Energy Modeling Can't Keep Pace — Energy Decision Intelligence Changes That

What do energy modelers and analysts actually spend their time on?  

Energy modelers and analysts are deeply skilled professionals with critical roles: they understand complex energy systems, run production grade models, and plan for reliability amidst great industry uncertainty. Their work is critical to maintaining reliable and affordable energy systems, helping to navigate changes in technology, policy, economics, and more. They turn uncertainty into the data decision makers need to help both their organizations and the grid succeed, performing rigorous and high-stakes studies for integrated resource plans, resource adequacy, transmission analyses, price forecasting, capacity expansion, and more.

However, as systems become more and more complex, and the problems being solved get bigger, we’ve heard from modelers and analysts that these highly skilled professionals are spending a disproportionate amount of their time on administrative, modeling-adjacent work. Manually copying and pasting base cases to set up scenario runs. Migrating data between teams working on different studies. Tracking down changes in data releases and reconciling differences. Reformatting outputs into charts and summaries that make sense to non-modeling audiences. Troubleshooting infeasibilities. Running iterative parameter variations one at a time.

These are time-consuming tasks that are eating up work hours for a highly skilled, strategic workforce.

Why are energy modeling teams under more pressure than ever?  

Energy modeling and analytic teams are being asked to do more than ever before. As the energy industry has evolved, new modeling use cases have piled on. The complexity of intermittent resources, storage dispatch, gas and power co-optimization, and data center load must all be considered. Additionally, the timeframes are shorter. Change is happening so rapidly that studies that used to be done annually now need to be done monthly — that’s a 12x increase in studies — often with the same resources available.  

Modelers and analysts are also being asked to share the results of these numerous studies with a much wider set of stakeholders: traders, finance, procurement, and boards. Modeling results must be dispositioned for non-modeling audiences so that critical, data-backed decisions can be made.  

The result? Energy modelers and analysts are under more pressure to deliver than ever — bigger changes, exponentially more studies, and additional stakeholders — often with the same resources, due to a critical gap in highly skilled modelers. 

What happens when modeling can’t keep pace with decisions? 

Modeling is critical to good decision making. It allows organizations to ensure that the decisions they make will support a variety of scenarios: high load, extreme weather, fuel price shocks, policy changes, and new technologies. But with current modeling resources and processes, modelers are no longer able to keep up with the demands being placed on them. Decisions need to be made in shorter timeframes, and conditions change rapidly. 

So, what happens when modeling can’t keep pace with the critical decisions that need to be made? This creates a problem that extends well beyond the modeling team. When modeling results do not arrive in time, or when the results are already outdated, the rigor behind the data and the decisions suffers. As a result, through no fault of the modeling team, the confidence decision makers have in the outputs erodes. Decisions need to be made anyway, so they're made on incomplete assumptions or instinct. In the worst-case scenario, decision makers stop asking the modeling team for inputs altogether.  

In order for modeling to continue to support transformative energy decisions, something needs to change. 

What is Energy Decision Intelligence?  

Energy Decision Intelligence is the change needed. It is the evolution of energy modeling and simulation built to support a rapidly changing industry. Energy Decision Intelligence applies purpose-built AI across the modeling workflow to accelerate time to insight, expand analytical capacity, and bring trusted outputs closer to the decision makers who rely upon them.  

Because Energy Decision Intelligence utilizes AI that is purpose-built for energy modeling and analytic workflows, it ensures that the mathematical rigor behind studies never suffers. It takes the power of a fundamental model and amplifies it so the same modeling questions get answered faster, at greater scale, and with more accessibility to stakeholders.  

How does Energy Decision Intelligence change analyst and modeler impact? 

Energy Decision Intelligence takes the impact of modelers and analysts and amplifies it. By purposefully applying AI to the manual, time-consuming activities that are eating up modelers’ time, it allows them to focus on the critical work their expertise is needed for. Energy Decision Intelligence allows modelers to:  

• Run more scenarios before the deadline. With Energy Decision Intelligence, scenarios are set up more quickly, and more options can be stress-tested so that defensible recommendations can be made that modeler and decision maker alike can have confidence in. 

• Model more use cases. Right now, only the highest priority use cases and decisions are modeled. When modelers can work faster, their influence can expand to use cases or studies that aren’t currently being considered. The backlog of studies that have gone untouched can get added to the queue, ensuring that more decisions are optimized and influenced by rigorous modeling and analytics. 

• Explain their work to key stakeholders more easily. Currently, when studies are complete, modeling teams then have to spend more time ensuring the outputs are digestible for a variety of stakeholders who depend on them. When stakeholders have questions, a series of back and forth ensues, taking precious time that could be spent on further modeling studies. Energy Decision Intelligence helps make key modeling outputs more accessible to stakeholders, improving reporting and allowing decision makers to query models themselves so they do not have to go to the team for every single question.  

With Energy Decision Intelligence, modelers save time on the work that doesn’t require their expertise, shifting them from model builder to strategic contributor.

How does Energy Exemplar deliver Energy Decision Intelligence?  

Energy Exemplar delivers Energy Decision Intelligence through a variety of solutions that are purpose-built to solve the real problems our customers tell us they are facing.  

PLEXOS® Intelligence embeds AI directly into the PLEXOS environment, providing faster support and automating manual processes.  

PLEXOS® Pulse provides calibrated, fundamental market forecasts and delivers them through a simple, AI interface.  

PLEXOS® AI Studio (Coming Soon) helps: build, deploy and scale AI powered energy workflows that are tailored to your organization.  

Energy Exemplar has spent decades building the modeling foundation the energy industry depends on. Energy Decision Intelligence is how that foundation evolves to meet what the industry needs now.