Method for considering distributed electricity and carbon distribution among multiple main bodies

Abstract

The invention provides a method for considering distributed power and carbon distribution among multiple subjects, and the method is characterized in that the method comprises the steps: S1, constructing a corresponding cost function for each subject according to the type of the subject; step S2-S4, in combination with a cost function and a constraint condition, performing multi-round iterative solution on the multi-subject electric energy cooperative sharing model to obtain an optimal electric energy transaction volume among the subjects; and S5-S7, in combination with the cost function and the constraint condition, performing multi-round iterative solution on the multi-subject bargaining income distribution model to obtain the optimal electric energy transaction price among the subjects. In a word, the method can carry out optimal electric energy and carbon quota distribution on multiple subjects.

Description

Technical Field

[0001] The present invention belongs to the field of energy conservation, and in particular relates to a method for distributing distributed electricity and carbon among multiple entities. Background Art

[0002] In recent years, with the intensification of global warming, green and low-carbon development has become the focus of attention of countries around the world. As the main mechanism for carbon emission reduction, the carbon emission trading system is a mandatory market tool to promote emission reduction in the whole society. In recent years, with the rapid expansion of the carbon market, the types of carbon trading products have become more diversified. As a gathering place for various enterprises, industrial parks play a vital role in industrial upgrading and economic development, and are also the main source of carbon emissions. In addition, with the development of distributed energy and new energy, the demand-side entities in these parks are changing from simple energy consumers to integrated producers and consumers, that is, prosumers. As emerging agents in the power market, the role of prosumers cannot be ignored. The uncertainty of distributed renewable energy within individual prosumers puts higher requirements on the flexibility of the distribution network system. In addition, due to the small scale of individual prosumers, they often fail to meet the entry threshold of the wholesale power market, cannot respond to market price signals, and objectively lack channels to participate in the operation of the power market. Therefore, with the increase in the number of demand-side prosumers in industrial parks, considering the energy interaction between multiple entities in the park has become an important means to solve these problems. At the technical level, advanced two-way communication networks and information technology can solve the problems of interaction and intelligent decision-making among multiple subjects. At the policy level, in the context of power and energy system reform, market-based transactions of distributed power generation can encourage distributed renewable energy on the demand side to actively participate in system operation and promote energy resource sharing. Due to the heterogeneity of different subjects in the park in terms of renewable energy output, load demand, equipment parameters, etc., there is potential for energy interaction among multiple subjects.

[0003] However, how to reasonably coordinate and optimize according to the needs of different entities to achieve the optimal electricity-carbon allocation is an urgent problem that needs to be solved. Summary of the invention

[0004] The present invention is made to solve the above-mentioned problem, and aims to provide a method for distributing distributed electricity and carbon among multiple entities.

[0005] The present invention provides a method for considering distributed electricity-carbon allocation among multiple subjects, which is used for each subject to obtain corresponding electricity allocation data according to corresponding carbon emission surplus and deficit data and electricity surplus and deficit data, and has the following characteristics, including the following steps: Step S1, each subject constructs a corresponding cost function according to the subject type; Step S2, each subject generates the electricity trading volume of the subject to other subjects according to the corresponding electricity surplus and deficit data, and sends each electricity trading volume to the corresponding subject; Step S3, each subject, according to all received electricity trading volumes, all electricity trading volumes generated by the subject and all carbon emission surplus and deficit data, combined with the cost function and constraints, solves the multi-subject electricity cooperation and sharing model constructed based on the Nash negotiation model, and updates each electricity trading volume generated by the subject according to the solution result, and sends each updated electricity trading volume to the corresponding subject; Step S4, repeats step S3 until the preset first iteration termination condition is reached, and obtains to the optimal electricity trading volume among various subjects; step S5, each subject initializes the electricity trading price corresponding to each optimal electricity trading volume corresponding to the subject, and sends each electricity trading price to the corresponding subject; step S6, each subject, according to the corresponding optimal electricity trading volume, all electricity trading prices generated by the subject, all received electricity trading prices and all carbon emission surplus and shortage data, combined with the cost function and constraints, solves the multi-subject bargaining profit distribution model constructed based on the Nash negotiation model, and updates each electricity trading price generated by the subject according to the solution result, and sends the updated electricity trading price to the corresponding subject; step S7, repeats step S6 until the preset second iteration termination condition is reached, and the optimal electricity trading price among various subjects is obtained, wherein the subject type includes key emission subjects and non-key emission subjects, and the subject's electricity distribution data includes all optimal electricity trading volumes and optimal electricity trading prices corresponding to the subject.

[0006] In the method for considering distributed electricity-carbon allocation among multiple entities provided by the present invention, there may also be such a feature: wherein the calculation expression of the objective function corresponding to the key emission entity is: Where U i is the cost of key emission entity i, T is the total number of times, is the gas consumption of the equipment of key emission entity i at the tth moment, c gas is the natural gas unit price, is the amount of electricity purchased by key emission entity i from the upper grid at time t, is the electricity price sold by the upper power grid at the tth moment, is the amount of electricity sold by key emission entity i to the upper grid at time t, is the repurchase price of the upper power grid at the tth moment, D is the set of other entities, λ is the unit power transmission fee, is the electric energy interaction between the key emission subject i and the jth other subject at the tth moment, is the electricity trading volume between the key emission subject i and the jth other subject at the tth moment, is the electricity transaction price between the key emission entity i and the jth other entity at the tth moment, The average unit price at the tth moment to offset the excess carbon quota by purchasing carbon quotas and carbon credits through the carbon platform, is the actual carbon emissions of key emission entity i at the tth moment, is the carbon quota of key emission entity i at the tth moment, is the carbon quota transaction price in the upper carbon market at the tth moment, α e,cut The compensation unit price for load reduction, is the electricity load reduction of key emission entity i at the tth moment, α e,tran is the compensation unit price for electric load transfer, is the electric load transfer amount of key emission entity i at the tth moment, α h,cut The compensation unit price for heat load reduction, is the heat load reduction of key emission entity i at time t, α h,tran is the compensation unit price for heat load transfer, is the heat load transfer amount of key emission entity i at the tth moment, The equipment operating costs of key emission entity i.

[0007] In the method for distributed electricity-carbon allocation among multiple entities provided by the present invention, the following features may also be provided: wherein the carbon quota of the key emission entity l is The calculation expression is: In the formula is the free carbon quota ratio coefficient of the key emission entity l, ε e is the carbon emission limit coefficient per unit of power supply, is the amount of electricity purchased by key emission entity l from the upper grid at time t, is the output power of the gas internal combustion engine purchased by the key emission entity l from the gas turbine at the tth moment, ε h is the carbon emission limit coefficient per unit of heating, is the output power of the waste heat boiler of the key emission entity l that purchases electricity from the gas turbine at the tth moment, is the output power of the gas boiler purchased by the key emission entity l at the tth moment, and the actual carbon emissions of the key emission entity l The calculation expression is: In the formula is the gas consumption of the key emission entity l at the tth moment, H g is the average lower calorific value of natural gas, is the CO2 emission factor for natural gas based on the lowest calorific value, It is the default value of electricity emission factor.

[0008] In the method for considering distributed electricity-carbon allocation among multiple entities provided by the present invention, there may also be such a feature: wherein the calculation expression of the objective function corresponding to the non-key emission entity is: Where U i is the cost of non-key emission entity i, T is the total number of moments, is the amount of electricity purchased by non-key emission entity i from the upper grid at time t, is the electricity price sold by the upper power grid at the tth moment, is the amount of electricity sold by non-key emission entity i to the upper grid at time t, is the repurchase price of the upper power grid at the tth moment, D is the set of other entities, λ is the unit power transmission fee, is the electric energy interaction between the non-key emission subject i and the jth other subject at the tth moment, is the electricity trading volume between non-key emission subject i and other subject j at the tth moment, is the electricity transaction price between non-key emission subject i and other subject j at the tth moment, is the selling price of the internal carbon credit at the tth moment, is the combined marginal emission factor of the regional power grid, The renewable energy output of non-key emission entity i at the th moment.

[0009] In the method for considering distributed electricity-carbon allocation among multiple entities provided by the present invention, there may also be such a feature: wherein the constraint conditions include power constraints of P2P transactions, cooperative transaction price constraints, demand response constraints and energy balance constraints, and the calculation expression of the power constraints of P2P transactions is: Where D is the set of all entities, is the electric energy transaction volume between subject i and subject j at time t, and the calculation expression of cooperative transaction price constraint is: In the formula is the on-grid electricity price at time t, is the electricity transaction price between subject i and subject j at time t, is the time-of-use electricity price at time t, is the carbon preferential price in the carbon trading market at time t, is the selling price of the internal carbon credit at time t, The average unit price at time t for purchasing carbon quotas and carbon credits through the carbon platform to offset excess carbon quotas. is the carbon quota trading price of the upper carbon market at time t, and the calculation expression of the demand response constraint is:

[0010] In the formula is the optimized electrical load of the key emission entity l at time t, is the electrical load of the key emission entity l at time t, is the electric power reduced by the key emission entity l at time t, for, The upper limit of the electric power reduction of key emission subject l, β e,tran is the maximum proportional coefficient of power load transfer, T is the total number at all times, is the optimized heat load of the key emission entity l at time t, is the heat load of the key emission entity l at time t, is the heat load reduced by the key emission entity l at time t, The upper limit of heat load reduction for key emission entities l, β h,tran is the maximum proportional coefficient of heat load transfer, and the calculation expression of energy balance constraint is: In the formula is the output power of the gas internal combustion engine of the key emission entity l at time t, is the power consumption of the electric boiler of the key emission entity l at time t, To focus on the heat generation power of the main body l at all times, is the renewable energy output of non-key emission entity f at time t, is the grid-connected power of non-key emission entity f at time t, is the P2P transaction volume of electricity between non-key emission subject f and other subjects j at time t.

[0011] In the method for considering distributed electricity-carbon allocation among multiple entities provided by the present invention, there may also be such a feature: wherein the expression of the multi-agent electric energy cooperative sharing model is: In the formula is the amount of electricity traded between subject i and subject j at time t, D is the set of subjects, U i is the cost function corresponding to subject i. In step S3, the calculation expression for the amount of electric energy transaction calculated by each subject in each iteration is:

[0012] In the formula is the Lagrange multiplier corresponding to subject i and subject j during the k-th iteration calculation, is the amount of electricity traded between subject i and subject j at time t during the k-th iteration calculation, is the amount of electricity traded between subject j and subject i at time t during the k-th iteration calculation, is the penalty coefficient corresponding to subject i, T is the total number of all moments, ρ P1 is the penalty coefficient. The termination condition of the first iteration is that the iteration converges or the iteration exceeds the preset maximum number of iterations. The calculation expression of the iteration convergence during the kth iteration calculation is: Where δ P1 is the original residual convergence threshold.

[0013] In the method for distributed electricity-carbon allocation among multiple entities provided by the present invention, there may also be such a feature: wherein the expression of the multi-agent bargaining benefit allocation model is: Where D is the set of all entities, The breaking point of the negotiations. is the operating cost of entity i under certain circumstances, is the energy interaction cost between subject i and other subjects. In step S6, the calculation expression for the energy transaction price calculated by each subject in each iteration is:

[0014]

[0015] In the formula is the Lagrange multiplier corresponding to subject i and subject j during the k-th iteration calculation, is the electricity transaction price between subject i and subject j at time t during the k-th iteration calculation, is the electricity transaction price between subject j and subject i at time t during the k-th iteration calculation, is the penalty coefficient corresponding to subject i, T is the total number of all moments, ρ P2is the penalty coefficient. The termination condition of the second iteration is that the iteration converges or the iteration exceeds the preset maximum number of iterations. The calculation expression of the iteration convergence during the kth iteration calculation is: Where δ P2 is the original residual convergence threshold.

[0016] In the method for distributed electricity-carbon allocation among multiple entities provided by the present invention, the following features may also be provided: wherein, according to the carbon emission surplus and shortage data, when the carbon quota shortage of each key emission entity is less than the upper limit of the voluntary emission reduction offset, the unit average price and selling price The calculation expression is:

[0017] In the formula is the carbon quota trading price in the upper carbon market at time t, is the carbon preferential price in the carbon trading market at time t, X t For R t The inverse of , F is the set of non-key emission entities, is the carbon credit supply of non-key emission subject f at time t, L is the set of key emission subjects, is the carbon emission reduction demand of the key emission entity l at time t, The total supply of carbon credits formed by the cooperative alliance of all entities at time t, is the total demand for carbon quotas of the cooperative alliance at time t.

[0018] In the method for distributed electricity-carbon allocation among multiple entities provided by the present invention, there may also be such a feature: wherein, according to the carbon emission surplus and shortage data, the carbon quota shortage of at least one key emission entity is greater than the voluntary emission reduction offset upper limit and the total supply Less than total demand When the unit price and selling price The calculation expression is:

[0019] In the formula is the carbon credit price in the carbon trading market at time t, β is the offset ratio of carbon credit regulations, is the actual carbon emissions of key emitter l at time t. According to the carbon emissions surplus and shortage data, at least one key emitter's carbon quota shortage is greater than the voluntary emission reduction offset limit and the total supply Greater than or equal to total demand When the unit price and selling price The calculation expression is:

[0020] Functions and Effects of the Invention

[0021] According to the method of considering distributed electricity-carbon allocation among multiple subjects involved in the present invention, on the one hand, the optimal electricity trading volume and the optimal electricity trading price are calculated in sequence through the cost function, the multi-subject electricity cooperation sharing model and the multi-subject bargaining benefit distribution model, and the carbon quota is considered therein; on the other hand, the distributed solution is performed through the multiplier alternating direction method, which effectively protects the information privacy of all parties. Therefore, the method of considering distributed electricity-carbon allocation among multiple subjects of the present invention can allocate the optimal electricity and carbon quota to multiple subjects. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of a method for considering distributed electric carbon allocation among multiple entities in an embodiment of the present invention;

[0023] Figure 2 is a schematic diagram of a multi-agent energy architecture formed by four agents in an embodiment of the present invention;

[0024] Figure 3 is a schematic diagram of the result of electric energy interaction between multiple entities in an embodiment of the present invention;

[0025] Figure 4 It is a schematic diagram of the electric energy transaction price at each moment of the cooperative operation of multiple subjects in an embodiment of the present invention;

[0026] Figure 5 is a schematic diagram of the surplus and shortage of carbon emission reduction within the park in an embodiment of the present invention;

[0027] Figure 6 It is a schematic diagram of the carbon trading price within the park in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the following embodiments and the accompanying drawings specifically illustrate the method of distributed electric carbon distribution among multiple entities considered in the present invention.

[0029] In this embodiment, a method for distributed electricity-carbon allocation among multiple subjects is provided, which is used for each subject to obtain corresponding electricity allocation data according to the corresponding carbon emission surplus and deficit data and electricity surplus and deficit data. Among them, the subject types include key emission subjects and non-key emission subjects. The subject's electricity allocation data includes all the optimal electricity trading volumes and optimal electricity trading prices corresponding to the subject.

[0030] In this embodiment, each subject is a member of a cooperative alliance, the key emission subject is the producer and consumer, and the non-key emission subject is the renewable energy operator. Different subjects have different carbon emission surplus and shortage data in operation, that is, remaining carbon quota or exceeding carbon quota, and also have different electricity profit and loss data, that is, insufficient electricity or excess electricity. In this regard, the method of considering distributed electricity-carbon allocation among multiple subjects in this embodiment can maximize the interests of each subject by calculating the optimal electricity trading price and the optimal electricity trading volume, and further coordinate the carbon quota and electricity of each subject to achieve the optimal interaction of carbon quota and electricity.

[0031] Figure 1 It is a flow chart of a method for distributing distributed electricity and carbon among multiple entities in an embodiment of the present invention.

[0032] like Figure 1 As shown, the method considering distributed electricity-carbon allocation among multiple entities includes the following steps:

[0033] Step S1, each subject constructs a corresponding cost function according to the subject type.

[0034] The calculation expression of the objective function corresponding to the key emission entities is:

[0035]

[0036] Where U i is the cost of key emission entity i, T is the total number of times, is the gas consumption of the equipment of key emission entity i at the tth moment, c gas is the natural gas unit price, is the amount of electricity purchased by key emission entity i from the upper grid at time t, is the electricity price sold by the upper power grid at the tth moment, is the amount of electricity sold by key emission entity i to the upper grid at time t, is the repurchase price of the upper power grid at the tth moment, D is the set of other entities, λ is the unit power transmission fee, is the electric energy interaction between the key emission subject i and the jth other subject at the tth moment, is the electricity trading volume between the key emission subject i and the jth other subject at the tth moment, is the electricity transaction price between the key emission entity i and the jth other entity at the tth moment, The average unit price at the tth moment to offset the excess carbon quota by purchasing carbon quotas and carbon credits through the carbon platform, is the actual carbon emissions of key emission entity i at the tth moment, is the carbon quota of key emission entity i at the tth moment, is the carbon quota transaction price in the upper carbon market at the tth moment, α e,cut The compensation unit price for load reduction, is the electricity load reduction of key emission entity i at the tth moment, α e,tran is the compensation unit price for electric load transfer, is the electric load transfer amount of key emission entity i at the tth moment, α h,cut The compensation unit price for heat load reduction, is the heat load reduction of key emission entity i at time t, α h,tran is the compensation unit price for heat load transfer, is the heat load transfer amount of key emission entity i at the tth moment, The equipment operating costs of key emission entity i.

[0037] Carbon quota of key emitters l The calculation expression is:

[0038]

[0039] In the formula is the free carbon quota ratio coefficient of the key emission entity l, ε e is the carbon emission limit coefficient per unit of power supply, is the amount of electricity purchased by key emission entity l from the upper grid at time t, is the output power of the gas internal combustion engine purchased by the key emission entity l from the gas turbine at the tth moment, ε h is the carbon emission limit coefficient per unit of heating, is the output power of the waste heat boiler of the key emission entity l that purchases electricity from the gas turbine at the tth moment, The output power of the gas boiler purchased by the key emission entity l from the gas boiler at the tth moment.

[0040] Actual carbon emissions of key emitters The calculation expression is:

[0041]

[0042] In the formula is the gas consumption of the key emission entity l at the tth moment, H g is the average lower calorific value of natural gas, is the CO2 emission factor for natural gas based on the lowest calorific value, It is the default value of electricity emission factor.

[0043] The calculation expression of the objective function corresponding to non-key emission entities is:

[0044]

[0045] Where U i is the cost of non-key emission entity i, T is the total number of moments, is the amount of electricity purchased by non-key emission entity i from the upper grid at time t, is the electricity price sold by the upper power grid at the tth moment, is the amount of electricity sold by non-key emission entity i to the upper grid at time t, is the repurchase price of the upper power grid at the tth moment, D is the set of other entities, λ is the unit power transmission fee, is the electric energy interaction between the non-key emission subject i and the jth other subject at the tth moment, is the electricity trading volume between non-key emission subject i and other subject j at the tth moment, is the electricity transaction price between non-key emission subject i and other subject j at the tth moment, is the selling price of the internal carbon credit at the tth moment, is the combined marginal emission factor of the regional power grid, The renewable energy output of non-key emission entity i at the th moment.

[0046] In this embodiment, the unit average price and selling price It is linked to the surplus and deficit data of carbon emissions of each entity, specifically:

[0047] According to the carbon emission surplus and shortage data, when the carbon quota shortage of each key emission entity is less than the voluntary emission reduction offset limit, the unit average price and selling price The calculation expression is:

[0048]

[0049] In the formula is the carbon quota trading price in the upper carbon market at time t, is the carbon preferential price in the carbon trading market at time t, X t For R t The inverse of , F is the set of non-key emission entities, is the carbon credit supply of non-key emission subject f at time t, L is the set of key emission subjects, is the carbon emission reduction demand of the key emission entity l at time t, The total supply of carbon credits formed by the cooperative alliance of all entities at time t, is the total demand for carbon quotas of the cooperative alliance at time t.

[0050] According to the carbon emission surplus and shortage data, at least one key emission entity's carbon quota shortage is greater than the voluntary emission reduction offset limit and the total supply Less than total demand hour,

[0051] Average price per unit and selling price The calculation expression is:

[0052]

[0053] In the formula is the carbon credit price in the carbon trading market at time t, β is the offset ratio of carbon credit regulations, is the actual carbon emissions of the key emission entity l at time t.

[0054] According to the carbon emission surplus and shortage data, at least one key emission entity's carbon quota shortage is greater than the voluntary emission reduction offset limit and the total supply Greater than or equal to total demand hour,

[0055] Average price per unit and selling price The calculation expression is:

[0056]

[0057]

[0058] Step S2: Each subject generates the electric energy transaction volume of the subject to other subjects according to the corresponding electric energy profit and loss data, and sends each electric energy transaction volume to the corresponding subject.

[0059] Step S3, each subject, based on all received electricity trading volumes, all electricity trading volumes generated by the subject and all carbon emission surplus and shortage data, combined with cost functions and constraints, solves the multi-subject electricity cooperation and sharing model based on the Nash negotiation model, updates each electricity trading volume generated by the subject according to the solution results, and sends the updated electricity trading volumes to the corresponding subjects.

[0060] Among them, the constraints include power constraints of P2P transactions, price constraints of cooperative transactions, demand response constraints and energy balance constraints.

[0061] The calculation expression of the power constraint of P2P transaction is:

[0062]

[0063] Where D is the set of all entities, is the electricity trading volume between subject i and subject j at time t.

[0064] The calculation expression of cooperative transaction price constraint is:

[0065]

[0066] In the formula is the on-grid electricity price at time t, is the electricity transaction price between subject i and subject j at time t, is the time-of-use electricity price at time t, is the carbon preferential price in the carbon trading market at time t, is the selling price of the internal carbon credit at time t, The average unit price at time t for purchasing carbon quotas and carbon credits through the carbon platform to offset excess carbon quotas. is the carbon quota trading price in the upper carbon market at time t.

[0067] The calculation expression of demand response constraint is:

[0068]

[0069] In the formula is the optimized electrical load of the key emission entity l at time t, is the electrical load of the key emission entity l at time t, is the electric power reduced by the key emission entity l at time t, for, The upper limit of the electric power reduction of key emission subject l, β e,tran is the maximum proportional coefficient of power load transfer, T is the total number at all times, is the optimized heat load of the key emission entity l at time t, is the heat load of the key emission entity l at time t, is the heat load reduced by the key emission entity l at time t, The upper limit of heat load reduction for key emission entities l, β h,tran is the maximum proportional coefficient of heat load transfer.

[0070] The calculation expression of energy balance constraint is:

[0071]

[0072] In the formula is the output power of the gas internal combustion engine of the key emission entity l at time t, is the power consumption of the electric boiler of the key emission entity l at time t, To focus on the heat generation power of the main body l at all times, is the renewable energy output of non-key emission entity f at time t, is the grid-connected power of non-key emission entity f at time t, is the P2P transaction volume of electricity between non-key emission subject f and other subjects j at time t.

[0073] The expression of the multi-agent electric energy cooperative sharing model in step S3 is:

[0074]

[0075] In the formula is the amount of electricity traded between subject i and subject j at time t, D is the set of subjects, U i is the cost function corresponding to subject i.

[0076] In the above multi-agent power cooperation sharing model, Decoupling is accomplished by introducing auxiliary variables And construct the corresponding augmented Lagrangian functional Then the calculation expression for each subject to calculate the amount of electric energy trading in each iteration is:

[0077]

[0078] In the formula is the Lagrange multiplier corresponding to subject i and subject j during the k-th iteration calculation, is the amount of electricity traded between subject i and subject j at time t during the k-th iteration calculation, is the amount of electricity traded between subject j and subject i at time t during the k-th iteration calculation, is the penalty coefficient corresponding to subject i, T is the total number of all moments, ρ P1 is the penalty coefficient.

[0079] Step S4, repeating step S3 until the preset first iteration termination condition is reached, and the optimal power trading volume between the entities is obtained. In this embodiment, the optimal power trading volume is the power trading volume obtained when the iteration is terminated.

[0080] The first iteration termination condition is that the iteration converges or the iteration exceeds the preset maximum number of iterations. In the first iteration termination condition, the calculation expression for the iteration convergence during the kth iteration calculation is:

[0081]

[0082] Where δ P1 is the original residual convergence threshold.

[0083] Step S5, each subject initializes the electric energy trading price corresponding to each optimal electric energy trading volume corresponding to the subject, and sends each electric energy trading price to the corresponding subject.

[0084] Step S6, each subject, according to the corresponding optimal electricity trading volume, all electricity trading prices generated by the subject, all received electricity trading prices and all carbon emission surplus and shortage data, combined with the cost function and constraints, solves the multi-subject bargaining profit distribution model based on the Nash negotiation model, and updates the electricity trading prices generated by the subject according to the solution results, and sends the updated electricity trading prices to the corresponding subjects.

[0085] Among them, the expression of the multi-agent bargaining profit distribution model is:

[0086]

[0087] Where D is the set of all entities, The breaking point of the negotiations. is the operating cost of entity i under certain circumstances, is the energy interaction cost between subject i and other subjects. is the operating cost of entity i when it only trades electricity from the upper grid, and the operating cost It is the operating cost of entity i when it trades with other entities and the upper-level power grid.

[0088] According to the above multi-agent bargaining profit distribution model, and through Decoupled builds and the augmented Lagrangian functional Then the calculation expression for each subject to calculate the electricity transaction price in each iteration is:

[0089]

[0090] In the formula is the Lagrange multiplier corresponding to subject i and subject j during the k-th iteration calculation, is the electricity transaction price between subject i and subject j at time t during the k-th iteration calculation, is the electricity transaction price between subject j and subject i at time t during the k-th iteration calculation, is the penalty coefficient corresponding to subject i, T is the total number of all moments, ρ P2 is the penalty coefficient.

[0091] Step S7, repeating step S6 until the preset second iteration termination condition is reached, and the optimal power transaction price between each subject is obtained. In this embodiment, the optimal power transaction price is the power transaction price obtained when the iteration is terminated.

[0092] The second iteration termination condition is that the iteration converges or the iteration exceeds the preset maximum number of iterations. Then the calculation expression for the iteration convergence in the kth iteration calculation in the second iteration termination condition is:

[0093]

[0094] Where δ P2 is the original residual convergence threshold.

[0095] In this embodiment, a cooperative alliance including four entities is constructed, that is, there are four entities in the park, and a simulation test is performed on the method of this embodiment considering distributed electricity-carbon allocation among multiple entities.

[0096] Figure 2 It is a schematic diagram of a multi-agent energy architecture formed by four agents in an embodiment of the present invention.

[0097] like Figure 2 As shown, the four entities are pharmaceutical companies, namely prosumer 1, food companies, namely prosumer 2, electronics companies, namely prosumer 3 and renewable energy operators, namely REO. The energy supply equipment involved includes wind turbines WT, photovoltaics PV, gas turbines GT, electric boilers EB, gas boilers GB and power storage ES.

[0098] The equipment parameters of the above energy supply equipment are shown in the following table:

[0099]

[0100] The first column in the table above is the various energy supply equipment, and the second and third columns are the energy efficiency and unit operation and maintenance cost of the corresponding energy supply equipment. Among them, the gas turbine includes a gas internal combustion engine and a waste heat boiler, and the power is stored in a battery. For example, the cell in the second column of the fourth row indicates that the energy efficiency of the gas internal combustion engine is 0.4.

[0101] In addition, the energy prices for different energy sources are set as shown in the following table:

[0102]

[0103]

[0104] The first column in the table above is the various energy price categories, and the second and third columns are the corresponding intervals and prices of each energy price category. For example, the cell in the second column of the fifth row indicates that the interval corresponding to the user's electricity sales price is the entire time period.

[0105] In this embodiment, the load data of the four entities are determined based on the typical daily load data of enterprises in a certain industrial park in Shanghai in winter, and the carbon quota price and PHCER are 100CNY / t and 80CNY / t respectively, and the offset coefficient value of the certified emission reduction is 10%.

[0106] In this embodiment, the electric energy coordination among the four entities is performed by considering the distributed electric carbon allocation method among multiple entities.

[0107] Figure 3 It is a schematic diagram of the result of electric energy interaction among multiple entities in an embodiment of the present invention.

[0108] like Figure 3 As shown, the horizontal axis is time, the unit is hour h, and the vertical axis is power, the unit is kw. Positive power represents the electric power sold, and negative power represents the electric power purchased. It can be seen that the electric power sold and purchased in each period are symmetrical about the horizontal axis. It shows that the supply and demand of electric power in the park in each period of this embodiment is balanced, and the method of considering distributed electric carbon allocation among multiple subjects is further verified to be effective. Since the distributed wind turbines inside REO are output all day and have no electric load demand, REO is the main supplier in the power sharing transaction. In the period of 9:00-16:00, the photovoltaic output is large, so prosumer 2 has abundant electricity to sell. However, since prosumer 2 is not equipped with gas-fired power generation equipment, it needs to purchase electricity from the outside during the photovoltaic non-output period. In the period of 9:00-16:00, the electricity demand of prosumer 1 increases sharply, and it is necessary to purchase electricity from REO and prosumer 2 at this time. Since the heat load of prosumer 1 increases significantly during the period of 18:00-21:00, the output of its gas internal combustion engine increases, thus it acts as a supplier in the power exchange. Prosumer 3 is always a demander in the transaction because of its large overall power load.

[0109] Figure 4 It is a schematic diagram of the electric energy transaction price at each moment of the cooperative operation of multiple entities in an embodiment of the present invention.

[0110] like Figure 4 As shown, the horizontal axis is time, in hours h, and the vertical axis is electricity price, in yuan / kw·h. It can be seen that when the power transaction is zero, the electricity price is the grid-connected price. At the moment when there is power transaction, due to constraints, the transaction price is higher than the grid-connected price. Since the difference between REO grid-connected price and the power purchase price of prosumers, and the difference between the purchase and sale price of power traded by prosumers and the upper-level power grid are the economic drivers for cooperative sharing, namely P2P transactions, the cooperative sharing transaction price is usually higher than the grid-connected price and lower than the purchase price from the power grid.

[0111] Figure 5 It is a schematic diagram of the surplus and shortage of carbon emission reduction within the park in an embodiment of the present invention.

[0112] like Figure 5 As shown, the horizontal axis is time, the unit is hour h, and the vertical axis is the carbon emission reduction surplus or deficit, the unit is kg.

[0113] Figure 6 It is a schematic diagram of the carbon trading price within the park in an embodiment of the present invention.

[0114] like Figure 6 As shown, the horizontal axis is time, the unit is hour h, and the vertical axis is price, the unit is yuan / t.

[0115] according to Figure 5 and Figure 6 It can be seen that the amount of carbon credit certified by REO is always greater than the overall carbon quota demand of producers and consumers, so the supply-demand ratio is always greater than 1, which leads to the internal carbon trading price of the park calculated based on the supply-demand ratio price mechanism being close to the carbon credit transaction price in the carbon market. When there is no internal carbon quota demand, the internal transaction price is the same as the carbon credit transaction price in the carbon market.

[0116] In summary, the method of this embodiment considering distributed electricity-carbon allocation among multiple entities has made important contributions in terms of electricity coordination among entities, carbon quota coordination, and promoting renewable energy consumption and reducing carbon emissions in the park:

[0117] (1) Coordinate the amount of electricity between entities

[0118] The model used in the method of considering distributed electricity carbon allocation among multiple subjects takes into account the electricity supply and demand of each subject, cost factors, and electricity P2P transaction constraints, so as to promote the reasonable distribution of electricity among different subjects. For example, in the analysis of electricity sharing transactions, according to the energy production and demand characteristics of each subject, such as the prosumer playing the role of supplier or demander of electricity at different time periods according to its own photovoltaic output, electricity load demand, and equipment operation. In this way, the balance of power supply and demand in the park is achieved in each time period, the efficiency of electricity utilization is improved, the dependence of each subject on the upper power grid is reduced, and the stability and reliability of the park energy system are enhanced.

[0119] (2) Coordination of carbon quotas

[0120] The method of allocating distributed electricity and carbon among multiple entities also designs a corresponding carbon trading mechanism based on the different characteristics of prosumers and renewable energy operators, thus achieving effective coordination of carbon quotas. For prosumers, the issuance of their carbon quotas is related to the output of their internal gas equipment and the amount of electricity purchased from their superiors. By reasonably arranging energy consumption and participating in carbon trading to meet carbon emission requirements, waste or shortage of carbon quotas is avoided. For renewable energy operators, participating in carbon trading by selling carbon benefits certified by renewable energy power generation not only brings benefits to themselves, but also increases the supply of carbon emission reductions in the park. At the same time, the carbon asset management platform formulates transaction prices within the park and acts as an agent for transactions based on the surplus and shortage information of carbon emission reductions fed back by various entities, further optimizing the allocation and utilization of carbon quotas and promoting the realization of the overall carbon emission reduction goals of the park.

[0121] (3) Promoting the consumption of renewable energy and reducing carbon emissions

[0122] The method of considering the distributed electricity-carbon allocation among multiple subjects significantly promotes the local consumption of distributed renewable energy and reduces the overall carbon emissions of the park. In the process of multi-subject cooperative operation, through power sharing transactions, each producer and consumer gives priority to the consumption of REO's renewable energy power generation, reducing the purchase of high-carbon electricity from the upper power grid. For example, in the case analysis, compared with the independent operation of each subject, after participating in the power sharing alliance, the renewable energy consumption rate in the park has been greatly increased to, and carbon emissions have been reduced. During the period of high REO output at night, producers and consumers reduce the purchase of electricity from the upper power grid and use more REO's wind turbines for power generation; during the period of high photovoltaic output during the day, producers and consumers can also achieve effective sharing and utilization of photovoltaic power. This optimization of energy consumption structure fundamentally reduces the carbon emission level of the park, effectively promotes the development of the park in the direction of green and low-carbon, and has important practical significance for achieving the dual carbon goals.

[0123] Functions and Effects of the Embodiments

[0124] According to the method of considering distributed electricity-carbon allocation among multiple subjects involved in this embodiment, on the one hand, the optimal electricity trading volume and the optimal electricity trading price are calculated in turn through the cost function, the multi-subject electricity cooperation sharing model and the multi-subject bargaining benefit distribution model, and the carbon quota is considered in it; on the other hand, the distributed solution is performed through the multiplier alternating direction method to effectively protect the information privacy of all parties. In short, this method can allocate the optimal electricity and carbon quota to multiple subjects.

[0125] Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for distributed electricity-carbon allocation among multiple entities, for each entity to obtain corresponding electricity allocation data according to corresponding carbon emission surplus and deficit data and electricity surplus and deficit data, characterized in that: The following steps are involved: Step S1, each of the subjects constructs a corresponding cost function according to the subject type; Step S2, each of the entities generates the electric energy transaction volume of the entity to each of the other entities according to the corresponding electric energy profit and loss data, and sends each of the electric energy transaction volumes to the corresponding entity; Step S3, each of the subjects solves the multi-subject electric energy cooperation and sharing model constructed based on the Nash negotiation model according to all the received electric energy trading volumes, all the electric energy trading volumes generated by the subject, and all the carbon emission surplus and shortage data, in combination with the cost function and the constraints, and updates each of the electric energy trading volumes generated by the subject according to the solution results, and sends the updated electric energy trading volumes to the corresponding subject; Step S4, repeatedly executing step S3 until a preset first iteration termination condition is reached, and the optimal electric energy transaction amount between each of the entities is obtained; Step S5, each of the subjects initializes the electric energy transaction price corresponding to each of the optimal electric energy transaction quantities corresponding to the subject, and sends each of the electric energy transaction prices to the corresponding subject; Step S6, each of the subjects solves the multi-subject bargaining profit distribution model constructed based on the Nash negotiation model according to the corresponding optimal electricity trading volume, all the electricity trading prices generated by the subject, all the received electricity trading prices and all the carbon emission surplus and shortage data, combined with the cost function and the constraints, and updates each of the electricity trading prices generated by the subject according to the solution results, and sends the updated each of the electricity trading prices to the corresponding subject; Step S7, repeating step S6 until the preset second iteration termination condition is reached, and obtaining the optimal electric energy transaction price between the entities. The types of entities include key emission entities and non-key emission entities. The electric energy distribution data of the subject includes all the optimal electric energy transaction volumes and the optimal electric energy transaction prices corresponding to the subject.

2. The method for distributing distributed electricity and carbon among multiple entities according to claim 1 is characterized in that: in, The calculation expression of the objective function corresponding to the key emission entities is: Where U i is the cost of key emission entity i, T is the total number of times, is the gas consumption of the equipment of key emission entity i at the tth moment, c gas is the natural gas unit price, is the amount of electricity purchased by key emission entity i from the upper grid at time t, is the electricity price sold by the upper power grid at the tth moment, is the amount of electricity sold by key emission entity i to the upper grid at time t, is the repurchase price of the upper power grid at the tth moment, D is the set of other entities, λ is the unit power transmission fee, is the electric energy interaction between the key emission subject i and the jth other subject at the tth moment, is the electricity trading volume between the key emission subject i and the jth other subject at the tth moment, is the electricity transaction price between the key emission entity i and the jth other entity at the tth moment, The average unit price at the tth moment to offset the excess carbon quota by purchasing carbon quotas and carbon credits through the carbon platform, is the actual carbon emissions of key emission entity i at the tth moment, is the carbon quota of key emission entity i at the tth moment, is the carbon quota transaction price in the upper carbon market at the tth moment, α e,cut The compensation unit price for the reduction of electric load, is the electricity load reduction of key emission entity i at the tth moment, α e,tran is the compensation unit price for electric load transfer, is the electric load transfer amount of key emission entity i at the tth moment, α h,cut The compensation unit price for heat load reduction, is the heat load reduction of key emission entity i at time t, α h,tran is the compensation unit price for heat load transfer, is the heat load transfer amount of key emission entity i at the tth moment, The equipment operating costs of key emission entity i.

3. The method for distributing distributed electricity and carbon among multiple entities according to claim 2 is characterized in that: in, Carbon quota of key emitters l The calculation expression is: In the formula is the free carbon quota ratio coefficient of the key emission entity l, ε e is the carbon emission limit coefficient per unit of power supply, is the amount of electricity purchased by key emission entity l from the upper grid at time t, is the output power of the gas internal combustion engine purchased by the key emission entity l from the gas turbine at the tth moment, ε h is the carbon emission limit coefficient per unit of heating, is the output power of the waste heat boiler of the key emission entity l that purchases electricity from the gas turbine at the tth moment, is the output power of the gas boiler purchased by the key emission entity l at the tth moment, The actual carbon emissions of the key emitter l The calculation expression is: In the formula is the gas consumption of the key emission entity l at the tth moment, H g is the average lower calorific value of natural gas, is the CO2 emission factor for natural gas based on the lowest calorific value, It is the default value of electricity emission factor.

4. The method for distributing distributed electricity and carbon among multiple entities according to claim 1 is characterized in that: in, The calculation expression of the objective function corresponding to the non-key emission subject is: Where U i is the cost of non-key emission entity i, T is the total number of moments, is the amount of electricity purchased by non-key emission entity i from the upper grid at time t, is the electricity price sold by the upper power grid at the tth moment, is the amount of electricity sold by non-key emission entity i to the upper grid at time t, is the repurchase price of the upper power grid at the tth moment, D is the set of other entities, λ is the unit power transmission fee, is the electric energy interaction between the non-key emission subject i and the jth other subject at the tth moment, is the electricity trading volume between non-key emission subject i and other subject j at the tth moment, is the electricity transaction price between non-key emission subject i and other subject j at the tth moment, is the selling price of the internal carbon credit at the tth moment, is the combined marginal emission factor of the regional power grid, The renewable energy output of non-key emission entity i at the th moment.

5. The method for distributing distributed electricity and carbon among multiple entities according to claim 1 is characterized in that: in, The constraints include power constraints of P2P transactions, cooperative transaction price constraints, demand response constraints and energy balance constraints. The calculation expression of the power constraint of the P2P transaction is: Where D is the set of all entities, is the amount of electricity traded between subject i and subject j at time t, The calculation expression of the cooperative transaction price constraint is: In the formula is the on-grid electricity price at time t, is the electricity transaction price between subject i and subject j at time t, is the time-of-use electricity price at time t, is the carbon preferential price in the carbon trading market at time t, is the selling price of internal carbon credit at time t, The average unit price at time t for purchasing carbon quotas and carbon credits through the carbon platform to offset excess carbon quotas. is the carbon quota trading price in the upper carbon market at time t, The calculation expression of the demand response constraint is: In the formula is the optimized electrical load of the key emission entity l at time t, is the electrical load of the key emission entity l at time t, is the electric power reduced by the key emission entity l at time t, for, The upper limit of the electric power reduction of key emission subject l, β e,tran is the maximum proportional coefficient of power load transfer, T is the total number at all times, is the optimized heat load of the key emission entity l at time t, is the heat load of the key emission entity l at time t, is the heat load reduced by the key emission entity l at time t, The upper limit of heat load reduction for key emission entities l, β h,tran is the maximum proportional coefficient of heat load transfer, The calculation expression of the energy balance constraint is: In the formula is the output power of the gas internal combustion engine of the key emission entity l at time t, is the power consumption of the electric boiler of the key emission entity l at time t, To focus on the heat generation power of the main body l at all times, is the renewable energy output of non-key emission entity f at time t, is the grid-connected power of non-key emission entity f at time t, is the P2P transaction volume of electricity between non-key emission subject f and other subjects j at time t.

6. The method for distributing distributed electricity and carbon among multiple entities according to claim 1 is characterized in that: in, The expression of the multi-agent electric energy cooperative sharing model is: In the formula is the amount of electricity traded between subject i and subject j at time t, D is the set of subjects, U i is the cost function corresponding to subject i, In step S3, the calculation expression for calculating the electric energy transaction amount by each subject in each iteration is: In the formula is the Lagrange multiplier corresponding to subject i and subject j during the k-th iteration calculation, is the amount of electricity traded between subject i and subject j at time t during the k-th iteration calculation, is the amount of electricity traded between subject j and subject i at time t during the k-th iteration calculation, is the penalty coefficient corresponding to subject i, T is the total number of all moments, ρ P1 is the penalty coefficient, The first iteration termination condition is that the iteration converges or the iteration exceeds a preset maximum number of iterations. The calculation expression of iterative convergence during the kth iteration calculation is: Where δ P1 is the original residual convergence threshold.

7. The method for distributing distributed electricity and carbon among multiple entities according to claim 1 is characterized in that: in, The expression of the multi-agent bargaining profit distribution model is: Where D is the set of all entities, The breaking point of the negotiations. is the operating cost of entity i under certain circumstances, is the electricity interaction cost between subject i and other subjects, In step S6, the calculation expression used by each of the entities to calculate the electric energy transaction price in each iteration is: In the formula is the Lagrange multiplier corresponding to subject i and subject j during the k-th iteration calculation, is the electricity transaction price between subject i and subject j at time t during the k-th iteration calculation, is the electricity transaction price between subject j and subject i at time t during the k-th iteration calculation, is the penalty coefficient corresponding to subject i, T is the total number of all moments, ρ P2 is the penalty coefficient, The second iteration termination condition is that the iteration converges or the iteration exceeds a preset maximum number of iterations. The calculation expression of iterative convergence during the kth iteration calculation is: Where δ P2 is the original residual convergence threshold.

8. The method for distributing distributed electricity and carbon among multiple entities according to claim 1 is characterized in that: in, According to the carbon emission surplus and shortage data, when the carbon quota shortage of each key emission entity is less than the upper limit of voluntary emission reduction offset, the unit average price and selling price The calculation expression is: In the formula is the carbon quota trading price in the upper carbon market at time t, is the carbon preferential price in the carbon trading market at time t, X t For R t The inverse of , F is the set of non-key emission entities, is the carbon credit supply of non-key emission subject f at time t, L is the set of key emission subjects, is the carbon emission reduction demand of the key emission entity l at time t, The total supply of carbon credits formed by the cooperative alliance of all entities at time t, is the total demand for carbon quotas of the cooperative alliance at time t.

9. The method for distributing distributed electricity and carbon among multiple entities according to claim 8 is characterized in that: in, According to the carbon emission surplus and shortage data, the carbon quota shortage of at least one of the key emission entities is greater than the voluntary emission reduction offset limit and the total supply Less than total demand hour, Average price per unit and selling price The calculation expression is: In the formula is the carbon credit price in the carbon trading market at time t, β is the offset ratio of carbon credit regulations, is the actual carbon emissions of the key emission entity l at time t, According to the carbon emission surplus and shortage data, the carbon quota shortage of at least one of the key emission entities is greater than the voluntary emission reduction offset limit and the total supply Greater than or equal to total demand hour, Average price per unit and selling price The calculation expression is:

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