Public Utilities Reports

PUR Guide 2012 Fully Updated Version

Available NOW!
PUR Guide

This comprehensive self-study certification course is designed to teach the novice or pro everything they need to understand and succeed in every phase of the public utilities business.

Order Now

Clearing the Air On Emissions

How utilities can take a portfolio-management approach to environmental compliance.

Fortnightly Magazine - August 2005

regional emissions, to estimate future allowance costs. If these models assume that investments in environmental controls will be reflected in the allowance prices, the equilibrium price of NO x allowances then can be defined as the price that equals the long-term marginal cost of emission controls for the last ton of NO X reduced under the cap.

3) Link Regional-Emissions Analysis to Portfolio-Specific Analysis. 

An optimal emissions management process calls for a price-coupled approach to assessing the wider strategic impact of alternative legislation on portfolio mixes by simulating the operation of the portfolio under different scenarios.

A portfolio simulation should monetize the value of generation assets and associated contracts against market prices and provide for alternative technologies, plant improvement options, and contracting or new build alternatives covering both medium and long-term horizons. With the portfolio planning solution, planners can evaluate all the aspects affecting the generation fleet, including:

  • The uncertainty surrounding multiple emissions market prices and their correlations with fuel markets;
  • Evaluation of the trade-offs of control technology versus renewable investment;
  • Evaluation of the impact of seasonal caps and emission credits; and
  • Evaluation of the cost of unit outage during investment.

4) Model Generation Alternatives: How Do You Create an Expansion Plan Taking Emissions Into Account? 

Another component of the portfolio-planning piece includes a methodology that assesses appropriate capacity expansion and retirement alternatives for the portfolio, and allows the automated evaluation and screening of alternative contracting strategies under various emission scenarios. Mixed-integer linear programming-based techniques that are integrated into the broader EPM framework have an advantage in that they quantify the "shadow cost" of portfolio alternatives, thereby allowing for a direct comparison of renewable alternatives versus retrofitting existing assets and participating in cap-and-trade programs.

Having established the strategic framework of market outcomes and portfolio options, each individual investment decision needs to be reviewed for both risk and return from a portfolio perspective. This can be achieved by adding the underlying market uncertainty on top of the scenario analysis. Neither a scenario with a high-gas case nor another with a low-gas case may reflect the underlying volatility of the commodity. We thus need to add another dimension-uncertainty-to the evaluation.

For a robust portfolio assessment, these regional uncertainty variables at a minimum should include:

  • Load growth;
  • Resource mix;
  • Fuel cost and availability;
  • Performance of future resource additions;
  • Emission allowances, restrictions, and costs;
  • Alternative emissions regulations or rules;
  • Alternative bidding strategies from market participants; and
  • Alternative resource and fuel technologies and retirement options.

Uncertainty and the relationship among key drivers (often referred to as volatility and correlation) need to be incorporated to ensure the flexibility of the portfolio is fully considered. For instance, a long-term contract may provide the lowest-cost solution on average under the scenario analysis, but it could impose significant risk if prices are volatile.

Having distilled the options available and assessed plausibility of different scenarios as well as underlying fundamental uncertainties through a systematic process, the road ahead becomes clearer and allows the organization to align its goals and actions accordingly. But is this enough? How do the insights from modeling and