For months now we have heard President-elect Joe Biden tout the job and economic growth that would come from transitioning to a clean electric grid. We’ve also heard from critics who say the new clean electric grid he is proposing will cost upwards of $2 trillion.
These assumptions about costs are misguided, as are other widely-held assumptions about a clean electric grid. Transitioning to a clean electric grid could actually cost less money and save us billions of dollars, create jobs, and result in a cleaner, more reliable grid across the United States.
We found that when you use better planning models and scale both local solar and storage, as well as utility-scale solar and wind, you maximize cost savings and unlock the path to the lowest cost grid. In fact, it could generate nearly half a trillion dollars in savings to ratepayers over the next 30 years.
In recent years, myriad studies conducted in more than twenty states have tried to evaluate the net value of distributed energy resources (DERs) like local solar and storage. While these studies have been used to influence the implementation of tariffs for DERs on a limited scale, none have been able to project those costs and benefits at scale into total system planning processes — in the all-important capacity expansion and production cost modeling that underpins utility system resource planning. In fact, most grid and system planning processes aren’t equipped to consider resources based on their total costs and benefits to the entire system. That’s because they analyze the grid in a piece-meal fashion in distribution, transmission, and generation modules, lack exhaustive data inputs, and can’t fairly consider smaller resources like DERs.
We wanted to know what the grid would look like, and cost, if we stopped ignoring the benefits of DERs and optimized the integration of these resources through a better modeling process aimed at a true least-cost development plan for the entire grid. So we engaged Dr. Christopher Clack of Vibrant Clean Energy to apply his advanced and big-data friendly WIS:dom(R) model to the task. What we found surprised even us.
What did we do?
We had the model compare multiple scenarios: 1.) a “dumbed down” scenario that mimics traditional models by only considering and weighing cost impacts from a central transmission-level grid perspective; 2.) a scenario that integrated and optimized for distributed solar and storage assets located closer to the customers; and 3.) a scenario that sets a clean electricity target of 95% reduction in carbon emissions in each state by 2050 from 1990.
The model spent days crunching data, analyzing over 1.5 trillion data points across every county in the continental U.S., down to a resolution of a single kilowatt, three square kilometers, and intervals of just five minutes—the fine resolution necessary to reveal distributed resource benefits. The model’s ability to work at this detailed scale solved for the complex resource choices that system planners face in the real world, reconciling costs and options across technologies, sizes, and locations.
What did we find?
Not surprisingly, the model built a lot of solar, wind, and storage—over 1,000 GW (a terawatt) of solar and over 800 GW of wind by 2050. What surprised us was how and where it built these resources and why that accounted for hundreds of billions of dollars in potential savings.
The model found that by scaling local solar and storage at the distribution level and closer to customer load, we don’t have to over-rely on the most expensive parts of the transmission system and under-utilize the distribution system as many traditional planners assume. The daily peaks that the system must ramp up and down to serve can be permanently and more cost-effectively managed by local solar assets, storage injections, and off-peak charging. These DERs cost-effectively reshape the load as seen by the large-scale grid, reducing bulk power system costs and smoothing volatility and variation in load across the system. This allows for a more efficient overall allocation of investments, and a more flexible and local electricity system through the addition of 247 GW of distributed solar and 160 GW of distributed storage by 2050.
(Average summer month utility-scale generation and distribution demand in business as usual vs. DER integration and optimization)
And how much does it cost?
Just by integrating and optimizing distributed solar and storage, we found potential for over $300 billion in grid savings. When we asked the model to also meet a 2050 clean electricity target, we found $473 billion in grid savings versus a clean electricity grid that doesn’t scale distributed solar and storage. And finally, and most notably considering current discussions around President-elect Biden’s clean electricity plans, the model found that a clean electricity grid that scales local solar and storage is $88 billion less expensive than maintaining the status quo. These savings are driven by reduced grid costs alone and do not include the massive societal benefits that also come with more local solar and storage.
On top of saving the grid lots of money, deploying more community and rooftop solar and storage will result in massive economic benefits, including jobs, and additional social and environmental indirect benefits. While this analysis didn’t account for these indirect benefits in resource selection, the model did calculate that a clean electricity grid that scales local solar and storage would result in over 2 million jobs by 2050.
But you may ask: if we’re seeing per unit costs for utility-scale solar and wind at less than five cents per kilowatt-hour, why not just build more of that and avoid the higher per-unit costs of local solar and storage? Embedded within the results of this analysis, we found that the lowest cost bulk renewables are optimized when local solar and storage are optimized as well. Analyzing resources on their per unit cost alone is misguided and misleading, and when you run a better analysis that chooses resources based on their net cost to the entire system, you achieve the lowest cost system with a portfolio of resources with varying per unit costs. The sub five cent wind alone still requires the ramping of gas combustion turbines and additional transmission, and the local solar alone still requires capacity support from the bulk power system. But together, they can deploy the maximum efficient amount of bulk power and local power to deliver the lowest cost system for all.
So how can we realize these savings?
As an analogy, while navigating with paper maps is of course still possible, we know there are better, more efficient maps that use modern technology and more and better data to get us where we need to go. If we want to build the lowest cost electric grid that serves the needs of all customers in ways that can adapt to the challenges of a new era, we must evolve the tools we are using in the electricity sector as well.
For too long we’ve used outdated tools, methodologies, and thinking, with too few actors in mind to inform our thinking about how we build a better electricity system. Using new tools that take advantage of technology and big data, we find that a local, clean electric grid isn’t as expensive as we thought—and far more local, too. It turns out we can build a clean grid that empowers consumers and strengthens communities with distributed solar and storage. We can build a grid that leverages private investment while also reducing overall costs on all customers. We can build a grid that increases resilience. And we can build a grid that improves the equity of our electricity system.
So what do we need to do to bring these benefits to life?
Policymakers should apply the outputs of this advanced modeling to all energy policy decisions today. They should demand better planning and analysis that focuses on the most tangible solutions right away, and let the data-driven results guide their decision making on everything from planning, to RPSs, to interconnection, equity, and local solar programs like community solar. They should also establish clear and consistent policies and programs that scale local solar and storage right now, because if we continue on our current trajectory of distributed solar and storage deployments, we will not be able to achieve the maximum cost savings uncovered by our analysis.
We have a data-driven roadmap to the lowest cost grid of the future. Now is the time we must start building and let the benefits start accruing. Because if we’re not saving ratepayers money, then we’re costing them money. We simply cannot afford to wait.
The views and opinions expressed in this article are the author’s own, and do not necessarily reflect those held by pv magazine.
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