Smarter Pumps, Lower Emissions: The Digital Reinvention Of San Jose Water
By Gary Wong
Pumps are power-hungry and thus expensive to run, but San Jose Water shows how data-driven technologies and strategies can bring the cost down for utilities.
San Jose Water (SJW) is a key player in the San Francisco Bay Area, bringing fresh water to over one million California residents every single day.
In a mission to optimize its performance, the investorowned provider launched an ambitious operations control and data initiative designed to reduce energy costs, lower carbon emissions, and unlock the full potential of its assets.
SJW operates a complex distribution network, comprising over 100 pressure zones, ranging from a handful of service connections to tens of thousands.
Monitoring its 84 stations for groundwater extraction and inter-zone pumping — as well as 229 booster pumps and 89 groundwater wells — has proven to be a large-scale challenge.
Conserving Power
Energy consumption continues to be a top focus for SJW. Pump operations account for over 90% of the utility’s annual energy costs. Yet, until recently, SJW lacked the data transparency to survey and optimize this usage effectively.
To address this blind spot, the utility set out to build a centralized data infrastructure capable of aggregating and analyzing operational and asset data in real time.
In tandem, the organization set a target to slash greenhouse gas emissions by 50% by 2030.
From Silos To Real-Time Success
From the start, SJW’s main pain point was a lack of operational visibility. Data was scattered across isolated systems, and real-time pump performance couldn’t be easily monitored. Maintenance was reactive, which meant repairs were often costly and disruptive. Operators couldn’t reliably track asset lifecycles, and downtime was frequent.
It was also a challenge to decide which pumps to operate throughout the day. With energy rates rising as much as 30% during peak hours, it was imperative to use pumps when they were the most affordable to operate.
The utility originally prioritized pumps based on the results of field-efficiency tests, which offered static information and were time-consuming and costly to perform. SJW conducted field tests infrequently, so the test results could be two to five years old.
Ethan Smith; EIT, capital planning, San Jose Water; summarizes the challenge: “We looked at improving our operating efficiency, reducing electrical costs, and lowering our carbon footprint at the same time. We also wanted to move toward more condition-based maintenance rather than reactive maintenance.”
The breakthrough came with the adoption of a centralized data platform, anchored by the AVEVA PI system. The solution combined data from flow meters, pressure monitors, and level sensors, as well as SCADA and HMI systems.
This setup enabled SJW to organize data hierarchically — from stations to individual assets, such as well pumps and tanks — providing a complete and contextual view of operations.
Defining Pump KPIs And Acting Early To Detect Issues
To make sense of the flood of new data, SJW worked with Casne Engineering to develop key performance indicators (KPIs) for pumps leveraging context and system templates on the data.
These metrics define how pumps should perform under ideal conditions, allowing SJW to detect underperforming equipment in real time and act before minor issues cascade into system failures.
Today, operators receive alerts when a pump deviates from its KPI targets or nears the end of its service interval. As a matter of course, maintenance is proactive, targeted, and informed.
Real-time dashboards also reveal the operating cost per million gallons of water. This data is then updated automatically as electricity prices fluctuate. When a low tank triggers a pump activation, the system evaluates current rates and selects the most energy-efficient option.
Toward A Smarter Future
The results have been transformative. SJW identified a 5% overcharge from its electric utility, tracing it back across 12 months. With this data in hand, the utility recovered the excess charges and renegotiated its rates. This adjustment alone means SJW now saves hundreds of thousands of dollars annually.
And that’s not all. By ensuring that pumps operate during offpeak hours, energy consumption decreased by 30%, resulting in even more cost savings. Greenhouse gas emissions have fallen by 206 tons annually, putting the utility on track to meet its 2030 climate target.
Now that the company has brought its data into a centralized location where it can view and measure asset performance against KPIs, it’s making plans to take the system to the next stage. SJW is currently adopting a system for enterprise asset management and plans to integrate the enterprise-asset-management (EAM) solution to further improve its asset-management capabilities.
In summary, SJW’s ambitious real-time data infrastructure initiative has transformed its everyday operations, cut costs, and positioned the utility on a clear path toward a sustainable and efficient future.
About The Author
Gary Wong is the global segment leader of power, utilities, and infrastructure at AVEVA, a leader in real-time, industrial, performance intelligence. He leads its global power, water, smart cities, facilities, and transportation businesses and has 25 years of extensive international experience providing sustainable, strategic, and costeffective digital solutions. Wong is also the chairman emeritus of the Smart Water Networks Forum (SWAN) Americas Alliance.