Industrial Facility - Energy Management System Strategy AS50001

Client

Industrial facility in QLD, 4 processing plants each with up to 11,000 kWh daily consumption, 1,000 kVa daily maximum demand.

Challenge

Conduct preliminary modelling to identify potential energy efficiency opportunities.

Industrial facilities are high-energy consumers. The rising cost of electricity and gas is a growing concern. Coupled with the pressures of carbon commitments for net-zero - Energy is a big ticket item on the industrial agenda. Clearly, there are many reasons to improve energy efficiency and productivity, and reduce emissions. Namely, to improve the bottom-line and achieve strategic sustainability objectives and goals.

Technology is moving at a rapid pace in the energy sector. New equipment, metering, electrification of gas, the efficiency of solar, battery technologies all have a role to play in improving the energy mix for efficiency whilst reducing emissions. On the flip side however, the rising costs of capital investment is making investment decisions ever more critical. Expenditure and cost management is crucial. The need for innovation must be weighed against the financial impact upon the bottom-line. To get buy-in for new technologies requires a solid business case evaluating the options.

Often, engineering teams have solutions yet lack the ability to translate the benefits in financial terms to secure buy-in. Traditional modelling in spreadsheets cannot handle the scale of calculations required to model complex energy systems coupled with changing internal and external operating conditions. And thus fail to answer fundamental questions like:

“What is the cost of inaction? - Forecast of rising electricity and gas prices vs the capital cost and payback period of investment in new technologies and systems.”

Concept

Achieving energy efficiency in an industrial facility is a significant task. In our experience, significant improvements require a holistic approach involving integrating a plan into management framework, systems and processes guided by performance objectives. From initial engagement with the client the following high-level objectives were defined in the following key phases:

1. Capex funding grant opportunity - comprehensive pathway to improve energy efficiency, leveraging financial support from the Powering the Regions Safeguard transformation program. By securing a minimum $500,000 grant, covering up to 50% of eligible expenses, we can promote sector 1 and sector emissions reductions. To access additional grant or capital loan options, a minimum cash contribution of 10% is required.

2. Commitment to managing energy systems - Conduct energy planning workshops for implementing energy management system strategy AS50001 to harness the superior energy performance framework it provides for industrial organisations in advancing energy efficiency objectives.

3. Energy planning workshops - Collate data on organisational drivers, the significant energy users (SEUs) analysis, energy assessments, and plans for system optimisation and any other existing energy planning efforts. Use this information to create and develop a list of Energy Conservation Opportunities (ECOs) for modelling during the workshop(s).

4. Identify ECOs for financial modelling.

5. Conduct modelling using the modelling tool:

   • Deliver indicative load profiles and maximum demand to enable rapid energy modelling.
   • Define input scenarios to assess feasibility across ECOs.
   • Output financial results BCR and NPV analysis to answer problem statement and seek stakeholder buy-in.

6. 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.

An overview of this process is shown in the diagram below:

Purpose

A Rapid Cost Assessment using Deep Energy is intended to provide a guide to the financial viability of various mixes of energy plant and equipment run as simulations to test various hypotheses. It further allows comparison between various network & retail tariffs and/or the wholesale price of electricity. The summary will inform project partners as to the content and approach contextualised by key objectives and goals.

Energy Management System Strategy AS50001

Key Objectives

1. 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.

2. 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 .

3. Provide briefs to consulting engineers.

4. Discovery of available data sources including utilities, SCADA, PLCs.

5. 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).

Key Goals

1. Identify the optimal solutions to achieve efficiencies.

2. Secure first round grant funding.

3. Install metering equipment on SEUs.

4. Secure second round grant funding.

5. 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.

Proposed Solution

In order to run data driven scenario modelling we need to:

• acquire recent 5-minute NEM12 electricity data for all supply authority meters;
• acquire gas boiler data consumption data from SCADA;
• acquire corresponding daily natural gas metering data from the utility;
• conduct supply options workshop (discuss status quo, consolidation, HV supply, transmission supply, zone sub-station build options);
• create baseline year from NEM12 + Gas data; and
• generate micro econometric scenario models for the agreed energy hypotheses to be tested.

Modelling Scenarios

The modelling utilises building metadata, tariffs, transformed precedent data, to run calculations and simulations for:

• A source-of-truth baseline scenario to deliver indicative maximum demand and load flexibility assessment.
• What-if scenarios of identified ECOs from modelling workshops for analysis outlined in the table below.
  
  

Supply & Demand	Cost
Solar sizing for full electrification	Change of retail tariff analysis
Existing client energy strategies	Change of network tariff analysis
Consolidated electrical supply option	Wholesale market participation
Zone substation for connection to transmission network option	Optimal load profile (for wholesale market participation)
Grid-scale battery energy storage system options (250MWh, 500MWh etc)	Battery operational mode: FCAS and/or arbitrage
Manufacturing equipment changes (e.g compressed air/gas boiler options)	Forward price projections
Onsite thermal/compressed air/other energy storage options to buffer energy usage	Sensitivity analysis on price
Add/upgrade controls over demand flex plant & equipment	Additional finance mechanisms
Add load shifting (pre-heat/pre-cool)	And more…
Reduce/increase electrical demand or consumption (e.g. EEOs, electrification)

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 will 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.