How The Energy Authority Uses PLEXOS® for Midterm and Long-Term Portfolio Strategy

Advancing Risk Intelligence for Public Power

The Energy Authority (TEA) provides power trading, gas trading, and portfolio management services to public power utilities across the United States. Serving municipals, joint action agencies, and an increasing number of electric cooperatives, TEA operates in nearly every organized U.S. power market.

Supporting public power entities across diverse markets requires a rigorous and adaptable analytical framework. Volatile commodity prices, evolving generation portfolios, renewable integration, load growth uncertainty, and extreme weather events all introduce material financial risk. To manage this complexity, TEA leverages Energy Exemplar’s PLEXOS® platform to conduct advanced risk analysis across both medium-term and long-term planning horizons.

Quantifying Financial Exposure Across Planning Horizons 

Public power utilities must manage risk on multiple time horizons. In the midterm, exposure stems from commodity price volatility, load variability, renewable output fluctuations, and forced outages. And, in the long term, capital investment and retirement decisions must be made under uncertainty regarding fuel prices, demand growth, renewable penetration, and structural market changes.

Traditional deterministic modeling provides expected-value projections but does not quantify the range of possible outcomes. For TEA’s clients, this limitation constrained decision-making. Utilities required clear answers to critical questions:

• What is the distribution of net power costs, including downside exposure?
• Does a financial hedge meaningfully reduce risk?
• How do long-term portfolios compare on a risk-adjusted basis?

To address these needs, TEA implemented a probabilistic modeling framework within PLEXOS® capable of simulating hundreds of correlated outcomes, quantifying downside risk, and evaluating portfolio resilience under extreme conditions.

Integrated Stochastic Simulation with PLEXOS® 

Having established a robust stochastic framework for operational and hedge analysis, TEA recognized the need to apply the same risk discipline to long-term resource planning. This evolution brought probabilistic insight into 20-year investment decisions.

Medium-Term Risk Analysis: Distribution-Based Cost Forecasting

In the midterm horizon, TEA generates approximately 700 stochastic iterations of key variables, including:

• Power prices

• Natural gas prices
• Utility-level load
• Renewable generation

This iteration count balances computational efficiency with stable estimation of extreme outcomes.

Each stochastic draw produces a unique set of model inputs that are fed into utility-specific PLEXOS® models. Unique forced outage draws are incorporated into each simulation, allowing operational uncertainty to be reflected in dispatch outcomes.

PLEXOS® performs hourly dispatch optimization across all iterations, producing a distribution of financial results.

Quantifying Risk

From these distributions of outputs such as net power costs, which includes generation costs, load purchases costs, and generation revenues for utilities operation within ISOs, TEA develops measurable risk metrics.

Risk is typically measured using “At-Risk”– the different between the 95^th percentile outcome and the mean but also use expected shortfall which is the average of all iterations above the 95^th percentile.

This framework enables utilities to evaluate both expected performance and downside exposure.

Evaluating Financial Hedges

A primary application of midterm stochastic modeling is evaluating prospective financial power or natural gas hedges.

After generating the distribution of net power costs, TEA layers in proposed hedges and measures their impact on risk metrics. The objective is to determine whether the hedge narrows the cost distribution, reduces value at risk, or aligns with the utility’s risk tolerance.

Hedge effectiveness varies depending on existing generation structure and existing hedge positions. This distribution-based evaluation replaces intuition with quantitative evidence.

Long-Term IRP: Risk-Adjusted Portfolio Planning

Long-term integrated resource planning (IRP) historically relied on deterministic modeling to identify optimal capacity additions or retirements over a 20-year horizon. Scenarios might reflect differing assumptions regarding:

• Load growth
• Economic conditions
• Data center demand
• Full requirements participation
• Renewable policy goals

While this approach supported expected cost comparison, it did not quantify financial variability across portfolios.

To enhance long-term decision-making, TEA extended its stochastic framework into IRP analysis.

Stochastic IRP Workflow

To embed risk transparency directly into long-term planning decisions, TEA integrates stochastic simulation into its traditional IRP modeling process.

The enhanced workflow consists of:

1. Running a deterministic PLEXOS® Long-Term (LT) Plan to identify an optimized portfolio
2. Generating stochastic forecasts for commodity prices, load, and renewable generation
3. Reintegrating the optimized portfolio into PLEXOS®
4. Running PASA, MT, and ST schedules across stochastic iterations
5. Post-processing results to generate cost distributions for each portfolio

This approach produces quantile ranges, mean outcomes, and outliers for each scenario, enabling direct risk-adjusted comparison.

Risk-Adjusted Decision-Making Across Horizons

By integrating stochastic simulation with PLEXOS® across both Medium-Term and Long-Term workflows, TEA delivers measurable value to public power clients:

Comprehensive Risk Visibility

• Robust net power cost distributions
• Clear Value at Risk and expected shortfall metrics
• Extreme event stress testing across portfolios

Improved Hedge and Portfolio Evaluation

• Evidence-based assessment of financial hedge effectiveness
• Risk-adjusted comparison of long-term resource portfolios
• Clear identification of cost variability and downside exposure

Post-processing tools make complex modeling outputs accessible beyond technical modeling teams, supporting broader strategic and financial decision-making.

This integrated approach ensures that risk is quantified consistently across operational and planning horizons.

Embedding Risk Intelligence into Portfolio Strategy 

Public power utilities operate in markets defined by uncertainty. Commodity volatility, evolving resource mixes, and extreme events require disciplined, forward-looking analytics.

By integrating stochastic simulation with PLEXOS® across both medium-term operations and long-term resource planning, The Energy Authority has built a unified risk analytics framework. From hedge evaluation to IRP portfolio comparison and stress testing, TEA delivers risk-adjusted insight that strengthens financial resilience.

The result is a structured, data-driven approach that aligns strategic planning with quantified risk—enabling public power utilities to make confident decisions in an increasingly complex energy landscape.