By making smart building data more accessible, the DCH will drive innovation amongst a broad ecosystem of participating service providers, leading to better products, increased competition, lower prices, and an increased value proposition for participants.
Location: Australia
Industry: Education
Project Funding: $30m
Project Website: http://ihub.org.au
Project Funder: This project was funded by the Australian Renewable Energy Association (ARENA), a federal government agency.
Scope
As a part of i-Hub Data Clearing House (DCH) initiative, CSIRO is working with NSW Department of Education, and Buildings Evolved on a project involving the installation of battery storage and HVAC control equipment in three schools:
- Jamison High School (Penrith, Greater Sydney);
- Singleton High School (Singleton, Hunter Valley); and
- Nimbin Central School (near Lismore, Northern Rivers).
The project provides proof-of-concept testing to onboard 3-trial sites from SINSW’s building portfolio and demonstrates DCH’s operational effectiveness. Namely, the demonstration of demand flexibility of building assets to support the energy network by ‘tuning’ of building optimisation strategies using various technology mixes achieved to favour occupant comfort over financial benefit or vice-versa depending on the requirements of the client. In this case, the NSW Department of Education has the objective of providing improved learning environments to deliver better educational outcomes while driving stronger economic outcomes for the state.
Source: https://github.com/intelligent-environments-lab/CityLearn
Modelling Outcomes
The modelling demonstrated HVAC controls without battery consistently return the best benefit-cost ratio (BCR) for all three sites in DCH 6.1, of above 2x BCR. These results mirror the preliminary modelling work, undertaken prior to procurement of the batteries and additional solar PV on the three school sites covered in this report. By comparison, the modelled business case for battery storage systems for the purpose of energy arbitrage shows a lower financial return, although this is improved by utilising batteries within the FCAS market (and wholesale spot pricing). The scenario with larger solar PV, without batteries or controls and using retail energy accounts, was also notable, showing an impressive BCR in its own right for two of the sites.
The results indicate that where batteries are introduced into the mix of technologies, the BCR and NPV are often reduced. Singleton HS, the largest energy consumer, produced the best financial returns of the three schools studied, and the best BCR – when using the CCP7A scenario (solar PV + HVAC controls). Over 15 years, the modelling indicates a saving of $1.19m can be made with a spend of $0.47m at this one school location.
Additionally, HVAC controls have the ability to improve financial returns while simultaneously improving occupant comfort and thus educational outcomes.
Works Completed
| System | Singleton HS | Jamison HS | Nimbin CS |
|---|---|---|---|
| Existing PV | 25KW | 35KW | ✘² |
| Added PV | +35KW | +25KW | +60KW |
| Total PV | 60KW | 60KW | 60KW |
| Added Battery (KWh/KW) | +60/30 | +60/30 | +60/30 |
| HVAC Controls | ✘¹ | ✔ | ✔ |
| Brick Modelling | ✔ | ✔ | ✔ |
| PV Control | ✔ | ✔ | ✔ |
| Monitor Battery | ✔ | ✔ | ✔ |
| Flow programming | ✔ | ✔ | ✔ |
| Data to CSIRO DCH | ✔ | ✔ | ✔ |
| Optimisation stage 1 | □ | □ | □ |
| Optimisation stage 2 | □ | □ | □ |
¹ Scope removed due to pandemic related cost increases
² Nimbin CS had an end-of-life 2KW system, producing <1KW, deemed to be non-material
The Challenge: How did we assess system sizing and the best options to proceed with?
Project Phases
The original intent of the project was to implement the infrastructure required to produce real-world data from which to draw conclusions regarding the viability of the various technologies and configurations. Given the delays in installing this infrastructure, due to a number of complications including the onset of the Covid-19 pandemic, the course of this project had veered to rely on a modelled/simulated output.
Modelling the “What-If”
Establish a Ground Truth & Validate
We gathered NEM12 electricity utility data for multiple years. We developed software to use machine learning for behavioural analysis to determine load variability and through that, flexibility. Loading the data into a chart reveals the following information, noting the rolling maximum demand calculations using prior 12 months of data to calculate the first day accurately. Invoice generation is used to complete further validation of the ground-truth.
Note also that this chart is from the metering data only - no sub-meters or other data sources were used to create this analysis.
It becomes extremely easy to see where the maximum demand events are occurring: late July and early August. Summer doesn’t get close to winter in terms of the impact of a maximum demand event. We can ascribe this phenomenon to the efficiency of HVAC compressors - the COP drops precipitously at low temperatures, rendering the HVAC no better than a resistive bar heater in terms of efficiency.
Finding a Maximum Demand Event
So. Let’s drill in to the peak demand event day on 15th August 2019 and look at raw data. Keeping in mind that we have raw 15 minute interval data here, so this will roughly double to get the 30 minute demand window.
OK, so we have identified two things: the classrooms are likely extremely cold in the mornings, and in response the staff and students turn on the HVAC roughly at the same time between 08:30 and 09:00 causing a huge spike in maximum demand. So we have uncomfortable classrooms AND a poor energy outcome.
The obvious question: What can we do about this?
What-If We Add More Solar?
Let’s add 25KW to the existing 35KW of Solar PV and make a 60KW system. What does that do to our ground-truth? The overall site demand is 292KVA, so the solar PV at maximum output constitutes less than 1/5 the total maximum demand. Applying some maths, we can modify the ground-truth consumption to render this chart:
Model: Add 30KW of solar PV to existing 30KW system.
Hm. Seems that Solar PV has some impact upon consumption figures. But in this circumstance, it has helped to mitigate a maximum demand event, to which we can attribute a sunny morning in mid-August, being 6 weeks after winter solstice. By 54KVA. Checking this against BOM data, there indeed was no cloud cover on this day.
We can conclude that the impact from solar system increase is good, but not a panacea for all ills.
What-If we Introduce Demand Management?
Next. What if we control the HVAC systems? We could turn them on early, allowing pre heating or cooling of the classroom. This would make the classroom more comfortable, and also eliminate the maximum demand issues. So, what happens if we aggressively load-shift while simultaneously improve efficiency by 30% (not an unreasonable number)?
This.
Model: Add 30KW of solar PV and use load-shifting to reduce max demand
Now we’re talking. All those maximum demand events in July and August are gone. Overall energy consumption has reduced per the assumption that HVAC controls will improve consumption and reduce demand - backed by many an academic article, study or pilot. We can see the KVA stepping down during the year as the maximum demand rolling window runs through the 12 month cycle.
It ends up at 115KVA. Down from 292KVA. Impressive.
Load Shifting
If we drill into the 15th August 2019 again, we can see what is happening to achieve this outcome. The yellow ‘parent’ is the ground-truth data - representing the ‘before’. The red line represents the ‘after’ while the blue line identifies non-variable load. The slight dip during the day is the impact of PV + HVAC controls.
Analysing the chart, the impact of the load-shift is apparent. The area of the chart between the red and yellow line during the middle of the day is being shifted to 3am - 8am.
Financial Output
From here, the software is built to load network and retail tariffs, wholesale spot price of electricity and other financial and engineering assumptions (capex, opex, replacement costs etc) to be able to produce 50-year discount cash flows, as well as 15 and 50 year BCR and NPV
Key outcomes
- Modelling demonstrated above 2x 15-50 year BCR
- Defined the optimal technology mix to achieve goals
- Demonstrated optimal thermal comfort model
- Quantified the benefit of demand flexibility and DER
- No on-site equipment, utilise IT Network & datacentres
- Open source software
- Light-weight modelling requirements
- Engineered to avoid vendor lock-in
iHub DCH 6
Project Partners & Participants
| Organisation | Role |
|---|---|
| NSW Department of Education | Project partner, beneficiary |
| CSIRO Energy | Project partner |
| Buildings Evolved | Project partner |
| Australian Institute of Refrigration Air Conditioning and Heating | Project governance |
| Australian Renewable Energy Association (ARENA) | Project funding |
Video shows the CSIRO predictive battery control algorithm in action, as used in this project and featured in the above battery peak-lopping and arbitrage output.
The TL;DR
Challenges
Preliminary financial modelling was undertaken by consultant Aeris Capital, under the direction of project partners. The modelling work consisted of using network and retail step tariffs and offered a broad understanding of the impacts of decision making but was not capable or intended to answer the primary question posed in the hypothesis underlying this project: that exposure to the wholesale spot price, or a wholesale price pass-through electricity retailer, such as Amber Electric – in conjunction with supporting technology – produces economic benefits that provide income to improve the educational outcomes of students in NSW.
The original intent of the project was to rapidly deploy equipment to gather data to support the preliminary modelling and then use the real-world to determine the benefit of exposure to the wholesale spot price of electricity. Unfortunately, the pandemic impacted the project in the first few months, causing a shift to requiring a more advanced modelling tool to provide a desktop simulation of what was intended to be installed.
Therefore in early 2021 it was resolved that more needed to be done in order to fully test the hypothesis – namely a requirement for interval by interval dynamic pricing from the wholesale spot market. The CSIRO battery control simulation was optimised around the wholesale spot price, so therefore the financial modelling needed to be able to handle wholesale spot in order to properly model the benefits of the battery control system.
The next steps include proceeding with the installation of the cabling and control systems to enable the collection of actual data for a period of 12 months. This data will then be analysed and compared with the modelling to provide insights and inform the direction SINSW will take with regard to the technology and future viability. It should be noted that the inclusion of wholesale spot pricing (without FCAS) found in some modelling scenarios can be implemented through procuring electricity from wholesale price pass-through retailers such as Amber Electric or PowerShop – however, this may conflict with whole-of-government electricity procurement contracts.
The other option: participation directly in the wholesale market, requires a virtual power plant to enable access to the FCAS markets (as modelled in scenarios labelled with FCAS); adding an additional layer of complexity for the Department of Education, and the NSW government more broadly, requiring additional consideration around the practicalities of implementation including resourcing.
Data-driven energy modelling
Buildings Evolved hired software development staff and reallocated existing resources towards agile software development with the aim of solving the hypothesis. This was undertaken only after market research proved existing modelling tools to either be targeted at completely different electricity markets (EnergyPlus, SAM), or likewise had similar limitations in only being able to model conventional retail step tariffs, or were not able to capture the complexity of the Australian energy market.
Extensive background IP was drawn upon to create a tool that could model an extensive array of what-if scenarios and deliver it in a method compatible with NSW Treasury business case guidelines. Major components of work include:
- extract, load transform scripts for NEM12, EDI retail data, BOM, AEMO & other data sources
- tariff engine to model and normalise the extreme complexity of network and retail stepped tariffs
- emissions factor calculations and repository of variables based on state, year and emissions source
- scenario and assumption generator/editor
- 50-year NPV & BCR report outputs
- software is written in Python using a PostgreSQL database back-end
- web user interface written using the React.JS framework
Undertaking this project now allows infinitely more sophisticated modelling to be undertaken, principally in being able to process each interval of data through a unique (wholesale) price. Central to the modelling effort is the ability to create infinite numbers of load profile assumptions. Buildings Evolved in conjunction with CSIRO produced over 10,000 days of unique load profiles for simulation and results generation in the advanced modelling tool.
Advanced energy management – proof of concept
The living lab configuration functions to replicate controls technologies and connection methods used for simulating a typical NSW air conditioning controls scenario used for testing controls strategies in the Buildings Evolved living lab.
The purpose of testing control strategies is to provide proof-of-concept testing to:
- develop full vertical proof-of-concept of the Operational Technology and Information Technology to be used at NSW Schools in DCH 6
- recreate and test SINSW control logic to determine baseline/BAU controls operation
- manipulate air-conditioning set-points in response to internal and external conditions, such as: internal/external temperature; humidity and internal C02 levels in context of ASHRAE Comfort Bands and control signals from AEMO (simulated).
- demonstrate control functionality in concert with solar inverter operation, energy generation and onsite consumption. Noting that testing of batteries operation and control are not available as batteries are not installed in the laboratory.
- integrate the living lab with DCH to demonstrate operational capability and response times through various IoT integration layers, on-premise server Node RED – DCH response and latency testing.
- for Buildings Evolved consultants and staff to “Live” the control system, meaning effective debugging of functionality and logic
- reduce risk in the specification, tendering and commissioning phases of works
- providing a local historian functionality with more detailed information than sent to DCH
- allowing CSIRO and Buildings Evolved teams to rapidly develop control algorithms
Key benefits – realised
The modelling demonstrates HVAC controls without battery consistently return the best benefit-cost ratio (BCR) for all three sites in DCH 6.1, of above 2x BCR. These results mirror the preliminary modelling work, undertaken prior to procurement of the batteries and additional solar PV on the three school sites covered in this report. By comparison, the modelled business case for battery storage systems for the purpose of energy arbitrage shows a lower financial return, although this is improved by utilising batteries within the FCAS market (and wholesale spot pricing). The scenario with larger solar PV, without batteries or controls and using retail energy accounts, was also notable, showing an impressive BCR in its own right for two of the sites.
The results indicate that where batteries are introduced into the mix of technologies, the BCR and NPV are often reduced. Singleton HS, the largest energy consumer, produced the best financial returns of the three schools studied, and the best BCR – when using the CCP7A scenario (solar PV + HVAC controls). Over 15 years, the modelling indicates a saving of $1.19m can be made with a spend of $0.47m at this one school location.
Additionally, HVAC controls have the ability to improve financial returns while simultaneously improving educational outcomes through strategies including the ability to:
- Set, achieve and measure thermal comfort (e.g. ASHRAE 55 );
- use night purge to provide fresh air to students at the start of the day;
- provide thermal comfort models to adjust set points based on external conditions;
- understand the thermal properties of each building and optimise against it (e.g. thermal mass of demountable buildings vs a triple brick building);
- improve ventilation to maintain a good indoor environment quality and reduce the risk of infection;
- making sensor data available to students studying STEM subjects;
- altering HVAC modes automatically based on external conditions & forecast, and favour fan and dry modes where possible; and
- reduce HVAC system downtime through better maintenance methods.
Based on the outcomes of this project, there may be opportunities for further modelling focusing on the HVAC controls opportunity in concert with the demand flexibility market opportunities that are emerging adjacent to the wholesale spot price of electricity (WSP) and the frequency control and ancillary service markets (FCAS). Now that the financial modelling tool has been built, adjusting assumptions or adding scenarios can be done with relative ease.
The project has wider implications for society, namely, energy efficiency, security and reliability. Smart controlled operation of assets can provide rapid response in a dynamic fashion according to the needs of the electricity network and market. This will contribute to improved power quality in the network, mitigate the threat of negative energy pricing and ensure cheaper, more reliable electricity with reduced emissions.