The time-honored discounted cash flow method for determining appropriate utility returns falls short when interest rates are low. Inadequate ROEs ultimately increase cost of capital and wipe away...
IRP Meets RPS
New green mandates force portfolio planners to re-think their models.
much to build, etc. ), and factoring in different price, demand, and regulatory scenarios involves modeling complex utility operations and optimizing new resource additions. The final output of the IRP is the optimal resource plan. Software modeling techniques can help resource planners with these optimal plans, but the different mathematical algorithms deployed to optimize the utility portfolio present certain strengths and weaknesses. Dynamic programming techniques, for example, show all of the feasible capacity plans to meet demand given constraints, rather than a single optimal plan, and sometimes the preferred plan is not actually the optimal plan. These models, however, generally have less detailed dispatch and transmission representations than do other solutions. On the other hand, linear programming or mixed integer programming techniques provide only a single optimal plan, but these models typically excel at providing a more realistic dispatch, transmission representation, and constraint treatment.
For an IRP that must include RPS, the planning process—and the computational models it uses—must incorporate the different renewable portfolio limits as a percentage of energy:
• Renewable limits are treated similar to operating reserve (as a constraint);
• Existing and potential generators must be characterized in terms of contributing renewable energy credits (REC) or production tax credits (PTC);
• Once the RPS requirement is set, only renewable generation can contribute towards the minimum renewable percentage requirement; and
• For areas that don’t meet the RPS requirements, renewable resources must be added.
So if the RPS functions just like any other portfolio constraint (such as operating reserve, for example) then it should be easy simply to incorporate these parameters into the traditional IRP process. Unfortunately, it’s not quite that simple. Each state has its own requirements (s ee Figure 1 ). There also are regional and national criteria that need to be met. Regulatory uncertainty is a factor, as not every state has a requirement, and even states that do have requirements may change them over time. For example, in 2001 the state of Nevada implemented a requirement of 15 percent renewable energy capacity by 2013 and then, in 2005, amended this to 20 percent by 2015. Resource planners also need to consider factors such as renewable energy credits (REC), green tags, white tags, and other evolving market mechanisms.
Multi-State RPS Modeling
While traditional IRP is portfolio-based, regional organizations such as independent system operators (ISO) and regional transmission organizations (RTO) also perform resource planning to monitor and assess the effects on transmission-expansion planning. For example, the Midwest ISO (MISO), PJM, and other ISO/RTOs regularly perform transmission-expansion assessments for their regions. Companies also might own assets across multiple states. For example, AEP, PacifiCorp, Entergy, Exelon, Duke, Progress, and others have multi-state service territories. Transmission upgrades increasingly are a resource option, and transmission availability or adequacy presents a major constraint for some utilities. In these cases, the portfolio must be expanded to include some level of regional impacts.
For example, in PacifiCorp’s 2004 IRP, the company’s plan modeled two zones (West and East) with an intertie between them. The analysis included thermal generating, wind, demand- side