PUCs are concerned that a rapid shutdown of coal-fired plants will start a full-tilt dash to gas—similar to the one that caused bankruptcies among independent power producers in the late 1990s and...
IRP Meets RPS
New green mandates force portfolio planners to re-think their models.
plans will influence when utilities build, or retire, generating resources. The makeup of the specific state or national plans—the percentage of renewable resources required, specific technology requirements, and even the definition of “renewable”—will influence what gets built or retired.
The locations of newly built capacity also will depend on specific RPS plans. Many feasible renewable technologies—wind, solar, geothermal—are location-dependent and have limited location options. This is why wind development is growing in Texas but not in Georgia.
Finally, resource planners need to balance renewable requirements with their demand forecasts and factor in retiring non-renewable resources.
Best practices in IRP require planners to model a variety of uncertainties. Traditionally these include fundamental uncertainties, such as commodity prices ( e.g., fuel, purchased power, emissions), basis, transmission, demand (peak and energy), long-term economic variables (escalation), generator energy limits for hydro or renewable resources, and generator availability (forced outage rates). These typically are best handled using Monte Carlo techniques. In addition to the traditional fundamental stochastic analysis, most IRPs will incorporate stress testing to capture extreme moves in fundamentals—for example, $200/barrel crude oil prices or major weather influences.
Regulatory uncertainty does not lend itself to traditional stochastic techniques, so planners generally will use scenario analysis to study and quantify the various regulatory possibilities. Because the IRP process is labor intensive, resource planners only can evaluate a limited number of scenarios. The art therefore is in choosing the most relevant scenarios. First, the planner needs to develop a finite, well-defined, and manageable number of scenarios based on known and possible state and national regulations. For example:
• States A and B both at 15 percent in 2020;
• State A at 15 percent, State B at 25 percent in 2020;
• National standard at 15 percent implemented in 2024; or
• National standard at 20 percent implemented in 2025.
Such scenarios can be combined with sensitivity analysis to measure the change in output relative to change in input (difference between the required 15 percent or 20 percent). Knowing the cross-over points for different technologies ( i.e., the points at which various technologies become or cease to be viable) based on the various possible regulations is valuable information. Of course, IRPs typically encompass between 20- and 30-year views, so everything is a bit uncertain.
A key uncertainty involves the evolution of markets for REC trading.
RECs—variously called green tags, renewable energy credits or certificates, tradable renewable certificates, and environmental attributes—represent a finite quantity of green or environmentally friendly energy generation or its equivalent. Buying such RECs can allow load-serving entities to meet their RPS obligations in more cost-effective ways than building or contracting for capacity in a non-optimal location.
Because electricity follows the path of least resistance rather than a direct, assignable route, purchasers of renewable (or green) energy don’t actually receive the renewable power, but they do expect it is being delivered to the grid. RECs provide a mechanism for tracking renewable energy and for decoupling it from energy production (that is, RECs can be purchased separately to offset energy purchases