Carbon quota dynamic monitoring processing method and system for community energy consumption

Abstract

The invention discloses a community energy consumption-oriented carbon quota dynamic monitoring processing method and system, and belongs to the technical field of dynamic monitoring. Constructing a risk management and scheduling model of the multi-scene day-ahead comprehensive transaction based on the conditional value-at-risk; constructing a community income distribution and adjustment method based on a VCG mechanism; constructing a three-stage management method of community energy and carbon quota based on the risk management and scheduling model and the community income distribution and adjustment method; firstly, the risk of energy transaction is quantified based on a risk management model, and stable operation is ensured; secondly, fairly distributing energy and carbon quotas by using a VCG mechanism, and optimizing cost; finally, energy scheduling is adjusted in real time through a three-stage management method, and the stability of a power grid is guaranteed. The energy utilization efficiency is integrally improved, the cost is reduced, the reasonable distribution of carbon quota is ensured, and the sustainable development of communities is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic monitoring, and specifically provides a method and system for dynamically monitoring and processing carbon quotas for community energy consumption. Background Art

[0002] With the increasingly serious global climate change problem, carbon emission control has become the focus of common concern for governments and enterprises around the world; in recent years, the carbon market and carbon quota trading mechanism have gradually become an important means of managing carbon emissions. Through the allocation and trading of carbon quotas, the relationship between energy consumption and carbon emissions can be effectively regulated, promoting the development of a low-carbon economy; traditional carbon quota trading mechanisms mainly rely on static market allocation rules, ignoring the dynamic changes in energy consumption and carbon emissions; with the development of distributed energy systems and smart grid technologies, how to achieve carbon quota trading and management based on dynamic monitoring and real-time adjustment has become a new research direction; especially at the community level, how to comprehensively consider the energy consumption patterns, carbon emission situations and their interaction behaviors of different types of users, and reasonably allocate and schedule carbon quotas to achieve the stability and economy of the energy system is one of the current research hotspots.

[0003] Although the existing technologies have made certain progress in carbon quota management, there are still some obvious deficiencies; traditional carbon quota trading models are mostly static models and cannot respond to fluctuations in energy consumption and carbon emissions in real time, resulting in unsatisfactory trading efficiency and resource allocation effects in the rapidly changing energy market; in the process of carbon quota allocation in existing technologies, the diversity of user behaviors and risk preferences are often ignored, and there is a lack of an effective personalized adjustment mechanism. This leads to certain fairness issues in carbon quota allocation. Especially within different communities, when the energy consumption and carbon emissions differences between users are large, the existing allocation methods cannot achieve precise adjustment. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for dynamically monitoring and processing carbon quotas for community energy consumption to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: A dynamic monitoring and processing method for carbon quotas for community energy consumption, the method comprising the following steps: constructing a risk management and scheduling model for multi-scenario day-ahead integrated trading based on conditional value at risk (CVaR) to quantify and manage the uncertainty of photovoltaic power generation and reduce the impact of fluctuations on system stability; constructing a community revenue allocation and adjustment method based on the Vickrey-Clarke-Groves (VCG) mechanism to ensure fair allocation by eliminating fraud and reduce voltage fluctuations, thereby enhancing system stability; at the same time, introducing the Jain fairness index to measure the fairness of allocation; based on the risk management and scheduling model and the community revenue allocation and adjustment method, constructing a three-stage management method for community energy and carbon quotas to achieve dynamic regulation at the day-ahead, real-time, and monthly levels, and ensure voltage stability and fair allocation of the system under uncertain conditions.

[0006] As a preferred solution of the dynamic monitoring and processing method for carbon quotas for community energy consumption described in the present invention, a community-based user model and management model are constructed, and the user model includes gas users, photovoltaic users, energy storage users, and general load users.

[0007] The management model is specifically as follows: After users conduct autonomous transactions within the community, the community administrator collects data based on the transaction volume of users and the impact on system stability and makes adjustments, and determines the adjustment amount for each user using technical indicators, where the technical indicators include voltage fluctuations and current.

[0008] Construct a day-ahead integrated trading model for community energy and carbon trading, specifically as follows: In the day-ahead trading stage, users submit the buying and selling situations of energy and carbon quotas for the next day to the community administrator, and the administrator determines the transaction volume based on the buying and selling situations and makes reasonable allocations: ; ; ; ; Among them, is the electricity purchase cost of user i at time t, is the electricity sale revenue of user i at time t, is the natural gas purchase cost of user i at time t, is the time interval in the day-ahead stage, refers to the set of decision variables of user i in the day-ahead trading, is the time in the day-ahead stage, is the carbon purchase cost, is the carbon emission quota sale revenue, and DA represents the day-ahead stage, is the objective function for users in the current stage, and respectively represent the electric energy purchased by user i from the outside and within the community at time t. gb represents the purchase from outside the community, and cb represents the purchase within the community. and respectively represent the electricity purchase prices outside and within the community at time t; and respectively represent the electric energy sold by user i to the outside and within the community at time t. gs represents the sale to the outside of the community, and cs represents the sale within the community; and respectively represent the amount of carbon quota purchased and sold by user i within the community at time t; and respectively represent the electricity prices for selling electricity outside and within the community at time t; and respectively represent the prices for purchasing carbon quota outside and within the community at time t; and respectively represent the prices for selling carbon quota outside and within the community; represents the natural gas consumption of the gas turbine unit of user i at time t. T represents the gas turbine unit, represents the price of natural gas at time t.

[0009] The satisfaction relationship formula of the carbon quota is as follows: ; ; Among them, is the initial carbon quota of each unit of user i at time t, is the carbon quota coefficient; is the carbon emission of user i at time t, is the carbon emission coefficient; and respectively represent the carbon emissions that user i needs to purchase additionally and can sell at time t, is the output of the gas turbine unit, is the power of the photovoltaic unit, is the power of the energy storage device.

[0010] After the user has solved, the community administrator collects and verifies the transaction volume that the user is ready to submit, and performs community cost minimization according to the community transaction balance constraint, as follows: ; Among them, is the set of decision variables of the community management administrator, is the objective function of the community administrator, and U is the set of user variables.

[0011] The specific community trading balance constraints are as follows: ; ; Among them, represents the total electrical energy bought or sold at time t; represents the total carbon quota amount bought or sold at time t, is the electrical energy purchased by user i during the internal community trading at time t, is the electrical energy sold by user i during the internal community trading at time t.

[0012] As a preferred solution of the carbon quota dynamic monitoring and processing method for community energy consumption described in the present invention, the process of constructing a risk management and scheduling model for multi-scenario day-ahead integrated trading based on conditional value at risk is as follows: By simulating the output scenarios of multiple photovoltaic units under different fluctuation conditions, for the output scenarios, CVaR is used to quantify the operation risk, specifically as follows: ; Among them, S represents the total number of scenarios; is the scenario probability of each scenario, is the risk preference coefficient of user i, is the CVaR value of user i, is the set of decision variables of user i in the kth scenario, is the electrical energy purchase quantity of user i at time t in the kth scenario, is the carbon quota purchase quantity of user i at time t in the kth scenario, is the electrical energy sales quantity of user i at time t in the kth scenario, is the carbon quota sales quantity of user i at time t in the kth scenario.

[0013] The specific calculation formula for the CVaR value of user i is as follows: ; ; Among them, is the trading risk value of user i; represents the confidence level; refers to the cost of user i exceeding the VaR part in the kth scenario.

[0014] As a preferred solution of the carbon quota dynamic monitoring and processing method for community energy consumption described in the present invention, the energy and carbon trading costs of users are determined as follows: ; ; ; ; Among them, and respectively represent the net values of the electricity and carbon quota trading of user i within time t; and respectively represent the average values of the electricity purchase volume and sales volume of user i within time t, b represents purchase, s represents sales, and respectively represent the purchase volume and sales volume of the carbon quota of user i within time t; and respectively represent the total electricity energy and carbon quota volume bought or sold in the community at time t, is the time of the day-ahead stage, is the amount of electricity purchased by the user from the outside, is the amount of electricity sold by the user to the outside world.

[0015] Use the VCG mechanism to re-determine the allocation adjustment amount and allocate the electricity and carbon quota as follows: Calculate the costs of the electricity and carbon quota purchase and sale of user i at time t, and the formula is as follows: ; ; Among them, and respectively are the average values of the electricity purchase and sales volumes of user i at time t in the real-time stage; and respectively represent the allocation coefficients of the electricity purchase and electricity sale of user i at time t, and respectively represent the allocation coefficients of the carbon quota purchase and carbon quota sale of user i at time t, represents the total electricity energy bought or sold at time t; represents the total carbon quota volume bought or sold at time t, and respectively represent the electricity purchase volume and electricity sales volume of user i at time t; and respectively represent the carbon quota purchase cost and carbon quota sales volume of user i at time t.

[0016] Calculate the total cost after removing user i at time t, and the calculation formula is as follows: ; ; where, is the total electricity purchase volume in the community after removing user i, is the average value of the electricity purchase volume of the j-th user in the community after removing user i within time t, and respectively represent the distribution coefficients of electricity purchase and electricity sale of user j at time t, is the total electricity sales volume in the community after removing user i, is the average value of the electricity sales volume of the j-th user in the community after removing user i within time t, is the total carbon quota purchase volume in the community after removing user i, is the average value of the carbon quota purchase volume of the j-th user in the community after removing user i within time t, is the total carbon quota sales volume in the community after removing user i, is the average value of the carbon quota sales volume of the j-th user in the community after removing user i within time t, and respectively represent the distribution coefficients of carbon quota purchase and sale of user j at time t.

[0017] Under the VCG mechanism, calculate the actual cost of user i, and the calculation formula is as follows: ; ; where, is the electricity purchase volume of user i at time t after VCG allocation, is the electricity sales volume of user i at time t after VCG allocation, is the carbon quota purchase volume of user i at time t after VCG allocation, is the carbon quota sales volume of user i at time t after VCG allocation.

[0018] Based on the actual cost of user i, calculate the distribution coefficient, and the calculation formula is: ; ; where, is the distribution coefficient of electricity purchase of user i at time t after VCG, is the distribution coefficient of electricity sales of user i at time t after VCG, The allocation coefficient of carbon quota purchase for user i after VCG at time t. The allocation coefficient of carbon quota sale for user i after VCG at time t.

[0019] As a preferred solution of the carbon quota dynamic monitoring and processing method for community energy consumption described in the present invention, calculate the costs of transactions between users, retailers, and within the community after using the VCG allocation mechanism. The calculation formula is as follows: ; ; ; ; ; ; ; ; ; Among them, and are respectively the average values of the electricity bought and sold by user i within time t in the real-time stage; and respectively represent the allocation coefficients of electricity purchase and electricity sale for user i at time t, and respectively represent the allocation coefficients of carbon quota purchase and carbon quota sale for user i at time t; and respectively represent the electricity and carbon quota transaction costs between users within the community for user i at time t; and respectively represent the electricity and carbon quota costs of the transaction between user i and the retailer at time t; represents the total transaction cost of user i at time t, is the line power of user i at time t in the real-time stage, is the time interval in the monthly allocation stage, is the electricity purchased by user i during the community internal transaction at time t, is the electricity sold by user i during the community internal transaction at time t.

[0020] Construct a fairness metric for the allocation of electricity-carbon costs: ; Among them, and respectively represent the fairness indices of electricity and carbon quota buying and selling, and N represents the total number of users in the community. is the total electricity purchased by user i within the time interval. is the total electricity sold by user i within the time interval. is the total carbon quota purchased by user i within the time interval. is the total carbon quota sold by user i within the time interval.

[0021] As a preferred solution of the carbon quota dynamic monitoring and processing method for community energy consumption described in the present invention, a real-time adjustment and voltage stability management model is constructed as follows: Correct the deviations in the day-ahead stage and the real-time stage. The correction formula is as follows: ; where and respectively represent the charge and discharge states of the energy storage device of user i at time t in the day-ahead stage and the real-time stage. represents the deviation amount of the charge and discharge of the energy storage device in the day-ahead stage and the real-time stage.

[0022] By adjusting the energy storage device, minimize the deviation before the day-ahead and real-time stages, and then submit the initial power to the community administrator as follows: ; where and respectively represent the line power of user i at time t in the day-ahead stage and the real-time stage. is the set of initial decision variables of user i in the real-time stage.

[0023] The constraint formula of the line power is as follows: ; where represents the net value of electricity of user i within time t. is the amount of electricity purchased by user i from the outside at time t. is the amount of electricity sold by user i to the outside at time t. is the electricity purchased by user i during the internal community transaction at time t. is the electricity sold by user i during the internal community transaction at time t.

[0024] The safety constraints that the community needs to satisfy are as follows: ; ; ; Among them, , , are respectively the current, active power, and reactive power flowing from the bus at user i through the resistance and reactance of the line to the bus at user j, represents the set of branches with the bus at user j as the head node, and are respectively the square of the voltage and the square of the current at the bus at user i, is the square of the active power from the bus at user i to the bus at user j at time t, is the square of the voltage at the bus at user i at time t, is the square of the reactive power from the bus at user i to the bus at user j at time t, is the active power flowing through the bus at user j at time t, represents the set of branches with the bus at user j as the head node.

[0025] If, after the community administrator verifies the initial decision variables submitted by the user, there is no voltage violation, it means that the power submitted by the user meets the requirements, , and no adjustment is required.

[0026] If there is a voltage violation, at this time , then optimization adjustment is performed through second-order cone relaxation as follows: ; Among them, is the minimum value of the square of the voltage at the bus at user i at time t, is the maximum value of the square of the voltage at the bus at user i at time t.

[0027] The community administrator performs the solution as follows: ; Among them, represents the network loss part of voltage regulation, and are weight coefficients, is the set of all decision variables of the community administrator in the real-time stage.

[0028] It should be noted that the present invention uses the VCG mechanism to fairly allocate energy and carbon quotas and optimize costs. The costs are only used as the input of the present invention for reasonable allocation of carbon quotas for users, so as to promote the sustainable development of the community and are not used for the research of economic management.

[0029] As a preferred solution of the carbon quota dynamic monitoring and processing method for community energy consumption described in the present invention, a monthly allocation and settlement model is constructed as follows: Calculate the costs of transactions between users, retailers, and within the community.

[0030] Calculate the voltage penalty cost, and the calculation formula is as follows: ; Wherein, represents the voltage penalty cost of user i at time t, is the penalty price.

[0031] By comparing and to calculate the grid service reward, and the calculation formula is as follows: ; Wherein, is the grid service reward of user i at time t, refers to the grid service reward price.

[0032] Calculate the total cost of each user: ; Wherein, is the total cost of user i at time t.

[0033] Calculate the community surplus, and the calculation formula is as follows: ; Wherein, is the community surplus.

[0034] Construct a monthly allocation and settlement model with the goal of minimizing the difference between the actual operating cost and the predicted cost in the day-ahead stage within this month, as follows: ; Wherein, is the total user cost of user i in the day-ahead stage at time t, is the total time interval of a month.

[0035] A carbon quota dynamic monitoring and processing system for community energy consumption, which includes: a risk management and scheduling model construction module, a revenue distribution and adjustment mechanism module, and an energy and carbon quota three-stage management module.

[0036] The risk management and scheduling model construction module is used to construct a community-based user model and management model. The user model includes gas users, photovoltaic users, energy storage users, and general load users. After users' autonomous transactions, community administrators collect data based on users' trading volumes and the impact on system stability and make adjustments, and use technical indicators (such as voltage fluctuations and current) to determine the adjustment amount for each user. A day-ahead comprehensive trading model for community energy and carbon trading is constructed, and the administrator determines the trading volume and makes a reasonable allocation according to the trading situations submitted by users.

[0037] The revenue distribution and adjustment mechanism module is used to determine the energy and carbon trading costs of users, calculate the net value of electric energy and carbon quotas of users within time t, the bought and sold quantities of electric energy, and the bought and sold quantities of carbon quotas; use the VCG mechanism to re-determine the allocation and adjustment amounts of users and conduct the allocation of electric energy and carbon quotas; calculate the total cost after removing user i, make allocation adjustments based on the actual cost of user i, and calculate the allocation coefficient; be used to calculate the fairness measurement index for the allocation of electricity-carbon costs to ensure the fairness of transaction cost allocation.

[0038] The three-stage management module for energy and carbon quotas adjusts the voltage and energy storage device management in real time to minimize the power deviation between the day-ahead and real-time stages and avoid voltage violations and the destruction of power constraints of power lines; uses the second-order cone relaxation method to solve the voltage problem first; uses the monthly allocation and settlement model to calculate and optimize the trading costs of electric energy and carbon quotas of users, voltage violation penalties, and grid service rewards of users, and calculate the community surplus.

[0039] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: In a carbon quota dynamic monitoring and processing method and system for community energy consumption provided by the present invention, by constructing a risk management and scheduling model for multi-scenario day-ahead integrated trading based on conditional value at risk, comprehensive quantitative risk management of various uncertain factors is realized, providing a scientific basis for subsequent energy trading and carbon quota allocation, effectively reducing the operation risk of the community energy system under different external scenarios, and laying a foundation for subsequent community energy scheduling decisions; The community revenue distribution and adjustment method based on the VCG mechanism calculates the costs and revenues of each user under different trading situations, ensuring the fairness of the allocation of carbon quotas and electric energy, and realizing the cost optimization of power and carbon quota trading by adjusting the allocation coefficient, avoiding the waste of energy and carbon resources. Especially in the optimization allocation process, the application of the VCG mechanism improves the fairness and transparency of the trading, helping to enhance the sense of participation and trust of community users; Finally, through the constructed three-stage management method, comprehensively applying the risk management and revenue distribution models, combined with the real-time scheduling and voltage stability management models, the real-time optimization and adjustment of the community energy system are further realized, effectively improving the accuracy and stability of electric energy and carbon quota management, ensuring the optimal balance of energy flow and carbon emissions; The scheduling and optimization steps in each stage not only ensure the efficient operation of the community energy system, but also ensure the reasonable use of carbon quotas and the realization of environmental protection goals, thus bringing the beneficial effects of reduced energy costs, reduced carbon emissions and the sustainable development of the community. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification, and are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention.

[0041] Figure 1 is the three-stage method flow; Figure 2 is the 55-node test system; Figure 3 is the photovoltaic output curve under different scenarios; Figures 4 - 6 is the community electric energy and carbon quota cost; Figures 7 - 9 is the risk management; Figures 10 - 11 is the real-time stage voltage adjustment; Figures 12 - 13 is the electric energy cost allocation result; Figures 14 - 15 is the carbon quota cost allocation result. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0043] Please refer to Figure 1 , in the first embodiment: A carbon quota dynamic monitoring and processing method for community energy consumption is provided. The method includes the following steps: Figure 1 Including Phase 1: Multi-scenario day-ahead integrated trading and risk management, Phase 2: Real-time operation scheduling, and Phase 3: Monthly allocation adjustment.

[0044] The above Phase 1 is described through steps S1 - S2 of a carbon quota dynamic monitoring and processing method for community energy consumption of the present invention: Step S1: Construct a risk management and scheduling model for multi-scenario day-ahead integrated trading based on conditional value-at-risk to quantify and manage the uncertainty of photovoltaic power generation, so as to reduce the impact of fluctuations on system stability.

[0045] Specifically, construct a user model and a management model based on the community to form a framework for the collective self-consumption mode; the user model includes gas users, photovoltaic users, energy storage users, and ordinary load users; the management model is specifically: after users independently trade within the community, the community administrator collects data based on the trading volume of users and the impact on system stability and makes adjustments, and determines the adjustment amount for each user using technical indicators, and the technical indicators include voltage fluctuation and current.

[0046] Furthermore, construct a day-ahead integrated trading model for community energy and carbon trading, specifically as follows: The optimization of the user local model in the above Phase 1 is specifically: in the day-ahead trading stage, users submit the purchase and sale situations of energy and carbon quotas for the next day to the community administrator, and the administrator determines the trading volume based on the above purchase and sale situations and makes reasonable allocations to optimize the user cost: ; ; ; ; Among them, is the electricity purchase cost of user i at time t, is the electricity sale income of user i at time t, is the natural gas purchase cost of user i at time t, is the time interval in the day-ahead stage, Refers to the set of decision variables of user i in day-ahead trading, is the time in the day-ahead stage, is the carbon purchase cost, is the income from selling carbon emission allowances. DA represents the day-ahead stage, is the objective function of the user in the day-ahead stage, and respectively represent the electricity energy purchased by user i from the outside and within the community at time t. gb represents purchasing from outside the community, and cb represents purchasing within the community, and respectively represent the electricity purchase prices outside and within the community at time t; and respectively represent the electricity energy sold by user i to the outside and within the community at time t. gs represents selling to the outside of the community, and cs represents selling within the community; and respectively represent the carbon emission allowance quantities purchased and sold by user i within the community at time t; and respectively represent the electricity selling prices outside and within the community at time t; and respectively represent the prices of purchasing carbon emission allowances outside and within the community at time t; and respectively represent the prices of selling carbon emission allowances outside and within the community; represents the natural gas consumption of the gas turbine unit of user i at time t. T represents the gas turbine unit, represents the price of natural gas at time t.

[0047] The relationship of the carbon emission allowances satisfies the following formula: ; ; Among them, is the initial carbon emission allowance of each unit of user i at time t, is the carbon emission allowance coefficient; is the carbon emission of user i at time t, is the carbon emission coefficient; and respectively represent the carbon emissions that user i needs to purchase additionally and can sell at time t, is the output of the gas turbine unit, is the power of the photovoltaic unit, is the power of the energy storage device.

[0048] The optimization of the community global model in Stage 1 is specifically as follows: For the community administrator, after the user finishes solving, the community administrator collects and verifies the transaction volume that the user is about to submit. Its purpose is to minimize the overall community cost while satisfying the community transaction balance: ; Among them, is the set of decision variables for the community management administrator, is the objective function of the community administrator, and U is the set of user variables.

[0049] The constraints to be satisfied are: ; ; Among them, represents the total electrical energy bought or sold at time t; represents the total carbon quota bought or sold at time t, is the electrical energy purchased by user i during the internal community transaction at time t, is the electrical energy sold by user i during the internal community transaction at time t.

[0050] Furthermore, the process of constructing the risk management and scheduling model for multi-scenario day-ahead integrated trading based on conditional value at risk is specifically as follows: By simulating the output scenarios of multiple photovoltaic units under different fluctuation conditions, the day-ahead trading decision is made closer to the actual operation situation; for these scenarios, CVaR is used to quantify the trading and system operation risks brought by the uncertainty of photovoltaic output, so as to evaluate and optimize the risks of users under uncertain conditions, thereby improving the stability of the system and the reliability of the decision-making; the risk management and scheduling model for multi-scenario day-ahead integrated trading based on conditional value at risk is as follows: ; Among them, S represents the total number of scenarios; is the scenario probability of each scenario, is the risk preference coefficient of user i, is the CVaR value of user i, is the set of decision variables of user i in the k-th scenario, is the electrical energy purchase volume of user i at time t in the k-th scenario, is the carbon quota purchase volume of user i at time t in the k-th scenario, is the electrical energy sales volume of user i at time t in the k-th scenario, is the carbon quota sales volume of user i at time t in the k-th scenario.

[0051] The specific calculation and constraints of the CVaR value of user i are as follows: ; ; Among them, is the value at risk of user i's transaction; represents the confidence level; refers to the cost of the part where user i exceeds VaR in the k-th scenario.

[0052] Step S2: Construct a community revenue allocation and adjustment method based on the Vickrey-Clarke-Groves mechanism to ensure fair allocation by eliminating fraud, reduce voltage fluctuations, and thus improve the stability of the system; at the same time, introduce the Jain fairness index to measure the fairness of the allocation.

[0053] In the settlement stage, the community administrator redistributes the community's electricity and carbon quotas to each user according to the actual transaction data of the users; the administrator calculates the total cost of each user by determining the adjustment amount of the electricity and carbon quotas; in the allocation process, the VCG allocation mechanism is adopted to identify and eliminate potential fraud behaviors, ensure fair allocation, and reduce system fluctuations; to further measure the fairness of the allocation results, the Jain fairness index is introduced to evaluate fairness.

[0054] Determine the energy and carbon trading costs of users: ; ; ; ; Among them, and respectively represent the net values of user i's electricity and carbon quota transactions within time t; and respectively represent the average values of user i's electricity purchase and sale volumes within time t, b represents purchase, s represents sale, and respectively represent the purchase and sale volumes of user i's carbon quota within time t; and respectively represent the total electricity and carbon quota volumes bought or sold in the community at time t, is the time of the day-ahead stage, is the amount of electricity purchased by the user from the outside, is the amount of electricity sold by the user to the outside world.

[0055] Use the VCG mechanism to re-determine the allocation adjustment amount and allocate electricity and carbon quotas as follows: During the trading process, some users may reduce their own costs by falsely reporting trading volumes; to eliminate such fraudulent behavior, ensure the fairness of allocation, and encourage users to conduct honest transactions and allocations, the VCG mechanism is introduced in the settlement stage to adjust and correct the trading volumes of users, so as to ensure the overall fairness and stability of the system.

[0056] First, calculate the costs of user i's electricity and carbon quota purchases and sales at time t: ; ; Among them, and are respectively the average values of the electricity bought and sold by user i at time t during the real-time stage; and respectively represent the allocation coefficients of user i's electricity purchase and electricity sale at time t, and respectively represent the allocation coefficients of user i's carbon quota purchase and carbon quota sale at time t, represents the total amount of electricity bought or sold at time t; represents the total amount of carbon quota bought or sold at time t, and respectively represent the amount of electricity purchased and the amount of electricity sold by user i at time t; and respectively represent the cost of carbon quota purchase and the amount of carbon quota sold by user i at time t.

[0057] Next, calculate the total cost after removing user i at time t: ; ; Among them, is the total amount of electricity purchased in the community after removing user i, is the average value of the electricity bought by the j-th user in the community after removing user i during time t, and respectively represent the allocation coefficients of user j's electricity purchase and electricity sale at time t, is the total amount of electricity sold in the community after removing user i, is the average value of the electricity sold by the j-th user in the community after removing user i during time t, is the total amount of carbon quota purchased in the community after removing user i, is the average value of the carbon quota bought by the j-th user in the community after removing user i during time t, The total carbon quota sales volume in the community after removing user i The average value of the carbon quota sales volume of the j-th user in the community within time t after removing user i in the community and Are the distribution coefficients representing the carbon quota purchase and sales of user j at time t, respectively

[0058] Then, under the VCG mechanism, the actual cost of user i is ; ; Among them, Is the amount of electric energy purchased by user i at time t after VCG allocation Is the amount of electric energy sold by user i at time t after VCG allocation Is the amount of carbon quota purchased by user i at time t after VCG allocation Is the amount of carbon quota sold by user i at time t after VCG allocation

[0059] Finally, the corresponding distribution coefficients can be obtained as ; ; Among them, Is the distribution coefficient of electric energy purchase by user i after VCG at time t Is the distribution coefficient of electric energy sales by user i after VCG at time t Is the distribution coefficient of carbon quota purchase by user i after VCG at time t Is the distribution coefficient of carbon quota sales by user i after VCG at time t

[0060] Calculate the costs of transactions between users and retailers and within the community after using the VCG allocation mechanism

[0061] ; ; ; ; ; ; ; ; ; Among them, and The average values of the electricity purchased and sold by user i at time t during the real-time stage, respectively; and respectively represent the distribution coefficients of electricity purchase and electricity sale of user i at time t, and respectively represent the distribution coefficients of carbon quota purchase and carbon quota sale of user i at time t; and respectively represent the electricity and carbon quota transaction costs among the internal users of the community of user i at time t; and respectively represent the electricity and carbon quota costs of user i in the transaction with the retailer at time t; represents the total transaction cost of user i at time t, is the line power of user i at time t during the real-time stage, is the time interval of the monthly allocation stage, is the electricity purchased by user i during the internal community transaction at time t, is the electricity sold by user i during the internal community transaction at time t.

[0062] Construct a fairness metric for the allocation of electricity-carbon costs: ; wherein, and respectively represent the fairness indices of electricity and carbon quota purchase and sale, N represents the total number of users in the community, is the total electricity purchased by user i within the time interval, is the total electricity sold by user i within the time interval, is the total carbon quota purchased by user i within the time interval, is the total carbon quota sold by user i within the time interval.

[0063] The above-mentioned stage 2 and stage 3 are described by step S3 of a method for dynamically monitoring and processing carbon quotas for community energy consumption according to the present invention: Step S3: Based on the risk management and scheduling model and the community revenue allocation and adjustment method, construct a three-stage management method for community energy and carbon quotas to achieve dynamic regulation at the day-ahead, real-time, and monthly levels, and ensure voltage stability and fair allocation of the system under uncertain conditions.

[0064] Construct a day-ahead comprehensive trading model for multi-scenario risk management.

[0065] Construct a real-time adjustment and voltage stability management model: The real-time stage mainly focuses on the real-time control of grid constraints, which is mainly divided into two parts. One is to correct the deviation between the day-ahead stage and the actual operation stage, and the other is to reduce voltage violations and make corrections.

[0066] The initial scheduling in the aforementioned stage 2 is first to correct the deviation between the day-ahead stage and the real-time stage: ; Among them, and respectively represent the charging and discharging states of the energy storage device of user i at time t in the day-ahead stage and the real-time stage, represents the deviation amount of the charging and discharging of the energy storage device between the day-ahead stage and the real-time stage.

[0067] Through the adjustment of the energy storage device, each user first solves the correction problem to minimize the deviation between the day-ahead stage and the real-time stage, and then submits the initial power to the community administrator : ; Among them, and respectively represent the line power of user i at time t in the day-ahead stage and the real-time stage, is the set of initial decision variables of user i in the real-time stage.

[0068] The constraints of the line power are as follows: ; Among them, represents the net value of electric energy of user i within time t, is the amount of electric energy purchased by user i from the outside at time t, is the amount of electric energy sold by user i to the outside at time t; is the electric energy purchased by user i during the internal community transaction at time t, is the electric energy sold by user i during the internal community transaction at time t.

[0069] Then comes voltage verification and control: The safety constraints that the entire community needs to satisfy are as follows: ; ; ; Among them, , , are the current, active power, and reactive power of the line from the bus of user i flowing through the resistance and reactance to the bus of user j, represents the set of branches with the bus at user j as the head node, and are the square of the voltage and the square of the current at the bus of user i respectively, is the square of the active power from the bus of user i to the bus of user j at time t, is the square of the voltage at the bus of user i at time t, is the square of the reactive power from the bus of user i to the bus of user j at time t, is the active power flowing through the bus of user j at time t, represents the set of branches with the bus at user j as the head node.

[0070] The rescheduling in the second stage is specifically as follows: If the community administrator collects the initial decision variables submitted by users After verification, if there is no situation such as voltage w violation and crossing the boundary, it means that the power submitted by users meets the requirements, , and no adjustment is required.

[0071] If voltage violation is detected after verification, at this time , then optimization adjustment needs to be carried out through second-order cone relaxation: ; Among them, is the minimum value of the square of the voltage at the bus of user i at time t, is the maximum value of the square of the voltage at the bus of user i at time t.

[0072] At this time, the problem that the community administrator needs to solve is as follows: ; Among them, represents the network loss part of voltage regulation, and are weight coefficients, is the set of all decision variables of the community administrator in the real-time stage.

[0073] A monthly allocation and settlement model is constructed as follows: The penalties and rewards in the third stage are specifically as follows: The community administrator distributes community energy to each user according to the decisions of users in the day-ahead and real-time stages, including the costs of transactions between users and retailers and within the community, the penalties for excessive deviation in the real-time stage, and the rewards obtained by users for participating in voltage adjustment.

[0074] First, calculate the costs of transactions between users and retailers and within the community.

[0075] Next is the voltage penalty cost: ; Among them, represents the voltage penalty cost of user i at time t, is the penalty price.

[0076] Finally, there is the grid service reward, which is calculated by comparing and : ; Among them, is the grid service reward of user i at time t, refers to the grid service reward price.

[0077] Therefore, the total cost of each user is: ; Among them, is the total cost of user i at time t.

[0078] The community administrator can adjust the difference between the buying and selling prices and use the surplus for user income or investment.

[0079] The community surplus can be expressed by the following formula: ; Among them, is the community surplus.

[0080] Based on the above transaction costs, a monthly allocation and settlement model is proposed with the goal of minimizing the difference between the actual operating cost within this month and the cost predicted in the day-ahead stage: ; Among them, is the total user cost of user i in the day-ahead stage at time t, is the total time interval of a month.

[0081] In the second embodiment: A carbon quota dynamic monitoring and processing system for community energy consumption is provided. The system includes: a risk management and scheduling model construction module, a revenue distribution and adjustment mechanism module, and an energy and carbon quota three-stage management module.

[0082] The risk management and scheduling model construction module is used to construct a user model and a management model based on the community. The user model includes gas users, photovoltaic users, energy storage users, and ordinary load users; after the users' independent transactions, the community administrator collects data based on the user transaction volume and the impact on system stability and makes adjustments, and determines the adjustment amount for each user using technical indicators (such as voltage fluctuation and current); constructs a day-ahead comprehensive trading model for community energy and carbon trading, and the administrator determines the trading volume and makes a reasonable allocation according to the trading situation submitted by the users.

[0083] The revenue distribution and adjustment mechanism module is used to determine the user's energy and carbon trading costs, calculate the net value of electric energy and carbon quotas, the purchase and sale volumes of electric energy, and the purchase and sale volumes of carbon quotas within time t for the user; re-determine the user's allocation adjustment amount using the VCG mechanism and perform the allocation of electric energy and carbon quotas; calculate the total cost after removing user i, perform allocation adjustment based on the actual cost of user i, and calculate the allocation coefficient; be used to calculate the fairness measurement index for the distribution of electricity-carbon costs to ensure the fairness of transaction cost distribution.

[0084] The three-stage management module for energy and carbon quotas adjusts the voltage and energy storage device management in real time, minimizes the power deviation between the day-ahead and real-time stages, and avoids voltage violations and the violation of power constraints of power lines; adopts the second-order cone relaxation method first to solve the voltage problem; uses the monthly allocation and settlement model to calculate and optimize the user's electric energy and carbon quota trading costs, voltage violation penalties, and grid service rewards, and calculates the community surplus.

[0085] Please refer to Figure 2 , in the third embodiment: Take the improved IEEE low-voltage 55-node test system as an example to introduce the present invention: Node 0 is the superior power grid, and users can conduct electric energy and carbon quota transactions with it; Nodes 1, 2, 9, 11, 15, 17, 18, 19, 20, 21, 24, 26, 28, 30, 31, 33, 34, 35, 40, 43, 45, 47 are ordinary load users without distributed power sources; Nodes 3, 5, 6, 13, 25, 27, 37, 38, 39, 41, 44, 48, 51, 52, 55 are photovoltaic power generation users; Nodes 4, 7, 8, 12, 14, 16, 23, 29, 32, 36, 42, 46, 49, 50, 54 are photovoltaic and energy storage users; Nodes 10, 22, 53 are other types of users. The test system is as Figure 2 shown. Four case scenarios are set to verify the practicability of the method: 1) Only consider electric energy transactions, do not use CVaR to measure risks, and adopt the distribution method according to the contribution ratio; 2) Consider carbon trading, but do not use CVaR to measure the photovoltaic fluctuation risk, and still distribute according to the contribution ratio; 3) On the basis of Example 2, change the distribution method to VCG distribution; 4) Based on Example 3, consider electricity-carbon trading and VCG distribution, and add multiple photovoltaic scenario fluctuations in the day-ahead stage, and use CVaR to measure risks.

[0086] The power of the photovoltaic scenario is as Figure 3As shown; five different photovoltaic output scenarios were designed to comprehensively analyze the uncertainty of photovoltaic power output; the first scenario simulated typical sunny weather conditions with small and stable power output fluctuations; the remaining four scenarios introduced larger fluctuations to simulate different weather conditions (such as cloud cover and overcast days), resulting in higher uncertainty outputs; these scenarios better captured the instability in actual photovoltaic output and helped with the design of subsequent strategies.

[0087] In the above example, the total cost, electricity trading cost, and carbon trading cost results of the community are as Figures 4 - 6 shown; compared with Example 1 that only considered electricity trading, after adding carbon trading, although the overall cost increased, the overall revenue increased significantly; by comparing Example 2 and Example 3, after using the VCG allocation method, the trading information submitted by users is more real and the social welfare of the community has increased; in Example 4, after users considered uncertainty and adopted CVaR, they better coped with risks and the cost decreased; the introduction of carbon trading significantly increased the electricity sales volume, promoted the power generation of users with new energy units, and thus provided more electricity for other users and the external power grid.

[0088] The risk management results of Example 4 are as Figures 7 - 9 shown; different confidence levels have different impacts on the overall cost and risk of users; by adopting CVaR, the cost and risk are quantified, and users can make risk preference decisions based on expectations and curves, thus better coping with uncertainty and reducing costs.

[0089] The voltage adjustment results of users' lines in the real-time stage are as Figures 10 - 11 shown; through the voltage adjustment in the real-time stage, the voltages of users are maintained within the normal upper and lower limits, maintaining the operation safety of the community.

[0090] The allocation results of electricity buying and selling are as Figures 12 - 13 shown.

[0091] The allocation results of carbon quota buying and selling are as Figures 14 - 15 shown.

[0092] The fairness results of the VCG allocation method are shown in Table 1; the Jain index value ranges from 0 to 1, and the larger the value, the fairer the allocation; the results show that the VCG allocation method is fairer than the traditional contribution-based allocation method, fully demonstrating its advantages in eliminating fraud and promoting fairness.

[0093] Table 1 Fairness of Different Allocation Methods

[0094] The simulation results verify the effectiveness and practicality of the model constructed in the present invention; through the risk management and voltage stability method for community energy and carbon trading, an optimized dispatching and planning scheme is formed, effectively coping with the risks brought by uncertainties, enhancing the security and stability of system operation, reducing the operation costs of users, and ensuring the fairness of community distribution; this scheme provides strong support for the decision-making of users and managers and has significant engineering application value.

[0095] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0096] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for dynamic monitoring and processing of carbon quota for community energy consumption, characterized in that: The method comprises the following steps: Step S1: Construct a risk management and scheduling model for multi-scenario day-ahead integrated transactions based on conditional value at risk; Step S2: Construct a community benefit distribution and adjustment method based on the Vickrey-Clarke-Groves mechanism; Step S3: Based on the risk management and scheduling model and the community benefit allocation and adjustment method, a three-stage management method for community energy and carbon quota is constructed.

2. According to claim 1, a carbon quota dynamic monitoring and processing method for community energy consumption is characterized in that: The specific implementation process of step S1 includes the following steps: Construct a community-based user model and management model, wherein the user model includes gas users, photovoltaic users, energy storage users, and ordinary load users; The management model is as follows: After the user trades autonomously in the community, the community administrator collects data and makes adjustments based on the user's trading volume and the stability of the system, and uses technical indicators to determine the adjustment amount for each user, including voltage fluctuations and current; The day-ahead integrated trading model for community energy and carbon trading is constructed as follows: In the day-ahead trading phase, users submit the next day's energy and carbon quota trading information to the community administrator. The administrator determines the trading volume and makes a reasonable allocation based on the trading information: ； ； ； ； in, is the electricity purchase cost of user i at time t, is the electricity sales revenue of user i at time t, The natural gas purchase cost of user i at time t is: is the time interval of the day-ahead phase, Refers to the set of decision variables of user i in the day-ahead transaction, is the time of the day before the present, For the carbon purchase cost, is the income from the sale of carbon emission quotas, DA is the day-ahead period, is the objective function of the user in the day-ahead phase, and They represent the amount of electricity purchased by user i from the outside and from within the community at time t, respectively. gb represents purchase from outside the community, and cb represents purchase within the community. and Represent the external and community electricity purchase prices at time t, and They represent the amount of electricity sold by user i to the outside world and within the community at time t, respectively. gs represents sales to the outside of the community, and cs represents sales within the community. and They represent the carbon quotas purchased and sold by user i in the community at time t, and They represent the electricity prices sold to the outside world and within the community at time t, and Represent the prices of purchasing carbon quotas from outside and within the community at time t, and Represent the prices of carbon quotas sold externally and within the community, represents the natural gas consumption of the gas unit of user i at time t, T represents the gas turbine unit, represents the price of natural gas at time t; The carbon quota satisfaction relationship formula is as follows: ； ； in, is the initial carbon quota of each unit of user i at time t, is the carbon quota coefficient; is the carbon emissions of user i at time t, is the carbon emission coefficient, and are the additional carbon emissions that user i needs to purchase and can sell at time t, is the output of the gas turbine unit, is the power of the photovoltaic unit, is the power of the energy storage device; When the user has completed the solution, the community administrator collects and verifies the transaction volume that the user is ready to submit, and minimizes the community cost according to the community transaction balance constraint, as follows: ； in, is the set of community administrator decision variables, is the objective function of the community administrator, and U is the set of user variables; The community transaction balance constraints are as follows: ； ； in, represents the total amount of electricity bought or sold at time t, represents the total amount of carbon quotas bought or sold at time t, is the electricity purchased by user i during the internal transaction in the community at time t, It is the electric energy sold by user i during the intra-community transaction at time t.

3. According to claim 2, a carbon quota dynamic monitoring and processing method for community energy consumption is characterized in that: The specific implementation process of step S1 also includes the following steps: The process of constructing a risk management and scheduling model for multi-scenario day-ahead integrated transactions based on conditional value at risk is as follows: By simulating the output scenarios of multiple photovoltaic units under different fluctuation conditions, CVaR is used to quantify the operation risks for the output scenarios, as follows: ； Where S represents the total number of scenes, is the scenario probability for each scenario, is the risk preference coefficient of user i, is the CVaR value of user i, is the set of decision variables for user i in the kth scenario, is the amount of electricity purchased by user i at time t in the kth scenario, is the carbon quota purchase amount of user i in the kth scenario at time t, is the electricity sales volume of user i at time t in the kth scenario, is the carbon quota sales volume of user i at time t in the kth scenario; The specific calculation formula of the CVaR value of user i is as follows: ； ； in, is the transaction risk value of user i, represents the confidence level; It refers to the cost of user i in excess of VaR in the kth scenario.

4. According to the method for dynamic monitoring and processing of carbon quota for community energy consumption in claim 1, it is characterized in that: The specific implementation process of step S2 includes the following steps: Determine the user's energy and carbon trading costs as follows: ； ； ； ； in, and They represent the net value of electricity and carbon quota transactions of user i in time t, and They represent the average amount of electricity purchased and sold by user i in time t, b represents purchase, s represents sale, and They represent the purchase and sale of carbon quotas by user i in time t, and They represent the total amount of electricity and carbon quota bought or sold in the community at time t, is the time of the day before the present, The amount of electricity purchased by the user from outside. The amount of electrical energy sold to users and the outside world; The VCG mechanism is used to redefine the allocation adjustment amount and allocate electricity and carbon quotas as follows: The cost of purchasing and selling electricity and carbon quotas for user i at time t is calculated as follows: ； ； in, and are the average values ​​of the amount of electricity bought and sold by user i at time t in the real-time stage, and They represent the allocation coefficients of electricity purchase and electricity sale of user i at time t, and They represent the allocation coefficients of carbon quota buying and carbon quota selling of user i at time t, represents the total amount of electricity bought or sold at time t, represents the total amount of carbon quotas bought or sold at time t, and They represent the amount of electricity purchased and sold by user i at time t, and They represent the cost of carbon quota purchase and the amount of carbon quota sales of user i at time t respectively; Calculate the total cost after removing user i at time t. The calculation formula is as follows: ； ； in, The total amount of electricity purchased in the community after removing user i is: is the average amount of electricity purchased by the jth user in the community, excluding user i, within time t. and They represent the allocation coefficients of electricity purchase and electricity sale of user j at time t, is the total electricity sales of the community after removing user i, is the average value of the electricity sales of the jth user in the community, excluding user i, within time t. To remove the total amount of carbon quota purchased in the community after user i is removed, is the average amount of carbon quota purchased by the jth user in the community, except user i, within time t. To remove the total carbon quota sales in the community after user i, is the average value of carbon quota sales of the jth user in the community, excluding user i, within time t. and are the allocation coefficients for the purchase and sale of carbon quotas by user j at time t; Under the VCG mechanism, the actual cost of user i is calculated using the following formula: ； ； in, is the amount of electricity purchased by user i at time t after VCG allocation, After VCG allocation, the amount of electricity sold by user i at time t, is the amount of carbon quota purchased by user i at time t after VCG allocation, The amount of carbon quota sold by user i at time t after VCG allocation; Based on the actual cost of user i, the allocation coefficient is calculated using the following formula: ； ； in, is the allocation coefficient of the electricity purchased by user i after VCG at time t, is the distribution coefficient of the electric energy sold by user i after passing through VCG at time t, is the allocation coefficient of carbon quota purchased by user i after VCG at time t, is the allocation coefficient of carbon quota sales of user i after VCG at time t.

5. A method for dynamic monitoring and processing of carbon quota for community energy consumption according to claim 4, characterized in that: The specific implementation process of step S2 also includes the following steps: The cost of transactions between users and retailers and within the community after using the VCG allocation mechanism is calculated as follows: ； ； ； ； ； ； ； ； ； in, and are the average values ​​of the amount of electricity bought and sold by user i at time t in the real-time stage, and They represent the allocation coefficients of electricity purchase and electricity sale of user i at time t, and They represent the allocation coefficients of carbon quota buying and carbon quota selling of user i at time t, and They represent the transaction costs of electricity and carbon quotas between users in the community at time t, and They represent the electricity and carbon quota costs traded by user i with the retailer at time t, represents the total cost of user i’s transaction at time t, is the line power of user i at time t in the real-time stage, The time interval for the monthly allocation phase, is the electricity purchased by user i during the internal transaction in the community at time t, The electric energy sold by user i in the intra-community transaction at time t; Constructing a fairness metric for carbon cost allocation: ； in, and They represent the fairness index of buying and selling electricity and carbon quotas, respectively, N represents the total number of users in the community, is the total amount of electricity purchased by user i in the time interval, is the total amount of electricity sold by user i in the time interval, is the total carbon quota purchased by user i in the time interval, is the total carbon quota sold by user i in the time interval.

6. The method for dynamic monitoring and processing of carbon quota for community energy consumption according to claim 1 is characterized in that: The specific implementation process of step S3 includes the following steps: Construct a real-time adjustment and voltage stability management model as follows: Correct the deviation between the day-ahead phase and the real-time phase. The correction formula is as follows: ； in, and They represent the charging and discharging status of the energy storage device of user i at time t in the day-ahead stage and the real-time stage respectively. Indicates the deviation between the charging and discharging of the energy storage device in the day-ahead phase and the real-time phase; By adjusting the energy storage device, the deviation between the day-ahead and real-time phases is minimized, and then the initial power is submitted to the community administrator, as follows: ； in, and They represent the line power of user i at time t in the day-ahead phase and the real-time phase respectively. is the initial decision variable set of user i in the real-time stage; The constraint formula of the line power is as follows: ； in, represents the net value of electric energy of user i in time t, is the amount of electricity purchased by user i from the outside at time t, is the amount of electric energy sold by user i to the outside world at time t, is the electricity purchased by user i during the internal transaction in the community at time t, The electric energy sold by user i in the intra-community transaction at time t; The security constraints that the community needs to meet are as follows: ； ； ； in, , and The busbar at user i flows through the resistor and reactance The current, active and reactive power of the line to the bus at user j, represents the branch set of the bus at user j as the head end node, and are the square voltage and square current of the bus at user i, is the square of the active power from the bus at user i to the bus at user j at time t, is the square of the voltage of the bus at user i at time t, is the square of the reactive power from the bus at user i to the bus at user j at time t, is the active power flowing through the bus at user j at time t, Indicates that the head-end node is the branch set of the bus at user j; If the community administrator collects the initial decision variables submitted by users After verification, if there is no voltage violation, it means that the power submitted by the user meets the requirements. , no adjustment is required; If the voltage exceeds the limit, , then the optimization adjustment is performed through the second-order cone relaxation, as follows: ； in, is the minimum value of the square of the bus voltage at user i at time t, It is the maximum value of the square of bus voltage at user i at time t; The community administrator performs the solution as follows: ； in, represents the network loss part of voltage regulation, and is the weight coefficient, It is the set of all decision variables of community managers in the real-time stage.

7. A method for dynamic monitoring and processing of carbon quota for community energy consumption according to claim 6, characterized in that: The specific implementation process of step S3 also includes the following steps: Construct a monthly allocation settlement model as follows: Calculate the costs of transactions between users and retailers and within the community; Calculate the voltage penalty cost, the calculation formula is as follows: ； in, represents the voltage penalty cost of user i at time t, To punish the price; By comparison and To calculate the grid service reward, the calculation formula is as follows: ； in, The grid service reward for user i at time t, incentive pricing for grid services; Calculate the total cost per user: ； in, is the total cost of user i at time t; Calculate the community surplus using the following formula: ； in, surplus for the community; A monthly allocation settlement model is constructed with the goal of minimizing the difference between the actual operating cost within the month and the cost predicted in the day-ahead stage. The details are as follows: ； in, is the total user cost of user i in the day-ahead phase at time t, For the time interval.

8. A carbon quota dynamic monitoring and processing system for community energy consumption, executing a carbon quota dynamic monitoring and processing method for community energy consumption as claimed in any one of claims 1 to 7, characterized in that: The system includes: a risk management and scheduling model building module, a revenue distribution and adjustment mechanism module, and an energy and carbon quota three-stage management module; The risk management and scheduling model building module is used to build a community-based user model and management model. The user model includes gas users, photovoltaic users, energy storage users and ordinary load users. After the user trades autonomously, the community administrator collects data and makes adjustments based on the user's trading volume and system stability impact, and uses technical indicators to determine the adjustment amount for each user. A day-ahead comprehensive trading model for community energy and carbon trading is built, and the administrator determines the trading volume and makes a reasonable allocation based on the trading situation submitted by the user.

9. The carbon quota dynamic monitoring and processing system for community energy consumption according to claim 8 is characterized by: The revenue distribution and adjustment mechanism module is used to determine the energy and carbon transaction costs of the user, calculate the net value of the user's electricity and carbon quota within time t, the purchase and sale of electricity, and the purchase and sale of carbon quotas; Use the VCG mechanism to redefine the user's allocation adjustment and allocate electricity and carbon quotas; Calculate the total cost after removing user i, make allocation adjustments based on the actual cost of user i, and calculate the allocation coefficient; A fairness metric used to calculate the distribution of electricity carbon costs to ensure the fairness of transaction cost distribution.

10. The carbon quota dynamic monitoring and processing system for community energy consumption according to claim 9 is characterized in that: The energy and carbon quota three-stage management module adjusts the voltage and energy storage equipment management in real time, minimizes the power deviation between the day-ahead and real-time stages, and avoids voltage violations and power constraints of power lines; adopts the second-order cone relaxation method to solve the voltage problem; uses the monthly allocation settlement model to calculate and optimize the user's electricity and carbon quota transaction costs, voltage violation penalties and grid service rewards, and calculates the community surplus.

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