Eight Data Points To Inform Which Clean Energy Projects Are The Right Investment
April 30, 2018 · By Chris Watmore
Unless you’re Google or Apple, most companies have modest energy needs and finite finances that limit their ability to purchase renewable power. Luckily, through energy aggregation there is no shortage of options when it comes to selecting developers worth partnering with.
More choices for building and operating renewable energy projects gives buyers both the flexibility to find the right match and the responsibility of vetting developers to identify a suitable investment. While the demand for clean energy is high — U.S. corporate buyers committed to adding 60 gigawatts (GW) of renewable energy to the grid by 2025 — there are far more developers willing to collaborate on a company’s energy needs, than companies ready to start buying.
Refer to these eight data points to inform which renewable energy projects are a smart investment, even though there’s no one-size-fits-all approach when buying power.
Forward Traded Hub Price
Developers can sell projects at their node, the interconnection location, a pricing node or at a trading hub, which impacts the range of pricing at a given settlement location. Historically, the easiest way for a developer to sell power from a project was to settle the sale at a project’s interconnection location, but that’s usually not the best option.
Consider settling instead at a trading hub — typically a collection of hundreds of nodes — to receive a more durable value for your power in a particular region on a consistent basis. It’s less likely that the pricing is going to fluctuate at a trading hub, reducing risk. Transferring energy from a project to the hub is a service that most experienced developers will provide.
It is also easier to measure the expected value of energy at a hub as there are traded forward curves available for most liquid hub locations. This provides a clearer indication of a project’s total cost.
Settlement Price Volatility
The settlement price is calculated for each renewable energy project contract to determine the expected profits and losses in a given day. It’s helpful for a buyer to understand the prices an energy contract has historically traded at to get a sense of how much pricing fluctuation to account for per project.
Reviewing the standard deviation of the settlement price helps to determine how energy prices performed historically at a particular settlement location and the associated level of volatility — the day-to-day or month-to-month differences of the energy’s price. While these calculations can be completed manually, it’s easy to access this data using the LevelTen marketplace to assess the settlement price volatility of a project.
It is often useful to look at both capped and uncapped standard deviations. Uncapped standard deviations allow large price spikes to flow through to the risk metric, which is useful if price spikes are showing up in the forward traded price. Capped standard deviations use a price cap — typically around $100/megawatt hour (MWh) — to give a better measure of downside volatility, or how the project may perform in the worst case.
Price Basis Volatility
The closer the settlement location is to a customer’s load, the better the hedge potentially is and the easier it is to achieve a fixed price for power.
Known as price basis volatility, this data point’s aim is to demonstrate how overall energy cost is affected by the correlation between where an organization is accessing electricity and where a clean energy project is located. A prudent hedger will want this number to be as low as possible. This is often accomplished most simply by the buyer choosing a project on the same regional grid.
A sophisticated energy hedger will take a closer look at project settlement location, generation shape and consider an appropriate blend of wind and solar at multiple locations in order to minimize price basis volatility.
Regional Renewables Growth Rate
The penetration of renewables in a given market refers to the percentage of electricity generated by wind, solar or hydro-power at a specific location.
Referencing the regional renewables growth rate allows a buyer to determine the amount of renewables currently on the grid and what’s projected to enter a market in the future at the same location as a potential project.
Evaluating the prevalence of renewables in a region is important as their presence can lower energy prices and affect the value of a buyer’s investment in a particular project.
Development Risk Score
Knowing the associated development risks of each project is essential for determining which investments are most likely to be constructed as planned at the estimated cost. This risk score is a calculation of the uncertainty related to a project’s development like site control, interconnection issues and local and state permitting costs that can derail its timeline or stall a project’s construction entirely.
Calculated in the LevelTen marketplace, the development risk score may indicate that even though a project has a favorable projected long-term value, the risk of the project not coming to fruition outweighs the potential benefits. Deal friction costs in large renewable energy transactions are substantial and development risk should not be ignored.
Correlation Between Customer Load and Shape of the Generation
The needs of an energy buyer vary dramatically impacting their energy load and the shape of the generation needed to match their specifications. Load matching a project is essential to ensure a buyer’s energy requirements are met, which is why measuring the correlation between a customer’s load and the shape of a project’s generation is informative.
For example, most of a retailer’s load is during the day, which wouldn’t be a match for a single wind project where most of the generation is at night. Picking a combination of projects in a portfolio that has a better load match is a much more effective choice.
Measure the correlation between monthly production or average production of each hour of the day compared to your average load or better yet, review hourly historical data from the last one to two years to determine the best match.
12×24 Price Scalar
This is the primary measure of how valuable the generation shape of a project is. It measures to what extent the project is producing energy at times when energy is most highly valued. Wind projects typically have scalars under one, while solar projects tend to have values above one, unless the project is located in or near California.
Technically this metric is defined as the value for this project forecasted over a PPA contract term divided by the value of energy produced in equal amounts throughout the day and year over the same contract period and at the same settlement location.
In order to calculate the 12×24 generation weighted value of the project, data is needed on average expected generation of the project during each hour of the day within each month of the year as well as historical hourly power prices for a representative historical time period and forward market traded curve data.
Negative Price Covariance
This data point is a secondary though vital layer in measuring the value of a project’s generation shape. This is referring to the coincidence of generation and grid prices in real time as opposed to in aggregate as is custom in the 12×24 price scalar.
For instance, when there are heavy winds and a given wind project is producing at maximum output, all the other wind projects in the region also tend to be generating high amounts of electricity as well. All of this generation can have an adverse effect on electricity prices just when the project is generating at its highest.
This is calculated as the ratio of two values:
- Historical value using simulated project generation with actual weather data over a representative historical time period.
- Historical value using a 12×24 generation profile. Care must be taken to ensure there are no biases or systematic differences in either of the values.
Neglecting to layer in historical backcast analysis can easily cost more than one million dollars on a 100,000 MWh per annual contract.
It’s understandably overwhelming when reviewing the many developers interested in partnering, but these key data points will help your team make more informed decisions.
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