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*Corresponding author’s e-mail: jnsi@foxmail.com
Based on the Carbon Emission Accounting System: Low-Carbon
Electricity Intelligent Management Technology
Jianan Si1*, Hujun Li1, Fangzhao Deng1, Fang Liu2, Zhenli Deng1
1State Grid Henan Economic Research Institute, Zhengzhou, Henan, 450018, China
2State Grid Henan Electric Power Company, Zhengzhou, Henan, 450018, China
Abstract: This paper discusses the possibility of combining low-carbon intelligent power management
technology with carbon emission accounting systems in the context of the pressing global climate change.
The article emphasizes the crucial role of these technologies in promoting sustainable power system
development. It introduces the basic principles of carbon emission accounting and its application in the
electricity industry, highlighting how the integration of intelligent power management with emission
accounting can help reduce carbon emissions. Furthermore, the paper provides insights into algorithm
development and real case studies, demonstrating the effectiveness of these methods. In summary, this
integrated approach supports a cleaner, more sustainable energy future.
1 Introduction
As global climate change increasingly becomes a key
issue, achieving a low-carbon economic transition has
become an urgent need. In this transition process, the
electricity industry, being one of the major sources of
carbon emissions, its intelligent and low-carbon
transformation is particularly important. This paper aims
to explore low-carbon electricity intelligent management
technology based on the carbon emission accounting
system, deeply analyzing its key role in promoting the
development of a sustainable power system.
2 Background
2.1 Definition and Purpose of the Carbon
Emission Accounting System
The carbon emission accounting system is an important
environmental management tool used to systematically
measure and record the greenhouse gas emissions of
enterprises or industries. This system includes the
identification of emission sources, collection and
processing of emission data, and compilation of emission
reports. Its purpose is to provide accurate, transparent
carbon emission data, helping enterprises and
governments better understand their carbon footprint[1].
With these data, relevant agencies can assess and manage
their carbon emissions, thereby formulating effective
emission reduction strategies and measures to address
climate change challenges. This not only promotes
environmental protection but also supports the sustainable
development of enterprises and the formulation of
government environmental policies.
2.2 Carbon Emission Status of the Electricity
Industry
The electricity industry is one of the major sources of
global carbon emissions, particularly due to reliance on
fossil fuels for power generation. The combustion of coal,
natural gas, and oil in the electricity production process
releases a large amount of carbon dioxide, causing
significant environmental impacts. Therefore, reducing
the carbon emissions of the electricity industry is crucial
for addressing climate change. By adopting clean energy,
improving energy efficiency, and implementing carbon
capture technologies, the electricity industry can
significantly reduce its carbon footprint, thereby
contributing to achieving global climate goals.
Strengthening the low-carbon transformation of the
electricity industry is a key step in achieving sustainable
development and environmental protection[2].
2.3 Application of Carbon Emission Accounting
in Power Management
In the electricity industry, the application of carbon
emission accounting mainly involves monitoring,
recording, and reporting the carbon emissions during the
production and distribution of electricity. These data are
used to evaluate and optimize the operational efficiency of
the power system, especially in balancing energy supply
and demand[3]. By analyzing these data, power companies
can identify key areas for reducing carbon emissions, such
as improving the efficiency of traditional power plants,
increasing the proportion of renewable energy use, and
reducing reliance on high-carbon energy sources.
Effectively using carbon emission data not only helps the
E3S Web of Conferences 520, 04024 (2024) https://doi.org/10.1051/e3sconf/202452004024
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© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution
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electricity industry achieve more environmentally friendly
operations but also promotes the entire energy system's
transition to low-carbon.
2.4 Integration of Intelligent Power Management
Technology with Carbon Emission Accounting
Combining intelligent power management technology
with the carbon emission accounting system can greatly
enhance the environmental sustainability of the electricity
industry. Smart grids and advanced data analysis tools
make energy use more efficient while reducing carbon
emissions. For example, smart grids can monitor energy
demand and supply in real-time, optimize the use of
renewable energy, and reduce reliance on fossil fuels.
Additionally, this integration also supports the
development and application of low-carbon technologies,
such as power storage and intelligent dispatching, thereby
improving the energy efficiency of the entire power
system[4]. Overall, this fusion provides a powerful impetus
for the electricity industry to move towards a cleaner, more
sustainable future.
3 Algorithm Development
3.1 Optimization Algorithms
Optimization algorithms for power generation and
distribution play a key role in achieving renewable energy
integration and reducing fuel use and emissions. Linear
programming and mixed-integer linear programming are
used to optimize the allocation of electricity resources,
mathematically represented as minimizing objective
functions while satisfying linear constraints. Genetic
algorithms, an evolutionary algorithm, simulate the
biological evolution process to search for the best
solutions. The application of these algorithms maximizes
the use of renewable energy and reduces the use of fossil
fuels, thereby decreasing carbon emissions[5]. Below is the
general form of linear programming and mixed-integer
linear programming:
(1) Linear Programming: Minimize ,subject
to the constraints,.
Here, c is the coefficient vector of the objective
function, x is the vector of decision variables, A is the
coefficient matrix, and b is the vector on the right side of
the constraints.
(2)Mixed-Integer Linear Programming:
Minimize ，subject to the constraints, ,
where x is either an integer or a non-negative real number.
3.2 Demand Forecasting Algorithms
Demand forecasting algorithms are a crucial part of power
management. Time series analysis, using historical
electricity consumption data for pattern recognition, often
employs the ARIMA (AutoRegressive Integrated Moving
Average) model. Machine learning models such as neural
networks and support vector machines can handle more
complex situations, including nonlinear relationships
among multiple variables. Their application can help
power companies more accurately predict demand,
thereby effectively planning energy supply, reducing
reliance on high-carbon energy sources, and promoting
the integration of renewable energy.
The commonly used ARIMA model formula in power
demand forecasting is as follows:
The ARIMA(p, d, q) model is represented as:
Here, is the power demand at time t, p is the order
of autoregression, d is the order of differencing, q is the
order of moving average, are autoregressive
coefficients, are moving average coefficients, and is
white noise error.
The formulas for neural networks and support vector
machines are more complex and depend on the specific
structure and parameter configuration of the model, and
thus a complete formula cannot be provided here[6]. The
application of these models requires detailed data
preprocessing and model training.
3.3 Carbon Emission Calculation Algorithms
The carbon emission calculation algorithms for the
electricity sector mainly include two methods:
(1) Emission Factor Method: This method calculates
emissions by multiplying the energy produced from each
type of fuel (such as coal, natural gas) by its corresponding
emission factor (like CO2 emissions per unit of energy).
The formula is:
Emissions = Energy Production × Emission Factor
(2) Activity Data Method: This method uses detailed
activity data, such as fuel consumption and operational
parameters, to calculate emissions. The formula varies
depending on the activity data used, making it more
adaptable to different scenarios.
These algorithms play a key role in quantifying and
tracking carbon emissions from various sources in the
electricity sector.
3.4 Machine Learning Algorithms for Data
Analysis
In power management, machine learning algorithms for
data analysis play a crucial role. Clustering algorithms are
used to group similar data points and identify patterns,
such as K-means clustering. Regression analysis is used to
determine the relationships between variables, like linear
regression[7]. The specific formulas are as follows:
(1) K-means Clustering Algorithm
Objective Function: Minimize the total squared
distance
,where k is the number of
clusters, n is the number of data points, is a data point,
andis the center of a cluster.
(2) Linear Regression
Model:
, where y is the dependent
variable, x is the independent variable,
are
regression coefficients, and ϵ is the error term.
These algorithms are used for data mining and analysis,
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E3S Web of Conferences 520, 04024 (2024) https://doi.org/10.1051/e3sconf/202452004024
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helping to identify key patterns and variables, thereby
better managing the power system and reducing emissions.
3.5 Renewable Energy Integration Algorithms
Renewable energy integration algorithms are vital in
power management. Intermittent renewable sources like
wind and solar energy require balancing supply and
demand, and optimizing energy storage and grid stability.
Typical algorithms include:
(1) Battery Storage Optimization
Uses dynamic programming or genetic algorithms to
maximize battery charging and discharging efficiency to
meet demand. Battery Storage Optimization (Dynamic
Programming Method):
Objective Function: Maximize total charging and
discharging efficiency
.
Constraints: Battery capacity constraints, charging rate
constraints, discharging rate constraints, etc.
(2) Grid Dispatch
Uses linear programming or mixed-integer linear
programming to coordinate renewable energy, storage, and
conventional generation to ensure stable grid operation.
Grid Dispatch (Mixed-Integer Linear Programming
Method):
Objective Function: Minimize total cost (including
generation costs and battery operation costs).
Constraints: Power balance, battery state constraints,
generator output range constraints, transmission line
capacity constraints, etc.
(3) Demand Response
Utilizes control algorithms to adjust power demand to
accommodate fluctuations in renewable energy, enhancing
the resilience of the power system. The specific formula
for demand response control algorithms in the power
system often depends on the control strategy adopted[8]. A
common method is based on feedback control,
dynamically adjusting power demand to balance
fluctuations in renewable energy. The formula includes:
where is the adjusted power demand at
time t, is the baseline demand, i s
the power generated by renewable sources, is
the forecasted value of renewable energy, and K is the
control gain.
These algorithms help achieve efficient integration of
renewable energy and ensure the reliability and stability of
the power system. The specific formulas depend on the
algorithms and scenarios and require detailed problem and
data analysis to determine.
4 Case Studies and Data Analysis
4.1 Case Study 1: Application of Intelligent Power
Management in Renewable Energy Integration
4.1.1 Case Description
In this case, a power company adopted intelligent power
management technology, combined with a carbon
emission accounting system, to optimize the integration of
renewable energy. They faced challenges of renewable
energy variability, such as the uncertainty of wind and
solar power. To address this issue, they used data analysis
methods, including time-series analysis and neural
network models, to predict the generation of renewable
energy. These predictive data were used to adjust
electricity demand to accommodate the fluctuations in
renewable energy, achieving a balance of power supply
and demand.
By implementing intelligent power management
technology, the company not only improved the efficiency
of renewable energy integration but also reduced carbon
emissions. This case emphasizes the crucial role of data
analysis and intelligent scheduling in renewable energy
management, laying a solid foundation for reducing
reliance on traditional high-carbon energy sources,
enhancing the resilience of the power system, and
achieving sustainable power production. This
comprehensive approach provides a powerful reference
for the future development of the power industry in the
field of renewable energy.
4.1.2 Data Analysis
Table 1: Comparison Table of Renewable Energy Generation
Data
Time Actual Renewable
Energy Generation (MW)
Predicted Renewable
Energy Generation
(MW)
08:00 AM 100 98
09:00 AM 120 115
10:00 AM 95 100
As show in table 1. Through data analysis, the power
company achieved better integration of renewable energy.
They were able to adjust power demand based on forecast
data to better match the supply of renewable energy. This
not only reduced carbon emissions but also improved the
reliability and efficiency of the power system, paving the
way for the development of a sustainable energy future.
This case highlights the importance and potential of
intelligent power management technology in the
renewable energy sector.
4.2 Case Study 2: Application of Battery Storage
Optimization in Microgrids
4.2.1 Case Description
In this case, the microgrid utilized battery storage
optimization algorithms aimed at efficient integration of
renewable energy. The microgrid faced challenges of
renewable energy variability, such as the instability of
wind and solar power capacities. To address this issue,
they introduced a battery storage system and optimized the
charging and discharging of the batteries using a mixed-
integer linear programming approach. Through this
method, they were able to store excess electricity in the
batteries when renewable energy was abundant, and
release energy from the batteries during shortages to meet
power demands. The following table shows the generation
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of renewable energy, battery charging, and discharging
over different time periods, to better illustrate the effects
of battery storage optimization. Through this approach, the
microgrid maximized the use of renewable energy,
reduced electricity costs, and improved grid stability,
providing strong support for the development of
sustainable energy. This case highlights the key role of
battery storage technology in microgrids, supporting
efficient integration of renewable energy.
4.2.2 Data Analysis
Table 2: Comparison Table of Battery Storage and Renewable
Energy Data
Time Renewable Energy
Generation (MW)
Battery
Charging
(MW)
Battery
Discharging
(MW)
08:00 AM 50 10 15
09:00 AM 60 5 20
10:00 AM 55 12 18
As show in table 2. We can see that through the application
of battery storage optimization algorithms, microgrids
have achieved the maximization of renewable energy
integration. Specifically, when renewable energy
production is high, the batteries are charged to store excess
electricity; conversely, when energy production is
insufficient, the batteries release stored energy to meet
power demands. This process occurs at different times and
adjusts dynamically according to real-time conditions. The
data in the tables show the charging and discharging status
of the batteries, as well as the generation of renewable
energy. Through battery storage optimization, microgrids
have achieved efficient power supply, enhanced the
efficiency of the power system, reduced energy waste,
lowered electricity costs, and ensured the stability of
power supply, providing strong support for the sustainable
integration of renewable energy.
5 Conclusion
In this paper, we explored low-carbon electricity
intelligent management technology based on the carbon
emission accounting system. Through case studies, we
highlighted the potential application of the integration of
intelligent power management technology and carbon
emission accounting in the electricity industry, as well as
the importance of battery storage in microgrids. We
introduced a range of algorithms, including optimization
algorithms, demand forecasting algorithms, carbon
emission calculation algorithms, and data analysis
machine learning algorithms, which provide key support
for achieving low-carbon power management.
The low-carbon electricity intelligent management
technology based on the carbon emission accounting
system holds great potential in achieving sustainable
development in the electricity sector and reducing carbon
emissions. The application of these technologies and
algorithms can enhance the efficiency of the power system,
support the integration of renewable energy, reduce
electricity costs, improve grid stability, thereby making a
significant contribution to reducing the carbon footprint
and achieving global climate goals. In the future, with the
continuous development and refinement of these
technologies, we look forward to moving towards a
cleaner, more sustainable power future.
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E3S Web of Conferences 520, 04024 (2024) https://doi.org/10.1051/e3sconf/202452004024
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