Rationalizing California’s Residential Electricity Rates

California is finally talking seriously about changing the way utilities price electricity for residential customers.  In particular, as a result of recent legislative actions, the CPUC now has some flexibility to modify the extreme increasing-block pricing (IBP) schedules that were adopted after California’s 2000-01 electricity crisis.  IBP means charging more for additional kilowatt-hours as a household consumes more over the billing period.  Pacific Gas & Electric’s current residential rate schedule, shown in the figure below, illustrates how IBP can charge drastically different prices for an additional kilowatt-hour (kWh) depending on how much the customer is consuming. (The rates of Southern California Edison and San Diego Gas & Electric look pretty similar.)

PGErates2014August

 PG&E’s current increasing-block residential electricity rate structure

This extreme form of IBP violates one of the basic tenets of utility price setting: cost causation. Prices should reflect the cost of serving a customer.  The cost of providing electricity does vary over time, an issue that I will return to below, but it does not vary with how much a household uses over the billing period.  That kilowatt-hour (kWh) for which PG&E charges one customer $0.32 costs the utility the same to provide as a kWh for which PG&E charges another (or the same) customer $0.15.

The implementation of IBP in California is both unfair and inefficient, a holdover from policy made during the darkest days of the electricity crisis without careful analysis of the impact.  While California’s IBP schedules are more extreme than in other states, the lessons from the Left Coast have much to teach in other locations as well.

To remind the young or forgetful (or the less-obsessed with electricity tariff history), prior to the electricity crisis, the regulated electric utilities in California had two-tier IBP rates with the second tier 15%-20% higher than the first tier.  The majority of customers consumed more than their first-tier baseline quantity for their region, paying the first-tier price up to the baseline and the second-tier price for any additional electricity beyond that.  But more than a third of customers only experienced first-tier rates.

After the crisis, utilities needed to raise revenues, but politicians said they wanted to protect the poor, so regulators instituted a 5-tier system, with rates on the first two tiers – designed to go up to 130% of baseline quantity, which is about the median household’s consumption – frozen at pre-crisis levels.  As a result, any revenue increase had to come on tiers 3, 4, and 5, which constituted only about one-third of the residential electricity sold. (Tiers 4 and 5 were merged into one tier a couple of years ago.)  To get the needed revenue, the price on those high-tier kWhs had to go up a lot.

SCEtiers1999-2009

            Southern California Edison’s tiered rates from 1999 to 2009 (Source Ito (2014))

That is how we got to the IBP schedules we have today where the highest-tier rates are more than twice as high as the low-tier rates (though the high tiers change frequently, so the exact ratio is a moving target).  This ratio is far out of line with other retail rate structures.  The majority of utilities in the U.S. don’t have IBP; they sell all electricity to residential customers at the same cost per kWh.  In the one-third or so that do have IBP, the rate structure typically looks more like pre-crisis California, with two tiers and only a small difference between them.

Lots of claims get lobbed into the political heat of the discussion about IBP, generally with little empirical basis, so it is worth reviewing the best evidence we have.  Increasing-block pricing proponents make three cases for IBP:

First, some proponents argue that high-usage customers are more expensive to serve on a per-kWh basis because they on average consume a higher share of their electricity at peak times.  It is ironic that some of the same organizations that make this argument also are the adamant foes of time-varying pricing that would reflect time-varying costs directly.  In any case, my own research (published in Review of Industrial Organization in 2013) shows that there is a tiny bit of truth to this claim, but the operative word is tiny.  That study shows that for PG&E and Southern California Edison, the different time-patterns of consumption between large and small residential consumers could justify at most about a few percent price difference. That would be less than a one cent per kWh average price differential, far smaller than would result even if we returned to the pre-crisis IBP structure, and vanishingly small compared to what we have today.

My study shows that the true incremental cost per kWh of serving large and small residential customers is virtually the same.  And that cost is much lower than the upper-tier prices under the IBP used by the California regulated utilities.  Even if you added the carbon cost of the emissions and priced those emissions at $37/ton (the latest estimate of the social cost of greenhouse gas emissions from the Obama administration), that would raise the appropriate price by less than two cents per kWh.

The second argument for IBP is the one that drove the political debate in 2001, that IBP is a way to raise rates while protecting the poor.  Implicit in this claim is that poor people consume less electricity than rich people do, which makes intuitive sense, but at the time the legislation was rushed through during the crisis there was just a sense, not a measure of the real difference.  A paper that I published in 2012 (in American Economic Journal: Economic Policy) shows that using the steep post-crisis IBP does help low-income customers relative to a flat rate, but by much less than you might think.  The reason is that the CARE program that is explicitly designed to give lower rates to low-income consumers – those up to 200% of poverty or $47,700/year for a family of 4 – has already taken most of the low-income customers out of the standard IBP tariff and put them on a separate low-income tariff.

Overall, I estimate that moving to a flat-rate tariff would raise the average bill of a customer making about $50,000/year or less (in today’s dollars) by about $5/month.  That’s not nothing, but it’s also not a huge effect to get in exchange for an increasing-block rate structure that greatly distorts prices away from real costs – which are essentially the same for the low-tier and high-tier kWh.  Furthermore, moving from IBP to a flat rate would likely incent more truly needy households to sign up for CARE.  If the goal is to help the poor, a program targeted directly at the poor makes more sense than one targeted at consumption level, which has a weak correlation with how wealthy a household is.

Using IBP to help low-income households has another problem.  It arbitrarily harms many low-income households that are heavy users for completely understandable reasons.  Household baselines are based on climate regions (PG&E has 10 regions, SCE has 6) and…well…nothing else.  They don’t adjust for how many people live in the house, how old those people are, how much time they spend at home (rather than using electricity somewhere else), or any other reason that we would expect even an energy-conscious household to consume more.  Instead, IBP rewards people who live alone and don’t spend much time at home.  Does that make sense?

The third case made for IBP is that it leads to conservation, because it raises the price of marginal consumption.  In theory, this could be right, but in practice it doesn’t work out that way.  The idea is that it raises price for the heavier uses and lowers price for the lighter users, compared to a single flat rate, but the response of heavy users is proportionately larger.

But path-breaking research that our former grad student Koichiro Ito published this year in the economics profession’s top journal (American Economic Review) shows that most customers don’t pay attention to the marginal price they face, but instead respond to the average price or total bill.  This isn’t surprising given that the vast majority of customers don’t even know we have tiered pricing and even for the electricity-obsessed it’s very hard to know what marginal price you will face at the end of the month.  Ito demonstrates that when customers respond to average price, IBP results in virtually no change in total consumption.[1]

Solar_panels_on_house_roof_winter_view

Residential Solar PV has been a big beneficiary of increasing-block pricing  (Source:http://256.com/solar/images/)

But what has become apparent in the last few years is that one small set of customers is very aware of IBP and the high marginal prices for heavy consumers: solar PV households, or actually the solar PV vendors who explain IBP to them.  While evidence that IBP incents energy efficiency is absent, the evidence is clear that IBP is a key driver behind the distributed solar PV movement in California.

In fact, solar PV installers “work with” customers to optimally design systems so they just shave off the high-tier usage without touching the low-tier usage where the price is way too low for PV to save customers money.  With federal subsidies that pay nearly half the cost of PV — and net metering that pays the residential customer for electricity supplied to the grid at the retail rate rather than the wholesale rate that other renewable generation sources receive – solar PV can beat the high-tier prices of IBP.  That’s why solar PV installers are the most vocal opponents of rational rate reform that would call a kilowatt-hour a kilowatt-hour regardless of how many other kilowatt-hours you consume during the month.

So, increasing-block pricing isn’t cost based.  It is a very indirect way to lower the average bills of low-income customers, while arbitrarily harming many of them and benefitting many wealthy people who live alone or don’t spend much time at home (or own a second home). And it’s only effect on conservation is to create incentives to install solar that just skims off the highest tiers of the IBP structure, an activity that is pursued overwhelmingly by the richest households (and which raises the bills of all other ratepayers).  The first step to rationalizing California’s electricity rates is to greatly reduce or eliminate increasing-block pricing.

Then we need to talk about time-varying pricing.  We should be transitioning to opt-in time-varying pricing immediately and to opt-out in the near future.  I’ve explained an efficient and equitable way to do this in a recent paper, but that will have to wait for another blog.

 

[1] Paul Chernick recently filed testimony on behalf of NRDC that mischaracterizes Ito’s finding, saying Ito claims consumers don’t respond to IBP.  That’s not at all what Ito finds, as I explain here.   A study by Energy & Environmental Economics argues that in British Columbia IBP does lower total consumption.  But the study looks only at whether high-consuming households consume less under IBP – which they do, not surprisingly—and ignores that low-consuming households consume more, because their price is lower than under a single flat rate.  What matters, of course, in the change in total consumption, on which the E3 study is silent.

I’m still tweeting interesting energy news articles most days @EnergyClippings

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Remember back when everybody hated electricity deregulation?

Today we take a break from our regularly scheduled blogging about environmental topics to provide a brief message concerning electricity restructuring.  Severin Borenstein and I are finishing up a draft paper for the Annual Review of Economics (available as a working paper here) that looks back over the last 20 years of electricity restructuring and it has given us a chance to update some data sets and revisit some topics that really haven’t gotten a lot of attention since around 2008.

Around that time I started giving a talk titled “If electricity restructuring is so great, why does everybody hate it?” Back then, several states like Illinois and Maryland were actively pursuing options to “re-regulate” markets that they had at least partially restructured.  The New York Times ran a series of articles that pointed to studies showing that rates had increased more rapidly in states that had restructured compared to those that did not.    The gist of my talk was that this could very well be true, but did not necessarily signal that restructuring was a failure.

Back in 2000, Severin and I had written a paper arguing that the motivations for restructuring were driven more by a desire by some groups to avoid paying for stranded assets (like nuclear and coal plants which looked like white elephants in the late 1990s) than by a belief that restructuring would reap massive efficiency gains.  In economic terms, customers preferred to pay market-based prices — which were based upon marginal cost in competitive markets — rather than regulated rates — which were based upon average production costs. During this period of relatively large capacity margins and low natural gas prices, market-based pricing appealed to customers and terrified utility shareholders whose assets would become stranded absent other compensation.  However, despite the allure of market-based pricing,  the regulatory and political process allowed utilities to recover the bulk of what appeared at the time to be stranded costs. So customers ended up paying for most of these costs anyway.

The great irony of this period is that a half decade after transition arrangements largely compensated utilities for the losses incurred in selling or transferring these assets, the market value of those same assets had fully recovered. By the mid-2000s the relationship between average and marginal cost had largely reversed, and states like Illinois and Maryland expressed a great deal of regret about the decision to restructure. However, since the formerly regulated generation assets were now largely held by private, deregulated firms, there was no clear path to dramatically “re-regulate” the industry without paying full market value for those assets. Looked at this way, one can view the disappointment with restructuring as being driven by magnificently poor market timing.  Utilities sold off their assets at the nadir of their value, and as natural gas prices climbed throughout the 2000s, those assets became quite valuable under market-based pricing.  Therefore, my take on this topic circa 2008 was that, with hindsight certain states would have been better off restructuring, but that was due to external shocks like natural gas price increases and changes in technologies.

ippStates by Percentage of Generation from IPP sources

Then a funny thing happened.  Since 2009, this story has largely reversed yet again.  Natural gas prices have declined sharply, nearly to the levels seen at the dawn of the restructuring movement. The attention of policymakers has now been consumed by environmental priorities, particularly the implications of coal generation decline and renewable generation growth for costs and greenhouse gas emissions.  A surge of subsidized renewable generation, combined with low natural gas prices, has driven wholesale prices steadily lower.  As one would expect, in the short run this has benefited consumers in market-based states disproportionately more than those in regulated states.  This figure plots the average prices of electricity and natural gas in “restructured” states (defined as a state with more than 40% of its energy produced from IPP sources – the states that are not blue in the figure above) and “regulated” states. The dashed line shows the difference between the two groups.  One can see the gap between the states growing as natural gas prices climb, and then following the natural gas prices right down again.

retail_rates_by_yearElectricity and Gas Prices

The table below summarizes the changes in rates using two different definitions of restructured that are explained in the paper. All together if you look at the changes in between the two groups now, restructured states actually come out a little better than the regulated states.  Rates kept rising at a consistent trend in regulated states, while they have declined slightly in the restructured ones.

This is not meant to be a comprehensive study of rate differences.  For example, we suspect that Renewable Portfolio Standards requirements in some states, like California, are keeping rates from declining faster.  But I do find it fascinating that the situation has reversed so dramatically over the last 5 or 6 years.  Not surprisingly, there isn’t much clamoring for re-regulation these days.

You also don’t see much clamoring for more restructuring either, however, which is equally interesting. The relationship between average costs and marginal costs appears again to be turning in favor of marginal costs, but today the resulting pressure is being manifested in the arena of distributed generation, rather than retail choice. Although rooftop solar and retail competition are not usually equated, from a consumer perspective the economics of each can be very similar.  Both have been pushed forward in large part by a desire to avoid paying for sunk costs – generation in the case of retail competition and distribution costs in the case of distributed generation.

However, looked at from a societal perspective, such motivations can be disheartening. Consider the prospect, hinted at in this article, of a future where homes install batteries and distributed generation in order to avoid paying for fixed distribution charges.  Even if this becomes economically viable from a household perspective, this is a potentially appalling waste of resources.  We would be talking about thousands of dollars of investment per household to avoid paying for assets that are already there.  Who pays for these wires? Either utility shareholders or those households that don’t leave the grid.

There are real and important technological and efficiency advances happening, and competition is providing some of the impetus for that. But much of the economics and politics of this industry have been and still are dominated by the not-very-noble desire to stick someone else with existing infrastructure costs.

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The Big Stick: Cap and Trade in the Peoples Republic of Carbon

Many of us have argued that a patchwork of national or subnational climate policies is a largely pointless undertaking unless the big two (United States and China) are part of that patchwork. The US has taken some steps towards national regulation recently and there are noises coming out of China that the next five-year plan will contain a significant piece of mitigation policy.

The sooner, the better. The averaged annual growth rate of Chinese emissions over the past decade was 9.1%, which implies a doubling of emissions every eight years.

In order to get emissions under control, there are inflexible standards or price-based mechanisms like cap and trade (CAT) and carbon taxes. The US lacks a national CAT, yet has functioning markets in California and on the East Coast. The EU has the super-national emissions trading system (ETS) supplemented by a significant portfolio of energy efficiency and renewable energy policies.

China has, in the recent past, started a few experimental cap and trade systems, which may well be regarded as blueprints for a larger national market. While trade volumes on these exchanges are relatively thin, the figure below shows that prices vary between 4 and 13 dollars per ton (which is not too far from the price on the California system and the ETS).

Daily permit prices on six Chinese regional exchanges (Dollars per ton)

The big stick CDW-2

The National Development and Reform Commission, which is the arm of the Chinese government responsible for economic planning, has provided coarse outlines of a national market starting in 2016, which will cover 3-4 billion tons of CO2 with a volume of roughly 65 billion US Dollars by 2020. This is twice the volume of the European market. And some simple algebra suggests that the planners expect permits to trade at just below $20, which is higher than anywhere else in the world currently or in any of the regional markets in China. This is exciting news and will surely provide some significant momentum in the upcoming climate talks.

I am somewhat puzzled by the apparent choice of a national cap and trade system over the other price based alternative to a CAT: a carbon tax. The reason I am puzzled is deeply rooted in some simple political economy. Cap and trade generates valuable assets (permits), which are frequently handed out to carbon intensive industries, which would otherwise fight the regulation and possibly prevent it from being implemented. If the government hands out permits, it gets no revenues from these permits.

A carbon tax, on the other hand, is charged on each carbon atom and most likely charged fairly far upstream the carbon river. It generates revenues for the government (especially at ~$20 a ton). My (limited) understanding of how environmental policy works in China is that industry gets much less say in what regulations will be implemented and how.

Here are a few simple advantages of a carbon tax over cap and trade in an economy that currently does not come close to collecting a universal income tax:

  • It is relatively simple to collect far upstream.
  • It provides carbon price certainty.
  • It does not suffer from the significant design issues involved in setting up a market in one of the most highly regulated economies in the world.
  • It generates significant revenue, which may prevent one from having to set up or ramp up income taxation schedules, which are distortionary.
  • It provides the regulator with direct control of the rates.
  • China has extensive experience at the regional level with collecting effluent fees (which are essentially emissions taxes of criteria pollutants).

One argument, which is often used against a carbon tax is that it does not guarantee what emissions reductions will be in any given period, as the marginal abatement costs are not known by the regulator. While this is certainly true, Lucas Davis points out from across the hall that with GHGs we care about cumulative emissions. When the marginal benefit of abatement curve is relatively flat, you want the flexibility afforded by taxes.  If costs end up being higher than expected, emission sources can abate less. One clear disadvantage of a tax is that it is somewhat harder to exempt individual sectors/firms from the tax. What this might mean in the Chinese context is that it puts smaller (already inefficient) coal mines at a further competitive disadvantage and risks significant local employment impacts.

The problem with almost all environmental regulation of course is that there will be some winners and some losers, but overall we make the pie bigger. I am thrilled to see the Chinese government get serious about climate regulation, but would hope that serious consideration is given to a tax instead of a cap and trade system. China has significant experience with taxes and the main institutions required to put a new one in place are, well, in place. Setting up a market is hard and the devil is in the details. Just ask California.

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Want to Schedule Your Electricity Use to Reduce Pollution? Here’s How

Some of us occasionally feel the urge to turn off the kitchen/porch/office light as our small step towards addressing global climate change. A Berkeley PhD student – Gavin McCormick – has started a nonprofit to provide information on exactly how our actions impact pollution.

Gavin’s organization, WattTime, is analyzing data from around the US to provide information like the map below. You can hover over an area to get almost live information on CO2 emissions.

WattTime

It turns out that it’s not straightforward to generate the information behind maps like this – far from it.

Consider two different scenarios. In both, I’m assuming that you’re doing something that you don’t usually do – turning off the porch light earlier, for instance – and that other consumers do not change what they do.

To understand what Gavin is doing, we first need a basic understanding of electricity grid operations. Many readers no doubt understand this better than I do, and this is a highly stylized description, but it works for these purposes.

Wherever you live, there’s a grid operator charged with balancing the electricity system and ensuring that there’s enough power generated at any given time to meet demand. The operator also needs to ensure that the plants are not generating too much as that could damage equipment.

GridOperator

The grid operator communicates with power plant operators in the region about how much they’re producing and with the grid operators in adjacent areas about imports and exports. Much of the communication is automated and, for instance, the grid operator’s “request” for less electricity when you turn off your light would likely be communicated through something called Automatic Generation Control (AGC).

Go back to turning off the porch light. If you live in Ohio and it’s a regular night, it’s possible that the AGC system will instruct a coal plant to reduce production. That’s great if you’re trying to reduce GHGs or other pollutants, as coal plants are as dirty as they come.

Turning off your light in West Texas may not be as helpful for reducing greenhouse gas emissions, especially if it’s windy. In this case, you well might be asking the grid operator to back off on the output from wind plants.

In fact, many wind producers receive subsidies from the federal government that increase in the amount of electricity they produce. This is called the production tax credit, and it was recently $23/MWh. So, wind producers actually want you to keep your lights on in the middle of a windy night. In fact, prices can be negative (though rarely below -$23/MWh), meaning that the wind turbine producers are willing to pay consumers to take their power at that point in time.

West Texas Market Clearing Prices for 2010

One thing that’s great about Gavin’s site is that he’s striving to use a better methodology than what’s out there now. There are other sites that simply report the average emissions in a given hour. So, for the Ohioan turning off the light, the other sites are averaging across zero-GHG-emissions nuclear power plants and the coal plants.

This calculation will provide a misleadingly low estimate of the impact of your actions, though, since the nuclear power plants won’t change their output when you turn off your light. They are not marginal, in the language of economists. So, it doesn’t make sense to account for their emissions, as your actions don’t affect them. While average GHG emissions vary by a small amount throughout the day, the marginal plant can vary a lot – from particularly dirty coal to emissions-free wind. The map above still reflects averages, but WattTime maps reflecting marginal emissions are coming very soon.

My example about a single porch light is actually below WattTime’s aim, as a 100W porch light is 1/5,000,000th of the output of a typical 500 MW plant. They’re really targeting the larger decisions by, for example, designing smart plugs for electric cars and industrial load controllers.

If Gavin’s company takes off, which we’re all cheering for, the calculations get a lot more complicated. If his company accounts for a large and predictable share of electricity demand, grid operators might anticipate that fewer people will turn on their porch lights when polluting plants are marginal, and adjust their decisions about which plants to turn on for the day. Ultimately, planners might anticipate this reaction and adjust the type of power plants they build. Gavin and crew are working on incorporating longer run decisions, so stay tuned for Version 2.0 of their site.

Information will help us make better decisions, and I’m delighted that there are innovators like Gavin out there who are perfecting the information that consumers get, and designing cool websites and apps to put the information in front of us.

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Renewable integration challenges create demand response opportunities

The power grid is getting greener. The graph below summarizes EIA projections of U.S. non-hydro electricity generation under different assumptions about greenhouse gas regulations, fuel prices, and technology costs:

EIA_ren

Source: http://www.eia.gov/todayinenergy/detail.cfm?id=16051

Although there is some debate over whether these EIA projections are too conservative, it seems we can all  agree that the penetration of non-hydro renewables will continue to increase in the coming years.

The power grid is also getting smarter.  The Smart Grid Investment program invested close to $8 billion in accelerating the deployment of smart grid infrastructure and technology. It is estimated that smart meters will have been deployed to over 50 percent of U.S. end users by 2015.

Some recent studies highlight some important complementarities between a greener grid and a smarter grid. Before connecting these dots, let’s first review the key operational challenges associated with increasing integration of renewables.

Why lose sleep over increasing grid-penetration of renewables?

Here in California, the “duck chart” has become emblematic of the challenges that increased renewable generation (and solar in particular) could present. The duck helps to illustrate some key issues (not just in California, but any place the sun rises in the morning and sets in the evening).

duck

The colored lines trace out actual (2012-2013) and forecast (through 2020) electricity demand less generation from variable renewables, including wind and solar (“net load”).  As solar PV accounts for a larger share of generation, the change in the net load profile takes on a duck-like shape. Note three key take-aways:

  • Ramping demands: Increased solar puts stress on the system when the sun rises and sets. Conventional generation must ramp down and up to compensate.
  • Over-generation can be a problem when solar output peaks in the early afternoon if demand levels are modest and  inflexible base load generation bumps up against minimum output constraints.
  • Declining marginal value: As the level of renewables penetration (solar in particular) increases, renewable energy output becomes less coincident with peak net load. This drives down the marginal value of the electricity generated, in part by reducing the capacity value of solar on the build margin and in part by driving up the marginal cost of managing variable energy output.

This duck is only an illustrative tool.  For one thing, the chart is based on a somewhat non-representative day in which solar output is high but temperatures are cool and demand for air conditioning low. Perhaps more importantly, the graph makes no attempt to account for adjustments in energy infrastructure and energy markets that can be deployed to mitigate stresses on the system. Fortunately, we have many options available when we think about re-optimizing the electricity sector to accommodate higher levels of renewable energy generation.

Meeting the renewables integration challenge

Over a year ago, Catherine wrote a great post raising key questions about the relative merits of storage versus demand response to renewable resource integration challenges. A year later, we have in hand some studies that systematically consider how different power system investments and operational changes can mitigate ramping and over-generation problems associated with increased renewables penetration.

One study in particular – recently released by Andrew Mills and Ryan Wiser of LBNL- caught my attention last week.   The paper considers alternative approaches to meeting the renewables integration challenge including: energy storage, demand response (assuming a demand elasticity of -0.1), investment in more flexible gas generation, and diversification in renewables deployment (to reduce variance of aggregate renewables output).

The study assesses the economic value of these response options, where “value” is defined in terms of the change in the marginal economic value of wind or solar relative to a base case where no measures are deployed.  That’s a mouthful. The basic idea is the following.  The authors simulate long-run investment decisions, generation dispatch, and wholesale market clearing in California’s electricity sector under a baseline scenario and calculate the marginal economic value of renewables (in terms of energy and capacity cost avoided). They then repeat the entire simulation exercise assuming one of the mitigating alternatives has been deployed.[1]

The following table summarizes some key results from the study:

mills_table

Qualitatively, the results are quite intuitive. At very high solar penetration rates, bulk storage is particularly valuable. Diversification is more effective in the high wind penetration scenarios. Demand response (RTP)  increases the marginal value of both wind and solar at low and high penetration rates.

Importantly, the study stops short of estimating costs.  Dedicated bulk storage is likely to be costly. In contrast, we have already made a significant investment in the smart grid infrastructure we would need to operationalize widespread demand response.

Smart renewables integration should leverage the smart grid

Increased penetration of variable renewable resources increases the potential value added by demand response. In this sense, renewables integration creates opportunity for demand response. In order to tap this potential,  we’ll need to enable broad based and highly flexible demand response.  This represents a significant departure from today’s standard DR programs.  There is much more we can do- both in terms of automation and pricing- to leverage investments in smart-grid infrastructure. With renewable energy penetration rates on the rise, the cost of overlooking these opportunities gets harder to justify.

 

[1] Consider, for example, the high solar penetration scenario.  The simulated marginal value of solar  is $25.32/MWh at a 30% PV penetration rate in the baseline scenario.  When demand response to real time pricing incentives is incorporated into the simulations, the marginal value of solar increases to $32.76/MWh. This implies a value of $7.44/MWh.

 

 

 

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What’s the goal and point of national biofuel regulation?

While preparing a lecture for the 4th Berkeley Summer School in Environmental and Energy Economics, I returned to contemplating the regulation of biofuels as part of a federal strategy to combat climate change and increase energy security. If we review policy approaches for increasing the share of biofuels in the transportation fuels supply across this great land, there are three main approaches. We have subsidies for the production of ethanol and biodiesel, renewable fuels standards (RFS) and low carbon fuels standards (LCFS).

The two main tools employed at the federal level are subsidies, which essentially provide a per gallon payment for producing a gallon of a certain type of biofuel, and renewable fuels standards, which require the production of different classes and quantities of biofuels over a prescribed time path. California has employed a low carbon fuel standard, whose goal it is to decrease the average carbon content of California’s gasoline by prescribed percentages over time. It relies on life cycle calculations for the carbon content of different fuels and allows producers to choose a mix of different fuels, which decrease the average carbon content, thus providing more flexibility in terms of fuels compared to the RFS.

If I were elected the social planner, I would recognize that I am most likely not smarter than the market, but also would not trust the market to make the right decisions when it comes to carbon reductions (see the demonstrated record of markets since 1850). The standard way an economist would approach the problem, assuming that we know what the right amount of carbon abatement is, is to set a cap on emissions and issue tradable rights to pollute (a cap and trade). This would in theory lead to the desired level of emissions reductions at least cost. While preparing for my lecture, I was thinking I should set up a simple model where profit-maximizing producers of fuels face different policy constraints (e.g., subsidies, RFS, LCFS or a cap and trade), a reasonable demand curve and my giant computer. As so often happens to many of us environmental and energy economists, EI@Haas’ all-star team captain Chris Knittel (MIT) and coauthors had already written the paper, which is titled “Unintended Consequences of Transportation Carbon Policies: Land-Use, Emission, and Innovation”.

[Skip this paragraph if you are not a fan of wonk]. The paper, which is a great and relatively quick read, simulates the consequences of the 2022 US RFS, current ethanol subsidies and constructs a fictional national LCFS and cap-and-trade (CAT) system, which are calibrated to achieve the same savings as the RFS. The paper assumes profit-maximizing firms which either face no policy, the RFS, subsidies, LCFS or CAT. Using an impressive county level dataset on agricultural production and waste, the authors set out to construct supply curves for corn ethanol and six different types of cellulosic ethanol. Chris’ daisy chained Mac Pros then maximize profits of the individual firms by choosing plant location, production technology, and output conditional on fuel price, biomass resources, conversion and transportation costs. Changing fuel prices and re-optimizing gets them county level supply curves. Assuming a perfectly elastic supply of gasoline and a constant elasticity demand curve for transportation fuels, they solve for market equilibria numerically.

They use the results to compare the consequences of each policy type for a variety of measures we might care about. Here is what happens:

The CAT leads to the greatest increase in gas prices and largest decrease in fuel consumption. It leads to no additional corn ethanol production and slight increases in second-generation biofuels. The RFS and LCFS both lead to less than half the price increase and fuel reduction compared to the CAT. Both policies see a four to nine fold increase in corn ethanol production relative to no policy and a massive ramp up in second generation biofuels production. All three measures lead to the same reductions in carbon emissions. The subsidies leave fuel costs constant, do not change fuel consumption and lead to a massive increase in first and second generation biofuels, but only achieve two thirds of the carbon reductions compared to the other policies (which is due to the authors using current subsidy rates rather than artificially higher ones which would lead to the same carbon savings).

Biofuels lead to lower gas prices and equivalent carbon savings! This is the point, where biofuels cheerleaders scream “everything is awesome!” But this ain’t a Lego movie. Especially since Legos are not made from corn. The paper evaluates the policies along a number of dimensions. First, compare the abatement cost curves for the CAT and the LCFS. When it comes to marginal abatement cost curves, the flatter, the better. What we see in the paper is a radically steeper marginal abatement cost curve from the LCFS compared to the CAT. In equilibrium the marginal abatement cost for the LCFS is almost five times higher that of the CAT. What about those emissions reductions? What happens in practice is that the CAT leads to higher emissions reductions from reduced fuel consumption (by driving less or more efficient cars) and a little bit of fuel switching. For the LCFS there is much more fuel switching and not much less driving.

What about land use? Well, since the non CAT policies incentivize ethanol production, significant amounts of crop and marginal lands will be pulled into production.

figure 3

The paper shows that total land use for energy crops goes up about ten fold under the biofuels policies and only by about 30% under the CAT. The paper calculates that damages from erosion and habitat loss from these policies can reach up to 20% of the social cost of carbon compared to essentially 0% for the CAT.

Further, ethanol policies create the wrong incentives for innovation, where in some settings the incentives are too strong and in others they are too weak. A further aspect of the paper, which is incredibly clever, is that they show the cost of being wrong in terms of the carbon intensity (e.g., you get the indirect land use effect wrong, which is almost certainly the case) of different fuels can lead to massive amounts of uncontrolled emissions. The carbon damage consequences of being wrong by 10% in terms of the emissions intensity of corn ethanol are an order of magnitude (read 10 times!) the number for the cap and trade. Before I wonk you to death, I will close with some more general thoughts, but staffers of carbon regulators should read this paper. Now.

What this work shows is that in the case of biofuels setting a simple universal policy, which lets market participants choose the least cost ways of finding emissions reductions, is vastly preferred to complex renewable fuels or low carbon fuels standards. While I understand that producers of ethanol enjoy their subsidies (much like I enjoy my home interest mortgage deduction), this paper argues that they are a bad deal for society. And so is the RFS, as would be a national LCFS. As we go ahead and design a national carbon policy, I would hope that we take the lessons from this paper and the decades of environmental economics insight it builds upon to heart. This does not say that first or second generation biofuels are a bad idea, but if they want to compete for emissions reductions, they need to be fully cost competitive with other and currently lower cost emissions reductions alternatives.

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Raising Gas Prices to Grow An Economy

Two weeks ago, Yemen increased gasoline prices from $2.20 to $3.50 per gallon, while increasing diesel prices from $1.70 to $3.40.

And last month, Egypt increased gasoline prices from $.47 to $.83 (premium gasoline went from $1.00 to $1.40), while increasing diesel prices from $.61 to $1.00.

These are significant increases.  The reforms in Yemen bring the price of both fuels up to market levels.  And although prices in Egypt remain well below market levels, this is an important step toward rolling back subsidies in a country that has some of the largest energy subsidies in the world.

Economists, including myself, always complain about energy subsidies and celebrate in cases like this when subsidies get rolled back. But what is the big deal?  What’s wrong with subsidizing gasoline?

 

As I discuss in the video, the economic cost of fuel subsidies can be summarized by this figure, straight out of Econ 101.

PowerPoint Presentation

If you are like most people, you tune out whenever anyone says, “deadweight loss”.  But this is just economist-speak for waste.  When prices are subsidized, gasoline and diesel end up being used in a whole host of low-value ways.  People buy fuel-inefficient vehicles and drive them too much. They produce goods and services using inefficient, fuel-intensive technologies.  And they consume too many fuel-intensive products.

Subsidizing energy shrinks the economy. The deadweight loss triangle means that it costs the economy more to supply this fuel than the value these consumers get out of consuming. So, with every transaction, economic value is destroyed.  This is the opposite of gains from trade.  This is losses from inefficient trade.

By my calculations, prior to the reform the deadweight loss from fuel subsidies in Yemen was $40 million per year.  Total fuels expenditures in Yemen is $1.2 billion annually, so this is small compared to the size of the market. Worldwide, there are many countries with larger subsidies than Yemen.  In fact, Yemen is not even in the top 20.

Egypt, incidentally, with much more generous subsidies and a larger population, was #5 in 2012 in terms of total welfare loss from fuel subsidies, behind only Saudi Arabia, Venezuela, Iran, and Indonesia. When I next redo these calculations, I expect to see Egypt slip back a couple of notches.

The price increases in Yemen and Egypt will also decrease the burden of pollution, traffic congestion, and vehicle accidents. A new IMF report finds that the total cost of externalities from driving exceeds $1.00 per gallon in most countries.  By my calculations, removing subsidies in Yemen will decrease fuels consumption by 90 million gallons per year (a 15% decrease).  So if external costs are $1.00 per gallon, this is $90 million annually in additional benefits.

Sana'a Traffic 2

Traffic jam near Sana’a, the capital of Yemen.

By the way, you might have noticed that there is a smaller purple triangle to the left of the externalities rectangle. To maximize welfare you really want to increase prices all the way to social cost. This would further decrease consumption, yielding this additional welfare gain. You can think of this as another deadweight loss triangle or, alternatively, think of the entire larger triangle as deadweight loss relative to the full social cost of fuels consumption.

(Yes, this is exactly the same as the discussion in California about including transportation fuels in the cap-and-trade program for carbon dioxide. See here and here. The whole point is to move California fuel prices closer to full social cost.)

There is more work to do in both countries. In Yemen, it will be important to once-and-for-all allow gasoline prices to float at market levels. These reforms have brought prices up to market levels, but if there continues to be price controls, these gains will erode over time with inflation. In Egypt, there is still a long way to go before market prices are reached.  The price increases last month went through with relatively little public protest (here), but it remains to be seen whether President Abdel Fattah el-Sisi will be able to push through deeper reforms.

I’m not claiming that subsidy reform is easy. The IMF has some interesting work aimed at trying to better understand the political challenges and potential approaches for facilitating reform (here).  But the economic analysis makes it clear that much is at stake. Pricing energy below cost imposes real inefficiencies, and these are enormous markets so the magnitude of the inefficiencies can be very large.

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