Optimization method for participation of production and elimination aggregator in power-carbon market decision making of integrated energy system

Abstract

The invention discloses an optimization method for participation of production and elimination aggregators in an integrated energy system in an electricity-carbon market decision. The optimization method comprises the following steps: determining a carbon emission free quota amount of each aggregator in each divided period and a carbon emission free quota increment obtained by using photovoltaic power generation equipment; establishing a total operation cost model of all the aggregators participating in the electricity-carbon transaction in the carbon emission assessment period; based on a dual problem method, introducing a Lagrange multiplier, and establishing an electricity-carbon transaction income model of each aggregator in the carbon emission assessment period by using a dual variable and a total operation cost model of each aggregator participating in the electricity-carbon transaction in the carbon emission assessment period; based on a game method, establishing an electricity-carbon transaction instruction optimization model; and based on the Nash equilibrium point of the electricity-carbon transaction instruction optimization model, determining an electricity-carbon transaction optimal instruction of each aggregator. According to the invention, the transaction cost and the carbon emission of each producer and consumer in the electricity-carbon market are reduced, the local renewable energy consumption is improved, and the privacy of different subjects is protected at the same time.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy systems, and in particular, relates to an optimization method for producers and consumers in integrated energy systems to participate in electricity-carbon market decision-making. Background Art

[0002] In order to achieve the "dual carbon" goals of carbon peak and carbon neutrality, the rapid development of renewable energy has become the key to energy transformation. At the same time, the growth of the electricity market and the rise of the carbon emission rights market have also provided financial support and economic impetus for the transformation of the energy industry.

[0003] The popularity of distributed renewable energy has transformed traditional electricity users into energy producers and consumers. They are often equipped with green photovoltaic, wind power and other renewable energy power generation equipment, which has great potential for carbon reduction and emission reduction. At present, the electricity generated by producers and consumers first meets their own electricity needs, and the remaining part is sold online. At the same time, they can also obtain income by participating in carbon emission rights trading. In theory, using peer-to-peer (P2P) trading technology, any energy producer and consumer can directly participate in the user-side electricity market transactions. However, the renewable energy generation of individual energy producers and consumers is generally small, resulting in a small amount of electricity trading. A large number of disorderly transactions will affect the safety of power grid operation; the carbon emission reductions owned by producers and consumers are also relatively small, making it difficult to meet the entry threshold of carbon emission rights market transactions. Even if they can participate in carbon emission rights trading, they are often in a disadvantageous position.

[0004] In response to the above problems, some solutions have been proposed in the existing technology, such as centrally managing the electricity transactions of producers and consumers through third-party platforms. However, such methods often focus on the centralized processing of electricity transactions and ignore the integration of carbon emission reductions and transaction optimization, and fail to fully solve the dual dilemma of producers and consumers in the electricity and carbon emission rights markets. Summary of the invention

[0005] In order to address the deficiencies in the prior art, the present invention provides an optimization method for prosumer aggregators to participate in electricity carbon market decision-making in an integrated energy system, helping different types of prosumer entities (campuses, households, commercial enterprises, etc.) to achieve economic carbon quotas and green electricity decisions, while reducing the transaction costs and carbon emissions of each prosumer in the electricity carbon market, and increasing the local renewable energy consumption, while protecting the privacy of different entities.

[0006] The present invention adopts the following technical solution.

[0007] The present invention proposes an optimization method for participating in the decision-making of the electricity-carbon market by the prosumer aggregator in the integrated energy system. The coverage of the electricity-carbon market includes multiple aggregators and multiple carbon emission units. Each aggregator includes energy storage equipment, photovoltaic power generation equipment and multiple electricity load units, including:

[0008] In a carbon emission assessment cycle, the carbon emissions of each aggregator and the carbon emission quota of each carbon emission unit are obtained, and the sum of the carbon emission quotas of all carbon emission units is allocated to each aggregator according to the proportion of each aggregator's carbon emissions in the sum of all aggregators' carbon emissions, so as to obtain the free carbon emission quota of each aggregator in a carbon emission assessment cycle; based on the free carbon emission quota of each aggregator in a carbon emission assessment cycle, the free carbon emission quota of each aggregator in each time period is determined according to the proportion of the electricity load of each aggregator in the time period in the sum of the electricity load of all aggregators in the time period; according to the predicted power generation value of the photovoltaic power generation equipment, the increment of the free carbon emission quota obtained by each aggregator in each time period by using the photovoltaic power generation equipment is determined;

[0009] Obtain the transaction costs between the aggregator and the upper-level power grid, the photovoltaic power generation costs of the aggregator, the operating costs of the energy storage equipment of the aggregator, the carbon trading costs of the aggregator, and the income obtained by the aggregator from using photovoltaic power generation equipment during a carbon emission assessment cycle, so as to establish a total operating cost model for all aggregators participating in electricity-carbon trading during the carbon emission assessment cycle;

[0010] Based on the dual problem method, the amount of electricity traded between aggregators and other aggregators and the amount of free carbon emission quotas traded between aggregators and other aggregators are used as a set of dual variables; Lagrange multipliers are introduced, and the total operating cost model of each aggregator participating in electricity-carbon trading during the carbon emission assessment period is used to establish the electricity-carbon trading revenue model of each aggregator during the carbon emission assessment period; based on the electricity-carbon trading revenue model of each aggregator during the carbon emission assessment period, the revenue of each aggregator participating in the electricity-carbon market decision-making in each time period is determined;

[0011] Based on the game theory method, an electricity carbon trading instruction optimization model is established using the set of various aggregators participating in the electricity carbon market decision-making within the time period, the set of electricity carbon trading instructions of various aggregators participating in the electricity carbon market decision-making within the time period, and the set of benefits of various aggregators participating in the electricity carbon market decision-making within the time period; the optimal electricity carbon trading instructions of each aggregator are determined based on the Nash equilibrium point of the electricity carbon trading instruction optimization model; among which, the electricity carbon trading instructions of the aggregator include: the amount of electricity traded with the superior power grid, the amount of free carbon emission quota traded with the carbon emission rights market, the charging and discharging control of energy storage equipment, the power generation control of photovoltaic power generation equipment, the amount of electricity traded with other aggregators, and the amount of free carbon emission quota traded with other aggregators.

[0012] Preferably, the optimal instructions for electricity-carbon trading of each aggregator include: the optimal value of electricity traded with the superior power grid, the optimal value of carbon emission free quota traded with the carbon emission rights market, the optimal control of energy storage equipment charging and discharging, the optimal control of photovoltaic power generation equipment, the optimal value of electricity traded with other aggregators, and the optimal value of carbon emission free quota traded with other aggregators;

[0013] Each aggregator will encrypt and share the optimal value of the electricity volume traded with other aggregators and the optimal value of the carbon emission free quota volume traded with other aggregators; each aggregator will update the dual variables based on the shared data; and use the updated dual variables to update the Lagrange multiplier; when the Lagrange multipliers before and after the update converge, the updated Lagrange multipliers and the updated dual variables will be used to determine the final revenue of each aggregator based on the electricity-carbon trading revenue model of each aggregator during the carbon emission assessment period;

[0014] The final revenue of each aggregator participating in the electricity-carbon market decision-making within the time period is used to update the Nash equilibrium point of the electricity-carbon trading instruction optimization model to obtain the updated optimal electricity-carbon trading instructions of each aggregator, so as to participate in the electricity-carbon market decision-making as the production and consumption aggregator in the integrated energy system.

[0015] Preferably, in a carbon emission assessment cycle, obtaining the carbon emissions of each aggregator includes:

[0016] Obtain the carbon emissions generated by the purchased electricity of each aggregator in different time periods and the carbon emissions generated by the charging and discharging of each aggregator's energy storage equipment, so as to establish a carbon emissions model for each aggregator in the carbon trading process in different time periods; based on the carbon emissions model for each aggregator in the carbon trading process in different time periods, determine the carbon emissions of each aggregator in a carbon emission assessment cycle;

[0017] Among them, the carbon emission model of each aggregator in the carbon trading process in different time periods satisfies the following relationship:

[0018]

[0019] In the formula, represents the carbon emissions of aggregator i in the carbon trading process in time period t, represents the carbon emissions generated by the purchased electricity of aggregator i in time period t, represents the carbon emissions generated by the charging and discharging of the energy storage equipment of aggregator i in time period t;

[0020] The carbon emissions caused by the outsourcing of electricity by aggregator i in time period t satisfy the following relationship:

[0021]

[0022] In the formula, kg represents the grid benchmark emission factor, represents the amount of electricity purchased by aggregator i in time period t;

[0023] The carbon emissions of energy storage equipment of aggregator i in time period t satisfy the following relationship:

[0024]

[0025] In the formula, γ ba Indicates the carbon emission coefficient of energy storage equipment during charging and discharging. represents the charging load of the energy storage device of aggregator i in time period t, represents the discharge load of the energy storage device of aggregator i in time period t;

[0026] The carbon emissions of each aggregator during the carbon emission assessment period satisfy the following relationship:

[0027]

[0028] In the formula, E i,T It represents the carbon emissions of aggregator i in the carbon trading process within the carbon emission assessment period T.

[0029] Preferably, within a carbon emission assessment cycle, the carbon emission quota of each carbon emission unit is obtained, including:

[0030] The historical emission method is combined with the annual attenuation coefficient method to correct the sum of the historical carbon emissions of all carbon emission units in a historical carbon emission assessment period to obtain the total carbon emission quota of all carbon emission units in the carbon emission assessment period; the total carbon emission quota of all carbon emission units is allocated to each carbon emission unit according to the proportion of the historical carbon emissions of each carbon emission unit in the sum of the carbon emission quotas of all carbon emission units, and the carbon emission quota of each carbon emission unit in a carbon emission assessment period is obtained;

[0031] Among them, the sum of the historical carbon emissions of each carbon emission unit within a historical carbon emission assessment period satisfies the following relationship:

[0032]

[0033] In the formula, E total It represents the sum of the historical carbon emissions of each carbon emission unit during the historical carbon emission assessment period, E j represents the historical carbon emissions of the jth carbon emission unit, j∈EU, and EU represents the set of all carbon emission units;

[0034] The total amount of carbon emission quotas of all carbon emission units within the carbon emission assessment period satisfies the following relationship:

[0035]

[0036] In the formula, Q total Indicates the total amount of carbon emission quotas of all carbon emission units within the carbon emission assessment period. is the annual decay coefficient, expressed as a percentage;

[0037] The annual attenuation coefficient satisfies the following relationship:

[0038]

[0039] In the formula, n represents the number of years required to achieve the emission reduction target, and r represents the total emission reduction target for the next n years, expressed as a percentage;

[0040] The carbon emission quota of each carbon emission unit in a carbon emission assessment cycle satisfies the following relationship:

[0041]

[0042] In the formula, Q j Represents the carbon emission quota of the j-th carbon emission unit.

[0043] Preferably, the amount of free carbon emission quotas of each aggregator in a carbon emission assessment cycle satisfies the following relationship:

[0044]

[0045] In the formula, represents the free carbon emission quota of aggregator i within the carbon emission assessment period T, Q total represents the total carbon emission quota of all carbon emission units within the carbon emission assessment period, and I represents the set of all aggregators;

[0046] The free carbon emission quota of each aggregator in each time period satisfies the following relationship:

[0047]

[0048] In the formula, represents the amount of free carbon emission quota of aggregator i in time period t, represents the load of aggregator i in time period t.

[0049] Preferably, the carbon emission offset corresponding to the photovoltaic power generation equipment of aggregator i in time period t satisfies the following relationship:

[0050]

[0051] In the formula, represents the carbon emission offset of photovoltaic power generation equipment of aggregator i in time period t, It represents the predicted value of power generation of photovoltaic power generation equipment of aggregator i in time period t;

[0052] The increment of free carbon emission quota obtained by aggregator i using photovoltaic power generation equipment in time period t satisfies the following relationship:

[0053]

[0054] In the formula, represents the carbon emission offset amount of photovoltaic power generation equipment used by aggregator i in time period t, which is determined by the offset ratio of the free carbon emission quota increment, μ c To offset the ratio.

[0055] Preferably, the total operating cost model of all aggregators participating in electricity-carbon trading during the carbon emission assessment period satisfies the following relationship:

[0056]

[0057] In the formula, Cost represents the total operating cost of all producers and consumers participating in the electricity-carbon trading during the carbon emission assessment period. represents the cost of transactions between aggregator i and the upper grid in time period t, represents the photovoltaic power generation cost of aggregator i in time period t, represents the operating cost of the energy storage equipment of aggregator i in time period t, represents the carbon trading cost of aggregator i in time period t, It represents the income obtained by aggregator i from using photovoltaic power generation equipment in time period t, T represents the carbon emission assessment period, and I represents the set of all aggregators.

[0058] Preferably, under the constraint that the aggregator purchases and sells electricity to the superior power grid, the cost of transactions between each aggregator and the superior power grid in different time periods is determined to satisfy the following relationship:

[0059]

[0060] In the formula, represents the cost of transactions between aggregator i and the upper grid in time period t, and They represent the electricity sales price and on-grid electricity price of the upper power grid in time period t respectively; and They represent the power purchased and sold by aggregator i to the upper grid in time period t respectively;

[0061] Among them, the aggregator meets the maximum exchange power constraint when purchasing and selling electricity to the upper-level power grid, as shown in the following relationship:

[0062]

[0063] In the formula, represents the upper limit of the switching power of aggregator i.

[0064] Preferably, the photovoltaic power generation cost of each aggregator in different time periods satisfies the following relationship:

[0065]

[0066] In the formula, represents the cost coefficient of photovoltaic power generation equipment of aggregator i, It represents the predicted value of power generation of photovoltaic power generation equipment of aggregator i in time period t.

[0067] Preferably, the operating cost of each aggregator's energy storage equipment is determined under the joint constraints of the aggregator's energy storage equipment, satisfying the following relationship:

[0068]

[0069] In the formula, represents the operating cost of the energy storage equipment of aggregator i in time period t, represents the cost coefficient of energy storage equipment of aggregator i, represents the charging load of the energy storage device of aggregator i in time period t, represents the discharge load of the energy storage device of aggregator i in time period t;

[0070] Among them, the joint constraints of the aggregator's energy storage equipment include:

[0071] 1) The energy storage equipment of the aggregator meets the maximum charging power constraint and the maximum discharging power constraint during the charging and discharging process, as shown in the following relationship:

[0072]

[0073] In the formula, They represent the upper limit of charging power and the upper limit of discharging power of the energy storage device of aggregator i respectively;

[0074] 2) The aggregator's energy storage equipment is charged and discharged at different times, satisfying the following relationship:

[0075]

[0076] 3) The energy storage equipment of the aggregator meets the state of charge constraint and satisfies the following relationship:

[0077]

[0078] In the formula, They represent the lower and upper limits of the state of charge of the energy storage equipment of aggregator i, respectively. represents the state of charge of the energy storage device of aggregator i in time period t, represents the capacity of the energy storage equipment of aggregator i;

[0079] 4) The energy storage equipment of the aggregator meets the balance of charge and discharge within a use cycle and satisfies the following relationship:

[0080]

[0081] The charge amount of an energy storage device during one usage cycle should be equal to the discharge amount.

[0082] Preferably, the carbon trading costs of each aggregator in different time periods satisfy the following relationship:

[0083]

[0084] In the formula, and They represent the purchase price and sale price of carbon quota in the carbon emission rights market in time period t, and They respectively represent the amount of free carbon emission quotas purchased and sold by aggregator i in the carbon emission rights market in time period t.

[0085] Preferably, the income obtained by aggregator i from using photovoltaic power generation equipment in time period t satisfies the following relationship:

[0086]

[0087] In the formula, represents the income obtained by aggregator i from selling CCER to the market in time period t, ω c is the unit price of carbon emission offset, μ c To offset the ratio, It represents the carbon emission offset of photovoltaic power generation equipment of aggregator i in time period t.

[0088] Preferably, the constraints of the total operating cost model for all aggregators participating in electricity-carbon trading during the carbon emission assessment period include: establishing power balance constraints and carbon emission free quota balance constraints for aggregators during the carbon emission assessment period, and power trading constraints and carbon emission free quota trading constraints for aggregators participating in electricity-carbon trading based on the electricity-carbon trading behavior of the aggregators.

[0089] Preferably, the power balance constraint of the aggregator in different time periods satisfies the following relationship:

[0090]

[0091] In the formula, represents the power purchased or sold by aggregator i in time period t, represents the predicted value of power generation of photovoltaic power generation equipment of aggregator i in time period t, represents the load of aggregator i in time period t, represents the charging load of the energy storage device of aggregator i in time period t, represents the discharge load of the energy storage device of aggregator i in time period t;

[0092] The power purchased or sold by aggregator i in time period t satisfies the following relationship:

[0093] When aggregator i purchases electricity

[0094]

[0095] When aggregator i sells electricity

[0096]

[0097] In the formula, and They represent the power purchased and sold by aggregator i to the upper grid in time period t, I represents the set of all prosumer aggregators, They represent the power purchased by aggregator i from aggregator j in time period t and the power sold by aggregator i to aggregator j in time period t respectively;

[0098] The balance constraint of the free carbon emission quota of the aggregator in different time periods satisfies the following relationship:

[0099]

[0100] In the formula, represents the amount of free carbon emission quotas sold by aggregator i to the market in time period t, represents the remaining amount of free carbon emission quota of aggregator i in time period t, represents the amount of free carbon emission quotas sold by aggregator i in time period t, They represent the free carbon emission quota of aggregator i in time period t, the free carbon emission quota purchased or sold by aggregator i in time period t, the free carbon emission quota obtained by aggregator i from using photovoltaic power generation equipment in time period t, and the carbon emissions of aggregator i in the carbon trading process in time period t;

[0101] The amount of free carbon emission quotas purchased or sold by aggregator i in time period t satisfies the following relationship:

[0102] When aggregator i purchases free carbon emission quotas:

[0103]

[0104] When aggregator i sells free carbon emission allowances:

[0105]

[0106] In the formula, represents the amount of free carbon emission quota purchased by aggregator i from the market in time period t, They respectively represent the amount of free carbon emission quotas purchased by aggregator i from aggregator j in time period t and the amount of free carbon emission quotas sold by aggregator i to aggregator j in time period t.

[0107] Preferably,

[0108] 1) Power trading constraints satisfy the following relationship:

[0109]

[0110] In the formula, denote the electricity purchased by aggregator i from aggregator j in time period t and the electricity sold by aggregator i to aggregator j in time period t, respectively. denote the electricity purchased by aggregator j from aggregator i in time period t and the electricity sold by aggregator j to aggregator i in time period t, respectively. represents the power cap of transactions between aggregator i and aggregator j;

[0111] 2) The trading constraints of free carbon emission quotas shall satisfy the following relationship:

[0112]

[0113]

[0114] In the formula, They represent the free carbon emission quotas purchased by aggregator i from aggregator j in time period t and the free carbon emission quotas sold by aggregator i to aggregator j in time period t, respectively. They represent the free carbon emission quotas purchased by aggregator j from aggregator i in time period t and the free carbon emission quotas sold by aggregator j to aggregator i in time period t, respectively. It represents the upper limit of free carbon emission quotas traded between aggregator i and aggregator j.

[0115] Preferably, based on the dual problem method, the amount of electricity traded between an aggregator that simultaneously satisfies the power balance constraint and the power trading constraint and other aggregators, and the amount of carbon emission free quota traded between an aggregator that simultaneously satisfies the carbon emission free quota balance constraint and the carbon emission free quota trading constraint and other aggregators are used as a set of dual variables;

[0116] The aggregator that satisfies both the power balance constraint and the power trading constraint simultaneously trades electricity with other aggregators, including: the total power sold by aggregator i to other aggregators in time period t, and the total power purchased by aggregator i from other aggregators in time period t; satisfying the following relationship:

[0117]

[0118] In the formula, represents the total power sold by aggregator i to other aggregators in time period t, represents the power vector of electricity sold by aggregator i to other aggregators in time period t, They represent the power sold by aggregator i to aggregator 1, ..., aggregator N in time period t; Y i represents an N-dimensional column vector whose i-th element is 0 and the rest of the elements are 1;

[0119]

[0120] In the formula, represents the total power purchased by aggregator i from other aggregators in time period t, represents the power vector of electricity purchased by aggregator i from other aggregators in time period t, They represent the power purchased by aggregator i from aggregator 1, ..., aggregator N in time period t respectively;

[0121] The sum of the power sold by aggregator i to other aggregators in time period t and the sum of the power purchased by aggregator i from other aggregators in time period t are both negative values;

[0122] Aggregators that simultaneously meet the carbon emission free quota balance constraint and the carbon emission free quota transaction constraint conditions trade carbon emission free quotas with other aggregators, including: the sum of carbon emission free quotas sold by aggregator i to other aggregators in time period t, and the sum of carbon emission free quotas purchased by aggregator i from other aggregators in time period t; satisfying the following relationship:

[0123]

[0124] In the formula, represents the total amount of free carbon emission quotas sold by aggregator i to other aggregators in time period t, represents the vector of free carbon emission quotas sold by aggregator i to other aggregators in time period t, They represent the amount of free carbon emission quotas sold by aggregator i to aggregator 1, ..., aggregator N in time period t respectively;

[0125]

[0126] In the formula, represents the total amount of free carbon emission quotas purchased by aggregator i from other aggregators in time period t, represents the vector of free carbon emission quotas purchased by aggregator i from other aggregators in time period t, They represent the free carbon emission quotas purchased by aggregator i from aggregator 1, ..., aggregator N in time period t respectively;

[0127] The sum of the free carbon emission quotas sold by aggregator i to other aggregators in time period t and the sum of the free carbon emission quotas purchased by aggregator i from other aggregators in time period t are both positive.

[0128] Preferably, the electricity carbon trading revenue model of each aggregator during the carbon emission assessment period satisfies the following relationship:

[0129]

[0130]

[0131] In the formula, represents the income of aggregator i participating in electricity-carbon trading in time period t; represents the operating cost of aggregator i participating in electricity-carbon trading in time period t; represents the set of Lagrange multipliers in time segment t, satisfying in, It represents the Lagrange multiplier corresponding to the amount of electricity traded between aggregator i and other aggregators in time period t, It represents the Lagrange multiplier corresponding to the amount of free carbon emission quotas traded between aggregator i and other aggregators in time period t; It represents the Lagrange multiplier vector corresponding to the amount of electricity traded between the aggregator and other aggregators in time period t; It represents the Lagrange multiplier vector corresponding to the amount of free carbon emission quotas traded between the aggregator and other aggregators in time period t; diag{} represents the diagonal elements.

[0132] Preferably, the electricity-carbon trading instruction optimization model satisfies the following relationship:

[0133]

[0134] In the formula, G AOPIt is the optimization model of electricity carbon trading instructions; {AOP1,…,AOP N} is the set of various aggregators participating in the decision-making of the electricity carbon market in different time periods, among which AOP1, ..., AOP N They are aggregators 1, ..., and N that participate in the decision-making of the electricity-carbon market in different time periods; is the set of electricity-carbon trading instructions of each aggregator participating in the electricity-carbon market decision-making within the time period t, where They are respectively the electricity-carbon trading instructions of aggregator 1, ..., and the electricity-carbon trading instructions of aggregator N in time period t; is the collection of the revenues of each aggregator participating in the electricity carbon market decision-making in time period t, where are the revenue of aggregator 1, ..., and N in time period t, respectively; N represents the total number of aggregators, which is a positive integer;

[0135] The optimal instruction set of each aggregator’s electricity-carbon trading in time period t is: in, They are the preferred electricity-carbon trading instructions of aggregator 1 in time period t, ..., and the preferred electricity-carbon trading instructions of aggregator N; the preferred electricity-carbon trading instructions of aggregator i in time period t are The set of preferred instructions for electricity-carbon trading of other aggregators is:

[0136] When the following relationship is satisfied, the optimal order set of electricity-carbon trading of each aggregator in time period t is A Nash equilibrium point for the electricity-carbon trading order optimization model:

[0137]

[0138] In the formula, is the revenue of aggregator i determined based on the electricity-carbon trading instructions in time period t, X i Preferred instructions for electricity-carbon trading for aggregator i Any electricity carbon trading instruction other than

[0139] Preferably, the Lagrange multiplier is updated using the updated dual variable to satisfy the following relationship:

[0140]

[0141] In the formula, They represent the Lagrange multipliers corresponding to the amount of electricity traded between aggregator i and other aggregators in time period t at the k+1th and kth iterations, respectively. They represent the Lagrange multipliers corresponding to the free carbon emission quotas traded between aggregator i and other aggregators in time period t at the k+1th and kth iterations, respectively. represents the price update step of the electricity traded between aggregator i and other aggregators at the kth iteration, represents the carbon price update step size of the carbon emission free quotas traded by aggregator i with other aggregators at the kth iteration, represents the power vector of electricity sold by aggregator i to other aggregators in time period t at the kth iteration, represents the total power sold by aggregator i to other aggregators in time period t at the kth iteration, represents the vector of free carbon emission quotas sold by aggregator i to other aggregators in time period t at the kth iteration, Y represents the sum of the free carbon emission quotas sold by aggregator i to other aggregators in time period t at the kth iteration; i represents an N-dimensional column vector whose i-th element is 0 and the rest of the elements are 1;

[0142] The difference between the Lagrange multipliers before and after the update is compared. If the difference is less than the set threshold, it means that the Lagrange multipliers before and after the update have converged. The threshold value is no more than 0.01.

[0143] A terminal comprises a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0144] A computer-readable storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0145] The beneficial effects of the present invention are that, compared with the prior art, at least the present invention proposes a method suitable for optimizing the decision-making of the aggregator P2P electric carbon trading market through the integration and utilization of distributed renewable energy and the key role of the electric carbon trading mechanism in energy transformation. The method fully considers the diversified needs and market conditions of energy producers and consumers, realizes the optimal allocation of electric carbon resources, and provides energy producers and consumers with a more flexible and efficient trading method. BRIEF DESCRIPTION OF THE DRAWINGS

[0146] Figure 1 It is a flow chart of the optimization method for the integrated energy system prosumer aggregator to participate in the decision-making of the electricity carbon market proposed by the present invention. DETAILED DESCRIPTION

[0147] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0148] The present invention proposes an optimization method for the participation of prosumer aggregators in the decision-making of the electricity-carbon market in an integrated energy system. The coverage of the electricity-carbon market includes multiple aggregators and multiple carbon emission units. Each aggregator includes energy storage equipment, photovoltaic power generation equipment and multiple electricity load units, such as Figure 1 As shown, including:

[0149] Step 1. Within a carbon emission assessment cycle, obtain the carbon emissions of each aggregator and the carbon emission quota of each carbon emission unit, and distribute the sum of the carbon emission quotas of all carbon emission units to each aggregator according to the proportion of the carbon emissions of each aggregator in the sum of the carbon emissions of all aggregators, so as to obtain the free carbon emission quota of each aggregator within a carbon emission assessment cycle; based on the free carbon emission quota of each aggregator within a carbon emission assessment cycle, determine the free carbon emission quota of each aggregator in each time period according to the proportion of the electricity load of each aggregator in the time period in the sum of the electricity load of all aggregators in the time period; determine the incremental free carbon emission quota obtained by each aggregator for using photovoltaic power generation equipment in each time period according to the predicted power generation value of photovoltaic power generation equipment; take the sum of the free carbon emission quota of each aggregator in each time period and the incremental free carbon emission quota as the total free carbon emission quota of each aggregator in each time period.

[0150] In an embodiment, before the carbon emission assessment cycle begins, the aggregator chooses whether to join the P2P electricity-carbon trading decision. If the choice is yes, the aggregator will participate in the optimization of the electricity-carbon market decision during the carbon emission assessment cycle. Otherwise, the aggregator will not participate in the optimization of the electricity-carbon market decision during the carbon emission assessment cycle.

[0151] Taking multiple carbon emission units and multiple aggregators participating in the electricity carbon market decision optimization during the carbon emission assessment cycle as the optimization objects, firstly initialize the parameters according to step 1 to obtain the carbon emission free quota of each aggregator in each time period within a carbon emission assessment cycle and the carbon emission free quota increment obtained by using photovoltaic power generation equipment.

[0152] Aggregators of various types that integrate the resources of producers and consumers in the comprehensive energy system can buy and sell electricity with other aggregators through P2P electricity trading, or trade electricity with their superiors to meet their own energy needs; they can buy and sell carbon emission rights with other aggregators through P2P carbon quota trading, or trade carbon with the carbon emission rights market to meet the inspection requirements of carbon emissions. The trading cycles of P2P electricity trading and carbon quota trading are consistent with the electricity trading cycle of the superior power grid and the carbon trading cycle with the carbon emission rights market, respectively. In the embodiment, the carbon emission assessment cycle T is set to 1 year, and the time period t for participating in the transaction is set to 1 week. Aggregators use P2P electricity trading and carbon trading to increase the absorption of distributed renewable energy, reduce dependence on the superior power grid, reduce electricity trading costs, and indirectly reduce carbon emissions.

[0153] Specifically, step 1 includes:

[0154] Step 1.1, obtain the carbon emissions generated by the purchased electricity of each aggregator in different time periods and the carbon emissions generated by the charging and discharging of the energy storage equipment of each aggregator, so as to establish the carbon emissions model of each aggregator in the carbon trading process in different time periods; based on the carbon emissions model of each aggregator in the carbon trading process in different time periods, determine the carbon emissions of each aggregator in a carbon emission assessment cycle;

[0155] Specifically, the carbon emission model of each aggregator in the carbon trading process in different periods satisfies the following relationship:

[0156]

[0157] In the formula, represents the carbon emissions of aggregator i in the carbon trading process in time period t, represents the carbon emissions generated by the purchased electricity of aggregator i in time period t, It represents the carbon emissions generated by charging and discharging the energy storage equipment of aggregator i in time period t.

[0158] A large part of the electricity demand in the integrated energy system still needs to be purchased from the superior power grid. However, purchasing electricity from outside will produce indirect carbon emissions. This is because the current electricity production still relies on the combustion of fossil fuels (such as coal, oil, and natural gas), which release a large amount of greenhouse gases and pollutants such as carbon dioxide during the combustion process. At the same time, indirect carbon emissions will also be generated during the transmission of electricity. Electricity transmission needs to be carried out through the power grid, and the construction and maintenance of the power grid requires a lot of energy and materials. In addition, electricity will also produce certain energy losses during transmission due to factors such as resistance and electromagnetic fields, and these lost energy will also be converted into carbon emissions. Therefore, the carbon emissions caused by the purchase of electricity by aggregator i in time period t satisfy the following relationship:

[0159]

[0160] In the formula, k g represents the grid benchmark emission factor, Represents the amount of electricity purchased by aggregator i in time period t.

[0161] The carbon emissions of photovoltaic power generation equipment in the integrated energy system are 0. Energy storage equipment also produces very little carbon emissions during use, and the emissions are proportional to the charging and discharging behavior of the prosumer. The carbon emissions of energy storage equipment of aggregator i in time period t satisfy the following relationship:

[0162]

[0163] In the formula, γ ba Indicates the carbon emission coefficient of the energy storage device during charging and discharging. In the embodiment, the value is 0.083kg / kWh. represents the charging load of the energy storage device of aggregator i in time period t, It represents the discharge load of the energy storage equipment of aggregator i in time period t.

[0164] Based on the carbon emission model of each aggregator in the carbon trading process in different time periods, the carbon emissions of each aggregator in the carbon emission assessment period are determined to meet the following relationship:

[0165]

[0166] In the formula, E i,T It represents the carbon emissions of producer-consumer aggregator i in the carbon trading process within the carbon emission assessment period T.

[0167] Step 1.2, using the historical emission method combined with the annual attenuation coefficient method, correct the sum of the historical carbon emissions of all carbon emission units in a historical carbon emission assessment period to obtain the total carbon emission quota of all carbon emission units in the carbon emission assessment period; according to the proportion of the historical carbon emissions of each carbon emission unit in the sum of the carbon emission quotas of all carbon emission units, allocate the total carbon emission quota of all carbon emission units to each carbon emission unit to obtain the carbon emission quota of each carbon emission unit in a carbon emission assessment period.

[0168] Specifically, step 1.2 includes:

[0169] Step 1.2.1, obtain the historical carbon emissions of each carbon emission unit within a historical carbon emission assessment period, and use the historical emission method combined with the annual attenuation coefficient method to correct the sum of the historical carbon emissions of all carbon emission units to obtain the total carbon emission quota of all carbon emission units within the carbon emission assessment period.

[0170] The sum of the historical carbon emissions of each carbon emission unit within a historical carbon emission assessment period satisfies the following relationship:

[0171]

[0172] In the formula, E total It represents the sum of the historical carbon emissions of each carbon emission unit during the historical carbon emission assessment period, E j represents the historical carbon emissions of the jth carbon emission unit, j∈EU, and EU represents the set of all carbon emission units.

[0173] The total amount of carbon emission quotas of all carbon emission units within the carbon emission assessment period satisfies the following relationship:

[0174]

[0175] In the formula, Q total Indicates the total amount of carbon emission quotas of all carbon emission units within the carbon emission assessment period. is the annual decay coefficient, expressed as a percentage.

[0176] Among them, the annual attenuation coefficient satisfies the following relationship:

[0177]

[0178] In the formula, n represents the number of years required to achieve the emission reduction target, and r represents the total emission reduction target for the next n years, expressed as a percentage.

[0179] At present, my country's electricity carbon market mainly implements the method of free allocation of quotas, and only a small part of the quotas are allocated for a fee, mainly through auctions. For small and medium-sized producers and consumers, their carbon emission sources are characterized by "wide distribution and many points", that is, they are widely distributed and dispersed. In order to effectively manage these carbon emission sources through aggregators, in terms of the initial carbon quota allocation, the use of the historical emission method for free allocation is a simple and easy way to be accepted by all parties. This method ensures the fairness of the allocation, so that those units with large historical emissions will not suffer too much impact from the new policy, and at the same time it also encourages emission reduction actions. It is worth noting that technicians in this field can use different methods to allocate carbon quotas according to the actual conditions of carbon trading.

[0180] Step 1.2.2, according to the proportion of the historical carbon emissions of each carbon emission unit in the total carbon emission quota of all carbon emission units, the total carbon emission quota of all carbon emission units is allocated to each carbon emission unit, and the carbon emission quota of each carbon emission unit in a carbon emission assessment cycle is obtained, which satisfies the following relationship:

[0181]

[0182] In the formula, Q j represents the carbon emission quota of the jth carbon emission unit, E j Represents the historical carbon emissions of the j-th carbon emission unit.

[0183] In the method proposed in the present invention, the carbon emission quota of each carbon emission unit is capped at the total historical carbon emissions, thereby achieving adaptive attenuation adjustment of the carbon emission quota under the emission reduction target.

[0184] Step 1.3, according to the proportion of the carbon emissions of each aggregator in the total carbon emissions of all aggregators in the carbon emission assessment period, the sum of the carbon emission quotas of all carbon emission units in the carbon emission assessment period is allocated to each aggregator, so as to obtain the free carbon emission quota of each aggregator in a carbon emission assessment period;

[0185] The free carbon emission quota of each aggregator during the carbon emission assessment period satisfies the following relationship:

[0186]

[0187] In the formula, represents the free carbon emission quota of producer-consumer aggregator i within the carbon emission assessment period T, Q total It represents the total carbon emission quota of all carbon emission units within the carbon emission assessment period, and I represents the collection of all production and consumption aggregators.

[0188] Step 1.4, according to the proportion of the electricity load of each aggregator in the time period to the total electricity load of all aggregators in the time period, and the carbon emission free quota of each aggregator in a carbon emission assessment cycle, determine the carbon emission free quota of each aggregator in each time period;

[0189] The free carbon emission quota of each aggregator in each time period satisfies the following relationship:

[0190]

[0191] In the formula, represents the amount of free carbon emission quota of aggregator i in time period t, It represents the load of aggregator i in time period t, and T represents the carbon emission assessment period.

[0192] The present invention proposes that when allocating free carbon emission quotas to various producers and consumers, it is not only refined to the carbon emission assessment cycle, but also to each time period, so that each producer and consumer aggregator can more accurately control carbon emissions according to the real-time changes in actual production activities and achieve optimal allocation of carbon resources. This "time-based refinement" strategy effectively avoids the traditional "loose at the beginning and tight at the end" carbon emission management model, that is, carbon emissions are relatively loose at the beginning of the assessment cycle, but at the end of the period, emergency emission reduction measures have to be taken because the cumulative emissions are close to the upper limit.

[0193] Step 1.5, based on the predicted power generation value of the photovoltaic power generation equipment, determine the incremental amount of free carbon emission quotas that each aggregator obtains from using photovoltaic power generation equipment in each time period.

[0194] For the integrated energy system, each aggregator is equipped with photovoltaic power generation equipment. Photovoltaic equipment has a carbon reduction effect, and the national certified emission reduction (CCER) generated by it can offset the carbon emissions of the aggregator in a certain proportion, that is, as an additional free carbon emission quota, and the remaining CCER can be sold to the national greenhouse gas voluntary emission reduction trading market. In the method proposed by the present invention, CCER is considered equivalent to the free carbon emission quota.

[0195] The carbon emission offset corresponding to the photovoltaic power generation equipment of aggregator i in time period t satisfies the following relationship:

[0196]

[0197] In the formula, represents the carbon emission offset of photovoltaic power generation equipment of aggregator i in time period t, k g represents the grid benchmark emission factor, It represents the predicted value of power generation of photovoltaic power generation equipment of aggregator i in time period t.

[0198] The increment of free carbon emission quota obtained by aggregator i using photovoltaic power generation equipment in time period t satisfies the following relationship:

[0199]

[0200] In the formula, represents the carbon emission offset amount of photovoltaic power generation equipment used by aggregator i in time period t, which is determined by the offset ratio of the free carbon emission quota increment, μ c To offset the ratio.

[0201] Step 1.6: The sum of the free carbon emission quota of each aggregator in each time period and the increment of the free carbon emission quota is taken as the total free carbon emission quota of each aggregator in each time period.

[0202] The total amount of free carbon emission quotas of each aggregator in each time period satisfies the following relationship:

[0203]

[0204] Where M i,t Represents the total amount of free carbon emission quota of aggregator i in time period t.

[0205] Step 2: Obtain the transaction costs between the aggregator and the upper-level power grid, the photovoltaic power generation costs of the aggregator, the operating costs of the energy storage equipment of the aggregator, the carbon trading costs of the aggregator, and the income obtained by the aggregator from the use of photovoltaic power generation equipment during a carbon emission assessment cycle, so as to establish a total operating cost model for all aggregators participating in electricity-carbon trading during the carbon emission assessment cycle.

[0206] Specifically, the total operating cost model of all aggregators participating in electricity-carbon trading during the carbon emission assessment period satisfies the following relationship:

[0207]

[0208] In the formula, Cost represents the total operating cost of all producers and consumers participating in the electricity-carbon trading during the carbon emission assessment period. represents the cost of transactions between aggregator i and the upper grid in time period t, represents the photovoltaic power generation cost of aggregator i in time period t, represents the operating cost of the energy storage equipment of aggregator i in time period t, represents the carbon trading cost of aggregator i in time period t, It represents the income obtained by aggregator i from using photovoltaic power generation equipment in time period t.

[0209] Specifically, under the constraint that the aggregator purchases and sells electricity to the superior power grid, the transaction costs of each aggregator and the superior power grid in different time periods are determined to satisfy the following relationship:

[0210]

[0211] In the formula, represents the cost of transactions between aggregator i and the upper grid in time period t, and They represent the electricity sales price and on-grid electricity price of the upper power grid in time period t respectively; and They respectively represent the power purchased and sold by aggregator i to the superior power grid in time period t.

[0212] Among them, the aggregator meets the maximum exchange power constraint when purchasing and selling electricity to the upper-level power grid, as shown in the following relationship:

[0213]

[0214] In the formula, represents the upper limit of the switching power of aggregator i.

[0215] Specifically, the photovoltaic power generation cost of each aggregator in different time periods satisfies the following relationship:

[0216]

[0217] In the formula, Represents the cost coefficient of photovoltaic power generation equipment of aggregator i.

[0218] Specifically, the operating cost of each aggregator's energy storage equipment is determined under the joint constraints of the aggregator's energy storage equipment, satisfying the following relationship:

[0219]

[0220] In the formula, represents the operating cost of the energy storage equipment of aggregator i in time period t, represents the cost coefficient of energy storage equipment of aggregator i.

[0221] Among them, the joint constraints of the aggregator's energy storage equipment include:

[0222] 1) The energy storage equipment of the aggregator meets the maximum charging power constraint and the maximum discharging power constraint during the charging and discharging process, as shown in the following relationship:

[0223]

[0224] In the formula, They represent the upper limit of charging power and the upper limit of discharging power of the energy storage equipment of aggregator i respectively.

[0225] 2) Constraints on the simultaneous charging and discharging of the aggregator’s energy storage equipment

[0226] The present invention analyzes the historical data of the charging and discharging costs of energy storage devices and finds that when a part of the batteries needs to be charged or discharged at a certain power while another part of the batteries is discharged or charged, it costs more than charging or discharging all the batteries at the same time. Therefore, the energy storage device cannot be charged and discharged at the same time. When some batteries are charged, the discharge power is 0, and when some batteries are discharged, the charging power is 0, satisfying the following relationship:

[0227]

[0228] During the operation of energy storage equipment, it is conventional control to charge some batteries and discharge some batteries. However, in the present invention, the goal is to minimize the charging and discharging cost of the energy storage equipment. Only based on the prosumer aggregator can the effective control of the non-simultaneous charging and discharging constraints of the energy storage equipment be achieved. Moreover, such non-simultaneous charging and discharging constraints of the energy storage equipment are also special constraints determined for minimizing the operating cost of the energy storage equipment of the prosumer aggregator. Although similar in form to the charging and discharging control based on BMS, the control objectives are completely different.

[0229] 3) The aggregator’s energy storage equipment meets the state of charge constraints

[0230] In order to maintain the service life of energy storage equipment and prevent it from overcharging and discharging, the battery state of charge (SOC) should be specified within a certain range, and the SOC should change its value as the battery is charged and discharged, satisfying the following relationship:

[0231]

[0232] In the formula, They represent the lower and upper limits of the state of charge of the energy storage equipment of aggregator i, respectively. represents the state of charge of the energy storage device of aggregator i in time period t, represents the capacity of the energy storage equipment of aggregator i.

[0233] 4) The energy storage equipment of the aggregator meets the balance of charge and discharge within a usage cycle

[0234] In actual operation, it is necessary to ensure that the initial state and final state of the energy storage device in a use cycle are completely consistent. Therefore, the charge amount of the energy storage device in a use cycle should be equal to the discharge amount, satisfying the following relationship:

[0235]

[0236] The energy storage equipment of the aggregator meets the balance of charge and discharge within a usage cycle. Combined with the constraints on the non-simultaneous charging and discharging of the energy storage equipment of the aggregator, it is a guarantee for minimizing the operating costs of the energy storage equipment of the prosumer aggregator.

[0237] Specifically, the carbon trading costs of each aggregator in different periods satisfy the following relationship:

[0238]

[0239] In the formula, and They represent the purchase price and sale price of carbon quota in the carbon emission rights market in time period t, and They respectively represent the amount of free carbon emission quotas purchased and sold by aggregator i in the carbon emission rights market in time period t.

[0240] The income obtained by aggregator i from using photovoltaic power generation equipment in time period t satisfies the following relationship:

[0241]

[0242] In the formula, represents the income obtained by aggregator i from selling CCER to the market in time period t, ω c The unit price of carbon emission offset.

[0243] In step 2, the constraints of the total operating cost model for all aggregators participating in electricity-carbon trading during the carbon emission assessment period include: establishing power balance constraints and carbon emission free quota balance constraints for aggregators during the carbon emission assessment period, and power trading constraints and carbon emission free quota trading constraints for aggregators participating in electricity-carbon trading based on the electricity-carbon trading behavior of the aggregators.

[0244] Specifically, based on the electricity-carbon trading behavior of the aggregators, power balance constraints and carbon emission free quota balance constraints of the aggregators are established during the carbon emission assessment period.

[0245] The power balance constraints of the aggregator in different time periods satisfy the following relationship:

[0246]

[0247] In the formula, represents the power purchased or sold by aggregator i in time period t, represents the predicted value of power generation of photovoltaic power generation equipment of aggregator i in time period t, represents the load of aggregator i in time period t, represents the charging load of the energy storage device of aggregator i in time period t, It represents the discharge load of the energy storage equipment of aggregator i in time period t.

[0248] Among them, the identity of the aggregator as a buyer or seller in time period t remains unchanged, so the power purchased or sold by aggregator i in time period t is Satisfies the following relationship:

[0249] When aggregator i purchases electricity:

[0250]

[0251] When aggregator i sells electricity

[0252]

[0253] In the formula, and They respectively represent the power purchased and sold by aggregator i to the superior power grid in time period t, and I represents the set of all prosumer aggregators.

[0254] The balance constraint of the free carbon emission quota of the aggregator in different time periods satisfies the following relationship:

[0255]

[0256] In the formula, represents the amount of free carbon emission quotas sold by aggregator i to the market in time period t, represents the remaining amount of free carbon emission quota of aggregator i in time period t, represents the amount of free carbon emission quotas sold by aggregator i in time period t, They respectively represent the amount of free carbon emission quota of aggregator i in time period t, the amount of free carbon emission quota purchased or sold by aggregator i in time period t, the increase in free carbon emission quota obtained by aggregator i through the use of photovoltaic power generation equipment in time period t, and the carbon emissions of aggregator i in the carbon trading process in time period t.

[0257] The carbon emission free quota balance constraint indicates that, unlike electricity trading, carbon quotas can be stored without the need to use all of them within time period t. Therefore, the carbon quotas sold by aggregator i should not exceed its remaining carbon quotas.

[0258] In the method proposed by the present invention, the remaining amount of carbon emission free quota of aggregator i in time period t includes the remaining amount of carbon emission free quota of aggregator i in the previous time period t-1, the carbon emission free quota of aggregator i in time period t, the carbon emission free quota of aggregator i in electricity purchase or sale in time period t, the carbon emission free quota obtained by aggregator i in time period t by using photovoltaic power generation equipment, and the carbon emissions of aggregator i in the carbon trading process in time period t. The storage characteristics of carbon emission free quota are fully utilized, and the corresponding remaining amount of carbon emission free quota is calculated in time periods, so that the balance constraint of carbon emission free quota is more accurate, especially considering that the energy storage equipment and photovoltaic power generation equipment set in the production and consumption aggregator have the characteristics of output uncertainty and volatility. Therefore, the time period calculation can overcome the influence of uncertainty and volatility and obtain a more accurate balance effect of carbon emission free quota.

[0259] Among them, the identity of aggregator i as a buyer or seller in time period t remains unchanged, so the amount of free carbon emission quotas purchased or sold by aggregator i in time period t Satisfies the following relationship:

[0260] When aggregator i purchases free carbon emission quotas:

[0261]

[0262] When aggregator i sells free carbon emission allowances:

[0263]

[0264] In the formula, represents the amount of free carbon emission quota purchased by aggregator i from the market in time period t, They respectively represent the amount of free carbon emission quotas purchased by aggregator i from aggregator j in time period t and the amount of free carbon emission quotas sold by aggregator i to aggregator j in time period t.

[0265] Among the lower-level optimization targets proposed in the present invention, power balance reflects the real-time nature of the trading behavior of prosumer aggregators, and carbon emission free quota balance reflects the continuity of the trading behavior of prosumer aggregators. Compared with the single optimization target of minimizing the total operating cost of prosumer aggregators participating in electricity-carbon trading, on the basis of the upper-level optimization target of minimizing the total operating cost of all prosumer aggregators participating in electricity-carbon trading, power balance and carbon emission free quota balance jointly constitute the lower-level optimization target, which can more accurately reflect the behavioral characteristics of prosumer aggregators, and can also more accurately provide data support for the trading behavior of prosumer aggregators.

[0266] Specifically, establish power trading constraints for aggregators to participate in electricity-carbon trading and carbon emission free quota trading constraints.

[0267] 1) Power trading constraints satisfy the following relationship:

[0268]

[0269]

[0270] In the formula, They represent the power purchased by aggregator i from aggregator j in time period t and the power sold by aggregator i to aggregator j in time period t, respectively. They represent the power purchased by aggregator j from aggregator i in time period t and the power sold by aggregator j to aggregator i in time period t, respectively. represents the power cap for transactions between aggregator i and aggregator j.

[0271] The power trading constraint means that the consistency of the transaction needs to be considered in P2P transactions, that is, the power purchased by aggregator i from aggregator j is equal to the power sold by aggregator j to aggregator i. Assume that the value of the P2P transaction power sold is always less than 0, the value of the P2P transaction power purchased is always greater than 0, and the transaction power is also subject to a maximum limit.

[0272] 2) The trading constraints of free carbon emission quotas shall satisfy the following relationship:

[0273]

[0274] In the formula, They represent the free carbon emission quotas purchased by aggregator i from aggregator j in time period t and the free carbon emission quotas sold by aggregator i to aggregator j in time period t, respectively. They represent the free carbon emission quotas purchased by aggregator j from aggregator i in time period t and the free carbon emission quotas sold by aggregator j to aggregator i in time period t, respectively. It represents the upper limit of free carbon emission quotas traded between aggregator i and aggregator j.

[0275] The trading constraints of free carbon emission quotas indicate that P2P carbon quota trading also needs to consider the consistency of transactions, and the value of P2P carbon quotas sold is always less than 0, and the value of P2P carbon quotas purchased is always greater than 0.

[0276] Step 3, based on the dual problem method, the amount of electricity traded between aggregators and other aggregators and the amount of free carbon emission quotas traded between aggregators and other aggregators are used as a set of dual variables; Lagrange multipliers are introduced, and the total operating cost model of each aggregator participating in electricity-carbon trading during the carbon emission assessment period is used to establish the electricity-carbon trading revenue model of each aggregator during the carbon emission assessment period; based on the electricity-carbon trading revenue model of each aggregator during the carbon emission assessment period, the revenue of each aggregator participating in the electricity-carbon market decision-making in each time period is determined.

[0277] Specifically, step 3 includes:

[0278] Step 3.1, based on the dual problem method, takes the amount of electricity traded between an aggregator that satisfies both the power balance constraint and the power trading constraint and other aggregators, and the amount of carbon emission free quota traded between an aggregator that satisfies both the carbon emission free quota balance constraint and the carbon emission free quota trading constraint and other aggregators as a set of dual variables.

[0279] The aggregator that satisfies both the power balance constraint and the power trading constraint simultaneously trades electricity with other aggregators, including: the total power sold by aggregator i to other aggregators in time period t, and the total power purchased by aggregator i from other aggregators in time period t; satisfying the following relationship:

[0280]

[0281] In the formula, represents the total power sold by aggregator i to other aggregators in time period t, represents the power vector of electricity sold by aggregator i to other aggregators in time period t, They represent the power sold by aggregator i to aggregator 1, ..., aggregator N in time period t; Y i represents an N-dimensional column vector whose i-th element is 0 and the rest of the elements are 1;

[0282]

[0283] In the formula, represents the total power purchased by aggregator i from other aggregators in time period t, represents the power vector of electricity purchased by aggregator i from other aggregators in time period t, They represent the power purchased by aggregator i from aggregator 1, ..., aggregator N in time period t respectively;

[0284] The total power sold by aggregator i to other aggregators in time period t and the total power purchased by aggregator i from other aggregators in time period t are both negative values.

[0285] Aggregators that simultaneously meet the carbon emission free quota balance constraint and the carbon emission free quota transaction constraint conditions trade carbon emission free quotas with other aggregators, including: the sum of carbon emission free quotas sold by aggregator i to other aggregators in time period t, and the sum of carbon emission free quotas purchased by aggregator i from other aggregators in time period t; satisfying the following relationship:

[0286]

[0287] In the formula, represents the total amount of free carbon emission quotas sold by aggregator i to other aggregators in time period t, represents the vector of free carbon emission quotas sold by aggregator i to other aggregators in time period t, They represent the amount of free carbon emission quotas sold by aggregator i to aggregator 1, ..., aggregator N in time period t respectively;

[0288]

[0289] In the formula, represents the total amount of free carbon emission quotas purchased by aggregator i from other aggregators in time period t, represents the vector of free carbon emission quotas purchased by aggregator i from other aggregators in time period t, They represent the free carbon emission quotas purchased by aggregator i from aggregator 1, ..., aggregator N in time period t respectively;

[0290] The sum of the free carbon emission quotas sold by aggregator i to other aggregators in time period t and the sum of the free carbon emission quotas purchased by aggregator i from other aggregators in time period t are both positive.

[0291] Step 3.2, based on the dual problem method, introduce two Lagrange multiplier vectors, and use the dual variables and the total operating cost model of each aggregator participating in the electricity-carbon trading during the carbon emission assessment period to establish the electricity-carbon trading revenue model of each aggregator during the carbon emission assessment period, satisfying the following relationship:

[0292]

[0293]

[0294] In the formula, represents the income of aggregator i participating in electricity-carbon trading in time period t; represents the operating cost of aggregator i participating in electricity-carbon trading in time period t; represents the set of Lagrange multipliers in time segment t, satisfying in, It represents the Lagrange multiplier corresponding to the amount of electricity traded between aggregator i and other aggregators in time period t, It represents the Lagrange multiplier corresponding to the amount of free carbon emission quotas traded between aggregator i and other aggregators in time period t; It represents the Lagrange multiplier vector corresponding to the amount of electricity traded between the aggregator and other aggregators in time period t; It represents the Lagrange multiplier vector corresponding to the amount of free carbon emission quotas traded between the aggregator and other aggregators in time period t; diag{} represents the diagonal elements.

[0295] Under the centralized optimization framework, the power generation and consumption information of each aggregator is shared, which may lead to the risk of privacy and commercial information leakage. Therefore, the present invention proposes an electricity-carbon trading revenue model for each aggregator based on the dual problem method, which decomposes the original complex problem to each aggregator level, so that each aggregator can make decisions independently with the goal of maximizing its own operating benefits, avoiding privacy and commercial information leakage, thereby effectively reducing carbon emissions while maximizing the overall benefits of the system.

[0296] After the above-mentioned distributed processing, the game model becomes a P2P trading model containing complex games. The present invention not only proposes an electricity-carbon trading revenue model for each aggregator based on the dual problem method, decomposing the original complex problem to the level of each aggregator, but also effectively maintains the privacy and security of each energy aggregator in participating in electricity trading and electricity-carbon markets.

[0297] Step 4, based on the game theory method, using the set of each aggregator participating in the electricity carbon market decision-making within the time period, the set of electricity carbon trading instructions of each aggregator participating in the electricity carbon market decision-making within the time period, and the set of benefits of each aggregator participating in the electricity carbon market decision-making within the time period, establish an electricity carbon trading instruction optimization model; determine the optimal electricity carbon trading instructions of each aggregator based on the Nash equilibrium point of the electricity carbon trading instruction optimization model; extract the optimal value of the electricity volume traded between the aggregator and other aggregators and the optimal value of the free carbon emission quota traded between the aggregator and other aggregators from the optimal electricity carbon trading instructions of each aggregator.

[0298] In a non-limiting preferred embodiment, the aggregator's electricity-carbon trading instructions include but are not limited to: the amount of electricity traded with the superior power grid, the amount of free carbon emission quotas traded with the carbon emission rights market, the charging and discharging control of energy storage equipment, the power generation control of photovoltaic power generation equipment, the amount of electricity traded with other aggregators, and the amount of free carbon emission quotas traded with other aggregators.

[0299] Each aggregator executes the optimal instructions for electricity-carbon trading, enabling the aggregator to participate in electricity-carbon market decision-making.

[0300] Specifically, the electricity-carbon trading instruction optimization model satisfies the following relationship:

[0301]

[0302] In the formula, G AOP It is the optimization model of electricity carbon trading instructions; {AOP1,…,AOP N} is the set of various aggregators participating in the decision-making of the electricity carbon market in different time periods, among which AOP1, ..., AOP N They are aggregators 1, ..., and N that participate in the decision-making of the electricity-carbon market in different time periods; is the set of electricity-carbon trading instructions of each aggregator participating in the electricity-carbon market decision-making within the time period t, where They are respectively the electricity-carbon trading instructions of aggregator 1, ..., and the electricity-carbon trading instructions of aggregator N in time period t; is the collection of the revenues of each aggregator participating in the electricity carbon market decision-making in time period t, where They are respectively the revenue of aggregator 1, ..., and the revenue of aggregator N in time period t; N represents the total number of aggregators, which is a positive integer.

[0303] In the embodiment, the preferred instruction set of each aggregator for electricity-carbon trading in time period t is in, They are the preferred electricity-carbon trading instructions of aggregator 1 in time period t, ..., and the preferred electricity-carbon trading instructions of aggregator N; the preferred electricity-carbon trading instructions of aggregator i in time period t are The set of preferred instructions for electricity-carbon trading of other aggregators is:

[0304] When the following relationship is satisfied, the optimal order set of electricity-carbon trading of each aggregator in time period t is A Nash equilibrium point for the electricity-carbon trading order optimization model:

[0305]

[0306] In the formula, is the revenue of aggregator i determined based on the electricity-carbon trading instructions in time period t, X i Preferred instructions for electricity-carbon trading for aggregator i Any electricity carbon trading instruction other than

[0307] A Nash equilibrium point in the optimization model of electricity-carbon trading instructions corresponds to an optimal electricity-carbon trading instruction of an aggregator, indicating that each aggregator cannot increase its own benefits by unilaterally changing its own electricity-carbon trading instructions. The optimal electricity-carbon trading instructions of all aggregators constitute the set of the optimal electricity-carbon trading instructions of all aggregators participating in the electricity-carbon market decision-making in the time period.

[0308] Among them, the optimal instructions for electricity and carbon trading of each aggregator include but are not limited to: the optimal value of electricity volume traded with the superior power grid, the optimal value of carbon emission free quota traded with the carbon emission rights market, the optimal control of charging and discharging of energy storage equipment, the optimal control of power generation of photovoltaic power generation equipment, the optimal value of electricity volume traded with other aggregators, and the optimal value of carbon emission free quota traded with other aggregators; from the optimal instructions for electricity and carbon trading of all aggregators, the optimal value of electricity volume traded by the aggregator with other aggregators and the optimal value of carbon emission free quota traded with other aggregators are obtained.

[0309] In the integrated energy system, different energy producers and consumers act as independent stakeholders, and their interactions constitute a complex game network. In order to effectively manage and optimize this system, it is necessary to first model these game behaviors, and then explore the solution strategy of the P2P electricity-carbon trading decision model under the multi-agent game framework.

[0310] The issue of P2P electricity-carbon trading between aggregators can essentially be understood as the issue of pursuing the maximization of their own interests. This process of maximizing profits is not carried out in isolation, but is affected by the behavior of other aggregators in electricity and carbon emission rights trading, including the size of trading volume and the setting of trading prices. Specifically, when an aggregator predicts that the amount of electricity and carbon quota it can provide is not enough to meet the needs of other aggregators, in order to increase profits, it may choose to increase the trading price of these resources. However, this strategic price increase behavior often triggers a chain reaction: other aggregators may reduce their purchases of these resources in the face of price increases in order to control costs or find more economical alternatives. This reduction in demand will in turn affect the trading volume of the price-raising aggregator, which may weaken the revenue growth it originally expected to obtain through price increases.

[0311] Therefore, each aggregator needs to take into account the reactions and strategy adjustments of other aggregators while pursuing its own interests, and strive to find an equilibrium in a dynamically changing market environment, that is, a trading plan that can both guarantee its own benefits and promote the overall low-carbon and efficient operation of the system. This game problem can be regarded as an optimization problem under multiple objectives and multiple constraints, which requires comprehensive consideration of market supply and demand, technical limitations, environmental policies, and the interaction between aggregators.

[0312] The optimization method for the participation of prosumers in the decision-making of the electricity carbon market in the comprehensive energy system proposed by the present invention also includes:

[0313] In step 5, each aggregator will encrypt and share the optimal value of the electricity volume traded with other aggregators and the optimal value of the free carbon emission quota traded with other aggregators; each aggregator will update the dual variable based on the shared data; the updated dual variable will be used to update the Lagrange multiplier; when the Lagrange multipliers before and after the update converge, the updated Lagrange multiplier and the updated dual variable will be used to determine the final profit of each aggregator based on the electricity-carbon trading profit model of each aggregator during the carbon emission assessment period.

[0314] Step 5 uses the iterative calculation of Lagrange multipliers to realize the supply and demand relationship of electricity and carbon quotas of various aggregators, and gradually converges to a state point that maximizes the interests of all parties and maintains the overall balance of the market.

[0315] Among them, the Lagrange multiplier is updated using the updated dual variable to satisfy the following relationship:

[0316]

[0317] In the formula, They represent the Lagrange multipliers corresponding to the amount of electricity traded between aggregator i and other aggregators in time period t at the k+1th and kth iterations, respectively. They represent the Lagrange multipliers corresponding to the free carbon emission quotas traded between aggregator i and other aggregators in time period t at the k+1th and kth iterations, respectively. represents the price update step of the electricity traded between aggregator i and other aggregators at the kth iteration, represents the carbon price update step size of the carbon emission free quotas traded by aggregator i with other aggregators at the kth iteration, represents the power vector of electricity sold by aggregator i to other aggregators in time period t at the kth iteration, represents the total power sold by aggregator i to other aggregators in time period t at the kth iteration, represents the vector of free carbon emission quotas sold by aggregator i to other aggregators in time period t at the kth iteration, It represents the sum of the carbon emission free quota vectors sold by aggregator i to other aggregators in time period t at the kth iteration.

[0318] The difference between the Lagrange multipliers before and after the update is compared. If the difference is less than the set threshold, it means that the Lagrange multipliers before and after the update have converged. The threshold value is no more than 0.01.

[0319] Using the updated Lagrange multipliers and updated dual variables, based on the electricity-carbon trading revenue model of each aggregator during the carbon emission assessment period, the final revenue of each aggregator is obtained;

[0320] The final revenue of each aggregator participating in the electricity-carbon market decision-making within the time period is used to update the Nash equilibrium point of the electricity-carbon trading instruction optimization model to obtain the updated optimal electricity-carbon trading instructions of each aggregator, so as to participate in the electricity-carbon market decision-making as the production and consumption aggregator in the integrated energy system.

[0321] The method proposed in the present invention belongs to the technical field of comprehensive energy production and consumption aggregators participating in electricity market and carbon emission rights market transactions, involving production and consumption resource aggregation technology, carbon emission quota allocation technology, and electricity-carbon coupling decision-making technology, and specifically involves the optimization strategy of each aggregator participating in electricity-carbon transactions in the comprehensive energy system. Through a specific analysis of the concept of aggregators and their important role in the point-to-point electricity-carbon market and the carbon trading mechanism, an aggregator P2P electricity-carbon trading model with the goal of minimizing total operating costs is constructed, and the model is distributedly processed and calculated. Through multi-scenario operation comparison, the optimization strategy is verified to have significant effects in reducing operating costs, increasing the proportion of local renewable energy consumption, and reducing carbon emissions, and effectively protects the transaction privacy of each aggregator and reduces information leakage.

[0322] Through comparative analysis with other trading scenarios, the present invention can significantly reduce system operating costs, effectively promote local consumption and efficient utilization of energy, while also reducing the system's carbon emissions, helping to promote the green transformation and sustainable development of the energy market.

[0323] When constructing a comprehensive energy system, the present invention fully considers the participation of diversified and differentiated prosumers, such as low-carbon campuses, households, and commercial enterprises. Subjects with the ability to produce and consume electricity in different fields are collectively referred to as prosumers. In order to effectively organize and promote these diverse subjects to participate in the trading of electricity and carbon emission rights, the present invention patent introduces energy prosumer aggregators as an intermediate bridge to make trading decisions on behalf of prosumers.

[0324] In the process of large-scale development of distributed renewable energy, the introduction of aggregators will have many advantages. The power sector will no longer directly face a large number of energy producers and consumers with small capacity. At this time, the role of aggregators is similar to that of business agents serving telecommunications companies such as China Mobile and China Telecom. Due to its existence, the difficulty and complexity of operation and maintenance of the power sector when consuming distributed renewable energy will be greatly reduced, providing convenience for the management of the power sector.

[0325] Aggregators, out of consideration for their own economic interests, will take the initiative to add some regulatory measures (such as installing energy storage equipment, developing user-side demand response resources, etc.), which can enhance the operational flexibility of the terminal power grid and help improve the level of distributed renewable energy consumption and system security. Aggregators can use distributed power generation resources to provide cheaper and greener electricity supply to nearby campuses, residents, commercial and industrial users, reduce corporate carbon emissions and electricity costs, and earn extra profits in the process by using electricity price differences. Aggregators can act as agents or service providers for small-capacity energy producers and consumers, pool their carbon emission reductions to form tradable carbon assets, participate in the electricity carbon market, and generate additional income, so they will inevitably be welcomed by small-capacity energy producers and consumers.

[0326] During the data collection and transaction process, the aggregator will desensitize the electricity consumption and power generation data of each prosumer to ensure that sensitive information will not be leaked. Although the aggregator centrally manages the data of multiple entities, the transaction process is still decentralized. Each entity trades through the aggregator without directly exposing its detailed information and transaction data to other entities, thereby protecting privacy. With the aggregator as an intermediate bridge, each prosumer entity can participate in electricity-carbon trading while maintaining anonymity, further enhancing the effectiveness of privacy protection. This mechanism not only makes participants feel at ease, but also promotes more types and sizes of entities to actively participate in the integrated energy system and jointly promote green and low-carbon transformation.

[0327] Each aggregator gathers or is equipped with photovoltaic power generation equipment and energy storage equipment, and collects electricity consumption and power generation data from all producers and consumers under its jurisdiction. Aggregators can flexibly conduct electricity-carbon transactions to achieve collaborative sharing of electricity and carbon emission rights. If the electricity demand in the entire system cannot be self-sufficient, electricity can be purchased from the superior power grid. Each aggregator gives priority to consuming its own photovoltaic power generation. If it is insufficient, it can be supplied by other aggregators with surplus electricity or the superior power grid; conversely, if photovoltaic power generation is in excess, the excess electricity can be shared with other aggregators or sold to the superior power grid. In terms of carbon emission rights, if an aggregator does not have enough carbon emission rights, other aggregators with sufficient carbon emission rights or external carbon trading markets can provide carbon quotas; if its carbon emission rights are in excess, it can sell carbon quotas to aggregators with insufficient carbon emission rights or external electricity-carbon markets to achieve effective allocation and utilization of carbon emission rights.

[0328] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0329] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0330] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0331] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0332] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An optimization method for participating in the decision-making of the electricity-carbon market by the prosumer aggregator in the integrated energy system. The electricity-carbon market covers multiple aggregators and multiple carbon emission units. Each aggregator includes energy storage equipment, photovoltaic power generation equipment and multiple electricity load units. The method is characterized in that: include: In a carbon emission assessment cycle, the carbon emissions of each aggregator and the carbon emission quota of each carbon emission unit are obtained, and the sum of the carbon emission quotas of all carbon emission units is allocated to each aggregator according to the proportion of each aggregator's carbon emissions in the sum of all aggregators' carbon emissions, so as to obtain the free carbon emission quota of each aggregator in a carbon emission assessment cycle; based on the free carbon emission quota of each aggregator in a carbon emission assessment cycle, the free carbon emission quota of each aggregator in each time period is determined according to the proportion of the electricity load of each aggregator in the time period in the sum of the electricity load of all aggregators in the time period; according to the predicted power generation value of the photovoltaic power generation equipment, the increment of the free carbon emission quota obtained by each aggregator in each time period by using the photovoltaic power generation equipment is determined; Obtain the transaction costs between the aggregator and the upper-level power grid, the photovoltaic power generation costs of the aggregator, the operating costs of the energy storage equipment of the aggregator, the carbon trading costs of the aggregator, and the income obtained by the aggregator from using photovoltaic power generation equipment during a carbon emission assessment cycle, so as to establish a total operating cost model for all aggregators participating in electricity-carbon trading during the carbon emission assessment cycle; Based on the dual problem method, the amount of electricity traded between aggregators and other aggregators and the amount of free carbon emission quotas traded between aggregators and other aggregators are used as a set of dual variables; Lagrange multipliers are introduced, and the total operating cost model of each aggregator participating in electricity-carbon trading during the carbon emission assessment period is used to establish the electricity-carbon trading revenue model of each aggregator during the carbon emission assessment period; based on the electricity-carbon trading revenue model of each aggregator during the carbon emission assessment period, the revenue of each aggregator participating in the electricity-carbon market decision-making in each time period is determined; Based on the game theory method, an optimization model for electricity-carbon trading instructions is established by using the set of various aggregators participating in the electricity-carbon market decision-making within the time period, the set of electricity-carbon trading instructions of various aggregators participating in the electricity-carbon market decision-making within the time period, and the set of benefits of various aggregators participating in the electricity-carbon market decision-making within the time period. The Nash equilibrium point of the electricity-carbon trading instruction optimization model is used to determine the optimal electricity-carbon trading instructions for each aggregator; among them, the electricity-carbon trading instructions of the aggregator include: the amount of electricity traded with the superior power grid, the amount of free carbon emission quotas traded with the carbon emission rights market, the charging and discharging control of energy storage equipment, the power generation control of photovoltaic power generation equipment, the amount of electricity traded with other aggregators, and the amount of free carbon emission quotas traded with other aggregators.

2. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 1 is characterized in that: Also includes: The optimal instructions for electricity-carbon trading of each aggregator include: the optimal value of electricity traded with the upper-level power grid, the optimal value of carbon emission free quota traded with the carbon emission rights market, the optimal control of energy storage equipment charging and discharging, the optimal control of photovoltaic power generation equipment, the optimal value of electricity traded with other aggregators, and the optimal value of carbon emission free quota traded with other aggregators; Each aggregator will encrypt and share the optimal value of the electricity volume traded with other aggregators and the optimal value of the carbon emission free quota volume traded with other aggregators; each aggregator will update the dual variables based on the shared data; and use the updated dual variables to update the Lagrange multiplier; when the Lagrange multipliers before and after the update converge, the updated Lagrange multipliers and the updated dual variables will be used to determine the final revenue of each aggregator based on the electricity-carbon trading revenue model of each aggregator during the carbon emission assessment period; The final revenue of each aggregator participating in the electricity-carbon market decision-making within the time period is used to update the Nash equilibrium point of the electricity-carbon trading instruction optimization model to obtain the updated optimal electricity-carbon trading instructions of each aggregator, so as to participate in the electricity-carbon market decision-making as the production and consumption aggregator in the integrated energy system.

3. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 1 is characterized in that: In a carbon emission assessment cycle, the carbon emissions of each aggregator are obtained including: Obtain the carbon emissions generated by the purchased electricity of each aggregator in different time periods and the carbon emissions generated by the charging and discharging of each aggregator's energy storage equipment, so as to establish a carbon emissions model for each aggregator in the carbon trading process in different time periods; based on the carbon emissions model for each aggregator in the carbon trading process in different time periods, determine the carbon emissions of each aggregator in a carbon emission assessment cycle; Among them, the carbon emission model of each aggregator in the carbon trading process in different time periods satisfies the following relationship: In the formula, represents the carbon emissions of aggregator i in the carbon trading process in time period t, represents the carbon emissions generated by the purchased electricity of aggregator i in time period t, represents the carbon emissions generated by the charging and discharging of the energy storage equipment of aggregator i in time period t; The carbon emissions caused by the outsourcing of electricity by aggregator i in time period t satisfy the following relationship: In the formula, k g represents the grid benchmark emission factor, represents the amount of electricity purchased by aggregator i in time period t; The carbon emissions of energy storage equipment of aggregator i in time period t satisfy the following relationship: In the formula, γ ba Indicates the carbon emission coefficient of energy storage equipment during charging and discharging. represents the charging load of the energy storage device of aggregator i in time period t, represents the discharge load of the energy storage device of aggregator i in time period t; The carbon emissions of each aggregator during the carbon emission assessment period satisfy the following relationship: In the formula, E i,T It represents the carbon emissions of aggregator i in the carbon trading process within the carbon emission assessment period T.

4. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 1 is characterized in that: In a carbon emission assessment cycle, obtain the carbon emission quota of each carbon emission unit, including: The historical emission method is combined with the annual attenuation coefficient method to correct the sum of the historical carbon emissions of all carbon emission units in a historical carbon emission assessment period to obtain the total carbon emission quota of all carbon emission units in the carbon emission assessment period; the total carbon emission quota of all carbon emission units is allocated to each carbon emission unit according to the proportion of the historical carbon emissions of each carbon emission unit in the sum of the carbon emission quotas of all carbon emission units, and the carbon emission quota of each carbon emission unit in a carbon emission assessment period is obtained; Among them, the sum of the historical carbon emissions of each carbon emission unit within a historical carbon emission assessment period satisfies the following relationship: In the formula, E total It represents the sum of the historical carbon emissions of each carbon emission unit during the historical carbon emission assessment period, E j represents the historical carbon emissions of the jth carbon emission unit, j∈EU, and EU represents the set of all carbon emission units; The total amount of carbon emission quotas of all carbon emission units within the carbon emission assessment period satisfies the following relationship: In the formula, Q total Indicates the total amount of carbon emission quotas of all carbon emission units within the carbon emission assessment period. is the annual decay coefficient, expressed as a percentage; The annual attenuation coefficient satisfies the following relationship: In the formula, n represents the number of years required to achieve the emission reduction target, and r represents the total emission reduction target for the next n years, expressed as a percentage; The carbon emission quota of each carbon emission unit in a carbon emission assessment cycle satisfies the following relationship: In the formula, Q j Represents the carbon emission quota of the j-th carbon emission unit.

5. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 3 is characterized in that: The amount of free carbon emission quotas of each aggregator in a carbon emission assessment cycle satisfies the following relationship: In the formula, represents the free carbon emission quota of aggregator i within the carbon emission assessment period T, Q total represents the total carbon emission quota of all carbon emission units within the carbon emission assessment period, and I represents the set of all aggregators; The free carbon emission quota of each aggregator in each time period satisfies the following relationship: In the formula, represents the amount of free carbon emission quota of aggregator i in time period t, represents the load of aggregator i in time period t.

6. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 3 is characterized in that: The carbon emission offset corresponding to the photovoltaic power generation equipment of aggregator i in time period t satisfies the following relationship: In the formula, represents the carbon emission offset of photovoltaic power generation equipment of aggregator i in time period t, It represents the predicted value of power generation of photovoltaic power generation equipment of aggregator i in time period t; The increment of free carbon emission quota obtained by aggregator i using photovoltaic power generation equipment in time period t satisfies the following relationship: In the formula, represents the carbon emission offset amount of photovoltaic power generation equipment used by aggregator i in time period t, which is determined by the carbon emission free quota increment determined by the offset ratio, μ c To offset the ratio.

7. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 1 is characterized in that: The total operating cost model of all aggregators participating in electricity-carbon trading during the carbon emission assessment period satisfies the following relationship: In the formula, Cost represents the total operating cost of all producers and consumers participating in the electricity-carbon trading during the carbon emission assessment period. represents the cost of transactions between aggregator i and the upper grid in time period t, represents the photovoltaic power generation cost of aggregator i in time period t, represents the operating cost of the energy storage equipment of aggregator i in time period t, represents the carbon trading cost of aggregator i in time period t, It represents the income obtained by aggregator i from using photovoltaic power generation equipment in time period t, T represents the carbon emission assessment period, and I represents the set of all aggregators.

8. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 7 is characterized in that: Under the constraints of the aggregator's purchase and sale of electricity to the superior power grid, the transaction costs of each aggregator and the superior power grid in different time periods are determined to satisfy the following relationship: In the formula, represents the cost of transactions between aggregator i and the upper grid in time period t, and They represent the electricity sales price and on-grid electricity price of the upper power grid in time period t respectively; and They represent the power purchased and sold by aggregator i to the upper grid in time period t respectively; Among them, the aggregator meets the maximum exchange power constraint when purchasing and selling electricity to the upper-level power grid, as shown in the following relationship: Where P i grid,max represents the upper limit of the switching power of aggregator i.

9. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 7 is characterized in that: The photovoltaic power generation cost of each aggregator in different periods satisfies the following relationship: In the formula, represents the cost coefficient of photovoltaic power generation equipment of aggregator i, It represents the predicted value of power generation of photovoltaic power generation equipment of aggregator i in time period t.

10. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 7 is characterized in that: Under the joint constraints of the aggregator's energy storage equipment, the operating cost of each aggregator's energy storage equipment is determined to satisfy the following relationship: In the formula, represents the operating cost of the energy storage equipment of aggregator i in time period t, represents the cost coefficient of energy storage equipment of aggregator i, represents the charging load of the energy storage device of aggregator i in time period t, represents the discharge load of the energy storage device of aggregator i in time period t; Among them, the joint constraints of the aggregator's energy storage equipment include: 1) The energy storage equipment of the aggregator meets the maximum charging power constraint and the maximum discharging power constraint during the charging and discharging process, as shown in the following relationship: Where P i ba,chg,max , P i ba,dischg,max They represent the upper limit of charging power and the upper limit of discharging power of the energy storage device of aggregator i respectively; 2) The aggregator's energy storage equipment is charged and discharged at different times, satisfying the following relationship: 3) The energy storage equipment of the aggregator meets the state of charge constraint and satisfies the following relationship: In the formula, They represent the lower and upper limits of the state of charge of the energy storage equipment of aggregator i, respectively. represents the state of charge of the energy storage device of aggregator i in time period t, represents the capacity of the energy storage equipment of aggregator i; 4) The energy storage equipment of the aggregator meets the balance of charge and discharge within a use cycle and satisfies the following relationship: The charge amount of an energy storage device during one usage cycle should be equal to the discharge amount.

11. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 7 is characterized in that: The carbon trading costs of each aggregator in different periods of time satisfy the following relationship: In the formula, and They represent the purchase price and sale price of carbon quota in the carbon emission rights market in time period t, and They respectively represent the amount of free carbon emission quotas purchased and sold by aggregator i in the carbon emission rights market in time period t.

12. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 7 is characterized in that: The income obtained by aggregator i from using photovoltaic power generation equipment in time period t satisfies the following relationship: In the formula, represents the income obtained by aggregator i from selling CCER to the market in time period t, ω c is the unit price of carbon emission offset, μ c To offset the ratio, It represents the carbon emission offset of photovoltaic power generation equipment of aggregator i in time period t.

13. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 1 is characterized in that: The constraints of the total operating cost model for all aggregators participating in electricity-carbon trading during the carbon emission assessment period include: based on the electricity-carbon trading behavior of the aggregators, establishing the power balance constraints and carbon emission free quota balance constraints of the aggregators during the carbon emission assessment period, and the power trading constraints and carbon emission free quota trading constraints of the aggregators participating in electricity-carbon trading.

14. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 13 is characterized in that: The power balance constraints of the aggregator in different time periods satisfy the following relationship: In the formula, represents the power purchased or sold by aggregator i in time period t, represents the predicted value of power generation of photovoltaic power generation equipment of aggregator i in time period t, represents the load of aggregator i in time period t, represents the charging load of the energy storage device of aggregator i in time period t, represents the discharge load of the energy storage device of aggregator i in time period t; The power purchased or sold by aggregator i in time period t satisfies the following relationship: When aggregator i purchases electricity When aggregator i sells electricity In the formula, and They represent the power purchased and sold by aggregator i to the upper grid in time period t, I represents the set of all prosumer aggregators, They represent the power purchased by aggregator i from aggregator j in time period t and the power sold by aggregator i to aggregator j in time period t respectively; The balance constraint of the free carbon emission quota of the aggregator in different time periods satisfies the following relationship: In the formula, represents the amount of free carbon emission quotas sold by aggregator i to the market in time period t, represents the remaining amount of free carbon emission quota of aggregator i in time period t, represents the amount of free carbon emission quotas sold by aggregator i in time period t, They represent the free carbon emission quota of aggregator i in time period t, the free carbon emission quota purchased or sold by aggregator i in time period t, the free carbon emission quota obtained by aggregator i from using photovoltaic power generation equipment in time period t, and the carbon emissions of aggregator i in the carbon trading process in time period t; The amount of free carbon emission quotas purchased or sold by aggregator i in time period t satisfies the following relationship: When aggregator i purchases free carbon emission quotas: When aggregator i sells free carbon emission allowances: In the formula, represents the amount of free carbon emission quota purchased by aggregator i from the market in time period t, They respectively represent the amount of free carbon emission quotas purchased by aggregator i from aggregator j in time period t and the amount of free carbon emission quotas sold by aggregator i to aggregator j in time period t.

15. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 13 is characterized in that: 1) Power trading constraints satisfy the following relationship: In the formula, denote the electricity purchased by aggregator i from aggregator j in time period t and the electricity sold by aggregator i to aggregator j in time period t, respectively. denote the electricity purchased by aggregator j from aggregator i in time period t and the electricity sold by aggregator j to aggregator i in time period t, respectively. represents the power cap of transactions between aggregator i and aggregator j; 2) The trading constraints of free carbon emission quotas shall satisfy the following relationship: In the formula, They represent the free carbon emission quotas purchased by aggregator i from aggregator j in time period t and the free carbon emission quotas sold by aggregator i to aggregator j in time period t, respectively. They represent the free carbon emission quotas purchased by aggregator j from aggregator i in time period t and the free carbon emission quotas sold by aggregator j to aggregator i in time period t, respectively. It represents the upper limit of free carbon emission quotas traded between aggregator i and aggregator j.

16. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 1 is characterized in that: Based on the dual problem method, the amount of electricity traded between an aggregator that satisfies both the power balance constraint and the power trading constraint and other aggregators, and the amount of carbon emission free quotas traded between an aggregator that satisfies both the carbon emission free quota balance constraint and the carbon emission free quota trading constraint and other aggregators are taken as a set of dual variables. The aggregator that satisfies both the power balance constraint and the power trading constraint simultaneously trades electricity with other aggregators, including: the total power sold by aggregator i to other aggregators in time period t, and the total power purchased by aggregator i from other aggregators in time period t; satisfying the following relationship: In the formula, represents the total power sold by aggregator i to other aggregators in time period t, represents the power vector of electricity sold by aggregator i to other aggregators in time period t, They represent the power sold by aggregator i to aggregator 1, ..., aggregator N in time period t; Y i represents an N-dimensional column vector whose i-th element is 0 and the rest of the elements are 1; In the formula, represents the total power purchased by aggregator i from other aggregators in time period t, represents the power vector of electricity purchased by aggregator i from other aggregators in time period t, They represent the power purchased by aggregator i from aggregator 1, ..., aggregator N in time period t respectively; The sum of the power sold by aggregator i to other aggregators in time period t and the sum of the power purchased by aggregator i from other aggregators in time period t are both negative values; Aggregators that simultaneously meet the carbon emission free quota balance constraint and the carbon emission free quota transaction constraint conditions trade carbon emission free quotas with other aggregators, including: the sum of carbon emission free quotas sold by aggregator i to other aggregators in time period t, and the sum of carbon emission free quotas purchased by aggregator i from other aggregators in time period t; satisfying the following relationship: In the formula, represents the total amount of free carbon emission quotas sold by aggregator i to other aggregators in time period t, represents the vector of free carbon emission quotas sold by aggregator i to other aggregators in time period t, They represent the amount of free carbon emission quotas sold by aggregator i to aggregator 1, ..., aggregator N in time period t respectively; In the formula, represents the total amount of free carbon emission quotas purchased by aggregator i from other aggregators in time period t, represents the vector of free carbon emission quotas purchased by aggregator i from other aggregators in time period t, They represent the free carbon emission quotas purchased by aggregator i from aggregator 1, ..., aggregator N in time period t respectively; The sum of the free carbon emission quotas sold by aggregator i to other aggregators in time period t and the sum of the free carbon emission quotas purchased by aggregator i from other aggregators in time period t are both positive.

17. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 16 is characterized in that: The electricity carbon trading revenue model of each aggregator during the carbon emission assessment period satisfies the following relationship: In the formula, represents the income of aggregator i participating in electricity-carbon trading in time period t; represents the operating cost of aggregator i participating in electricity-carbon trading in time period t; represents the set of Lagrange multipliers in time segment t, satisfying in, It represents the Lagrange multiplier corresponding to the amount of electricity traded between aggregator i and other aggregators in time period t, It represents the Lagrange multiplier corresponding to the amount of free carbon emission quotas traded between aggregator i and other aggregators in time period t; It represents the Lagrange multiplier vector corresponding to the amount of electricity traded between the aggregator and other aggregators in time period t; It represents the Lagrange multiplier vector corresponding to the amount of free carbon emission quotas traded between the aggregator and other aggregators in time period t; diag{} represents the diagonal elements.

18. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 1 is characterized in that: The optimization model of electricity-carbon trading instructions satisfies the following relationship: In the formula, G AOP It is the optimization model of electricity carbon trading instructions; {AOP1,…,AOP N } is the set of various aggregators participating in the decision-making of the electricity carbon market in different time periods, among which AOP1, ..., AOP N They are aggregators 1, ..., and N that participate in the decision-making of the electricity-carbon market in different time periods; is the set of electricity-carbon trading instructions of each aggregator participating in the electricity-carbon market decision-making within the time period t, where They are respectively the electricity-carbon trading instructions of aggregator 1, ..., and the electricity-carbon trading instructions of aggregator N in time period t; is the collection of the revenues of each aggregator participating in the electricity carbon market decision-making in time period t, where are the revenue of aggregator 1, ..., and N in time period t, respectively; N represents the total number of aggregators, which is a positive integer; The optimal instruction set of each aggregator’s electricity-carbon trading in time period t is: in, They are the preferred electricity-carbon trading instructions of aggregator 1 in time period t, ..., and the preferred electricity-carbon trading instructions of aggregator N; the preferred electricity-carbon trading instructions of aggregator i in time period t are The set of preferred instructions for electricity-carbon trading of other aggregators is: When the following relationship is satisfied, the optimal order set of electricity-carbon trading of each aggregator in time period t is A Nash equilibrium point for the electricity-carbon trading order optimization model: In the formula, is the revenue of aggregator i determined based on the electricity-carbon trading instructions in time period t, X i Preferred instructions for electricity-carbon trading for aggregator i Any electricity carbon trading instruction other than 19. The optimization method for participating in the decision-making of the electricity carbon market by the prosumer aggregator in the integrated energy system according to claim 2 is characterized in that: The Lagrange multiplier is updated using the updated dual variables to satisfy the following relationship: In the formula, They represent the Lagrange multipliers corresponding to the amount of electricity traded between aggregator i and other aggregators in time period t at the k+1th and kth iterations, respectively. They represent the Lagrange multipliers corresponding to the free carbon emission quotas traded between aggregator i and other aggregators in time period t at the k+1th and kth iterations, respectively. represents the price update step of the electricity traded between aggregator i and other aggregators at the kth iteration, represents the carbon price update step size of the carbon emission free quotas traded by aggregator i with other aggregators at the kth iteration, represents the power vector of electricity sold by aggregator i to other aggregators in time period t at the kth iteration, represents the total power sold by aggregator i to other aggregators in time period t at the kth iteration, represents the vector of free carbon emission quotas sold by aggregator i to other aggregators in time period t at the kth iteration, Y represents the sum of the free carbon emission quotas sold by aggregator i to other aggregators in time period t at the kth iteration; i represents an N-dimensional column vector whose i-th element is 0 and the rest of the elements are 1; The difference between the Lagrange multipliers before and after the update is compared. If the difference is less than the set threshold, it means that the Lagrange multipliers before and after the update have converged. The threshold value is no more than 0.

01.

20. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 19.

21. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 19 are implemented.

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