News

July 8, 2020

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

BY BROCK MOSOVSKY AND STEVEN DAHLKE

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.

April 14, 2020

Advanced Analytics and Long-Term Planning

Rapid change in the power sector adds to the complexity of evaluating investment decisions. Implementing advanced analytics and improving optionality can help companies make informed investment decisions during rapid sector changes. The following Q&A was an interview conducted by UtilityDive with our Energy Analyst, David Manning. Below you will see David’s comments on advanced utility investments. 

What are long-term planning challenges utilities face in determining what assets to invest in and how to time those investments?

Broadly, the changing power mix, increased generator intermittency, more distribution-connected generation, flat or declining load, and uncertainty around the pace of vehicle electrification are all contributing to planning challenges. These sources of uncertainty create challenges in determining what generator attributes will be optimal in the medium and long term. 

For example, is it preferable to invest in a generator that can produce power at a low marginal cost or a generator that has a higher cost/MWh, but is very flexible and can provide ancillary services at a low cost? The answer will likely be a moving target for the foreseeable future. In light of these uncertainties, strategies that allow flexibility in decision-making may provide utilities and IPP’s an edge in managing risk.

One approach to increasing flexibility is to deploy modular investments that can be constructed quickly. For example, deploying a small natural gas plant or PV+Storage project to meet an increase in load may be a lower risk approach than building a large combined cycle plant that may not be heavily utilized. Given uncertainty, bigger is not necessarily better.

Lastly, another approach to increasing investment return is to foster optionality. For example, when siting a new wind farm, making sure that there is space and line capacity to add a storage system provides flexibility which may increase long term project utilization.

Can you list what analytic tools will be important for evaluating long-term investment planning in the current market climate?

Because of the increased complexity of power market dynamics, more sophisticated analytical tools are critical for effective investment decision making.

Constrained optimization that comprehensively captures a unit’s operational constraints (e.g. ramp up and ramp down limits, minimum generational level) is critical for evaluating how a potential asset will perform under changing market dynamics. 

Increased market complexity also requires additional sophistication in effectively modeling power price dynamics and market volatility. Analytical tools will need to model prices at hourly and sub-hourly granularity, co-simulate asset performance in energy and ancillary service markets, and model locational price dynamics.

Analytical tools will also need to be able to perform stochastic scenario analysis that evaluates a range of possible scenarios, such as different forward curves or daily price shapes. Thankfully, the need for more complex analytical modeling is being met by improvements in both cloud computing power and analytic software tools. 

How can enhanced analytics improve utility planning?

Robust analytics can improve the evaluation of investments in renewable, storage or flexible natural gas generators, where granular market dynamics are critical to understanding asset performance. Analytics can also support detailed risk modeling of a full portfolio of assets, and new asset investment decisions can be evaluated based on how they fit into a larger utility portfolio.

Conclusion

In light of a rapidly-changing power sector, more sophisticated analytics can help evaluate how potential investments perform in an uncertain future. Effectively modeling the uncertainty of future scenarios, at both hourly and full portfolio level, will help companies more effectively manage investment risk.

Are you ready to improve your energy analytics? Request your free demo today.

SCHEDULE YOUR DEMO TODAY

March 8, 2020

A Quick Review of Outage Cost Analysis

Outages can pose a significant risk to generation asset managers. Some outages are under managers’ control, such as routine planned maintenance, while other outages are unexpected, including forced generator outages due to mechanical failure or transmission outages from severe weather events. Outages add to the complexity of energy risk management – risk managers may have a strategy for effectively hedging market risk but could be significantly over-hedged during an unexpected outage. cQuant’s Outage Cost Analysis (OCA) model gives risk managers a powerful tool to improve scheduled maintenance planning and more effectively manage outage events.

Methodology

The OCA model is smoothly integrated within the cQuant Analytics Platform, allowing it to leverage cQuant’s stochastic Monte Carlo price simulation and asset dispatch models to evaluate outage risk across a range of simulated future price scenarios. This facilitates a robust analysis of outage costs, allowing risk managers to model the expected cost of an outage as well as the distribution of possible outage costs around the mean. OCA has the flexibility to model the costs of both partial (e.g. one turbine of a combined cycle plant) and full plant outages, and users can easily specify different outage durations to investigate, ranging from just a few hours to a full month.

Let’s Review Some Outage Cost Analysis Use-Cases:

Scheduled Maintenance Optimization: Generators need to be taken offline for planned routine maintenance to ensure they continue to operate reliably. Depending on when a generator is taken offline, the asset could incur significant lost revenue. OCA can help asset managers assess the optimal timing for scheduled maintenance to minimize expected lost revenue.

Unscheduled Outage Risk Assessment: These outages can occur due to a range of unexpected events, from technical or mechanical issues at a plant to extreme weather events or natural disasters, such as PG&E’s recent outages due to California wildfires. OCA provides a simulated range of costs from future outages to help understand and manage outage risk.

Outage Insurance Valuation: Some plant managers may consider procuring outage insurance to cover their downside risk due to unplanned outages. While this insurance can provide a tangible benefit in the case of an outage, it can be costly and the benefits can be difficult to value. By assessing outage costs stochastically using market-specific price dynamics, OCA can help price outage insurance and indicate the average monetary benefit it could be expected to provide.

Outage Cost Analysis Conclusion

The Outage Cost Analysis model can easily be integrated into cQuant Analytics Platform modeling workflows. It provides a flexible tool for risk managers to use cQuant’s simulation-based methodology to evaluate outage risk. OCA can be flexibly deployed to serve a number of applications and use cases including maintenance schedule optimization, unplanned outage cost assessment, and complex outage insurance product valuation.

About cQuant.io

cQuant.io is an industry-leading provider of cloud-based energy analytics solutions. cQuant’s advanced analytics platform provides sophisticated energy-focused models across a wide range of industry verticals, from renewable energy project development and contract structuring to thermal generation asset analysis, hedge optimization, and exotic derivative valuation. Visit www.cquant.io for more information.

SCHEDULE YOUR DEMO TODAY

November 20, 2019

Excel Spreadsheet Analytics vs. SaaS Analytics

When does is make sense to upgrade your analytics?

Most corporations use Excel spreadsheets for a variety of business requirements, and for good reason. Excel is inexpensive, easy to learn, easy to customize and has useful computational features. However, as companies and their analytic needs grow, the strengths of Excel can quickly become its faults. Excel spreadsheet flexibility and customizability can enable unseen mistakes and financial errors, especially when being used by multiple individuals.

Software-as-a-service (SaaS) analytics, as compared to Excel analytics, are not only more robust, but are also highly secure and scalable. SaaS typically means a software solution that is cloud-based and accessible via a web browser.  SaaS analytics are generally superior to local (downloaded) software as they eliminate any associated hardware, costly implementation headaches and IT costs. Below, we summarize specifics as to why SaaS analytics, in some cases, should replace Excel spreadsheets.

Spreadsheets are vulnerable to human error.

The flexibility and customizability of Excel spreadsheets makes them easy to use.  However, these characteristics also make spreadsheets more likely to contain errors as complexity increases. The use of inter-cell and inter-tab formulas makes error tracking time-consuming and difficult within spreadsheets. With every cell subjected to manual manipulation, one miscalculation can compromise further calculations and results. This article, “The 7 Biggest Excel Mistakes of All Time”, describes common human errors in corporate spreadsheets which resulted in millions of dollars in losses for those organizations.   

SaaS analytics are less susceptible to these errors since typical end-users of SaaS analytics do not adjust the formulas or the underlying analytical code.  There are three main steps in order to run a SaaS analytics model:

  1. Company data is uploaded
  2. Model is run
  3. Results are downloaded

The analytic models are usually hosted in a cloud-based platform, significantly decreasing the risk of introducing unforeseen human errors. Model formulas can be adjusted, but this is reserved for specific personnel trained for that purpose.

SaaS analytics facilitates collaboration and control.

The fastest and most common way to share spreadsheets internally is through email.  Not only does this compromise data security, but it also can also make keeping track of multiple file versions and edits difficult. Here are a few common and potentially damaging mistakes:

  • Sally sends John a spreadsheet.  John makes 10 edits but only tells Sally about 7 changes. How does Sally find and validate the other changes?
  • Sally is updating the corporate spreadsheet and has asked other team members to review different tabs.  As the other team members make changes, there are several versions of the spreadsheet in circulation.  Bringing all changes together into a single updated spreadsheet is time-consuming and error prone. 
  • Jenny accidentally mis-titled a spreadsheet. Ben needs to share this spreadsheet with the accountant but sends the wrong version resulting in several days of confusion.
  • John has been managing the corporate spreadsheet for years and suddenly retires.   A new analyst may take weeks to fully understand John’s complex spreadsheet.  Often the new analyst finds mistakes that John had missed for years.

SaaS analytics can solve these problems.  These analytic tools are often accessed within a standard, easy to use platform that is accessed in a web-browser.  Multiple users or groups can use these tools simultaneously without the need to share models via email.  An intuitive user interface means that new or junior staff can successfully manage complex analytic processes.  Finally, if model changes are required, this can be managed in a systematic way.  Spreadsheet fans will point out that incorporating new changes may require days for SaaS analytics, rather than minutes for a spreadsheet.  That is true.  But if your corporate model is mission-critical, you probably want to manage these changes more systematically anyway. 

SaaS analytics are more secure.

Excel does allow users to password protect many elements, hide formulas and lock cells.  When using spreadsheets this is a good first line of defense but does not solve many underlying issues.  Complex spreadsheets are still prone to human error and locking or hiding those errors compounds the problem.  Also, there are many ‘un-protect’ programs available to unlock a spreadsheet and gain access to its data.  If a hacker does get a hold of a confidential spreadsheet, Excel can’t stop them from copying information and pasting it to another workbook.

SaaS analytics are able to leverage many cloud security features to ensure that sensitive data remains secure.  At cQuant.io, we use numerous security features in our SaaS platform that ensure our customer’s data is protected. These features include:

  • Secure communication (HTTPS)
  • Encryption of user and company data
  • 2-factor authentication
  • User session time outs/lock outs
  • Password complexity & change management

Like many SaaS providers, cQuant.io deals with sensitive customer data every day and works hard to ensure that we are employing the latest security tools and protocols.

SaaS analytics provides a more robust workflow.

A large downside to Excel is that if calculations are complex enough, Excel has difficulty managing the workflow.  Some corporate spreadsheets have become so large that they take tens of minutes to open and hours to calculate when new data is introduced.  This is due to Microsoft Excel’s software being relatively slow, even when the calculations are simplistic.    

For most spreadsheet models, the computations are single-threaded and deterministic.  Most companies would benefit from seeing scenarios and simulations of future outcomes.  This is especially true in energy and commodities organizations where market prices and weather are key drivers of risk and are quite volatile.   The company CFO and/or Risk Manager may like to see a “gross-margin-at-risk” report which provides an expected gross margin with useful distributions around the mean.  However, this is very difficult to enable and manage in Excel, so the risk analysis is often not performed.   

In the analytics world we call this “model limited choice”.  Your spreadsheet model cannot manage a robust analytical process, so that process is simply not performed. In this case, the company has less information on which to make decisions because of its choice of model. 

SaaS analytics are typically built using robust statistical analysis software. cQuant.io’s models are built in R, Python, C++, and other high-performance programming languages.  These languages are extremely fast, flexible and powerful.   The models are managed by our cloud platform, providing many user tools and data visualizations.  Multiple users can run the same models at the same time and share results.  cQuant’s analytics also preserve your workflow through time. Because cQuant’s platform saves every model run, the user(s) can look back to historical runs, model settings, and results.

Our Conclusion

Overall, there are many upsides to using Excel spreadsheets and not all corporate models should be converted to SaaS analytics. But organizations should consider converting spreadsheets to SaaS analytics when those models become more complex, mission-critical, multi-user and especially when mistakes can cost the company millions.

Are you ready to improve your energy analytics? Request your free demo today.

SCHEDULE YOUR DEMO TODAY

October 8, 2019

Fundamental vs Simulation Models for Energy Portfolios

There seems to be some confusion regarding the differences between, and best uses for, fundamental and simulation models.  In part, this is the fault of the vendors, who try to sell their solution as good for everything. There is no individual model that should be used for every analytical task. 

Recently I heard a chief officer of an energy company ask why his company needs simulation-based analytics, when they already have a fundamental model?  This article is designed to answer that question.

Fundamental vs Monte Carlo Simulation models

Fundamental power system analytics are typically used to build up an analytical representation of a market (generation, transmission, load). Making assumptions about grid topology, available generation capacity, maintenance outages, weather patterns, and aggregate load, these fundamental models will compute least-cost dispatch and optimal market pricing at an hourly granularity for many years into the future.  This is no small feat and fundamental models are quite good at this. A fundamental model is best used to develop a long-term forecast of hourly prices, particularly for use to inform resource planning efforts. Of course, this “single-path“ market forecast is always wrong, but if the model assumptions and inputs are good, these models can produce useful results. 

Knowing that the single-path forecast is going to be wrong in unforeseen ways, companies will turn to simulation models to understand a portfolio’s risk, or sensitivity to uncertainty, as prices deviate from the expected forecast. The simulation model will run hundreds or thousands of market simulations and probabilistically weight the results.  This yields a distribution of outcomes where the expected value is reported as well as the size, shape and magnitude of uncertainty within the distribution. Distributions can be inspected at any time granularity (hourly, monthly, annually) on metrics like portfolio gross margin, total costs, generation, fuel burn, and more. The left side of the distribution is interpreted as the “downside risk”.  Companies use the magnitude and timing of these downside risk events to hedge their portfolio and increase certainty around future cash flows. 

Fundamental models are not suited to generate robust distributions of value and therefore are inappropriate for assessing the effectiveness of existing hedging strategies or developing new ones.

Market Price Simulation

Price simulation is the key component to any simulation framework since market prices are a primary driver of risk. Different markets have very different pricing dynamics. Hourly power price shapes vary by day-of-week, month-of-year, and pricing location. Correlations between pricing locations and across commodities (e.g., between power and gas) vary seasonally. Volatility, the likelihood of price spikes, and mean reversion rates, all vary seasonally as well.  These price dynamics can strongly influence cycling behavior for thermal assets and impact both value and risk for intermittent renewable resources.

Many portfolio managers ask about fundamental drivers such as weather, grid topology, available generators, demand, etc. In most cases, these fundamental drivers are well represented in each market’s unique price dynamics, and therefore can be captured in the price simulations.

Interestingly, a commonly used integration between fundamental and simulation models involves using the forward-looking market price forecast generated by the fundamental model as input to the simulation model.  The simulation model will then produce thousands of hourly price paths around it, yielding the distributions needed for risk and hedging analysis while remaining consistent with the fundamental forecast itself.

Physical Asset Optimization & Valuation

Many years ago, energy analysts routinely used simple “closed-form spread option” models to value power plants.  Spread option models use a price for fuel and a price for electricity, as well as the volatility of each and the correlation between the two.  In a given month, if the expected spark spread was sufficient, the power plant was assumed to run.  If not, it was assumed to shut down.  Of course, this is an over-simplification of how power plants operate.  There are dozens of physical operating characteristics and costs that “constrain” the optimal operation of a power plant.  Modern dispatch models use a very detailed set of characteristics to accurately represent an asset so that modeled output is as close as possible to true asset operations.  Most simulation and fundamental models both use these modern techniques.  The difference is the simulation model dispatches assets to hundreds or thousands of hourly price paths, while still maintaining all operating constraints.  This yields hourly/daily/monthly/annual distributions of values for generation, costs, fuel burn, gross margin allowing for more effective hedge decisions. 

Other assets, such as renewables and storage, require separate analytical methodologies but still need to be integrated into a comprehensive portfolio analysis to properly track their effects on value and risk.  Renewable assets are non-dispatchable, but their hourly production can still be simulated and combined with market price simulations to develop distributions of generation and analyze the effects of volumetric uncertainty (swing risk) on asset value.  Such an analysis provides insight into production variability, price uncertainty, and even basis risk between a generation node and corresponding settlement hub.

Unlike renewables, batteries are dispatchable and their charge/discharge operations can be optimized to market prices or retail rates. Since batteries generate no electricity themselves, their value is entirely derived by operating them intelligently relative to various sources and sinks of energy at the battery site along with their corresponding values and/or costs (e.g., customer load, co-sited renewable generation, retail rates including TOU schedules and demand charges, and wholesale prices or export compensation rates).  Other asset types can and should be included in a comprehensive portfolio analysis. 

Contract valuation, Risk Management and Hedge Optimization

At a high level, we have discussed some of the differences between sophisticated fundamental and simulation models.  Using a simulation model, we generate thousands of hourly price paths that capture market dynamics and represent a very large set of possible future outcomes. These outcomes are probability-weighted and allow reporting not only on expected (mean) prices and portfolio value, but also left-tail and right-tail risk levels.  We can use these price simulations to optimize and value our physical assets, yielding many other useful statistics to support asset management and budgeting activities.  Finally, we can also use the same price simulations to value our physical and financial contracts.  These contracts can come in the form of standard swaps & options, index deals, heat-rate-call-options, revenue-put-options, PPAs, or other exotic derivatives and structured transactions.  Each of these contracts have unique payoff functions. 

Most importantly, we can aggregate all the above analysis based on both forward and spot price simulations to forecast distributions of portfolio cash flow through time. By bringing together all of these values, we can calculate summary statistics for the total portfolio – including both physical assets and financial contracts.  This affords the analyst / portfolio manager insight into the timing of all cash flows and how they are being generated. 

By looking at the distributions of value across months, peak periods, asset types, sub-portfolios, and many other dimensions, one can understand how, when, and why risks enter the portfolio.  This leads to the final piece of the puzzle: hedging the portfolio. 

Deciding on and implementing a hedging strategy depends on many input factors.  But armed with the analysis described above, we have all the analytical components we need to measure the risk-reduction-value (RRV) of a prospective hedging strategy.  The process above has given us distributions of value through time for every item in the energy portfolio and has accounted for the interactions between all the individual components to provide a “rolled-up” total portfolio view.  We can now assess various hedge strategies or evaluate individual contracts, assets, etc. to see the net effect they produce on the entire energy portfolio. 

No other model methodology will allow this type of analysis.  Fundamental models are very useful in their own right, but their deterministic outlook has limitations for portfolio analytics. Traders, portfolio analysts and energy risk managers should be utilizing a robust simulation-based methodology for deal valuation, cash-flow analysis, asset and hedge optimization.   

Why cQuant.io?

cQuant.io is a team of experienced PhD quants and energy analysts. cQuant develops sophisticated analytic models and delivers these solutions in a cloud-based application.  Our team has extensive experience delivering portfolio & risk analytic solutions to energy companies, including IPPs, utilities, trading floors, community choice aggregations (CCAs) and other load serving entities (LSEs). 

A cQuant solution can typically be set up for less than the cost of adding a senior quantitative analyst.

David Leevan is the CEO of cQuant.io, a SaaS platform for energy analytics.    

June 6, 2019

Five Key Mistakes Companies Make When Buying Software

After 20+ years in software sales, it is safe to say that I have seen almost everything. At a recent energy analytics event, we presented the “Top 5 Mistakes” that companies make when buying enterprise software.  Amazingly, most companies make the same mistakes, over and over. Why is that?

Vendors engage in sales processes dozens of times per year, but energy companies may only buy enterprise software once every few years. Vendors have developed many ways to fool the customer.

Frequently, I am dealing with buyers that are far less experienced that I am in the software procurement process.  While I engage in dozens of buying processes per year, the buyer may only deal with a few major software purchases per career. Amazingly, buyers seem to make the same mistakes over and over. 

While I have often tried to help the buyer, their process is usually flawed from the beginning and very difficult to correct. Here you will find the top five recurring mistakes that I encounter, along with suggestions on best practices. I hope that my time spent in the trenches can help you avoid some of these common mistakes.

Software Buying Mistakes, The Context:

  • Vendors have the upper hand
  • Vendors work with dozens of buyers each year. Buyers may only have
    1-2 experiences in an entire career
  • Vendors have gamed the system
  • Vendors want high revenue, captured customers
  • Buyers want low cost, high-value, flexibility, freedom of movement

Software Buying Mistake #1

Buying software without extensive solution testing

  • Vendors often use bespoke demo version
  • Demo version often use ‘vapor-ware’ to get the sale
  • Buyers don’t find out it was vapor-ware until after the contracts are done

Your Solution: Always engage in a Proof of Concept or Pilot in the exact configuration that you will be buying.

Software Buying Mistake #2

Evaluating too few companies

  • Buyers often “lock into” only one type of solution. 
  • In every part of the energy industry there are dozens of potential solutions.

Your Solution: Spend extra time investigating a wider range of potential options.  If nothing else, you will learn more about the solution space and feel better about your original choice.

Software Buying Mistake #3

Relying on a Solution Expert, or Solution Consultant

  • Companies often bring in solution consultants to help with their selection.
  • The consultants often have a lot of experience with the major market player.
  • The consultant may be looking for a role after you buy, as a system expert.
  • They will often insure that the most expensive solution wins.

Your Solution: Understand that consultants want longer engagements.  They do not want easy solutions.  Make your own decisions!

Software Buying Mistake #4

Requesting only 3 references

  • Any vendor can find 3 references.
  • What percentage of their customers could be a reference?
  • Ask references about initial cost estimate vs actual, initial time estimate vs actual, etc.

Your Solution: Ask for ALL customers contacts and call them at random. Or, do your own homework and find companies using the solution that the vendor will not provide as a reference. 

Software Buying Mistake #5

Using a vendor for too much.  ‘Best in Class’ vs ‘Single Source’

  • Vendors love building their product at customer’s expense.
  • 20% of the project takes 80% of the time and money.

Your Solution: Use vendors for their core offering only.  But if you do decide to ask the vendor to enhance their solution, the vendor should do this for free!

David Leevan is the Managing Director of cQuant.io, a SaaS platform for energy analytics.    

Are you ready to improve your energy analytics? Request your free demo today.

SCHEDULE YOUR DEMO TODAY

November 14, 2018

Top 5 Energy Trends in Mexico

Mexico has become one of the most attractive emerging energy markets in the world. Since 2016, when energy reform was enacted to liberate the state-run power and fuel markets, there are only a handful of places in the world that shine brighter with opportunity for new business. However, opportunity in Mexico comes wrapped with uncertainty and risk. Like in any new market the panorama is always shifting. Market dynamics are subject to improvement and feedback.  The infrastructure that is to be the cornerstone of Mexico’s energy revolution is just now being built. Two years into this Mexican energy revolution there are 5 big trends that will define the near future not only of the Mexican energy market but also the market in the United States:

Renewable energy supply is entering the power market at an accelerated rate.

Although renewable energy projects were by far the biggest winners of the first tenders in Mexico, many of these projects had slow starts. Going from the drawing board to becoming a reality was harder than many expected. Developers run into zoning and interconnection restrictions that they did not anticipate. They were also faced with a maze of bureaucracy, bouncing around the many different (and new) government entities that now regulate the power market.

However, by 2018, many of these projects were able move forward and become realities. Solar energy grew by more than 400% from 2016 to 2017 and continues to grow. Moreover, the Mexican government has established new procedures and is introducing online interfaces so that developers can process their permits all in one place. Developers can now access zoning maps that not only clearly show restricted areas, but also show what areas of the country have the highest potential for renewable energy development. Further growth of the renewable energy sector should happen at a quicker pace. There is optimism that Mexico will be able to meet its goal of sourcing 35% of its energy needs with renewables by 2024, followed by 50% by 2050.

The Energy Market will evolve past its auction/tender centric form.

Currently in Mexico energy market opportunities come from auctions that the CFE puts on periodically. Since 2016 there have been 3 auctions with a 4th auction currently taking place. Although these auctions have been very successful at increasing capacity and reducing the costs for the system, entering the auctions can be a complicated process. The terms for the tenders can favor some technologies over others and even those who win tenders can often face unexpected costs and restrictions as they try to integrate their new projects into the grid.

Semi-governmental institutions are still the only off takers of power. This is not an ideal situation and market players are hungry for other off-takers for the power that they are producing. PPAs and other bilateral contracts are possible under the current framework. However, there are many restrictions and risks that make these avenues of business difficult to execute. For example, it is difficult to get long term financing for PPAs when lenders are still weary of the risk that comes with such a new market. Short term PPAs are hard to sell as the spot market is ripe with volatility and hedges are not widely available.

As the market matures, perceived risk will likely be reduced. The barriers that impede bilateral deals will start to fade and the ability to strike contracts with a variety of off takers will become the norm. This market should evolve to become something similar to what is seen in the United States.

Gas pipelines are coming.

Currently Mexico produces around 2 BCF (Billion Cubic Feet) of Natural Gas every year. About half of that is used by PEMEX, the national petroleum and fuels company, for their own operations and supply. Outside of PEMEX those who are looking to buy fuel are left to fight over that last BCF of fuel.  The United States is seen as the best secondary gas market for Mexico. 

The current gas pipeline system in Mexico is not sufficient to meet current demand. As far back as 2012, Mexico saw long periods of gas shortages that forced the power grid to turn off more efficient gas power plant in favor of oil and diesel burning plants. In 2018 Mexico faces a similar gas shortage that is putting pressure on an emerging power market. Both the government and the private sector are making changes in order to be able to address this lack of pipeline infrastructure. Today, 7.1 GW of new gas-fired generation is under construction and gas imports are expected to grow by 27% in the next twelve months.

Just like the power market was de-regulated in 2016, so was the fuel market in 2017. This spurred an influx of investment into new pipelines. Mexico added 2.7 Bcf/d of new pipeline in 2018, with another 6.9 Bcf/d of pipe currently under construction. These new pipeline projects will start to become operational in 2019 and 2020, increasing pipeline import capacity from 7.5 to 13.5 BCFD in the next 3 years. With more pipeline projects coming, Mexico, should not only get caught up with current demand but also be enough to allow room for future growth. 

Gas storage is needed.

Pipelines are not the only culprit for Mexico’s gas shortage woes. Currently, Mexico only has the capacity to store enough gas to cover 3 days of demand. Mexico wants to expand their storage capacity to accommodate 30 days of demand. Initially this increase in storage will come from refurbishing depleted gas basins and converting them into storage locations.

The tenders for the first storage project, in Veracruz, are scheduled to be auctioned in early 2019, with 3 other locations that have the potential to be refurbished. There will also be opportunity for private companies to build other new storage infrastructure.  There are already 62 new storage projects announced coming from the private sector. Mexico will be poised to have more control over its energy prices and increase stability and energy security for the whole country.

Mexico and the US will face new and interesting energy dynamics.

As a result of the four trends mentioned above, there will be new energy dynamics emerging between the U.S and Mexico over the next 5 years. There will be push and pull effects on prices as demand and supply evolve across the borders. Initially new pipelines will allow U.S natural gas to flow to new consumers who previously were unable to access the fuel. These new pipelines will also facilitate physical contracts and trades within Mexico (physical contracts rarely occur currently because of infrastructure limitations). In general, the new pipelines will allow the gas market to become more liquid, which is favorable for both countries.

By 2022, it is likely that Mexico will have acquired a significant amount of gas storage infrastructure. Mexican demand for U.S gas will be much more stable. Gas price spikes, from which U.S firms profit, will happen less often and in smaller magnitude. In a case of excess supply, the U.S will face much less favorable prices to off-load that excess south of the border. The result could be downward pressure for U.S gas prices. Aided by new pipelines and storage, cross-border arbitrage margins may become slimmer.

By 2024 Mexico should see 35% of its generation come from renewables, likely shifting baseload generation away from natural gas plants. If the drop in demand for gas due to renewables is larger than decreases in gas supply, it is likely that U.S gas prices would face additional downward pressure.  Now that is all good news for buyers of gas, but producers will need to look for other sources of demand if they are to maintain profits.

Could all these changes make it possible that one day power is consistently imported from Mexico into the United States? It seems that may well be in the cards, but only time will tell.

Written by Sebastian KadamanyManager of Latin America at cQuant.io

cQuant.io is an industry-leading provider of cloud-based energy analytics solutions. cQuant’s advanced analytics platform provides sophisticated energy-focused models across a wide range of industry verticals, from renewable energy project development and contract structuring to thermal generation asset analysis, hedge optimization, and exotic derivative valuation. 

SCHEDULE YOUR DEMO TODAY

October 16, 2018

Renewable PPA Primer: Financial Risk Assessment for Corporate Energy Buyers

Renewable energy purchases by large corporations are accelerating.  In 2017, large corporates bought over 5 gigawatts of renewable energy and we are on-pace to shatter this number in 2018 with over 7 gigawatts already purchased this year (Bloomberg article August 2018).  Telecoms and large technology companies are leading this charge and mainstream Fortune 1000 companies are not far behind.  In this rush to acquire renewable energy for sustainability and green energy supply, many companies are forgetting that these deals can come with substantial financial exposure.  Google may be able to afford significant losses in its renewable energy portfolio.  Can you?

The effective lifetime of renewable energy deals, such as a power purchase agreements (PPAs), is measured in years.  Increasingly, corporate entities are opting for financially-settled PPAs known as “contracts for differences” (CFDs).  The number of CFD variations in the market is staggering, with each delivering a slightly different risk profile to the buyer.  If the buyer is on the wrong side of this risk when market conditions change, he can end up with many years of making monthly “wrong way” payments to the seller instead of collecting revenue from his PPA.

Obviously, it is impossible to predict the weather, energy prices, availability, and demand over this entire future horizon, though each of these components can materially impact the contract value.  Experienced energy companies, such as large utilities and global energy trading firms have been managing these risks for a long time. How, do they do it?  They use sophisticated simulation models to predict the range of possible future outcomes and the most likely outcome.  This allows the company to develop an expectation of their future cash flows, quantify the uncertainty around future cash flows, and mitigate risk using custom-tailored risk-reduction strategies. 

If the renewable energy seller is using these techniques to construct a renewable PPA for the buyer, you can be sure the deal’s terms are skewed in their favor.  My hope is that this primer will give corporate renewable energy buyers a better understanding of the financial risks involved and how to mitigate them.

Electricity Prices

Electricity (or power) prices exhibit dynamics that are fundamentally different from other financial products or commodities.  One driver of these differences is that it is not cost-effective to store large quantities of electricity, except over short time periods. Another driver is that power system reliability (i.e., “keeping the lights on”) requires a constant balance between production and consumption. At the same time, electricity consumption (demand) depends both on weather and the intensity of business activities and can be highly uncertain. The availability of electric generating assets to meet demand and the associated costs to do so ultimately determine electricity prices at any particular time and location on the grid. These unique and specific characteristics lead to price dynamics not observed in any other market, such as seasonal and intra-day fluctuations that tend to differ widely by geographic location.

Since it is impossible to know with complete certainty what the electricity price will be in any given day/hour in the future, price simulation techniques have evolved to capture the unique dynamics of electric power markets and to simulate future outcomes in a probability-weighted format. Analysts want to know both the range of possible prices and the probability that a particular scenario will occur.  For example, we know that in some electricity markets prices can range between negative values and up to $9000/MWh.  However, it is equally important to understand the probability and timing of large price movements, as well as how prices move in relation to other factors.

MidC_DA_TimeSeries-energy-analytics-cquant

Example of simulated hourly day-ahead electricity prices for the Mid-Columbia trading hub.  This chart shows 5 simulation paths.  Notice the strong seasonal shapes in each of the four seasons, as well as marked differences in price volatility throughout each year.

Robust price simulation models employ a Monte Carlo, mean-reversion, jump-diffusion methodology to simulate electricity prices.  This methodology was first introduced to the financial markets in the 1990s and migrated to the energy markets a few years later.  They have been widely used by traditional energy companies for nearly 20 years.  This robust price simulation methodology allows the financial analyst to have confidence that their contracts are being valued against hundreds of internally consistent future versions of the electricity markets and that the “expected future value” can be relied upon. 

Wind & Solar Generation

Renewable energy is inherently intermittent.  Unlike a natural gas generator that can be ramped up or down at-will, the wind blows and the sun shines based on the irregularities of nature. This makes wind and solar production difficult to predict over long time periods with a high degree of accuracy.  As with electricity prices, methodologies have emerged to simulate future renewable energy production.

The preferred methodology to forecast future renewable generation uses curated databases to access historical wind speed and solar irradiance patterns at any geographic location.  Robust simulation models use these historical weather patterns and the physical characteristics of the renewable generation facilities (e.g. geographic location, wind turbine type and height, solar panel tilt angle, etc.) to generate Monte Carlo simulations of future energy production at the specific asset location.

MthlyGenBoxplot-energy-renwable

“Box-and-whisker” plot of total monthly solar production for the 10 MW Pennsylvania Solar Park. Red horizontal lines denote the expected value (mean), boxes range from the P25 to P75, and whiskers extend to the min and max of total monthly generation, as implied by historical weather data.

The combination of hourly Monte Carlo simulations of renewable energy generation and real-time market prices at the resource node and/or settlement hub provides hourly distributions of market value for the renewable energy produced. The stochastic time series models used to generate the simulations are parameterized from historical market prices and solar irradiance or wind data. The parameterization process ensures the modeled output reflects locational patterns in expected price and renewable generation as well as variability around the mean. As such, simulations capture the real-time interplay between energy production and market prices, allowing users to understand the true value of and uncertainty in energy production at the seasonal, daily, and hourly level for a particular generation facility.

The simulated hourly generation and market prices can also be used to directly value renewable PPAs along with the unique “contract-for-differences” (CFD) financial structures they may entail.  Examples of a few settlement structures that can be directly modeled include fixed price, $0 minimum, collar, revenue share, and many others.

Cash-flow-analytics-energy

Simulated contract-for-differences monthly settlement amounts (year 2022 only) for a PPA struck at $35/MWh for the Pennsylvania Solar Park.

How does this analysis help Renewable PPA buyers?

Some corporate buyers of renewable PPAs have experienced significant “wrong-way payments” and financial losses since contract signing.  In almost every case, those buyers used the analysis provided by the seller as their reference of fair value for the contract.  It comes as no surprise that the sellers often used optimistic forecasts of future value and downplayed the effects of uncertainty.  A robust financial risk assessment and valuation model helps buyers of wind and solar renewable energy understand the true value and risks associated with long-term PPAs. Moreover, it provides an unbiased assessment of fair contract value from an independent third party that has no stake in the deal. This can add significant leverage to the buyer’s position at the negotiating table.

With a proper financial analysis, PPA buyers should be able to report fair market value for existing or proposed PPAs, as well as the degree of uncertainty around monthly settlement cash flows and the effect this uncertainty has on overall contract value. Armed with this information, renewable PPA buyers are in a much better position to negotiate on contract terms and pricing, resulting in higher rates of return over the contract lifetime. Buyers no longer need to rely solely on the seller’s own assessment of value when signing long-term renewable energy contracts.

ReAssure® for Renewable PPA Buyers

cQuant.io’s ReAssure model uses well-established valuation methods to demonstrate fair value and financial exposure for renewable assets and power purchase agreements (PPAs).  The use of Monte Carlo market price simulations coupled with simulations of generator production is a technique that has been used for decades to manage portfolios that include dispatchable thermal generation assets and financial contracts. With ReAssure, cQuant.io has applied these seasoned techniques to the valuation of wind and solar projects and PPAs.

ReAssure combines simulations of hourly electricity prices and hourly renewable energy generation to calculate a broad range of possible future scenarios of the true market value of generation. Each simulation is custom-tailored to project-specific parameters such as precise geographic location, nameplate capacity, tilt angle (solar), turbine manufacturer and model number (wind), and market pricing node or hub.  These simulations capture the real-time interplay between energy production and market prices, providing a comprehensive bottom-up analysis of the true value of and uncertainty in renewable asset or PPA cash flows.

The example output image below shows the expected fair market value of a 5-year renewable PPA, as well as the simulated distribution of possible.  The expected value gives the renewable PPA buyer a single number to consider when making a buying decision while the spread in the distribution provides an indicator of the risk in the deal related to variability in renewable energy generation and wholesale electricity market prices.  With these key metrics in-hand, buyers can make an informed decision to purchase a particular renewable energy contract. They can also quickly compare value and risk across many different proposed contracts and locations to determine the best deal for their renewable energy portfolio.

Simulated market value of electricity generated from 2018 through 2022 for the Pennsylvania Solar Park.

About cQuant.io

cQuant.io is an industry-leading provider of cloud-based energy analytics solutions. cQuant’s advanced analytics platform provides sophisticated energy-focused models across a wide range of industry verticals, from renewable energy project development and contract structuring to thermal generation asset analysis, hedge optimization, and exotic derivative valuation. Visit www.cquant.io for more information.

SCHEDULE YOUR DEMO TODAY

August 29, 2018

Energy Risk Management Software: Build, Buy or Assemble?

When it comes to energy risk management analytics, the classic dilemma that most energy companies face is “build or buy”.  A dilemma is a situation in which a difficult choice must be made between two equally undesirable alternatives.  Later I will introduce the new alternative of assembling a risk solution.  First, let’s spend a moment describing how most energy companies get themselves into the build versus buy dilemma.

A typical risk management life-cycle might look like this:  A young energy company (let’s call them Big Energy) operates with a financial spreadsheet that forecasts revenue, costs and margin.  At some point, this simple view proves insufficient.  So Big Energy hires a risk analyst and/or quantitative modeler to calculate exposure under various scenarios.  The risk analyst creates new tools and happily describes these creations to his/her colleagues.  Soon Big Energy has dozens of independent analytic tools, often in spreadsheets. For the risk analyst, this is a lot of fun!  It’s so much fun that they might not want to openly describe the deficiencies in those analytic tools.  We all know what comes next….

At some point, Big Energy experiences a large unforeseen financial loss and senior management realizes their mistake.  Their risk analyst was not able to calculate or mitigate the portfolio risk from simple in-house models.  So, Big Energy hires a new senior risk manager who promptly puts out an RFP for Energy Risk Management Software.   All the big-name risk vendors apply as well as consultants that act as system selectors and system integrators.  Of course, the consultants are in bed with the big software vendors.  If you pick any of the big system selectors, they will funnel your business to their favorite (massively difficult to install) risk software company.  That is the way the game is played.

Big Energy spends 12-18 months picking a system integrator and energy risk management software vendor. They are about to buy, install and maintain an enterprise risk management solution. Based almost entirely on their ability to spend money, Big Energy is now facing a $2M-$10M expenditure over several years.  Very typically, they will only receive 60%-80% of the functionality they requested.  To fill these gaps, the company continues to buy new services from the software vendor.  Big Energy has no choice; they are essentially stuck.

Like a drunk on a bender, the risk management software vendor happily spends Big Energy’s money until they are cut off.  Eventually, Big Energy does just this, but they still have only received a portion of the functionality they need.  What happens now?  Big Energy’s staff will continue to build solutions outside their risk management software, and new processes will be created to manage multiple independent analytic tools, again.

Raise your hand if you know what I am talking about.  In which stage of this cycle do you find yourself? 

In the energy industry, build or buy are equally undesirable choices.  What if you could access risk models on-demand, with no deployment projects, upgrades or long-term commitments? 

At cQuant.io, we allow our customers to access sophisticated energy models, on demand.  Based on the requirements of each energy portfolio, customers assemble component models into proprietary analytic chains creating a solution just for them.  Energy companies can also access our team of PhD quants to quickly build new analytic models at a fraction of the cost of hiring their own quants.

Energy Risk Management Software Done Right

Nobody likes a dilemma.  The choice to build or buy is a trap on both sides.  In this dilemma, energy companies must decide to either go into the software business (build) or bet the farm (buy) on a single software vendor. cQuant.io is offering a 3rd choice – assemble and start using your risk solution at cQuant.io today.

This article was written by David Leevan. David is the Managing Director of cQuant.io, a SaaS platform for energy analytics

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

Start Your Risk Management Analysis Today

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.

JP-Morgan-Energy-Analytics-cQuant.io

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.

Max-Generation-Energy-cQuant

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.

monthly-cash-flow-energy-

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 cQuant.io’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 cQuant.io, a SaaS energy analytics platform that helps companies understand their physical and financial exposure in today’s energy markets.

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

SCHEDULE YOUR DEMO TODAY

Scroll to top