Simulate Your Savings. Discover Your Potential.

A complimentary energy insight by SynVista Energy.

SynVista – Peak Shaving Simulation

⚡ Discover How Much You Can Save with SynVista

Every facility faces demand peaks that drive up electricity costs.

With SynVista’s Peak Shaving Simulation, you’ll receive a complimentary analysis showing how Battery Energy Storage Systems (BESS) can help reduce your Maximum Demand (MD) charges and improve energy efficiency.

Upload your load profile — our engineers will handle the rest.

The Challenge Many Businesses Face

  • Electricity bills keep rising, but cost structures remain unclear.
  • Maximum Demand (MD) charges take up a large portion of the monthly bill.
  • Demand spikes are hard to predict — and one bad month can inflate the entire quarter’s cost.
  • Traditional energy audits are time-consuming and costly, with no guarantee of ROI.
  • Many teams lack the tools or data visibility to identify peak hours or simulate savings.

💡 SynVista’s Peak Shaving Simulation solves these challenges — providing a data-driven preview of how BESS can flatten peaks, reduce MD, and optimise investment decisions before any capital commitment.

How It Works

1.Upload your Load Profile Data (Excel preferred)

2.Our engineers simulate your tariff and multiple BESS scenarios

3.Receive your report, including:

  • Demand-charge savings potential
  • Payback & ROI estimation
  • Peak-shaving performance charts

Why Join This Complimentary Simulation

  • Quantify real-world savings before you invest
  • Visualise your load curve improvements
  • Identify the ideal BESS configuration
  • Receive a presentation-ready report from SynVista experts

The SynVista Technology Stack

Stack1

SynVista Cube

Factory-built, highly-modular, standardized form factor leads to safe, scalable, cost-effective systems configurable with the latest storage components.

Stack2

Safety Management

Developed a large-scale pre-training model, PERB1.0 (Pre-training Encoder Representation for Battery) for battery time series data. The data is of great significant to the real status of battery, contributing to advanced SOC management and predictive maintenance.

Contact Information

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