Charging for non-customer-specific fixed costs

UC Berkeley economist Severin Borenstein has a really, really great post at the Energy at Haas blog on utility fixed charges to recoup system fixed costs. If you want a primer on volumetric versus two-part pricing, this is a good one. After a very clear and cogent explanation and illustration of the differences among variable costs, customer-specific fixed costs, and system fixed costs, he says

Second, as everyone who studies electricity markets knows (and even much of the energy media have grown to understand), the marginal cost of electricity generation goes up at higher-demand times, and all generation gets paid those high peak prices.  That means extra revenue for the baseload plants above their lower marginal cost, and that revenue that can go to pay the fixed costs of those plants, as I discussed in a paper back in 1999. …

The same is not true, however, for distribution costs.  Retail prices don’t rise at peak times and create extra revenue that covers fixed costs of distribution.  That creates a revenue shortfall that has to be made up somewhere. Likewise, the cost of customer-specific fixed costs don’t get compensated in a system where the volumetric charge for electricity reflects its true marginal cost.

He continues with a good discussion of the lack of a theoretical economic principle informing distribution fixed costs.

I want to take it in another, complementary, direction. The asymmetry he points out is, of course, an artifact of cost-based regulated rate recovery, which means that even under retail competition this challenge will arise, even though his explanation of it is articulated under fixed, regulated rates. And the fact that late night regulated rates are higher than energy costs may not generate a revenue excess that would be sufficient to pay the system fixed costs portion in the way he describes as happening in wholesale markets and transmission fixed costs. This is a thorny problem of cost-based regulation.

Consider a regulated, vertically-integrated distribution utility. This utility offers a menu of contracts — a fixed price, a TOU price, and a real-time price (the attentive among you will notice that this setup approximates what we studied in the GridWise Olympic Peninsula Project). It’s possible, as David Chassin and Ross Guttromson demonstrated, for the utility to find an efficient frontier among these three contract types to maximize expected revenue in aggregate across the groups of customers choosing among those contracts. That’s a situation in which retail revenue does vary, driven especially by the RTP customers, and revenue can be higher to the extent that there’s a core of inelastic retail demand. But they still have to figure out a principle, a rule, a metric, an algorithm for sharing those distribution system fixed costs, or for taking them into account when setting their fixed and TOU prices. And then to be non-discriminatory, they’d probably have to allocate the same system fixed costs to the RTP customers too. So we’re back where we started.

And this is also the case under retail competition. Take, for example, this table of delivery charges in Texas, where the regulated utilities are transmission and distribution wires companies.  It breaks them down between customer fixed charges and system fixed charges, but it’s still the same type of scenario as Severin describes.

As long as there’s a component of the value chain that’s cost-recovery regulated, and as long as that component has system-specific and customer-specific fixed costs, this question will have to be part of the analysis.

A related question is whether, or how, the regulated utility will be permitted to provide services that generate new revenue streams that will allow them to cover those costs. That’s a thicket I’ll crawl into another day.

Ride sharing and overcoming taxi discrimination

An interesting story from NPR caught my eye: how ride sharing apps make it easier for riders to overcome racial discrimination. The story tells the tale of Skinny Pants Guy, “a dude who was in his mid-20s — slim, with neat, shoulder-length locks, skinny chinos, loafers and a leather briefcase slung across his torso — standing on the corner, his arm raised skyward. He was trying without luck to hail a cab. …”If I was carrying a gun, where could I even hide it?” he said to us in exasperation.”

Although technically illegal and opening taxi drivers to regulatory punishment and fines if they refuse to accept passengers, racial discrimination among taxi drivers does occur.

Many people of color are embracing these services as a way to avoid discrimination from traditional taxicab drivers. There’s more than anecdotal evidence that that discrimination is widespread. A yearlong investigation by a local reporter in Washington, Russ Ptacek, found last year that taxicab drivers were significantly less likely to stop for black fares than for white fares who were dressed the same.

Phenomena like taxi driver discrimination are an economic conundrum — even in the case of Skinny Pants Guy, who is professionally dressed and unlikely to pose a threat to the driver, why choose no fare rather than pick up the passenger?

Gary Becker’s 1957 book The Economics of Discrimination analyzed this phenomenon, and Becker’s model (inserting a “race taste” parameter into a standard utility function) showed that such discrimination was harmful to both the person discriminated against and the person practicing the discrimination. This model has its limits, of course; if you’re working in a standard framework with stable preferences, this taste parameter represents inherent racism and uses that framework to estimate the cost of such preferences. It doesn’t allow for changing preferences as time and circumstances change. An alternative model that can explain the same phenomenon without such a taste parameter would be a screening model — based on location (good/bad neighborhood) and/or race and/or clothing, a taxi driver could draw inferences based on history or preconceptions and choose whether or not to accept the passenger.

Ride sharing, and technology features such as the ability of riders to rate drivers and vice versa, give riders competing alternatives. What drives this beneficial outcome? Are drivers more willing to pick up riders because they know that they will get to submit a rider rating ex post? Are drivers more willing to pick up riders because the fact that they actively chose Uber or Lyft signals to the driver that such a rider in less likely to be a threat? If that’s the case, why is that the case?

Whatever the motivation, I’m intrigued by this aspect of ride sharing because it’s an unintended benefit, and a broader social benefit of ride sharing. One reason why I’m so interested in ride sharing (see how many posts Mike and I have done!) is that in the process of enabling asset owners to monetize their “dead capital”, it taps into what Adam Smith called our “fellow-feeling” — it inclines us more to sympathy (in the Smithian sense) with the other person, and to act with tolerance. I also think that the reciprocal/mutual ratings system is an institutional design that harnesses fellow-feeling, by giving both drivers and riders an incentive to imagine being in the situation of the other party, and to consider the effects of their behavior on the other party.

Solar generation in key states

I’ve been playing around with some ownership type and fuel source data on electricity generation, using the EIA’s annual data going back to 1990. I looked at solar’s share of the total MWH of generated electricity in eight states (AZ CA IL NC NJ NY OH TX), 1990-2012, and express it as a percentage of that total, here’s what I got:

solar share since 1990

In looking at the data and at this graph, a few things catch my attention. California (the green line) clearly has an active solar market throughout the entire period, much of which I attribute to the implementation of PURPA qualifying facilities regulations starting in 1978 (although I’m happy to be corrected if I’m mistaken). The other seven states here have little or no solar market until the mid-2010s; Arizona (starts having solar in 2001) and Texas (some solar before restructuring, then none, then an increase) are exceptions to the general pattern.

Of course the most striking pattern in these data is the large uptick in solar shares in 2011 and 2012. That uptick is driven by several factors, both economic and regulatory, and trying to distentangle that is part of what I’m working on currently. I’m interested in the development and change in residential solar market, and how the extent and type of regulatory policy influences the extent and type of innovation and changing market boundaries that ensue. Another way to parse the data is by ownership type, and how that varies by state depending on the regulatory institutions in place. In a state like North Carolina (teal), still vertically-integrated, both the regulated utility and independent power producers own solar. The path to market, and indeed whether or not you can actually say that a residential solar market qua market exists, differs in a vertically-integrated state from, say, New Jersey (orange) or Illinois (purple, but barely visible), where thus far the residential solar market is independent, and the regulated utility does not participate (again, please correct me if I’m mistaken).

It will be interesting to see what the 2013 data tell us, when the EIA release it in November. But even in California with that large uptick, solar’s share of total MWH generated does not go above 2 percent, and is substantially smaller in other states.

What do you see here? I know some of you will want to snark about subsidies for the uptick, but please keep it substantive :-).

Why does a theory of competition matter for electricity regulation?

For the firms in regulated industries, for the regulators, for their customers, does the theory underlying the applied regulation matter? I think it matters a lot, even down in the real-world trenches of doing regulation, because regulation’s theoretical foundation influences what regulators and firms do and how they do it. Think about a traditional regulated industry like electricity — vertically integrated because of initial technological constraints, with technologies that enable production of standard electric power service at a particular voltage range with economies of scale over the relevant range of demand.

When these technologies were new and the industry was young, the economic theory of competition underlying the form that regulation took was what we now think of as a static efficiency/allocation-focused model. In this model, production is represented by a known cost function with a given capital-labor ratio; that function is the representation of the firm and of its technology (note here how the organization of the firm fades into the background, to be re-illuminated starting in the mid-20th century by Coase and other organizational and new institutional economists). In the case of a high fixed cost industry with economies of scale, that cost function’s relevant characteristic is declining long-run average cost as output produced increases. On the demand side, consumers have stable preferences for this well-defined, standard good (electric power service at a particular voltage range).

In this model, the question is how to maximize total surplus given the technology, cost function, and preferences. This is the allocation question, and it’s a static question, because the technology, cost function, and preferences are given. The follow-on question in an industry with economies of scale is whether or not competition, rivalry among firms, will yield the best possible allocation, with the largest total surplus. The answer from this model is no: compared to the efficient benchmark where firms compete by lowering price to marginal cost, a “natural monopoly” industry/firm/cost structure cannot sustain P=MC because of the fixed costs, but price equal to average cost (where economic profits are “normal”) is not a stable equilibrium. The model indicates that the stable equilibrium is the monopoly price, with associated deadweight loss. But that P=AC point yields the highest feasible total surplus given the nature of the cost function. Thus this static allocative efficiency model is the justification for regulation of prices and quantities in this market, to make the quantity at which P=AC a stable outcome.

The theory of competition underlying this regulatory model is the static efficiency model, that competition is beneficial because it enables rival firms to bid prices down to P=MC, simultaneously maximizing firm profits, consumer surplus, and output produced (all the output that’s worth producing gets produced). Based on this model, legislators, regulators, and industry all influenced the design of regulation’s institutional details — rate-of-return regulation to target firm profits at “normal” levels, deriving retail prices from that, and erecting an entry barrier to exclude rivals while requiring the firm to serve all customers.

So what? I’ve just argued that regulatory institutional design is grounded in a theory of competition. If institutional designers hold a particular theory about what competition does and how it does it, that theory will inform their design to achieve their policy objectives. Institutional design is a function of the theory of competition, the policy objectives, and the ability/interest of industry to influence the design. If your theory of competition is the static allocative efficiency theory, you will design institutions to target the static efficient outcome in your model (in this case, P=AC). You start with a policy objective or a question to explore and a theory of competition, and out of that you derive an institutional design.

But what if competition is beneficial for other reasons, in other ways? What if the static allocative efficiency benefits of competition are just a single case in a larger set of possible outcomes? What if the phenomena we want to understand, the question to explore, the policy objective, would be better served by a different model? What if the world is not static, so the incumbent model becomes less useful because our questions and policy objectives have changed? Would we design different regulatory institutions if we use a different theory of competition? I want to try to treat that as a non-rhetorical question, even though my visceral reaction is “of course”.

These questions don’t get asked in legislative and regulatory proceedings, but given the pace and nature of dynamism, they should.

Technology market experimentation in regulated industries: Are administrative pilot projects bad for retail markets?

Since 2008, multiple smart grid pilot projects have been occurring in the US, funded jointly through regulated utility investments and taxpayer-funded Department of Energy cost sharing. In this bureaucratic market environment, market experimentation takes the form of the large-scale, multi-year pilot project. The regulated utility (after approval from the state public utility commission) publishes a request for proposals from smart grid technology vendors to sell devices and systems that provide a pre-determined range of services specified in the RFP. The regulated utility, not the end user, is thus the vendor’s primary customer.

When regulated incumbent distribution monopolists provide in-home technology to residential customers in states where retail markets are nominally competitive but the incumbent is the default service provider, does that involvement of the regulated incumbent have an anti-competitive effect? Does it reduce experimentation and innovation?

In markets with low entry and exit barriers, entrepreneurship drives new product creation and product differentiation. Market experimentation reveals whether or not consumers value such innovations. In regulated markets like electricity, however, this experimentation occurs in a top-down, procurement-oriented manner, without the organic evolution of market boundaries as entrants generate new products and services. Innovations do not succeed or fail based on their ability to attract end-use customers, but rather on their ability to persuade the regulated monopolist that the product is cost-reducing to the firm rather than value-creating for the consumer (and, similarly, their ability to persuade regulators).

The stated goal of many projects is installing digital technologies that increase performance and reliability of the basic provision of basic wires distribution service. For that reason, the projects emphasize technologies in the distribution wires network (distribution automation) and the digital meter at each home. The digital meter is the edge of the wires network, from the regulated utility’s perspective, and in restructured states it is the edge of its business, the edge of the regulated footprint. A secondary goal is to explore how some customers actually use technology to control and manage their own energy use; a longer-run consequence of this exploration may be consumer learning with respect to their electricity consumption, now that digital technology exists that can enable them to reduce consumption and save money by automating their actions.

In these cases, consumer technology choices are being made at the firm level by the regulated monopolist, not at the consumer level by consumers. This narrowed path to market for in-home technology changes the nature of the market experimentation – on one hand, the larger-volume purchases by regulated utilities may attract vendors and investors and increase rivalry and experimentation, but on the other hand, the margin at which the technology rivalry occurs is not at the end-user as decision-maker, but instead at the regulated utility. The objective functions of the utility and their heterogeneous residential customers differ substantially, and this more bureaucratic, narrowed experimentation path reduces the role of the different preferences and knowledge of those heterogeneous consumers. In that sense, the in-home technology choice being in the hands of the regulated utility stifles market experimentation with respect to the preferences of the heterogeneous consumers, although it increases experimentation with respect to the features that the regulated monopolist thinks that its customers want.

Focusing any burgeoning consumer demand on a specific technology, specific vendor, and specific firm, while creating critical mass for some technology entrepreneurs, rigidifies and channels experimentation into vendors and technologies chosen by the regulated monopolist, not by end-use consumers. Ask yourself this counterfactual: would the innovation and increase in features and value of mobile technologies have been this high if instead of competing for the end user’s business, Apple and Google had to pitch their offerings to a large, regulated utility?

These regulated incumbent technology choices may have anti-competitive downstream effects. They reduce the set of experimentation and commercialization opportunities available to retail entrants to provide product differentiation, product bundling, or other innovative value propositions beyond the scope of those being tested by the incumbent monopolist. Bundling and product differentiation are the dominant forms that dynamic competition take, and in this industry such retail bundling and product differentiation would probably include in-home devices. The regulated incumbent providing in-home technology to default customers participating in pilot projects reduces the scope for competing retail providers to engage in either product differentiation or bundling. That limitation undercuts their business models and is potentially anti-competitive.

The regulated incumbent’s default service provision and designation of in-home technology reduces a motive for consumers to search for other providers and other competing products and services. While they may argue that they are providing a convenience to their customers, they are substituting their judgment of what they think their customers want for the individual judgments of their customers.

By offering a competing regulated retail service and leveraging it into the provision of in-home devices for pilot projects, the incumbent reduces the set of feasible potentially valuable profit opportunities facing the potential retail competitors, thus reducing entry. They have to be that much more innovative to get a foothold in this market against the incumbent, in the face of consumer switching costs and inertia, when incumbent provision of in-home devices reduces potential demand facing potential entrants. Even if the customer pays for and owns the device, the anti-competitive effect can arise from the monopolist offering the device as a complement to their regulated default service product.

Leaving in-home technology choice to retailers and consumers contributes to healthy retail competition. Allowing the upstream regulated incumbent to provide in-home technology hampers it, to the detriment of both entrepreneurs and the residential customers who would have gotten more value out of a different device than the one provided by the regulated incumbent. By increasing the number of default service customers with in-home smart grid devices, these projects decrease the potential demand facing these independent retailers by removing or diluting one of the service dimensions on which they could compete. Their forays into in-home technology may not have anti-competitive intent, but they still may have anti-competitive consequences.

The sharing economy and the electricity industry

In a recent essay, the Rocky Mountain Institute’s Matthew Crosby asks “will there ever be an AirBnB or Uber for the electricity grid?” It’s a good question, a complicated question, and one that I have pondered myself a few times. He correctly identifies the characteristics of such platforms that have made them attractive and successful, and relates them to distributed energy resources (DERs):

What’s been missing so far is a trusted, open peer-to-peer (P2P) platform that will allow DERs to “play” in a shared economy. An independent platform underlies the success of many shared economy businesses. At its core, the platform monetizes trust and interconnection among market actors — a driver and a passenger, a homeowner and a visitor, and soon, a power producer and consumer — and allows users to both bypass the central incumbent (such as a taxi service, hotel, or electric utility) and go through a new service provider (Uber, Airbnb, or in the power sector, Google).

Now, as millions gain experience and trust with Airbnb, Uber and Lyft, they may likely begin to ask, “Why couldn’t I share, sell or buy the energy services of consumer-owned and -sited DERs like rooftop solar panels or smart thermostats?” The answer may lie in emerging business models that enable both peer-to-peer sharing of the benefits of DERs and the increased utilization of the electric system and DERs.

A P2P platform very explicitly reduces transaction costs that prevent exchanges between buyer and seller, earning revenue via a commission per transaction (and this is why Uber has in its sights such things as running your errands for you (video)). That reduction allows owners of underutilized assets (cars, apartments, solar panels, and who knows what else will evolve) to make someone else better off by selling them the use of that asset. Saying it that way makes the static welfare gain to the two parties obvious, but think also about the dynamic welfare gain — you are more likely, all other things equal, to invest in such an asset or to invest in a bigger/nicer asset if you can increase its capacity utilization. Deregulation catalyzed this process in the airline industry, and digital technology is catalyzing it now in rides and rooms. This prospect is exciting for those interested in accelerating the growth of DERs.

Note also that Crosby makes an insightful observation when he says that such P2P networks are more beneficial if they have access to a central backbone, which in this case would be the existing electricity distribution grid. Technologically, the edge of the network (where all of the cool distributed stuff is getting created) and the core of the network are complements, not substitutes. That is not and has not been the case in the electricity network, in large part because regulation has largely prevented “innovation at the edge of the network” since approximately the early 20th century and the creation of a standard plug for lights and appliances!

The standard static and dynamic welfare gain arguments, though, are not a deep enough analysis — we need to layer on the political economy analysis of the process of getting from here to there. As the controversies over Uber have shown, this process is often contentious and not straightforward, particularly in industries like rides and electricity, the incumbents in which have had regulatory entry barriers to create and protect regulatory rents. The incumbents may be in a transitional gains trap, where the rents are capitalized into their asset values, and thus to avoid economic losses to themselves and/or their shareholders, they must argue for the maintenance of the regulatory entry barrier even if overall social welfare is higher without it (i.e., if a Kaldor-Hicks improvement is possible). The concentration of benefits from maintaining the entry barrier may make this regulation persist, even if in aggregate the diffuse benefits across the non-incumbents is larger than the costs.

That’s one way to frame the current institutional design challenge in electricity. Given that the incumbent utility business model is a regulatory construct, what’s a useful and feasible way to adapt the regulatory environment to the new value propositions that new digital and distributed energy technologies have made possible? If it is likely that the diffuse economic and environmental benefits of P2P electricity exchange are larger than the costs, what does a regulatory environment look like that would enable P2P networks and the distribution grid to be complements and not substitutes? And how would we transfer the resources to the incumbents to get them out of the transitional gains trap, to get them to agree that they will serve as the intelligent digital platform for such innovation?

I think this is the question at the guts of all of the debate over the utility “death spiral”, the future utility business model, and other such innovation-induced dynamism in this industry. I’ve long argued that my vision of a technology-enabled value-creating electricity industry would have such P2P characteristics, with plug-level sensors that enable transactive automated control within the home, and with meshed connections that enable neighbors with electric vehicles and/or rooftop solar to exchange with each other (one place I made that argument was in my 2009 Beesley lecture at the IEA, captured in this 2010 Economic Affairs article). Crosby’s analysis here is consistent with that vision, and that future.

A sabbatical note

Modern life is full of bustle and inattention, with too many activities and tasks and opportunities competing for our limited cognitive bandwidth. Even in the relatively staid academic life this is true; my regular teaching requirements and other campus commitments have meant that my mind is stretched, particularly over the past few years as some of the aspects of my job have shifted around. I have to fit in research when and where I can, and for the past few years it’s felt like the space for it is in the interstitial bits between my other responsibilities. Combine that with my natural inclination towards short attention, encouraged by the Internet, and I haven’t made the time or mental space to work on new ideas. But for the next few months, that will change.

One of the most appealing aspects of an academic job is the opportunity to take a sabbatical, and I am taking one now. I am thrilled and honored to be visiting in the Department of Political Economy at King’s College London, where I’ll be working with my good friends Mark Pennington and Paul Lewis. I’ll be here for six months, and am planning to work on a couple of different papers on the effects of regulation on experimentation by producers and consumers,alternative regulatory frameworks that could move us toward permissionless innovation in the electricity industry, and regulation and innovation in the residential rooftop solar market.

The KP Spouse (who will work in his regular job from London) and I left Chicago last Monday, laden with luggage and bicycles. After a week of grocery shopping, exploration, and a trip to Edinburgh to celebrate our godson’s birthday, I have been welcomed enthusiastically by my colleagues at King’s this week. It’s a diverse department, full of economists and political scientists working at the intersection of various theoretical strands, and I’m looking forward to the exchange of ideas and cross-pollination. And to living in London, of course, one of my favorite places in the world.

Please stay tuned …