How to Value DERs
And an open-source path to get there
Entirely written without AI, which means that it rambles, uses the passive voice too much, and “honestly” doesn’t have too many “load-bearing” sentences. This series will run ahead of the OpenEAC Alliance meeting on August 20th where we will discuss our plans to release an open source ontological model of the grid.
Part 1 - What’s Different Now?
For a long time, the value of a distributed energy resource like solar panels or an energy efficiency upgrade was measured in the bill savings a customer would see. The benefits could also include comfort (i.e., cold beer and warm showers) and health, but more or less the cost/benefit would be established relative to the customer experience.
Utility programs introduced new concepts like Total System Benefits that expanded cost-benefit analysis to include non-customer benefits like infrastructure savings. Renewable energy credits widened the lens even further by explicitly valuing the environmental benefits from a select subset of energy generation resources.
Because bill savings are generally the most straightforward to model, customer-centric business models drove most of the first wave of DER deployments. Energy Services Companies (ESCOs) developed performance contracting models that shared savings with their customers. Solar companies invented power purchase agreements to defray the upfront costs of new systems.
Utility programs spawned an entire ecosystem of rebate-centric firms that pushed technology-specific solutions subsidized by utility ratepayers. The concept of a system benefit also launched Non-Wires Alternatives programs that specifically targeted the deferral of certain infrastructure upgrades. These types of investments led to standardization of DER value in regulatory proceedings. For example, in California, the system benefits of DERs are defined by an official Avoided Cost Calculator developed under the guidance of the California Public Utilities Commission, which is used to determine how cost-effectively public resources are spent on incentive programs.
Now that data centers are looking to DERs to offset their grid impacts, new questions are arising that expose gaps in existing DER valuation frameworks. How would we know if a battery in someone’s home actually helped reduce transmission costs for the power required by a new data center? Would a distribution substation and a transmission substation receive the same benefit from EV load shifting? How would we compare two different locations on the same grid? How would a utility quantify the benefits? How would a data center claim credit?
These types of questions led us to question some of our own assumptions. We had been working on a “GridScore” concept, reflecting the degree to which DERs delivered load reductions while demand was at its highest for that balancing authority. Was that the right number? What about distribution grid impacts? Was the system peak the same as the local peak? How would we think about transmission costs? If transmission costs are high, but the energy is mostly clean, do we care that we have to pay a little more to get it to where it needs to go?
It felt like each time we answered one question, three more appeared. We had met the energy complexity hydra.
Out of these questions we started looking for data that could help. There is plenty of information out there about generation and transmission systems. The major ISOs publish varying degrees of detailed maps and pricing and other information. But information becomes much scarcer the farther down into the stack you go. Some utilities publish distribution grid data, but there is nowhere that you can look that would tell you what the value of a DER would be at any given point on the grid.
It seems like being able to answer this question will be essential if we are truly going to move to robust demand-side energy markets where the counterparty is a data center trying to justify its capacity allocation. If we get the number wrong because it’s based on a 5-year old study, the consequences are severe!
Last month we released GridSolver to illustrate how widely variable the different needs across the country are and how different DERs are going to be differently valuable in different places. Since then we’ve layered in a prediction algorithm that allows us to predict the hourly load carried by every transmission substation in the country. And we’ve continued to build out the mapping of distribution infrastructure across utilities that publish this information. The result is a unique dataset that pulls together in a mostly comprehensive way all of the information that you would need to be able to answer the question: what is a DER worth here?
We’ve identified 7 major categories of value that could be captured by a DER. Some of these are mutually exclusive and some are limited to certain types of DERs, but generally this the universe of value that might be available to DER developers.
Resource adequacy/capacity - at any given time, the grid needs to be able to deliver power to meet aggregate demand. The cheaper and cleaner the better.
Transmission congestion - some areas need power that cannot be supplied adequately by local power resources and must be carried over longer distance power lines or rerouted to reach the desired destination
Energy arbitrage - energy supply and demand can be mismatched during the day and the ability to shift one side or the other can result in lower costs
Ancillary services - voltage regulation is an important part of grid stability and can be facilitated by DERs
Line loss reduction - generally the farther energy has to travel, the more is lost along the way and DERs that can shorten the distance can keep our system more efficient
Infrastructure deferral - when grids are sized for peak loads and those peaks are significantly higher than normal operations, cost recovery is difficult. Reducing peaks and increasing usage during off peak hours helps to lower infrastructure spending.
Bill savings - ultimately customers pay the bill for their energy consumption and DERs can help reduce exposure to high demand charges, time-of-use rates, and reduce wasteful spending.
Given these categories of value, the challenge comes in mapping these to any given DER. How do you simultaneously measure the benefits that might accrue?
This was the challenge we set out for ourselves when working on GridSolver. Could we actually gather all of these bits of information for any given DER anywhere on the grid in the United States?
Part 2 of this series will deep dive into all of the different sources of information and how we pulled them together into a single data model.