Case study: Reducing costs with cloud-based energy management

The expense of water recycling

The Monterey Regional Water Pollution Control Agency claims to operate the world’s largest water recycling facility designed for raw food crop irrigation, using 25 pumping stations to treat 20 million gallons of wastewater every day. Pumping, treatment and conveyance are a few of the most expensive and energy exhaustive processes for regional municipalities, comprising more than 30% of their budgets, according to a case study provided by Candi Controls. The MRWPCA chose an energy management and control solution to try to reduce operating costs associated with its operations.

source: Candi

There are a number of ways to cut costs using the “internet of things” and data. For example, fine tuning the timing of operations relative to each other and to the time of day. But operators generally lack the tools to monitor, analyze and adjust energy usage with enough granularity to reduce energy related costs. According to Candi Controls, traditional building energy management systems are complex and expensive to deploy and provide submetered data for plant processes.

Enter the cloud. Cloud-based energy monitoring and control systems allow plant operators to use web-based technology to gather and analyze real-time data at lower costs. Because of this, MRWPCA decided on using a cloud-based monitoring approach to save energy and money.

The facility and the approach

MRWPCA’s treatment plant draws its power from Pacific Gas and Electric. In 2011, Solar City installed a 1.12 megawatt solar system to augment the plant’s PG&E power source and reduced its carbon footprint.

The approach taken to further reduce power is by monitoring and controlling “time of use” power requirements at the plant. This would, in theory, allow the facility to optimize its monthly peak power demand to below 500 kilowatts, and qualify for alternative rates from PG&E.

source: Candi

The MRWPCA energy monitoring system was developed by MC Engineering and Candi Controls. Water treatment systems analysts began the project by gathering knowledge of plant operations, conducting a facility audit and targeting strategic loads for monitoring. Energy monitors were then installed throughout the plant to meter instantaneous power usage. The encrypted data was streamed through a local network to a hosted cloud service for archiving and processing, and additional network-based control switches were installed to manage industrial lighting during high-use periods.

The data and controls are managed through an API and presented to operators using a web-based graphical interface on laptops, computer screens and tablets.

Granular data for optimized energy management

The system archives energy usage and instantaneous demand data at user-defined intervals and stores it on a remote server using an encrypted connection. More than 100,000 data records per day are streamed to Candi Controls’ cloud service, requiring up to 300 gigabytes of data records management per year for the site.

One of the main features of the system is its ability to control loads in a facility using different device protocols. This was done in an attempt to add flexibility by assigning the required protocol drivers to an on-site server to enable control of strategic loads. Real-time control signal latency is typically less than 200 milliseconds, according to the case study.

Controllable loads may include lighting, heating, ventilation and air conditioning, and large process-related devices traditionally managed by supervisory control and data acquisition.

Industry trends

Candi Controls says as the smart grid evolves, data-driven cloud-based solutions will become increasingly important to improve utility efficiencies. According to the case study, electricity providers are transitioning to peak day pricing, and the associated hourly rates during peak day events will result in higher energy bills for many water utilities operating under time-of-use tariffs. This increases the relevance of relying on real-time data and leveraging low-cost power monitors and cloud-based monitoring and control.