Assessing Carbon Emission Reduction Potential: A Case Study of Low Carbon Demand Response Technology in Fangshan District, Beijing
The aforementioned studies have explored the application of demand-side resources in reducing electrical carbon emissions, offering valuable insights. However, they are confined to demand-side interactions within the framework of an “electricity perspective”. Their primary approach involves influencing user electricity consumption behavior through signals from electricity and carbon market prices, thereby conducting demand response. This approach fundamentally considers the reduction of electrical carbon emissions from a global perspective within the power system.
Nevertheless, the primary responsibility for carbon emissions in the power industry lies with electricity users. The aforementioned efforts have yet to account for the time-varying characteristics of electricity carbon emission factors and their influence on the electricity consumption behavior of users. Electricity consumption-based emission factors serve as critical indicators for transferring carbon emission responsibilities from the source side to the load side. Users primarily rely on these factors to understand the amount of carbon emissions produced from their own electricity consumption.
On the one hand, with the successive introduction of Carbon Border Adjustment Mechanism (CBAM) implementation plans by international organizations such as the European Union and the United Kingdom, indirect carbon emissions resulting from electricity consumption will be incorporated into the overall carbon emissions of products, subjecting them to corresponding carbon tariffs. This will lead to a decline in the product competitiveness of high-carbon-emitting electricity users, impacting their profitability. On the other hand, the Chinese government has set the goals of achieving a carbon peak by 2030 and carbon neutrality by 2060. Currently, local governments in China are actively promoting the transition from a dual-control policy on energy consumption (a policy restricting both total energy consumption and intensity) to a dual-control policy on carbon emissions (a policy limiting both the total and intensity of carbon emissions). Guided by the aforementioned policies, electricity users with lower total carbon emissions per unit of output will have better development prospects under equivalent production conditions.
Hence, there is a necessity to conduct research on the low-carbon demand response from the perspective of dynamic carbon emission factors. This will facilitate a more effective transmission of carbon emission responsibilities from the power system to the user side.
Adopting a “carbon perspective”, akin to the conventional “electricity perspective”, this study calculates the electricity consumption-based carbon emissions for various user behaviors. It guides users to modify their consumption patterns, thereby enabling carbon emission management on the demand side of the power system. This approach is termed “low-carbon demand response”. Within the low-carbon demand response, discrepancies in electricity consumption-based carbon emissions arising from different behaviors are primarily characterized by distinct consumption-based carbon emission factors. These factors serve to reflect the direct carbon emissions associated with the unit of electricity consumed by the user. This metric serves as a bridge connecting the direct carbon emissions resulting from source-side fuel consumption with the indirect carbon emissions generated from load-side electricity consumption.
In response to the aforementioned issues, this study focuses on the low-carbon demand response approach in the power system, utilizing dynamically changing carbon emission factors. Additionally, it evaluates the potential for carbon reduction for electricity users after implementing this method. Through the established assessment model, empirical research is conducted on the expected carbon emission reductions for 108 enterprises across 6 industries in Fangshan District, Beijing. Furthermore, the study analyzes the influencing factors of a low-carbon demand response in different industries from the “carbon perspective”, providing a basis for the transmission of carbon emission rights and responsibilities in the power system as well as for evaluating its effectiveness.
Addressing the aforementioned issues, this study innovatively proposes a decarbonization approach for electricity users based on dynamic carbon emission factors, introducing a method to assess the carbon reduction potential of electricity users. Furthermore, empirical research on the anticipated carbon emission reduction effects for 108 enterprises across 6 industries in Fangshan District, Beijing, has been conducted using the established assessment model. This marks the first instance of such theoretical assessment work being carried out at a regional level. Lastly, the study analyzes the influencing factors of a low-carbon demand response in different industries from a “carbon perspective”, providing a foundation for the transmission of carbon emission rights and responsibilities in the power system as well as for evaluating their effectiveness.
In the Methodology section, this paper outlines the calculation methodology for dynamic carbon emission factors. Building upon this foundation, it introduces real-time procedures and effectiveness assessment methods for a low-carbon demand response. Furthermore, empirical analysis was conducted on the power grid in Fangshan District, Beijing, China, evaluating the carbon emission reduction potential across various industries. The findings validate the effectiveness of the proposed methodology in this study.
2.1. Metrics for Carbon Emission Flow
Carbon flux: Carbon flux is the fundamental physical quantity used to describe the magnitude of carbon flow within a system, denoted by the symbol F. The unit of carbon flux is the same as that of carbon emissions, typically expressed as metric tons of CO2 (tCO2) or kilograms of CO2 (kgCO2). Numerically, carbon flux is equivalent to the carbon emissions generated in the generation phase for sustaining a particular generator unit or branch flow that supplies load or network losses to users within a specified time period.
Branch/Node carbon flow rate (BCFR/NCFR): Carbon flow rate is defined as the accumulation of carbon flux over a unit of time, denoted by the symbol R. The formula for defining branch/node carbon flow rate is
The unit for branch/node carbon flow rate is typically tCO2/h or kgCO2/s. Clearly, branch carbon flux is the integral value of branch carbon flow rate over a given period of time.
Branch carbon flux density (BCFD): Branch carbon flux density is defined as the ratio of the carbon flow rate (R) to the active power flow (P) on any given branch in the power system. It is denoted by the symbol ρ, and its formula is
The unit of branch carbon flux density is the same as that of emission intensity on the generation side, typically expressed as kgCO2/kWh. In the outgoing lines of power plants, branch carbon flux density is equal to the carbon emission intensity of the generator unit. However, in the lines entering the load terminals, it represents the carbon emissions on the generation side caused by the consumption of one unit of electricity transmitted through the branch.
Node Carbon Potential (NCP): Branch carbon flux density is used to describe the relationship between current flow and carbon flow on branches in the power system. In regard to the relationship between carbon emission flow and active power flow at nodes in the system, it will be described using the concept of node carbon potential. Denoted by the symbol , the formula for the carbon potential of node n is as follows
In the equation, represents the set of all branches connected to node n where current is injected into node n; denotes the active power of branch i. Node carbon potential shares the same dimensional unit as branch carbon flux density, which is typically expressed as kgCO2/kWh.
Network Loss Carbon Flow Rate (NLCFR): Corresponding to active power losses within the system, network loss carbon flow rate in the power system can be categorized into two types: losses on the lines and losses at nodes (such as losses from transformers and other equipment). Regarding line losses, there are
In the equation, represents the active power loss of the branch; represents the carbon flow rate corresponding to the active power loss; is the carbon flux density of the branch.
2.2. Calculation Method for Low-Carbon Demand Response
Dynamic carbon emission factors for users are calculated based on the composition of electricity sources consumed by users during different time periods and the corresponding carbon emission information associated with each source.
The baseline load profile represents the original load profile of a user before implementing a low-carbon demand response. By utilizing the user’s baseline load profile and the dynamic carbon emission factors corresponding to the given day, the user’s original indirect carbon emissions from electricity consumption prior to low-carbon response can be calculated.
Upon recognizing the variability in carbon emission factors for electricity consumption during different time periods, users adjust their future electricity consumption behavior based on their own carbon reduction requirements, in conjunction with their past carbon reduction achievements. By autonomously planning for carbon reduction in the future, users achieve a reduction in carbon emissions from electricity consumption while maintaining a nearly constant daily electricity consumption level.
Based on the actual dynamic carbon emission factors and the actual load profile for the given day, the actual indirect carbon emissions from electricity consumption by the user can be determined. By comparing the actual indirect carbon emissions from electricity consumption with the original values, the reduction in carbon emissions resulting from the low-carbon demand response can be assessed.
In the equation, represents the carbon emission factor for regional grid in time period ; denotes the set of nodes covered by regional grid ; signifies the load at node in time period ; and represents the magnitude of the carbon potential at node .
It is worth noting that, while theoretically, adjusting electricity prices can also influence user behavior and facilitate decarbonization, the low-carbon demand response mechanism proposed in this paper does not transmit guiding signals to users in the form of electricity prices. Instead, it opts to convey this information to users through dynamic carbon emission factors.
2.3. Benefit Assessment Model of Low-Carbon Demand Response
The low-carbon demand response mechanism will have an impact on both users and the entire power system. For users, it allows for a reduction in electricity consumption and carbon emissions by altering their electricity consumption behavior, potentially leading to tangible benefits within the carbon market environment. For grid operators, the adjustment of user electricity consumption behavior also promotes the integration of clean energy sources into the system, thereby lowering the overall carbon emissions level of the entire power system. This paper will evaluate the decarbonization potential of the low-carbon demand response from both the system and user perspectives. The evaluation steps are as follows:
Step 1: Utilizing historical operational data of the power system, simulate the year-round operation scenario to obtain hourly-level data on power flow along transmission lines and generator outputs for the entire year.
Step 2: Obtain the dynamic carbon emission factors for users. Based on the simulation results provided in Step 1 and in conjunction with Equation (6), the hourly dynamic carbon emission factors for the corresponding time periods of the power system are derived.
In the equations, represents the daily carbon emission reduction for the electricity user; TD denotes the total number of time periods in a single day; represents the unit time interval (1 h); and represent the load increase and load decrease in time period for the electricity user participating in low-carbon response; represents the upper limits of load adjustment available to the electricity user in each time period; ₜ and ₜ are binary variables indicating whether the user is in a load-increasing or load-reducing state (0 or 1); represents the maximum daily electricity consumption variation value for the electricity user (accounting for the incremental energy consumption due to the impact of energy storage efficiency); represents the baseline load in time period .
In the equations, and represent the annual decarbonization quantity and the corresponding annual decarbonization revenue for the power company, respectively; pCO2 denotes the carbon price; and TY represents the total number of time periods in a year.
At the system level, the decarbonization benefit assessment model proposed in this paper can be employed to analyze the maximum annual decarbonization achievable by implementing a low-carbon demand response under a given level of load flexibility.
At the user level, the model presented in this paper can be utilized to analyze the maximum annual decarbonization and corresponding benefits achieved by users through adjustments in their electricity consumption patterns, using specified adjustment methods such as energy storage and load flexibility.
From the case analysis, it can be observed that, as a receiving-end grid, the carbon emission factor of the Fangshan grid significantly decreases when the local new energy output increases. Meanwhile, the impact of carbon flow rates from external power sources on the carbon emission intensity of the Fangshan grid is substantial. To reduce carbon emissions in the Fangshan area, the proportion of renewable energy in the power transmission lines needs to be further increased.
From the perspective of users, under the condition of constant total power demand, implementing a low-carbon demand response and redistributing loads to increase operational loads when the regional carbon emission factor is lower can effectively reduce the carbon emissions of enterprises. Additionally, there are variations in the expected carbon reduction across different industries after implementing low-carbon demand response. The construction and non-metallic mineral product industries exhibit better emission reduction potential compared to livestock and agriculture. The larger the electricity load of the enterprise, the more significant the emission reduction effect.
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