Virtual power plant multi-market coupling collaborative pricing and distributed clearing method

Abstract

This invention discloses a method for multi-market coupled collaborative pricing and distributed clearing in virtual power plants. The method acquires operational data from the generation side, independent energy storage side, load side, and intermediaries within the virtual power plant, as well as multi-market transaction data such as electricity, green certificates, and carbon allowances. It constructs a two-layer optimization model centered on upper-layer intermediary collaborative pricing and lower-layer multi-entity response. The lower layer coordinates electricity and green certificate consistency using an alternating direction multiplier method, while the upper layer updates multi-type transaction prices based on intermediary profits, lower-layer comprehensive objectives, and price deviation penalties. Simultaneously, it employs a combination of continuous optimization and integer settlement for green certificates, and utilizes an external market proxy trading mechanism for carbon allowance gaps or surpluses. This method enables collaborative price updates across multiple markets and distributed clearing for multiple entities within the virtual power plant.

Description

Technical Field

[0001] This invention relates to the fields of virtual power plant optimized operation and electricity market trading technology, and in particular to a method for multi-market coupled collaborative pricing and distributed clearing of virtual power plants. Background Technology

[0002] With the continuous expansion of distributed resource access such as new energy, independent energy storage, and adjustable loads, virtual power plants are gradually becoming an important form of aggregating multiple types of resources and participating in market transactions. During operation, virtual power plants not only need to coordinate the supply and demand of electricity among the generation side, independent energy storage side, and load side, but also need to consider the supply of green certificates corresponding to new energy output, the demand for green certificates corresponding to load electricity consumption, and the carbon emissions and carbon quota trading demand generated by conventional energy power generation.

[0003] Existing virtual power plant optimization methods often focus on operation scheduling or revenue optimization within a single electricity market, failing to adequately consider the coupling relationships between the electricity market, green certificate market, and carbon market. They also struggle to simultaneously characterize the interactive effects between intermediary pricing, generation-side response, independent energy storage charging and discharging response, and load-side demand response. Furthermore, existing methods typically employ a centralized optimization approach, making it difficult to balance the independent decision-making needs and distributed coordination requirements of different stakeholders.

[0004] Furthermore, existing methods have shortcomings in handling green certificate trading and carbon allowance trading. If integer variables are directly used in the optimization of the entire green certificate trading process, it easily increases the complexity of the model solution; if settlement is based entirely on continuous variables, it is difficult to adapt to the actual requirement of settling green certificates by integer number. Carbon allowance trading is mainly determined by the difference between carbon emissions from the generation side and the amount of free allowances, and it is not suitable to simply incorporate it into the internal energy clearing process between the generation side, independent energy storage side, and load side.

[0005] Therefore, it is necessary to propose a virtual power plant optimization method that can simultaneously consider the multi-market coupling relationship of electricity, green certificates, and carbon quotas. Through upper-level collaborative pricing by intermediaries and lower-level distributed clearing by multiple entities, the method can achieve coordinated operation of the generation side, independent energy storage side, and load side. Furthermore, by combining external market backing transactions, continuous optimization and integer settlement of green certificates, and external agency trading mechanisms for carbon quotas, the method can improve the collaborative optimization capability and settlement applicability of virtual power plants in multi-market trading scenarios. Summary of the Invention

[0006] A method for multi-market coupled collaborative pricing and distributed clearing in virtual power plants, the method comprising the following steps:

[0007] Step 1: Acquire multi-entity operation data and multi-market transaction data within the virtual power plant, and construct time-of-use price sequences for various transaction prices under each dispatch period according to preset time-of-use division rules. The multi-entity entities include intermediaries, generators, independent energy storage, and loads. The multi-entity operation data includes generator operation data, independent energy storage operation data, load operation data, green certificate conversion data, carbon emission data, and carbon quota data. The multi-market transaction data includes electricity trading price boundaries, green certificate trading price boundaries, carbon trading price boundaries, and external market transaction parameters. The time-of-use price sequences serve as price signals issued by intermediaries to the corresponding entities.

[0008] Step two involves constructing a two-layer collaborative optimization model based on multi-entity operational data, multi-market transaction data, and time-of-use price sequences. The model uses intermediaries as the upper-layer model, while the generation-side response model, independent energy storage-side response model, and load-side response model serve as the lower-layer models. The upper-layer model uses intermediary transaction profits, lower-layer comprehensive objective terms, and various types of price deviation penalty terms as optimization criteria to update various transaction prices. The lower-layer models solve for the generation-side response, independent energy storage-side response, and load-side response respectively under a given price signal.

[0009] Step 3: In the lower-level model, the power generation side optimizes the power supply, green certificate supply, and carbon quota agency trading behavior based on conventional energy trading prices, new energy trading prices, green certificate trading prices, and carbon trading prices; the independent energy storage side optimizes the charging and discharging behavior of independent energy storage based on charging and discharging trading prices and forms the independent energy storage side's power response; the load side optimizes the load's electricity consumption behavior based on the load side's electricity purchase price and green certificate trading price and forms the load side's green certificate demand.

[0010] Step 4: Construct an energy consistency coordination mechanism and a green certificate consistency coordination mechanism, and use the alternating direction multiplier method to perform distributed coordination on the generation side, independent energy storage side, and load side. The energy consistency coordination mechanism is used to coordinate the energy supply on the generation side, the energy response on the independent energy storage side, and the electricity demand on the load side; the green certificate consistency coordination mechanism is used to coordinate the green certificate supply on the generation side and the green certificate demand on the load side. The independent energy storage side does not participate in the green certificate consistency coordination. Based on the original energy residual, the dual energy residual, the original green certificate residual, and the dual green certificate residual, determine whether the lower-level distributed coordination has converged. When the above residuals meet the preset lower-level convergence conditions, the lower-level distributed coordination result under the current price conditions is obtained; otherwise, continue to execute the alternating direction multiplier method for iterative updates until the lower-level convergence conditions are met or the preset maximum number of iterations is reached.

[0011] Step 5: Based on the results of the lower-level distributed coordination, calculate the profits of intermediaries in electricity trading, green certificate trading service fees, carbon quota agency trading service fees, and external market guarantee transactions. Solve the upper-level model to obtain updated transaction prices. Internal coordination deviations after electricity and green certificate trading are handled through external market guarantees. Carbon quota gaps or surpluses on the power generation side are traded between intermediaries and the external carbon market. In the lower-level distributed coordination process, green certificate supply and demand are used as continuous variables in the local response optimization, green certificate consistency variable update, green certificate dual variable update, and the judgment of original and dual residuals on the power generation and load sides. In the upper-level model solution stage involving green certificate transaction amount calculation and final settlement output, hourly green certificate supply and demand are integerized. Based on the integerized green certificate settlement amount, calculate the internal green certificate trading volume, external green certificate guarantee transaction volume, green certificate transaction amount, and green certificate trading service fee revenue.

[0012] Step 6: Normalize the various transaction prices before and after the update according to the corresponding allowed price range, and calculate the upper-level price convergence residual based on the normalized price change. When the upper-level price convergence residual meets the preset upper-level convergence condition and the lower-level distributed coordination meets the preset lower-level convergence condition, output the virtual power plant multi-market collaborative pricing result and multi-entity collaborative optimization result. Otherwise, use the updated various transaction prices as the input for the next round of upper-level iteration, and repeat the lower-level distributed coordination and upper-level intermediary pricing update process. Attached Figure Description

[0013] Figure 1 This is a framework diagram of a virtual power plant multi-market coupled two-layer collaborative optimization model;

[0014] Figure 2 This is a flowchart of the virtual power plant's two-tier collaborative pricing and ADMM distributed coordination process;

[0015] Figure 3 This is a diagram of a mechanism for virtual power plants to be backed by an external market, with green certificate settlement in whole numbers and carbon quota agency trading. Detailed Implementation

[0016] The present invention will now be further described with reference to the accompanying drawings.

[0017] A method for multi-market coupled collaborative pricing and distributed clearing in virtual power plants, the method comprising the following steps:

[0018] Step 1: Acquire multi-entity operation data and multi-market transaction data within the virtual power plant, and construct time-of-use price sequences for various transaction prices under each dispatch period according to preset time-of-use division rules. The multi-entity entities include intermediaries, generators, independent energy storage, and loads. The multi-entity operation data includes generator operation data, independent energy storage operation data, load operation data, green certificate conversion data, carbon emission data, and carbon quota data. The multi-market transaction data includes electricity trading price boundaries, green certificate trading price boundaries, carbon trading price boundaries, and external market transaction parameters. The time-of-use price sequences serve as price signals issued by intermediaries to the corresponding entities.

[0019] Step two: Based on multi-entity operational data, multi-market transaction data, and time-of-use price series, a two-layer collaborative optimization model is constructed. The intermediary serves as the upper-layer model, while the generation-side response model, independent energy storage-side response model, and load-side response model serve as lower-layer models. The upper-layer model uses the intermediary's transaction profit, the lower-layer comprehensive objective term, and various types of price deviation penalty terms as optimization criteria to update various transaction prices. The lower-layer models solve for the generation-side response, independent energy storage-side response, and load-side response under a given price signal. The coupling relationships between the intermediary upper-layer model, the generation-side response model, the independent energy storage-side response model, the load-side response model, and the external electricity market, external green certificate market, and external carbon market are as follows: Figure 1 As shown;

[0020] Step 3: In the lower-level model, the power generation side optimizes the power supply, green certificate supply, and carbon quota agency trading behavior based on conventional energy trading prices, new energy trading prices, green certificate trading prices, and carbon trading prices; the independent energy storage side optimizes the charging and discharging behavior of independent energy storage based on charging and discharging trading prices and forms the independent energy storage side's power response; the load side optimizes the load's electricity consumption behavior based on the load side's electricity purchase price and green certificate trading price and forms the load side's green certificate demand.

[0021] Step 4: Construct an energy consistency coordination mechanism and a green certificate consistency coordination mechanism, and use the alternating direction multiplier method to perform distributed coordination on the generation side, independent energy storage side, and load side. The energy consistency coordination mechanism is used to coordinate the energy supply on the generation side, the energy response on the independent energy storage side, and the electricity demand on the load side; the green certificate consistency coordination mechanism is used to coordinate the green certificate supply on the generation side and the green certificate demand on the load side. The independent energy storage side does not participate in the green certificate consistency coordination. Based on the original energy residual, the dual energy residual, the original green certificate residual, and the dual green certificate residual, determine whether the lower-level distributed coordination has converged. When the above residuals meet the preset lower-level convergence conditions, the lower-level distributed coordination result under the current price conditions is obtained; otherwise, continue to execute the alternating direction multiplier method for iterative updates until the lower-level convergence conditions are met or the preset maximum number of iterations is reached.

[0022] Step 5: Based on the results of the lower-level distributed coordination, calculate the profits of intermediaries in electricity trading, green certificate trading service fees, carbon quota agency trading service fees, and external market-backed transactions. Solve the upper-level model to obtain updated transaction prices. Internal coordination deviations after electricity and green certificate trading are covered by the external market. Carbon quota gaps or surpluses on the power generation side are traded between intermediaries and the external carbon market. In the lower-level distributed coordination process, green certificate supply and demand are treated as continuous variables on both the power generation and load sides. The system optimizes the response, updates green certificate consistency variables and dual variables, and determines the original and dual residuals of green certificates. In the upper-level model solution stage, involving green certificate transaction amount calculation and final settlement output, hourly green certificate supply and demand are integerized. Based on the integerized green certificate settlement amount, internal green certificate transaction volume, external green certificate guarantee transaction volume, green certificate transaction amount, and green certificate transaction service fee revenue are calculated. The mechanism for external market guarantee, integer settlement of green certificates, and carbon quota agency trading based on the lower-level distributed coordination results is as follows: Figure 3 As shown;

[0023] Step Six: Normalize the various transaction prices before and after the update according to the corresponding allowable price range, and calculate the upper-level price convergence residual based on the normalized price change. When the upper-level price convergence residual meets the preset upper-level convergence condition, and the lower-level distributed coordination meets the preset lower-level convergence condition, output the virtual power plant multi-market collaborative pricing result and multi-entity collaborative optimization result. Otherwise, use the updated transaction prices as the input for the next round of upper-level iteration, and repeat the lower-level distributed coordination and upper-level intermediary pricing update process. The overall solution process for upper-level price iteration and lower-level ADMM distributed coordination is as follows: Figure 2 As shown.

[0024] The upper-level model is a pricing and settlement model for intermediaries, such as... Figure 1 As shown, this is used to update various transaction prices under the condition of fixing the current lower-level distributed coordination results. These transaction prices include conventional energy transaction prices, new energy transaction prices, load-side electricity purchase prices, independent energy storage charging and discharging transaction prices, green certificate transaction prices, and carbon transaction prices. The objective function of the upper-level pricing model is... for: in, This is the overall target value for the upper level of the intermediary. For the profit of the middlemen, For lower-level integrated target items, For multiple types of price deviation penalties, , , These are the weighting coefficients for the intermediary profit item, the lower-level comprehensive target item, and the price deviation penalty item, respectively. and These are the normalized scales for the intermediary profit item and the lower-level comprehensive objective item, respectively, and we have: in, After the lower-level initial pre-clearing phase is completed, the upper-level layer calculates the intermediary profit reference value based on the lower-level initial pre-clearing result and the initial price. In the initial pre-clearing phase, after the initial price is given, the generation side and the independent energy storage side first solve for the initial pre-clearing result based on the initial price, while the load side directly uses the initial data as the initial pre-clearing result. To initialize the lower-level main body comprehensive target reference value obtained in the pre-clearing stage; To prevent positive numbers with a denominator of zero;

[0025] Middleman's Profit It consists of profits from electricity trading, service fees from green certificate trading, and service fees from carbon quota agency trading, expressed as follows: in, For profits from electricity trading, Revenue from green certificate transaction service fees. Revenue from carbon quota agency trading service fees;

[0026] Profits from electricity trading are expressed as follows: in, Indicates time period, For the set of scheduling periods, The amount paid by the load side to the intermediary for electricity purchase transactions. The amount paid to intermediaries for electricity purchases when charging independent energy storage systems. This refers to the transaction amount of electricity sold by intermediaries to the external electricity market. This refers to the amount paid by intermediaries to conventional energy companies for electricity purchases. This refers to the amount paid by intermediaries to new energy companies for electricity purchases. This refers to the amount paid by intermediaries to independent energy storage providers for the electricity they purchase during discharge transactions. This refers to the transaction amount of electricity purchased by intermediaries from the external electricity market;

[0027] The amounts for each electricity transaction are expressed as follows: in, for The electricity purchase price on the load side at any given time. for The price of independent energy storage charging at any time for The current external electricity market price for Regular energy trading prices at all times for Real-time new energy trading prices for The price of independent energy storage discharge at any time. for The current external electricity market purchase price; for Electricity consumption on the load side at all times for Independent energy storage and charging power at all times. for Intermediaries sell electricity to the external electricity market at any time. for Always using conventional energy output, for Real-time output of new energy sources for Independent energy storage and discharge power at all times for Intermediaries purchase electricity from the external electricity market. For scheduling time intervals, For electricity unit conversion factor;

[0028] Green certificate transaction service fee revenue is represented as follows: in, Service fee rate for green certificate transactions, for The amount of green certificate transactions within the time frame. for The transaction amount of green certificates purchased through the external green certificate market at all times. for The transaction amount of green certificates sold through the external green certificate market is constantly recorded, and the service fee is charged by the intermediary to the seller of the green certificates.

[0029] The transaction amounts for green certificates are expressed as follows: in, for The trading price of the green certificate at any time for The volume of internal green certificate transactions between the generation and load sides at any given time. To fix the trading price in the external green certificate market, for The amount of green certificates supplemented by the external green certificate market when there is a shortage of green certificates on the load side. for The amount of green certificates sold through the external green certificate market when there is a surplus of green certificates on the power generation side;

[0030] Carbon quota agency trading service fee revenue is expressed as follows: in, For carbon quota trading service rates, for The amount of carbon allowance agency transactions between power generators and intermediaries at any given time. for The transaction amount of carbon allowances between intermediaries and external carbon markets is recorded at any time, and the agency service fee is charged by the intermediaries to both parties in the transaction.

[0031] The amounts for carbon quota agency transactions are expressed as follows: in, for Carbon trading prices at all times for External purchases corresponding to the carbon quota gap on the power generation side at any given time. for The amount of external sales corresponding to the surplus of carbon allowances on the power generation side at any given time. Indicates the conversion factor for carbon quota trading units;

[0032] Lower-level integrated objectives Represented as: in, For the revenue from power generation side transactions, For power generation side operating costs and penalty costs, For independent energy storage side discharge revenue, The charging cost and operational degradation cost of independent energy storage side, The overall cost on the load side;

[0033] Multiple types of price deviation penalties are represented as follows: in, For a set of price types, For the first Class of transaction prices, For the first Trading prices during the period The optimized value, For the first Trading prices during the period initial value, and The first The upper and lower limits of the transaction price. For the quantity of price types, Represents the set of scheduling periods The number of time periods included;

[0034] The upper-level model satisfies the following constraints: in, This is the minimum profit requirement for middlemen.

[0035] The generator-side response model is represented using an augmented Lagrangian form, such as... Figure 1 As shown, it is used to optimize the output of conventional power generation, the output of new energy, the supply of green certificates, and the trading volume of carbon quotas, given conventional energy trading prices, new energy trading prices, green certificate trading prices, and carbon trading prices; the objective function corresponding to the power generation side is... for: in, For the augmented Lagrangian function corresponding to the power generation side, For the power generation side electrical energy consistency enhancement term, This is an augmentation item for consistency of green certificates on the power generation side;

[0036] Revenue from power generation-side transactions includes revenue from the sale of conventional energy electricity, revenue from the sale of renewable energy electricity, revenue from green certificate trading, and revenue from carbon quota trading, expressed as: in, Revenue from the sale of electricity from conventional energy sources, For revenue from selling electricity from new energy sources, For the profits from green certificate trading, For carbon quota trading revenue;

[0037] Revenue from the sale of conventional energy electricity is expressed as follows: in, A collection of conventional energy units, For conventional energy units exist Efforts during a specific time period;

[0038] Revenue from the sale of electricity from new energy sources is expressed as follows: in, For the collection of new energy power stations, For new energy power stations exist Actual grid-connected power output during the time period;

[0039] The proceeds from green certificate trading are expressed as follows: in, For the power generation side The supply of green certificates during a given period;

[0040] Green certificate supply It is calculated from the output of new energy sources and expressed as: in, The conversion factor for converting renewable energy electricity into the number of green certificates;

[0041] The power generation side operating costs and penalty costs are expressed as follows: in, For conventional energy operating costs, The cost of penalizing the curtailment of wind and solar power;

[0042] The operating cost of conventional energy sources is expressed as follows: in, , , They are conventional energy units The secondary operating cost coefficient, the primary operating cost coefficient, and the fixed operating cost coefficient;

[0043] The penalty cost for curtailing wind and solar power is expressed as follows: in, For new energy power stations The penalty cost coefficient for curtailing wind and solar power, For new energy power stations exist The amount of wind and solar power curtailed during a given period;

[0044] The available output constraints of renewable energy power plants are: in, For new energy power stations exist The forecast for a given time period can be used to determine the output.

[0045] Conventional power generating units meet the upper and lower limits of output and the ramp-up constraint: in, and They are conventional energy units Minimum and maximum output, and They are conventional energy units Uphill and downhill climbing abilities;

[0046] Carbon allowance trading revenue is determined based on the difference between the actual carbon emissions on the power generation side and the amount of free carbon allowances. Carbon allowance trading does not participate in the internal consistency clearing among the power generation side, independent energy storage side, and load side. Instead, the power generation side conducts agency transactions and settlements with the external carbon market through intermediaries. Specifically, the power generation side generates actual carbon emissions and free carbon allowances based on conventional energy output, and determines the carbon allowance gap or carbon allowance surplus based on the difference between the two. The intermediary determines the carbon trading price for the corresponding period through an upper-level model. The power generation side submits the carbon allowance gap or carbon allowance surplus to the intermediary according to the carbon trading price, and the intermediary completes equivalent listing-style agency settlements with the external carbon market. Actual carbon emissions during the period and free carbon credits They are represented as follows: in, For conventional energy units carbon emission coefficient, For conventional energy units Free carbon allowance coefficient;

[0047] The carbon allowance purchase volume and carbon allowance sale volume meet the following requirements: in, for External purchases corresponding to the carbon quota gap on the power generation side during a given period. for External sales volume corresponding to surplus carbon allowances on the power generation side during a given period;

[0048] The revenue from carbon quota trading is expressed as follows:

[0049] Response variables of the generation side participating in power consistency coordination Represented as:

[0050] Response variables of power generation side participating in green certificate consistency coordination Represented as:

[0051] Electrical energy consistency augmentation Represented as:

[0052] Green Certificate Consistency Augmentation Represented as: in, and These are the electricity consistency variables on the power generation side and the green certificate consistency variables, respectively. and These are the corresponding dual variables. and These are the penalty parameters for electrical energy consistency coordination and green certificate consistency coordination, respectively.

[0053] The independent energy storage-side response model is represented using an augmented Lagrangian form, such as... Figure 1 As shown, it is used to optimize the charging power, discharging power, energy state, and charge / discharge state of independent energy storage under given independent energy storage charging and discharging trading prices. The independent energy storage side does not participate in green certificate consistency coordination, but only in energy consistency coordination. The objective function corresponding to the independent energy storage side is... for: in, For the augmented Lagrangian function corresponding to the independent energy storage side, For independent energy storage side discharge revenue, The charging cost and operational degradation cost of independent energy storage side, For the energy consistency enhancement term on the independent energy storage side;

[0054] The discharge benefit of independent energy storage is expressed as:

[0055] The charging cost and operating degradation cost of independent energy storage are expressed as follows: in, The cost of purchasing electricity for charging independent energy storage devices. For the degradation cost of independent energy storage operation;

[0056] The cost of purchasing electricity for charging on the independent energy storage side is expressed as follows:

[0057] The degradation cost of independent energy storage operation is expressed as: in, The charge / discharge degradation cost coefficient for an independent energy storage unit;

[0058] The energy state of independent energy storage satisfies the following state transition constraints: in, For independent energy storage Energy storage status during a given period For independent energy storage Energy storage status during a given period For charging efficiency, For discharge efficiency;

[0059] The energy state of independent energy storage meets the upper and lower capacity constraints: in, and These are the lower and upper limits of the energy state of independent energy storage, respectively;

[0060] Independent energy storage satisfies the energy state consistency constraint at the beginning and end of the scheduling cycle: in, This refers to the energy state of independent energy storage at the beginning of the scheduling cycle. The energy state of independent energy storage at the end of the scheduling cycle;

[0061] The independent energy storage charging and discharging power meet the upper and lower power limits and the charging and discharging mutual exclusion constraints: in, and These are the maximum charging power and maximum discharging power of independent energy storage, respectively. For independent energy storage The state variables of charging and discharging during a given period; when At that time, independent energy storage allows charging but not discharging; when At that time, independent energy storage allows discharging but not charging;

[0062] The independent energy storage charging power meets the following power variation constraints: in, This refers to the initial charging power of independent energy storage before the start of the scheduling cycle. for Initial charging power of independent energy storage at any given time. Limits on the variation of charging power for independent energy storage;

[0063] The independent energy storage discharge power satisfies the following power variation constraints: in, This represents the initial discharge power of the independent energy storage before the start of the scheduling cycle. for Initial discharge power of independent energy storage at any given time. For the limit of independent energy storage discharge power variation;

[0064] The net output of independent energy storage is expressed as: in, For independent energy storage Net output during the period;

[0065] Response variables of independent energy storage side participating in power consistency coordination Represented as:

[0066] Independent energy storage side power consistency enhancement term Represented as: in, For independent energy storage side electrical energy consistency variables, These are the corresponding dual variables.

[0067] The load-side response model is represented using an augmented Lagrangian form, such as... Figure 1 As shown, it is used to optimize the power consumption and green certificate demand in each region of the load side under given load-side electricity purchase price and green certificate trading price; the load side participates in both power consistency coordination and green certificate consistency coordination, and the objective function corresponding to the load side is... for: in, For the augmented Lagrangian function corresponding to the load side, As a load-side satisfaction indicator, The weighting coefficient for the satisfaction index. For load-side electrical energy consistency enhancement term, This is an augmentation item for consistency of green certificates on the load side;

[0068] The load-side comprehensive cost consists of the cost of purchasing electricity and the cost of purchasing green certificates, expressed as: in, For the cost of purchasing electricity on the load side, Cost of purchasing green certificates on the load side;

[0069] The load-side electricity purchase cost is expressed as:

[0070] The total power consumption on the load side is expressed as follows: in, For load-side region set, For the region exist Power consumption during a given time period;

[0071] The cost of purchasing load-side green certificates is expressed as follows: in, For the load side The demand for green certificates during a given period;

[0072] The demand for green certificates on the load side is calculated based on the total power consumption on the load side, and is expressed as follows: in, The conversion factor for converting load-side electricity consumption into green certificate demand;

[0073] The load-side demand response boundary is determined based on the load-side electricity purchase price change rate and price elasticity matrix, whereby... Price period index in price response calculation, relative rate of price change Represented as: in, for The benchmark electricity purchase price on the load side during the time period, for Optimized electricity purchase price for the time-of-use load side;

[0074] area Price response forecast load Represented as: in, For the region exist Baseline load for the time period For the region The price elasticity matrix elements represent Time-of-use load The response coefficient to price changes over a period of time;

[0075] area exist The actual optimized load during the time period satisfies the following demand response boundary constraints: in, For the region exist The actual optimized load after demand response within a given time period;

[0076] The regional load meets the power consumption conservation constraint during the dispatch cycle:

[0077] area exist Load offset during the period Represented as:

[0078] area Flexible load reduction index Represented as:

[0079] Economic efficiency indicators on the load side Represented as:

[0080] area exist Satisfaction indicators for different time periods Represented as: in, and They are respectively regions The weighting coefficients of the flexible load reduction index and the economic efficiency index.

[0081] Load-side satisfaction index Represented as: in, For the region The weight of satisfaction indicators;

[0082] Response variables of load side participating in power consistency coordination Represented as:

[0083] Response variables of load side participating in green certificate consistency coordination Represented as:

[0084] Load-side power consistency enhancement term Represented as:

[0085] Load-side green certificate consistency enhancement item Represented as: in, and These are the load-side electrical energy consistency variables and the green certificate consistency variables, respectively. and These are the corresponding dual variables.

[0086] like Figure 2 As shown, the iterative logic of the upper and lower layer models is described. The lower layer model is a distributed coordination of the generation side, independent energy storage side and load side under the condition of a certain round of upper layer price, including the coordination of electricity consistency and the coordination of green certificate consistency.

[0087] Electricity consistency coordination is used to coordinate the electricity supply on the generation side, the net output of independent energy storage, and the electricity demand on the load side. The electricity consistency balance relationship that should be satisfied at the time of convergence is expressed as:

[0088] Green certificate consistency coordination is used to coordinate the supply of green certificates on the generation side and the demand for green certificates on the load side. The green certificate consistency balance relationship that should be satisfied at the time of convergence is expressed as:

[0089] Before executing the first lower-level iteration, set the initial consistency variables and initial dual variables for the 0th iteration; among them, the load-side reference total power consumption is... Represented as:

[0090] The baseline green certificate demand is expressed as follows: in, , Both represent the baseline green certificate demand on the load side;

[0091] The initial values ​​of the electrical energy consistency variable are expressed as follows:

[0092] The initial value of the green certificate consistency variable is expressed as follows:

[0093] The initial values ​​of the dual variables for electrical energy and green certificates are both set to zero, that is: in, , and These represent the energy consistency variables for the generation side, independent energy storage side, and load side at the 0th iteration, respectively. , , For the corresponding dual variable; and These are the green certificate consistency variables for the power generation side and the load side at the 0th iteration, respectively. , For the corresponding dual variable;

[0094] In the In the next lower-level iteration, the generation side, the independent energy storage side, and the load side are respectively based on the first... The consistent variables and dual variables obtained in the second iteration are used to solve the local response model, resulting in the... The electrical energy response variable for the next iteration: in, , , These respectively represent the generation side, the independent energy storage side, and the load side in the first... The electrical energy response variable in the next iteration For the first In the next iteration, the power generation side is... Total power generation during the period For the first In the next iteration, the independent energy storage side is... Net output during the period and The first In the next iteration, the independent energy storage side is... Discharge power and charging power during the time period For the first In the next iteration, the load side is Total power consumption during the time period;

[0095] No. The green certificate response variable for the next iteration is expressed as: in, , These represent the generation side and the load side respectively in the first... The green certificate response variable in the next iteration. For the first In the next iteration, the power generation side is... Green certificate supply during a given period For the first In the next iteration, the load side is The demand for green certificates during a given period;

[0096] According to the middleman The electrical energy response variable in the second iteration and the first iteration For the dual variable of electrical energy in the next iteration, construct the auxiliary variable of electrical energy: in, , , These respectively represent the generation side, the independent energy storage side, and the load side in the first... The auxiliary variable for electrical energy in the next iteration;

[0097] No. Hourly average of the auxiliary variable of electrical energy in the next iteration Represented as:

[0098] The intermediary projects the auxiliary variable of electrical energy onto the electrical energy uniformity balance space, obtaining the first... Electrical energy consistency variable after the next iteration: in, , , These respectively represent the generation side, the independent energy storage side, and the load side in the first... The electrical energy consistency variable in the next iteration satisfies the following balance relationship:

[0099] According to the middleman The green certificate response variables in the second iteration and the first iteration For the dual variable of the green certificate in the next iteration, construct the auxiliary variable of the green certificate: in, , These represent the generation side and the load side respectively in the first... Auxiliary variables for green certificates in the next iteration;

[0100] No. The hourly average of the auxiliary variable for green certificates in the next iteration Represented as:

[0101] The intermediary projects the green certificate auxiliary variables onto the green certificate consistency equilibrium space, obtaining the first... Consistent variables for green certificates after the next iteration: in, , These represent the generation side and the load side respectively in the first... The green certificate consistency variables in the next iteration satisfy the following balance relationship:

[0102] No. The dual variable of electrical energy after the next iteration is updated as follows: in, , , These respectively represent the generation side, the independent energy storage side, and the load side in the first... The dual variable of electrical energy in the next iteration;

[0103] No. The dual variable of the green certificate after the next iteration is updated as follows: in, , These represent the generation side and the load side respectively in the first... The dual variables of the green certificate in the next iteration;

[0104] No. The original residual of electrical energy after the next iteration Represented as:

[0105] No. The dual residual of electrical energy after the next iteration Represented as:

[0106] No. Original residuals of green certificates after the second iteration Represented as:

[0107] No. Green Certificate Dual Residual After the Second Iteration Represented as: Wherein, when the following conditions are met: If the current price is reached, determine if the lower-level distributed coordination has converged and obtain the lower-level distributed coordination result at the current price; otherwise, let... Continue to perform local response solving on the generation side, independent energy storage side, and load side, as well as updating the intermediate consistency variables and dual variables, until the lower-level convergence condition is met or the preset maximum number of lower-level iterations is reached. , , and These are the original residual threshold for electrical energy, the dual residual threshold for electrical energy, the original residual threshold for green certificates, and the dual residual threshold for green certificates, respectively.

[0108] like Figure 2 As shown, the iterative logic of the upper and lower layer models is described. The upper layer model updates the prices of multiple types of transactions according to the upper layer iterative method. In each round of upper layer price iteration, the upper layer model performs a round of price optimization solution under the condition of fixing the lower layer distributed coordination result obtained in the previous feedback stage, and uses the multi-type transaction prices obtained by the solution as the price signal of the current upper layer iteration.

[0109] To describe the upper-level price iteration process, an upper-level iteration sequence number is introduced. ,in, , This represents the number of iterations required for the upper layer to reach convergence. To predetermine the maximum number of iterations at the upper level, the aforementioned transaction prices are general price variables in a single upper-level pricing calculation, and in the [number]th iteration... In the next outer price iteration, all transaction prices are denoted by the outer iteration index. The price variable; when At that time, the previous feedback phase was the initial pre-clearing phase; when At that time, the previous feedback phase was the [number]th [stage]. The lower-level convergence result obtained after completing the lower-level distributed coordination under the condition of fixed price in the next upper-level price iteration;

[0110] No. The first iteration after the next upper-level iteration Class of transactions price at The value of the time period is , No. The first iteration after the next upper-level iteration Class of transactions price at The value of the time period is The 0th upper-level price is taken from the initial time-sharing price of each type of price; the 1st... After the solution of the next higher-level model is completed, the first... Class of transactions price at Normalized price change over time period Represented as:

[0111] No. The price convergence residual after the next upper-level iteration Represented as: Among them, when the following conditions are met When the lower-level distributed coordination satisfies the lower-level convergence condition, the two-level collaborative optimization process is determined to have converged, and the virtual power plant multi-market collaborative pricing results and multi-entity collaborative optimization results are output. Set the upper-level price convergence residual threshold; otherwise, set the first... The various transaction prices obtained from the previous upper-level iteration are used as the input prices for the next upper-level iteration. The lower-level state of the next upper-level price iteration is determined based on whether the upper-level hot-start strategy is adopted. The lower-level distributed coordination and upper-level intermediary pricing update process is re-executed until the upper-level price convergence condition and the lower-level distributed coordination convergence condition are met simultaneously, or the preset maximum number of upper-level iterations is reached.

[0112] External market-backed trading and settlement mechanisms are used to settle and process internal coordination discrepancies in electricity supply, internal supply and demand discrepancies in green certificates, and external agency transactions involving carbon allowances, such as... Figure 3 As shown; and the green certificate trading adopts a combination of continuous optimization and integer settlement; in the lower-level distributed coordination process, the green certificate supply and demand are used as continuous variables to participate in the local response optimization of the generation side and the load side, the green certificate consistency variable update, the green certificate dual variable update, and the judgment of the green certificate original residual and the green certificate dual residual; in the upper-level model solution involving the calculation of green certificate transaction amount and the final settlement output stage, the hourly green certificate supply and demand are integerized.

[0113] For electricity trading, after completing the lower-level distributed coordination, if there is still an electricity discrepancy between the electricity supply on the generation side, the net output of the independent energy storage side, and the electricity demand on the load side, then an intermediary will conduct a guarantee transaction through the external electricity market; electricity discrepancy Represented as: Among them, when When this occurs, it indicates a surplus of internal electrical energy supply, and intermediaries sell this surplus energy to the external electrical energy market; when... When this occurs, it indicates that the internal power supply is insufficient, and intermediaries purchase the missing power from the external power market.

[0114] The volume of external electricity market guarantee transactions is expressed as follows: in, for Electricity sold by intermediaries to the external electricity market during the time period. for The electrical energy purchased by intermediaries from the external electricity market during the time period;

[0115] For green certificate trading, after completing the lower-level distributed coordination, if a discrepancy still exists between the supply of green certificates on the generation side and the demand on the load side, an intermediary will conduct a guarantee transaction through the external green certificate market; green certificate discrepancy Represented as: Among them, when When this indicates a surplus of internal green certificates, intermediaries sell the surplus green certificates to the external green certificate market; when This indicates that there is a shortage of internal green certificates, and intermediaries are purchasing the missing green certificates from the external green certificate market.

[0116] The volume of transactions in the external green certificate market is represented as follows: in, for The amount of green certificates sold through the external green certificate market during a given period. for The amount of green certificates supplemented through the external green certificate market during the period;

[0117] The green certificate settlement adopts a combination of continuous optimization and integer settlement. In the lower-level distributed coordination process, the supply and demand of green certificates participate as continuous variables in optimization and convergence determination. In the final settlement stage, the hourly supply and demand of green certificates are integerized, expressed as follows: in, for The total supply of integer green certificates for the power generation side participating in the settlement during the specified time period. for The number of whole-number green certificates required for settlement on the load side during a given time period. This indicates a floor function; the internal green certificate settlement amount is determined based on the integerized green certificate supply and demand. in, for Integer settlement amount of internal green certificates between the generation side and the load side during a given time period;

[0118] The external green certificate backing transaction volume after integer settlement is expressed as follows: in, The amount of green certificates sold through the external green certificate market after settlement in whole numbers. The amount of green certificates supplemented through the external green certificate market after integer settlement;

[0119] Carbon allowance trading does not participate in the coordination of electricity energy consistency and green certificate consistency, and does not constitute an internal clearing object among the generation side, independent energy storage side and load side; carbon allowance trading is determined based on the difference between the actual carbon emissions of the generation side and the amount of free carbon allowances, and the generation side conducts agency trading and settlement with the external carbon market through intermediaries.

[0120] The carbon allowance deficit and carbon allowance surplus on the power generation side are expressed as follows:

[0121] Intermediaries charge service fees based on the green certificate trading service fee rate and the carbon quota agency trading service fee rate. The green certificate trading service fee is calculated based on the internal green certificate trading amount, the external green certificate purchase trading amount, and the external green certificate sale trading amount. The carbon quota agency trading service fee is calculated based on the carbon quota agency trading amount between the power generation side and the intermediary, as well as the carbon quota trading amount between the intermediary and the external carbon market.

[0122] To further illustrate the implementation effect of the virtual power plant multi-market coupled collaborative pricing and distributed clearing method described in this embodiment, collaborative optimization is performed on the generation side, independent energy storage side, load side, and intermediaries based on the aforementioned multi-entity operating data, multi-market transaction data, time-of-use price boundaries, and convergence thresholds. During the optimization process, initial pre-clearing is first performed under the initial time-of-use price conditions to form the upper-level normalized reference value and the lower-level distributed coordination initial state. Subsequently, upper-level intermediary price updates and lower-level ADMM distributed coordination are alternately executed until the upper-level price change residuals and the lower-level energy consistency residuals and green certificate consistency residuals all meet the preset convergence conditions. The obtained multi-market collaborative pricing results, the final revenue and cost results of each entity, and the distributed coordination convergence results are shown in Tables 1 to 3, respectively.

[0123] Table 1. Results of Multi-Market Coordinated Pricing As shown in Table 1, within the preset price allowable range, the upper-level model of the intermediary collaboratively updates the prices of conventional energy transactions, new energy transactions, load-side electricity purchase prices, independent energy storage charging and discharging transactions, green certificate transactions, and carbon transactions. After optimization, all types of prices still satisfy the corresponding price boundary constraints and form differentiated price signals at different times of valley, flat, and peak. Among them, the prices of new energy transactions, load-side electricity purchase prices, energy storage charging and discharging prices, green certificate transactions, and carbon transactions are all adjusted according to the response results of the lower-level entities, indicating that the method can achieve joint optimization of multiple types of transaction prices of electricity, green certificates, and carbon quotas under a unified pricing framework, providing a coordinated and consistent price signal for distributed clearing on the generation side, independent energy storage side, and load side.

[0124] Table 2 Summary of Final Revenues and Costs for Each Entity As shown in Table 2, under the final collaborative optimization result, the generation side, independent energy storage side, and intermediaries all generate positive net revenue, while the load side, as the energy consumer, represents net cost. Specifically, the net revenue of the generation side is formed by the combined effects of conventional energy sales, renewable energy sales, green certificate trading, and carbon quota-related settlements; the net revenue of the independent energy storage side is obtained by deducting charging costs and operational degradation costs from discharge revenue; the total cost of the load side consists of electricity purchase costs and green certificate purchase costs; and the net revenue of the intermediaries is formed by electricity trading, green certificate trading service fees, carbon quota agency trading service fees, and external market-backed trading settlements. This result demonstrates that the proposed method can, while preserving the independent optimization decisions of each entity, uniformly calculate the revenues and costs of the generation side, independent energy storage side, load side, and intermediaries, yielding multi-entity collaborative optimization results that can be used for final settlement output.

[0125] Table 3. ADMM Distributed Coordination Convergence Results As shown in Table 3, in the final iteration results, both the original residual and the dual residual of electrical energy are less than the corresponding convergence thresholds, as are the original residual and the dual residual of green certificates. This indicates that the lower-level generation side, independent energy storage side, and load side have all achieved the preset convergence conditions in terms of electrical energy consistency coordination and green certificate consistency coordination. Simultaneously, the outer-layer price RMS residual is less than the preset upper-layer price convergence threshold, and both the number of outer-layer iterations and the final number of lower-layer ADMM iterations do not exceed the preset maximum number of iterations. This indicates that the upper-layer intermediary pricing process and the lower-layer distributed clearing process can converge collaboratively. Therefore, the multi-market price results, the revenue and cost results of each entity, and the distributed coordination results obtained in this embodiment can be used as the final outputs of the virtual power plant's multi-market collaborative pricing and distributed clearing.

Claims

1. A method for multi-market coupled collaborative pricing and distributed clearing in virtual power plants, characterized in that, The method includes the following steps: Step 1: Acquire multi-entity operation data and multi-market transaction data within the virtual power plant, and construct time-of-use price sequences for various transaction prices under each scheduling period according to preset time-of-use division rules. The multi-entity entities include intermediaries, generators, independent energy storage, and loads. The multi-entity operation data includes generator operation data, independent energy storage operation data, load operation data, green certificate conversion data, carbon emission data, and carbon quota data. The multi-market transaction data includes electricity trading price boundaries, green certificate trading price boundaries, carbon trading price boundaries, and external market transaction parameters. The time-of-use price sequences serve as price signals issued by intermediaries to the corresponding entities. Step two: Based on the multi-entity operation data, multi-market transaction data, and time-of-use price series, a two-layer collaborative optimization model is constructed. The intermediary serves as the upper-layer model, while the generation-side response model, independent energy storage-side response model, and load-side response model serve as the lower-layer models. The upper-layer model uses the intermediary's transaction profit, the lower-layer comprehensive objective term, and multiple types of price deviation penalty terms as optimization criteria to update various transaction prices. The lower-layer models solve for the generation-side response, independent energy storage-side response, and load-side response respectively under a given price signal. Step 3: In the lower-level model, the generation-side response model optimizes the generation-side electricity supply, green certificate supply, and carbon quota agency trading behavior based on conventional energy trading prices, new energy trading prices, green certificate trading prices, and carbon trading prices; the independent energy storage-side response model optimizes the independent energy storage-side charging and discharging behavior and forms the independent energy storage-side electricity response based on charging and discharging trading prices; the load-side response model optimizes the load-side electricity demand and forms the load-side green certificate demand based on the load-side electricity purchase price and green certificate trading price. Step four involves constructing an energy consistency coordination mechanism and a green certificate consistency coordination mechanism, and employing the alternating direction multiplier method to perform distributed coordination on the generation side, independent energy storage side, and load side. The energy consistency coordination mechanism coordinates the energy supply on the generation side, the energy response on the independent energy storage side, and the electricity demand on the load side; the green certificate consistency coordination mechanism coordinates the green certificate supply on the generation side and the green certificate demand on the load side. Independent energy storage does not participate in green certificate consistency coordination. Based on the distributed coordination results, it is determined whether the lower-level distributed coordination has converged. If the lower-level distributed coordination converges, the lower-level distributed coordination result under the current price conditions is obtained; otherwise, the alternating direction multiplier method is used for iterative updates until the lower-level convergence condition is met or the preset maximum number of iterations is reached. Step 5: Based on the results of the lower-level distributed coordination, calculate the profits of intermediaries in electricity trading, green certificate trading, carbon quota agency trading, and external market backing trading, and solve the upper-level model to obtain the updated trading prices for each type of transaction. Step 6: Normalize the various transaction prices before and after the update according to the corresponding allowed price range, and calculate the upper-level price convergence residual based on the normalized price change. When the upper-level price convergence residual meets the preset upper-level convergence condition and the lower-level distributed coordination meets the preset lower-level convergence condition, output the virtual power plant multi-market collaborative pricing result and multi-entity collaborative optimization result. Otherwise, use the updated various transaction prices as the input for the next round of upper-level iteration, and repeat the lower-level distributed coordination and upper-level intermediary update process.

2. The virtual power plant multi-market coupled collaborative pricing and distributed clearing method according to claim 1, characterized in that, In step two, the upper-level model is an intermediary price update and settlement model, which is used to update various transaction prices under the condition of fixing the current lower-level distributed coordination results. The various transaction prices include conventional energy transaction prices, new energy transaction prices, load-side electricity purchase prices, independent energy storage-side charging transaction prices, independent energy storage-side discharging transaction prices, green certificate transaction prices, and carbon transaction prices. The upper-level model consists of a middleman profit item, a lower-level comprehensive objective item, and multiple types of price deviation penalty items. The middleman profit item is determined based on load-side power purchase transactions, independent energy storage-side charge / discharge transactions, conventional energy power purchase transactions, new energy power purchase transactions, external energy market power purchase and sale transactions, green certificate trading service fees, and carbon quota agency trading service fees. The lower-level comprehensive objective item is determined based on generation-side transaction revenue and operating costs, independent energy storage-side charge / discharge revenue and costs, and load-side power purchase and green certificate purchase costs. The multiple types of price deviation penalty items are determined based on the normalized deviation of various transaction prices relative to their corresponding benchmark prices. The upper-level model is solved under various upper and lower price constraints and intermediary minimum profit constraints, and the obtained transaction prices are used as price signals for the next round of lower-level distributed coordination. Among them, the green certificate transaction service fee is charged by the intermediary to the green certificate seller, and the carbon quota agency transaction service fee is charged by the intermediary to the corresponding trading party based on the agency buying and selling transaction amount between the power generation side and the external carbon market.

3. The virtual power plant multi-market coupled collaborative pricing and distributed clearing method according to claim 1, characterized in that, In step three, the power generation-side response model is represented in augmented Lagrange form and is used to optimize conventional energy output, actual grid-connected output of new energy, curtailed wind and solar power, green certificate supply, and carbon quota trading volume, given conventional energy trading prices, new energy trading prices, green certificate trading prices, and carbon trading prices. The objective terms of the generation-side response model include generation-side trading revenue, generation-side operating costs and penalty costs, electricity consistency augmentation term, and green certificate consistency augmentation term; among them, generation-side trading revenue includes conventional energy electricity sales revenue, renewable energy electricity sales revenue, green certificate trading revenue, and carbon quota trading revenue, while generation-side operating costs and penalty costs include conventional energy operating costs and renewable energy curtailment penalty costs. The power generation-side response model satisfies the upper and lower limits of conventional energy unit output, the ramp-up constraint of conventional energy unit, the available output constraint of new energy power plants, the green certificate supply conversion constraint, and the carbon quota trading constraint. Among them, the actual grid-connected output of new energy and the curtailed wind and solar power are determined based on the predicted available output of new energy, and the green certificate supply is calculated based on the actual grid-connected power of new energy. The volume of carbon allowances traded is determined based on the difference between the actual carbon emissions on the power generation side and the amount of free carbon allowances. When the actual carbon emissions are greater than the amount of free carbon allowances, a carbon allowance deficit is formed; when the actual carbon emissions are less than the amount of free carbon allowances, a carbon allowance surplus is formed. Carbon allowance trading on the power generation side does not participate in the internal consistency clearing among the power generation side, independent energy storage side, and load side. Instead, the power generation side conducts transactions and settlements with the external carbon market through intermediaries. The response variables for the power generation side's participation in the coordination of power energy consistency are determined by the output of conventional energy and the actual grid-connected output of new energy sources, while the response variables for the power generation side's participation in the coordination of green certificate consistency are determined by the supply of green certificates.

4. The virtual power plant multi-market coupled collaborative pricing and distributed clearing method according to claim 1, characterized in that, In step three, the independent energy storage side response model is represented in augmented Lagrangian form, which is used to optimize the charging power, discharging power, energy storage state, and charge / discharge state of the independent energy storage side under the given independent energy storage side charging transaction price and independent energy storage side discharging transaction price. The objective terms of the independent energy storage side response model include the independent energy storage side discharge revenue, the independent energy storage side charging cost, the independent energy storage side operation degradation cost, and the independent energy storage side energy consistency augmentation term. Among them, the independent energy storage side discharge revenue is determined based on the independent energy storage side discharge power and the independent energy storage side discharge transaction price; the independent energy storage side charging cost is determined based on the independent energy storage side charging power and the independent energy storage side charging transaction price; and the independent energy storage side operation degradation cost is determined based on the independent energy storage side charging power and discharge power. The independent energy storage side response model satisfies the following constraints: energy storage energy state transition, upper and lower limits of energy storage energy state, consistency of energy state at the beginning and end of the scheduling cycle, upper and lower limits of charging and discharging power, mutual exclusion of charging and discharging, and variation of charging and discharging power. Among them, the energy storage energy state is updated according to the energy storage energy state, charging power, discharging power, charging efficiency, and discharging efficiency of the previous time period. The charging and discharging state is used to limit the independent energy storage from charging and discharging at the same time within the same time period. The response variable for independent energy storage side to participate in power consistency coordination is determined by the difference between the discharge power and the charging power of independent energy storage side.

5. The virtual power plant multi-market coupled collaborative pricing and distributed clearing method according to claim 1, characterized in that, In step three, the load-side response model is represented in augmented Lagrange form to optimize the power consumption and green certificate demand in each region of the load side, given the load-side electricity purchase price and green certificate trading price. The target terms of the load-side response model include the load-side comprehensive cost, the load-side satisfaction index, the electricity consistency augmentation term, and the green certificate consistency augmentation term. Among them, the load-side comprehensive cost includes the electricity purchase cost and the green certificate purchase cost. The electricity purchase cost is determined based on the total electricity consumption on the load side and the electricity purchase price on the load side, while the green certificate purchase cost is determined based on the green certificate demand on the load side and the green certificate transaction price. The load-side response model determines the price response forecast load for each region during each dispatch period based on the rate of change of the load-side electricity purchase price relative to the benchmark electricity purchase price and the price elasticity matrix. The price response forecast load is used as the demand response boundary reference for the electricity consumption of each region. The electricity consumption of each region satisfies the demand response boundary constraint and the electricity consumption conservation constraint during the dispatch period. The load-side satisfaction index is determined based on the degree of deviation of the optimized power consumption in each region from the baseline load, and is used to constrain the adjustment range of the load-side demand response; the load-side green certificate demand is calculated based on the total power consumption on the load side. The load side participates in both power consistency coordination and green certificate consistency coordination. The response variable for the load side's participation in power consistency coordination is determined by the total power consumption of the load side, while the response variable for the load side's participation in green certificate consistency coordination is determined by the load side's green certificate demand.

6. The virtual power plant multi-market coupled collaborative pricing and distributed clearing method according to claim 1, characterized in that, In step four, given a certain round of upper-level price, the alternating direction multiplier method is used to carry out lower-level distributed coordination on the generation side, independent energy storage side and load side. The lower-level distributed coordination includes energy consistency coordination and green certificate consistency coordination. In each lower-level iteration, the generation side, independent energy storage side, and load side solve the corresponding local response models based on the current price signal, the consistency variables of the previous round, and the dual variables of the previous round, respectively, to obtain the current round generation side electric energy response variables, independent energy storage side electric energy response variables, load side electric energy response variables, generation side green certificate response variables, and load side green certificate response variables. The intermediary updates the electrical energy consistency variable based on the electrical energy response variables of each lower-level entity in this round, and updates the green certificate consistency variable based on the green certificate response variables of the generation side and the green certificate response variables of the load side in this round; subsequently, the electrical energy dual variable and the green certificate dual variable are updated based on the deviation between the response variables of each lower-level entity and the corresponding consistency variable. After completing the updates of the consistency variables and dual variables, the original residuals of electrical energy, the dual residuals of electrical energy, the original residuals of green certificates, and the dual residuals of green certificates are solved based on the results of the lower-level distributed coordination. The convergence of the lower-level model is then determined based on these residuals. Among them, the original residual of electrical energy is calculated based on the deviation between the electrical energy response variables of each lower-level subject and the corresponding electrical energy consistency variables; the dual residual of electrical energy is calculated based on the change between the electrical energy consistency variables of each lower-level subject in two adjacent iterations; the original residual of green certificate is calculated based on the deviation between the green certificate response variables of the generation side and the load side and the corresponding green certificate consistency variables; the dual residual of green certificate is calculated based on the change between the green certificate consistency variables of the generation side and the load side in two adjacent iterations.

7. The virtual power plant multi-market coupled collaborative pricing and distributed clearing method according to claim 1, characterized in that, In step six, the upper-level model updates various transaction prices according to the outer-level iteration method, and determines whether the upper-level price converges based on the price change before and after the update; In the In the outer-layer iteration, the intermediary publishes current transaction prices to the generation side, independent energy storage side, and load side. After the lower-layer distributed coordination converges, the upper-layer model is solved based on the current lower-layer distributed coordination results to obtain the first... Prices for various types of transactions; No. The various transaction prices include conventional energy transaction prices, new energy transaction prices, load-side electricity purchase prices, independent energy storage-side charging transaction prices, independent energy storage-side discharging transaction prices, green certificate transaction prices, and carbon transaction prices, and all types of transaction prices meet the corresponding upper and lower price limits. According to the middleman Round of various transaction prices and the first The difference between various transaction prices and the price allowable range corresponding to each transaction price are used to calculate the normalized price change, and the upper-level price convergence residual is determined based on the normalized price change. Based on the convergence residuals of the upper-level prices and the convergence status of the lower-level distributed coordination, determine whether the two-level model has converged. If it has not converged, use the updated transaction prices as the input prices for the next round of outer-level iterations. At the beginning of the next round of outer-level iterations, use the energy consistency variable, green certificate consistency variable, and their corresponding dual variables at the end of the previous round of outer-level iterations as the initial variables for the lower-level distributed coordination; or reset the initial variables for the lower-level distributed coordination according to the preset initialization rules.

8. The virtual power plant multi-market coupled collaborative pricing and distributed clearing method according to claim 1, characterized in that, In step five, the green certificate transactions use continuous variables to participate in optimization during the lower-level distributed coordination process, and are integerized during the upper-level model solution stage involving green certificate transaction amount calculation and final settlement output. In the lower-level distributed coordination process, the supply of green certificates on the generation side and the demand of green certificates on the load side are used as continuous variables in the local response optimization of the generation side, the local response optimization of the load side, the update of green certificate consistency variables, the update of green certificate dual variables, and the judgment of green certificate original residuals and green certificate dual residuals. In the upper-level model solution, the calculation of green certificate transaction amount and the final settlement output stage are respectively rounded down for the hourly green certificate supply on the power generation side and the hourly green certificate demand on the load side to obtain the integer green certificate supply on the power generation side and the integer green certificate demand on the load side participating in the settlement. The hourly internal green certificate settlement volume is determined based on the integer green certificate supply and demand. The hourly internal green certificate settlement volume is determined by the smaller of the integer green certificate supply and demand, and the difference is covered by transactions in the external market. When the integer green certificate demand is greater than the internal green certificate settlement volume, the difference is used as the amount of green certificates supplemented through the external green certificate market. When the integer green certificate supply is greater than the internal green certificate settlement volume, the difference is used as the amount of green certificates sold through the external green certificate market. Intermediaries calculate green certificate transaction amount and green certificate transaction service fee income based on internal green certificate settlement volume, external green certificate replenishment volume, external green certificate sales volume, green certificate transaction price, external green certificate market fixed transaction price, and green certificate transaction service fee rate. Intermediaries calculate the amount of carbon quota agency transactions and the income from carbon quota agency transactions based on carbon trading prices, the amount of external purchases corresponding to carbon quota gaps, the amount of external sales corresponding to carbon quota surpluses, and the carbon quota agency transaction service fee rate.

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