Introduction

Marginal emissions refer to the amount of greenhouse gases or other pollutants emitted per unit of energy generated or consumed by the last power plant brought online or taken offline to meet fluctuating demand. It represents the emissions associated with the incremental change in energy production or consumption. In simpler terms, it's the emissions produced by the last source of power that is used to meet changes in demand on the energy grid.
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Let's imagine a simplified energy grid with three types of power plants: coal-fired, natural gas-fired, and renewable (wind and solar). This grid operates in a wholesale market where energy is bought and sold based on demand and supply conditions.

1. Coal-fired power plant: This plant is the cheapest to operate but also emits the highest amount of greenhouse gases per unit of energy generated.
2. Natural gas-fired power plant: This plant is slightly more expensive to operate than the coal-fired plant but emits fewer greenhouse gases.
3. Renewable energy sources (Wind and Solar): These sources are intermittent and depend on weather conditions. They have zero marginal emissions as they do not produce greenhouse gases during energy generation.

Now, let's consider a scenario where energy demand increases suddenly due to a heatwave, and the grid operator needs to meet this increased demand by bringing additional power plants online.
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Initially, the grid is meeting demand primarily with the coal-fired power plant, as it is the cheapest option. However, as demand increases beyond the capacity of the coal plant, the grid operator needs to bring additional sources of power online.
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At this point, the grid operator decides to bring a natural gas-fired power plant online to meet the increased demand. The natural gas plant becomes the marginal generator, meaning it is the last source of power brought online to meet the incremental increase in demand. Since the natural gas-fired power plant emits fewer greenhouse gases per unit of energy generated compared to the coal-fired plant, the marginal emissions associated with meeting the increased demand are lower than if the additional demand had been met solely by the coal plant.

Now, let's say the demand continues to increase, and the grid operator needs even more energy. In this case, they decide to utilise renewable energy sources like wind and solar to meet the remaining demand. Since these sources have zero marginal emissions, any additional energy generated from renewables further reduces the overall marginal emissions associated with meeting the increased demand.

In summary, marginal emissions in this scenario would be determined by the emissions produced by the natural gas-fired power plant, as it is the last source of power brought online to meet the incremental increase in demand. The use of renewable energy sources further reduces marginal emissions, highlighting the importance of integrating clean energy sources into the grid to mitigate environmental impact.

Standard practice

Today, standard practice for measuring marginal emissions involves sophisticated modelling techniques that consider various factors such as:

1. Dispatch order: The sequence in which different power plants are brought online or taken offline to meet fluctuating energy demand.
2. Emission factors: Accurate emission factors are essential for each energy source, reflecting the amount of greenhouse gases emitted per unit of energy generated.
3. Grid integration of renewables: Incorporating the intermittent nature of renewable energy sources and their impact on overall grid emissions.

Accuracy and data requirements

The accuracy of marginal emissions calculations hinges on the availability of reliable data. Key data requirements include:

1. Real-time generation data: Continuous monitoring of power generation from various sources to determine the current mix of energy production.
2. Emission factor data: Accurate and up-to-date emission factors for different energy sources based on factors like fuel quality, combustion efficiency, and pollution control technologies.
3. Weather data for renewables: Detailed weather forecasts to predict the output of renewable energy sources such as solar and wind power.

Reliability of data sources

Data sources for marginal emissions calculations vary in reliability:

1. Government databases: National and international agencies provide comprehensive datasets on energy generation and emission factors.
2. Utility records: Power utilities maintain records of their own generation and emission data, which can offer valuable insights at the regional level.
3. Independent research: Academic institutions and research organisations conduct studies to refine emission factors and improve the accuracy of calculations.

Challenges in calculating marginal emissions

Electricity grids are highly dynamic, with constantly changing patterns of supply and demand. Determining which power plants are brought online or taken offline to meet fluctuations in demand requires real-time monitoring and sophisticated modelling techniques.

Modern energy grids often rely on a diverse mix of energy sources, including coal, natural gas, renewables, and sometimes nuclear power. Each type of power plant has its own emission intensity and operational characteristics, making it challenging to accurately assess the emissions associated with incremental changes in energy production.

Renewable energy sources such as wind and solar are intermittent and depend on weather conditions. Incorporating these sources into marginal emissions calculations requires forecasting their output and understanding how they interact with other sources on the grid.

Emission factors, which represent the amount of greenhouse gases emitted per unit of energy generated, can vary based on factors such as fuel quality, combustion efficiency, and pollution control technologies. Obtaining accurate and up-to-date emission factors for different energy sources is essential for precise marginal emissions calculations.

Marginal emissions calculations rely on comprehensive and reliable data on power generation, emissions, and grid operations. Obtaining such data can be challenging, especially in regions with limited monitoring infrastructure or incomplete reporting mechanisms.

Marginal emissions calculations often involve complex modelling techniques that incorporate numerous variables and assumptions. Uncertainties in these models can arise from factors such as weather forecasts, fuel prices, and policy changes, further complicating the accuracy of the calculations.

While factors such as the dynamic nature of energy grids, the complexity of power generation mix and the intermittent nature of renewable energy sources pose challenges in calculating marginal emissions, they are generally manageable with the advancement of technology. However, the key driving factors contributing to the unreliability of marginal emissions calculations are the availability and quality of data and the uncertainties inherent in modelling technique.

Average Emissions

Average emissions, in the context of energy generation, refer to the total amount of greenhouse gases or other pollutants emitted over a specific period (such as an hour, day, week, month or year) divided by the total energy generated during that period. Unlike marginal emissions, which focus on the emissions associated with the last unit of energy generated or consumed, average emissions provide a broader measure of the environmental impact of energy generation over time.

To calculate average emissions, the following steps are typically involved:

1. Collect emission data: Gather data on the total amount of greenhouse gases or other pollutants emitted by each power plant over the specified period. This data can include direct measurements, emissions reports from power plant operators, or estimates based on emission factors.
2. Determine energy generation: Determine the total amount of energy generated by each power plant over the specified period. This data can be obtained from utility records, grid operators, or national energy agencies.
3. Calculate average emissions: Divide the total emissions by the total energy generated to calculate the average emissions per unit of energy generated. This can be expressed in units such as grams of CO2 per kilowatt-hour (g CO2/kWh) or kilograms of CO2 per megawatt-hour (kg CO2/MWh).
4. Aggregate data: Aggregate the average emissions data from all power plants within a region or country to calculate the overall average emissions for that area.

For example, let's say we want to calculate the average emissions for a country over the course of a year. We would gather data on the total emissions (in kilograms of CO2) from each power plant in the country and the total energy generated (in kilowatt-hours) by each plant. We would then divide the total emissions by the total energy generated to obtain the average emissions per kilowatt-hour of energy generated for the entire country.

This average emissions value provides insight into the overall environmental impact of energy generation within the given timeframe and can be used to track progress towards emissions reduction goals, evaluate the effectiveness of environmental policies, and compare the environmental performance of different regions or power generation technologies.

Powerledger’s take on Scope 2 emissions

Powerledger's approach takes the concept of average emissions a step further by incorporating real-time matching of energy generation to consumption at the same time. By doing so, Powerledger can extrapolate the proportionate amount of Scope 2 emissions for individual consumers or entities. This method provides a more precise and granular understanding of the environmental impact associated with energy consumption, allowing consumers to make more informed decisions about their energy usage and carbon footprint.

Calculating emissions by matching generation to consumption in real-time offers several advantages:

1. Precision, Granularity and Transparency: Powerledger's method allows for a granular breakdown of emissions at the individual consumer or entity level. Rather than relying on average emissions data for an entire region or grid, consumers can access personalised emission estimates based on their specific energy usage patterns and the emissions intensity of the energy consumed during those times. This is all done in real-time creating absolute transparency of emissions data.
2. Accountability: By providing consumers with personalised emission estimates, Powerledger fosters accountability for energy consumption and its associated environmental consequences. Consumers are encouraged to take responsibility for their energy usage and actively seek ways to minimise their emissions, whether through energy efficiency measures, renewable energy adoption, or demand response strategies.
3. Decision support: The granular emission data provided by Powerledger can serve as valuable decision support information for consumers, businesses, and policymakers. Armed with insights into the emissions intensity of their energy consumption, stakeholders can develop more effective sustainability strategies, evaluate the environmental impact of different energy sources or consumption patterns, and prioritise investments in clean energy technologies.

All of the aforementioned functionalities are encapsulated within a cutting-edge software application and made accessible through a sleek and user-friendly interface crafted by Powerledger, known as Vision.

Conclusion

Powerledger's mission is to provide an end to end solution that not only allows their consumers to visualise and track their emissions but also achieve 24/7 carbon-free energy. We facilitate the trading of renewable energy certificates (RECs) between producers and consumers. By enabling consumers to purchase RECs associated with renewable energy generation, we ensure that the energy they consume is sourced from renewable sources such as solar, wind, or hydroelectric power.

Furthermore, through our peer-to-peer energy trading platform, consumers can buy and sell excess renewable energy directly with other participants in the market. This enables consumers to access renewable energy from nearby sources, reducing reliance on fossil fuel-based energy from the grid. Powerledger's platform incentivises demand response and energy efficiency measures by providing real-time data on energy consumption and pricing. Consumers can adjust their energy usage patterns to align with periods of high renewable energy generation, reducing the need for backup fossil fuel generation and lowering overall carbon emissions.

As part of our ongoing efforts, we are exploring the integration of both marginal emissions and average emissions into our product. While this integration holds promise for providing a more comprehensive understanding of the environmental impact of energy consumption, it currently faces challenges related to data acquisition and modelling complexities. However, with advancements in technology and data infrastructures, these challenges are likely to diminish in the future. 

As such, the consideration of marginal emissions alongside average emissions in calculating total emissions for a given period holds potential for enhancing the accuracy and granularity of environmental impact assessments. By continuing to innovate and collaborate with stakeholders, Powerledger aims to play a pivotal role in accelerating the transition to a sustainable energy future.

Learn how our solutions can help you stay at the forefront of energy innovation. Book a demo.

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