Port virtual power plant interval game method and device considering transaction uncertainty

Abstract

The invention discloses a port virtual power plant interval game method and device considering transaction uncertainty, and the method comprises the steps: building an electric energy transaction model based on the electric energy supply and demand capability interval of each subsystem of a port virtual power plant in combination with quotation parameters and power grid parameters; based on the electric energy transaction model, adopting a pre-constructed transaction matching mechanism to give a transaction pair matching list; based on the transaction pair matching list, determining a transaction electric quantity and a settlement electricity price, executing a transaction, and generating an alliance transaction record; based on the electric energy transaction model and the alliance transaction record, obtaining an alliance utility interval, a marginal contribution interval of each subsystem in the alliance and an interval Shapley value of each subsystem; and based on the interval Shapley value of each subsystem, according to a pre-constructed distribution rule, giving an income distribution interval of each subsystem. Resource matching optimization and stable alliance structure construction under uncertainty disturbance are realized, and the self-coordination capability and cooperation benefit in the port multi-energy system are improved.

Description

Technical Field

[0001] This invention belongs to the field of port virtual power plants, specifically relating to a port virtual power plant interval game method and apparatus that takes into account transaction uncertainty. Background Technology

[0002] Currently, with the continuous growth of new energy output and energy load in port energy systems, port virtual power plants (VPPs), as important operating platforms aggregating shore power systems, wind, solar, and energy storage resources, and various adjustable loads, have become key platforms for improving the green operating efficiency and energy self-coordination capabilities of ports. Port VPPs contain multiple subsystems with independent operating intentions, including load-driven systems such as ship shore power, cold chain storage, and electric heavy-duty truck charging stations, as well as energy storage devices, offshore wind power, and distributed photovoltaic systems with energy supply capabilities. The interconnection and trading of electricity between these heterogeneous systems makes it possible for port areas to achieve local consumption and reduce electricity purchase costs.

[0003] However, in actual operation, the electricity trading between port subsystems is subject to significant uncertainty due to factors such as unstable renewable energy output, fluctuating load demand, and uncertain market response. This uncertainty makes it difficult for traditional static matching strategies to guarantee the stability and economy of system operation. Furthermore, the various subsystems within a port VPP typically belong to different operators, and their trading behavior is characterized by game theory and profit-driven factors. Therefore, constructing a robust and fair cooperation mechanism becomes a core challenge.

[0004] Patent CN119106832A provides a consensus-based decentralized scheduling method for the collaborative operation of a virtual power plant in a port, comprising the following steps: S1. Constructing a virtual power plant system model (SVPP) for the port; S2. Constructing an energy service model; S3. Designing a decentralized scheduling method for the virtual power plant in the port. This method addresses the serious challenges brought about by the accelerated electrification of ports, enhances the overall potential and flexibility of the SVPP, protects the privacy of all relevant entities, and strengthens energy interaction between the shore side and the ship side, enabling not only the sale of energy to the shore side but also participation in mutual energy sharing. However, this method does not consider the uncertainties and disturbances in the port energy system, only addresses the ship energy trading scenario, and its collaborative structure is prone to collapse, failing to dynamically optimize the structure of the trading collective.

[0005] Patent CN120280930B discloses a multi-microgrid collaborative scheduling method and system based on game theory. The method includes: generating a dynamic game initial strategy set through a multi-agent strategy network based on the user's energy storage device's state of charge, charging and discharging efficiency, and real-time grid scheduling needs; based on the dynamic game initial strategy set, using an asymmetric Nash bargaining model for distributed negotiation to generate an equilibrium benefit allocation scheme; based on the equilibrium benefit allocation scheme, using a time-decaying reinforcement learning algorithm to iteratively correct the energy storage priority index, generating dynamic bidding rules including supply and demand elasticity coefficients and risk compensation; based on the dynamic bidding rules, allocating energy storage resources in real time through a decentralized gradient consensus mechanism, generating final collaborative scheduling instructions and synchronizing them to each microgrid terminal. This method can improve the operating efficiency and adaptability of multi-microgrid systems, meeting the needs of future smart distribution networks. However, this method lacks a mechanism for quantifying uncertainty, making it difficult to adapt to high-fluctuation scenarios in ports. Furthermore, it lacks a continuous alliance evolution mechanism, does not consider the marginal contribution of subsystems to the alliance, and cannot continuously optimize the dynamic supply and demand relationship in ports.

[0006] To address the problems in the existing technologies, there is an urgent need for a multi-entity collaborative method for internal trading scenarios in port virtual power plants. This method can guide participating subsystems to form a stable cooperative alliance while taking into account the uncertainties of electricity trading, rationally match supply and demand, and ensure the fair distribution of trading benefits, thereby improving the collaborative operation efficiency and anti-interference capability of the entire port energy system. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a port virtual power plant interval game theory method and apparatus that considers transaction uncertainty. By constructing a port virtual power plant power trading model, it comprehensively considers factors such as renewable energy output fluctuations, random load response, and electricity price changes in the power supply and demand process of various port subsystems, effectively characterizing the interval uncertainty of trading power and price. A subsystem alliance trading matching and structural evolution mechanism is adopted to achieve stable multi-entity power cooperation. Based on the marginal contribution of each subsystem to the overall alliance utility, the comprehensive benefits brought by the transaction are rationally allocated. Ultimately, resource matching optimization and stable alliance structure construction under uncertainty disturbances are achieved, improving the self-coordination capability and cooperation efficiency within the port's multi-energy system.

[0008] In a first aspect, the present invention provides a port virtual power plant interval game method that takes into account transaction uncertainty, comprising the following steps: Based on the power supply and demand capacity range of each subsystem of the port virtual power plant, and combined with the bidding parameters and grid parameters, a power trading model is constructed. Based on the electricity trading model, a pre-built trading matching mechanism is used to provide a list of matching trading pairs; Based on the trading pair matching list, the transaction volume and settlement price are determined, the transaction is executed, and the alliance transaction record is generated; Based on the electricity trading model and the alliance trading records, the alliance utility range, the marginal contribution range of each subsystem in the alliance, and the range Shapley value of each subsystem are obtained. Based on the interval Shapley values ​​of each subsystem, the revenue distribution interval of each subsystem is given according to the pre-built allocation rules.

[0009] Furthermore, the subsystems include an electricity sales system and an electricity purchase system; the electricity supply and demand capacity ranges include a power supply capacity range and a power purchase demand range; the pricing parameters include an electricity sales price range and a power purchase price range; and the grid parameters include the interconnection resistance and rated voltage between each subsystem and the main grid. A power trading model is constructed, specifically including: Obtain the power supply capacity range and electricity sales price range for each electricity sales system, and the electricity purchase demand range and electricity purchase price range for each electricity purchase system, and establish transaction feasibility criteria. A transmission loss model is constructed by combining the interconnection resistance and rated voltage between each subsystem and the interconnection resistance and rated voltage between each subsystem and the main power grid. Define the initial alliance structure, construct the alliance utility function, and update the alliance structure in conjunction with the alliance evolution mechanism.

[0010] Furthermore, based on the electricity trading model, a pre-built trading matching mechanism is used to provide a list of matching trading pairs, specifically including: In each round of transactions, for each electricity sales system, based on the electricity sales price range and the electricity purchase price range of all electricity purchase systems, and combined with the transaction feasibility criteria, the feasibility of the transaction is judged, and feasible transaction pairs are given. Among all feasible transaction pairs, the upper limit of the electricity purchase price range of the electricity purchase system is used as the maximum willingness to pay indicator. The electricity purchase system with the best maximum willingness to pay indicator is selected as the transaction object, the optimal transaction pair is given, and the optimal transaction pair is included in the transaction pair matching list. Iterate through all the electricity sales systems in sequence, provide the optimal trading pair and update the trading pair matching list until the electricity purchase demand of all the electricity purchase systems is met or the power supply capacity of all the electricity sales systems is consumed, and provide the trading pair matching list.

[0011] Furthermore, based on the matching list of trading pairs, the transaction volume and settlement price are determined, the transaction is executed, and a consortium transaction record is generated, specifically including: For each trading pair in the trading pair matching list, the transaction volume is obtained by combining the power supply capacity range of the power supply system and the power purchase demand range of the power purchase system. Based on the electricity sales price range of the power supply system and the electricity purchase price range of the power purchase system, the settlement price is obtained by using the median value of the range; Based on the transmission loss model and combined with the transaction volume, the power transmission loss is obtained. For each electricity purchase system, the total received electricity is obtained based on all the traded electricity volume and the corresponding electricity transmission loss. The compensation electricity is obtained based on the electricity purchase demand range and the total received electricity volume. The main grid electricity price is determined, and the compensation electricity is supplemented through the main grid. For each electronic sales system, based on all transaction volumes and power supply capacity ranges, the surplus volume is obtained, the main grid electricity price is determined, and the surplus volume is sold to the main grid. Execute transactions and generate alliance transaction records, which include transaction pairs, transaction volume, settlement price, power transmission loss, compensated volume, surplus volume and main grid price.

[0012] Furthermore, based on the electricity trading model and the alliance's trading records, the alliance's utility range, the marginal contribution range of each subsystem within the alliance, and the interval Shapley value of each subsystem are obtained, specifically including: Based on the coalition utility function, obtain the coalition utility interval; For each subsystem in the alliance, obtain its alliance utility before and after joining the alliance, and give the marginal contribution range; Get all sub-alliances of the current alliance. For all sub-alliances that do not contain a certain subsystem, get the marginal contribution range of that subsystem to the sub-alliances, and give the weighted marginal contribution as the interval Shapley value of that subsystem. Repeat the process of obtaining the interval Shapley values ​​of the subsystems until the interval Shapley values ​​of each subsystem are obtained. Among them, the alliance utility range is obtained based on the alliance utility function, including: obtaining the first and second transaction costs of the alliance based on the electricity trading model and the alliance transaction records; By combining the first and second transaction costs, we can obtain the utility of the alliance and give the range of the alliance utility.

[0013] Furthermore, updating the alliance structure in conjunction with the alliance evolution mechanism specifically includes: Based on the initial alliance structure, the driving forces of alliance evolution are identified, and based on the driving forces of alliance evolution and the alliance evolution rules, alliance evolution operations are executed, including merging and splitting. For the alliance structure after performing the alliance evolution operation, a Pareto dominance relationship-based alliance structure screening mechanism is adopted to eliminate inferior alliances and retain non-dominant alliances as candidate alliance structures. Based on the principle of no individual deviation incentive, it is determined whether the candidate alliance structure is stable. If it is unstable, the evolution and screening continue until the alliance structure is stable.

[0014] Furthermore, no individual deviates from the incentive principle, including: No two alliances satisfy the alliance merger rule, and no alliance satisfies the alliance split rule; and, The rate of change of the marginal contribution interval of all subsystems is less than the preset threshold; and, The current Pareto alliance's replacement update has stopped.

[0015] Furthermore, before executing a transaction, a sorting mechanism is used to determine the order in which transactions are executed, specifically including: By considering the combined effectiveness of the alliance, the fairness of benefits, and the efficiency of transactions, a multi-objective optimization function is constructed. Iterate through all transaction sequences and select the set of non-dominant transaction sequences based on Pareto dominance. Based on a multi-objective optimization function and combined with objective weights, the comprehensive score of each non-dominated transaction sequence in the non-dominated transaction sequence set is obtained, and the non-dominated transaction sequence with the highest comprehensive score is selected as the transaction execution order.

[0016] Furthermore, based on the interval Shapley values ​​of each subsystem, and according to the pre-built allocation rules, the revenue interval of each subsystem is given, specifically including: For the interval Shapley values ​​of each subsystem, combined with the coalition utility interval, the collective constraints are verified and the first adjustment is made; Obtain the retention utility of each subsystem, combine it with the interval Shapley value after the first adjustment, verify the individual constraints, and make a second adjustment; The second adjusted interval Shapley value is used as the subsystem's revenue interval.

[0017] Secondly, the present invention also provides a port virtual power plant interval game device that takes into account transaction uncertainty. Employing the aforementioned port virtual power plant interval game method that takes into account transaction uncertainty, the device includes: The model building module is used to construct an energy trading model based on the energy supply and demand capacity range of each subsystem of the port virtual power plant, combined with bidding parameters and grid parameters. The transaction matching module is used to generate a list of matching transaction pairs based on the electricity trading model and a pre-built transaction matching mechanism. The transaction execution module is used to determine the transaction volume and settlement price based on the transaction pair matching list, execute the transaction, and generate alliance transaction records; The utility acquisition module is used to obtain the utility range of the alliance, the marginal contribution range of each subsystem in the alliance, and the range Shapley value of each subsystem based on the power trading model and the alliance transaction records. The revenue distribution module, based on the interval Shapley values ​​of each subsystem, provides the revenue distribution interval for each subsystem according to the pre-built distribution rules.

[0018] The port virtual power plant interval game method and apparatus considering transaction uncertainty provided by this invention have at least the following beneficial effects: (1) Through price competition game in the transaction matching stage, evolution game in the alliance evolution stage, and cooperative game in the profit distribution stage, the competitive conflict between the subsystems of the port virtual power plant is transformed into synergistic efficiency, achieving a stable equilibrium of "shared risk and shared benefits" under uncertainty, and also ensuring the fairness of profit distribution of each subsystem.

[0019] (2) By constructing an electricity trading model and introducing range modeling of trading power and electricity price, the uncertainty in the actual operation of the port virtual power plant can be effectively reflected, and the robustness and adaptability of the port virtual power plant can be improved.

[0020] (3) Combining the rules of alliance merger and split, an evolution strategy based on Pareto optimality is introduced, which can dynamically optimize the alliance structure, improve the overall transaction efficiency of the alliance and ensure the stable operation of the port virtual power plant.

[0021] (4) By adopting the coalition utility and interval Shapley value allocation mechanism, the fairness and interpretability of income distribution can be achieved. Attached Figure Description

[0022] Figure 1 A flowchart illustrating a port virtual power plant interval game method that takes into account transaction uncertainty, provided by the present invention; Figure 2 This is a schematic diagram illustrating the process of constructing an electricity trading model according to a certain embodiment of the present invention; Figure 3 This is a schematic diagram of the process for obtaining the interval Shapley value of a subsystem according to a certain embodiment of the present invention; Figure 4 This is a schematic diagram of the process of obtaining the updated alliance structure of the subsystem according to a certain embodiment of the present invention; Figure 5 A schematic diagram of the revenue range process of a subsystem is provided for one embodiment of the present invention; Figure 6 A schematic diagram of the port virtual power plant interval game device that takes into account transaction uncertainty provided by the present invention. Detailed Implementation

[0023] To better understand the above technical solutions, a detailed description of the solutions will be provided below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0024] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0025] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0026] Within a port virtual power plant, multiple energy subsystems face challenges related to power trading matching, utility allocation, and alliance structure stability under conditions of output and load uncertainty. The port virtual power plant comprises several energy subsystems with either power purchase or sales capabilities, such as shore power supply nodes, wind / solar power access points, electric heavy-duty truck charging areas, cold chain storage systems, and energy storage equipment. These subsystems are connected point-to-point via flexible switching devices, forming a collaborative trading network within the port area.

[0027] Firstly, such as Figure 1 As shown, a port virtual power plant interval game method that takes into account transaction uncertainty includes the following steps: Based on the power supply and demand capacity range of each subsystem of the port virtual power plant, and combined with the bidding parameters and grid parameters, a power trading model is constructed. Based on the electricity trading model, a pre-built trading matching mechanism is used to provide a list of matching trading pairs; Based on the trading pair matching list, the transaction volume and settlement price are determined, the transaction is executed, and the alliance transaction record is generated; Based on the electricity trading model and the alliance trading records, the alliance utility range, the marginal contribution range of each subsystem in the alliance, and the range Shapley value of each subsystem are obtained. Based on the interval Shapley values ​​of each subsystem, the revenue distribution interval of each subsystem is given according to the pre-built allocation rules.

[0028] In this method, the power supply and demand capacity of each subsystem is represented as an interval variable, and a power trading model is established. Based on this model, transaction matching, transaction execution, and benefit distribution are carried out. Through price competition game in the transaction matching stage and cooperative game in the benefit distribution stage, the competitive conflict between the subsystems of the port virtual power plant is transformed into synergistic efficiency, improving the cooperative benefits of each subsystem, obtaining the benefit distribution range of each subsystem, and ensuring the fairness of the benefit distribution of each subsystem.

[0029] Understandably, the Shapley value is a fair distribution method in cooperative game theory used to quantify each member's contribution to the alliance and allocate alliance payoffs accordingly. It ensures each member receives a share of payoffs commensurate with their contribution by calculating a weighted average of the marginal contributions of members across all possible alliance combinations. The interval Shapley value is an extension of the Shapley value, used to handle cooperative games with uncertainty. It considers that each member's contribution may fall within a range, calculating the payoff range for each member under different contribution scenarios, thus achieving fair distribution under uncertainty.

[0030] Specifically, the subsystem includes the electricity sales system and the electricity purchase system; the power supply and demand capacity range includes the power supply capacity range and the power purchase demand range; the pricing parameters include the electricity sales price range and the power purchase price range; and the grid parameters include the interconnection resistance and rated voltage between each subsystem and the main grid.

[0031] In one implementation, such as Figure 2 As shown, the electricity trading model is constructed, specifically including: Obtain the power supply capacity range and electricity sales price range for each electricity sales system, and the electricity purchase demand range and electricity purchase price range for each electricity purchase system, and establish transaction feasibility criteria. A transmission loss model is constructed by combining the interconnection resistance and rated voltage between each subsystem and the interconnection resistance and rated voltage between each subsystem and the main power grid. Define the initial alliance structure, construct the alliance utility function, and update the alliance structure in conjunction with the alliance evolution mechanism.

[0032] Understandably, subsystems with power supply capabilities, such as energy storage devices, offshore wind power, and distributed photovoltaic systems, are power-selling systems and require a sales price when selling electricity. Load-driven subsystems, such as ship shore power, cold chain warehousing, and electric heavy-duty truck charging stations, have electricity purchase needs and are power-purchasing systems, requiring a purchase price when purchasing electricity.

[0033] In this example, considering the large fluctuations in power output and the uncertainty of load response in the port scenario, the power supply and demand capacity and bidding behavior of each subsystem are represented by an interval method. An electricity trading model that considers the uncertainty of power and price is constructed to provide a mathematical basis for subsequent alliance matching and utility allocation.

[0034] Specifically, the power supply and demand capacity of a subsystem is represented by the transaction volume per unit time (i.e., transaction power). The transaction volume range per unit time for the k-th subsystem in the port virtual power plant is represented as follows: in , Let be the minimum and maximum trading power of the k-th subsystem, respectively, in MW; if and This indicates that the subsystem has the capability to sell electricity, and is therefore an electricity sales system. This refers to the power supply capacity range; if and This indicates that the subsystem has a need to purchase electricity, and is therefore an electronic system. This represents the range of electricity demand. Furthermore, this model does not consider hybrid behaviors involving both purchasing and selling electricity.

[0035] Considering the volatility of trading intentions, both electricity purchase and sales prices are expressed as ranges. The electricity sales price range for electronic sales system j is expressed as follows: in, This represents the lower limit of the electricity sales price range, indicating the lowest price at which the seller is willing to transact. The upper limit of the electricity sales price range represents the ideal expected return price. The electricity sales price can be estimated using the following method: in, The electronic power sales system can choose to sell surplus electricity back to the main grid, in MW. The electricity actually sent by the electronic sales system to the electronic purchase system, in MW. The main grid electricity price.

[0036] The electricity purchase price range for electronic system i is represented as follows: in, This represents the lower limit of the electricity purchase price range, corresponding to the lowest electricity purchase price under the expected optimal trading scenario. This represents the upper limit of the electricity purchase price range, indicating the maximum price that the electricity system is willing to accept during periods of tight supply and demand. Main grid electricity price; The compensation electricity obtained by the purchasing electronic system i from the main grid when the demand cannot be met through transactions with the selling electronic system, in MW; The actual amount of electricity received by the purchasing electronic system from the selling electronic system, in MW; The highest electricity purchase price that can be afforded for purchasing electronic systems.

[0037] The feasibility criterion for a transaction is specifically expressed as follows: if a certain transaction pair (purchasing electronic system i, selling electronic system j) meets the following conditions: This indicates that the trading pair is likely to be traded at a reasonable price. This is the upper limit of the electricity purchase price range for the electronic system i. It is the lower limit of the electricity sales price range for the electronic sales system j.

[0038] Since power transmission losses exist between the subsystems of the port virtual power plant and between each subsystem and the main power grid, a transmission loss model can be constructed based on the interconnection resistance and rated voltage between each subsystem and the main power grid. The power transmission loss between purchasing electronic system i and selling electronic system j is specifically expressed as follows: in, The amount of electricity purchased by electronic system i from electronic system j, in MW. The power transmission loss between electronic system i (purchasing system i) and electronic system j (selling system j); U is the resistance of the connection line between electronic system i and electronic system j, in Ω; U is the rated voltage between electronic system i and electronic system j, in kV.

[0039] The power transmission loss between subsystem k and the main power grid is specifically expressed as: in, R represents the power transmission loss between subsystem k and the main grid, in MW; R represents the power transmission between subsystem k and the main grid, in MW. k The interconnection resistance between subsystem k and the main power grid; This is the rated voltage between subsystem k and the main power grid, i.e., the bus voltage.

[0040] Understandably, the subsystems of the port virtual power plant form a trading alliance to reduce transmission losses through resource complementarity and internally share the risks of fluctuations in renewable energy output and load, thereby achieving cost reduction and efficiency improvement. In this example, a certain number of subsystems are first grouped into a trading alliance based on their actual cooperative relationships or historical experience, i.e., the initial alliance structure is defined. Then, the alliance structure is updated through an evolution mechanism to respond to the dynamic changes in supply and demand of the subsystems of the port virtual power plant.

[0041] In this example, alliance utility is the quantification of the overall energy-saving and loss-reduction effect of transactions between subsystems in a port virtual power plant trading alliance. If an alliance C includes a set of purchasing electronic systems B and a set of selling electronic systems S, then the alliance utility is defined as: in, This is the alliance utility of alliance C, expressed in yuan; The first transaction cost represents the transaction fees incurred if all electronic systems purchased electricity directly from the main grid; The power required for purchasing electronic systems; Main grid electricity price; The second transaction cost represents the line loss cost incurred through transactions within the alliance. The power transmission loss between electronic system i (purchasing system i) and electronic system j (selling system j); This represents the settlement price for purchasing electronic system i and selling electronic system j through transactions within the alliance.

[0042] Considering that transaction parameters such as electricity sales price, electricity purchase price, main grid electricity price, and electricity supply and demand capacity are interval quantities, the alliance utility is also expressed in interval form, that is: in, , These represent the lower limit and upper limit of the coalition utility range, respectively, indicating the minimum and maximum benefits generated under the most unfavorable and most favorable trade combinations.

[0043] It is understandable that the definition of coalition utility and the representation of coalition utility interval constitute the coalition utility function, which shows the specific way to obtain the coalition utility interval.

[0044] In another implementation, based on the electricity trading model, a pre-built trading matching mechanism is used to provide a list of matching trading pairs, specifically including: In each round of transactions, for each electricity sales system, based on the electricity sales price range and the electricity purchase price range of all electricity purchase systems, and combined with the transaction feasibility criteria, the feasibility of the transaction is judged, and feasible transaction pairs are given. Among all feasible transaction pairs, the upper limit of the electricity purchase price range of the electricity purchase system is used as the maximum willingness to pay indicator. The electricity purchase system with the best maximum willingness to pay indicator is selected as the transaction object, the optimal transaction pair is given, and the optimal transaction pair is included in the transaction pair matching list. Iterate through all the electricity sales systems in sequence, provide the optimal trading pair and update the trading pair matching list until the electricity purchase demand of all the electricity purchase systems is met or the power supply capacity of all the electricity sales systems is consumed, and provide the trading pair matching list.

[0045] In this example, a multi-to-one highest-range sealed auction mechanism is adopted to construct a sealed auction mechanism led by the electricity sales system. The system matches the electricity purchaser with the highest upper limit of the purchase price range from the candidate electricity purchasers to complete the transaction and provides a transaction matching list.

[0046] Specifically, in each round of transactions, the electronic sales system takes the lead, while the electronic purchase system acts as the bidder, participating in the bidding based on its electricity purchase price range. If there are m electronic sales systems in the port virtual power plant, their set is denoted as... There are n electronic purchasing systems, denoted as . The specific process of using a transaction matching mechanism to generate a list of matching transaction pairs includes: Step 1): Select one electronic vending system S from the set S of electronic vending systems that have not yet completed a transaction. j Participate in this round of transactions; acquire all electronic systems. Submitted electricity purchase price range; based on the transaction feasibility criterion, determine the electricity purchase price range and its compatibility with the electricity sales system S. j Does the electricity sales price range meet the relationship? If satisfied, then B i As a candidate electricity purchaser, (S) j B i ( ) as a viable trading pair.

[0047] Step 2): For all feasible transaction pairs, match the best bidder, i.e., use the upper limit of the electricity purchase price range of the electronic system as the indicator of their maximum willingness to pay, denoted as... The rules for selecting the optimal electronic purchasing system are as follows: Then purchase electronic systems Selected as the best electronic purchasing system, (S) j , This is selected as the optimal trading pair and included in the trading pair matching list.

[0048] Step 3): Traverse all purchasing electronics systems, provide the optimal trading pair, and update the trading pair matching list until the purchasing demand of all purchasing electronics systems is met or the power supply capacity of all selling electronics systems is consumed, thus obtaining the final trading pair matching list.

[0049] In another implementation, based on the trading pair matching list, the traded electricity volume and settlement price are determined, the transaction is executed, and a consortium trading record is generated, specifically including: For each trading pair in the trading pair matching list, the transaction volume is obtained by combining the power supply capacity range of the power supply system and the power purchase demand range of the power purchase system. Based on the electricity sales price range of the power supply system and the electricity purchase price range of the power purchase system, the settlement price is obtained by using the median value of the range; Based on the transmission loss model and combined with the transaction volume, the power transmission loss is obtained. For each electricity purchase system, the total received electricity is obtained based on all the traded electricity volume and the corresponding electricity transmission loss. The compensation electricity is obtained based on the electricity purchase demand range and the total received electricity volume. The main grid electricity price is determined, and the compensation electricity is supplemented through the main grid. For each electronic sales system, based on all transaction volumes and power supply capacity ranges, the surplus volume is obtained, the main grid electricity price is determined, and the surplus volume is sold to the main grid. Execute transactions and generate alliance transaction records, which include transaction pairs, transaction volume, settlement price, power transmission loss, compensated volume, surplus volume and main grid price.

[0050] Understandably, for each trading pair in the matching list (S) j , The transaction volume is represented as follows: in, For purchasing electronic systems Electronic Sales System S j The transaction volume, in MW. For purchasing electronic systems The upper limit of the electricity purchase demand range, For the sale of electronic systems S j The upper limit of the power supply capacity range.

[0051] The settlement price is obtained using the median value of the interval, specifically expressed as follows: in, For purchasing electronic systems Electronic Sales System S j The settlement price of electricity, For purchasing electronic systems The upper limit of the electricity purchase price range reflects their maximum willingness to pay. For the sale of electronic systems S jThe lower limit of the electricity sales price range. This method balances the highest willingness to pay by the electricity purchase system and the lowest acceptable price by the electricity sales system, ensuring fairness in transactions.

[0052] Based on the transaction volume, obtain the electronic purchase system. Electronic Sales System S j The power transmission loss is specifically expressed as: in, Purchase electronic systems Electronic Sales System S j Power transmission loss, For purchasing electronic systems Electronic Sales System S j Interconnection resistance, For purchasing electronic systems Electronic Sales System S j The transaction volume, For purchasing electronic systems Electronic Sales System S j The rated voltage between.

[0053] Purchase of electronic systems The amount of electricity received in this transaction is represented as: in, For purchasing electronic systems Electronic Sales System S j The amount of electricity received in the transaction.

[0054] For each electricity purchasing system, the total received electricity from all its transactions is summed to obtain the total received electricity. This is then compared with the actual electricity purchase demand to obtain the demand electricity that was not met through transactions within the alliance, i.e., the compensation electricity. This portion of electricity is purchased from the main grid at the main grid electricity price.

[0055] Similarly, for each electronic power sales system, the total transaction volume of all its transactions is summed and compared with the actual power supply capacity to obtain the unsold volume, i.e., the surplus volume, which can be sold to the main grid at the main grid electricity price.

[0056] After a transaction is executed, a consortium transaction record is generated, which serves as the basis for subsequent utility acquisition, revenue distribution, and the evolution of the consortium structure. The consortium transaction record includes data such as the trading parties, the transaction volume, the settlement price, and the power transmission loss for each transaction within the consortium, as well as the compensation volume, surplus volume, and main grid price for transactions with the main grid outside the consortium.

[0057] In another embodiment, such as Figure 3As shown, based on the electricity trading model and the alliance's transaction records, the alliance's utility range, the marginal contribution range of each subsystem within the alliance, and the interval Shapley value of each subsystem are obtained, specifically including: Based on the coalition utility function, obtain the coalition utility interval; For each subsystem in the alliance, obtain its alliance utility before and after joining the alliance, and give the marginal contribution range; Get all sub-alliances of the current alliance. For all sub-alliances that do not contain a certain subsystem, get the marginal contribution range of that subsystem to the sub-alliances, and give the weighted marginal contribution as the interval Shapley value of that subsystem. Repeat the process of obtaining the interval Shapley values ​​of the subsystems until the interval Shapley values ​​of each subsystem are obtained. Among them, based on the coalition utility function, the coalition utility interval is obtained, including: Based on the electricity trading model and the alliance's trading records, the alliance's first and second transaction costs are obtained. By combining the first and second transaction costs, we can obtain the utility of the alliance and give the range of the alliance utility.

[0058] It is understandable that by incorporating the various transaction data from the consortium's transaction records into the consortium utility function, the consortium utility range can be obtained, specifically expressed as: in, , These represent the lower limit and upper limit of the utility range for Alliance C, respectively.

[0059] Furthermore, for each subsystem in the alliance, by obtaining its alliance utility before and after joining the alliance, the marginal contribution of that subsystem can be derived. For a certain subsystem k in alliance C, its marginal contribution to alliance C is specifically expressed as: in, Let k be the marginal contribution of subsystem k to alliance C. For the alliance utility of alliance C, The utility of the alliance is obtained after removing subsystem k and re-matching transactions.

[0060] Since the utility of the coalition exists in the form of an interval, the marginal contribution is also represented in the form of an interval, resulting in the marginal contribution interval, specifically expressed as: in, , These are the lower limit and upper limit of the marginal contribution interval for subsystem k, respectively.

[0061] Understanding this approach involves viewing a consortium as a set of subsystems, with sub-consortia being subsets of the consortium. By removing a subsystem from the consortium, we iterate through all the sub-consortia of the consortium after the removal of the subsystem, obtaining the sub-consortium utility before and after the removal of the subsystem. This yields the marginal contribution of the subsystem to each sub-consortium, which is then weighted to obtain the weighted marginal contribution, which serves as the interval Shapley value for that subsystem. The interval Shapley value of subsystem k in consortium C is specifically represented as follows: in, Let k be the interval Shapley value for subsystem k. For a sub-alliance that does not contain subsystem k, This represents the alliance after removing subsystem k. Let k be the marginal contribution of subsystem k to sub-alliance S, and satisfy: in, This represents the utility of the alliance after subsystem k joins sub-alliance S. This represents the alliance utility when subsystem k is not part of sub-alliance S.

[0062] Since coalition utility exists in interval form, interval Shapley values ​​are also represented in interval form, specifically as follows: in, , These are the lower limit and upper limit of the interval Shapley value for subsystem k, respectively.

[0063] In another embodiment, such as Figure 4 As shown, updating the alliance structure in conjunction with the alliance evolution mechanism specifically includes: Based on the initial alliance structure, the driving forces of alliance evolution are identified, and based on the driving forces of alliance evolution and the alliance evolution rules, alliance evolution operations are executed, including merging and splitting. For the alliance structure after performing the alliance evolution operation, a Pareto dominance relationship-based alliance structure screening mechanism is adopted to eliminate inferior alliances and retain non-dominant alliances as candidate alliance structures. Based on the principle of no individual deviation incentive, it is determined whether the candidate alliance structure is stable. If it is unstable, the evolution and screening continue until the alliance structure is stable.

[0064] Understandably, alliance structures are not fixed. When the efficiency of collaboration or the distribution of benefits among the subsystems in an alliance fluctuates, it may create the motivation for alliance mergers or splits.

[0065] In this example, marginal utility improvement drivers and overall utility enhancement drivers are introduced as the drivers of alliance evolution, serving as the triggering conditions for alliance evolution. Marginal utility improvement drivers refer to the significant increase in the expected return range of a subsystem within the original alliance after it joins another alliance. Overall utility enhancement drivers refer to the increase in overall transaction volume, reduced losses, and improved total alliance utility after the merger of two alliances.

[0066] When a coalition evolves, if the coalition splitting rule is met, a coalition splitting operation is performed. The coalition splitting rule is specifically expressed as follows: For alliance C, if any sub-alliance exists... , so that: (In the formula, For the Alliance The lower bound of the alliance utility range, Remove the sub-alliance from the original alliance C Sub-alliance The lower bound of the alliance utility range, (The upper limit of the utility range of the original alliance C) Then, a split operation is performed, splitting Alliance C into two alliances. With Alliance .

[0067] When there is a motivation for alliance evolution, if the alliance merger rules are met, then an alliance merger operation will be performed. The alliance merger rules are specifically expressed as follows: If alliance C1 and alliance C2 satisfy: and ,have

[0068] (In the formula, This indicates that Alliance C1 and Alliance C2 have merged into a new alliance. The lower bound of the subsequent alliance utility range, This represents the upper limit of the utility range of alliance C1. This represents the upper limit of the utility range of alliance C2, where k represents subsystem k. Indicates the subsystem k in the new alliance after the merger. The lower limit of the profit distribution range in the middle. (This represents the upper limit of the profit distribution range for subsystem k within the original alliance C1 or C2) Then, a merger operation will be performed, merging alliances C1 and C2 into a single alliance. .

[0069] Understandably, due to the existence of multiple evolutionary possibilities, the alliance structure after alliance evolution is not a stable structure. Furthermore, alliance evolution may also lead to local optima. Therefore, this method employs an alliance structure screening mechanism based on Pareto dominance to eliminate weaker alliances and retain Pareto non-dominant alliances as candidate alliance structures.

[0070] In practice, the evaluation of a particular alliance can include multiple dimensions of indicators, such as the alliance's overall economic benefits, renewable energy absorption rate, line loss costs, and load fulfillment rate. Alliance C a Pareto dominates C b This refers to: Alliance C a All metrics are no worse than those of Alliance C. b And Alliance C a At least one metric is superior to that of Alliance C. b If no other alliance, Pareto dominates alliance C. a Then Alliance C a This is a non-dominant alliance, which can serve as a candidate alliance structure. Based on the principle of no individual deviation incentive, we determine whether all candidate alliance structures are stable. If they are unstable, we continue to evolve and filter until the alliance structure is stable.

[0071] In another implementation, no individual deviates from the incentive principle, including: No two alliances satisfy the alliance merger rule, and no alliance satisfies the alliance split rule; and, The rate of change of the marginal contribution interval of all subsystems is less than the preset threshold; and, The current Pareto alliance's replacement update has stopped.

[0072] Once the above conditions are met, the alliance structure is considered to have entered a stable state, and no subsystem has the motivation to obtain better utility by changing the alliance structure, thereby ensuring the long-term stable operation of the port virtual power plant.

[0073] The rate of change of the marginal contribution interval of a subsystem refers to the relative degree of change of the marginal contribution interval of the subsystem during the evolution of the alliance. It is used to quantify the convergence of the alliance structure evolution (i.e., whether there is still room for improvement). By obtaining the marginal contribution interval of the subsystem in each iteration, we can obtain the rate of change of the upper and lower limits of the marginal contribution interval or the rate of change of the interval length, etc. Combined with a preset rate of change threshold, we can determine whether the alliance evolution has converged, that is, whether the alliance structure is stable.

[0074] Pareto replacement updates for the current alliance cease: no better alliance structure exists that can replace the current alliance through Pareto dominance.

[0075] This method utilizes an alliance evolution mechanism and Pareto-based alliance selection to achieve iterative reconstruction and optimization of the alliance structure in dynamic scenarios, thereby enhancing the overall coordination and sustainability of the port virtual power plant operation.

[0076] In another implementation, a sorting mechanism is used to determine the order of transaction execution before the transactions are executed, specifically including: By considering the combined effectiveness of the alliance, the fairness of benefits, and the efficiency of transactions, a multi-objective optimization function is constructed. Iterate through all transaction sequences and select the set of non-dominant transaction sequences based on Pareto dominance. Based on a multi-objective optimization function and combined with objective weights, the comprehensive score of each non-dominated transaction sequence in the non-dominated transaction sequence set is obtained, and the non-dominated transaction sequence with the highest comprehensive score is selected as the transaction execution order.

[0077] It is understandable that "alliance" refers to the division between the electronic sales system and the electronic purchase system, and "transaction sequence" refers to the order in which the alliance conducts transactions (e.g., "process alliance C1 first, then process alliance C2"). After determining the trading parameters such as the trading pair, the traded volume, and the settlement price, it is also necessary to determine the order of trading execution before the trading can be carried out.

[0078] In this example, the multi-objective optimization function, which integrates coalition utility, revenue fairness, and transaction efficiency, is expressed as: in, Represents a multi-objective optimization function; Indicates the use of transaction sequences The alliance utility of alliance C; Indicates the use of transaction sequences The payoff fairness function of the alliance C; Indicates the use of transaction sequences The transaction efficiency of Alliance C.

[0079] Optionally, in, These represent the lower limit and upper limit of the utility range for Alliance C, respectively.

[0080] Optionally, in, The standard deviation of the median of the revenue intervals for each subsystem. This is the mean of the median values ​​of the revenue intervals for each subsystem.

[0081] Optionally, Where n is the number of alliances, t i Let be the transaction time for the i-th alliance.

[0082] Based on the existing alliance structure, all possible transaction sequences can be traversed. The multi-objective function value for each transaction sequence is obtained, and based on Pareto dominance, a set of non-dominated transaction sequences that are not dominated by other transaction sequences is selected.

[0083] Using transaction sequences and The multi-objective function values ​​are as follows: like Each item in ( , , (None of them are inferior to) The corresponding sub-items, and At least one sub-item is better than The corresponding sub-items are then considered as transaction sequences. Pareto Dominant Transaction Sequence If no other trading sequence exists, the Pareto dominant trading sequence will be... Then the transaction sequence This is a non-dominated transaction sequence, which is then included in the non-dominated transaction sequence set.

[0084] For each non-dominated transaction sequence in the set of non-dominated transaction sequences, based on a multi-objective optimization function, an objective weight is assigned to each component of the multi-objective optimization function, and the weighted multi-objective optimization function value is obtained as the comprehensive score. The non-dominated transaction sequence with the highest comprehensive score is selected as the final transaction execution order. The objective weights can be determined using methods such as the entropy weight method or the analytic hierarchy process, depending on actual needs; no specific limitations are made here.

[0085] In this example, the optimality of the transaction sequence is ensured by Pareto non-dominated screening, and the group multi-objective sorting combined with objective weights adapts to the actual needs of the port virtual power plant, realizing the optimal transaction order of the port virtual power plant under uncertainty conditions.

[0086] In another embodiment, such as Figure 5 As shown, based on the interval Shapley values ​​of each subsystem, and according to the pre-built allocation rules, the revenue intervals of each subsystem are given, specifically including: For the interval Shapley values ​​of each subsystem, combined with the coalition utility interval, the collective constraints are verified and the first adjustment is made; Obtain the retention utility of each subsystem, combine it with the interval Shapley value after the first adjustment, verify the individual constraints, and make a second adjustment; The second adjusted interval Shapley value is used as the subsystem's revenue interval.

[0087] After obtaining the interval Shapley values ​​for each subsystem, the interval Shapley values ​​cannot be directly used as the revenue interval of the subsystem. It is also necessary to verify the collective constraints and individual constraints and make corresponding adjustments to ensure the fairness of revenue distribution.

[0088] Specifically, collective constraints are represented as: in, Let k be the lower bound of the interval Shapley value for subsystem k. This represents the lower bound of the utility range for alliance C. Let be the upper limit of the interval Shapley value for subsystem k. This represents the upper limit of the alliance utility range for alliance C.

[0089] If the interval Shapley values ​​of each subsystem do not satisfy the collective constraints, a scaling factor is introduced for the first adjustment. The scaling factor is specifically expressed as: in, The lower bound scaling factor, This is the upper bound scaling factor.

[0090] The first adjusted interval Shapley value is specifically represented as follows: in, , These represent the lower and upper limits of the Shapley value in the first adjusted interval, respectively.

[0091] Individual constraints, specifically, are expressed as: in, The reserved utility of subsystem k represents the benefit of subsystem k operating independently without joining any alliance.

[0092] If the interval Shapley value of a subsystem does not meet the individual constraints, a second adjustment is performed to raise the lower bound of the interval Shapley value after the first adjustment for that subsystem. The interval Shapley value after the second adjustment is specifically expressed as follows: in, This is the lower bound of the second adjusted interval Shapley value, representing the minimum return that subsystem k can obtain when its marginal contribution is low; This represents the upper limit of the second adjusted interval Shapley value, indicating the highest return obtainable when the marginal contribution of subsystem k is large.

[0093] Finally, the adjusted Shapley value for the second interval is used as the revenue interval for the subsystem. The allocation rules in this method are based on the marginal contribution of each subsystem to the overall alliance transaction, comprehensively considering factors such as the order of joining, the importance of the subsystem, and the elasticity of its contribution. Through collective constraints (total allocation does not exceed the alliance's utility) and individual constraints (subsystem revenue is not less than that of individual transactions), this method solves the revenue allocation problem arising from differences in resource contributions among the subsystems in the port virtual power plant, ensuring fairness in revenue allocation.

[0094] Secondly, the present invention also provides a port virtual power plant interval game device that takes into account transaction uncertainty, employing the aforementioned port virtual power plant interval game method that takes into account transaction uncertainty, such as... Figure 6 As shown, the device includes: The model building module is used to construct an energy trading model based on the energy supply and demand capacity range of each subsystem of the port virtual power plant, combined with bidding parameters and grid parameters. The transaction matching module is used to generate a list of matching transaction pairs based on the electricity trading model and a pre-built transaction matching mechanism. The transaction execution module is used to determine the transaction volume and settlement price based on the transaction pair matching list, execute the transaction, and generate alliance transaction records; The utility acquisition module is used to obtain the utility range of the alliance, the marginal contribution range of each subsystem in the alliance, and the range Shapley value of each subsystem based on the power trading model and the alliance transaction records. The revenue distribution module, based on the interval Shapley values ​​of each subsystem, provides the revenue distribution interval for each subsystem according to the pre-built distribution rules.

[0095] To verify the applicability and effectiveness of the method of this invention in a real-world port virtual power plant, a large coastal port was selected as the simulation object. This port deployed five typical energy subsystems, namely: Subsystem 1: Shore power supply station (electronic sales system) Subsystem 2: Electric heavy-duty truck charging station (electronic system purchased) Subsystem 3: Cold Chain Warehousing System (Electronic Procurement System) Subsystem 4: Photovoltaic Energy Storage System (Electronic Sales System) Subsystem 5: Port Lighting System (Electronic System Purchased) Each subsystem has independent power metering and trading functions, and can conduct inter-subsystem trading and energy dispatch through a virtual power plant management platform.

[0096] Within the simulation period (set as one day, divided into 24 hours), the power supply and demand capacity and willingness to purchase and sell electricity of each subsystem are modeled, and the trading power range and price range for each time period are constructed.

[0097] To verify the advantages of this method, three types of schemes were set up: Option A (this method): Interval game theory + interval Shapley allocation + Pareto evolution.

[0098] Comparison with Option B (Deterministic Game): This option does not consider interval uncertainty and only uses deterministic value games.

[0099] Comparison Scheme C (Fixed Alliance): Adopts a fixed alliance and average distribution method, without evolving the alliance structure.

[0100] Based on the simulation results, the alliance utility, subsystem benefit fairness, and alliance structure stability of each scheme are analyzed, resulting in Tables 1 and 2, as shown below: Table 1. Comparison of Coalition Utility

[0101] It is evident that, under different levels of predicted perturbation, the average coalition utility obtained by Scheme A is higher than that of the comparative scheme, and the fluctuation range is significantly smaller.

[0102] Table 2 Comparison of Fairness and Stability of Benefits

[0103] The marginal revenue coefficient of variation (CV) and hp stability rate are used to measure the fairness of revenue and the stability of the alliance structure. Scheme A has the lowest CV (small fluctuation) and the highest stability rate (over 94%), indicating that the interval Shapley value allocation and alliance evolution mechanism have stronger fairness and stability.

[0104] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A port virtual power plant interval game method considering transaction uncertainty, characterized in that, Includes the following steps: Based on the power supply and demand capacity range of each subsystem of the port virtual power plant, and combined with the bidding parameters and grid parameters, a power trading model is constructed. Based on the electricity trading model, a pre-built trading matching mechanism is used to provide a list of matching trading pairs; Based on the trading pair matching list, the transaction volume and settlement price are determined, the transaction is executed, and the alliance transaction record is generated; Based on the electricity trading model and the alliance trading records, the alliance utility range, the marginal contribution range of each subsystem in the alliance, and the range Shapley value of each subsystem are obtained. Based on the interval Shapley values ​​of each subsystem, the revenue distribution interval of each subsystem is given according to the pre-built allocation rules.

2. The port virtual power plant interval game method considering transaction uncertainty as described in claim 1, characterized in that, The subsystem includes an electricity sales system and an electricity purchase system; the electricity supply and demand capacity range includes the power supply capacity range and the electricity purchase demand range; the pricing parameters include the electricity sales price range and the electricity purchase price range; and the grid parameters include the interconnection resistance and rated voltage between each subsystem and the main grid. The electricity trading model is constructed, specifically including: Obtain the power supply capacity range and electricity sales price range for each electricity sales system, and the electricity purchase demand range and electricity purchase price range for each electricity purchase system, and establish transaction feasibility criteria. A transmission loss model is constructed by combining the interconnection resistance and rated voltage between each subsystem and the interconnection resistance and rated voltage between each subsystem and the main power grid. Define the initial alliance structure, construct the alliance utility function, and update the alliance structure in conjunction with the alliance evolution mechanism.

3. The port virtual power plant interval game method considering transaction uncertainty as described in claim 2, characterized in that, Based on the electricity trading model, a pre-built trading matching mechanism is used to provide a list of matching trading pairs, specifically including: In each round of transactions, for each electricity sales system, based on the electricity sales price range and the electricity purchase price range of all electricity purchase systems, and combined with the transaction feasibility criteria, the feasibility of the transaction is judged, and feasible transaction pairs are given. Among all feasible transaction pairs, the upper limit of the electricity purchase price range of the electricity purchase system is used as the maximum willingness to pay indicator. The electricity purchase system with the best maximum willingness to pay indicator is selected as the transaction object, the optimal transaction pair is given, and the optimal transaction pair is included in the transaction pair matching list. Iterate through all the electricity sales systems in sequence, provide the optimal trading pair and update the trading pair matching list until the electricity purchase demand of all the electricity purchase systems is met or the power supply capacity of all the electricity sales systems is consumed, and provide the trading pair matching list.

4. The port virtual power plant interval game method considering transaction uncertainty as described in claim 3, characterized in that, Based on the matching list of trading pairs, the transaction volume and settlement price are determined, the transaction is executed, and a consortium transaction record is generated, specifically including: For each trading pair in the trading pair matching list, the transaction volume is obtained by combining the power supply capacity range of the power supply system and the power purchase demand range of the power purchase system. Based on the electricity sales price range of the power supply system and the electricity purchase price range of the power purchase system, the settlement price is obtained by using the median value of the range; Based on the transmission loss model and combined with the transaction volume, the power transmission loss is obtained. For each electricity purchase system, the total received electricity is obtained based on all the traded electricity volume and the corresponding electricity transmission loss. The compensation electricity is obtained based on the electricity purchase demand range and the total received electricity volume. The main grid electricity price is determined, and the compensation electricity is supplemented through the main grid. For each electronic sales system, based on all transaction volumes and power supply capacity ranges, the surplus volume is obtained, the main grid electricity price is determined, and the surplus volume is sold to the main grid. Execute transactions and generate alliance transaction records, which include transaction pairs, transaction volume, settlement price, power transmission loss, compensated volume, surplus volume and main grid price.

5. The port virtual power plant interval game method considering transaction uncertainty as described in claim 4, characterized in that, Based on the electricity trading model and consortium trading records, the consortium utility range, the marginal contribution range of each subsystem within the consortium, and the interval Shapley value of each subsystem are obtained, specifically including: Based on the coalition utility function, obtain the coalition utility interval; For each subsystem in the alliance, obtain its alliance utility before and after joining the alliance, and give the marginal contribution range; Get all sub-alliances of the current alliance. For all sub-alliances that do not contain a certain subsystem, get the marginal contribution range of that subsystem to the sub-alliances, and give the weighted marginal contribution as the interval Shapley value of that subsystem. Repeat the process of obtaining the interval Shapley values ​​of the subsystems until the interval Shapley values ​​of each subsystem are obtained. Among them, the alliance utility range is obtained based on the alliance utility function, including: obtaining the first and second transaction costs of the alliance based on the electricity trading model and the alliance transaction records; By combining the first and second transaction costs, we can obtain the utility of the alliance and give the range of the alliance utility.

6. The port virtual power plant interval game method considering transaction uncertainty as described in claim 5, characterized in that, Updating the alliance structure in conjunction with the alliance evolution mechanism specifically includes: Based on the initial alliance structure, the driving forces of alliance evolution are identified, and based on the driving forces of alliance evolution and the alliance evolution rules, alliance evolution operations are executed, including merging and splitting. For the alliance structure after performing the alliance evolution operation, a Pareto dominance relationship-based alliance structure screening mechanism is adopted to eliminate inferior alliances and retain non-dominant alliances as candidate alliance structures. Based on the principle of no individual deviation incentive, it is determined whether the candidate alliance structure is stable. If it is unstable, the evolution and screening continue until the alliance structure is stable.

7. The port virtual power plant interval game method considering transaction uncertainty as described in claim 6, characterized in that, The principle of no individual deviation from incentives includes: No two alliances satisfy the alliance merger rule, and no alliance satisfies the alliance split rule; and, The rate of change of the marginal contribution interval of all subsystems is less than the preset threshold; and, The current Pareto alliance's replacement update has stopped.

8. The port virtual power plant interval game method considering transaction uncertainty as described in claim 5, characterized in that, Before executing a transaction, a sorting mechanism is used to determine the order in which transactions are executed, specifically including: By considering the combined effectiveness of the alliance, the fairness of benefits, and the efficiency of transactions, a multi-objective optimization function is constructed. Iterate through all transaction sequences and select the set of non-dominant transaction sequences based on Pareto dominance. Based on a multi-objective optimization function and combined with objective weights, the comprehensive score of each non-dominated transaction sequence in the non-dominated transaction sequence set is obtained, and the non-dominated transaction sequence with the highest comprehensive score is selected as the transaction execution order.

9. The port virtual power plant interval game method considering transaction uncertainty as described in claim 5, characterized in that, Based on the interval Shapley values ​​of each subsystem, and according to the pre-built allocation rules, the profit intervals of each subsystem are given, specifically including: For the interval Shapley values ​​of each subsystem, combined with the coalition utility interval, the collective constraints are verified and the first adjustment is made; Obtain the retention utility of each subsystem, combine it with the interval Shapley value after the first adjustment, verify the individual constraints, and make a second adjustment; The second adjusted interval Shapley value is used as the subsystem's revenue interval.

10. A port virtual power plant interval game device considering transaction uncertainty, employing any of the port virtual power plant interval game methods considering transaction uncertainty as described in claims 1-9, characterized in that, include: The model building module is used to construct an energy trading model based on the energy supply and demand capacity range of each subsystem of the port virtual power plant, combined with bidding parameters and grid parameters. The transaction matching module is used to generate a list of matching transaction pairs based on the electricity trading model and a pre-built transaction matching mechanism. The transaction execution module is used to determine the transaction volume and settlement price based on the transaction pair matching list, execute the transaction, and generate alliance transaction records; The utility acquisition module is used to obtain the utility range of the alliance, the marginal contribution range of each subsystem in the alliance, and the range Shapley value of each subsystem based on the power trading model and the alliance transaction records. The revenue distribution module, based on the interval Shapley values ​​of each subsystem, provides the revenue distribution interval for each subsystem according to the pre-built distribution rules.

Images