Real Deployments, Measurable Results

Background & Challenge

The campus runs a centralized district energy system anchored by a high-capacity cogeneration plant — heavy-duty gas turbines (such as the Solar Titan 130), heat recovery boilers, and a network of chillers — serving thermal and electrical demand that swings hard through extreme Canadian winters. The plant lacked continuous, real-time visibility into asset efficiency: operating decisions were driven by static annual energy audits, leaving blind spots on gradual degradation. Without dynamic tracking of actual Heat Rate against the manufacturer (OEM) curves, operators could not catch early-stage issues — compressor fouling, boiler inefficiency — before they became major natural gas waste.

Solution

VL Energy deployed a Dynamic Heat Rate and Economic Dispatch Module inside the EMIS platform:

• Software Engineering: Ingests high-frequency field telemetry — natural gas mass flow, net electrical output (kW), and ambient temperature and pressure.
• Continuous Analytics: Continuously calculates real-time Heat Rate against the OEM baseline and runs a multivariable regression model (CUSUM) that flags efficiency degradation the moment it deviates from statistical norms.

Projected Results

Projected and expected results based on facility data and modeling.

Background & Challenge

This heavy industrial facility thermally processes and mills magnesium oxide (MgO), with an intensive electrical load profile dominated by Motor Control Centers (MCCs) and Variable Frequency Drives (VFDs), operating inside the deregulated and highly volatile Alberta electricity market (AESO). It faces steep transmission demand charges — the 12 Coincident Peaks, or 12-CP. Plant-floor telemetry is fragmented: modern VFDs provide high-fidelity data, but dozens of fixed-speed motors sharing the same MCCs have no physical sub-metering, which blocks the accurate Energy Performance Indicators (EnPIs) required for ISO 50001.

Solution

VL Energy deployed an EMIS platform with a Market Arbitrage Engine and Adaptive Virtual Sub-Metering:

• Demand Response Automation: A wholesale market-prediction API anticipates grid peaks and sends automated alerts to the SCADA system, letting operators shift load predictively — for example, throttling primary crushing circuits — before regional peaks hit.
• Adaptive Virtual Sub-Metering: A proprietary residual-ratio algorithm subtracts instrumented VFD consumption from the main MCC meter, then allocates the remainder to unmetered fixed-speed motors using live SCADA runtimes, nameplate capacity, electrical efficiency, and process load factors.

Projected Results

Projected and expected results based on facility data and modeling.