A demand-side electricity-carbon coupling trading method

Abstract

The present invention discloses a demand-side electricity-carbon coupling trading method, comprising the following steps: S100: submitting a virtual quotation to an electricity-carbon coupling trading market, participating in the first stage of the electricity-carbon coupling trading market, the electricity-carbon coupling trading market obtains the electricity clearing price of each time period based on the virtual quotation of each market participant; S200: obtaining the electricity clearing price of each time period given by the electricity-carbon coupling trading market, and calculating a carbon quotation strategy based on the electricity clearing price, the carbon quotation strategy including the bid electricity and the corresponding carbon intensity; S300: submitting a carbon quotation strategy to the electricity-carbon coupling trading market, participating in the second stage of the electricity-carbon coupling trading market, the electricity-carbon coupling trading market obtains the carbon quotation strategy of each market participant, and clears to obtain the clearing electricity and carbon intensity of each time period; S400: according to the difference between the clearing electricity of the electricity-carbon coupling trading market and its own bid electricity, using corresponding compensation to eliminate the difference.

Description

Technical Field

[0001] The present invention belongs to the technical field of power trading, and specifically is a demand-side electricity-carbon coupling trading method. Background Art

[0002] Warming caused by greenhouse gas emissions has always been a hot topic of global concern. Carbon dioxide emissions account for more than 70% of greenhouse gas emissions, of which more than 40% are generated by the combustion of fossil fuels during power generation. Therefore, as the main source of carbon emissions, the power sector needs to optimize power system dispatch, improve trading mechanisms, and reduce carbon emissions.

[0003] However, the existing power trading framework has the following shortcomings:

[0004] (1) There is a lack of clear price signals to guide users to change their electricity consumption behavior in order to achieve the goal of increasing the proportion of renewable energy in their electricity consumption;

[0005] The main reason for this shortcoming is that although power plants directly generate carbon emissions, the carbon emissions in electricity consumption are still from consumers rather than producers, because carbon emissions are driven by the demand side; but under the current trading framework, the carbon surcharge that users need to pay is proportional to the electricity consumption and has nothing to do with the specific consumption period; but in fact, the carbon emissions caused by electricity consumption are different under different renewable energy output conditions;

[0006] (2) Poor flexibility in carbon trading;

[0007] The main reasons for this shortcoming are: the real-time calculation of carbon emission intensity is the basis for carbon emission cost allocation. The proportional sharing theorem (PST) has been widely used in many works. It requires that the carbon emission costs caused by power trading need to be settled immediately, but this imposes unnecessary real-time balance constraints on carbon trading, greatly reducing the flexibility of carbon trading;

[0008] (3) Carbon emissions are only considered as an additional cost of electricity consumption, ignoring the carbon emission interactions between users. The unit additional cost caused by carbon emissions is the same for all users, making it impossible for users to fully utilize their carbon emission reduction capabilities;

[0009] (4) Only the electricity trading mechanism on the demand side is considered, and a flexible trading mechanism for carbon emissions on the demand side is still missing. Summary of the invention

[0010] Purpose of the invention: In order to address the shortcomings of the existing electricity trading framework, the present invention proposes a demand-side electricity-carbon coupling trading method to reduce carbon emissions from demand-side physical microgrids through market transactions and demand-side management.

[0011] Technical solution: A demand-side electricity-carbon coupling trading method, applied in the electricity-carbon coupling trading market; including the following steps:

[0012] Phase 1: Based on the virtual bids of each market participant, the clearing price of electricity in each period is obtained. Based on the clearing price of electricity, each market participant can calculate their own carbon bidding strategy, which includes the bid electricity and the corresponding carbon intensity.

[0013] Phase 2: Obtain the carbon quotation strategy of each market participant, and obtain the cleared electricity and carbon intensity in each period. Based on the cleared electricity and carbon intensity, each microgrid adopts corresponding compensation.

[0014] Furthermore, in step 2, the carbon quotation strategy of each market participant is obtained, and the clearing electricity and carbon intensity of each period are obtained by solving equations (16)-(18). The specific steps include:

[0015]

[0016]

[0017]

[0018] In the formula, is the carbon quotation strategy of the ith seller in period t; is the carbon bidding strategy of the jth buyer in period t; is the amount of electricity sold by the i-th seller in period t in stage 2, The amount of electricity purchased by the jth buyer in period t in stage 2.

[0019] The present invention also discloses a demand-side electricity-carbon coupling transaction method for guiding a microgrid as a buyer and seller in an electricity-carbon coupling transaction market to conduct electricity transactions; the method comprises the following steps:

[0020] S100: Submit a virtual quotation to the electricity-carbon coupling trading market to participate in the first phase of the electricity-carbon coupling trading market, and the electricity-carbon coupling trading market will calculate the electricity clearing price for each period;

[0021] S200: Obtain the electricity clearing price for each time period given by the electricity-carbon coupling trading market, and calculate the carbon quotation strategy based on the electricity clearing price, where the carbon quotation strategy includes the bid electricity and the corresponding carbon intensity;

[0022] S300: Submit carbon quotation strategy to the electricity-carbon coupling trading market and participate in the second phase of the electricity-carbon coupling trading market. The electricity-carbon coupling trading market calculates the clearing electricity and carbon intensity in each period;

[0023] S400: Obtain the cleared electricity volume and carbon intensity for each time period given by the electricity-carbon coupling trading market, and use corresponding compensation to eliminate the difference between the cleared electricity volume of the electricity-carbon coupling trading market and its own bid electricity volume.

[0024] Furthermore, in S200, the carbon quotation strategy is calculated based on the electricity clearing price, and the specific steps are as follows:

[0025] A microgrid model under the electricity-carbon coupling mechanism is constructed. The internal carbon emission flow balance of the microgrid model is satisfied, which is expressed as:

[0026]

[0027] Among them, P in,t is the amount of electricity purchased by the microgrid during period t, P out,t is the amount of electricity sold by the microgrid during period t, P BES,t is the charge and discharge capacity of the energy storage device during period t, P L,t is the load power consumption during period t, P PV,t is the photovoltaic power generation in period t, ρ in,t is the carbon intensity of electricity purchase, ρ BES,t is the energy storage charging and discharging carbon intensity of energy storage equipment, ρ out,t is the carbon intensity of electricity sales, ρ L,t is the carbon intensity of load electricity, ρ PV,t is the photovoltaic carbon intensity;

[0028] Based on the proximal policy optimization algorithm, set the agent observation state space s t is the state of the microgrid in each period, and the action space a t =[P in,t ,ΔP L,t ,P BES,t ,ρ BES,t ], reward r t =-C MG,t , where ΔP L,t is the load reduction, C MG,t is the microgrid operation cost function in period t, expressed as:

[0029]

[0030] In the formula, C BES,t is the energy storage equipment cost function in period t, C L,t is the user comfort loss cost function in period t, is the microgrid electricity purchase cost function in period t, is the carbon emission cost function of the microgrid in period t, is the profit function of the microgrid electricity sales in period t;

[0031] Through the continuous interaction of the intelligent agent with the electricity-carbon coupled trading market environment, a strategy that can obtain the maximum cumulative reward is learned. The strategy includes energy storage strategy for energy storage charging and discharging capacity and charging and discharging carbon intensity, load reduction strategy and carbon quotation strategy. The carbon quotation strategy includes the bid electricity and the corresponding carbon intensity.

[0032] Furthermore, in formula (1), by introducing the concept of virtual carbon storage, the energy storage charging and discharging carbon intensity ρ BES,t From uncontrollable variables to controllable variables;

[0033] Average carbon intensity of electricity stored in energy storage devices It is expressed as:

[0034]

[0035] in, is the average carbon intensity of electricity in the energy storage device during period t+1, C BES is the capacity of the energy storage device, is the charging state of the energy storage device during period t, is the average carbon intensity of electricity stored in the energy storage device during period t; ensure that the following constraints are met:

[0036]

[0037] in, is the maximum value of the average carbon intensity of electricity stored in the battery;

[0038] Carbon intensity of load electricity consumption ρ L,t It is expressed as:

[0039]

[0040] According to the proportional allocation principle, the carbon intensity of electricity sales ρ out,t It is expressed as:

[0041]

[0042] Furthermore, the energy storage equipment cost function C in period t is BES,t , expressed as:

[0043] C BES,t =τ|SOC t -SOC t-1 | (11)

[0044] In the formula, τ is the cost coefficient, SOC t is the charge / discharge state of the energy storage device during period t, SOC t-1 is the charge / discharge state of the energy storage device during period t-1;

[0045] User comfort loss cost function C during period t L,t , expressed as:

[0046] C L,t =α L (ΔP L,t ) 2 +β L ΔP L,t (12)

[0047] Where ΔP L,t is the load reduction, α L and β L is the coefficient of the user comfort loss cost function;

[0048] Microgrid power purchase cost function in period t It is expressed as:

[0049]

[0050] In the formula, λ t is the electricity clearing price in period t, P in,t is the amount of electricity purchased by the microgrid during period t;

[0051] Carbon emission cost function It can be expressed as:

[0052]

[0053] Among them, λ c is the annual carbon price in the carbon market, ρ L,t is the carbon intensity of the load electricity, P L,t is the load power consumption during period t;

[0054] Microgrid power sales profit function It is expressed as:

[0055]

[0056] Among them, λ t is the electricity clearing price in period t, P out,t is the amount of electricity sold by the microgrid during period t.

[0057] Furthermore, in S400, the use of corresponding compensation to eliminate the difference includes:

[0058] First, local energy storage equipment output compensation is adopted. If the difference is still not eliminated, local load reduction compensation is adopted until the difference is eliminated.

[0059] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0060] 1. The concept of "virtual carbon storage" is proposed to relax the unnecessary real-time balance constraints on carbon emissions caused by the proportional allocation principle. Carbon emission responsibilities can be temporarily stored and do not need to be paid immediately, thereby achieving flexible interaction between demand-side microgrids.

[0061] 2. In order to achieve the effective allocation of carbon emission responsibilities of microgrids, a demand-side electricity-carbon coupling trading mechanism is proposed. Through trading, highly flexible microgrids can further reduce carbon emissions while improving their own interests, thereby reducing the total carbon emissions of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a microgrid structure;

[0063] Figure 2 It is a two-stage market structure. DETAILED DESCRIPTION

[0064] The technical solution of the present invention is now further described in conjunction with the accompanying drawings and embodiments.

[0065] like Figure 2 As shown in Figure 1, n interconnected microgrid models act as buyers / sellers in the electricity-carbon coupling trading market; Figure 1 As shown in the figure, each microgrid model is equipped with at least energy storage equipment, photovoltaics, loads and energy routers. Microgrid operators can control the amount of energy storage and load reduction at different times according to external environmental factors such as external electricity prices and photovoltaic output to reduce the operating costs of a single microgrid, and electricity can be traded between microgrids to improve social benefits. As an intelligent agent of the microgrid, the energy router can realize the internal control decision-making of the microgrid and the energy interaction with other microgrid models.

[0066] The microgrid involved in the present invention is characterized by the following microgrid model.

[0067] The carbon emission flow balance is satisfied within the microgrid model, which can be expressed as:

[0068]

[0069] Among them, P in,t is the amount of electricity purchased by the microgrid during period t, P out,t is the amount of electricity sold by the microgrid during period t, P BES,t is the battery charge and discharge amount during period t, P L,t is the load power consumption during period t, P PV,t is the photovoltaic power generation in period t. ρ in,t is the carbon intensity of electricity purchase, ρ BES,t is the carbon intensity of battery energy storage charging and discharging, ρout,t is the carbon intensity of electricity sales, ρ L,t is the carbon intensity of load electricity, ρ PV,t is the photovoltaic carbon intensity. In general, we believe that the carbon emissions of renewable energy are 0, that is, the photovoltaic carbon intensity ρ PV,t =0.

[0070] The parameters appearing in formula (1) are explained as follows.

[0071] The concept of virtual carbon storage is proposed. Virtual carbon storage can be understood as follows: since carbon emissions related to electricity can only be deducted when it is consumed, when electricity is stored in batteries, additional carbon emissions and their carbon emission liabilities will also be considered to be temporarily stored in the batteries. BES,t From uncontrollable variables to controllable variables. Through virtual carbon storage, the unnecessary real-time balance constraints of carbon emissions caused by the proportional allocation principle are relaxed, and the power demand of the microgrid can be adjusted more flexibly to track changes in the external market.

[0072] Since the battery energy storage charging and discharging carbon intensity ρ BES,t different, so the average carbon intensity of electricity stored in batteries Also changing:

[0073]

[0074] in, is the average carbon intensity of the electricity in the battery during period t+1, C BES is the capacity of the battery, is the battery charging state during period t, is the average carbon intensity of the electricity stored in the battery during period t. In order to keep the carbon intensity of the electricity stored in the battery within a reasonable range, the following constraints need to be met:

[0075]

[0076] in, is the maximum value of the average carbon intensity of the electricity stored in the battery.

[0077] The carbon intensity of load electricity in formula (1) is L,t Affected by many factors, the electricity purchasing behavior and electricity consumption behavior of the microgrid are divided into two situations, which can be expressed as:

[0078]

[0079] According to the proportional allocation principle, the carbon intensity of electricity sales in formula (1) is out,t It can be expressed as:

[0080]

[0081] When the microgrid sells electricity during period t, it will not purchase electricity during that period, i.e., P in,t = 0. Combined with P in,t =0 and formula (4), formula (5) can be rewritten as:

[0082]

[0083] Based on the above disclosed microgrid, the present invention provides a demand-side electricity-carbon coupling trading method, and the specific operation steps are as follows:

[0084] Step 1: Each microgrid participates in the first stage of the electricity-carbon coupling trading market, and the virtual quotation is cleared to obtain the electricity clearing price λ for each period t ; The main calculation process is:

[0085] Each participant submits its bidding strategy to the market. The intersection of the bid and ask curves determines the market clearing price. The goal of market clearing is to maximize social welfare. By solving equations (7) and (8), we can get the clearing price λ t ;

[0086]

[0087]

[0088] In the formula, N is the number of sellers; M is the number of buyers; is the amount of electricity sold by the i-th seller in period t in a stage; is the amount of electricity purchased by the jth buyer in period t in a phase; is the quotation strategy of the i-th seller in period t, is a monotonically increasing function; is the bidding strategy of the jth buyer in period t, is a monotonically decreasing function.

[0089] Step 2: Each microgrid operator calculates the energy clearing price λ of the current period t Based on the reinforcement learning algorithm (such as the proximal strategy optimization algorithm), the carbon bidding strategy and the control strategy inside the microgrid are solved. The carbon bidding strategy includes the bid electricity and its corresponding carbon intensity. The specific calculation process is:

[0090] The microgrid model is based on the current period of electricity clearing price λ t , internal photovoltaic output power and other information to control energy storage and load reduction, change the power demand of the microgrid in different time periods to reduce the operating cost of a single microgrid. The specific control steps are:

[0091] The operating cost of each microgrid model is expressed as:

[0092]

[0093] In the formula, C MG,t is the microgrid operation cost function in period t, C BES,t is the battery cost function for period t, C L,t is the user comfort loss cost function in period t, is the microgrid electricity purchase cost function in period t, is the carbon emission cost function of the microgrid in period t, It is the profit function of the microgrid electricity sales in period t.

[0094] In order to minimize the cost at multiple time periods during a day, the optimization objective of the microgrid can be expressed as:

[0095]

[0096] The present invention converts the life loss caused by the charge / discharge of the battery into economic cost, so the charge / discharge state SOC is used t The absolute value of the change is used to calculate the battery cost. Therefore, the battery cost function C for the t period in the operating cost function is BES,t , expressed as:

[0097] C BES,t =τ|SOC t -SOC t-1 | (11)

[0098] In the formula, τ is the cost coefficient, SOC t is the charge / discharge state of the battery during period t, SOC t-1 It is the charge / discharge status of the battery during the t-1 period.

[0099] Flexible loads in microgrids can be reduced during peak hours, but this will result in a loss of user comfort. Therefore, the user comfort loss cost function C in period t in the operation cost function is L,t , which can be expressed as:

[0100] C L,t =α L (ΔP L,t ) 2 +β L ΔP L,t (12)

[0101] Where ΔP L,t is the load reduction. In order to ensure the basic load demand of users, ΔP L,t Will be limited to a certain range, αL and β L is the coefficient of the user comfort loss cost function.

[0102] Microgrid power purchase cost function in period t According to the electricity clearing price λ determined in the first-stage electricity transaction t The calculation can be expressed as:

[0103]

[0104] In the formula, λ t is the electricity clearing price in period t, P in,t is the amount of electricity purchased by the microgrid during period t.

[0105] In each period, the carbon emissions corresponding to the consumed power should be borne by the microgrid. Therefore, the carbon emission cost can be determined based on the carbon price in the annual carbon market, that is, the carbon emission cost function It can be expressed as:

[0106]

[0107] Among them, λ c is the annual carbon price in the carbon market, ρ L,t is the carbon intensity of the load electricity, P L,t is the load power consumption during period t.

[0108] If there is surplus power in the microgrid, the surplus power can be sold for profit, so the microgrid power sales profit function is It can be expressed as:

[0109]

[0110] Among them, λ t is the electricity clearing price in period t, P out,t is the amount of electricity sold by the microgrid during period t.

[0111] Therefore, the microgrid optimization problem to be solved can be expressed as a mathematical problem with equation (10) as the optimization target and equations (1) to (6) as constraints. The quotation is based on the proximal strategy optimization algorithm, setting the agent observation state space s t is the state of the microgrid in each period, and the action space a t =[P in,t ,ΔP L,t ,P BES,t ,ρ BES,t ], reward r t =-C MG,t, through the continuous interaction between the intelligent agent and the environment, the strategy that can obtain the maximum cumulative reward is learned, which includes the energy storage strategy of energy storage charging and discharging amount and charging and discharging carbon intensity, the load reduction strategy and the carbon bidding strategy of the microgrid participating in step 3. The carbon bidding strategy includes the bid electricity and the corresponding carbon intensity.

[0112] Step 3: The electricity-carbon coupling trading market obtains the cleared electricity and carbon intensity based on the carbon quotation strategy of each microgrid;

[0113] In this step, the carbon trading implicit in electricity trading is mainly considered, based on the electricity clearing price λ determined in the first stage t The buyer and seller (the microgrid in the transaction scenario can be either the seller or the buyer, but the same microgrid has only one identity) bid for the cleared electricity and its corresponding carbon intensity. In this step, carbon trading is considered, which is called the second stage. Therefore, the cleared electricity is determined by the attached carbon intensity.

[0114] Each participant submits its carbon quotation strategy to the market. The intersection of the buyer and seller quotation curves determines the market clearing price. The goal of market clearing is to maximize social welfare. By solving equations (16)-(18), we can obtain the clearing carbon intensity ρ for the current period: t and the clearing power P t :

[0115]

[0116]

[0117]

[0118] In the formula, is the carbon quotation strategy of the i-th seller in period t, is a monotonically increasing function; is the carbon quotation strategy of the jth buyer in period t, is a monotonically decreasing function. The carbon bidding strategies of buyers and sellers are obtained from step 2. is the amount of electricity sold by the i-th seller in period t in the second stage, The amount of electricity purchased by the jth buyer in period t in the second stage.

[0119] Step 4: Based on the difference between the clearing volume of the electricity-carbon coupling trading market and its own bid volume, each microgrid operator first adopts local energy storage equipment output compensation, and then adopts the local load reduction compensation mechanism.

[0120] The difference between the clearing volume of the electricity-carbon coupling trading market and its own bidding volume ΔP in,t , expressed as:

[0121] ΔPin,t =P t -P in,t (19)

[0122] Where P t is the clearing quantity of electricity-carbon coupling trading market in period t, P in,t is the bid electricity of the microgrid in period t.

[0123] When the difference in power does not exceed the maximum allowable storage capacity of the battery, the difference in power will be provided by the energy storage device in the microgrid or stored in the battery. When the output compensation of the energy storage device cannot completely offset the difference, the local load reduction compensation mechanism is adopted.

[0124] The compensation strategy is expressed as:

[0125]

[0126] Where ΔP BES,t is the compensation amount of energy storage equipment during period t, is the amount of electricity stored in the energy storage device during period t, C BES is the capacity of the energy storage device, where the energy storage device is generally a battery; ΔP reduction,t To compensate for load reduction.

[0127] The following four examples are set up for comparison with Example 0 using the method of the present invention. The comparison results are shown in Table 1.

[0128] Example 1: Consider the case where the regulation capacity of battery storage in the microgrid and virtual carbon storage are not utilized.

[0129] Example 2: Consider the case where demand response is not adopted.

[0130] Example 3: Consider the case of using battery energy storage but not considering the virtual carbon storage of the microgrid.

[0131] Example 4: Consider the case where the microgrid only participates in the electricity market and does not participate in the electricity-carbon coupling market proposed in the present invention.

[0132] Table 1 Average cost

[0133]

[0134] Among the four examples, the total cost of the microgrid using the proposed mechanism and optimization strategy is the lowest. Compared with Examples 1 and 2, the total cost of Example 0 is reduced by 26.6% and 1.6%, respectively, which shows that the optimal control of battery energy storage and demand response can reduce the cost of the microgrid. Compared with Example 3, the total cost of Example 0 is reduced by 11.9%, indicating that the virtual carbon storage proposed in this invention can further reduce the total cost of the microgrid. The comparison between Example 0 and Example 4 illustrates the superiority of the proposed electricity-carbon coupling trading mechanism, and the total cost of the microgrid is reduced by 39.3%, which shows that the coupling market is of great significance to reducing the operating cost of the microgrid.

Claims

1. A demand-side electricity-carbon coupling trading method, applied in the electricity-carbon coupling trading market; characterized by: It consists of two stages: Phase 1: Based on the virtual bids of each market participant, the clearing price of electricity in each period is obtained. Based on the clearing price of electricity, each market participant calculates its own carbon bidding strategy, which includes the bid electricity and the corresponding carbon intensity. Phase 2: Obtain the carbon quotation strategies of each market participant, and obtain the cleared electricity and carbon intensity in each period. Based on the cleared electricity and carbon intensity, each microgrid adopts corresponding compensation; Prior to Phase 1, a microgrid model was established; The established microgrid model satisfies the carbon emission flow balance, which can be expressed as: Among them, P in,t is the amount of electricity purchased by the microgrid during period t, P out,t is the amount of electricity sold by the microgrid during period t, P BES,t is the battery charge and discharge amount in period t, P L,t is the load power consumption during period t, P PV,t is the photovoltaic power generation in period t, ρ in,t is the carbon intensity of electricity purchase, ρ BES,t is the carbon intensity of battery energy storage charging and discharging, ρ out,t is the carbon intensity of electricity sales, ρ L,t is the carbon intensity of load electricity, ρ PV,t is the photovoltaic carbon intensity; Since the battery energy storage charging and discharging carbon intensity ρ BES,t different, so the average carbon intensity of electricity stored in batteries is also changing, as shown by: in, is the average carbon intensity of the electricity in the battery during period t+1, C BES is the capacity of the battery, is the battery charging state during period t, is the average carbon intensity of electricity stored in the battery during period t; The carbon intensity of battery energy storage charging and discharging meets the following constraints: in, is the maximum value of the average carbon intensity of electricity stored in the battery; The carbon intensity of load electricity in formula (1) is L,t According to the power purchasing behavior and power consumption behavior of the microgrid, it can be divided into two situations, expressed as: According to the proportional allocation principle, the carbon intensity of electricity sales in formula (1) is out,t It is expressed as: When the microgrid sells electricity during period t, it will not purchase electricity during that period, i.e., P in,t = 0, combined with P in,t =0 and formula (4), formula (5) is rewritten as: The clearing of the virtual bids of each market participant to obtain the clearing price of electric energy in each period specifically includes: Each participant submits its bidding strategy to the market. The intersection of the bid and ask curves determines the market clearing price. The goal of market clearing is to maximize social welfare. By solving equations (7) and (8), we can get the clearing price λ t ; Where N is the number of sellers; M is the number of buyers; is the amount of electricity sold by the i-th seller in period t in a stage; is the amount of electricity purchased by the jth buyer in period t in a phase; is the quotation strategy of the i-th seller in period t, is a monotonically increasing function; is the bidding strategy of the jth buyer in period t, is a monotonically decreasing function; Among them, based on the electricity clearing price, each market participant calculates their own carbon quotation strategy, which specifically includes: The operating cost of the microgrid is expressed as: In the formula, C MG,t is the microgrid operation cost function in period t, C BES,t is the battery cost function for period t, C L,t is the user comfort loss cost function in period t, is the microgrid electricity purchase cost function in period t, is the carbon emission cost function of the microgrid in period t, is the profit function of the microgrid electricity sales in period t; The optimization objective of the microgrid is expressed as: By solving equation (10), we get the carbon quotation strategy.

2. The demand-side electricity-carbon coupling trading method according to claim 1, characterized in that: In the second stage, the carbon quotation strategy of each market participant is obtained. By solving equations (16)-(18), the clearing electricity and carbon intensity of each period are obtained. The specific steps include: In the formula, is the carbon quotation strategy of the i-th seller in period t; is the carbon bidding strategy of the jth buyer in period t; is the amount of electricity sold by the i-th seller in period t in stage 2, The amount of electricity purchased by the jth buyer in period t in stage 2, ρ t To clear carbon intensity.

3. A demand-side electricity-carbon coupling trading method for guiding microgrids acting as buyers and sellers in an electricity-carbon coupling trading market to conduct electricity trading; characterized in that: The following steps are involved: S100: Submit a virtual quotation to the electricity-carbon coupling trading market to participate in the first phase of the electricity-carbon coupling trading market, and the electricity-carbon coupling trading market will calculate the electricity clearing price for each period; S200: Obtain the electricity clearing price for each time period given by the electricity-carbon coupling trading market, and calculate the carbon quotation strategy based on the electricity clearing price, where the carbon quotation strategy includes the bid electricity and the corresponding carbon intensity; S300: Submit carbon quotation strategy to the electricity-carbon coupling trading market and participate in the second phase of the electricity-carbon coupling trading market. The electricity-carbon coupling trading market calculates the clearing electricity and carbon intensity in each period; S400: Obtain the cleared electricity and carbon intensity of each time period given by the electricity-carbon coupling trading market, and use corresponding compensation to eliminate the difference between the cleared electricity of the electricity-carbon coupling trading market and its own bid electricity; Before executing S100, a microgrid model is established; The established microgrid model satisfies the carbon emission flow balance, which can be expressed as: Among them, P in,t is the amount of electricity purchased by the microgrid during period t, P out,t is the amount of electricity sold by the microgrid during period t, P BES,t is the battery charge and discharge amount in period t, P L,t is the load power consumption during period t, P PV,t is the photovoltaic power generation in period t, ρ in,t is the carbon intensity of electricity purchase, ρ BES,t is the carbon intensity of battery energy storage charging and discharging, ρ out,t is the carbon intensity of electricity sales, ρ L,t is the carbon intensity of load electricity, ρ PV,t is the photovoltaic carbon intensity; Since the battery energy storage charging and discharging carbon intensity ρ BES,t different, so the average carbon intensity of electricity stored in batteries is also changing, as shown by: in, is the average carbon intensity of the electricity in the battery during period t+1, C BES is the capacity of the battery, is the battery charging state during period t, is the average carbon intensity of electricity stored in the battery during period t; The carbon intensity of battery energy storage charging and discharging meets the following constraints: in, is the maximum value of the average carbon intensity of electricity stored in the battery; The carbon intensity of load electricity in formula (1) is L,t According to the power purchasing behavior and power consumption behavior of the microgrid, it can be divided into two situations, expressed as: According to the proportional allocation principle, the carbon intensity of electricity sales in formula (1) is out,t It is expressed as: When the microgrid sells electricity during period t, it will not purchase electricity during that period, i.e., P in,t = 0, combined with P in,t =0 and formula (4), formula (5) is rewritten as: Among them, the stage 1 of submitting virtual quotations to the electricity-carbon coupling trading market and participating in the electricity-carbon coupling trading market is that the electricity-carbon coupling trading market calculates the electricity clearing prices for each period, specifically including: Each participant submits its bidding strategy to the market. The intersection of the bid and ask curves determines the market clearing price. The goal of market clearing is to maximize social welfare. By solving equations (7) and (8), we can get the clearing price λ t ; Where N is the number of sellers; M is the number of buyers; is the amount of electricity sold by the i-th seller in period t in a stage; is the amount of electricity purchased by the jth buyer in period t in a phase; is the quotation strategy of the i-th seller in period t, is a monotonically increasing function; is the bidding strategy of the jth buyer in period t, is a monotonically decreasing function; The obtaining of the electricity clearing prices for each period given by the electricity-carbon coupling trading market and the calculation of the carbon quotation strategy based on the electricity clearing prices specifically include: The operating cost of the microgrid is expressed as: In the formula, C MG,t is the microgrid operation cost function in period t, C BES,t is the battery cost function for period t, C L,t is the user comfort loss cost function in period t, is the microgrid electricity purchase cost function in period t, is the carbon emission cost function of the microgrid in period t, is the profit function of the microgrid electricity sales in period t; The optimization objective of the microgrid is expressed as: By solving equation (10), we get the carbon quotation strategy; In S400, the use of corresponding compensation to eliminate the difference includes: First, local energy storage equipment output compensation is adopted. If the difference is still not eliminated, local load reduction compensation is adopted until the difference is eliminated.

4. The demand-side electricity-carbon coupling trading method according to claim 3 is characterized by: In S200, the carbon quotation strategy is obtained by solving equation (10), and the specific steps are as follows: Based on the proximal policy optimization algorithm, set the agent observation state space s t is the state of the microgrid in each period, and the action space a t =[P in,t ,ΔP L,t ,P BES,t ,ρ BES,t ], reward r t =-C MG,t ; Through the continuous interaction of the intelligent agent with the electricity-carbon coupled trading market environment, a strategy for obtaining the maximum cumulative reward is learned. The strategy includes energy storage strategy for energy storage charging and discharging capacity and charging and discharging carbon intensity, load reduction strategy, and carbon quotation strategy. The carbon quotation strategy includes the bid electricity volume and the corresponding carbon intensity.

5. The demand-side electricity-carbon coupling trading method according to claim 3 is characterized by: Energy storage equipment cost function C in period t BES,t , expressed as: C BES,t =τ|SOC t -SOC t-1 | (11) In the formula, τ is the cost coefficient, SOC t is the charge / discharge state of the energy storage device during period t, SOC t-1 is the charge / discharge state of the energy storage device during period t-1; User comfort loss cost function C during period t L,t , expressed as: C L,t =a L (ΔP L,t ) 2 +b L ΔP L,t (12) Where ΔP L,t is the load reduction, α L and β L is the coefficient of the user comfort loss cost function; Microgrid power purchase cost function in period t It is expressed as: In the formula, λ t is the electricity clearing price in period t, P in,t is the amount of electricity purchased by the microgrid during period t; Carbon emission cost function It can be expressed as: Among them, λ c is the annual carbon price in the carbon market, ρ L,t is the carbon intensity of the load electricity, P L,t is the load power consumption during period t; Microgrid power sales profit function It is expressed as: Among them, λ t is the electricity clearing price in period t, P out,t is the amount of electricity sold by the microgrid during period t.

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