Author: Brock Mosovsky

July 8, 2020

Retail Rate Structures for Electric Distribution Networks in Transition: A Case for Automation


Clean energy technologies are increasingly being deployed on electric distribution systems and retail electricity pricing is evolving to support the transition. This evolution involves moving from rates characterized by flat energy charges and net metering policies for distributed energy resources (DERs) towards modern structures that more accurately reflect a utility’s costs to supply and deliver electricity. These include time-of-use schedules, demand charges, feed-in tariffs (FITs) for over-generation by DERs, and other dynamic pricing signals. These modern rate structures provide economic signals that encourage energy consumption during periods when supply is abundant and discourage consumption during periods when demand is higher and grid resources are more constrained.

Historically, net energy metering (NEM) policies have been the dominant compensation mechanism driving renewable DER growth in the United States, the large majority of which has been small-scale solar photovoltaics.(1) NEM requires utilities to compensate excess production from customer-owned generation at the relatively static retail electricity price. Under this paradigm, small-scale (<1MW) solar generation has grown an average of 27% per year from 2014-2018, and currently provides 33% of all solar energy in the United States.(2) Clearly, NEM policies have been an effective tool to stimulate early investment in distributed clean energy; however, policymakers have begun to shift away from this model for future distribution systems. (3)

NEM becomes less efficient as DER penetrations increase to substantial levels. As this occurs, the grid can become oversupplied with a particular form of generation (e.g., solar). This decreases the marginal value of each kilowatt-hour generated and increases grid management costs… read more here on page 5 of 44.

Article snippet from the IAEE Energy Forum – Third Quarter 2020. Issn 1944-3188. Full article found here.

July 17, 2018

J.P. Morgan Center for Commodities: Three Steps to Understanding Value and Risk in Renewable PPAs

This article is based on the author’s recently-published article in the JP Morgan Center for Commodities Global Commodities Applied Research Digest (GCARD).

Renewable power purchase agreements (PPAs) have long been important enablers of renewable energy development. However, like any structured transaction, PPAs come with a certain set of risks that the buyer should fully understand before committing to the deal. Each wind or solar facility has its own unique signature governing both the timing of the renewable generation and the value of that generation at the time of production. A proper understanding of value and risk for a renewable PPA requires analyzing how generation and market prices align in real-time at the specific location of the facility underlying the contract. This includes a detailed statistical understanding of three important components of the deal:

  • The real-time generation signature of the project underlying the PPA
  • The real-time market price signature where energy from the PPA is fed into the grid
  • The real-time alignment of generation and market prices

The Generation Signature

Each renewable project site comes with its own unique generation profile, or signature. This signature defines not only how much energy will be produced, but, importantly, when the energy will be produced and how uncertain the production will be. For example, consider the signature of a solar farm in New York compared to a solar farm in Arizona. Even if the total annual energy from the two facilities is the same, these two locations will have starkly different generation signatures.

The New York solar farm, being further north, will have more seasonal fluctuation in generation due to the dramatically changing number of daylight hours over the course of the year. Since Arizona is further south, it will have less of a difference between the length of day in summer and winter. Being east of the Great Lakes, New York has much more moisture in its atmosphere than Arizona, which leads to more cloudy days and correspondingly greater variability in solar generation day-to-day. In particular, New York winters tend to be particularly cloudy and produce significant snowfall, further contributing to depressed solar generation during the coldest months of the year compared to the warm, arid Arizona desert.

By analyzing historical weather patterns and historical solar radiation, we can compose a fairly complete statistical picture of the solar generation signature in each of these locations. It is important to understand the generation signature at the hourly level, and how it changes over the course of the year. By doing so, we obtain not only an expectation of how much electricity each facility will produce in each hour of the year, but we gain an understanding of how uncertain this estimate is. We’ll come back to this uncertainty in the third component of understanding PPA value and risk.


The Price Signature

The second component of the PPA to understand is the real-time price signature. Just as each location has its own patterns of generation, so too does each price location have its own unique price signature. The price signature is highly complex and depends on many different local factors including weather patterns, demand for electricity, generation availability and type (e.g., natural gas, coal, hydro, renewable, etc.), transmission infrastructure, fuel price, and governmental policy and regulations, to name a few. Despite all this complexity, there are two useful indicators of a particular location’s price signature that provide insight when assessing PPA value and risk: historical real-time spot prices and current forward contract prices.

The historical real-time spot prices provide a record for how the particular price location has behaved in the past. Some prices may be extremely volatile while others may be more predictable. Some may show enormous variability across each day while others may have a flatter shape. Some may change shape seasonally while others may remain more constant throughout the year. Teasing out these price trends from historical price history is essential to building an understanding of when electricity is most valuable at a particular price location and when value is most uncertain. Both of these components are directly tied to the value and risk the PPA will have over its contract term.

The second pricing component that sheds light on market expectation at the pricing location is the current forward contract price. This is the price agreed upon today for electricity to be delivered at some point in the future. Together the set of forward prices across a range of future delivery periods is called the “forward curve”, and this forward curve is one reflection of the current market expectation of what prices will be in the future. Not every pricing location has a liquidly-traded forward curve, so, in many cases, seasoned insight and understanding of financial electricity markets must be used to develop a forward curve specific to a given project. Whereas the historical spot price record provides insight on real-time price shape and variability, the forward curve provides insight on the absolute price level out in the future. Together, these two pricing components form the real-time price signature for the PPA.

Generation and Price Alignment

Having an understanding of the generation and price signatures individually is not sufficient to understand their effects on PPA value and risk; we have to also understand how they align in real-time. The value of the electricity generated under a PPA is the product of generation and the market price at the time the generation occurs. The multiplicative effect can magnify uncertainty and risk in one of these quantities relative to the other. For example, if market prices tend to dip down at times when generation is highest, the overall net effect will be a reduction in average PPA value. However, the high generation will magnify the downside risk of the low market prices, causing a greater net loss than if generation were at lower levels. Similarly, if market prices tend to spike when generation is low, the benefit to the PPA of high market prices will be reduced by lower-than-average generation.


The alignment between the generation and market prices signatures is a primary driver of both PPA value and risk. Because this alignment is constantly changing as markets evolve and more renewable generation is placed in service on the grid, a simulation-based approach to generation-price alignment can be an excellent way to probe a wide range of possible scenarios and assess the effect of each on overall value. A Monte Carlo simulation approach like’s ReAssure PPA valuation and risk assessment model can uncover the signature of generation and real-time prices for a particular PPA location while also providing insight into how the alignment of these two signatures drives PPA value and risk.

The next time you or your organization is looking to sign a long-term renewable PPA, make sure you don’t go into the transaction blinded by “greenwashing” claims and promises of positive NPV based on optimistic (or worse, faulty) analysis and forecasting. Make sure you demand to understand the signatures of real-time generation and real time market prices and how the two interact to drive value and risk for your particular PPA. Anything less, and you just may be signing a deal that could come back to bite you.

This article is based on the author’s recently-published article in the JP Morgan Center for Commodities Global Commodities Applied Research Digest (GCARD). For more details, please see the full article beginning on page 29 of the Summer 2018 issue of the GCARD here.

Brock Mosovsky, Ph.D., is Director of Operations and Analytics at, a SaaS energy analytics platform that helps companies understand their physical and financial exposure in today’s energy markets.

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March 20, 2018

Duck with a Side of Energy Storage: Why batteries pair perfectly with high-penetration solar

In 2013, the California Independent System Operator (CAISO) first published the iconic “Duck Curve“, forecasting what would happen to the shape of hourly net electricity demand under high solar photovoltaic (PV) penetration scenarios. Since this initial publication, the situation in California has unfolded in remarkably similar fashion to what was forecasted. Mid-day solar generation has dramatically reduced the net electricity demand that must be supplied by fossil-fueled power plants. However, the large amount of mid-day solar generation begins to ramp down exactly as the evening electricity peak is ramping up. This results in a more pronounced afternoon-to-evening ramp and puts added strain on the more traditional power plants that must be relied on to pick up solar’s slack after the sun sets. By helping to reduce the strain on these generators, adding batteries and other flexible generation resources to the grid can help support the integration of high levels of solar PV while maintaining reliable electricity supply. What is a bit less obvious at first glance is that this trend also holds in reverse: high levels of solar PV on the grid actually make it easier to integrate high levels of battery storage. 

A recent report from the National Renewable Energy Laboratory (NREL) outlines exactly why this is the case. Essentially, batteries are great at providing power over short timeframes but less ideal for maintaining high power output over long periods of time. Put another way, batteries are well-suited to deliver power but are less adept at delivering energy. As increased solar penetration levels reduce system net demand on the front-end of the evening peak, the overall system peak in net demand becomes “sharper”. That is, the highest levels of electricity demand persist for a shorter amount of time than in the absence of solar generation. This allows batteries to more easily provide peak reduction benefits to the grid because they can discharge for a shorter period of time while achieving the same reduction in overall system peak demand.

“Those that fail to leverage today’s ever-more-accessible data analytics to monitor their portfolios amid a changing market may find themselves eating crow while the rest of the industry sits down to duck.”

The overall dynamic between solar and batteries, as the authors note, is one of synergism. In the language of system dynamics, it’s a reinforcing feedback loop. Increases in battery storage and flexible generation on the grid allow more solar to be integrated, which in turn increases the value proposition of battery storage and flexible generation. That is, batteries are good for renewable energy is good for batteries–and round and round we go. 

There’s also a third component to this dynamic that the NREL report doesn’t really address: market prices. To-date, the Duck Curve has already lead to decreases in the mid-day price of electricity in California, with marginal wholesale electricity prices frequently even dipping below zero. This means that generators (regardless of type) that put electricity onto the grid may actually have to pay to do so. In two recent articles, we discussed how the net load dynamic that results from high levels of renewable generation is killing the economics of the baseload operational paradigm where large centralized generators run at constant output for long periods of time. However, battery storage has the unique ability to increase both electricity production and electricity consumption on the grid, albeit at different times. If properly scheduled, the increased electricity demand from batteries could become a powerful stabilizing force for both the electricity demand curve and, correspondingly, for market prices.

As the grid transforms and market expectations evolve, we are likely to see dramatic shifts in what becomes “normal” for wholesale electricity market prices. Stabilization of the hourly shape of electricity demand could mean reduced energy prices and less market volatility. Organizations with physical assets or long-term power purchase agreements could see the erosion of value in their existing assets and contracts. Load serving entities could find it more economically optimal to purchase power on the spot market than to fire up aging generators, forcing these units into early retirement.

Whatever these fundamental shifts in electricity supply will bring, businesses with a stake in the game should carefully monitor the value of their assets and actively pursue downside protection through targeted risk mitigation strategies. Those that fail to leverage today’s ever-more-accessible data analytics to monitor their portfolios amid a changing market may find themselves eating crow while the rest of the industry sits down to duck.

Brock Mosovsky, Ph.D., is Director of Operations and Analytics at, a SaaS energy analytics platform that helps companies understand their physical and financial exposure in today’s energy markets.

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February 15, 2018

Renewables Have Killed Baseload…Now What?

***This article is the second in a two-part series on the effects of high renewable penetration on thermal generation operational paradigms. The first article discussed how intermittent renewable generation has transformed the grid to the point where many combined cycle plants are unable to operate profitably.***

So baseload power is on its way out and intermittent renewable generation is on its way in. There’s been a lot of buzz about how this represents a “tremendous transformation” or a “historic transition“, and the trend has even received attention in the national political arena with the controversial Grid Resiliency Pricing Rule proposal of late 2017, since defeated by the Federal Energy Regulatory Commission. Suffice it to say there’s been a lot of rhetoric around the declining relevance of baseload electricity supply. However, there has been little practical insight or guidance offered to organizations with a stake in the energy game—precisely those that will be most affected by the transformation. For example:

  • What does the end of the baseload supply paradigm mean for energy companies and purchasers of long-term renewable PPAs?
  • How can companies participate in the renewable energy revolution without being destroyed by it?
  • And what role should data analytics and energy risk management play throughout the impending transition.

In this article, we offer some practical perspectives on these questions and discuss what the death of baseload power really means both today and in the near future. For more on how and why baseload is becoming increasingly irrelevant, be sure to check out the first article in this two-part series.

Long-standing structural energy market dynamics will be challenged

You can’t talk about the future of electricity supply without addressing the future of electricity demand, and we’ve observed an important trend over the better part of the past decade that is becoming harder and harder to dispute: demand growth has stagnated. Continued growth in electricity demand is a long-standing assumption that has been baked into electric utility supply planning and budget forecasting for decades. A late 2016 report by Lawrence Berkeley National Laboratory includes a striking figure that illustrates just how resistant supply planners are to giving up the load growth assumption (see Figure 1 below). Challenging this assumption will provide an important perspective that can ultimately inform an outlook on future wholesale electricity prices.



Figure 1. Comparison of forecasted and actual customer demand for a utility in the western U.S. Image credit: LBNL Report

When stagnant electricity demand is viewed in the context of the evolving electricity supply stack that is undergoing rapid expansion of renewable generation capacity and significant retirement of fossil-fueled generation, additional long-held market assumptions come into question. One of these is the assumption that natural gas and electricity prices will tend to move in the same direction at the same time. The ratio of the electricity price in $/MWh to the natural gas price in $/MMBtu is referred to as the “implied market heat rate”. Measured in MMBtu per MWh, the market heat represents a measure of generation efficiency akin to a miles-per-gallon rating for a car. As market prices fluctuate, the real-time market heat rate loosely reflects the efficiency of the generator called on to provide the “next megawatt”, or the marginal unit of power to satisfy electricity demand. Since this marginal unit often runs on natural gas, the price of natural gas determines the cost of production, and correspondingly influences the marginal price of electricity. 

However, as the electricity supply stack evolves, there is increasing likelihood that the marginal power needed to satisfy demand could be provided by a generation unit that does not run on natural gas, e.g., by renewables, batteries, or hydroelectric generation. In this case, the price of natural gas may have little to do with the marginal cost of power, causing electricity and natural gas prices to decouple. While it’s not likely the decoupling effect will persist across all hours of the day anytime soon, even its occurrence across a few hours when renewable generation is peaking could produce significant financial consequences for organizations with natural gas or power positions on the books. This includes power producers, load serving entities, and counter-parties to physical or financial power purchase agreements (PPAs). Such a break-down of market heat rates would directly contradict fundamental assumptions that underpin many long-term energy procurement and physical/financial hedging strategies. As such, these strategies should be reevaluated within the context of a market where baseload generation has become obsolete.

Wholesale electricity market prices over the coming decades are extremely uncertain

Demand stagnation and decoupling of market heat rates are virtually unprecedented dynamics in wholesale energy markets, and the market’s response as these trends become more pronounced is anyone’s guess. In particular, long-held assumptions that electricity prices will continue to rise into the future should be viewed as highly suspect. In fact, it is possible that wholesale electricity prices could actually fall in the coming years. The combination of demand stagnation, added renewable generation with little to no production cost, and continued retirement of outdated and inefficient fossil generation implies that the average cost of generating electricity will likely decrease.

As the grid evolves in response to the changing supply paradigm, new transmission and distribution costs, or other costs associated with managing grid reliability, may partially offset declines in the cost of electricity itself. However, long-term agreements that reference wholesale electricity prices may not capture these added grid-based costs, resulting in significant downside risk to counterparties. For example, many corporations today are signing long-term renewable PPAs that are financially settled against wholesale electricity prices at a particular grid location. In a scenario of falling electricity prices, these PPA buyers could see themselves making significant monthly payments for the losses incurred on their contracts. As a result, any purchase decisions that hinge on a long-term forecast of electricity prices should incorporate analysis that challenges the conventional notion that these prices will increase over time.

Generation flexibility will be highly valuable

Because renewable generation facilities can exhibit dramatic fluctuations in production output on short timescales, there will be a serious need for dispatchable generation that is responsive enough to make up the difference. These generation facilities must be able to ramp up and down quickly and to cycle on and off frequently in response to highly variable renewable production output. Such desirable operational traits have become synonymous with the term “generation flexibility”, and they will by highly valued in an era without traditional baseload generation. The increased emphasis on flexibility means that generators with high fixed startup costs incurred each time the unit turns on will be at a disadvantage. Additionally, generators whose efficiency significantly degrades as they ramp down will have a difficult time remaining profitable. Essentially, the same dynamic that is hurting baseload units today will be taken to its logical conclusion, and will eventually elbow out all but the most responsive technologies for delivering power to the grid.

The elephant in the room here is–you guessed it–batteries, or more broadly, energy storage. Batteries, flywheels, and other forms of energy storage can be extremely responsive, providing almost instantaneous power when needed. However, there is an important interplay between a storage technology’s ability to provide power and its ability to provide energy. For example, a battery may be able to provide a large amount of power to fill a short-lived gap in renewable energy generation caused by a large cloud passing over a solar facility or a momentary lapse in wind. However, using batteries to supply the total energy needed to fill a gap in solar production from an entire day of rain showers or snowfall is much more difficult. Batteries and other storage technologies have come a long way in recent years, but their ability to provide sustained power output over long periods of time still has a long way to go before they represent viable solutions for completely replacing flexible fossil generation. At least for the foreseeable future, there will be a significant need for highly-flexible fossil-fueled generation to ensure electricity demand in a baseload-free era is always met with adequate supply.

Data-driven energy risk management will be more critical than ever before

The impending fundamental shifts in electricity supply and demand and the corresponding impacts on market prices all mean that uncertainty and risk will permeate every aspect of the energy sector, from physical operation management to financial decision making. An intimate relationship with risk is nothing new for participants in electricity markets where wholesale prices that normally hover around $30/MWh can spike as high as $9000/MWh in the blink of an eye. However, in response to aggressive corporate sustainability targets and pressure to display environmental stewardship, there has been a growing trend of new businesses directly procuring long-term energy through PPAs. In many cases, the core business function of these companies is not directly tied to the energy markets, and they often do not have the in-house expertise to properly evaluate and continually monitor risk associated with their purchases. These organizations are particularly exposed to downside risk resulting from wrong-way market moves or breakdowns in long-standing structural market relationships. As more and more companies begin interacting directly with volatile energy markets, they will experience a growing need for sophisticated energy risk management and targeted hedging strategies that protect them from downside exposure.

Even for seasoned veterans of the energy industry, the fundamental shifts resulting from the end of baseload generation as we know it mean that many of the risk management strategies and assumptions they are familiar with will break down. Developing and testing new risk management strategies that leverage all data available will require access to sophisticated modeling capabilities and sound analytical methods. Models must be flexible enough to accommodate rapid scenario analysis custom-tailored to an organization’s particular portfolio. Just as flexible generation will be essential to reliable electricity supply, flexible analytics will be essential to maintaining long-term profitability and solvency throughout the energy transformation. 

So renewables have killed baseload…now what? Now we adapt. At, we’re helping organizations adapt to the ever-changing energy landscape with our industry-leading cloud-based energy analytics platform. From renewable PPA valuation, risk assessment, and ongoing value monitoring to thermal asset valuation, cash flow reporting, and hedge analysis, we provide access to the advanced analytics your organization needs in an era without baseload generation.

Need a solution for your energy analytics? We can help! Request your free demo today.

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January 22, 2018

cQuant Insight: GE Rings Funeral Toll for Baseload Generation

***This article is the first in a two-part series on the effects of high renewable penetration on thermal generation operational paradigms. The next article will discuss how utility supply planners, generation asset managers, and renewable PPA purchasers can leverage today’s best practices in energy analytics to position their portfolios for a future without baseload power.*** 

The fact that our generation mix is transforming is no surprise. What may surprise you, however, is just how transformed it’s already become. General Electric, a global leader that produces some of the world’s most efficient natural gas combined cycle generating technology, is already finding its plants uneconomical in electricity markets with high renewable penetration. A recent post on GE’s blog cites intermittent patterns in renewable generation as the main cause for this operational transformation.

It’s no secret that renewable energy has been coming onto the grid at an ever-accelerating pace. Over the past decade, technology costs for solar and wind have experienced dramatic declines and consumers have advocated with increasing fervor for “green” sustainable energy. The result has been a focus on renewable generation for utility-scale capacity expansion like never before. Since 2014, renewables have accounted for more installed capacity in the U.S. than all other forms of generation combined, while at the same time over 40 gigawatts of outdated coal and natural gas fossil generation have retired. The way electricity is generated in both the U.S. and around the world is rapidly transforming.

One of the most insightful windows into this transformation is the way thermal generation technology is being forced to respond. Combined cycle natural gas plants used to run for long periods of time to satisfy baseload electricity demand. Baseload demand is essentially the lowest point on the daily and weekly cyclic patterns of electricity demand in a given region; that is, it’s the amount of demand that can always be counted on no matter what the time of day or day of week. When renewable generation peaks, it reduces the amount of “net demand” that fossil-fueled generators are needed to satisfy. When that net demand begins dropping below the capacity of some of the baseload power plants themselves, these plants have to ramp down to follow suit, or, in some cases, shut down completely. This becomes extremely expensive, since fossil plants often incur high fixed costs each time they start back up and usually run at significantly lower efficiency when ramped down below their maximum generation capacity. The increased operational costs of cycling can quickly eat away at profits for once-baseload generating facilities, forcing them into early retirement.

Here’s the kicker: this dynamic is already happing. GE’s article notes that the Irsching combined cycle plant in Germany was forced to close despite being the most efficient power plant in the world at the time. Despite its high efficiency under baseload operation, it was simply not flexible enough to operate within Germany’s electricity supply stack, which had been transformed by large quantities of wind and solar generation. Other European markets are seeing similar baseload-destroying trends, such as the UK, where the country’s largest energy services provider has noted it’s giving up on combined cycle generation altogether. In these markets, it’s not an exaggeration to say that the very concept of baseload power has become obsolete.

So how much renewable generation does it take to kill baseload power? If you want to see the energy issues the U.S. will likely face ten years from now, just look at Western Europe today. Take Germany, for example: today, roughly 27% of Germany’s electricity comes from renewable energy, whereas this number was just 9% a decade ago. According to the U.S. Energy Information Administration, renewable energy accounted for about 15% of U.S. electric generation in 2016. With recent trends showing an acceleration in renewable generating capacity, it’s likely we’re already well within a decade of where the German and British electricity supply stacks are today.

The U.S. is firmly on a path toward a future where baseload power is a thing of the past. Now, with GE second-guessing its own efficient baseload technology, the funeral dirge for baseload power in the U.S. may have already begun:

“And therefore never send to know for whom the bell tolls; it tolls for thee, Baseload. It tolls for thee.”

 Read Full Original Article Here: Evolution of Combined Cycle Performance: From Baseload to Backup

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