The Federal Energy Regulatory Commission (FERC) docket on power system resilience and the recently-leaked Department of Energy memo suggesting that Secretary of Energy Rick Perry is contemplating measures to prop up struggling coal and nuclear generators are volleys in a wide-ranging and highly-spirited regulatory debate around electricity markets. 

On one hand, there is a debate surrounding how to account for state subsidies for certain generators through minimum offer pricing rules (MOPRs) and FERC's recent 3-2 decision provisionally approving ISO New England's alternative approach. There is also a conversation about the right regulatory structure for making capacity decisions, with many eyes on high wholesale prices in ERCOT's energy-only market and only a few wonky American eyes on Alberta's measured transition to a capacity market. And in the West, the War of the Markets continues.

These debates generally focus on defining market structure and determining how state policy can be harmonized with sound capacity decision making. However, the debate ignores a vital part of the conversation — making effective capacity investment decisions is hard.

The uncertainties of new generation investment

Investing in new generation involves innate risks, and this uncertainty is compounded by structural changes in electricity markets driven by higher penetrations of renewables and coal retirements. Deterministic least-cost capacity expansion models can paint an incomplete picture; optimal capacity decisions should reflect a range of possible futures, and resource plans should have the flexibility to adjust to changing conditions.

A number of factors contribute to uncertainties in utility investment decision-making, including load growth, demand-side management (DSM) and distributed energy resource (DER) deployment, fuel prices, macro-economic factors and the regulatory environment.

An LBNL report indicated that utilities generally adapt resource decisions to changing conditions, such as an unexpected change in load growth, in real-time. However, in states with regulated utilities, the ability of a utility to incorporate new information in investment decisions could be constrained by an Integrated Resource Plan (IRP), or the utility may have to modify the original IRP. The authors of the report suggest that there is an intrinsic challenge in maintaining the balance between the long-term planning objectives of IRPs and the need to be responsive to changing conditions.

However, flexibility in decision-making, which is determined by the regulatory environment and/or the culture of a utility, is only part of the challenge of determining an optimal capacity investment strategy. Properly quantifying uncertainty presents an additional challenge.

Increasing computational power is giving utilities the ability to incorporate uncertainty into modeling, which is helping shift the focus of resource planning from a simple least-cost framework to least-risk, least-cost. Not all utilities systematically quantify risk in evaluating capacity investment decisions, and those that do vary in their level of sophistication.

Calibrating credible planning scenarios

Scenario analysis with a few sensitivities, such as a scenario evaluating a new natural gas combined cycle investment with natural gas price sensitivities, are unlikely to fully capture the risks of a proposed portfolio decision. Even for utilities using sophisticated sensitivity analysis that can model thousands of different possible futures, calibrating credible planning scenarios under uncertainty is a non-trivial problem.

For most of the 20th and early 21st centuries, systematic uncertainty analysis was used much less frequently than it is now for a few reasons.

First, computational power was not as cheap and accessible as it is today, so uncertainty metrics are easier to quantify for today's analysts. More importantly, the smaller and modular generators that can be deployed quickly today were simply not available, and energy efficiency was the only option for meeting small increases in load. Now, utilities can call upon a range of modular resources in addition to energy efficiency, such as DERs, solar and small natural gas generators.

Additionally, the need for uncertainty analysis is being driven by changing power sector dynamics. Flat and declining loads mean that regulators and utilities have to adapt to additional uncertainty in anticipating future demand.

Furthermore, ongoing increases in renewable penetration and the falling cost of electricity storage create uncertainty about which generators will be needed in the future energy system. Finally, regulatory uncertainty around state and federal electricity sector policy adds to the potential risk of investing in large new generators.

DER challenges

DER deployment presents a clear example of the type of challenge resource planners face making investment decisions under uncertainty, because DER adoption patterns are influenced by a range of factors and can be challenging to forecast. Another LBNL report outlines how some utilities have adapted planning practices to strategically make decisions more robust to uncertainty in DER deployment, which the authors dub acquisition path analysis.

The report notes that more simplistic approaches evaluate a range of distributed solar photovoltaic deployment cases through different sensitivities or scenarios. While this helps assess whether particular plans are preferred across a range of uncertainties, acquisition path analysis involves more flexible planning that develops strategies that are more robust to changes.

The same principles apply for larger bulk system investment decisions as well. With more regulatory or load forecast uncertainty, there may not be as strong a case for making a large investment in generation that will take decades to recoup.

For example, if a state is considering a cap-and-trade policy for carbon emissions, a utility may hold off on a large natural gas combined cycle investment in that state until it is clear whether the policy will be enacted. Absent regulatory certainty, a utility may also defer investing in such a plant to meet increasing load if it sees demand forecasts as highly uncertain.

Flexibility in decision making

As the uncertainty of investment decisions increases, different resource attributes that emphasize flexibility in decision making become more valuable.

For example, resources with shorter construction lead times can help reduce the risk that a utility makes large capital investments that turn out not to be needed because of inaccurate demand forecasts. Smaller, more modular generators that can be deployed at smaller sizes and size increments, such as solar, also become more valuable, as they allow a utility to more precisely build to meet smaller increases in demand through just-in-time investments, which can help reduce the risk of over- or under-building.

Solar, storage, energy efficiency, DR and demand-side flexibility, combined heat and power generators, and internal combustion engines can all provide this additional flexibility. These flexible attributes allow utilities to make just-in-time investments to meet changing load and can help reduce overall financial exposure.

If emphasizing flexibility in resource decision-making can improve financial performance, then why don't all utilities use this resource decision framework that systematically incorporates uncertainty?

For some smaller utilities, this approach may exceed computational capabilities or financial know-how. Utilities in traditionally regulated states may not have as strong an incentive to adopt this framework, as ratepayers can bear the risk of larger investments and utilities get a rate-of-return on investments, which could discourage smaller investments.

However, some utilities are using this approach by incorporating a systematic financial analysis of uncertainty. For example TVA evaluates a range of different strategies designed to adapt to changing conditions. Similarly, the Northwest Power and Conservation Council's GENESYS model uses probabilistic Monte Carlo simulations to assess resource needs, which helps inform resource decisions that are robust across a range of possible futures.

In a deregulated state like Texas, investors may have more of an incentive to use a more flexible investment strategy in a more uncertain environment, as investors that pursue larger, more risky investments fully bear the consequences if the investment isn't profitable.

How can planning entities incorporate these considerations?

In regulated states, commissions can work with utilities to consider just-in-time investments when load forecast or regulatory uncertainty is higher, sharing risk between ratepayers and shareholders. In capacity markets, the system operator could conduct systematic uncertainty analysis in determining future resource needs and hold shorter-term forward auctions to help address issues like load forecast uncertainty.

In short, while the ISOs and FERC are arguing about the finer points of harmonizing state policy objectives with the sanctity of interstate commerce, they are missing an important part of the capacity market design discussion that could implicitly favor more expensive, higher risk investments. Especially given the high levels of uncertainty in the electricity sector, ignoring a framework for flexible just-in-time investment to meet shorter-term capacity needs could saddle ratepayers with additional risks and cost.