A Green Community Energy Sharing Method Considering Carbon Credits and Electric Energy Traceability

Abstract

The present invention discloses a green community energy sharing method considering carbon credits and electricity traceability, belonging to the technical field of energy market analysis. The present invention includes: (1) determining the prosumer parameters and the operating characteristic constraints of distributed resources; (2) establishing an energy clearing model considering the electricity traceability path based on each prosumer parameter; (3) establishing a carbon credit clearing model based on the established energy clearing model; (4) establishing a green community energy-carbon credit joint clearing model based on the established energy clearing model and carbon credit clearing model; (5) using the alternating direction method of multipliers to solve the energy-carbon credit joint clearing model to obtain the optimal trading plan for the green community and the energy low-carbon management method. The proposed method can provide technical and mechanism references for motivating prosumers on the distribution side to actively conduct low-carbon energy management and promote the consumption substitution of green electricity at the system terminal.

Description

Technical Field

[0001] The present invention relates to a green community energy sharing method considering carbon credits and electricity traceability, belonging to the technical field of energy market analysis. Background Art

[0002] The development of large-scale distributed resources and advanced intelligent metering and information communication technologies has made it possible for prosumers to directly share energy on the distribution network side. With the concept of tradable energy proposed, prosumers with power generation capabilities can directly supply surplus energy to other consumers through market-based transactions, which helps to stimulate prosumers to explore their flexibility and initiative to actively participate in system operation, thereby promoting the nearby consumption of new energy and local supply-demand matching.

[0003] In addition, the increasingly prominent environmental problems are prompting the rapid transformation of the power system, and a series of supporting low-carbon and environmentally friendly market mechanisms have also emerged on the power wholesale side, adding environmental value to renewable energy. However, in the prosumer energy sharing environment on the distribution side, as a subsidy mechanism to encourage the development of new energy, the above mechanisms do not assess or subsidize the end prosumers of the distribution network. The existing energy sharing on the distribution side still regards new energy power generation and fossil energy power generation as homogeneous products, ignoring the environmental value of green power generation and consumption behaviors, which is not conducive to the sustainable development of distributed renewable energy. Summary of the Invention

[0004] Technical Problem: The purpose of the present invention is to address the deficiencies of the above background art and propose a green community energy sharing method considering carbon credits and electricity traceability. The proposed method designs a carbon credit mechanism based on the traceability of electricity production pathways and establishes an energy-carbon credit joint clearing trading model to achieve traceable pricing transactions of heterogeneous electricity, so as to encourage prosumers to conduct low-carbon energy management.

[0005] Technical Solution: To achieve the above objectives, the present invention adopts the following technical solutions:

[0006] A green community energy sharing method considering carbon credits and electricity traceability, characterized by including the following steps:

[0007] (1) Determine the prosumer parameters within the green community and the constraint conditions of the distributed resource operation characteristics;

[0008] (2) Establish an energy trading model considering the electricity traceability path based on the prosumer parameters;

[0009] (3) Establish a carbon credit trading model based on the prosumer parameters and the energy trading model;

[0010] (4) Establish a green community energy-carbon credit joint clearing model based on the established energy trading model and carbon credit trading model;

[0011] (5) Solve the energy-carbon integrated clearing model using the alternating direction method of multipliers to obtain the optimal trading plan and energy low-carbon management method for the green community, so that each prosumer within the green community can share energy and arrange the distributed resource operation plan in an optimal manner.

[0012] Specifically, the step (1) includes:

[0013] (1-1) Determine the prosumer parameters within the green community. The prosumer parameters within the green community include:

[0014] 1) Load demand prediction parameter P i load (t) and the photovoltaic output prediction parameter

[0015] 2) Heterogeneous electricity trading preference parameters for green electricity, local electricity, and temperature-controlled load electricity

[0016] (1-2) Determine the constraints on the operation characteristics of the distributed resources of the prosumers within the green community. The constraints on the operation characteristics of the distributed resources of the prosumers within the green community include:

[0017] 1) Constraints on the operation characteristics and cost of the micro gas turbine:

[0018]

[0019] Where I = {1, 2,..., I}, i ∈ I represents the set of prosumers, T = {1, 2,..., T}, t ∈ T represents the set of time intervals, P i MT (t) and represent the active power output and its limit of the micro gas turbine of prosumer i at time interval t.

[0020] 2) Constraints on the operation characteristics of the distributed photovoltaic:

[0021]

[0022] Where P i PV (t) and respectively represent the active power output and its day-ahead predicted maximum value of the distributed photovoltaic of prosumer i at time interval t.

[0023] 3) Constraints on the operation characteristics of the temperature-controlled load:

[0024] T i in(t + 1) = T i out (t + 1) - [T i out (t + 1) - T i in (t)]e -1 / (RC) -ηP i TCL (t)(40)

[0025]

[0026] Wherein, P i TCL (t), T i in (t), T i out (t) respectively represent the operating power of the temperature control load of prosumer i in the time interval t, the internal and external ambient temperatures; R, C, and η respectively represent the equivalent thermal resistance, equivalent heat capacity, and performance coefficient of the temperature control load; represents the temperature limit corresponding to the comfort range of prosumer i.

[0027] 4) Constraints on the operating characteristics and cost constraints of electrical energy storage:

[0028]

[0029]

[0030]

[0031]

[0032] Wherein, respectively represent the charging and discharging power and state of charge of the electrical energy storage of prosumer i in the time interval t; respectively represent the upper limits of the charging and discharging power, the state of charge limit, and the capacity of the electrical energy storage of prosumer i.

[0033] Specifically, step (2) includes:

[0034] (2-1) Determine the objective function of the energy trading model, and the energy trading model takes minimizing the overall energy cost of the green community as the objective function:

[0035]

[0036] In the formula, to achieve product traceability classification of electric energy based on different electric energy production sources and then achieve classified pricing of heterogeneous electric energy, set \(K = \{PV, MT, grid\}\), and \(k\in K\) respectively represents three electric energy production methods: distributed photovoltaic power generation, micro gas turbine power generation, and purchasing electric energy from the grid. \(C_{p}\) represents the energy trading cost of all prosumers in the community within a trading period; \(C_{MT,i}(t)\) represents the operating cost of the micro gas turbine of prosumer \(i\) in the time interval \(t\); \(C_{loss,i}(t)\) represents the loss cost of the electric energy storage of prosumer \(i\) in the time interval \(t\); \(C_{k,i}(t)\) respectively represents the trading cost of prosumer \(i\) for the electric energy of production method \(k\) in the time interval \(t\); \(U_{k,i}(t)\) represents the additional utility of prosumer \(i\) for obtaining the electric energy of production method \(k\) in the time interval \(t\); \(D_{i}(t)\) represents the dissatisfaction degree of prosumer \(i\) with the temperature control load deviating from the set temperature in the time interval \(t\).

[0037] (2-2) Determine the constraint conditions of the energy trading model, and the constraint conditions of the energy trading model include:

[0038] 1) The prosumer energy balance constraint condition based on the electric energy traceability path:

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048]

[0049]

[0050] In the formula, \(P\) i load (t) represents the rigid load prediction parameter of prosumer \(i\) in the time interval \(t\); Denote the electric energy consumed by prosumer i for production mode k of the * passing route during time interval t. Constraints (47)-(50) indicate that for any energy consumption passing route *, there are only three sources of electric energy: distributed photovoltaic power generation, micro gas turbine power generation, and power purchased from the power grid. Denote the net demand power of prosumer i for the electric energy of production mode k during time interval t; Denote the power generation of prosumer power source k for self-use and internal community circulation; and Denote the net power purchased and sold by the prosumer to the external power grid respectively; Denote the power of prosumer i selling electric energy directly to the external power grid for production mode k during time interval t; Denote the state of charge of the energy storage for the electric energy of production mode k.

[0051] 2) Constraints on the cost and utility of prosumers based on the electric energy traceability route:

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059] In the formula, a i , b i , c i Denote the cost coefficients of the micro gas turbine; Denote the loss coefficient of the electric energy storage; Denote the price of the electric energy of production mode k during time interval t; Denote the real-time electricity price and the feed-in tariff of the external power grid respectively; are the trading preference parameters of prosumer i for green electricity and local electricity, which can be interpreted as the additional value that the prosumer is willing to pay for a unit of specific electricity; is the trading preference parameter of prosumer i for the electric energy of the temperature control load, indicating the sensitivity of the prosumer to the temperature of the temperature control load; T i set Denote the most comfortable temperature of the temperature control load set by prosumer i.

[0060] Specifically, the step (3) includes:

[0061] (3-1) Define the carbon credit as an indicator for confirming an environmentally friendly power generation and consumption method, which is a tradable product and is only bought, sold and circulated within the community. The carbon credit is only used as a tool to measure the environmental contribution of prosumers. In addition to different power generation methods that may obtain or consume carbon credits, the carbon credits are also transferred among prosumers along with power transactions. Prosumers can circulate carbon credits through internal sharing or directly purchase the deficit from the external market. The carbon credits are cleared and settled daily to help achieve the zero-carbon goal of the green community. Determine the objective function of the carbon credit trading model based on the parameters of each prosumer and the energy trading model. The carbon credit trading model aims to minimize the overall carbon credit cost of the green community as the objective function:

[0062]

[0063] In the formula, represents the carbon credit trading cost of all prosumers in the community within a trading period, and λ s represents the carbon credit price within the community, represents the carbon credit deficit of all prosumers within the time interval t.

[0064] (3-2) Determine the constraint conditions of the carbon credit trading model established based on the parameters of each prosumer and the energy trading model. The constraint conditions of the carbon credit trading model include:

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] In the formula, respectively represent the changes in carbon credits generated by different types of electric energy production or market circulation within the time interval t; ω PV 、ω MT respectively represent the carbon credit conversion coefficients under unit heterogeneous electric energy; λ REC 、λ ETS respectively represent the prediction parameters of green certificates and carbon prices in the external market; σMT Indicates the carbon emission parameters of the community micro gas turbine unit; Indicates the net trading demand for carbon credits within the time interval t. Equations (66) and (67) indicate that to avoid double counting of environmental value in energy sharing, carbon credit rewards can be obtained for producing green electricity, and when selling green electricity to other prosumers, the automatic transfer of the carbon credit rewards corresponding to this part of the green electricity will occur. During the above process of selling green electricity, the buyer prosumer only needs to pay for the price of the green electricity. Similarly, equations (68) and (69) represent the rules for obtaining and circulating carbon credits for gas turbine power generation.

[0074] Specifically, step (4) includes:

[0075] (4-1) Determine the objective function of the green community energy-carbon credit joint clearing model, and the objective function of the green community energy-carbon credit joint clearing model is to minimize the sum of the overall community energy and carbon credit costs:

[0076]

[0077] (4-2) Determine the constraint conditions of the green community energy-carbon credit joint clearing model, and the constraint conditions of the green community energy-carbon credit joint clearing model include: equations (38)-(44), (47)-(64), (66)-(73).

[0078] Specifically, step (5) includes the following steps:

[0079] (5-1) Decompose the original green community energy-carbon credit joint clearing model into prosumer sub-problems and community manager sub-problems;

[0080] (5-2) Initialize the global variables and dual variables of the green community energy-carbon credit joint clearing model;

[0081] (5-3) Solve the prosumer sub-problem and formulate an energy and carbon credit management method;

[0082] (5-4) The prosumer updates the net trading demand for heterogeneous electricity and carbon credits and interacts with the community manager;

[0083] (5-5) The community manager solves the community manager sub-problem and formulates an energy and carbon credit sharing method;

[0084] (5-6) The community manager updates the dual variables and interacts with each prosumer;

[0085] (5-7) Repeat the iteration until the accuracy requirements of the residuals of the global variables and dual variables are met, and output the optimal trading plan and energy low-carbon management strategy for the green community.

[0086] Beneficial effects: A green community energy sharing method considering carbon credits and electricity traceability proposed in the embodiment of the present invention constructs an energy trading model and a carbon credit trading model based on the traceability of electricity production pathways. On this basis, a trading model for joint clearing of energy - carbon credits in a green community is established to realize the traceable pricing transaction of heterogeneous electricity, so as to encourage prosumers to conduct low - carbon energy management. In addition, to ensure the fair and safe operation of the market and take into account the trading preferences and privacy information such as equipment parameters of each prosumer, the above models are solved by the alternating direction method of multipliers, which helps to reduce the energy cost of prosumers and promote the substitution of terminal green electricity consumption through market means, thereby promoting the sustainable development of distributed new energy. Brief Description of the Drawings

[0087] Figure 1 It is the overall flowchart of the method of the present invention.

[0088] Figure 2 It is the specific implementation steps of Step Five. Specific Embodiment

[0089] The present invention will be further described in detail below with reference to the drawings.

[0090] As Figure 1 shown is a green community energy sharing method considering carbon credits and electricity traceability, and each step will be specifically described below.

[0091] Step One: Determine the prosumer parameters within the green community and the constraint conditions of the operation characteristics of distributed resources.

[0092] The prosumer parameters include the load demand prediction parameter P i load (t) and the photovoltaic output prediction parameter including the heterogeneous electricity trading preference parameters for green electricity, local electricity, and thermostatic load electricity

[0093] The constraint conditions of the operation characteristics of distributed resources within the green community prosumers include:

[0094] 1) The operation characteristic constraint conditions and cost constraint conditions of micro - gas turbines:

[0095]

[0096] In the formula, I = {1, 2,..., I}, i ∈ I represents the set of prosumers, T = {1, 2,..., T}, t ∈ T represents the set of time intervals, P i MT (t) and Indicates the active power output and its limit of the micro gas turbine of prosumer i in time interval t.

[0097] 2) Constraints on the operating characteristics of distributed PV:

[0098]

[0099] In the formula, P i PV (t) and respectively represent the active power output and its maximum daily predicted value of the distributed PV of prosumer i in time interval t.

[0100] 3) Constraints on the operating characteristics of thermostatic loads:

[0101] T i in (t + 1) = T i out (t + 1) - [T i out (t + 1) - T i in (t)]e -1 / (RC) -ηP i TCL (t) (77)

[0102]

[0103] In the formula, P i TCL (t), T i in (t), T i out (t) respectively represent the operating power, internal and external ambient temperatures of the thermostatic load of prosumer i in time interval t; R, C, and η respectively represent the equivalent thermal resistance, equivalent heat capacity, and performance coefficient of the thermostatic load; Indicates the temperature limit corresponding to the comfort range of prosumer i.

[0104] 4) Constraints on the operating characteristics and cost constraints of electrical energy storage:

[0105]

[0106]

[0107]

[0108]

[0109] In the formula, respectively represent the charging and discharging power and state of charge of the electrical energy storage of prosumer i in time interval t; respectively represent the upper limits of the charging and discharging power, the state-of-charge limit, and the capacity of the electrical energy storage of prosumer i.

[0110] Step 2: Establish an energy trading model considering the power tracing path based on the parameters of each prosumer.

[0111] The energy trading model takes minimizing the overall energy cost of the green community as the objective function:

[0112]

[0113] In the formula, to achieve product traceability classification of electrical energy based on different electrical energy production sources and then realize classified pricing of heterogeneous electrical energy, set K = {PV, MT, grid}, and k ∈ K respectively represent three electrical energy production methods: distributed photovoltaic power generation, micro gas turbine power generation, and purchasing electrical energy from the grid. represents the energy trading cost of all prosumers in the community within a trading period; represents the operating cost of prosumer i's micro gas turbine in time interval t; represents the loss cost of prosumer i's electrical energy storage in time interval t; respectively represent the trading costs of prosumer i for electrical energy of production method k in time interval t; represents the additional utility of prosumer i for obtaining electrical energy of production method k in time interval t; represents the dissatisfaction degree of prosumer i with the temperature control load deviating from the set temperature in time interval t.

[0114] The constraint conditions of the energy trading model include:

[0115] 1) The prosumer energy balance constraint condition based on the power tracing path:

[0116]

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127] In the formula, P i load (t) represents the rigid load prediction parameter of prosumer i in the time interval t; represents the electric energy of prosumer i used for production mode k passing through * in the time interval t. The constraint conditions (84)-(87) indicate that for any energy consumption path *, there are only three ways of electric energy sources: distributed photovoltaic power generation, micro gas turbine power generation, and power purchase from the power grid. represents the net demand power of prosumer i for the electric energy of production mode k in the time interval t; represents the power generation power of prosumer power source k for self-use and internal circulation in the community; and respectively represent the net power purchase and sale power of the prosumer to the external power grid; represents the power selling power of prosumer i to the external power grid directly for the electric energy of production mode k in the time interval t; represents the state of charge of the energy storage for the electric energy of production mode k.

[0128] 2) Constraints on the cost and utility of prosumers based on the electric energy traceability path:

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136] In the formula, a i , b i , c i represent the cost coefficients of the micro gas turbine; represents the loss coefficient of the electric energy storage; represents the price of the electric energy of production mode k in the time interval t; respectively represent the real-time electricity price and the feed-in tariff of the external power grid; $\alpha_{i}$ is the trading preference parameter of prosumer $i$ for green electricity and local electricity, which can be interpreted as the additional value that the prosumer is willing to pay for a unit of specific electricity; $\beta_{i}$ is the trading preference parameter of prosumer $i$ for the electricity of the temperature control load, indicating the sensitivity of the prosumer to the temperature of the temperature control load; $T_{i}^{s}$ i set represents the most comfortable temperature of the temperature control load set by prosumer $i$.

[0137] Step 3: Establish a carbon credit trading model based on the parameters of each prosumer and the energy trading model.

[0138] Define carbon credit as an indicator to confirm an environmentally friendly power generation and consumption method. As a tradable product, it is only bought, sold and circulated within the community. Carbon credits are only used as a tool to measure the prosumer's contribution to the environment. In addition to different electricity production methods that may obtain or consume carbon credits, carbon credits are also transferred among prosumers along with electricity trading. Prosumers can circulate carbon credits through internal sharing or directly purchase the deficit from the external market. Carbon credits are cleared and settled daily to help achieve the zero-carbon goal of the green community. The carbon credit trading model takes minimizing the overall carbon credit cost of the green community as the objective function:

[0139]

[0140] In the formula, $C_{c}$ represents the carbon credit trading cost of all prosumers in the community within a trading period, and $\lambda$ s represents the carbon credit price within the community, $\Delta C_{t}$ represents the carbon credit deficit of all prosumers within the time interval $t$.

[0141] The constraint conditions of the carbon credit trading model include:

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148]

[0149]

[0150] In the formula, respectively represent the changes in carbon credits generated by different types of electric energy production or market circulation within the time interval t; ω PV and ω MT respectively represent the carbon credit conversion coefficients under unit heterogeneous electric energy; λ REC and λ ETS respectively represent the prediction parameters of green certificates and carbon prices in the external market; σ MT represents the carbon emission parameter of the community micro gas turbine unit; represents the net trading demand for carbon credits within the time interval t.

[0151] Step Four: Establish a green community energy-carbon credit joint clearing model based on the established energy trading model and carbon credit trading model.

[0152] The objective function of the green community energy-carbon credit joint clearing model is to minimize the sum of the overall community energy and carbon credit costs:

[0153]

[0154] The constraint conditions of the green community energy-carbon credit joint clearing model include: Equations (75)-(81), (84)-(101), (103)-(110).

[0155] Step Five: Use the alternating direction multiplier method to solve the energy-carbon credit joint clearing model, and obtain the optimal trading plan and energy low-carbon management method for the green community, so that each prosumer within the green community can share energy and arrange the distributed resource operation plan in an optimal manner.

[0156] As Figure 2 shown is the solution process of the alternating direction multiplier method in Step Five. The model solution method includes the following steps:

[0157] 1) Decompose the original green community energy-carbon credit joint clearing model into a prosumer sub-problem and a community manager sub-problem. The objective function of the prosumer sub-problem:

[0158]

[0159] where X i,k and Y i,k respectively represent the vectors composed of the terms on the left and right ends of Equation (88); Z i and W i respectively represent the vectors composed of the terms on the left and right ends of Equation (109); C i represents the vector composed of the MT power generation cost, ES loss cost and external grid power purchase cost of prosumer i; U i represents the utility corresponding to the trading and energy consumption preferences of prosumer i; μ i,kThe dual variable of expression (88); υ i The dual variable of expression (109); ρ represents the penalty term coefficient of the alternating direction multiplier method, and ν represents the number of iterations.

[0160] The constraints of the prosumer sub-problem include: expressions (75)-(81), (84)-(87), (89)-(90), (92)-(96), (98)-(101), (103)-(108).

[0161] The objective function of the community manager sub-problem:

[0162]

[0163] where is calculated according to the following formula:

[0164]

[0165]

[0166] The constraints of the community manager sub-problem include: expressions (91), (103)-(108), (110).

[0167] 2) Initialize the global variables of the green community energy-carbon integral joint clearing model and the dual variables

[0168] 3) The prosumer solves the prosumer sub-problem and formulates an energy and carbon integral management method and

[0169] 4) Based on the solution results of the prosumer sub-problem in step 3), the prosumer updates and according to expressions (114)-(115) in step 1) and interacts with the community manager;

[0170] 5) The community manager solves the community manager sub-problem and formulates an energy and carbon integral sharing method and

[0171] 6) The community manager updates the dual variables and and interacts with the prosumer. The method for updating the dual variables:

[0172]

[0173]

[0174] 7) Repeat the iteration until the accuracy requirements of the global variable and the dual variable residual are met, and output the optimal trading plan and energy low-carbon management strategy for the green community. The accuracy requirements for the global variable and the dual variable residual are as follows:

[0175]

[0176] where ε represents the set convergence accuracy of the alternating direction multiplier method.

[0177] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A green community energy sharing method considering carbon credits and electricity traceability, characterized in that, it includes the following steps: Determine the parameters of prosumers within the green community and the operating characteristic constraints of distributed resources; Based on the parameters of each prosumer, establish an energy clearing model considering the electricity traceability path. The energy clearing model takes minimizing the overall energy cost of the green community as the objective function, and the model constraints include the prosumer energy balance constraint, cost, and utility constraints based on the electricity traceability path; Based on the parameters of each prosumer and the energy trading model, establish a carbon credit clearing model. The carbon credit clearing model takes minimizing the overall carbon credit cost of the green community as the objective function, and the model constraints include the carbon credit change constraint and the balance constraint; Based on the established energy trading model and carbon credit trading model, establish a green community energy-carbon credit joint clearing model. The green community energy-carbon credit joint clearing model takes minimizing the sum of the overall energy and carbon credit costs of the community as the objective function; Use the alternating direction multiplier method to solve the energy-carbon credit joint clearing model to obtain the optimal trading plan and energy low-carbon management method for the green community, so that each prosumer within the green community can share energy and arrange the distributed resource operation plan in an optimal manner; The operating characteristic constraints of the distributed resources of the prosumers within the green community include: Operating characteristic constraints and cost constraints of micro gas turbines: Operating characteristic constraints of distributed photovoltaics: Operating characteristic constraints of temperature-controlled loads: T i in (t + 1) = T i out (t + 1)-[T i out (t + 1)-T i in (t)]e -1 / (RC) -ηP i TCL (t)(3) Operating characteristic constraints and cost constraints of electrical energy storage: where \(I = \{1, 2, \ldots, I\}\), \(i\in I\) represents the set of prosumers, \(T=\{1, 2, \ldots, T\}\), \(t\in T\) represents the set of time intervals, \(P\) i MT (t) and represent the active power output and its limit of the micro gas turbine of prosumer \(i\) in time interval \(t\); \(P\) i PV (t) and respectively represent the active power output and its maximum day-ahead prediction value of the distributed PV of prosumer \(i\) in time interval \(t\); \(P\) i TCL (t), \(T\) i in (t), \(T\) i out (t) respectively represent the operating power, internal and external ambient temperatures of the thermostatic load of prosumer \(i\) in time interval \(t\); \(R\), \(C\), \(\eta\) respectively represent the equivalent thermal resistance, equivalent heat capacity and performance coefficient of the thermostatic load; represents the temperature limit corresponding to the comfort range of prosumer \(i\); respectively represent the charging and discharging power and state of charge of the electrical energy storage of prosumer \(i\) in time interval \(t\); respectively represent the upper limits of the charging and discharging power, state of charge limit and capacity of the electrical energy storage of prosumer \(i\).

2. A green community energy sharing method considering carbon credits and electricity traceability according to claim 1, characterized in that, the energy clearing model takes minimizing the overall energy consumption cost of the green community as the objective function: In the formula, to achieve product traceability classification of electric energy based on different electric energy production sources and then realize classified pricing of heterogeneous electric energy, set \(K = \{PV, MT, grid\}\), where \(k\in K\) respectively represents three electric energy production methods: distributed photovoltaic power generation, micro gas turbine power generation, and purchasing electric energy from the power grid; represents the energy transaction cost of all prosumers in the community during a trading period; represents the operating cost of the micro gas turbine of prosumer \(i\) during the time interval \(t\); represents the loss cost of the electric energy storage of prosumer \(i\) during the time interval \(t\); respectively represent the transaction costs of prosumer \(i\) for electric energy of production method \(k\) during the time interval \(t\); represents the additional utility of prosumer \(i\) for obtaining electric energy of production method \(k\) during the time interval \(t\); represents the dissatisfaction degree of prosumer \(i\) with the temperature control load deviating from the set temperature during the time interval \(t\).

3. A green community energy sharing method considering carbon credits and electricity traceability according to claim 1, characterized in that, the constraints of the energy clearing model include: Prosumers' energy balance constraint conditions based on the electricity traceability path: where P i load (t) represents the rigid load prediction parameter of prosumer i in the time interval t; represents the electric energy of prosumer i used for production mode k passing through * in the time interval t; Constraints (10)-(13) indicate that for any energy consumption path *, the power sources of electric energy are only three paths: distributed photovoltaic power generation, micro gas turbine power generation, and power purchase from the power grid; represents the net demand power of prosumer i for the electric energy of production mode k in the time interval t; represents the power generation power of prosumer power source k for self-use and internal circulation in the community; and respectively represent the net power purchase and sale of prosumer to the external power grid; represents the power of prosumer i selling electricity directly to the external power grid for the electric energy of production mode k in the time interval t; represents the state of charge of the energy storage for the electric energy of production mode k.

4. A green community energy sharing method considering carbon credits and electricity traceability according to claim 1, characterized in that, Prosumers' cost and utility constraints based on the electricity traceability path: Where a i , b i , c i represent the cost coefficients of the micro gas turbine; represents the loss coefficient of the electrical energy storage; represents the price of electrical energy for production mode k at time interval t; respectively represent the real-time electricity price and the feed-in tariff of the external power grid; is the trading preference parameter of prosumer i for green electricity and local electricity, which can be interpreted as the additional value that the prosumer is willing to pay for a unit of specific electricity; is the trading preference parameter of prosumer i for the electrical energy of the temperature control load, indicating the sensitivity of the prosumer to the temperature of the temperature control load; T i set represents the most comfortable temperature of the thermostatic load set by prosumer i.

5. A green community energy sharing method considering carbon credits and electricity traceability according to claim 1, characterized in that, the carbon credit clearing model takes minimizing the overall carbon credit cost of the green community as the objective function: In the formula, represents the carbon credit trading cost of all prosumers in the community within a trading cycle, and λ s represents the carbon credit price within the community, represents the carbon credit deficit of all prosumers within the time interval t.

6. A green community energy sharing method considering carbon credits and electricity traceability according to claim 1, characterized in that, the constraints of the carbon credit clearing model include: In the formula, respectively represent the changes in carbon credits generated by different types of electrical energy produced or in the market circulation within the time interval t; ω PV and ω MT respectively represent the carbon credit conversion coefficients under unit heterogeneous electrical energy; λ REC and λ ETS respectively represent the prediction parameters of green certificates and carbon prices in the external market; σ MT represents the carbon emission parameter of the community micro gas turbine unit; represents the net trading demand for carbon credits within the time interval t; Formulas (29) and (30) indicate that in order to avoid double accounting of environmental value in energy sharing, producing green electrical energy can obtain carbon credit rewards, and selling green electricity to other prosumers will be accompanied by the automatic transfer of the carbon credit rewards corresponding to this part of the green electricity. During the process of selling green electricity, the buyer prosumer only needs to pay for the price of the green electricity; Formulas (31) and (32) represent the carbon credit acquisition and circulation rules for gas turbine power generation.

7. A green community energy sharing method considering carbon credits and electricity traceability according to claim 1, characterized in that, the green community energy-carbon credit joint clearing model takes minimizing the sum of the overall energy and carbon credit costs of the community as the objective function: The constraint conditions include equations (1)-(7), (10)-(27), (29)-(36).

8. A green community energy sharing method considering carbon credits and electricity traceability according to claim 1, characterized in that, Solving the energy-carbon integration joint clearing model using the alternating direction multiplier method to obtain the optimal trading plan and energy low-carbon management method for green communities includes the following steps: Decompose the original green community energy-carbon integration joint clearing model into a prosumer sub-problem and a community manager sub-problem; Initialize the global variables and dual variables of the green community energy-carbon integration joint clearing model; Solve the prosumer sub-problem and formulate the energy and carbon integration management method; The prosumer updates the net demand for heterogeneous electricity and carbon integration trading and interacts with the community manager; The community manager solves the community manager sub-problem and formulates the energy and carbon integration sharing method; The community manager updates the dual variables and interacts with each prosumer; Repeat the iteration until the accuracy requirements of the global variable and dual variable residuals are met, and output the optimal trading plan and energy low-carbon management strategy for the green community.

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