Optimization method for integrated energy system producer-consumer aggregator to participate in electric-carbon market decision-making

Abstract

The optimization method for the participation of the power and carbon market decision of the integrated energy system producer and consumer aggregation enterprise is used to determine the carbon emission free quota amount of each aggregation enterprise in each time period and the carbon emission free quota increment obtained by using photovoltaic power generation equipment; a total operation cost model of all aggregation enterprises participating in the electricity and carbon transaction in the carbon emission assessment period is established; based on the dual problem method, the Lagrange multiplier is introduced, and the electricity and carbon transaction income model of each aggregation enterprise in the carbon emission assessment period is established by using the dual variable and the total operation cost model of each aggregation enterprise participating in the electricity and carbon transaction in the carbon emission assessment period; based on the game method, an electricity and carbon transaction instruction optimization model is established; and the electricity and carbon transaction optimal instruction of each aggregation enterprise is determined based on the Nash equilibrium point of the electricity and carbon transaction instruction optimization model. The application can reduce the transaction cost and carbon emission amount of each producer and consumer in the electricity and carbon market, improve the local renewable energy consumption amount, and protect the privacy of different subjects.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of integrated energy systems, and in particular relates to an optimization method for decision-making of an electricity-carbon market by a producer-consumer aggregator in an integrated energy system. BACKGROUND

[0002] To achieve the "double carbon" goal of carbon peak and carbon neutrality, the rapid development of renewable energy has become the key to energy transformation. At the same time, the expansion of the electricity market and the rise of the carbon emission market also provide financial support and economic motivation for energy industry transformation.

[0003] The popularity of distributed renewable energy has turned traditional electricity users into energy producers and consumers, who often have green photovoltaic, wind power and other renewable energy power generation equipment, and have good potential for carbon reduction. At present, the electricity generated by the producer-consumer first meets its own electricity demand, and the remaining part is sold on the grid, and they can also obtain income by participating in carbon emission trading. In theory, using peer-to-peer (P2P) transaction technology, any energy producer-consumer can directly participate in user-side electricity market transactions. However, the renewable energy generation of a single energy producer-consumer is generally small, resulting in small electricity transaction volume, and a large number of disordered transactions will affect the safe operation of the power grid; the carbon reduction capacity of the producer-consumer is also relatively small, which is difficult to meet the access threshold of carbon emission trading market, and even if they can participate in carbon emission trading, they are often at a disadvantage.

[0004] To solve the above problems, some solutions have been proposed in the prior art, such as centralized management of electricity transactions of producer-consumers through a third-party platform, but such methods often focus on centralized processing of electricity transactions, ignoring the integration and transaction optimization of carbon reduction capacity, and failing to fully solve the dual dilemma of producer-consumers in the electricity and carbon emission market. SUMMARY

[0005] To solve the problems in the prior art, the present application provides an optimization method for decision-making of an electricity-carbon market by a producer-consumer aggregator in an integrated energy system, which helps different types of producer-consumer subjects (campus, family residents, commercial enterprises, etc.) to make economic carbon quota and green electricity decisions, reduces the transaction cost and carbon emission of each producer-consumer in the electricity-carbon market, improves the local renewable energy consumption, and protects the privacy of different subjects.

[0006] The present application adopts the following technical solutions.

[0007] The present application provides an optimization method for decision-making of an electricity-carbon market by a producer-consumer aggregator in an integrated energy system, which covers a plurality of aggregators and a plurality of carbon emission units in the electricity-carbon market, each aggregator includes energy storage equipment, photovoltaic power generation equipment and a plurality of electricity load units, comprising:

[0008] In a carbon emission assessment period, the carbon emission amounts of each aggregator and the carbon emission quota amounts of each carbon emission unit are obtained, and the total of the carbon emission quota amounts of all carbon emission units is allocated to each aggregator according to the proportion of the carbon emission amount of each aggregator in the total of the carbon emission amounts of all aggregators, to obtain the carbon emission free quota amount of each aggregator in a carbon emission assessment period; based on the carbon emission free quota amount of each aggregator in a carbon emission assessment period, the carbon emission free quota amount 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 total of the electricity loads of all aggregators in the time period; and the carbon emission free quota increment of each aggregator obtained by using the photovoltaic power generation equipment in each time period is determined according to the power generation amount prediction value of the photovoltaic power generation equipment.

[0009] The cost of the transaction between the aggregator and the superior power grid, the photovoltaic power generation cost of the aggregator, the operation cost of the energy storage equipment of the aggregator, the carbon transaction cost of the aggregator, and the income obtained by the aggregator using the photovoltaic power generation equipment in a carbon emission assessment period are obtained to establish a total operation cost model of all aggregators participating in the electricity-carbon transaction in the carbon emission assessment period;

[0010] Based on the dual problem method, the electricity traded between the aggregator and other aggregators and the carbon emission free quota traded between the aggregator and other aggregators are used as a set of dual variables; a Lagrange multiplier is introduced, and the dual variables and the total operation cost model of each aggregator participating in the electricity-carbon transaction in the carbon emission assessment period are used to establish an electricity-carbon transaction income model of each aggregator in the carbon emission assessment period; based on the electricity-carbon transaction income model of each aggregator in the carbon emission assessment period, the income of each aggregator participating in the electricity-carbon market decision in each time period is determined.

[0011] Based on the game method, the set of aggregators participating in the electricity-carbon market decision in each time period, the set of electricity-carbon transaction instructions of the aggregators participating in the electricity-carbon market decision in each time period, and the set of incomes of the aggregators participating in the electricity-carbon market decision in each time period are used to establish an electricity-carbon transaction instruction optimization model; the Nash equilibrium point of the electricity-carbon transaction instruction optimization model is used to determine the optimal instruction of the aggregator; wherein the electricity-carbon transaction instruction of the aggregator includes: the electricity traded with the superior power grid, the carbon emission free quota traded with the carbon emission right market, the charge-discharge control of the energy storage equipment, the power generation control of the photovoltaic power generation equipment, the electricity traded with other aggregators, and the carbon emission free quota traded with other aggregators.

[0012] Preferably, the electricity and carbon trading optimal instruction of each aggregator comprises: an electricity quantity optimal value traded with a superior power grid, a carbon emission free quota quantity optimal value traded with a carbon emission market, optimal control of charging and discharging of an energy storage device, optimal control of power generation of a photovoltaic power generation device, an electricity quantity optimal value traded with other aggregators, and a carbon emission free quota quantity optimal value traded with other aggregators.

[0013] The electricity quantity optimal value traded with other aggregators and the carbon emission free quota quantity optimal value traded with other aggregators of each aggregator are encrypted and shared; each aggregator updates a dual variable according to the shared data; a Lagrange multiplier is updated by using the updated dual variable; when the Lagrange multipliers before and after the update converge, a final income of each aggregator is determined based on an electricity and carbon trading income model of each aggregator in a carbon emission assessment period by using the updated Lagrange multiplier and the updated dual variable.

[0014] The final income of each aggregator participating in electricity and carbon market decision in each time period is used to update a Nash equilibrium point of the electricity and carbon trading instruction optimization model, so as to obtain updated electricity and carbon trading optimal instructions of each aggregator as electricity and carbon market decision of a producer and consumer aggregator in a comprehensive energy system.

[0015] Preferably, in a carbon emission assessment period, the carbon emission quantity of each aggregator comprises:

[0016] The carbon emission quantity of each aggregator generated by external purchased power and the carbon emission quantity of each aggregator generated by charging and discharging of an energy storage device in different time periods are obtained, so as to establish a carbon emission quantity model of each aggregator in a carbon trading process in different time periods; the carbon emission quantity of each aggregator in a carbon emission assessment period is determined based on the carbon emission quantity model of each aggregator in a carbon trading process in different time periods.

[0017] The carbon emission quantity model of each aggregator in a carbon trading process in different time periods satisfies the following relationship:

[0018]

[0019] In the formula, k represents a carbon emission factor of each aggregator, and represents a carbon emission factor of a power grid. represents the carbon emission quantity of aggregator i in a carbon trading process in time period t, represents the carbon emission quantity of aggregator i generated by external purchased power in time period t, represents the carbon emission quantity of aggregator i generated by charging and discharging of an energy storage device in time period t.

[0020] The carbon emission quantity of aggregator i generated by external purchased power in time period t satisfies the following relationship:

[0021]

[0022] In the formula, k represents a carbon emission factor of each aggregator, and represents a carbon emission factor of a power grid.g Indicates the grid reference emission factor. This represents the purchased electricity volume of aggregator i during time period t;

[0023] The carbon emissions of energy storage devices at time-period aggregator i satisfy the following relationship:

[0024]

[0025] In the formula, γ ba This represents the carbon emission coefficient of energy storage devices during charging and discharging. This represents the charging load of energy storage devices at aggregator i during time period t. This represents the discharge load of the energy storage device at time interval t for aggregator i;

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

[0027]

[0028] In the formula, E i,T This represents the amount of carbon emissions emitted by aggregator i during the carbon trading process within the carbon emission assessment period T.

[0029] Preferably, within a carbon emission assessment cycle, the carbon emission allowances for each carbon emission unit are obtained, including:

[0030] By combining the historical emissions method with the annual decay coefficient method, the sum of the historical carbon emissions of all carbon emission units within a historical carbon emission assessment period is corrected to obtain the total carbon emission quota of all carbon emission units within the carbon emission assessment period. Based on the proportion of each carbon emission unit's historical carbon emissions 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 to obtain the carbon emission quota of each carbon emission unit within a carbon emission assessment period.

[0031] The sum of historical carbon emissions of all carbon emission units within a historical carbon emission assessment period satisfies the following relationship:

[0032]

[0033] In the formula, E total E represents the sum of historical carbon emissions of all carbon emission units within the historical carbon emission assessment period. j Let $\mathbf{j}$ represent the historical carbon emissions of the $j$-th carbon emission unit, where $j \in $EU$ and $EU$ represents the set of all carbon emission units.

[0034] The total carbon emission allowances of all carbon-emitting entities during the carbon emission assessment period satisfy the following relationship:

[0035]

[0036] Q total denotes the total amount of carbon emission quota of all carbon emission units in the carbon emission assessment period, is the annual attenuation coefficient, expressed in percentage;

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

[0038]

[0039] wherein n represents the number of years required to achieve the emission reduction target, and r represents the total emission reduction target in the future n years, expressed in percentage;

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

[0041]

[0042] Q j denotes the amount of carbon emission quota of the jth carbon emission unit.

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

[0044]

[0045] wherein denotes the amount of carbon emission free quota of aggregator i in the carbon emission assessment period T, Q total denotes the total amount of carbon emission quota of all carbon emission units in the carbon emission assessment period, and I denotes the set of all aggregators;

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

[0047]

[0048] wherein denotes the amount of carbon emission free quota of aggregator i in sub-period t, denotes the load of aggregator i in sub-period t.

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

[0050]

[0051] wherein denotes the amount of carbon emission offset of the photovoltaic power generation equipment of aggregator i in sub-period t, represents the power generation prediction value of the photovoltaic power generation device of the aggregator i at the sub-period t;

[0052] The carbon emission free quota increment obtained by the aggregator i using the photovoltaic power generation device at the sub-period t satisfies the following relationship:

[0053]

[0054] In the formula, represents the carbon emission free quota increment determined by the carbon emission offset amount of the aggregator i using the photovoltaic power generation device at the sub-period t according to the offset ratio, μ c is the offset ratio.

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

[0056]

[0057] In the formula, Cost represents the total operation cost of all production and consumption aggregators participating in the electricity-carbon transaction in the carbon emission assessment period, represents the cost of the aggregator i trading with the upper grid at the sub-period t, represents the photovoltaic power generation cost of the aggregator i at the sub-period t, represents the operation cost of the energy storage device of the aggregator i at the sub-period t, represents the carbon transaction cost of the aggregator i at the sub-period t, represents the benefit obtained by the aggregator i using the photovoltaic power generation device at the sub-period t, T represents the carbon emission assessment period, and I represents the set of all aggregators.

[0058] Preferably, the cost of each aggregator trading with the upper grid in different sub-periods is determined under the constraint condition that the aggregator purchases and sells electricity to the upper grid, and satisfies the following relationship:

[0059]

[0060] In the formula, represents the cost of the aggregator i trading with the upper grid at the sub-period t, and respectively represent the selling price and the on-grid price of the upper grid at the sub-period t; and respectively represent the power purchased and sold by the aggregator i to the upper grid at the sub-period t;

[0061] Wherein, the maximum exchange power constraint is satisfied when the aggregator purchases and sells electricity to the upper grid, as shown in the following relationship:

[0062]

[0063] wherein, represents the upper limit of the exchange power of the aggregator i.

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

[0065]

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

[0067] Preferably, the operation cost of the energy storage equipment of each aggregator under the joint constraint condition of the energy storage equipment of the aggregator satisfies the following relationship:

[0068]

[0069] wherein, represents the operation cost of the energy storage equipment of the aggregator i in the time period t, represents the cost coefficient of the energy storage equipment of the aggregator i, represents the charging load of the energy storage equipment of the aggregator i in the time period t, represents the discharging load of the energy storage equipment of the aggregator i in the time period t;

[0070] The joint constraint condition of the energy storage equipment of the aggregator includes:

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

[0072]

[0073] wherein, respectively represent the upper limit of the charging power and the upper limit of the discharging power of the energy storage equipment of the aggregator i;

[0074] 2) The different constraints of the charging and discharging of the energy storage equipment of the aggregator satisfy the following relationship:

[0075]

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

[0077]

[0078] wherein, respectively represent the lower limit and the upper limit of the state of charge of the energy storage device of the aggregator i, represents the state of charge of the energy storage device of the aggregator i in the sub-period t, represents the capacity of the energy storage device of the aggregator i;

[0079] 4) The energy storage device of the aggregator satisfies the charge and discharge quantity balance in a use cycle, satisfying the following relationship:

[0080]

[0081] The charging quantity of the energy storage device in a use cycle should be equal to the discharging quantity.

[0082] Preferably, the carbon trading cost of each aggregator in different sub-periods satisfies the following relationship:

[0083]

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

[0085] Preferably, the income obtained by the aggregator i using the photovoltaic power generation device in the sub-period t satisfies the following relationship:

[0086]

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

[0088] Preferably, the constraint condition of the total operation cost model of all aggregators participating in the electricity-carbon transaction in the carbon emission assessment period includes: according to the electricity-carbon transaction behavior of the aggregator, the power balance constraint condition and the carbon emission free quota balance constraint condition of the aggregator in the carbon emission assessment period, the power transaction constraint condition and the carbon emission free quota transaction constraint condition of the aggregator participating in the electricity-carbon transaction are established.

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

[0090]

[0091] wherein, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t,

[0092] Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t,

[0093] When the aggregator i purchases electricity,

[0094]

[0095] When the aggregator i sells electricity,

[0096]

[0097] wherein, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t,

[0098] Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t,

[0099] Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t,

[0100] Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t,

[0101] Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t,

[0102] When the aggregator i purchases carbon emission free quota, Pi(t) represents the power of the purchase or sale of electricity of aggregator i at the time interval t,

[0103]

[0104] When the aggregator i sells the amount of carbon emission free quota:

[0105]

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

[0107] Preferably,

[0108] 1) Power transaction constraint condition, satisfying the following relationship:

[0109]

[0110] In the formula, respectively represent the amount of electric energy purchased by the aggregator i to the aggregator j and the amount of electric energy sold by the aggregator i to the aggregator j in the time interval t, respectively represent the amount of electric energy purchased by the aggregator j to the aggregator i and the amount of electric energy sold by the aggregator j to the aggregator i in the time interval t, represents the upper limit of power traded by the aggregator i and the aggregator j;

[0111] 2) Carbon emission free quota amount transaction constraint condition, satisfying the following relationship:

[0112]

[0113]

[0114] In the formula, respectively represent the amount of carbon emission free quota purchased by the aggregator i to the aggregator j and the amount of carbon emission free quota sold by the aggregator i to the aggregator j in the time interval t, respectively represent the amount of carbon emission free quota purchased by the aggregator j to the aggregator i and the amount of carbon emission free quota sold by the aggregator j to the aggregator i in the time interval t, represents the upper limit of carbon emission free quota amount traded by the aggregator i and the aggregator j.

[0115] Preferably, based on the dual problem method, the aggregated merchant and other aggregated merchants trade the power quantity satisfying the power balance constraint condition and the power transaction constraint condition, the aggregated merchant and other aggregated merchants trade the carbon emission free quota quantity satisfying the carbon emission free quota quantity balance constraint condition and the carbon emission free quota quantity transaction constraint condition, as a set of dual variables;

[0116] The aggregated merchant and other aggregated merchants trade the power quantity satisfying the power balance constraint condition and the power transaction constraint condition, including: the total power of the aggregated merchant i selling to other aggregated merchants in the time period t, the total power of the aggregated merchant i buying from other aggregated merchants in the time period t; satisfying the following relationship:

[0117]

[0118] In the formula, represents the total power of the aggregated merchant i selling to other aggregated merchants in the time period t, represents the power vector of the aggregated merchant i selling to other aggregated merchants in the time period t, respectively represent the power of the aggregated merchant i selling to the aggregated merchant 1, …, the aggregated merchant N in the time period t; Y i represents an N-dimensional column vector with the i-th element being 0 and the rest being 1;

[0119]

[0120] In the formula, represents the total power of the aggregated merchant i buying from other aggregated merchants in the time period t, represents the power vector of the aggregated merchant i buying from other aggregated merchants in the time period t, respectively represent the power of the aggregated merchant i buying from the aggregated merchant 1, …, the aggregated merchant N in the time period t;

[0121] The total power of the aggregated merchant i selling to other aggregated merchants in the time period t and the total power of the aggregated merchant i buying from other aggregated merchants in the time period t are both negative;

[0122] The aggregated merchant and other aggregated merchants trade the carbon emission free quota quantity satisfying the carbon emission free quota quantity balance constraint condition and the carbon emission free quota quantity transaction constraint condition, including: the total carbon emission free quota quantity of the aggregated merchant i selling to other aggregated merchants in the time period t, the total carbon emission free quota quantity of the aggregated merchant i buying from other aggregated merchants in the time period t; satisfying the following relationship:

[0123]

[0124] In the formula, represents the total carbon emission free quota quantity of the aggregated merchant i selling to other aggregated merchants in the time period t, a vector representing the amount of carbon emission free allowances sold by aggregator i to other aggregators in time period t, respectively representing the amount of carbon emission free allowances sold by aggregator i to aggregator 1, …, aggregator N in time period t;

[0125]

[0126] wherein, a sum of the amount of carbon emission free allowances purchased by aggregator i from other aggregators in time period t, a vector representing the amount of carbon emission free allowances purchased by aggregator i from other aggregators in time period t, respectively representing the amount of carbon emission free allowances purchased by aggregator i from aggregator 1, …, aggregator N in time period t;

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

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

[0129]

[0130]

[0131] wherein, represents the revenue of aggregator i participating in electric-carbon trading in time period t; represents the operating cost of aggregator i participating in electric-carbon trading in time period t; represents a set of Lagrange multipliers in time period t, satisfying wherein, represents the Lagrange multiplier corresponding to the electric quantity traded between aggregator i and other aggregators in time period t, represents the Lagrange multiplier corresponding to the carbon emission free allowances traded between aggregator i and other aggregators in time period t; represents a vector of Lagrange multipliers corresponding to the electric quantity traded between aggregator i and other aggregators in time period t; represents a vector of Lagrange multipliers corresponding to the carbon emission free allowances traded between aggregator i and other aggregators in time period t; diag{} represents off-diagonal elements.

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

[0133]

[0134] wherein, G AOPAOP1, AOP2, …, AOPN N} is a set of aggregators participating in the electricity-carbon market decision in the time interval, wherein AOP1, AOP2, …, AOPN N respectively represent aggregator 1, …, aggregator N participating in the electricity-carbon market decision in the time interval; is a set of electricity-carbon trading instructions of each aggregator participating in the electricity-carbon market decision in the time interval t, wherein respectively represent the electricity-carbon trading instruction of aggregator 1, …, the electricity-carbon trading instruction of aggregator N in the time interval t; is a set of profits of each aggregator participating in the electricity-carbon market decision in the time interval t, wherein respectively represent the profit of aggregator 1, …, the profit of aggregator N in the time interval t; N represents the total number of aggregators, which is a positive integer;

[0135] The set of electricity-carbon trading optimization instructions of each aggregator in the time interval t is wherein, respectively represent the electricity-carbon trading optimization instruction of aggregator 1, …, the electricity-carbon trading optimization instruction of aggregator N in the time interval t; the electricity-carbon trading optimization instruction of aggregator i in the time interval t is The set of electricity-carbon trading optimization instructions of the remaining aggregators is

[0136] When the following relationship is satisfied, the set of electricity-carbon trading optimization instructions of each aggregator in the time interval t is a Nash equilibrium point of the electricity-carbon trading instruction optimization model:

[0137]

[0138] In the formula, is the profit of aggregator i determined based on the electricity-carbon trading instruction in the time interval t, X i is any electricity-carbon trading instruction other than the electricity-carbon trading optimization instruction of aggregator i .

[0139] Preferably, the Lagrange multipliers are updated using the updated dual variables, satisfying the following relationship:

[0140]

[0141] In the formula, respectively represent the Lagrange multipliers corresponding to the electricity amount traded by aggregator i with other aggregators in the time interval t in the k+1th and kth iterations, respectively represent the Lagrange multipliers corresponding to the carbon emission free quota traded by aggregator i with other aggregators in the time interval t in the k+1th and kth iterations, an electricity price update step representing the electricity quantity traded by aggregator i with other aggregators at the kth iteration, an electricity price update step representing the electricity quantity traded by aggregator i with other aggregators at the kth iteration, an electricity price update step representing the electricity quantity traded by aggregator i with other aggregators at the kth iteration, an electricity price update step representing the electricity quantity traded by aggregator i with other aggregators at the kth iteration, an electricity price update step representing the electricity quantity traded by aggregator i with other aggregators at the kth iteration, an electricity price update step representing the electricity quantity traded by aggregator i with other aggregators at the kth iteration, i an N-dimensional column vector with the i-th element being 0 and the rest being 1;

[0142] The difference between the updated and the previous Lagrange multipliers is compared, and if the difference is less than a set threshold, it is indicated that the updated and the previous Lagrange multipliers converge, wherein the threshold is not greater than 0.01.

[0143] A terminal comprising 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 having stored thereon a computer program, which, when executed by a processor, implements the steps of the method.

[0145] The beneficial effects of the present application at least include, compared with the prior art, by integrating and utilizing distributed renewable energy and the key role of the electricity-carbon trading mechanism in energy transformation, the present application proposes a method suitable for P2P electricity-carbon trading market decision optimization of aggregators. The method fully considers the diversified needs and market conditions of energy producers and consumers, realizes the optimal allocation of electricity-carbon resources, and provides more flexible and efficient trading methods for energy producers and consumers. BRIEF DESCRIPTION OF DRAWINGS

[0146] Figure 1 is a flowchart of the optimization method for decision-making of aggregators participating in the electricity-carbon market of the comprehensive energy system proposed by the present application. DETAILED DESCRIPTION

[0147] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. The embodiments described in the present application are only a part of the embodiments of the present application, not all the embodiments. Based on the spirit of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0148] The application provides an optimization method for participation of a producer-consumer aggregator in decision-making of an electricity-carbon market in a comprehensive energy system. Figure 1 As shown in the figure, the method comprises the following steps:

[0149] Step 1: In a carbon emission evaluation period, the carbon emission amount of each aggregator and the carbon emission quota amount of each carbon emission unit are obtained, and the total sum of the carbon emission quota amounts of all carbon emission units is distributed to each aggregator according to the proportion of the carbon emission amount of each aggregator in the total sum of the carbon emission amounts of all aggregators, so as to obtain the carbon emission free quota amount of each aggregator in the carbon emission evaluation period; based on the carbon emission free quota amount of each aggregator in the carbon emission evaluation period, the carbon emission free quota amount 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 total sum of the electricity loads of all aggregators in the time period; the carbon emission free quota increment of each aggregator in each time period obtained by using the photovoltaic power generation equipment is determined according to the power generation amount prediction value of the photovoltaic power generation equipment; and the sum of the carbon emission free quota amount and the carbon emission free quota increment of each aggregator in each time period is taken as the total carbon emission free quota amount of each aggregator in each time period.

[0150] In the embodiment, before the start of the carbon emission evaluation period, the aggregator selects whether to join the P2P electricity-carbon transaction decision-making, if yes, the aggregator will participate in the electricity-carbon market decision-making optimization of the carbon emission evaluation period, otherwise, the aggregator will not participate in the electricity-carbon market decision-making optimization of the carbon emission evaluation period.

[0151] With the multiple carbon emission units and the multiple aggregators participating in the electricity-carbon market decision-making optimization of the carbon emission evaluation period as optimization objects, first, the parameter initialization is performed according to step 1 to obtain the carbon emission free quota amount of each aggregator and the carbon emission free quota increment obtained by using the photovoltaic power generation equipment in each time period in the carbon emission evaluation period.

[0152] The various types of aggregators in the integrated energy system that integrate producer and consumer resources can buy and sell electricity with other aggregators through P2P electricity trading, and can also trade electricity with the upper level 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 carbon emission inspection requirements. The trading cycle of P2P electricity trading and carbon quota trading is consistent with the electricity trading cycle of the upper grid and the carbon trading cycle with the carbon emission rights market. In the embodiment, the carbon emission assessment period T is set to 1 year, and the time period t for participating in the transaction is set to 1 week. The aggregators trade electricity and carbon through P2P to increase the consumption of distributed renewable energy, reduce dependence on the upper grid, reduce electricity trading costs, and indirectly reduce carbon emissions.

[0153] Specifically, step 1 includes:

[0154] Step 1.1, obtaining the carbon emissions of the purchased electricity of each aggregator in different time periods and the carbon emissions of the charging and discharging of the energy storage devices of each aggregator, to establish a carbon emission model of each aggregator in the carbon trading process in different time periods; 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 a carbon emission assessment period are determined;

[0155] Specifically, the carbon emission model of each aggregator in the carbon trading process in different time 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 of the purchased electricity of aggregator i in time period t, represents the carbon emissions of the charging and discharging of the energy storage devices 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 upper grid. However, the purchased electricity will produce indirect carbon emissions, because the current electricity production still relies on the combustion of fossil fuels (such as coal, oil, and natural gas), which will release a large amount of carbon dioxide and other greenhouse gases and pollutants during the combustion process. At the same time, indirect carbon emissions will also be produced 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 large amount of energy and materials. In addition, a certain amount of energy loss will be generated during the transmission of electricity due to factors such as resistance and electromagnetic field, and these lost energies will also be converted into carbon emissions. Therefore, the carbon emissions caused by the purchased electricity of aggregator i in time period t satisfy the following relationship:

[0159]

[0160] wherein k g represents the grid reference emission factor, represents the amount of purchased electricity of aggregator i in time interval t.

[0161] The carbon emission of the photovoltaic power generation equipment in the integrated energy system is 0. The energy storage equipment also produces a small amount of carbon emission during use, and the emission amount is proportional to the charging and discharging behavior of the producer and consumer. The carbon emission of the energy storage equipment of aggregator i in time interval t satisfies the following relationship:

[0162]

[0163] wherein γ ba represents the carbon emission coefficient when the energy storage equipment is charged and discharged, and in the embodiment, the value is 0.083 kg / kWh, represents the charging load of the energy storage equipment of aggregator i in time interval t, represents the discharging load of the energy storage equipment of aggregator i in time interval t.

[0164] Based on the carbon emission model of each aggregator in the carbon trading process in different time intervals, the carbon emission of each aggregator in the carbon emission assessment period is determined, which satisfies the following relationship:

[0165]

[0166] wherein E i,T represents the carbon emission of the producer and consumer aggregator i in the carbon trading process in the carbon emission assessment period T.

[0167] Step 1.2, the historical emission method and the annual decay coefficient method are combined to correct the sum of the historical carbon emissions of all carbon emission units in a historical carbon emission assessment period, so as to obtain the total amount of carbon emission quota of all carbon emission units in the carbon emission assessment period. According to the proportion of the historical carbon emission of each carbon emission unit in the total amount of carbon emission quota of all carbon emission units, the total amount of carbon emission quota of all carbon emission units is allocated to each carbon emission unit, so as 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, obtaining the historical carbon emission of each carbon emission unit in a historical carbon emission assessment period, and combining the historical emission method and the annual decay coefficient method to correct the sum of the historical carbon emissions of all carbon emission units, so as to obtain the total amount of carbon emission quota of all carbon emission units in the carbon emission assessment period.

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

[0171]

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

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

[0174]

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

[0176] The annual decay 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 in the next n years, expressed in percentage.

[0179] At present, the carbon market in China mainly implements the free allocation of quotas, and only a small part of the quotas is allocated for a fee, mainly through auction. For small and medium-sized producers and consumers, their carbon emission sources are characterized by "wide distribution and scattered location". In order to effectively manage these carbon emission sources through aggregators, in the initial carbon quota allocation, 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 allocation, so that those units with larger historical emission amounts will not be greatly impacted by the new policy, and at the same time, it also encourages emission reduction actions. It is worth noting that those skilled in the art can allocate carbon quotas in different ways according to the actual conditions of carbon trading.

[0180] Step 1.2.2, according to the proportion of the historical carbon emission amount of each carbon emission unit in the total amount of carbon emission quotas of all carbon emission units, the total amount of carbon emission quotas of all carbon emission units is allocated to each carbon emission unit, to obtain the carbon emission quota amount of each carbon emission unit in a carbon emission assessment period, 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 emission of the jth carbon emission unit.

[0183] In the method, the carbon emission quota of each carbon emission unit is limited by the total historical carbon emission, and the adaptive attenuation adjustment of the carbon emission quota under the emission reduction target is realized.

[0184] Step 1.3, according to the proportion of the carbon emission of each aggregator in the carbon emission assessment period in the total carbon emission of all aggregators in the carbon emission assessment period, the total carbon emission quota of all carbon emission units in the carbon emission assessment period is allocated to each aggregator, and the carbon emission free quota of each aggregator in a carbon emission assessment period is obtained.

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

[0186]

[0187] In the formula, represents the carbon emission free quota of the aggregator i in the carbon emission assessment period T, Q total represents the total carbon emission quota of all carbon emission units in the carbon emission assessment period, and I represents the set 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 in the total electricity load of all aggregators in the time period, the carbon emission free quota of each aggregator in a carbon emission assessment period is determined.

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

[0190]

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

[0192] In the method, the free carbon emission quota is allocated to each producer-consumer aggregator, and the allocation is not only refined to the carbon emission assessment period, but also to each time period, so that each producer-consumer aggregator can control the carbon emission more accurately according to the real-time changes of actual production activities, and realize the optimal allocation of carbon resources. The "time refinement" strategy effectively avoids the traditional "loose at the beginning and tight at the end" carbon emission management mode, that is, the carbon emission is relatively loose at the beginning of the assessment period, and emergency emission reduction measures have to be taken at the end of the period due to the accumulated emission close to the upper limit.

[0193] Step 1.5, according to the power generation prediction value of the photovoltaic power generation equipment, determine the carbon emission free quota increment of each aggregator using photovoltaic power generation equipment in each time period.

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

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

[0196]

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

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

[0199]

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

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

[0202] The total amount of carbon emission free quota of each aggregator in each sub-period meets the following relationship:

[0203]

[0204] In the formula, M i,t represents the total amount of carbon emission free quota of aggregator i in sub-period t.

[0205] Step 2, obtaining the cost of the aggregator trading with the upper grid, the photovoltaic power generation cost of the aggregator, the operation cost of the energy storage device of the aggregator, the carbon trading cost of the aggregator, and the income obtained by the aggregator using the photovoltaic power generation device in a carbon emission assessment period, to establish a total operation cost model of all aggregators participating in electricity-carbon trading in the carbon emission assessment period.

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

[0207]

[0208] In the formula, Cost represents the total operation cost of all production and consumption aggregators participating in electricity-carbon trading in the carbon emission assessment period, represents the cost of aggregator i trading with the upper grid in sub-period t, represents the photovoltaic power generation cost of aggregator i in sub-period t, represents the operation cost of the energy storage device of aggregator i in sub-period t, represents the carbon trading cost of aggregator i in sub-period t, represents the income obtained by aggregator i using the photovoltaic power generation device in sub-period t.

[0209] Specifically, the cost of each aggregator trading with the upper grid in different time periods is determined under the constraint condition that the aggregator purchases and sells electricity to the upper grid, and meets the following relationship:

[0210]

[0211] In the formula, represents the cost of aggregator i trading with the upper grid in sub-period t, and respectively represent the selling price and the on-grid price of the upper grid in sub-period t; and respectively represent the power of aggregator i purchasing and selling electricity to the upper grid in sub-period t.

[0212] Wherein, the aggregator satisfies the maximum exchange power constraint when purchasing and selling electricity to the upper grid, as shown in the following relationship:

[0213]

[0214] In the formula, represents the upper limit of the exchange power of the 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 the photovoltaic power generation equipment of the aggregator i.

[0218] Specifically, the operation cost of the energy storage equipment of each aggregator is determined under the joint constraint condition of the energy storage equipment of the aggregator, and satisfies the following relationship:

[0219]

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

[0221] The joint constraint condition of the energy storage equipment of the aggregator includes:

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

[0223]

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

[0225] 2) Different constraints of the charging and discharging of the energy storage equipment of the aggregator

[0226] The present application finds that when a part of the battery needs to be charged or discharged at a certain power while the other part of the battery is discharged or charged, more cost is needed than when all the batteries are charged or discharged at the same time. Therefore, the energy storage equipment cannot be charged and discharged at the same time, and the discharging power is 0 when part of the battery is charged, and the charging power is 0 when part of the battery is discharged, satisfying the following relationship:

[0227]

[0228] In the operation of the energy storage device, part of the battery pack is charged and part of the battery pack is discharged, which is a conventional control. In the present application, the minimum cost of charging and discharging of the energy storage device is taken as the target. Only based on the production and consumption aggregator, the effective control of the different time constraints of the charging and discharging of the energy storage device can be realized, and such different time constraints of the charging and discharging of the energy storage device are special constraint conditions determined for the minimization of the operation cost of the energy storage device of the production and consumption aggregator. Although the charging and discharging control based on BMS is similar in form, the control target is completely different.

[0229] 3) The energy storage device of the aggregator meets the state of charge constraint

[0230] In order to maintain the service life of the energy storage device and prevent it from being overcharged and discharged, the state of charge (SOC) of the battery should be within a certain range, and the SOC should change its value along with the charging and discharging of the battery, satisfying the following relationship:

[0231]

[0232] In the formula, SOCmin and SOCmax represent the lower limit and the upper limit of the state of charge of the energy storage device of the aggregator i, respectively, SOCi(t) represents the state of charge of the energy storage device of the aggregator i at the time interval t, Ci represents the capacity of the energy storage device of the aggregator i.

[0233] 4) The energy storage device of the aggregator meets the charge and discharge balance in one service cycle

[0234] In actual operation, it is necessary to ensure that the initial state and the final state of the energy storage device in one service cycle are completely consistent, so the charging capacity of the energy storage device in one service cycle should be equal to the discharging capacity, satisfying the following relationship:

[0235]

[0236] The energy storage device of the aggregator meets the charge and discharge balance in one service cycle, which, combined with the different time constraints of the charging and discharging of the energy storage device of the aggregator, is the guarantee for the minimization of the operation cost of the energy storage device of the production and consumption aggregator.

[0237] Specifically, the carbon trading cost of each aggregator in different time intervals satisfies the following relationship:

[0238]

[0239] In the formula, and Pbuy(t) and Psell(t) represent the purchase price and the sale price of the carbon quota in the carbon emission right market at the time interval t, respectively, and respectively represent the amount of carbon emission free allowances purchased and sold by aggregator i in the carbon emission market in the time interval t.

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

[0241]

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

[0243] In step 2, the constraint conditions of the total operation cost model of all aggregators participating in the electricity-carbon transaction in the carbon emission assessment period include: according to the electricity-carbon transaction behavior of the aggregator, the power balance constraint condition and the carbon emission free allowance balance constraint condition of the aggregator in the carbon emission assessment period, the power transaction constraint condition and the carbon emission free allowance transaction constraint condition of the aggregator participating in the electricity-carbon transaction are established.

[0244] Specifically, according to the electricity-carbon transaction behavior of the aggregator, the power balance constraint condition and the carbon emission free allowance balance constraint condition of the aggregator in the carbon emission assessment period are established.

[0245] The power balance constraint condition of the aggregator in different time intervals satisfies the following relationship:

[0246]

[0247] In the formula, ω represents the power purchased or sold by aggregator i in the time interval t, ω represents the power generation prediction value of the photovoltaic power generation equipment of aggregator i in the time interval t, ω represents the load of aggregator i in the time interval t, ω represents the charging load of the energy storage equipment of aggregator i in the time interval t, ω represents the discharging load of the energy storage equipment of aggregator i in the time interval t.

[0248] Wherein, the buyer or seller identity of the aggregator in the time interval t is unchanged, so the power purchased or sold by aggregator i in the time interval t satisfies the following relationship:

[0249] When aggregator i purchases electricity:

[0250]

[0251] When aggregator i sells electricity

[0252]

[0253] In the formula, and Let represent the power purchased and sold by aggregator i from the upper-level grid during time period t, respectively, and let I represent the set of all production and consumption aggregators.

[0254] The balance constraints of aggregators' free carbon emission allowances during different time periods satisfy the following relationship:

[0255]

[0256] In the formula, This represents the amount of free carbon emission allowances sold to the market by aggregator i during a specific time period t. This represents the remaining amount of free carbon emission allowance for aggregator i in time period t. This represents the amount of free carbon emission allowances sold by aggregator i during a specific time period t. These represent the amount of free carbon emission allowances of aggregator i in time period t, the amount of free carbon emission allowances purchased or sold by aggregator i in time period t, the increase in free carbon emission allowances obtained by aggregator i through the use of photovoltaic power generation equipment in time period t, and the amount of carbon emissions emitted by aggregator i during the carbon trading process in time period t, respectively.

[0257] The carbon emission allowance balance constraint means that, unlike electricity trading, carbon allowances can be stored and do not need to be used up within a time period t. Therefore, the carbon allowances sold by aggregator i should not exceed its remaining carbon allowances.

[0258] In the method proposed in this invention, the remaining amount of free carbon emission allowances for aggregator i in time period t includes the remaining amount of free carbon emission allowances for aggregator i in the previous time period t-1, the amount of free carbon emission allowances for aggregator i in time period t, the amount of free carbon emission allowances for aggregator i from purchasing or selling electricity in time period t, the amount of free carbon emission allowances obtained by aggregator i from using photovoltaic power generation equipment in time period t, and the amount of carbon emissions of aggregator i in the carbon trading process in time period t. By fully utilizing the storable nature of free carbon emission allowances, the remaining amount of free carbon emission allowances is calculated in time periods, thereby making the balance constraint of free carbon emission allowances more accurate. Especially considering the uncertainty and volatility of output from energy storage devices and photovoltaic power generation equipment installed in the aggregators, time-period calculation can overcome the impact of uncertainty and volatility, achieving a more accurate balance effect of free carbon emission allowances.

[0259] In this case, aggregator i's status as a buyer or seller remains unchanged within time period t. Therefore, the amount of free carbon emission allowances purchased or sold by aggregator i in time period t... The following relationship must be satisfied:

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

[0261]

[0262] When the aggregator i sells the amount of carbon emission free quota:

[0263]

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

[0265] In the lower layer optimization target, the power balance reflects the real-time of the transaction behavior of the producer and consumer aggregators, and the carbon emission free quota balance reflects the continuity of the transaction behavior of the producer and consumer aggregators. Compared with the single optimization target of minimizing the total operation cost of the producer and consumer aggregators participating in the electricity-carbon transaction, on the basis of the upper layer optimization target of minimizing the total operation cost of all the producer and consumer aggregators participating in the electricity-carbon transaction, the lower layer optimization target constituted by the power balance and the carbon emission free quota balance can more accurately reflect the behavior characteristics of the producer and consumer aggregators and can more accurately support the transaction behavior of the producer and consumer aggregators with data.

[0266] Specifically, the power transaction constraint condition and the carbon emission free quota transaction constraint condition of the aggregator participating in the electricity-carbon transaction are established.

[0267] 1) The power transaction constraint condition satisfies the following relationship:

[0268]

[0269]

[0270] In the formula, respectively represent the power purchased by the aggregator i to the aggregator j and the power sold by the aggregator i to the aggregator j in the time interval t, respectively represent the power purchased by the aggregator j to the aggregator i and the power sold by the aggregator j to the aggregator i in the time interval t, represents the upper limit of the power traded by the aggregator i and the aggregator j.

[0271] The power transaction constraint condition indicates that the consistency of the transaction needs to be considered in the P2P transaction, that is, the electric energy purchased by the aggregator i from the aggregator j is equal to the electric energy sold by the aggregator j to the aggregator i. It is assumed that the value of the P2P transaction power sold to the outside is always less than 0, the value of the P2P transaction power purchased is always greater than 0, and the transaction power also has a maximum limit.

[0272] 2) the carbon emission free quota quantity transaction constraint condition satisfies the following relationship:

[0273]

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

[0275] The carbon emission free quota quantity transaction constraint condition indicates that the consistency of the transaction also needs to be considered in the P2P carbon quota transaction, and the value of the P2P carbon quota sold outwards is always less than 0, and the value of the P2P carbon quota purchased is always greater than 0.

[0276] Step 3, based on the dual problem method, taking the electricity traded by the aggregator with other aggregators and the carbon emission free quota quantity traded by the aggregator with other aggregators as a set of dual variables; introducing a Lagrange multiplier, using the dual variables and the total operating cost model of each aggregator participating in the electricity-carbon transaction in the carbon emission assessment period to establish an electricity-carbon transaction revenue model of each aggregator in the carbon emission assessment period; based on the electricity-carbon transaction revenue model of each aggregator in the carbon emission assessment period, determining the revenue of each aggregator participating in the electricity-carbon market decision in each time period.

[0277] Specifically, step 3 includes:

[0278] Step 3.1, based on the dual problem method, taking the electricity traded by the aggregator with other aggregators while satisfying the power balance constraint condition and the power transaction constraint condition, and the carbon emission free quota quantity traded by the aggregator with other aggregators while satisfying the carbon emission free quota quantity balance constraint condition and the carbon emission free quota quantity transaction constraint condition, as a set of dual variables.

[0279] The electricity traded by the aggregator with other aggregators while satisfying the power balance constraint condition and the power transaction constraint condition includes: the total power sold by the aggregator i to other aggregators in the time period t, and the total power purchased by the aggregator i from other aggregators in the time period t; satisfying the following relationship:

[0280]

[0281] In the formula, represents the total power sold by the aggregator i to other aggregators in the time period t, denotes the power vector of the power sold by aggregator i to other aggregators at time interval t, denotes the power sold by aggregator i to aggregator 1, …, aggregator N at time interval t, respectively; Y i denotes an N-dimensional column vector with the i-th element being 0 and the rest being 1;

[0282]

[0283] wherein, denotes the total power purchased by aggregator i from other aggregators at time interval t, denotes the power vector of the power purchased by aggregator i from other aggregators at time interval t, denotes the power purchased by aggregator i from aggregator 1, …, aggregator N at time interval t, respectively;

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

[0285] The aggregators satisfying the carbon emission free quota balance constraint condition and the carbon emission free quota trade constraint condition trade carbon emission free quotas with other aggregators, including: the total carbon emission free quota sold by aggregator i to other aggregators at time interval t, the total carbon emission free quota purchased by aggregator i from other aggregators at time interval t; satisfying the following relationship:

[0286]

[0287] wherein, denotes the total carbon emission free quota sold by aggregator i to other aggregators at time interval t, denotes the carbon emission free quota vector sold by aggregator i to other aggregators at time interval t, denotes the carbon emission free quota sold by aggregator i to aggregator 1, …, aggregator N at time interval t, respectively;

[0288]

[0289] wherein, denotes the total carbon emission free quota purchased by aggregator i from other aggregators at time interval t, denotes the carbon emission free quota vector purchased by aggregator i from other aggregators at time interval t, denotes the carbon emission free quota purchased by aggregator i from aggregator 1, …, aggregator N at time interval t, respectively;

[0290] The sum of the carbon emission free quota amount sold by the aggregator i to other aggregators in the time interval t, and the sum of the carbon emission free quota amount purchased by the aggregator i from other aggregators in the time interval t, are positive values.

[0291] In step 3.2, based on the dual problem method, two Lagrange multiplier vectors are introduced, and an electricity-carbon trading revenue model of each aggregator in the carbon emission assessment period is established by using the dual variables and a total operation cost model of each aggregator participating in the electricity-carbon trading in the carbon emission assessment period, and the following relationship is met:

[0292]

[0293]

[0294] In the formula, t represents a time interval, i represents an aggregator, and represents a revenue of the aggregator i participating in the electricity-carbon trading in the time interval t. diag{} represents a de-diagonal element.

[0295] Under the centralized optimization framework, the power generation and consumption information of each aggregator is shared, which may cause the risk of privacy and commercial information leakage. Therefore, the electricity-carbon trading revenue model of each aggregator obtained based on the dual problem method is proposed, the original complex problem is decomposed to the level of each aggregator, each aggregator can independently make decisions with the goal of maximizing its own operation benefit, and the privacy and commercial information leakage is avoided, so that the overall revenue of the system is maximized, and the carbon emission amount is effectively reduced.

[0296] After the above distributed processing, the game model becomes a P2P transaction model containing complex games, the electricity-carbon trading revenue model of each aggregator obtained based on the dual problem method is proposed, the original complex problem is decomposed to the level of each aggregator, and the privacy and security of each energy aggregator in the participation in the electricity trading and the electricity-carbon market are effectively maintained.

[0297] ​​​​​​​Step 4, based on the game method, using the set of each aggregator participating in the decision of the electricity-carbon market in the time period, the set of electricity-carbon transaction instructions of each aggregator participating in the decision of the electricity-carbon market in the time period, and the set of benefits of each aggregator participating in the decision of the electricity-carbon market in the time period, an electricity-carbon transaction instruction optimization model is established; the Nash equilibrium point of the electricity-carbon transaction instruction optimization model is used to determine the optimal electricity-carbon transaction instruction of each aggregator; the optimal electricity quantity traded between the aggregator and other aggregators and the optimal carbon emission free quota quantity traded between the aggregator and other aggregators are extracted from the optimal electricity-carbon transaction instruction of each aggregator.

[0298] In the non-limiting preferred embodiment, the electricity-carbon transaction instruction of the aggregator includes but is not limited to the electricity quantity traded with the upper-level power grid, the carbon emission free quota quantity traded with the carbon emission right market, the charge-discharge control of the energy storage device, the power generation control of the photovoltaic power generation device, the electricity quantity traded with other aggregators, and the carbon emission free quota quantity traded with other aggregators.

[0299] The implementation of the optimal electricity-carbon transaction instruction by each aggregator realizes the participation of the aggregator in the decision of the electricity-carbon market.

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

[0301]

[0302] In the formula, G AOP is the electricity-carbon transaction instruction optimization model; {AOP1, …, AOP N} is the set of each aggregator participating in the decision of the electricity-carbon market in the time period, wherein AOP1, …, AOP N are aggregator 1, …, aggregator N participating in the decision of the electricity-carbon market in the time period, respectively; is the set of electricity-carbon transaction instructions of each aggregator participating in the decision of the electricity-carbon market in the time period, wherein are the electricity-carbon transaction instruction of aggregator 1, …, the electricity-carbon transaction instruction of aggregator N in the time period t, respectively; is the set of benefits of each aggregator participating in the decision of the electricity-carbon market in the time period, wherein are the benefit of aggregator 1, …, the benefit of aggregator N in the time period t, respectively; N represents the total number of aggregators, which is a positive integer.

[0303] In the embodiment, the set of optimal electricity-carbon transaction instructions of each aggregator in the time period t is wherein, are the optimal electricity-carbon transaction instruction of aggregator 1, …, the optimal electricity-carbon transaction instruction of aggregator N in the time period t, respectively; the optimal electricity-carbon transaction instruction of aggregator i in the time period t is The set of electricity-carbon trading preference instructions of the rest of the aggregators is

[0304] When the following relationship is satisfied, the set of electricity-carbon trading preference instructions of each aggregator in the time interval t is A Nash equilibrium point of the electricity-carbon trading instruction optimization model:

[0305]

[0306] In the formula, is the income of the aggregator i based on the electricity-carbon trading instruction in the time interval t, X i is the electricity-carbon trading preference instruction of the aggregator i Any electricity-carbon trading instruction other than the electricity-carbon trading preference instruction of the aggregator i.

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

[0308] The optimal electricity-carbon trading instructions of each aggregator include but are not limited to: the optimal value of electricity traded with the upper grid, the optimal value of carbon emission free quota traded with the carbon emission market, the optimal control of charging and discharging of energy storage devices, the optimal control of photovoltaic power generation, the optimal value of electricity traded with other aggregators, and the optimal value of carbon emission free quota traded with other aggregators. From the optimal electricity-carbon trading instructions of all aggregators, the optimal value of electricity traded with other aggregators and the optimal value of carbon emission free quota traded with other aggregators are obtained.

[0309] In a comprehensive energy system, different energy production and consumption aggregators are independent interest subjects, and their interactive behaviors 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-making model under the multi-agent game framework.

[0310] The issue of peer-to-peer (P2P) electricity and carbon trading among aggregators can essentially be understood as a matter of maximizing individual profits. This profit maximization process is not isolated but influenced by the behavior of other aggregators in electricity and carbon emission rights trading, including the size of the trading volume and the setting of trading prices. Specifically, when an aggregator predicts that its available electricity and carbon allowances are insufficient to meet the demand of other aggregators, it may choose to raise the trading price of these resources in order to increase its profits. However, this strategic price increase often triggers a chain reaction: other aggregators, faced with rising prices, may reduce their purchases of these resources to control costs or seek more economical alternatives. This reduced demand, in turn, affects the trading volume of the aggregator that raised the price, potentially weakening its original expectation of increased profits through the price increase.

[0311] Therefore, each aggregator needs to maximize its own profits while considering the reactions and strategy adjustments of other aggregators, striving to find an equilibrium state in the dynamically changing market environment—a trading scheme that can both guarantee its own profits and promote the overall low-carbon and efficient operation of the system. This game theory problem can be viewed as an optimization problem under multiple objectives and constraints, requiring comprehensive consideration of market supply and demand, technological limitations, environmental policies, and the interactions among aggregators.

[0312] The optimization method for producer-consumer aggregators participating in the electricity carbon market decision-making process in an integrated energy system proposed in this invention further includes:

[0313] Step 5: Each aggregator shares the optimal value of electricity traded with other aggregators and the optimal value of free carbon emission allowances traded with other aggregators in encrypted form; each aggregator updates its dual variables based on the shared data; the updated dual variables are used to update the Lagrange multipliers; when the Lagrange multipliers before and after the update converge, the updated Lagrange multipliers and the updated dual variables are used to determine the final revenue of each aggregator based on the electricity-carbon trading revenue model of each aggregator within the carbon emission assessment period.

[0314] Step 5 uses Lagrange multiplier iterative calculations to realize the supply and demand relationship of each aggregator based on electricity and carbon quotas, gradually converging to a state point that maximizes the interests of all parties while maintaining overall market balance.

[0315] The Lagrange multipliers are updated using the updated dual variables, satisfying the following relation:

[0316]

[0317] In the formula, These represent the Lagrange multipliers corresponding to the electricity traded between aggregator i and other aggregators in time period t at the (k+1)th and kth iterations, respectively. respectively represent the Lagrange multiplier corresponding to the carbon emission free quota traded by the aggregator i with other aggregators in the time interval t in the k+1th iteration and the kth iteration, represent the electricity price update step of the electricity traded by the aggregator i with other aggregators in the kth iteration, represent the carbon price update step of the carbon emission free quota traded by the aggregator i with other aggregators in the kth iteration, represent the power vector of the electricity sold by the aggregator i to other aggregators in the time interval t in the kth iteration, represent the total power of the electricity sold by the aggregator i to other aggregators in the time interval t in the kth iteration, represent the carbon emission free quota vector sold by the aggregator i to other aggregators in the time interval t in the kth iteration, represent the total carbon emission free quota vector sold by the aggregator i to other aggregators in the time interval t in the kth iteration.

[0318] The difference between the updated and the previous Lagrange multipliers is compared, and if the difference is less than a set threshold, it indicates that the updated and the previous Lagrange multipliers converge, wherein the threshold value is not greater than 0.01.

[0319] The updated Lagrange multipliers and the updated dual variables are used to obtain the final revenue of each aggregator based on the electricity-carbon trading revenue model of each aggregator in the carbon emission assessment period;

[0320] The final revenue of each aggregator participating in the electricity-carbon market decision in the time interval is used to update the Nash equilibrium point of the electricity-carbon trading instruction optimization model to obtain the updated electricity-carbon trading optimal instruction of each aggregator, which is used for the electricity-carbon market decision of the producer-consumer aggregator in the comprehensive energy system.

[0321] The method provided by the application belongs to the technical field of producer-consumer aggregators participating in electricity market and carbon emission right market transaction, relates to producer-consumer resource aggregation technology, carbon emission quota allocation technology, and electricity-carbon coupling decision technology, and specifically relates to an optimization strategy for electricity-carbon transaction of each aggregator in a comprehensive energy system. Through specific analysis of the concept of aggregator and its important role in the point electricity-carbon market and the carbon transaction mechanism, an aggregator P2P electricity-carbon transaction model with the target of minimizing the total operation cost is constructed, and the model is distributed for processing and calculation. Through multi-scenario operation comparison, the significant effect of the optimization strategy in reducing operation cost, improving the proportion of local renewable energy consumption, and reducing carbon emission is verified, and the transaction privacy of each aggregator is effectively protected, and information leakage is reduced.

[0322] By comparison with other transaction scenarios, the application can significantly reduce system operation cost, effectively promote on-site consumption and efficient use of energy, and reduce carbon emissions of the system, which helps to promote green transformation and sustainable development of the energy market.

[0323] In the construction of the comprehensive energy system, the application fully considers the participation of diversified and differentiated producers and consumers, such as low-carbon campus, family residents, and commercial enterprises, etc. The subjects with power generation and consumption capacity in different fields are collectively referred to as producers and consumers. In order to effectively organize and promote the participation of these diversified subjects in the transaction of electric energy and carbon emission rights, the application introduces an energy producer and consumer aggregator as an intermediate bridge to represent the producers and consumers to make transaction decisions.

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

[0325] The aggregator will actively increase some control means (such as installing energy storage devices and developing user-side demand response resources) for its own economic interests, which can enhance the flexibility of the end power grid and help to improve the level of distributed renewable energy consumption and system security. The aggregator can use distributed power generation resources to provide cheaper and greener power supply for nearby campuses, residents, commercial and industrial users, reduce carbon emissions and electricity costs of enterprises, and earn additional profits by taking advantage of price differences in the process. The aggregator can act as an agent or service provider for small-capacity energy producers and consumers, aggregate the carbon emission reductions they own to form tradable carbon assets, participate in the electric carbon market, and generate additional income, so it will be welcomed by small-capacity energy producers and consumers.

[0326] In the process of data collection and transaction, the aggregator will desensitize the power consumption and generation data of each producer and consumer subject to ensure that sensitive information is not leaked, and although the aggregator centrally manages the data of multiple subjects, the transaction is still carried out in a decentralized manner. Each subject transacts through the aggregator without directly exposing their detailed information and transaction data to other subjects, thereby protecting privacy. Through the aggregator as an intermediate bridge, each producer and consumer subject can participate in electric carbon transactions 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 subjects to actively participate in the comprehensive energy system and jointly promote green and low-carbon transformation.

[0327] Each aggregator collects or is equipped with photovoltaic power generation equipment and energy storage equipment, and collects electricity and power generation data of all producers and consumers in its jurisdiction. The various aggregators can flexibly trade electricity and carbon, and realize the coordinated 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 upper-level power grid. Each aggregator preferentially consumes its own photovoltaic power generation, and if insufficient, it can be supplied by other aggregators with rich electricity or the upper-level power grid; conversely, if photovoltaic power generation is excessive, the excess electricity can be shared with other aggregators or sold to the upper-level power grid. In terms of carbon emission rights, if a certain aggregator is short of carbon emission rights, it can be provided with carbon quotas by other aggregators with sufficient carbon emission rights or the external carbon trading market; if it has excess carbon emission rights, it can sell carbon quotas to aggregators with insufficient carbon emission rights or the external electricity-carbon market, realizing effective allocation and utilization of carbon emission rights.

[0328] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

[0329] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic 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 (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, 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 disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or punched tape, a magneto-optical or other optical medium, and / or any suitable combination of the foregoing. Computer readable storage media are non-transitory devices because they do not "change" over time. The computer readable storage medium excludes modulated data signals, carrier waves, signals per se, such as those delivered via radio or other

[0330] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0331] Computer readable program instructions for carrying out operations of the present disclosure can be assembly instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, for example, through the Internet using an Internet Service Provider. In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0332] Finally, it should be noted that the above-mentioned embodiments are merely intended for describing and illustrating, not limiting the technical solutions of the present application. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered within the protection scope of the claims of the present application.

Claims

1. An optimization method for the participation of producer-consumer aggregators in the decision-making of the electricity carbon market in an integrated energy system, wherein the electricity carbon market covers multiple aggregators and multiple carbon emitting units, and each aggregator includes energy storage equipment, photovoltaic power generation equipment, and multiple electricity load units, characterized in that, include: Within a carbon emission assessment cycle, the carbon emissions of each aggregator and the carbon emission allowances of each carbon emission unit are obtained. Based on the proportion of each aggregator's carbon emissions in the total carbon emissions of all aggregators, the total carbon emission allowances of all carbon emission units are allocated to each aggregator, resulting in the free carbon emission allowance for each aggregator within a carbon emission assessment cycle. Based on the free carbon emission allowances of each aggregator within a carbon emission assessment cycle, and based on the proportion of each aggregator's electricity load in each time period in the total electricity load of all aggregators in each time period, the free carbon emission allowance for each aggregator in each time period is determined. Based on the predicted power generation of photovoltaic power generation equipment, the incremental free carbon emission allowances obtained by each aggregator for using photovoltaic power generation equipment in each time period are determined. To obtain the costs of aggregators trading with the upper-level power grid, the aggregators' photovoltaic power generation costs, the aggregators' energy storage equipment operating costs, the aggregators' carbon trading costs, and the revenue aggregators obtain from using photovoltaic power generation equipment within a carbon emission assessment cycle, in order to establish a model of the total operating cost of all aggregators participating in electricity carbon trading within the carbon emission assessment cycle. Based on the dual problem approach, the electricity traded between aggregators and other aggregators, and the amount of free carbon emission allowances traded between aggregators and other aggregators are used as a set of dual variables. Lagrange multipliers are introduced, and the electricity carbon trading revenue model of each aggregator in the carbon emission assessment period is established using the dual variables and the total operating cost model of each aggregator participating in electricity carbon trading within the carbon emission assessment period. Based on the electricity carbon trading revenue model of each aggregator in the carbon emission assessment period, the revenue of each aggregator participating in electricity carbon market decision-making in each time period is determined. Based on game theory, an optimization model for electricity carbon trading orders is established using the set of aggregators participating in the electricity carbon market decision-making within a specific time period, the set of electricity carbon trading orders of the aggregators participating in the electricity carbon market decision-making within a specific time period, and the set of returns of the aggregators participating in the electricity carbon market decision-making within a specific time period. The relationship is as follows: In the formula, Optimize the model for electricity carbon trading orders; This refers to the aggregation of various aggregators participating in the decision-making of the electricity carbon market within different time periods, where, ... Aggregators 1, ..., and ... aggregators participating in the carbon market decision-making process during different time periods are listed below. ; In order to divide into time periods The collection of electricity carbon trading orders from various aggregators participating in electricity carbon market decision-making, among which, ... They are divided into time periods. Aggregator 1's carbon trading orders, ..., Aggregator Electricity carbon trading orders; In order to divide into time periods The sum of the profits of all aggregators involved in the decision-making process of the carbon market, where, ... In different time periods Aggregator 1's revenue, ..., Aggregator The benefits; This represents the total number of aggregators, and is a positive integer. In time periods The preferred order set for each aggregator's electricity carbon trading is as follows: ,in, ... They are divided into time periods. Aggregator 1's preferred carbon trading order, ..., Aggregator Preferred orders for electricity carbon trading; in time slots Aggregator The preferred order for electricity carbon trading is The set of preferred orders for electricity carbon trading from the remaining aggregators is as follows: ; When the following relationship is satisfied, then time periods are used. Preferred order sets for electricity carbon trading from various aggregators A Nash equilibrium point for optimizing the carbon trading order model: In the formula, In order to divide into time periods Aggregators determined based on carbon trading orders The benefits, For aggregators Preferred Orders for Electricity Carbon Trading Any other than the following: carbon trading order; The optimal carbon trading instructions for each aggregator are determined by the Nash equilibrium point of the carbon trading instruction optimization model. The carbon trading instructions of the aggregator include: the amount of electricity traded with the upstream grid, the amount of free carbon emission allowances 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 allowances traded with other aggregators.

2. The optimization method for the participation of producer-consumer aggregators in the electricity carbon market decision-making process in an integrated energy system according to claim 1, characterized in that, Also includes: The optimal carbon trading instructions for each aggregator include: the optimal value of electricity traded with the upstream grid, the optimal value of free carbon emission allowances 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 traded with other aggregators, and the optimal value of free carbon emission allowances traded with other aggregators. Each aggregator will share the optimal value of electricity traded with other aggregators and the optimal value of free carbon emission allowances traded with other aggregators in encrypted form; each aggregator will update its dual variables based on the shared data; the updated dual variables will be used to update the Lagrange multipliers; 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 within the carbon emission assessment period. By utilizing the final revenues of each aggregator participating in the electricity carbon market decision-making process within different time periods, the Nash equilibrium point of the electricity carbon trading instruction optimization model is updated to obtain the optimal electricity carbon trading instructions for each aggregator after the update, thus enabling the producer-consumer aggregator in the integrated energy system to participate in the electricity carbon market decision-making process.

3. The optimization method for the participation of producer-consumer aggregators in the electricity carbon market decision-making process in an integrated energy system according to claim 1, characterized in that, During a carbon emissions assessment cycle, the carbon emissions of each aggregator are obtained, including: The carbon emissions generated by each aggregator's purchased electricity and the carbon emissions generated by each aggregator's energy storage equipment charging and discharging are obtained at different time periods to establish a carbon emission model for each aggregator in the carbon trading process at different time periods. Based on the carbon emission model of each aggregator in the carbon trading process at different time periods, the carbon emissions of each aggregator in a carbon emission assessment cycle are determined. The carbon emission models for each aggregator during different time periods in the carbon trading process satisfy the following relationship: In the formula, Indicates time period Aggregator Carbon emissions during the carbon trading process Indicates time period Aggregator Carbon emissions from purchased electricity Indicates time period Aggregator The carbon emissions generated by the charging and discharging of energy storage devices; In time periods Aggregator Carbon emissions from purchased electricity satisfy the following relationship: In the formula, Indicates the grid reference emission factor. Indicates time period Aggregator Purchased electricity volume; In time periods Aggregator The carbon emissions of energy storage devices satisfy the following relationship: In the formula, This represents the carbon emission coefficient of energy storage devices during charging and discharging. Indicates time period Aggregator The charging load of energy storage devices Indicates time period Aggregator The discharge load of the energy storage device; The carbon emissions of each polymerizer during the carbon emission assessment period satisfy the following relationship: In the formula, Indicates the carbon emission assessment cycle Carbon emissions of aggregater i during the carbon trading process.

4. The optimization method for the participation of producer-consumer aggregators in the electricity carbon market decision-making process in an integrated energy system according to claim 1, characterized in that, Within a carbon emission assessment cycle, obtain the carbon emission allowances for each carbon emission unit, including: By combining the historical emissions method with the annual decay coefficient method, the sum of the historical carbon emissions of all carbon emission units within a historical carbon emission assessment period is corrected to obtain the total carbon emission quota of all carbon emission units within the carbon emission assessment period. Based on the proportion of each carbon emission unit's historical carbon emissions 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 to obtain the carbon emission quota of each carbon emission unit within a carbon emission assessment period. The sum of historical carbon emissions of all carbon emission units within a historical carbon emission assessment period satisfies the following relationship: In the formula, This represents the sum of historical carbon emissions of all carbon-emitting entities within the historical carbon emission assessment period. Indicates the first Historical carbon emissions of each carbon emission unit , This represents the set of all carbon emission units; The total carbon emission allowances of all carbon-emitting entities during the carbon emission assessment period satisfy the following relationship: In the formula, This represents the total carbon emission allowances of all carbon-emitting entities within the carbon emission assessment period. The annual attenuation factor is 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. Within a carbon emission assessment cycle, the carbon emission allowances of each carbon emission unit satisfy the following relationship: In the formula, Indicates the first The amount of carbon emission allowance per carbon emission unit.

5. The optimization method for the participation of producer-consumer aggregators in the electricity carbon market decision-making process in an integrated energy system according to claim 3, characterized in that, The amount of free carbon emission allowances for each aggregator during a carbon emission assessment cycle satisfies the following relationship: In the formula, Indicates the carbon emission assessment cycle Intra-aggregator The amount of free carbon emission allowances, This represents the total carbon emission allowances of all carbon-emitting entities within the carbon emission assessment period. Represents the set of all aggregators; The amount of free carbon emission allowances for each aggregator in each time period satisfies the following relationship: In the formula, Indicates time period Aggregator The amount of free carbon emission allowances, Indicates time period Aggregator The load.

6. The optimization method for the participation of producer-consumer aggregators in the electricity carbon market decision-making process in an integrated energy system according to claim 3, characterized in that, In time periods Aggregator The carbon emission offset corresponding to photovoltaic power generation equipment satisfies the following relationship: In the formula, Indicates time period Aggregator The carbon emission offset of photovoltaic power generation equipment. Indicates time period Aggregator The predicted power generation of photovoltaic power generation equipment; In time periods Aggregator The incremental carbon emission allowances obtained from using photovoltaic power generation equipment satisfy the following relationship: In the formula, Indicates time period Aggregator The carbon emission offset from using photovoltaic power generation equipment is the increase in free carbon emission allowances determined by the offset ratio. This is the offsetting ratio.

7. The optimization method for the participation of producer-consumer aggregators in the electricity carbon market decision-making process in an integrated energy system according to claim 1, characterized in that, The total operating cost model for all aggregators participating in electricity carbon trading during the carbon emission assessment period satisfies the following relationship: In the formula, This represents the total operating cost of all producers and consumers participating in carbon trading during the carbon emission assessment period. Indicates time period Aggregator The cost of trading with the upper-level power grid, Indicates time period Aggregator The cost of photovoltaic power generation Indicates time period Aggregator The operating cost of energy storage devices, Indicates time period Aggregator Carbon trading costs, Indicates time period Aggregator The benefits obtained from using photovoltaic power generation equipment Indicates the carbon emission assessment cycle. This represents the set of all aggregators.

8. The optimization method for the participation of producer-consumer aggregators in the electricity carbon market decision-making process in an integrated energy system according to claim 7, characterized in that, Under the constraint that aggregators buy and sell electricity from the upstream power grid, the costs of transactions between each aggregator and the upstream power grid in different time periods are determined, satisfying the following relationship: In the formula, Indicates time period Aggregator The cost of trading with the upper-level power grid, and These represent the time periods of the upper-level power grid. The retail electricity price and the on-grid electricity price; and These represent time periods. Aggregator The power consumed when purchasing and selling electricity to the upstream power grid; Among them, when aggregators buy or sell electricity from the upper-level power grid, they must satisfy the maximum exchange power constraint, as shown in the following formula: In the formula, Indicates aggregator The upper limit of the switching power.

9. The optimization method for the participation of producer-consumer aggregators in the electricity carbon market decision-making process in an integrated energy system according to claim 7, characterized in that, The photovoltaic power generation costs of various aggregators at different time periods satisfy the following relationship: In the formula, Indicates aggregator The cost coefficient of photovoltaic power generation equipment, Indicates time period Aggregator The predicted power generation value of the photovoltaic power generation equipment.

10. The optimization method for the participation of producer-consumer aggregators in the electricity carbon market decision-making process in an integrated energy system according to claim 7, characterized in that, Under the joint constraints of the energy storage devices of the aggregators, the operating costs of each aggregator's energy storage devices are determined, satisfying the following relationship: In the formula, Indicates time period Aggregator The operating cost of energy storage devices, Indicates aggregator The cost coefficient of energy storage equipment, Indicates time period Aggregator The charging load of energy storage devices Indicates time period Aggregator The discharge load of the energy storage device; The joint constraints on 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 formula: In the formula, , They represent aggregators The upper limits of charging power and discharging power of energy storage devices; 2) The timing constraints of charging and discharging of the energy storage equipment of the aggregator satisfy the following relationship: 3) The energy storage devices of the aggregator satisfy the state of charge constraints, which satisfy the following relationship: In the formula, , They represent aggregators The lower and upper limits of the state of charge of energy storage devices. Indicates time period Aggregator The state of charge of energy storage devices, Indicates aggregator The capacity of energy storage devices; 4) The energy storage equipment of the aggregator meets the balance of charge and discharge within a service cycle, satisfying the following relationship: The amount of charge a storage device receives during a single usage cycle should equal the amount of discharge.

11. The optimization method for the participation of producer-consumer aggregators in the electricity carbon market decision-making process in an integrated energy system according to claim 7, characterized in that, The carbon trading costs of various aggregators at different times satisfy the following relationship: In the formula, and These represent time periods. The purchase and sale prices of carbon allowances in the carbon emissions trading market. and These represent time periods. Aggregator Free carbon emission allowances that are bought and sold in the carbon emission rights market.

12. The optimization method for the participation of producer-consumer aggregators in the electricity carbon market decision-making process in an integrated energy system according to claim 7, characterized in that, In time periods Aggregator The revenue obtained from using photovoltaic power generation equipment satisfies the following relationship: In the formula, Indicates time period Aggregator The revenue gained from selling CCERs on the market. The unit price for offsetting carbon emissions. To offset the ratio, Indicates time period Aggregator The carbon emission offset of photovoltaic power generation equipment.

13. The optimization method for the participation of producer-consumer aggregators in the electricity carbon market decision-making process in an integrated energy system according to claim 1, 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: establishing power balance constraints and carbon emission free quota balance constraints for aggregators during the carbon emission assessment period, as well as power trading constraints and carbon emission free quota trading constraints for aggregators participating in electricity carbon trading, based on the aggregators' electricity carbon trading behavior.

14. The optimization method for the participation of producer-consumer aggregators in the electricity carbon market decision-making process in an integrated energy system according to claim 13, characterized in that, The power balance constraints of aggregators at different time periods satisfy the following relationship: In the formula, Indicates time period Aggregator The power of electricity purchased or sold. Indicates time period Aggregator The predicted power generation value of the photovoltaic power generation equipment, Indicates time period Aggregator The load, Indicates time period Aggregator The charging load of energy storage devices Indicates time period Aggregator The discharge load of the energy storage device; In time periods Aggregator The power purchased or sold satisfies the following relationship: When Aggregator When purchasing electricity When Aggregator When selling electricity In the formula, and These represent time periods. Aggregator The power volume purchased and sold from the upstream power grid. This represents the collection of all producers and consumers. , These represent time periods. Aggregator to aggregator The power purchased and in time of day Aggregator to aggregator The power output of electricity sold; The balance constraints of aggregators' free carbon emission allowances during different time periods satisfy the following relationship: In the formula, Indicates time period Aggregator The amount of free carbon emission allowances sold on the market. Indicates time period Aggregator The remaining amount of free carbon emission allowances. Indicates time period Aggregator Free carbon emission allowances sold through trading , , , These represent time periods. Aggregator Free carbon emission allowances, in different time periods Aggregator Free carbon emission allowances purchased or sold, in time slots Aggregator The amount of free carbon emission allowances obtained from using photovoltaic power generation equipment, in time slots Aggregator Carbon emissions during the carbon trading process; In time periods Aggregator The amount of free carbon emission allowances purchased or sold satisfies the following relationship: When Aggregator When purchasing free carbon emission allowances: When Aggregator When selling free carbon emission allowances: In the formula, Indicates time period Aggregator The amount of free carbon emission allowances purchased from the market. , These represent time periods. Aggregator to aggregator Purchased free carbon emission allowances and time-sharing Aggregator to aggregator Free carbon emission allowances for sale.

15. The optimization method for the participation of producer-consumer aggregators in the electricity carbon market decision-making process in an integrated energy system according to claim 13, characterized in that, 1) Power trading constraints satisfy the following relationship: In the formula, , These represent time periods. Aggregator to aggregator Purchased electricity and time-of-use Aggregator to aggregator The electricity sold , These represent time periods. Aggregator to aggregator Purchased electricity and time-of-use Aggregator to aggregator The electricity sold Indicates aggregator and aggregator The power limit for transactions; 2) The trading constraints for free carbon emission allowances satisfy the following relationship: In the formula, , These represent time periods. Aggregator to aggregator Purchased free carbon emission allowances and time-sharing Aggregator to aggregator The amount of free carbon emission allowances sold. , These represent time periods. Aggregator to aggregator Purchased free carbon emission allowances and time-sharing Aggregator to aggregator The amount of free carbon emission allowances sold. Indicates aggregator and aggregator The upper limit on the amount of free carbon emission allowances traded.

16. The optimization method for the participation of producer-consumer aggregators in the electricity carbon market decision-making process in an integrated energy system according to claim 1, characterized in that, Based on the dual problem approach, the electricity traded between aggregators that simultaneously satisfy power balance constraints and power trading constraints, and the amount of free carbon emission allowances traded between aggregators that simultaneously satisfy carbon emission free allowance balance constraints and carbon emission free allowance trading constraints are used as a set of dual variables. Aggregators that simultaneously meet both power balance and power trading constraints can trade electricity with other aggregators, including: during time slots. Aggregator The total power sold to other aggregators, in time-of-use Aggregator The total power purchased from other aggregators; satisfying the following relationship: In the formula, Indicates time period Aggregator The total power sold to other aggregators. Indicates time period Aggregator Power vector of electricity sold to other aggregators ... These represent time periods. Aggregator To Aggregator 1, ..., Aggregator The power output of electricity sold; Indicates the first One element is 0, and the rest are 1. 3D column vector; In the formula, Indicates time period Aggregator The total power purchased from other aggregators. Indicates time period Aggregator Power vector of electricity purchased from other aggregators ... These represent time periods. Aggregator To Aggregator 1, ..., Aggregator The power consumption of the purchased electricity; In time periods Aggregator The total power sold to other aggregators, in time-of-use Aggregator The total power purchased from other aggregators is negative; Aggregators that simultaneously meet both the carbon emission free allowance balance constraint and the carbon emission free allowance trading constraint can trade carbon emission free allowances with other aggregators, including: in time slots. Aggregator The total amount of free carbon emission allowances sold to other aggregators, in different time periods Aggregator The total amount of free carbon emission allowances purchased from other aggregators; satisfying the following relationship: In the formula, Indicates time period Aggregator The total amount of free carbon emission allowances sold to other aggregators. Indicates time period Aggregator The amount of free carbon emission allowances sold to other aggregators. ... These represent time periods. Aggregator To Aggregator 1, ..., Aggregator The amount of free carbon emission allowances sold; In the formula, Indicates time period Aggregator The total amount of free carbon emission allowances purchased from other aggregators. Indicates time period Aggregator The amount of free carbon emission allowances purchased from other aggregators. ... These represent time periods. Aggregator To Aggregator 1, ..., Aggregator Purchased free carbon emission allowances; In time periods Aggregator The total amount of free carbon emission allowances sold to other aggregators, in different time periods Aggregator The total amount of free carbon emission allowances purchased from other aggregators is positive.

17. The optimization method for the participation of producer-consumer aggregators in the electricity carbon market decision-making process in an integrated energy system according to claim 16, characterized in that, The revenue model for each aggregator's electricity carbon trading during the carbon emission assessment period satisfies the following relationship: In the formula, Indicates time period Aggregator Benefits from participating in electricity carbon trading; Indicates time period Aggregator The operating costs of participating in carbon trading; Indicates time period The set of Lagrange multipliers satisfies ,in, Indicates time period Aggregator Lagrange multipliers corresponding to the electricity traded with other aggregators Indicates time period Aggregator Lagrange multipliers corresponding to the amount of free carbon emission allowances traded with other aggregators; Indicates time period The Lagrange multiplier vector corresponding to the electricity volume traded between aggregators and other aggregators; Indicates time period The Lagrange multiplier vector corresponding to the amount of free carbon emission allowances traded between aggregators and other aggregators; This indicates removing diagonal elements.

18. The optimization method for the participation of producer-consumer aggregators in the electricity carbon market decision-making process in an integrated energy system according to claim 2, characterized in that, The Lagrange multipliers are updated using the updated dual variables, satisfying the following relation: In the formula, , They represent the first sequence During the next iteration, in time segments Aggregator The Lagrange multipliers corresponding to the electricity traded with other aggregators , They represent the first sequence During the next iteration, in time segments Aggregator The Lagrange multipliers corresponding to the amount of free carbon emission allowances traded with other aggregators. Indicates the first Aggregator in the next iteration The electricity price update step for electricity traded with other aggregators. Indicates the first Aggregator in the next iteration The carbon price update step for free carbon emission allowances traded with other aggregators. Indicates the first During the next iteration, in time segments Aggregator Power vector of electricity sold to other aggregators Indicates the first During the next iteration, in time segments Aggregator The total power sold to other aggregators. Indicates the first During the next iteration, in time segments Aggregator The amount of free carbon emission allowances sold to other aggregators. Indicates the first During the next iteration, in time segments Aggregator The total amount of free carbon emission allowances sold to other aggregators; Indicates the first One element is 0, and the rest are 1. 3D column vector; Compare the difference between the Lagrange multipliers before and after the update. If the difference is less than a set threshold, it means that the Lagrange multipliers before and after the update have converged. The threshold value is no greater than 0.

01.

19. 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 perform the steps of the method according to any one of claims 1-18.

20. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-18.

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