Real-time energy trading for a greener tomorrow
Posted: November 26, 2025
Global electricity consumption is expected to rise by 3,500 TWh over the next three years[1]—an amount of energy that could power 350 billion homes for an hour, roughly equal to Japan’s global electricity consumption per year. Renewable energy sources are expected to meet about 95% of this demand.1 But renewables such as solar and wind are often limited by geography and available resources. In the U.S., for example, Arizona gets more sunshine than Oregon, which has more hydropower than the California desert, the location of a significant amount of wind power.
Renewable energy introduces new volatility to the grid, new products and technology, and the need for much more granular real-time trading. In the past, energy trading was simpler. Energy companies were largely vertically integrated—with the same company handling generation, transmission, and distribution—which made coordinating supply and demand seamless. Now, the influx of renewables in the market requires balancing output and demand across different regions. The energy trading market has had to adapt.
A real-time energy market to meet real-time power needs
Energy trading used to be mostly bilateral, in which a power producer and a buyer make a deal at an agreed price and time. With the added complexity of renewables, there has been a shift, particularly in the U.S., to centralized energy markets operated by Independent System Operators (ISOs). These operators match many buyers and sellers at once and set prices based on overall supply and demand.
This means ISOs can harness market competition to provide better purchasing options for customers while managing the integration of renewable energy into the grid.[2]
ISOs, such as the California Independent System Operator’s (CAISO), use imbalance markets, such as the Western Energy Imbalance Market (WEIM), to balance energy supply and demand in the short term—every 5-15 minutes—as renewables such as wind and solar fluctuate. The goal of the imbalance market is to maintain grid reliability while automatically finding participants lower-cost resources to meet real-time power needs.[3]
Launched in 2014, the WEIM allows participants—currently including portions of Arizona, California, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Canada—to buy and sell power when it’s needed.
Some of the benefits of this type of model include:
- Real-time efficiency and cost savings: WEIM has saved participants billions of dollars through more efficient dispatch and reduced curtailments of renewables.
- Better integration: Renewables, such as wind and solar, are variable, and their output doesn’t always match local demand. With the WEIM, quick trading spreads renewable energy across geographically diverse places. For example, California solar oversupply can be sold to Nevada or Arizona in real time
- Enhanced reliability: A larger pool of resources means unexpected outages or demand spikes due to heatwaves, drought, and flooding are easier to cover. The WEIM has prevented blackouts, for example, during extreme heat events by accessing generation across the region.[4]
- Cleaner energy: Since it began operation in 2014, the WEIM has saved more than three-quarters of a million tons of carbon pollution, equivalent to the electricity use of 150,000 homes for one year.
“In energy trading, we are in the business of managing uncertainty. The more accurate and digestible the data, the better our decisions, and the more successful we are.”
— Nick Jacobs, Market Operations Instructor in the Supply and Trading department, SRP
Data management helps CAISO ensure accurate, up-to-date information for traders
For CAISO, which manages and operates the WEIM, new energy generators means new types of data, new operating characteristics, and new challenges. Managing the data requires an in-depth understanding of where that data is and what it means. Using the AVEVA™ PI System™ portfolio—with over 65 AVEVAä PI Server deployments—CAISO has a highly available, secure system, and one that’s scalable.
CAISO used to consult static displays in dashboards about equipment, processes, and loads that required manual calculations and updates. Today, CAISO uses dynamic AVEVA™ PI Vision™ displays with automatic updates. CAISO shares these displays both internally with its more than 400 internal company users, and externally with other organizations.
Using AVEVA PI System’s asset framework templates, CAISO is able to build everything on the back end, so that when it gets new resources and parameters, it can update all the displays at once, providing a common operating picture. This sets the foundation for advanced predictive analytics and forecasting.
Managing uncertainty with trusted real-time data
CAISO can see batteries side-by-side to immediately get a picture of stored energy capacity
The Salt River Project (SRP) is one of two electric providers in Phoenix, Arizona. A not-for-profit organization, SRP provides reliable, affordable and sustainable water and energy to more than two million people. When SRP shifted from a traditional bilateral energy trading model to CAISO’s WEIM, the utility needed a way to manage the massive volume of real-time data required for participation.
The WEIM dispatches resources every five minutes across the regional grid based on economic optimization. This means that participating providers such as SRP must continuously provide accurate data on generator operating parameters, outages, forecasts, reserves, and transactions—any delay or error can carry significant financial consequences.
“In energy trading, we are in the business of managing uncertainty,” said Nick Jacobs, Market Operations Instructor, Supply and Trading, SRP. “The more accurate and digestible the data, the better our decisions, and the more successful we are.”
To meet this challenge, SRP deployed AVEVA PI System as the backbone of its trading and optimization operations. With more than 900,000 tags feeding data from plants, weather stations, sensors, and even market pricing inputs, the platform collects, validates, and translates raw information into actionable insights for traders. Integrated tools like AVEVA PI Vision, AVEVA™ PI DataLink™, and Power BI allow SRP’s trading team to visualize generator health, battery state-of-charge, hydro reservoir levels, and forecast data in real time. This transparency enables traders to quickly assess opportunities—for example, cycling down a unit at midday to import low-cost solar and wind, saving its customers over $100,000 in a single decision.
AVEVA PI System also powers SRP’s optimization engine, ensuring gas burns, hydro operations, storage management, and renewable integration are aligned with WEIM’s economic dispatch. By giving traders immediate visibility into both operational and financial impacts, the system has fostered a more data-driven culture, improved decision-making, and enhanced reliability.
Traders can monitor frequency of the grid, see system reserves, variable generation, and the day’s temperature and load mapped against the day before, among other things, with the ability to drill down if needed.
Connected ecosystems power the energy transition
The success of the energy transition hinges on the power ecosystem and the ability of different power players—power producers, power schedulers, markets, utilities, and prosumers—to all be able to communicate and share accurate data quickly and securely.
ZGlobal, another part of CAISO’s connected ecosystem, works with Silicon Valley Clean Energy (SVCE) to ensure cost-effective, reliable energy to the Silicon Valley. SVCE is a public, non-profit agency that provides clean electricity for 270,000 customers across 13 Silicon Valley communities. ZGlobal is a power scheduler that provides power management services to SVCE, including scheduling coordination and asset analysis for maintenance purposes.
Using AVEVA’s industrial intelligence platform, CONNECT data services, the power producers, ZGlobal, and SVCE can securely share real-time and historical data with speed and ease. In this cloud-based data community, ZGlobal can combine the power producer’s datasets with other information, such as schedule data from CAISO as well as market and meter data, to provide its customers with a full picture of exactly how their assets are performing.
“It’s a big deal to be this transparent. We have the same data as our clients, and we can look at the same data with them at the same time. This helps to differentiate us from competitors.”
—Kevin Coffee, Vice President of Electric Operations, ZGlobal
New energy trading models for a smarter, more resilient grid
As advanced analytics and AI continue to transform the energy trading landscape, traders and organizations that have set up a secure, highly available data framework will be better positioned as markets become even more real-time. The ability to manage and harness data, and securely and easily share that data—from market data to weather data—will be the game-changer.
The energy transition is transforming power markets at an unprecedented pace, and organizations like CAISO are proving that a smart data foundation can turn volatility into opportunity. Real-time trading platforms such as the Western Energy Imbalance Market enable cleaner, more reliable, and more affordable energy by unlocking regional cooperation and renewable integration at scale. Digital offerings like AVEVA™ PI Data Infrastructure, which includes access to the AVEVA PI System portfolio and the CONNECT platform, provide the visibility and agility needed to manage complex data streams and make confident decisions in seconds, not hours. New energy trading models are laying the foundation for a smarter, more resilient grid—one where energy traders and utilities can thrive in a dynamic, low-carbon future.
[1] IEA. 2025 Electricity Analysis and Forecast to 2027. 2025.
[2] https://cebi.org/programs/designing-the-21st-century-electricity-system/organized-wholesale-market-expansion/
[3] https://www.westerneim.com/Pages/About/HowItWorks.aspx
[4] https://www.nrdc.org/bio/kelsie-gomanie/3-billion-milestone-western-grid
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