Tag Archives: M.Cubed

Transmission: the hidden cost of generation

The cost of transmission for new generation has become a more salient issue. The CAISO found that distributed generation (DG) had displaced $2.6 billion in transmission investment by 2018. The value of displacing transmission requirements can be determined from the utilities’ filings with FERC and the accounting for new power plant capacity. Using similar methodologies for calculating this cost in California and Kentucky, the incremental cost in both independent system operators (ISO) is $37 per megawatt-hour or 3.7 cents per kilowatt-hour in both areas. This added cost about doubles the cost of utility-scale renewables compared to distributed generation.

When solar rooftop displaces utility generation, particularly during peak load periods, it also displaces the associated transmission that interconnects the plant and transmits that power to the local grid. And because power plants compete with each other for space on the transmission grid, the reduction in bulk power generation opens up that grid to send power from other plants to other customers.

The incremental cost of new transmission is determined by the installation of new generation capacity as transmission delivers power to substations before it is then distributed to customers. This incremental cost represents the long-term value of displaced transmission. This amount should be used to calculate the net benefits for net energy metered (NEM) customers who avoid the need for additional transmission investment by providing local resources rather than remote bulk generation when setting rates for rooftop solar in the NEM tariff.

  • In California, transmission investment additions were collected from the FERC Form 1 filings for 2017 to 2020 for PG&E, SCE and SDG&E. The Wholesale Base Total Revenue Requirements submitted to FERC were collected for the three utilities for the same period. The average fixed charge rate for the Wholesale Base Total Revenue Requirements was 12.1% over that year. That fixed charge rate is applied to the average of the transmission additions to determine the average incremental revenue requirements for new transmission for the period. The plant capacity installed in California for 2017 to 2020 is calculated from the California Energy Commission’s “Annual Generation – Plant Unit”. (This metric is conservative because (1) it includes the entire state while CAISO serves only 80% of the state’s load and the three utilities serve a subset of that, and (2) the list of “new” plants includes a number of repowered natural gas plants at sites with already existing transmission. A more refined analysis would find an even higher incremental transmission cost.)

Based on this analysis, the appropriate marginal transmission cost is $171.17 per kilowatt-year. Applying the average CAISO load factor of 52%, the marginal cost equals $37.54 per megawatt-hour.

  • In Kentucky, Kentucky Power is owned by American Electric Power (AEP) which operates in the PJM ISO. PJM has a market in financial transmission rights (FTR) that values relieving the congestion on the grid in the short term. AEP files network service rates each year with PJM and FERC. The rate more than doubled over 2018 to 2021 at average annual increase of 26%.

Based on the addition of 22,907 megawatts of generation capacity in PJM over that period, the incremental cost of transmission was $196 per kilowatt-year or nearly four times the current AEP transmission rate. This equates to about $37 per megawatt-hour (or 3.7 cents per kilowatt-hour).

Outages highlight the need for a fundamental revision of grid planning

The salience of outages due to distribution problems such as occurred with record heat in the Pacific Northwest and California’s public safety power shutoffs (PSPS) highlights a need for a change in perspective on addressing reliability. In California, customers are 15 times more likely to experience an outage due to distribution issues rather than generation (well, really transmission outages as August 2020 was the first time that California experienced a true generation shortage requiring imposed rolling blackouts—withholding in 2001 doesn’t count.) Even the widespread blackouts in Texas in February 2021 are attributable in large part due to problems beyond just a generation shortage.

Yet policymakers and stakeholders largely focus almost solely on increasing reserve margins to improve reliability. If we instead looked the most comprehensive means of improving reliability in the manner that matters to customers, we’d probably find that distributed energy resources are a much better fit. To the extent that DERs can relieve distribution level loads, we gain at both levels and not just at the system level with added bulk generation.

This approaches first requires a change in how resource adequacy is defined and modeled to look from the perspective of the customer meter. It will require a more extensive analysis of distribution circuits and the ability of individual circuits to island and self supply during stressful conditions. It also requires a better assessment of the conditions that lead to local outages. Increased resource diversity should lead to improved probability of availability as well. Current modeling of the benefits of regions leaning on each other depend on largely deterministic assumptions about resource availability. Instead we should be using probability distributions about resources and loads to assess overlapping conditions. An important aspect about reliability is that 100 10 MW generators with a 10% probability of outage provides much more reliability than a single 1,000 MW generator also with a 10% outage rate due to diversity. This fact is generally ignored in setting the reserve margins for resource adequacy.

We also should consider shifting resource investment from bulk generation (and storage) where it has a much smaller impact on individual customer reliability to lower voltage distribution. Microgrids are an example of an alternative that better focuses on solving the real problem. Let’s start a fundamental reconsideration of our electric grid investment plan.

A new agricultural electricity use forecast method holds promise for water use management

Agricultural electricity demand is highly sensitive to water availability. Under “normal” conditions, the State Water Project (SWP) and Central Valley Project (CVP), as well as other surface water supplies, are key sources of irrigation water for many California farmers. Under dry conditions, these water sources can be sharply curtailed, even eliminated, at the same time irrigation requirements are heightened. Farmers then must rely more heavily on groundwater, which requires greater energy to pump than surface water, since groundwater must be lifted from deeper depths.

Over extended droughts, like between 2012 to 2016, groundwater levels decline, and must be pumped from ever deeper depths, requiring even more energy to meet crops’ water needs. As a result, even as land is fallowed in response to water scarcity, significantly more energy is required to water remaining crops and livestock. Much less pumping is necessary in years with ample surface water supply, as rivers rise, soils become saturated, and aquifers recharge, raising groundwater levels.

The surface-groundwater dynamic results in significant variations in year-to-year agricultural electricity sales. Yet, PG&E has assigned the agricultural customer class a revenue responsibility based on the assumption that “normal” water conditions will prevail every year, without accounting for how inevitable variations from these circumstances will affect rates and revenues for agricultural and other customers.

This assumption results in an imbalance in revenue collection from the agricultural class that does not correct itself even over long time periods, harming agricultural customers most in drought years, when they can least afford it. Analysis presented presented by M.Cubed on behalf of the Agricultural Energy Consumers Association (AECA) in the 2017 PG&E General Rate Case (GRC) demonstrated that overcollections can be expected to exceed $170 million over two years of typical drought conditions, with the expected overcollection $34 million in a two year period. This collection imbalance also increases rate instability for other customer classes.

Figure-1 compares the difference between forecasted loads for agriculture and system-wide used to set rates in the annual ERRA Forecast proceedings (and in GRC Phase 2 every three years) and the actual recorded sales for 1995 to 2019. Notably, the single largest forecasting error for system-wide load was a sales overestimate of 4.5% in 2000 and a shortfall in 2019 of 3.7%, while agricultural mis-forecasts range from an under-forecast of 39.2% in the midst of an extended drought in 2013 to an over-forecast of 18.2% in one of the wettest years on record in 1998. Load volatility in the agricultural sector is extreme in comparison to other customer classes.

Figure-2 shows the cumulative error caused by inadequate treatment of agricultural load volatility over the last 25 years. An unbiased forecasting approach would reflect a cumulative error of zero over time. The error in PG&E’s system-wide forecast has largely balanced out, even though the utility’s load pattern has shifted from significant growth over the first 10 years to stagnation and even decline. PG&E apparently has been able to adapt its forecasting methods for other classes relatively well over time.

The accumulated error for agricultural sales forecasting tells a different story. Over a quarter century the cumulative error reached 182%, nearly twice the annual sales for the Agricultural class. This cumulative error has consequences for the relative share of revenue collected from agricultural customers compared to other customers, with growers significantly overpaying during the period.

Agricultural load forecasting can be revised to better address how variations in water supply availability drive agricultural load. Most importantly, the final forecast should be constructed from a weighted average of forecasted loads under normal, wet and dry conditions. The forecast of agricultural accounts also must be revamped to include these elements. In addition, the load forecast should include the influence of rates and a publicly available data source on agricultural income such as that provided by the USDA’s Economic Research Service.

The Forecast Model Can Use An Additional Drought Indicator and Forecasted Agricultural Rates to Improve Its Forecast Accuracy

The more direct relationship to determine agricultural class energy needs is between the allocation of surface water via state and federal water projects and the need to pump groundwater when adequate surface water is not available from the SWP and federal CVP. The SWP and CVP are critical to California agriculture because little precipitation falls during the state’s Mediterranean-climate summer and snow-melt runoff must be stored and delivered via aqueducts and canals. Surface water availability, therefore, is the primary determinant of agricultural energy use, while precipitation and related factors, such as drought, are secondary causes in that they are only partially responsible for surface water availability. Other factors such as state and federal fishery protections substantially restrict water availability and project pumping operations greatly limiting surface water deliveries to San Joaquin Valley farms.

We found that the Palmer Drought Stress Index (PDSI) is highly correlated with contract allocations for deliveries through the SWP and CVP, reaching 0.78 for both of them, as shown in Figure AECA-3. (Note that the correlation between the current and lagged PDSI is only 0.34, which indicates that both variables can be included in the regression model.) Of even greater interest and relevance to PG&E’s forecasting approach, the correlation with the previous year’s PDSI and project water deliveries is almost as strong, 0.56 for the SWP and 0.53 for the CVP. This relationship can be seen also in Figure-3, as the PDSI line appears to lead changes in the project water deliveries. This strong relationship with this lagged indicator is not surprising, as both the California Department of Water Resources and U.S. Bureau of Reclamation account for remaining storage and streamflow that is a function of soil moisture and aquifers in the Sierras.

Further, comparing the inverse of water delivery allocations, (i.e., the undelivered contract shares), to the annual agricultural sales, we can see how agricultural load has risen since 1995 as the contract allocations delivered have fallen (i.e., the undelivered amount has risen) as shown in Figure-4. The decline in the contract allocations is only partially related to the amount of precipitation and runoff available. In 2017, which was among the wettest years on record, SWP Contractors only received 85% of their allocations, while the SWP provided 100% every year from 1996 to 1999. The CVP has reached a 100% allocation only once since 2006, while it regularly delivered above 90% prior to 2000. Changes in contract allocations dictated by regulatory actions are clearly a strong driver in the growth of agricultural pumping loads but an ongoing drought appears to be key here. The combination of the forecasted PDSI and the lagged PDSI of the just concluded water year can be used to capture this relationship.

Finally, a “normal” water year rarely occurs, occurring in only 20% of the last 40 years. Over time, the best representation of both surface water availability and the electrical load dependent on it is a weighted average across the probabilities of different water year conditions.

Proposed Revised Agricultural Forecast

We prepared a new agricultural load forecast for 2021 implementing the changes recommended herein. In addition, the forecasted average agricultural rate was added, which was revealed to be statistically valid. The account forecast was developed using most of the same variables as for the sales forecast to reflect similarities in drivers of both sales and accounts.

Figure-5 compares the performance of AECA’s proposed model to PG&E’s model filed in its 2021 General Rate Case. The backcasted values from the AECA model have a correlation coefficient of 0.973 with recorded values,[1] while PG&E’s sales forecast methodology only has a correlation of 0.742.[2] Unlike PG&E’s model almost all of the parameter estimates are statistically valid at the 99% confidence interval, with only summer and fall rainfall being insignificant.[3]

AECA’s accounts forecast model reflects similar performance, with a correlation of 0.976. The backcast and recorded data are compared in Figure-6. For water managers, this chart shows how new groundwater wells are driven by a combination of factors such as water conditions and electricity prices.




The State Water Board needs to act to start Flood MAR pilot projects

I recently presented to CDWR’s Lunch-MAR group the findings for a series of studies we conducted on the universe of benefits from floodwater managed aquifer recharge (MAR) and the related economic and financing issues. I also proposed that an important next step is to run a set of pilots to study the acceptability of on-farm floodwater recharge projects to growers, including how do they respond to incentives and program design, and what are the potential physical consequences.

The key to initiating these pilots is getting a clear declaration from the State Water Resources Control Board that excess floodwaters are surplus and available. Unfortunately, the Water Board has not provided sufficient clarification on how these projects can take “advantage of seasonal or occasional flood waters that overtop the banks of a stream and are then directed into a designated recharge area.” Instead, the Board’s website says that such diverted floodwaters cannot be stored for future beneficial use–which obviates the very purpose of retaining the floodwaters in the first place.

The Board should be at least issuing temporary use permits for floodwaters above certain designated levels as being available for pilot projects on the basis that non-use of those floodwaters constitute a surrender of that right for the year. Then those agencies interested in flood MAR can design projects to experiment with potential configurations.

Can Net Metering Reform Fix the Rooftop Solar Cost Shift?: A Response

A response to Severin Borenstein’s post at UC Energy Institute where he posits a large subsidy flowing to NEM customers and proposes an income-based fixed charge as the remedy. Borenstein made the same proposal at a later CPUC hearing.

The CPUC is now considering reforming the current net energy metering (NEM) tariffs in the NEM 3.0 proceeding. And the State Legislature is considering imposing a change by fiat in AB 1139.

First, to frame this discussion, economists are universally guilty of status quo bias in which we (since I’m one) too often assume that changing from the current physical and institutional arrangement is a “cost” in an implicit assumption that the current situation was somehow arrived at via a relatively benign economic process. (The debate over reparations for slavery revolve around this issue.) The same is true for those who claim that NEM customers are imposing exorbitant costs on other customers.

There are several issues to be considered in this analysis.

1) In looking at the history of the NEM rate, the emergence of a misalignment between retail rates that compensate solar customers and the true marginal costs of providing service (which are much more than the hourly wholesales price–more on that later) is a recent event. When NEM 1.0 was established residential rates were on the order of 15 c/kWh and renewable power contracts were being signed at 12 to 15 c/kWh. In addition, the transmission costs were adding 2 to 4 c/kWh. This was the case through 2015; NEM 1.0 expired in 2016. NEM 2.0 customers were put on TOU rates with evening peak loads, so their daytime output is being priced at off peak rates midday and they are paying higher on peak rates for usage. This despite the fact that the difference in “marginal costs” between peak and off wholesale costs are generally on the order of a penny per kWh. (PG&E NEM customers also pay a $10/month fixed charge that is close to the service connection cost.) Calculating the net financial flows is more complicated and deserve that complex look than what can be captured in a simple back of the envelope calculation.

2) If we’re going to dig into subsidies, the first place to start is with utility and power plant shareholders. If we use the current set of “market price benchmarks” (which are problematic as I’ll discuss), out of PG&E’s $5.2 billion annual generation costs, over $2 billion or 40% are “stranded costs” that are subsidies to shareholders for bad investments. In an efficient marketplace those shareholders would have to recover those costs through competitively set prices, as Jim Lazar of the Regulatory Assistance Project has pointed out. One might counter those long term contracts were signed on behalf of these customers who now must pay for them. Of course, overlooking whether those contracts were really properly evaluated, that’s also true for customers who have taken energy efficiency measures and Elon Musk as he moves to Texas–we aren’t discussing whether they also deserve a surcharge to cover these costs. But beyond this, on an equity basis, NEM 1.0 customers at least made investments based on an expectation, that the CPUC did not dissuade them of this belief (we have documentation of how at least one county government was mislead by PG&E on this issue in 2016). If IOUs are entitled to financial protection (and the CPUC has failed to enact the portfolio management incentive specified in AB57 in 2002) then so are those NEM customers. If on the other hand we can reopen cost recovery of those poor portfolio management decisions that have led to the incentive for retail customers to try to exit, THEN we can revisit those NEM investments. But until then, those NEM customers are no more subsidized than the shareholders.

3) What is the true “marginal cost”? First we have the problem of temporal consistency between generation vs. transmission and distribution grid (T&D) costs. Economists love looking at generation because there’s a hourly (or subhourly) “short run” price that coincides nicely with economic theory and calculus. On the other hand, those darn T&D costs are lumpy and discontinuous. The “hourly” cost for T&D is basically zero and the annual cost is not a whole lot better. The current methods debated in the General Rate Cases (GRC) relies on aggregating piecemeal investments without looking at changing costs as a whole. Probably the most appropriate metric for T&D is to calculate the incremental change in total costs by the number of new customers. Given how fast utility rates have been rising over the last decade I’m pretty sure that the “marginal cost” per customer is higher than the average cost–in fact by definition marginal costs must be higher. (And with static and falling loads, I’m not even sure how we calculated the marginal costs per kwh. We can derive the marginal cost this way FERC Form 1 data.) So how do we meld one marginal cost that might be on a 5-minute basis with one that is on a multi-year timeframe? This isn’t an easy answer and “rough justice” can cut either way on what’s the truly appropriate approximation.

4) Even if the generation cost is measured sub hourly, the current wholesale markets are poor reflections of those costs. Significant market distortions prevent fully reflecting those costs. Unit commitment costs are often subsidized through out of market payments; reliability regulation forces investment that pushes capacity costs out of the hourly market, added incremental resources–whether for added load such as electrification or to meet regulatory requirements–are largely zero-operating cost renewables of which none rely on hourly market revenues for financial solvency; in California generators face little or no bankruptcy risk which allows them to underprice their bids; on the flip side, capacity price adders such as ERCOT’s ORDC overprices the value of reliability to customers as a backdoor way to allow generators to recover investments through the hourly market. So what is the true marginal cost of generation? Pulling down CAISO prices doesn’t look like a good primary source of data.

We’re left with the question of what is the appropriate benchmark for measuring a “subsidy”? Should we also include the other subsidies that created the problem in the first place?

Is the NASDAQ water futures market transparent enough?

Futures markets are settled either physically with actual delivery of the contracted product, or via cash based on the difference in the futures contract price and the actual purchase price. The NASDAQ Veles California Water Index future market is a cash settled market. In this case, the “actual” price is constructed by a consulting firm based on a survey of water transactions. Unfortunately this method may not be full reflective of the true market prices and, as we found in the natural gas markets 20 years ago, these can be easily manipulated.

Most commodity futures markets, such at the crude oil or pork bellies, have a specific delivery point, such as Brent North Sea Crude or West Texas Intermediate at Cushing, Oklahoma or Chicago for some livestock products. There is also an agreed upon set of standards for the commodities such as quality and delivery conditions. The problem with the California Water Index is that these various attributes are opaque or even unknown.

Two decades ago I compiled the most extensive water transfer database to date in the state. I understand the difficulty of collecting this information and properly classifying it. The bottom line is that there is not a simple way to clearly identify what is the “water transfer price” at any given time.

Water supplied for agricultural and urban water uses in California has many different attributes. First is where the water is delivered and how it is conveyed. While water pumped from the Delta gets the most attention, surface water comes from many other sources in the Sacramento and San Joaquin Valleys, as well as from the Colorado River. The cost to move this water greatly varies by location ranging from gravity fed to a 4,000 foot lift over the Tehachapis.

Second is the reliability and timing of availability. California has the most complex set of water rights in the U.S. and most watersheds are oversubscribed. A water with a senior right delivered during the summer is more valuable than a junior right delivered in the winter.

Third is the quality of the water. Urban districts will compete for higher quality sources, and certain agricultural users can use higher salinity sources than others.

A fourth dimension is that water transfers are signed for different periods and delivery conditions as well as other terms that directly impact prices.

All of these factors lead to a spread in prices that are not well represented by a single price “index”. This becomes even more problematic when a single entity such as the Metropolitan Water District enters the market and purchases one type of water which they skews the “average.” Bart Thompson at Stanford has asked whether this index will reflect local variations sufficiently.

Finally, many of these transactions are private deals between public agencies who do not reveal key attributes these transfers, particularly price, because there is not an open market reporting requirement. A subsequent study of the market by the Public Policy Institute of California required explicit cooperation from these agencies and months of research. Whether a “real time” index is feasible in this setting is a key question.

The index managers have not been transparent about how the index is constructed. The delivery points are not identified, nor are the sources. Whether transfers are segmented by water right and term is not listed. Whether certain short term transfers such as the State Water Project Turnback Pool are included is not listed. Without this information, it is difficult to measure the veracity of the reported index, and equally difficult to forecast the direction of the index.

The housing market has many of these same attributes, which is one reason why you can’t buy a house from a central auction house or from a dealer. There are just too many different dimensions to be considered. There is housing futures market, but housing has one key difference from the water transfer market–the price and terms are publicly reported to a government agency (usually a county assessor). Companies such as CoreLogic collect and publish this data (that is distributed by Zillow and Redfin.)

In 2000, natural gas prices into California were summarized in a price index reported by Natural Gas Intelligence. The index was based a phone survey that did not require verification of actual terms. As part of the electricity crisis that broke that summer, gas traders found that they could manipulate gas prices for sales to electricity generators higher by simply misreporting those prices or by making multiple sequential deals that ratcheted up the price. The Federal Energy Regulatory Commission and Commodity Futures Trading Commission were forced to step in and establish standards for price reporting.

The NASDAQ Veles index has many of the same attributes as the gas market had then but perhaps with even less regulatory protections. It is not clear how a federal agency could compel public agencies, including the U.S. Bureau of Reclamation, to report and document prices. Oversight of transactions by water districts is widely dispersed and usually assigned to the local governing board.

Trying to introduce a useful mechanism to this market sounds like an attractive option, but the barriers that have impeded other market innovations may be too much.

What is driving California’s high electricity prices?

This report by Next10 and the University of California Energy Institute was prepared for the CPUC’s en banc hearing February 24. The report compares average electricity rates against other states, and against an estimate of “marginal costs”. (The latter estimate is too low but appears to rely mostly on the E3 Avoided Cost Calculator.) It shows those rates to be multiples of the marginal costs. (PG&E’s General Rate Case workpapers calculates that its rates are about double the marginal costs estimated in that proceeding.) The study attempts to list the reasons why the authors think these rates are too high, but it misses the real drivers on these rate increases. It also uses an incorrect method for calculating the market value of acquisitions and deferred investments, using the current market value instead of the value at the time that the decisions were made.

We can explore the reasons why PG&E’s rates are so high, much of which is applicable to the other two utilities as well. Starting with generation costs, PG&E’s portfolio mismanagement is not explained away with a simple assertion that the utility bought when prices were higher. In fact, PG&E failed in several ways.

First, PG&E knew about the risk of customer exit as early as 2010 as revealed during the PCIA rulemaking hearings in 2018. PG&E continued to procure as though it would be serving its entire service area instead of planning for the rise of CCAs. Further PG&E also was told as early as 2010 (in my GRC testimony) that it was consistently forecasting too high, but it didn’t bother to correct thee error. Instead, service area load is basically at the save level that it was a decade ago.

Second, PG&E could have procured in stages rather than in two large rounds of request for offers (RFOs) which it finished by 2013. By 2011 PG&E should have realized that solar costs were dropping quickly (if they had read the CEC Cost of Generation Report that I managed) and that it should have rolled out the RFOs in a manner to take advantage of that improvement. Further, they could have signed PPAs for the minimum period under state law of 10 years rather than the industry standard 30 years. PG&E was managing its portfolio in the standard practice manner which was foolish in the face of what was occurring.

Third, PG&E failed to offer part of its portfolio for sale to CCAs as they departed until 2018. Instead, PG&E could have unloaded its expensive portfolio in stages starting in 2010. The ease of the recent RPS sales illustrates that PG&E’s claims about creditworthiness and other problems had no foundation.

I calculated the what the cost of PG&E’s mismanagement has been here. While SCE and SDG&E have not faced the same degree of exit by CCAs, the same basic problems exist in their portfolios.

Another factor for PG&E is the fact that ratepayers have paid twice for Diablo Canyon. I explain here how PG&E fully recovered its initial investment costs by 1998, but as part of restructuring got to roll most of its costs back into rates. Fortunately these units retire by 2025 and rates will go down substantially as a result.

In distribution costs, both PG&E and SCE requested over $2 billion for “new growth” in each of its GRCs since 2009, despite my testimony showing that growth was not going to materialize, and did not materialize. If the growth was arising from the addition of new developments, the developers and new customers should have been paying for those additions through the line extension rules that assign that cost responsibility. The utilities’ distribution planning process is opaque. When asked for the workpapers underlying the planning process, both PG&E and SCE responded that the entirety were contained in the Word tables in each of their testimonies. The growth projections had not been reconciled with the system load forecasts until this latest GRC, so the totals of the individual planning units exceeded the projected total system growth (which was too high as well when compared to both other internal growth projections and realized growth). The result is a gross overinvestment in distribution infrastructure with substantial overcapacity in many places.

For transmission, the true incremental cost has not been fully reported which means that other cost-effective solutions, including smaller and closer renewables, have been ignored. Transmission rates have more than doubled over the last decade as a result.

The Next10 report does not appear to reflect the full value of public purpose program spending on energy efficiency, in large part because it uses a short-run estimate of marginal costs. The report similarly underestimates the value of behind-the-meter solar rooftops as well. The correct method for both is to use the market value of deferred resources–generation, transmission and distribution–when those resources were added. So for example, a solar rooftop installed in 2013 was displacing utility scale renewables that cost more than $100 per megawatt-hour. These should not be compared to the current market value of less than $60 per megawatt-hour because that investment was not made on a speculative basis–it was a contract based on embedded utility costs.

The PCIA is heading California toward another energy crisis

The California ISO Department of Market Monitoring notes in its comments to the CPUC on proposals to address resource adequacy shortages during last August’s rolling blackouts that the number of fixed price contracts are decreasing. In DMM’s opinion, this leaves California’s market exposed to the potential for greater market manipulation. The diminishing tolling agreements and longer term contracts DMM observes is the result of the structure of the power cost indifference adjustment (PCIA) or “exit fee” for departed community choice aggregation (CCA) and direct access (DA) customers. The IOUs are left shedding contracts as their loads fall.

The PCIA is pegged to short run market prices (even more so with the true up feature added in 2019.) The PCIA mechanism works as a price hedge against the short term market values for assets for CCAs and suppresses the incentives for long-term contracts. This discourages CCAs from signing long-term agreements with renewables.

The PCIA acts as an almost perfect hedge on the retail price for departed load customers because an increase in the CAISO and capacity market prices lead to a commensurate decrease in the PCIA, so the overall retail rate remains the same regardless of where the market moves. The IOUs are all so long on their resources, that market price variation has a relatively small impact on their overall rates.

This situation is almost identical to the relationship of the competition transition charge (CTC) implemented during restructuring starting in 1998. Again, energy service providers (ESPs) have little incentive to hedge their portfolios because the CTC was tied directly to the CAISO/PX prices, so the CTC moved inversely with market prices. Only when the CAISO prices exceeded the average cost of the IOUs’ portfolios did the high prices become a problem for ESPs and their customers.

As in 1998, the solution is to have a fixed, upfront exit fee paid by departing customers that is not tied to variations in future market prices. (Commissioner Jesse Knight’s proposal along this line was rejected by the other commissioners.) By doing so, load serving entities (LSEs) will be left to hedging their own portfolios on their own basis. That will lead to LSEs signing more long term agreements of various kinds.

The alternative of forcing CCAs and ESP to sign fixed price contracts under the current PCIA structure forces them to bear the risk burden of both departed and bundled customers, and the IOUs are able to pass through the risks of their long term agreements through the PCIA.

California would be well service by the DMM to point out this inherent structural problem. We should learn from our previous errors.

“What are public benefits of conveyance?” presented to the California Water Commission

Maven’s Notebook posted a summary of presentations to the California Water Commission by Richard McCann of M.Cubed, Steve Hatchett of Era Economics, and David Sunding of the Brattle Group. Many of my slides are included.

The Commission is developing a framework that might be used to identify how shares of conveyance costs might be funded by the state of California. The Commission previously awarded almost $3 billion in bond financing for a dozen projects under the Proposition 1B Water Storage Investment Program (WSIP). That process used a prescribed method including a Technical Guide that determined the eligible public benefits for financing by the state. M.Cubed supported the application by Irvine Ranch Water District and Rio Bravo-Rosedale Water Storage District for the Kern Fan water bank.

We’ve already paid for Diablo Canyon

As I wrote last week, PG&E is proposing that a share of Diablo Canyon nuclear plant output be allocated to community choice aggregators (CCAs) as part of the resolution of issues related to the Integrated Resource Plan (IRP), Resource Adequacy (RA) and Power Charge Indifference Adjustment (PCIA) rulemakings. While the allocation makes sense for CCAs, it does not solve the problem that PG&E ratepayers are paying for Diablo Canyon twice.

In reviewing the second proposed settlement on PG&E costs in 1994, we took a detailed look at PG&E’s costs and revenues at Diablo. Our analysis revealed a shocking finding.

Diablo Canyon was infamous for increasing in cost by more than ten-fold from the initial investment to coming on line. PG&E and ratepayer groups fought over whether to allow $2.3 billion dollars.  The compromise in 1988 was to essentially shift the risk of cost recovery from ratepayers to PG&E through a power purchase agreement modeled on the Interim Standard Offer Number 4 contract offered to qualifying facilities (but suspended as oversubscribed in 1985).

However, the contract terms were so favorable and rich to PG&E, that Diablo costs negatively impacted overall retail rates. These costs were a key contributing factor that caused industrial customers to push for deregulation and restructuring. As an interim solution in 1995 in anticipation of forthcoming restructuring, PG&E and ratepayer groups arrived at a new settlement that moved Diablo Canyon back into PG&E’s regulated ratebase, earning the utilities allowed return on capital. PG&E was allowed to keep 100% of profit collected between 1988 and 1995. The subsequent 1996 settlement made some adjustments but arrived at essentially the same result. (See Decision 97-05-088.)

While PG&E had borne the risks for seven years, that was during the plant startup and its earliest years of operation.  As we’ve seen with San Onofre NGS and other nuclear plants, operational reliability is most at risk late in the life of the plant. PG&E’s originally took on the risk of recovering its entire investment over the entire life of the plant.  The 1995 settlement transferred the risk for recovering costs over the remaining life of the plant back to ratepayers. In addition, PG&E was allowed to roll into rate base the disputed $2.3 billion. This shifted cost recovery back to the standard rate of depreciation over the 40 year life of the NRC license. In other words, PG&E had done an end-run on the original 1988 settlement AND got to keep the excess profits.

The fact that PG&E accelerated its investment recovery over the first seven years and then shifted recovery risk to ratepayers implies that PG&E should be allowed to recover only the amount that it would have earned at a regulated return under the original 1988 settlement. This is equal to the discounted net present value of the net income earned by Diablo Canyon, over both the periods of the 1988 (PPA) and 1995 settlements.

In 1996, we calculated what PG&E should be allowed to recover in the settlement given this premise.  We assumed that PG&E would be allowed to recover the disputed $2.3 billion because it had taken on that risk in 1988, but the net income stream should be discounted at the historic allowed rate of return over the seven year period.  Based on these assumptions, PG&E had recovered its entire $7.7 billion investment by October 1997, just prior to the opening of the restructured market in March 1998.  In other words, PG&E shareholders were already made whole by 1998 as the cost recovery for Diablo was shifted back to ratepayers.  Instead the settlement agreement has caused ratepayers to pay twice for Diablo Canyon.

PG&E has made annual capital additions to continue operation at Diablo Canyon since then and a regulated return is allowed under the regulatory compact.  Nevertheless, the correct method for analyzing the potential loss to PG&E shareholders from closing Diablo is to first subtract $5.1 billion from the plant in service, reducing the current ratebase to capital additions incurred since 1998. This would reduces the sunk costs that are to be recovered in rates from $31 to $3 per megawatt-hour.

Note that PG&E shareholders and bondholders have earned a weighted return of approximately 10% annually on this $5.1 billion since 1998. The compounded present value of that excess return was $18.1 billion by 2014 earned by PG&E.