Sustainability Measures for Energy Systems

Introduction

Energy systems are responsible for tremendous increases in standards of living (on average) for much of the world. However, it is clear that current usage and generation technology mix is not sustainable. This is especially true if you consider increasing the standard of living in other parts of the world to levels enjoyed in countries like Australia.

We need more energy and lower carbon emissions. To achieve this we need to understand, define, and quantify, the “whole of system” impacts on sustainability. There are many elements to sustainability and many elements to a “whole of system” analysis. A comprehensive sustainability analysis of something as complex as an energy system requires the involvement of many disciplines: engineers, environmental scientist, economists, sociologists, etc.

In this assignment, you will present a short report in a clear and engaging manner the carbon intensity (as one aspect of sustainability) of different large-scale (100 MW+) stationary electricity generation options:

1. Wind turbines using gas-fired open cycle turbine for peaking, and
2. Wind turbines using battery storage for peaking.

To do this, you will investigate for both (or one if working by yourself):

• the energy flows and losses to deliver electricity to customers (homes and businesses).
• the life cycle carbon intensity of the options, including what is required for feeds and construction.

You will then compare the overall carbon intensity with the carbon intensity of using a new combined cycle gas turbine power stations. This has a carbon intensity of approximately 550 kgCO2e/MWh (delivered to customers).

Pair or Individual

Complete the assignment as a self-formed pair or as an individual:

• If completing as a pair, you analyse both options. You will both receive the same mark. There is no peer assessment.
• If you complete as an individual, you need to look at one of the options above but will still need to complete all sections of the report.

Options

The two options will be part of Victoria’s total energy system. This is in turn part of the National Energy Market (NEM). This is a bit of a misnomer since the NEM only connects the east coast states and South Australia but the point is that these two technology options form part of a complex energy system. A simplified diagram of a complex energy system is in Figure 1 below. The blue boundaries in Figure 1 cover what is in scope for this case study.

For both options, wind turbines provide primary generation. What is different between the two proposals is how “low wind” time is managed by the system. As an initial estimate, we will assume that 75 % of the energy that reaches customers comes directly from wind turbines and 25% during low wind periods is supplied via one of the options below. Assume that the number of wind turbines per MWh is the same for each option.

Option 1: Wind turbines using gas-fired open cycle turbine for peaking

In this option, power required during low wind periods is supplied using open cycle gas turbines as “peaking plants”. This type of turbine can be started and shutdown quickly which means they can supplement the power provided by wind turbines. Combined cycle plants are more efficient but cannot start up and shutdown quickly.

Option 2: Wind turbines using battery storage for peaking

In this option, we will use utility scale battery storage. Some additional wind turbine capacity is required to be able to supply an excess of electricity to the battery system.

Traditional linear energy system versus modern energy system with a greater range of generators and storage. From: https://energy.nsw.gov.au/renewables/renewable-generation/renewable-energy-nsw

Report Sections

The sections of the summary are:

• Title page (1 page, not included in page or word count)
• Introduction
• Energy flows
• Carbon intensity
• Other impacts
• References (not included in page or word count)
• Appendix A: Energy flow assumptions and data (up to 2 pages, not in page / word count)
• Appendix B: Carbon intensity assumptions and data (up to 2 pages, not in page / word count)

Title page

The title page must include: Title, date, student details (both for pairs), and a related image to give some visual impact (this must be referenced).

Introduction

A short introduction on why this type of analysis is important, what you did, and the key conclusions. This should not be more than 200 words. Yes, that is brief!

Energy Flows

A diagrammatic representation and description of the major energy flows. The purpose is to clearly show the amount, and types, of energy transfers required to deliver 1 MWh of electricity to customers (households / businesses). The diagram is the most important part of representing this clearly.

You should end up with a number for the primary energy required for the different systems (i.e. amount of fuel, wind load, etc.). The types of energy flows to consider are:

• Mass flow
  • Heat
  • Shaft work
  • Electrical work (may need to be differentiated into DC and AC, high and low voltage) The sub-systems to consider as part of the analysis are:
• Generation of primary work (e.g. shaft work) from primary energy input (e.g. wind, combustion etc.).
• Consumption of primary work: shaft or electrical work depending on the type of system.
• Conversion of primary work to/from electrical work to/from high/low voltage (if applicable).
• High voltage transmission (note, we give you some numbers for this).
• Grid distribution (note, we give you some numbers for this).

The energy flows should be determined from typical efficiencies (e.g. conversion of shaft work to electrical work) and energy densities (e.g. energy intensity of wind). Tabulate the numbers in an appendix and reference from reputable sources. Often you will see several values for the same thing. Pick what seems most feasible for a system built in 5 years’ time and include an indication of range in your diagrams.

You can present this analysis with a flow diagram(s) and/or a plot(s). It is up to how you feel it is best to convey the data. Some things to note:

• Keep systems at a useful level so that flows and losses are clear without overwhelming detail. This clarity is very important in the body of your report. Put explanatory notes / diagrams in the appendix to maintain clarity.
• Draw any diagram by hand first to see what looks good in terms of systems and flows. This is the fastest way to prototype you diagrams. Once you have a good idea of what you want to do, then start in Word/Power Point/ etc.

Carbon Intensity

A description and diagrammatic representation of the average lifetime carbon intensity of the two options. The carbon intensity should be presented on the basis of kg CO2e per MWh (to the customer). The carbon intensity should be broken down into following (not all are applicable to, or significant for, each option):

• Direct CO2e emissions from primary energy generation (e.g. burning fuel).
  • Embedded CO2e emissions from any fuel used (e.g. how much CO2e is generated in producing the fuel used).
  • Embedded CO2e emissions from the manufactured assets. This needs to be broken down into:
    • Generation facility(ies).
    • Transmission and grid (we give you this).
  • Anything else from operations that generates significant CO2e
  • Note: Not all of these categories are important for each option.

The purpose is to try and make a comparison of the CO2e intensity of the two options. The CO2e should be determined from typical CO2e for direct emissions (e.g. from combustion) and facilities (e.g. per MWh for turbines). Tabulate supporting numbers an appendix and reference from reputable sources. Often you will see several values for the same thing. Select what seems most feasible for a system built in 5 years’ time but give some sense of the spread of reputable data.

You can present this analysis as one or several flow diagrams and/or with plots. It is up to how you feel it is best to convey the data. Some things to note:

• The assets have an operating life of 20 years. This typical operating life matches well with most life cycle studies.
• The embedded emissions associated with utility scale battery storage is likely to be very uncertain.

Other Impacts

In this section you have are expected to identify two significant sustainability impact associated with that energy option outside of carbon intensity. This does not have to be quantified, just clearly described and supported by a reputable reference(s). It may not be possible to identify the most significant impacts but it should be among the top few impacts for that technology and/or its supply chain. These may be impacts on ecosystems, water systems, communities, etc.

Supplied information Transmission network

When electricity leaves your wind turbine site, it goes into the “transmission network” and “the grid”. The transmission network is the high voltage part of the network that delivers power over large distances. To get to consumers, electricity goes through the grid (unless you are a very large user). In this analysis, do not differentiate between the two. Just refer to the two in combination as the “transmission network”. This is what will carry electricity from one place to another. You do not need to source data about the transmission network, you can use the numbers below:

• The efficiency of the transmission network from any electricity production to any electricity use (including storage) is 90%.
• The emissions associated with the transmission network (embedded and indirect from maintenance and operations) is 2 kgCO2e/MWh. This is per MWh delivered to an energy use (including storage).
• The numbers are above do not require separate references, you can cite this brief.

Report Format

You are producing a short and informative report of the similarities and differences between the carbon intensity of the two energy systems. The aim is that a fellow engineer can read your short report in 5 minutes, understand the key differences, and believe that what you have presented is a reasonable and reputable estimate.

You report should be:

• 4 pages or less (excluding title page, references and appendices), and
• less than 1200 words (excluding title page, references and appendices)

To achieve this, carefully select the figures and tables you use and your writing will need to be very concise and clear.

You will submit your summary as a PDF in Turnitin (for plagiarism detection) and as a Blackboard Assignment (for marking). All work should be possible to do in the Microsoft Office suite of products or the equivalent on macOS, Linux etc. Recommendations on software are below but you can use other equivalent software:

• Report: Word
• Plots: Excel
• Flow Diagrams: Word or Power Point or Visio (can paste diagrams from other software into Word using screen shots)

Other details:

• For body text, use 12 pt Calibri or Times New Roman font (or similar like Arial, Helvetica, etc),

1.1 paragraph spacing, and justified format.

• For tables and captions for tables and figures, use a 2 pt smaller font (e.g. 10 pt Calibri).
• At least 2 cm margins for pages (top, bottom, and sides).
• Headers and footers on each page after the title page.
• Referencing style: In text citation using Chicago Style with Author-Date in text and an alphabetical listing in your reference list. A good guide is available here. Similar variants like APA or UQ Havard are fine but must use Author-Date in text and an alphabetical listing in your reference list and be consistent for all references.

References

To support your data, you will need references to reputable sources of data and information. You will likely need somewhere between 6 and 12 references. Reputable sources tend to come from:

1. Books. These should be from established publishers (e.g. Wiley) that require a review process and whose authors have established reputations in the field. A reputation in the field can be demonstrated by: many papers and citations on a topic area or an esteemed position at a research institute or (non-partisan) policy centre. Books usually aggregate the best available data at the time. The downside of a book is that some information can become outdated.
2. Peer reviewed articles (especially review articles). A review article pulls together data from lots of existing peer-reviewed articles. This allows the author to make general conclusions from the existing articles studies and/or highlight where disagreement still exists in the literature. The advantage is that “averaged” outcomes are determined so the information is less likely to be impacted by the nuances of an individual paper. This is helpful for something like embedded CO2e as the estimates can vary widely depending on what has been assumed.
3. Reportsanddatafrom(non-partisan)publicorganisations. Such public organisations come in a few different flavours and are a valuable source of information.
   1. International multi-nation agencies. Important examples in the energy space include the Intergovernmental Panel on Climate Change (IPCC) and the International Energy Agency (IEA).
   1. Government agencies. These are public agencies funded by the government to provide some sort of public outcome. This can range from the Bureau of Meteorology in Australia to NASA in the United States. They have deep expertise in a particular area and make much of their data publicly available for others to use. Important agencies in the energy space in Australia include: the Australian Energy Market Operator (AEMO), The Clean Energy Regulator (cleanenergyregulator.gov.au), Department of Energy and the Environment (energy.gov.au), Australian Renewable Energy Agency (ARENA), CSIRO, etc.
4. Reports and data from public, or publicly listed, companies. Energy companies are typically owned by the government (public company) or listed on a stock exchange somewhere (publicly listed). In both cases, they will have minimum reporting requirements to keep the government or investors informed. Companies like: Ergon, Hydro Tasmania, Stanwell, AGL, etc.

Content providers (e.g. media outlets) and aggregators. Sites like Wikipedia are great for getting an overview of information and is often accurate in the information it presents. It is a convenient first place to look on many topics. However, you must refer to the information source that those sites reference. If they do not have a reference, the information they present is not reputable.

Library guide on evaluating information is available here.

Appendices A & B

The appendices clearly present the:

• Data and assumptions used in the analysis and calculations.
• Uncertainty with the data (if applicable).
• References for the data and assumptions (if needed). The references are in your reference section and cited here.
• Anything else that needs to be shown / explained so your analysis can be understood (if applicable).

We do not require that you present your calculations, just the assumptions and data that you used.

Assumptions

List of assumptions that were required in your analysis. You may need to be specific about which parts of an analysis the assumptions apply to.

Example of assumptions (you can use these if you feel they are appropriate):

1. Scope 3 emissions of business operations not considered in emissions as these are likely very small contributions compared with other emissions.
2. Efficiencies based on current expected values and do not account for the use of old infrastructure or future improvements in technology.

Data

Table 1. Example table for data used in summary.

Data	Uncertainty	Reference(s)
Density of water at 25 oC = 997 kg/m3	Established fact.	Cengel et al. (2018)
Life cycle emissions intensity of beef = 100 kg of CO2e per kg of beef. Range from references = 25 – 300 kg CO2e/kg.	Very High. Depends upon the LCA method used and the context of the evaluation (e.g. pasture vs high intensity farming). Higher estimates expand the boundaries to account for land clearing for animals and crops required to feed animals.	Eady et al. (2011), 25.2 kg CO2e/kg Koneswaran and Nierenberg (2008), 36 kg CO2e/kg Food and Agriculture Organization of United Nations (n.d.), 295 kg CO2e/kg

Additional plots or steps in analysis

Include additional plots or steps in analysis as required so other people can understand what you did and can verify where key numbers have come from. Even in an appendix, you must be selective. It is not a dumping ground for all possible plots and analysis steps. You may not require anything in this sub-heading.

Uncertainty

Label your uncertainty for you data based on the following legend:

• Established fact: Measured accurately and verified across several sources. For example, the density of water at 25 ^oC or atmospheric pressure at sea level or the distance between cities. No commentary required.
• Low (+/- 5%): Known with a high degree of certainty. Consistently evaluated within a small range across multiple independent sources and/or independent methods. For example, Brisbane’s average temperature in December. Limited or no commentary required.
• Moderate (+/- 20%): Known with a reasonable degree of certainty. Consistently evaluated within a moderate range across multiple independent sources and/or independent methods. The variation can be due to measurement difficulty or method variation. For example, Australia’s population in 10 years’ time. Different methods of estimation and different scenarios give a range of possible future populations but this range is not massive. Limited commentary required.
• High (+/- 100 %): As above, but just more uncertain. You are confident of the order of magnitude (1, 10, 100, etc) but you are not too confident beyond that. For example, the price of a commodity (e.g. iron ore) in 5 years’ time. This is something that is inherently uncertain due to a wide range of unpredictable factors.
• Very high (+/- 1000 % or more): This is something that you do not know within an order of magnitude. It could be 10 times (or more) bigger or smaller depending on the source you use. These are things that are inherently difficult to measure (e.g. the population of a rare and hard to find bird) or have many varied methodologies (e.g. life cycle assessment of beef CO2e emissions) or are a prediction of something uncertain. However, you can use even highly uncertain data to help draw conclusions.