Industrial Energy Strategy AS50001 Alignment
Energy Management System Strategy for a paper mill in Queensland, Australia.
Client
A paper mill in Queensland with four processing plants, each consuming up to 11,000 kWh daily and reaching 1,000 kVA maximum demand.
Challenge
Rising electricity and gas costs were putting pressure on operational margins. The client needed preliminary modelling to identify energy efficiency opportunities and align with AS50001 standards — without disrupting production.
A data-driven, AI simulation based approach was required to answer fundamental questions...
“What is the cost of inaction?
Can we evaluate projected electricity and gas prices against the cost and benefits of new technologies?”
Approach
Deep Energy AI ingested NEM12 data across all sites and performed baseline analysis, tariff validation, and load disaggregation. Scenario modelling was used to test solar PV, battery storage, and smart controls — with financial metrics calculated over 15 and 50 years.
Outcome
The feasibility analysis identified a 22% reduction in energy costs and a 35% reduction in emissions under the optimal scenario. The client used the outputs to brief internal stakeholders and secure funding for a staged implementation strategy.
Results
Deep Energy produces fully simulated yearly outputs of 20 data-driven energy models utilizing the client’s own data. In addition to mutated load profiles, a flexible load analysis, costs & tariff analysis, the key output is the 15 & 50 year benefit-cost-ratio (BCR) and net-present value (NPV) cash flow tables for each ECU scenario simulated.The report outputs enable:
- The executive management team to make confident investment decisions over the top ECUs based on the business cases for each option.
- Data as inputs for a capital equipment replacement program as part of their long-term planning and ISO 50001 compliance.
- Data to support any grant application or federal funding requests.
More about AS50001
ISO 50001 assists facilities in evaluating and prioritizing the implementation of new energy-efficient technologies and in improving energy efficiency, energy use and consumption
Key Objectives
- Identify SEUs - the most significant energy consuming plant & equipment. High level tasks include:
- Generating a motor list.
- Generating a boiler/steam consumer list.
- Identifying if sub-metering or data via VSD is available.
- Identifying the age and lifecycle of the plant & equipment.
- Merge into an SEU register.
- Provide briefs to consulting engineers.
- Against each SEU, determine Energy Performance Indicators (EnPIs):
- targets for energy savings, initially coarse estimates until data-driven decisions can be made as part of the implementation of the energy management system strategy AS50001.
- Provide briefs to consulting engineers.
- Discovery of available data sources including utilities, SCADA, PLCs.
- Consolidate energy data for calculations, modelling & simulation:
- Scope for scenario modelling;
- Capital and operational cost assumptions;
- Energy cost and inflation projection curves; and
- Other financial variables such as cost of capital (discount cash rate).
- Take a bite of the grant funding available for feasibility studies.
Key goals
- Identify the optimal solutions to achieve efficiencies.
- Secure first round grant funding.
- Install metering equipment on SEUs.
- Secure second round grant funding.
- Monitoring EnPIs against baselines and preventative maintenance regimes to deliver;
- Annual cost savings ranging from $36,000 to $938,000.
- A 12% average reduction in energy costs within 15 months of initial implementation.
- Energy performance improvements of 5.6% to 30.6% over three years.
- Annual savings of $430,000 or more through low- or no-cost operational improvements.
- AS/NZS ISO 50001:2021 adoption.
- Take a second bite of the substantial grant funding available, potentially reaching up to $50 million. This endeavour will be powered by the valuable data insights gathered in Phase 1.
