Green power-conventional power same-station clearing method oriented to overall social welfare optimization

Abstract

The invention discloses a green electricity-conventional electricity same-station clearing method oriented to overall social welfare optimization. The green electricity-conventional electricity same-station clearing method comprises the following steps: 1) disassembling electricity transactions of a green electricity market into two types of electric energy services and environmental attributes; 2) quantity price declarations of supply and demand sides on the electric energy service and the environment attribute are obtained; 3) constructing an overall social welfare optimal-oriented green power-conventional power same-station competition clearing model; (4) quantity price declarations of the supply side and the demand side on the electric energy service and the environment attribute are input into a clearing model for the green power-conventional power same-station competition oriented to the overall social welfare optimization, and the optimal electric energy clearing price and the optimal environment attribute clearing price are obtained; and 5) determining the electricity transaction settlement price of the green electricity market based on the optimal electricity energy clearing price and the optimal environment attribute clearing price.The green electricity-conventional electricity transaction settlement price determination method can be widely applied to a green electricity-conventional electricity same-platform clearing scene, and can provide a basis for the design of a green electricity transaction and conventional electricity transaction collaborative development mechanism.

Description

Technical Field

[0001] This invention relates to the field of power systems and their automation technology, specifically to a method for the simultaneous clearing of green electricity and conventional electricity in order to optimize overall social welfare. Background Technology

[0002] The green electricity market offers two trading models: "certificate-for-electricity" green electricity trading and "certificate-for-electricity" green certificate trading. Both aim to promote green electricity consumption through market mechanisms while meeting users' green electricity needs. Green electricity trading is a type of medium- to long-term electricity trading, following the basic rules of such transactions. Compared to green certificate trading, the "certificate-for-electricity" model gives the green attributes of green electricity transactions greater international recognition. In green electricity trading, suppliers can increase their profits, while consumers can build a green corporate image. Green electricity trading has become a key pathway to channeling the green value of renewable energy.

[0003] To effectively reflect the green value of new energy sources, the development of green electricity trading should meet the following market operation objectives: 1) Maximize the overall social welfare of green electricity trading and conventional electricity trading; 2) The electricity portion of green electricity trading is homogeneous with that of conventional electricity trading, and the market price should be unified; 3) Green electricity prices can be broken down, and the total price is higher than that of conventional electricity to reflect the additional environmental value contained in green electricity. At the same time, both the environmental price component and the electricity price component should reflect the market supply and demand relationship.

[0004] Under the current market mechanism, green electricity trading markets in most regions are organized separately before non-green electricity trading markets. The remaining electricity demand after the green electricity trading ends then participates in conventional medium- and long-term electricity trading, which focuses solely on electricity services. The green electricity trading clearing process obtains the overall market price signal (including electricity price and environmental price) based on the total price declared by new energy sources and users. The average market price of green certificates from the previous settlement period is then used as the environmental price, and the remaining portion is used as the electricity price, thus decomposing the total price of green electricity. However, this platform-based market competition model, which prioritizes green electricity trading, focuses on maximizing the social welfare of green electricity trading while neglecting the greater social welfare of conventional electricity trading. This results in a loss of overall system social welfare and fails to meet the aforementioned market operation objective 1), incentivizing market participants to overstate their green electricity demand. Furthermore, under this market mechanism, green electricity trading and conventional electricity trading are priced separately based on the quantity and price declarations of their respective entities. On the one hand, this cannot ensure that the price of green electricity is higher than that of conventional electricity; on the other hand, it cannot achieve a scientific decomposition of green electricity prices. The decomposition method based on the average price of green certificates in the previous settlement period cannot accurately reflect the current demand for green electricity, and it cannot guarantee that the prices of green electricity and conventional electricity are unified, thus failing to meet the aforementioned market operation objectives 2) and 3).

[0005] In summary, the current green electricity-conventional electricity trading mechanism cannot effectively meet market operation objectives, and the failure to achieve optimal overall social welfare of the system is the key factor causing this problem. Relevant market clearing methods still need further research. Summary of the Invention

[0006] The purpose of this invention is to provide a method for the co-exit of green electricity and conventional electricity that optimizes overall social welfare, comprising the following steps:

[0007] 1) The electricity trading in the green electricity market is broken down into two categories: electricity energy services and environmental attributes.

[0008] 2) Obtain the quantity and price declarations for electricity services and environmental attributes from both the supply and demand sides.

[0009] 3) Construct a clearing model for green electricity and conventional electricity to compete on the same stage, which is aimed at optimizing overall social welfare.

[0010] 4) Input the quantity and price declarations of electricity services and environmental attributes from both the supply and demand sides into a clearing model that optimizes overall social welfare by having green electricity and conventional electricity compete on the same stage, and obtain the optimal clearing price of electricity services and the optimal clearing price of environmental attributes.

[0011] 5) Determine the electricity trading settlement price in the green electricity market based on the optimal energy clearing price and the optimal environmental attribute clearing price.

[0012] Furthermore, the supply and demand sides include the supply side and the demand side.

[0013] The supply side includes conventional energy units and new energy units.

[0014] The demand side includes users.

[0015] The electricity trading in the green electricity market includes conventional electricity trading and green electricity trading.

[0016] Furthermore, the conventional energy units include thermal power units and hydropower units.

[0017] The new energy units include wind turbines and solar power units.

[0018] Furthermore, the quantity and price declaration of the electricity service includes the user's regular electricity declaration quantity, the user's regular electricity price, the regular electricity declaration quantity of the conventional energy unit, the regular electricity price of the conventional energy unit, the regular electricity declaration quantity of the new energy unit, and the regular electricity price of the new energy unit.

[0019] Furthermore, the environmental attribute quantity and price declaration includes the user's declared green electricity volume, the user's environmental attribute price, the declared green electricity volume of the new energy unit, and the environmental attribute price of the new energy unit.

[0020] Furthermore, the objective function of the clearing model for competition between green electricity and conventional electricity, which aims at optimizing overall social welfare, is as follows:

[0021] (1)

[0022] In the formula, This indicates the user index. This indicates the index of energy units on the supply side. This represents the index for new energy generating units. t represents the index for the trading session. , users respectively Energy units The regular electricity price quote. , users respectively Energy units The amount of electricity cleared. , users respectively New energy units Quotation based on environmental attributes. , users respectively New energy units Environmental attribute clearing volume.

[0023] Furthermore, the constraints of the clearing model for green electricity and conventional electricity competing for the optimal overall social welfare include supply and demand balance constraints for electrical energy and environmental attributes, constraints on the clearing amount of electrical energy by the purchasing and selling entities, constraints on the clearing amount of environmental attributes by the purchasing and selling entities, and constraints on line power flow.

[0024] Furthermore, the supply and demand balance constraints of electrical energy and environmental attributes are as follows:

[0025] (2)

[0026] (3)

[0027] In the formula, This indicates the user index. This indicates the index of energy units on the supply side. This represents the index for new energy generating units. t represents the index for the trading session. , users respectively Energy units The amount of electricity cleared. , users respectively New energy units Environmental attribute clearing volume. , These are the dual multipliers of the electrical energy supply and demand balance constraint and the environmental attribute supply and demand balance constraint, respectively.

[0028] The energy clearing constraints for the purchasing and selling entities are as follows:

[0029] (4)

[0030] (5)

[0031] In the formula, , users respectively Energy units The total declared amount of electrical energy. , , , Both are dual multipliers of the energy clearing constraints of the purchasing and selling entities.

[0032] The clearing constraints for the environmental attributes of the purchasing and selling entities are as follows:

[0033] (6)

[0034] (7)

[0035] In the formula, The proportion of green electricity demand declared by users. , , , All are dual multipliers of the clearing quantity constraints of the environmental attributes of the buying and selling entities.

[0036] The power flow constraints of the line are as follows:

[0037] (8)

[0038] In the formula, This is a route index. , The lines are respectively Upper and lower limits of power transfer. For energy units The injected power at the node relative to the line The power transfer distribution factor. For users The injected power at the node relative to the line The power transfer distribution factor. , Both are dual multipliers of line power flow constraints.

[0039] Furthermore, the optimal energy clearing price and the optimal environmental attribute clearing price are as follows:

[0040] (9)

[0041] (10)

[0042] In the formula, , These represent the optimal energy clearing price and the optimal environmental attribute clearing price, respectively. , These represent the dual multipliers of the electrical energy supply and demand balance constraint and the environmental attribute supply and demand balance constraint, respectively. This is a route index. This indicates the node index where the user and energy unit are located. Represents a node Injected power relative to line The power transfer distribution factor. , Both are dual multipliers of line power flow constraints.

[0043] Furthermore, the settlement price for the conventional electricity trading is... .

[0044] The settlement price for the green electricity trading is: .

[0045] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned method for co-emptying green electricity and conventional electricity in order to optimize overall social welfare.

[0046] The technical effects of this invention are undeniable. This invention proposes a green electricity-conventional electricity co-clearing method that simultaneously considers the supply and demand relationship of electrical energy and environmental attributes, in order to explore the impact of different clearing methods on market resource allocation.

[0047] The green electricity-conventional electricity competition model established by this invention provides an important foundation for fair competition between green electricity and conventional energy on the same platform. It breaks down green electricity trading into two categories of commodities: electricity services and environmental attributes. The electricity services part competes with conventional electricity trading entities such as thermal power and hydropower, while the environmental attributes part is competed by entities with the qualifications and willingness to purchase and sell green electricity. This is conducive to accurately representing the supply and demand relationship of the two types of commodities.

[0048] The green electricity-conventional electricity competitive clearing method proposed in this invention, which aims to optimize overall social welfare, demonstrates excellent performance in terms of market clearing effectiveness. It forms an electricity balance constraint based on the electricity declarations of both buyers and sellers, thereby achieving supply and demand matching for electricity transactions. It also forms an environmental attribute supply and demand balance constraint based on the environmental attribute quantity and price declarations of entities with green electricity purchase and sale qualifications and willingness, thereby achieving supply and demand matching for environmental attribute transactions. Based on the dual multipliers of the two balance constraints, it forms the electricity clearing price and the environmental attribute clearing price, respectively. This pricing mechanism satisfies the incentive compatibility property of market participants.

[0049] This invention can ensure the maximization of overall social welfare in the clearing scheme, incentivize market participants to declare their true demands, and the market competition-based quantity and price declaration method can adapt to the differentiated green electricity demands of different types of users. The price settlement mechanism can meet the pricing objectives of green electricity and provide a reference for the co-trading of green electricity and conventional electricity in my country.

[0050] This invention can be widely applied to scenarios where green electricity and conventional electricity are traded on the same platform, and can provide a basis for the design of a mechanism for the coordinated development of green electricity trading and conventional electricity trading. Attached Figure Description

[0051] Figure 1 A schematic diagram illustrating a competitive model between green electricity and conventional electricity that optimizes overall social welfare.

[0052] Figure 2 This is a schematic diagram of the system network topology for an example embodiment;

[0053] Figure 3 This is a schematic diagram of the system power flow distribution during the S1 green electricity trading phase;

[0054] Figure 4 This is a schematic diagram of the system power flow distribution during the S1 regular power trading phase;

[0055] Figure 5 This is a schematic diagram of the system power flow distribution during the S2 energy trading phase;

[0056] Figure 6 This is a schematic diagram of the S3 power flow distribution.

[0057] Figure 7 This is a diagram illustrating the market clearing price for node 3 under S1.

[0058] Figure 8 This is a diagram illustrating the market clearing price at node 3 under S2.

[0059] Figure 9 This is a diagram illustrating the market clearing price for node 3 under S3. Detailed Implementation

[0060] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.

[0061] Example 1:

[0062] See Figures 1 to 9 A method for co-purchasing green electricity and conventional electricity to optimize overall social welfare includes the following steps:

[0063] 1) The electricity trading in the green electricity market is broken down into two categories: electricity energy services and environmental attributes.

[0064] 2) Obtain the quantity and price declarations for electricity services and environmental attributes from both the supply and demand sides.

[0065] 3) Construct a clearing model for green electricity and conventional electricity to compete on the same stage, which is aimed at optimizing overall social welfare.

[0066] 4) Input the quantity and price declarations of electricity services and environmental attributes from both the supply and demand sides into a clearing model that optimizes overall social welfare by having green electricity and conventional electricity compete on the same stage, and obtain the optimal clearing price of electricity services and the optimal clearing price of environmental attributes.

[0067] 5) Determine the electricity trading settlement price in the green electricity market based on the optimal energy clearing price and the optimal environmental attribute clearing price.

[0068] Example 2:

[0069] A method for clearing out green electricity and conventional electricity in a manner that optimizes overall social welfare is described in Example 1. Furthermore, the supply and demand sides include the supply side and the demand side.

[0070] The supply side includes conventional energy units and new energy units.

[0071] The demand side includes users.

[0072] The electricity trading in the green electricity market includes conventional electricity trading and green electricity trading.

[0073] Example 3:

[0074] A method for clearing out green electricity and conventional electricity in a manner that optimizes overall social welfare is described in any one of Examples 1 and 2. Furthermore, the conventional energy units include thermal power units and hydropower units.

[0075] The new energy units include wind turbines and solar power units.

[0076] Example 4:

[0077] A method for clearing out green electricity and conventional electricity in a manner that optimizes overall social welfare is described in any one of Examples 1 to 3. Further, the quantity and price declaration of the electricity service includes the user's declared quantity of conventional electricity, the user's quoted price of conventional electricity, the declared quantity of conventional electricity of conventional energy units, the quoted price of conventional electricity of conventional energy units, the declared quantity of conventional electricity of new energy units, and the quoted price of conventional electricity of new energy units.

[0078] Example 5:

[0079] A method for clearing out green electricity and conventional electricity in a manner that optimizes overall social welfare is described in any one of Examples 1 to 4. Further, the quantity and price declaration of environmental attributes includes the user's declared green electricity volume, the user's environmental attribute quotation, the declared green electricity volume of the new energy unit, and the environmental attribute quotation of the new energy unit.

[0080] Example 6:

[0081] A method for clearing out green electricity and conventional electricity in a competitive environment, aimed at optimizing overall social welfare, is described in any one of Examples 1 to 5. Furthermore, the objective function of the clearing model for green electricity and conventional electricity competition, aimed at optimizing overall social welfare, is as follows:

[0082] (1)

[0083] In the formula, This indicates the user index. This indicates the index of energy units on the supply side. This represents the index for new energy generating units. t represents the index for the trading session. , users respectively Energy units The regular electricity price quote. , users respectively Energy units The amount of electricity cleared. , users respectively New energy units Quotation based on environmental attributes. , users respectively New energy units Environmental attribute clearing volume.

[0084] Example 7:

[0085] A green electricity-conventional electricity co-exit clearing method oriented towards overall social welfare optimization is described in any one of Examples 1 to 6. Further, the constraints of the clearing model for green electricity-conventional electricity co-exit competition oriented towards overall social welfare optimization include supply and demand balance constraints of electrical energy and environmental attributes, electrical energy clearing constraints of purchasing and selling entities, environmental attribute clearing constraints of purchasing and selling entities, and line power flow constraints.

[0086] Example 8:

[0087] A method for co-purging green electricity and conventional electricity to optimize overall social welfare is described in any one of Examples 1 to 7. Furthermore, the supply and demand balance constraints of electrical energy and environmental attributes are as follows:

[0088] (2)

[0089] (3)

[0090] In the formula, This indicates the user index. This indicates the index of energy units on the supply side. This represents the index for new energy generating units. t represents the index for the trading session. , users respectively Energy units The amount of electricity cleared. , users respectively New energy units Environmental attribute clearing volume. , These are the dual multipliers of the electrical energy supply and demand balance constraint and the environmental attribute supply and demand balance constraint, respectively.

[0091] The energy clearing constraints for the purchasing and selling entities are as follows:

[0092] (4)

[0093] (5)

[0094] In the formula, , users respectively Energy units The total declared amount of electrical energy. , , , Both are dual multipliers of the energy clearing constraints of the purchasing and selling entities.

[0095] The clearing constraints for the environmental attributes of the purchasing and selling entities are as follows:

[0096] (6)

[0097] (7)

[0098] In the formula, The proportion of green electricity demand declared by users. , , , All are dual multipliers of the clearing quantity constraints of the environmental attributes of the buying and selling entities.

[0099] The power flow constraints of the line are as follows:

[0100] (8)

[0101] In the formula, This is a route index. , The lines are respectively Upper and lower limits of power transfer. For energy units The injected power at the node relative to the line The power transfer distribution factor. For users The injected power at the node relative to the line The power transfer distribution factor. , Both are dual multipliers of line power flow constraints.

[0102] Example 9:

[0103] A method for co-clearing green and conventional electricity to optimize overall social welfare is described in any one of Examples 1 to 8. Furthermore, the optimal energy clearing price and the optimal environmental attribute clearing price are as follows:

[0104] (9)

[0105] (10)

[0106] In the formula, , These represent the optimal energy clearing price and the optimal environmental attribute clearing price, respectively. , These represent the dual multipliers of the electrical energy supply and demand balance constraint and the environmental attribute supply and demand balance constraint, respectively. This is a route index. This indicates the node index where the user and energy unit are located. Represents a node Injected power relative to line The power transfer distribution factor. , Both are dual multipliers of line power flow constraints.

[0107] Example 10:

[0108] A method for co-exiting green and conventional electricity to optimize overall social welfare is described in any one of Examples 1 to 9. Further, the settlement price for the conventional electricity trading is... .

[0109] The settlement price for the green electricity trading is: .

[0110] Example 11:

[0111] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the green electricity-conventional electricity co-exit method for optimal overall social welfare as described in any one of Examples 1-10.

[0112] Example 12:

[0113] See Figures 1 to 9 A method for the co-exit of green and conventional electricity to optimize overall social welfare, the main technical contents of which include:

[0114] First, a competitive model for green and conventional electricity trading, oriented towards optimal overall social welfare, was constructed. Then, green electricity trading was decomposed into two categories: electricity services and environmental attributes. The electricity services category competes with conventional electricity trading entities such as thermal and hydropower, while the environmental attributes category is contested by entities with the qualifications and willingness to purchase and sell green electricity. Finally, dual multipliers based on the balance constraints of the electricity and environmental attributes categories were used to form the clearing prices for electricity and environmental attributes, respectively. Simulation results using numerical examples verified the effectiveness of the proposed method in market resource allocation compared to existing methods. Simulation results on the PJM5 node system show that the proposed method can achieve a reasonable and optimized allocation of green and conventional electricity, resulting in greater social welfare, and rationally decomposes the prices of green and conventional electricity, while comprehensively considering the market participants' quantity and price declarations and the transmission capacity of the system's transmission lines.

[0115] The specific steps are as follows:

[0116] I. Green Electricity vs. Conventional Electricity Competition Model

[0117] This invention constructs a competitive model for green electricity and conventional electricity that optimizes overall social welfare (e.g., Figure 1As shown, by declaring the quantity and price of electrical energy and environmental attributes and competing on the same stage, the purchasing and selling intentions of both supply and demand sides are clarified, forming a clearing scheme that maximizes the overall social welfare of the system.

[0118] The proposed competition model breaks down green electricity trading into two categories: electricity services and environmental attributes. The electricity services segment competes with conventional electricity trading entities such as thermal and hydropower companies, while the environmental attributes segment is contested by entities with the qualifications and willingness to purchase and sell green electricity. The following details the proposed green electricity-conventional electricity co-marketing model from three aspects: bidding model, clearing method, and pricing and settlement mechanism.

[0119] Regarding the bidding model, both supply and demand sides will submit quantity and price declarations for electricity services and environmental attributes respectively. Specifically, electricity users will submit quantity and price declarations for electricity services and environmental attributes based on their own electricity needs (i.e., whether they need to purchase green electricity). The quantity and price declaration for electricity services represents the amount of electricity users wish to purchase and the price they are willing to pay for conventional electricity services. The quantity and price declaration for environmental attributes represents the amount of green electricity users wish to purchase and the additional price they are willing to pay on top of conventional electricity for green electricity. The demand for environmental attributes can be declared according to the proportion of the user's winning bid for electricity. Electricity sellers with green electricity trading qualifications, such as wind and solar power, will submit quantity and price declarations for electricity services and environmental attributes. The quantity and price declaration for electricity services represents the amount of electricity that renewable energy sources wish to sell and the expected selling price. The quantity and price declaration for environmental attributes represents the amount of electricity that renewable energy sources wish to sell in the form of green electricity and the additional revenue they expect to earn from green electricity. Electricity sellers that do not yet have green electricity trading qualifications, such as thermal power and hydropower, will only submit quantity and price declarations for electricity services.

[0120] Regarding the clearing mechanism, electricity is traded as a commodity divided into two parts: electrical energy and environmental attributes. The clearing of both categories aims to maximize the overall social welfare of both. All market participants can participate in conventional electricity trading. The electrical energy portion of green electricity is homogeneous with conventional electricity, and a balance constraint will be formed based on the electrical energy declarations of both buyers and sellers, thereby achieving supply and demand matching in electrical energy trading. Simultaneously, a supply and demand balance constraint will be formed based on the quantity and price declarations of environmental attributes by entities with the qualifications and willingness to purchase and sell green electricity, thereby achieving supply and demand matching in environmental attribute trading. Since environmental attributes represent an added value to the electrical energy traded by green electricity buyers and sellers, the winning bid for environmental attributes cannot exceed the winning bid for electrical energy.

[0121] Regarding the pricing and settlement mechanism, the dual multipliers based on the balance constraints of electrical energy and environmental attributes can respectively form the clearing price of electrical energy and the clearing price of environmental attributes. If the influence of line power flow constraints is considered, the nodal clearing price of electrical energy is jointly formed by the dual multipliers of electrical energy balance constraints and the dual multipliers of power flow constraints. The clearing price of electrical energy is the conventional electricity clearing price, and the sum of the prices of electrical energy and environmental attributes is the green electricity clearing price. This ensures that the price of green electricity is higher than the price of conventional electrical energy, and naturally captures the electrical energy component and the environmental price component of the green electricity price. Based on the bidding results of market participants for electrical energy and environmental attributes, their winning bids for conventional electricity and green electricity can be determined. Subsequently, the corresponding clearing prices can be used to settle the two winning bids.

[0122] II. A Green Electricity-Conventional Electricity Competition and Clearing Model Aimed at Optimizing Overall Social Welfare

[0123] 1. Objective function

[0124] The proposed model takes the maximization of overall social welfare from electricity and environmental attributes as its objective function, as shown in equation (1). Here, the first term represents the social welfare generated by electricity trading; the second term represents the social welfare generated by environmental attribute trading.

[0125] (1)

[0126] In the formula, , and These are the user's, all power generators', and power generators qualified for green electricity trading, respectively; t is the trading period number; , The electricity price quotes are for users and power generators, respectively. , The cleared electricity volume is for users and power generators, respectively; , Quotations are provided based on the environmental attributes of both the user and the distributor. , The clearing amounts are based on environmental attributes for both users and generators.

[0127] 2. Constraints

[0128] 1) Constraints on the supply and demand balance of electrical energy and environmental attributes

[0129] (2)

[0130] (3)

[0131] In the formula, , These are the dual multipliers for the electrical energy supply and demand balance constraint and the environmental attribute supply and demand balance constraint, respectively.

[0132] 2) Constraints on the clearing of electrical energy by purchasing and selling entities

[0133] (4)

[0134] (5)

[0135] In the formula, , These are the electricity energy declarations for users and power generators, respectively. , , , For the dual multipliers of the corresponding constraints.

[0136] 3) Environmental attribute clearing constraints for purchasing and selling entities

[0137] (6)

[0138] (7)

[0139] In the formula, The green electricity demand ratio declared by users, that is, the proportion of green electricity consumption to their total electricity consumption. , , , For the dual multipliers of the corresponding constraints.

[0140] 4) Power flow constraints of the line

[0141] (8)

[0142] In the formula, l is the line number; , These are the upper and lower limits of line power transmission, respectively; Injecting power into the node where the power generator g is located relative to the line The power transfer distribution factor; Inject power relative to the line at the node where user d is located. The power transfer distribution factor; , These are the dual multipliers of the corresponding constraints.

[0143] III. Pricing and Settlement Mechanism and Nature of Green Electricity and Conventional Electricity Co-existing Markets

[0144] Based on relevant theories of economic dispatch, in the proposed clearing model of green electricity and conventional electricity competing on the same platform, the dual multiplier of the power supply and demand balance constraint is... Dual multiplier with line power flow constraints , Together they constitute the price of electrical energy, as shown in equation (9). The dual multiplier of the environmental attribute supply and demand balance constraint. Price, when considered as an environmental attribute, is non-negative.

[0145] (9)

[0146] Under this pricing mechanism, market participants face three settlement scenarios: 1) The winning bids for electricity by market participants are all for conventional electricity, and the settlement will be based on the price of electricity. Settlement of all winning bids; 2) The winning bids for all electricity by market participants are green electricity that includes environmental attributes (i.e., the winning bid for environmental attributes equals the winning bid for electrical energy), and will be settled at the total green electricity price ( ) Settle all winning bids for electricity; 3) Part of the winning bids for electricity by market participants are green electricity that includes environmental attributes (i.e., the amount of winning bids for environmental attributes is less than the amount of winning bids for electrical energy), and will be settled at the total green electricity price ( The settlement is based on green electricity consumption, while the remaining conventional electricity consumption is settled based on the electricity price. Settlement will be conducted. Since the price of environmental attributes is non-negative, under this pricing and settlement mechanism, the settlement price of green electricity trading will never be lower than the settlement price of conventional electricity trading, and the difference will reflect the additional environmental value included in green electricity trading.

[0147] The proposed pricing and settlement mechanism forms separate electricity prices and environmental attribute prices based on the supply and demand balance constraints of electricity and environmental attributes, respectively, and uses the sum of the electricity price and environmental attribute price as the green electricity settlement price. This achieves a scientific and natural decomposition of green electricity prices, forming a unified electricity clearing price for both green electricity and conventional electricity trading, and ensures that green electricity prices are higher than conventional electricity prices. Furthermore, both the electricity price and the environmental attribute price effectively reflect the market supply and demand matching relationship. Therefore, the proposed pricing and settlement mechanism meets market operation objectives 2) and 3).

[0148] In addition to the aforementioned market operation objectives related to green electricity, to ensure the interests of market participants, maintain their participation, and improve market efficiency, the pricing and settlement mechanism must also meet the incentive compatibility requirements of market participants. That is, the revenue earned or costs incurred by market participants should align with their declared volume and price intentions. The following section uses green electricity generators as an example. For example, the theory proves that the proposed pricing and settlement mechanism satisfies the incentive compatibility property of market participants.

[0149] Under the proposed pricing and settlement model, green electricity generators The expressions for the net revenue from conventional electricity and the net revenue from green electricity are as follows:

[0150] (10)

[0151] (11)

[0152] In the formula, For green power generators The conventional electrical energy gain; For green power generators The revenue from green electricity.

[0153] Based on the proposed joint clearing model, the following expression can be obtained using the KKT conditions:

[0154] (12)

[0155] (13)

[0156] (14)

[0157] (15)

[0158] (16)

[0159] (17)

[0160] Equations (12)-(13) represent the stationary conditions, and equations (14)-(17) represent the complementary relaxation constraints. Substituting the above equations into equations (10)-(11), we can obtain the green electricity generator... The expressions for net revenue from conventional electricity and net revenue from green electricity are rewritten as follows:

[0161] (18)

[0162] (19)

[0163] As can be seen from equation (7), green electricity generators Conventional power clearing volume ( ) and green electricity clearing volume All are non-negative, and the dual multipliers in this model are also non-negative. It exhibits non-negativity. Therefore, the net revenue from conventional electricity generated by green energy generators... and net income from green electricity Neither of them are negative.

[0164] Therefore, under the proposed pricing and settlement mechanism, the market revenue of green electricity generators is in line with their own volume and price declaration intentions, which satisfies the incentive compatibility of market entities. Similarly, this can be extended to other market entities.

[0165] In summary, the proposed pricing and settlement mechanism satisfies the price characteristics and incentive compatibility of market participants desired by the green electricity trading market.

[0166] Example 13:

[0167] See Figures 1 to 9 A method for the co-exit of green and conventional electricity to optimize overall social welfare, the main technical contents of which include:

[0168] (1) Example data settings

[0169] This embodiment is based on a simulation analysis of a five-node system of the US PJM (Pennsylvania-New Jersey-Maryland) electricity market. The system network topology is as follows: Figure 2 As shown. The purpose of conducting simulation examples based on small-scale systems is to facilitate the presentation and analysis of results. The proposed model is an LP problem with low computational complexity, which will also be applicable to large-scale systems.

[0170] Figure 2 In this system, W1, W2, and W3 are wind turbine units with installed capacities of 280MW, 360MW, and 660MW, respectively. G1 and G2 are thermal power units with installed capacities of 400MW and 800MW, respectively. The maximum transmission capacities of lines L1-L6 are 250MW, 400MW, 400MW, 250MW, 600MW, and 400MW, respectively. D1, D2, and D3 are electricity users. D1 has green electricity demand and participates in both green electricity and conventional electricity trading, while D2 and D3 have no green electricity demand and only participate in conventional electricity trading. The load curve and renewable energy curve are collected from actual data from a provincial power grid in my country and appropriately scaled to match the system parameters. Green electricity trading is a medium- to long-term transaction involving multiple trading days and periods. To facilitate the demonstration of clearing results using different methods, the clearing results of a specific 24-hour period on a particular day are selected for detailed analysis. The proposed clearing method is also applicable to scenarios involving multiple trading days, such as annual, monthly, and weekly transactions.

[0171] The comparison methods in simulation include:

[0172] S1: The currently commonly used "green electricity-conventional electricity" sequential clearing method uses the average price of green certificate transactions as the environmental attribute price of green electricity transactions.

[0173] S2: The sequential clearing method of "electrical energy - environmental attributes" adopted by a certain province.

[0174] S3: The "Electrical Energy-Environmental Attributes" clearing method proposed in this invention.

[0175] (2) Construction of a green electricity-conventional electricity competition and clearing model for optimal overall social welfare

[0176] The clearing model adopted in this embodiment takes the maximization of the overall social welfare of electrical energy and environmental attributes as the objective function, as shown in equation (1). The first term is the social welfare generated by electrical energy trading; the second term is the social welfare generated by environmental attribute trading. The constraints include the supply and demand balance constraints of electrical energy and environmental attributes, the electrical energy clearing amount constraints of the purchasing and selling entities, the environmental attribute clearing amount constraints of the purchasing and selling entities, and the line power flow constraints, as shown in equations (2)-(8), respectively. Under this clearing model, electricity commodities are decomposed into two categories of trading commodities: electrical energy and environmental attributes. Green electricity trading and conventional electricity trading compete on the same stage under a unified market framework. Under the relevant coupling constraints, coordinated trading and joint clearing are achieved, which has the following advantages:

[0177] 1) Ensuring the maximization of overall social welfare in the clearing scheme and incentivizing market participants to declare their true demands. Under this clearing model, the market organizes the clearing process with the goal of optimizing the overall social welfare of electricity and environmental attributes, which can guarantee the maximization of the social welfare of the clearing scheme and meet market operation objective 1). At the same time, compared with the existing sequential clearing model, the proposed co-market clearing model does not have a situation where one type of transaction is dominant. Market participants can purchase or sell green electricity based on their overall bidding advantage, without having to deviate their true intentions in terms of quantity and price declarations in order to win priority in bidding for a certain type of transaction (for example, in the "green electricity-conventional electricity" sequential clearing model, they hope to win priority in bidding for green electricity to occupy transmission channels; in the "electric energy-environmental attributes" sequential clearing model, they hope to win priority in bidding for electricity to participate in subsequent environmental attribute transactions).

[0178] 2) The volume and price declaration method is adapted to the differentiated green electricity demands of different types of users. For users with no demand for green electricity, they can declare zero price for environmental attributes to ensure that they will not purchase green electricity and pay extra fees; for users with rigid demand for green electricity, they can declare low price for electricity volume and high price for environmental attributes to ensure that all the electricity they purchase is green electricity; for users with non-rigid demand for green electricity, they can declare the price of environmental attributes according to their own green electricity purchase intentions, thereby competing for green electricity within their acceptable price range.

[0179] This model is a linear programming (LP) problem, which can be solved using commercial solvers. Furthermore, the model uses the maximization of overall social welfare as its objective function, and LP problems can find a globally optimal solution; therefore, the proposed solution can achieve the optimal overall social welfare of the system.

[0180] (3) Comparative analysis of market clearing volume

[0181] Based on the objective function and constraints established above, simulations can be performed in a 5-node system to obtain market clearing results under different methods.

[0182] 1. Comparative Analysis of Social Welfare

[0183] The social welfare under the three clearing methods S1-S3 is shown in Table 1:

[0184] Table 1 Social Welfare under S1-S3

[0185]

[0186] As shown in Table 1, S3 yields the greatest social welfare, while S1 yields the least. This is because both S1's sequential clearing method ("green electricity-electric energy") and S2's sequential clearing method ("electric energy-environmental attributes") have clearing priorities, prioritizing the maximization of social welfare in green electricity trading and electrical energy trading, respectively. This neglects trading combinations with high social welfare in another category of goods, impacting the overall social welfare of the system and failing to meet the aforementioned market operation objective 1). S3, on the other hand, uses a competitive clearing model ("electric energy-environmental attributes"), aiming to maximize the overall social welfare of both categories. It does not have clearing priorities, thus resulting in a higher overall social welfare than S1 and S2. Furthermore, comparing S1 and S2 reveals that S2's social welfare is higher than S1's in this example. This is because S2 achieves competition between wind turbines and thermal power units in electrical energy trading, optimizing the allocation of transmission line capacity between wind power and thermal power sales in the power transmission stage, thereby generating greater social welfare.

[0187] It is evident that, compared to the current sequential clearing method, the "electrical energy-environmental attribute" simultaneous clearing method proposed in this invention can ensure the maximum overall social welfare of the system and meet market operation objectives (1).

[0188] 2. Comparative Analysis of Electricity Volume Winning Bids by Market Entities

[0189] Table 2 shows the total cleared electricity volume under the three clearing methods S1-S3. Table 3 shows the unsuccessful electricity volume of each user. Among them, D1 has green electricity trading needs, and the unsuccessful green electricity volume will be considered separately.

[0190] Table 2 Overall Market Clearing Volume

[0191]

[0192] Table 3. Number of users who did not win bids under S1-S3

[0193]

[0194] First, as shown in Tables 2 and 3, wind power (non-green electricity) cleared out the lowest amount in S1, while thermal power cleared out the highest amount. D2 had a relatively large amount of unsold electricity. This is because wind power in S1 had already been cleared out during the priority green electricity trading phase, occupying a certain amount of transmission capacity. This meant that during the regular electricity trading phase, wind turbines could not provide sufficient electricity to users due to line congestion. Ultimately, thermal power units with more abundant transmission capacity on relevant lines met the users' electricity needs, hence the lower clearing out of wind power (non-green electricity) and the higher clearing out of thermal power. Simultaneously, due to line congestion, the generators that could supply electricity to user D2 during the regular electricity trading phase could not fully meet D2's electricity demand, resulting in unsold electricity in D2.

[0195] To further clarify the above clearing results, the following section uses period 14 as an example to specifically illustrate the clearing results for S1. Table 4 shows the volume and price declarations of each market participant during this period.

[0196] Table 4: Market Entities' Quantity and Price Reports for Period 14

[0197]

[0198] The system power flow distribution during the S1 green electricity trading phase and the conventional electricity trading phase during this period is as follows: Figure 3 , Figure 4 As shown.

[0199] Depend on Figure 3 , Figure 4 It is evident that D1's participation in green electricity trading prioritizes the use of most of the transmission capacity of line L1 (209.12 MW of L1 capacity already used, with 40.88 MW remaining). In this example system, the power transfer distribution factors of nodes 1, 4, and 5 for line L1 are positive. During the regular electricity trading phase, the electricity bids of units W1-W3 and G2 are relatively lower, but when these units meet the electricity demand of user D2, the power transmitted from node 1 to node 2 of line L1 will continue to increase, causing the transmission power of line L1 to reach its upper limit. Conversely, the power transfer distribution factor of node 3 for line L1 is negative. Therefore, using unit G1 to meet the electricity demand of user D2 can reduce the power transmitted from node 1 to node 2 of line L1, alleviating the congestion of line L1. Ultimately, user D2's electricity demand is mainly met by G1, resulting in a higher amount of thermal power generation and a lower amount of non-green wind power generation in the system. Meanwhile, although the cleared power of W1, W3, and G2 did not reach the upper limit, the power generation of these units could not continue to increase due to the blockage of line L1. As a result, D2, which had the highest bid, could not purchase enough power, resulting in 54.09 MWh of unsuccessful bids.

[0200] In summary, the sequential clearing method of "green electricity - conventional electricity" adopted by S1 results in the pre-locking of transmission capacity by green electricity transactions. This fails to achieve optimized allocation of transmission capacity that considers the power transmission needs of both green and conventional electricity transactions, leading to a suboptimal overall market clearing effect. Furthermore, due to the priority given to green electricity transactions in occupying transmission capacity, D2, with its higher electricity price, failed to purchase sufficient electricity. Consequently, even if D2 has no demand for green electricity, it has an incentive to participate in green electricity transactions to increase its own purchase volume. Ultimately, this prevents the market clearing from accurately reflecting the green electricity purchase and sale needs of various participants.

[0201] Secondly, as shown in Tables 2 and 3, in S2, wind power had the highest cleared volume in non-green electricity form, while wind power cleared volume in green electricity form and thermal power cleared volume were the lowest. D1 had a relatively large amount of unsold green electricity and unsold electricity. This is because, in the priority-organized electricity trading phase, wind power and thermal power competed equally for transmission line capacity and user electricity demand. Wind power, with its price advantage in electricity bidding, obtained a higher cleared volume, while thermal power cleared a lower volume, thus improving the system's social welfare compared to S1. Simultaneously, because user D1's electricity bid was lower, it could not purchase sufficient electricity during the electricity trading phase due to transmission line congestion, resulting in a large amount of unsold electricity. In the subsequent environmental attribute trading phase, the environmental attributes that user D1 could purchase depended on its electricity bidding success, ultimately resulting in user D1 having a large amount of unsold green electricity. As a result, the amount of wind power that could be sold in green electricity form was relatively small, leading to a relatively large amount that could only be sold in non-green electricity form. To further clarify the reasons why D1 failed to purchase sufficient electrical energy and green electricity, the following section will use time period 14 as an example to specifically demonstrate the clearing results of S2.

[0202] The system power flow distribution during the S2 energy trading phase in this period is as follows: Figure 5 As shown, since environmental attribute trading does not change the system's power transmission status, the system power flow distribution during the environmental attribute trading phase is not displayed.

[0203] Depend on Figure 4 , Figure 5 It can be seen that user D1, who has a demand for green electricity, has a relatively low bid for electricity. Due to congestion on line L1, D1 is unable to purchase sufficient electricity during the electricity trading phase (resulting in 106.84 MWh of unsuccessful bids), affecting the total amount of environmental attributes it can purchase in the subsequent environmental attribute trading phase. Ultimately, even though D1 has the highest overall bid for green electricity, it still cannot fully purchase the green electricity it needs (again, 106.84 MWh of unsuccessful bids).

[0204] In summary, under the sequential clearing method of "electricity energy - environmental attributes" adopted by S2, even though D1 has the highest overall green electricity bid, it still cannot purchase a sufficient amount of green electricity due to its low electricity energy bid. Under this method, entities with green electricity purchase and sale needs must ensure they win bids in both the electricity energy and environmental attribute trading stages, making decision-making difficult. With the total price remaining constant, market participants will be forced to adjust the ratio of their electricity energy bids to their environmental attribute bids, causing the final bids to deviate from their actual purchase and sale needs.

[0205] Finally, as shown in Tables 2 and 3, the market participants in S3 had the highest electricity purchase and sale volume, thus generating greater social welfare. Furthermore, user D1's green electricity demand was fully cleared. This is because the proposed clearing method S3 simultaneously conducts electricity and environmental attribute trading. Under the constraints of line power flow, the market clears transactions entirely according to maximizing overall social welfare. On one hand, it achieves optimized allocation of transmission line capacity among the purchase and sale demands of different market participants, and the electricity purchase and sale demands of market participants are well met. Only during certain periods did D2 experience unsold electricity due to line L1 congestion. On the other hand, D1 was able to purchase sufficient green electricity due to the higher overall green electricity bid level, and there was no unsold electricity or green electricity. To further clarify why S3 can increase the total electricity purchase and sale volume of market participants, the clearing results of S3 will be specifically presented below using time period 14 as an example.

[0206] The system power flow distribution of S3 during this period is as follows: Figure 6 As shown.

[0207] As shown in Table 4, the electricity price and total green electricity price of wind turbine units W1 and W2 are lower than those of W3, G1, and G2. Therefore, considering only the price, W1 and W2 should be prioritized to meet the user's electricity demand. However, if... Figure 3 , Figure 4 As shown, the generation of electricity by W1 and W2 will cause congestion on line L1, ultimately resulting in the inability to effectively meet the electricity demand of user D2, leading to a significant loss of social welfare. In S3, to effectively meet the electricity demand of users D1-D3 and thus maximize the overall social welfare of the system, the total winning bid power of W1 and W2 will be limited by the transmission capacity of line L1. Meanwhile, although W3's total green electricity bid is higher, the trading method where W3 meets the green electricity demand of D1, and the remaining power of W1, W2, and W3 meets the energy demand of D2 and D3, can create greater social welfare. Therefore, W3 ultimately wins the bid for environmental attributes, while W1 and W2 only win the bid for energy. Furthermore, since W2's energy bid is higher than W1's, all the generateable power of node 1 will ultimately be provided by W1.

[0208] In summary, the S3 "green electricity-conventional electricity" co-marketing approach can achieve a reasonable and optimized allocation of green electricity and conventional electricity, thereby creating greater social welfare, by comprehensively considering the market participants' quantity and price declarations and the transmission capacity of the system's transmission lines.

[0209] Market Clearing Price Comparison Analysis

[0210] The following analysis uses node 3 as an example to examine the market clearing prices in S1-S3. The market clearing prices in S1-S3 are as follows: Figures 7-9 As shown:

[0211] Depend on Figure 7 It is evident that the prices of green electricity and conventional electricity in S1 are not uniform, and there are instances where green electricity prices are lower than conventional electricity prices, failing to meet market operation objectives 2) and 3). Furthermore, the environmental attribute price of green electricity remains consistent across all time periods, not changing with market supply and demand, similarly failing to meet market operation objective 3). The reason for these phenomena lies in the fact that green electricity trading and conventional electricity trading are organized on separate platforms in S1. The corresponding clearing price depends on the bidding situation of market participants in both stages, making it impossible to guarantee that the green electricity price will not be lower than the conventional electricity price. Simultaneously, the method of using the average price of green certificates as the environmental attribute price cannot guarantee the uniformity of green electricity prices with conventional electricity prices, and the environmental attribute price fails to reflect the supply and demand relationship of environmental attributes.

[0212] Depend on Figure 8 , Figure 9 It is evident that in S2 and S3, green electricity and conventional electricity compete on the same platform, achieving price unification between the two. Furthermore, the total price of green electricity is the sum of the electricity price and the market-clearing price in the subsequent environmental attribute trading phase, ensuring that the price of green electricity is not lower than that of conventional electricity. In addition, the electricity price and environmental attribute price are formed based on the quantity and price declarations of market participants, respectively. Therefore, the pricing mechanisms of S2 and S3 can reflect the supply and demand relationship of both electricity and green electricity. Thus, the pricing mechanisms of S2 and S3 are consistent with market operation objectives 2) and 3). However, as the foregoing analysis shows, the bidding decision-making of green electricity trading participants is more difficult under S2. Market participants may be forced to adjust their bidding behavior, resulting in a final price that fails to effectively reflect the true supply and demand intentions of market participants.

[0213] In addition, by Figure 8 , Figure 9 It is evident that there is a difference in market clearing prices between S2 and S3. This is because the different clearing models lead to differences in the market clearing volumes of S2 and S3. Figure 5 , Figure 6 As shown, this ultimately leads to differences in clearing prices.

[0214] In summary, the pricing mechanism of the proposed S3 method can achieve the natural decomposition of green electricity prices, enabling green electricity trading to include environmental value in addition to conventional electricity trading, and the resulting price can reflect the true supply and demand intentions of market participants, thus meeting market operation objectives 2) and 3).

[0215] In summary, this invention proposes a green-conventional electricity co-marketing clearing method aimed at optimizing overall social welfare. This method first constructs a green-conventional electricity co-marketing competition model optimized for overall social welfare, decomposing green electricity trading into two categories: electrical energy services and environmental attributes. Market participants submit quantity and price declarations for electrical energy services and environmental attributes respectively. Then, based on the dual multipliers of the balance constraints of electrical energy and environmental attributes, the clearing prices for electrical energy and environmental attributes are formed respectively, and the incentive compatibility property of the price formation mechanism is proven. Finally, the effectiveness of the proposed method is verified through simulation examples. Simulation results on the PJM5 node system show that the proposed method can achieve a reasonable and optimized allocation of green and conventional electricity, resulting in greater social welfare, and reasonably decomposes the prices of green and conventional electricity, while comprehensively considering the quantity and price declarations of market participants and the transmission capacity of the system's transmission lines.

Claims

1. A method for co-purchasing green electricity and conventional electricity to optimize overall social welfare, characterized in that, Includes the following steps: 1) The electricity trading in the green electricity market is broken down into two categories: electricity energy services and environmental attributes; 2) Obtain the quantity and price declarations for electricity services and environmental attributes from both the supply and demand sides; 3) Construct a clearing model for competition between green electricity and conventional electricity that optimizes overall social welfare; 4) Input the quantity and price declarations of electricity services and environmental attributes from both the supply and demand sides into a clearing model that optimizes overall social welfare by having green electricity and conventional electricity compete on the same stage, and obtain the optimal electricity clearing price and the optimal environmental attribute clearing price. 5) Determine the electricity trading settlement price in the green electricity market based on the optimal energy clearing price and the optimal environmental attribute clearing price.

2. The green electricity-conventional electricity co-exit method for optimal overall social welfare as described in claim 1, characterized in that, The supply and demand sides include the supply side and the demand side; The supply side includes conventional energy units and new energy units; The demand side includes users; The electricity trading in the green electricity market includes conventional electricity trading and green electricity trading.

3. The green electricity-conventional electricity co-exit method for optimal overall social welfare as described in claim 2, characterized in that, The conventional energy units include thermal power units and hydropower units; The new energy units include wind turbines and solar power units.

4. The green electricity-conventional electricity co-exit method for optimal overall social welfare as described in claim 2, characterized in that, The electricity service quantity and price declaration includes the user's regular electricity declaration quantity, the user's regular electricity price, the regular electricity declaration quantity of conventional energy units, the regular electricity price of conventional energy units, the regular electricity declaration quantity of new energy units, and the regular electricity price of new energy units.

5. The green electricity-conventional electricity co-exit method for optimal overall social welfare as described in claim 2, characterized in that, The environmental attribute quantity and price declaration includes the user's declared green electricity volume, the user's environmental attribute price, the declared green electricity volume of the new energy unit, and the environmental attribute price of the new energy unit.

6. The green electricity-conventional electricity co-exit method for optimal overall social welfare as described in claim 2, characterized in that, The objective function of the clearing model for competition between green electricity and conventional electricity, which aims at optimizing overall social welfare, is as follows: （1） In the formula, Indicates the user index; Indicates the index of energy units on the supply side; This represents the index for new energy generating units; t represents the index for the trading session. , users respectively Energy units Standard electricity price quote; , users respectively Energy units The amount of electricity cleared; , users respectively New energy units Quotation based on environmental attributes; , users respectively New energy units Environmental attribute clearing volume.

7. The green electricity-conventional electricity co-exit method for optimal overall social welfare as described in claim 2, characterized in that, The constraints of the clearing model for green electricity and conventional electricity competing for the optimal overall social welfare include supply and demand balance constraints for electrical energy and environmental attributes, constraints on the clearing amount of electrical energy by the purchasing and selling entities, constraints on the clearing amount of environmental attributes by the purchasing and selling entities, and line power flow constraints.

8. The green electricity-conventional electricity co-exit method for optimal overall social welfare as described in claim 7, characterized in that, The supply and demand balance constraints of electrical energy and environmental attributes are as follows: （2） （3） In the formula, Indicates the user index; Indicates the index of energy units on the supply side; This represents the index for new energy generating units; t represents the index for the trading session. , users respectively Energy units The amount of electricity cleared; , users respectively New energy units Environmental attribute clearing volume; , These are the dual multipliers of the electrical energy supply and demand balance constraint and the environmental attribute supply and demand balance constraint, respectively. The energy clearing constraints for the purchasing and selling entities are as follows: （4） （5） In the formula, , users respectively Energy units The total declared amount of electrical energy; , , , Both are dual multipliers of the energy clearing constraints of the main purchasing and selling entities; The clearing constraints for the environmental attributes of the purchasing and selling entities are as follows: （6） （7） In the formula, The proportion of green electricity demand declared by users; , , , All are dual multipliers of the clearing quantity constraints of the environmental attributes of the purchasing and selling entities; The power flow constraints of the line are as follows: （8） In the formula, For route indexing; , The lines are respectively Upper and lower limits of power transfer; For energy units The injected power at the node relative to the line The power transfer distribution factor; For users The injected power at the node relative to the line The power transfer distribution factor; , Both are dual multipliers of line power flow constraints.

9. A method for co-purchasing green electricity and conventional electricity to optimize overall social welfare, as described in claim 8, is characterized in that... The optimal energy clearing price and the optimal environmental attribute clearing price are as follows: （9） （10） In the formula, , These represent the optimal energy clearing price and the optimal environmental attribute clearing price, respectively. , These represent the dual multipliers of the electrical energy supply and demand balance constraint and the environmental attribute supply and demand balance constraint, respectively. For route indexing; This indicates the node index where the user and energy unit are located. Represents a node Injected power relative to line The power transfer distribution factor; , Both are dual multipliers of line power flow constraints.

10. A method for co-purchasing green electricity and conventional electricity to optimize overall social welfare, as described in claim 9, is characterized in that... The settlement price for the conventional electricity trading is: ; The settlement price for the green electricity trading is: .

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