exports
• Accounting basis (generation vs. sales)
• Temporal matching (real-time generation vs. annual netting)
  

Decarbonization strategies, including:

• Wind, solar, geothermal, hydropower
• Bioenergy and H2 combustion
• Carbon capture and storage (CCS)
• Seasonal storage
• Demand-side measures
• Carbon dioxide removal (CDR)
  

The planning model must be capable of representing these diverse technologies with appropriate constraints and cost structures.

Energy, Environmental, and Climate Justice

Addressing Historical Inequities

Energy justice emphasizes the fair distribution of energy benefits and burdens. Modeling should include:

• Recognition Justice: Identifying disadvantaged communities
• Procedural Justice: Involving these communities meaningfully
• Distributive Justice: Ensuring equitable outcomes
  

IRPs should:

• Incorporate metrics for pollution and energy burden
• Enable participation through outreach
• Quantify impacts by demographics and location
  

Grid Resilience and Extreme Events

Defining Resilience

While reliability deals with expected system performance, resilience addresses how the grid withstands extreme or unpredictable events.

Models must begin to reflect:

• Geographic dependencies (e.g., hurricane zones)
• Interdependencies (e.g., electric and gas systems)
• Microgrids and backup systems

Integrated Planning and Transparency

Cross-Disciplinary Planning

Historically, generation, transmission, and distribution planning have been siloed. Enhanced coordination helps:

• Align assumptions and timelines
• Maximize shared resource value

Joint review processes, as described on page 12, are helping integrate these traditionally separate domains.

Transparency and Public Engagement

Open-source models and published assumptions (e.g., cost tables, load forecasts) foster trust and allow stakeholders to provide informed feedback.

• Review committees during IRP development
• Public availability of data and models
• Clear documentation of methods
  

Conclusion

As power systems transition toward decarbonization, increased electrification, and distributed resources, modeling sophistication must grow accordingly. The NREL Advanced Guide highlights that:

• Trade-offs between reliability, equity, and cost are inevitable
• Demand-side and clean energy technologies must be valued accurately
• Stakeholder engagement and transparency are central to effective planning

By embracing these principles, utilities, regulators, and stakeholders can shape a resilient, inclusive, and sustainable energy future.

See our Article: Advanced PSCAD Modeling for Substations, Renewables & Grid Compliance

FAQs: Power System Model Results for Long-Term Resource Plans

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