Point-to-point transaction strategy for urban multi-type subjects based on Nash bargaining

Abstract

The invention discloses a point-to-point transaction strategy for urban multi-type subjects based on Nash bargaining, and belongs to the technical field of power transaction. The point-to-point transaction strategy comprises the following steps: S1, data collection; s2, constructing a transaction model; s3, proposing a transaction scheme; and S4, determining a transaction scheme. According to the invention, by comprehensively considering the power utilization characteristics and demands of multiple types of users, the optimal transaction effect is realized, the problem that a traditional transaction method only considers a single type of users and has an unfairness phenomenon is solved, the fairness and flexibility of energy transaction are improved, and all users can be treated fairly in the market.

Description

Technical Field

[0001] This invention relates to the field of power trading technology, specifically to a peer-to-peer trading strategy based on Nash bargaining for various types of entities in a city. Background Technology

[0002] Renewable energy sources such as solar and wind power are affected by weather conditions, exhibiting intermittency and unpredictability, making them difficult to adapt to traditional centralized energy management and distribution methods. With the widespread application of distributed energy, different types of users can not only be self-sufficient but also engage in buying and selling in the energy market.

[0003] Traditional energy trading methods rely primarily on centralized market mechanisms and fixed transaction prices, lacking the ability to plan and tailor strategies across all time periods. Furthermore, traditional methods typically only provide a uniform market transaction price, failing to support free trading between users or the design of customized transaction prices.

[0004] Patent CN114358756B discloses a peer-to-peer electricity trading method and system based on a two-layer blockchain. The patent enables the trading entity to prioritize the scheduling and matching of its energy consumption plan or energy supply plan twice within its own microgrid blockchain, effectively alleviating the pressure on power flow overload, information storage capacity, and computing power requirements caused by too many block nodes.

[0005] The aforementioned patents address the challenges posed by the large influx of producers and consumers into the electricity trading system to traditional centralized electricity operations. However, there is still room for improvement in terms of fair trading. This application achieves optimal trading results and solves the problems of traditional trading methods that only consider a single type of user and exhibit unfairness.

[0006] To this end, this application proposes a peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in the city, which aims to achieve optimal transaction results. Summary of the Invention

[0007] The purpose of this invention is to provide a peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, in order to solve the technical problems of traditional transaction methods mentioned in the background art, which only consider a single type of user and have unfair phenomena.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, comprising the following steps:

[0009] S1. Data Collection: Identify the types of energy users in the city and establish the utility function and constraints for each type of user;

[0010] S2. Construction of the transaction model: Taking the utility value obtained by each user when not participating in peer-to-peer transactions as the negotiation breakdown point, a peer-to-peer transaction model is constructed based on Nash bargaining theory.

[0011] S3. Proposal of a Trading Plan: Users report their trading plans to the trading platform. Based on the peer-to-peer trading model, the trading platform proposes an initial trading plan. Users then evaluate the utility of the trading plan and provide feedback to the trading platform.

[0012] S4. Determination of the transaction plan: Based on user feedback, the trading platform adjusts the transaction plan using a peer-to-peer trading model until all users accept the transaction plan and the final transaction plan is determined.

[0013] Preferably, step S2 further includes pricing for network access fees, which includes the following steps:

[0014] S21. Calculation of network access fees: Using the power flow transmission factor model, calculate the electrical distance between each user, and calculate the network access fees for transactions between users based on the electrical distance and the network access fee price coefficient set by the power distribution company.

[0015] S22. Incorporating network access fees into the transaction model: Adding a network access fee cost item to the user's utility function, correcting the negotiation breakdown point, and adding network access fee sharing constraints to the constraints of the peer-to-peer transaction model.

[0016] S23. Transaction matching priority optimization: Classify users based on electrical distance and set the priority for matching with transaction platforms.

[0017] 3. The peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, as described in claim 1, is characterized in that: step S3 further includes multi-timescale optimization scheduling, which includes the following steps:

[0018] S31. Submission of trading plans at multiple time scales: In addition to submitting a trading plan for a single point in time to the trading platform, users of all types can submit trading plans at multiple time scales, including day-to-day, intraday, and real-time.

[0019] S32. Integration of multi-timescale information: The trading platform collects and integrates the multi-timescale trading plans of all users to generate a trading information database that reflects the overall energy supply and demand situation and market price fluctuation trends of the system at different time scales.

[0020] Preferably, step S3 further includes user assistance services, which include the following steps:

[0021] Ancillary service contribution quantification: Collect data on flexibility resources provided by various types of users, evaluate the potential contribution of flexibility resources in providing ancillary services, including voltage regulation effect and standby capacity call rate, establish a contribution quantification model, and calculate the specific contribution of users;

[0022] Market Mechanism Design: Design the ancillary services market architecture, define the roles and responsibilities of market participants, establish trading instruments and trading cycles, and formulate ancillary services trading rules that include pricing mechanisms, clearing mechanisms, and settlement mechanisms;

[0023] Incentive and compensation mechanism design: Establish an incentive mechanism based on contribution, including direct subsidies, priority transaction rights, and tax incentives, and provide a compensation mechanism for users who incur additional costs due to providing auxiliary services, including cost subsidies and tax reductions.

[0024] Preferably, the trading strategy further includes the construction and application of a trading platform, which includes the following steps:

[0025] Setting up a blockchain network: Design the blockchain network architecture, establish a decentralized peer-to-peer trading platform, deploy smart contracts, and perform digital identity authentication for users joining the blockchain network;

[0026] Transaction plan broadcasting: The smart contract verifies the transaction plan submitted by the user, verifies the format and content of the transaction plan, and broadcasts the verified transaction plan to all nodes in the blockchain network.

[0027] Preferably, step S2 further includes incomplete information processing, which includes the following steps:

[0028] Identify incomplete information due to data deviation, transmission delay, and user information protection needs, and transform the incomplete information into complete but imperfect information with a probability distribution according to the user type.

[0029] Based on the peer-to-peer transaction model, and combined with probability distribution, a Nash bargaining model that considers incomplete information is constructed.

[0030] Preferably, step S4 further includes adaptive state tree pruning, which includes the following steps:

[0031] S41. After receiving user feedback, the trading platform uses adaptive state tree pruning technology to calculate the transition probability between each state, filter out invalid and redundant states, and optimize the state space.

[0032] S42. Based on the optimized state space, the trading platform dynamically adjusts the trading plan in real time, taking into account user interactions and incomplete information.

[0033] Preferably, the energy user types include: temperature-controlled main users equipped with distributed renewable energy generator sets, manufacturing users, commercial users, and users equipped with distributed combined heat and power (CHP) power generation equipment.

[0034] Preferably, the function for obtaining the negotiation breakdown point is:

[0035]

[0036] Where N is the number of users, u n , These represent the user's utility and workload, respectively. For transaction costs between users and the distribution network, The cost of using distributed energy storage devices for users.

[0037] Preferably, the peer-to-peer transaction model is as follows:

[0038]

[0039] in, For the user's production costs, The price for transactions between users. Let n and m be the transaction volumes between any two users. A positive value indicates that n is purchasing energy from m. A negative value indicates that n sells energy to m.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] 1. This invention achieves optimal transaction results by comprehensively considering the electricity consumption characteristics and needs of multiple types of users, solving the problems of traditional transaction methods that only consider a single type of user and are unfair, improving the fairness and flexibility of energy transactions, and ensuring that all users can receive fair treatment in the market;

[0042] 2. This invention achieves accurate cost allocation by incorporating grid access fees, thus solving the problem of imbalanced benefit distribution caused by ignoring grid access fees in traditional models, improving the transparency of the electricity market, and enhancing transaction efficiency and energy utilization.

[0043] 3. This invention achieves dynamic balance between supply and demand and cost optimization throughout the entire time period through multi-timescale optimized scheduling, improves the flexibility and stability of energy trading, reduces information asymmetry between users, and enhances the transparency and fairness of transactions between users;

[0044] 4. This invention enables accurate assessment of the value of flexible resources through user-assisted services, solves the unfairness problem of traditional compensation mechanisms, ensures the safe and stable operation of the power grid, and improves the overall operating efficiency and security of the power grid. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the trading strategy process of the present invention;

[0046] Figure 2 This is a schematic diagram illustrating the pricing process for network access fees according to the present invention;

[0047] Figure 3 This is a schematic diagram of the multi-time-scale optimized scheduling process of the present invention;

[0048] Figure 4 This is a schematic diagram of the user assistance service process of the present invention;

[0049] Figure 5 This is a schematic diagram illustrating the construction and application process of the trading platform of the present invention;

[0050] Figure 6 This is a schematic diagram of the incomplete information processing flow of the present invention;

[0051] Figure 7 This is a schematic diagram of the adaptive state tree pruning process of the present invention;

[0052] Figure 8 This is a schematic diagram of the energy user types of the present invention. Detailed Implementation

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0054] Example 1, please refer to Figure 1 and Figure 8 A peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, characterized by the following steps:

[0055] S1. Data Collection: Identify the types of energy users in the city and establish the utility function and constraints for each type of user;

[0056] S2. Construction of the transaction model: Taking the utility value obtained by each user when not participating in peer-to-peer transactions as the negotiation breakdown point, a peer-to-peer transaction model is constructed based on Nash bargaining theory.

[0057] S3. Proposal of a Trading Plan: Users report their trading plans to the trading platform. Based on the peer-to-peer trading model, the trading platform proposes an initial trading plan. Users then evaluate the utility of the trading plan and provide feedback to the trading platform.

[0058] S4. Determination of the transaction plan: Based on user feedback, the trading platform adjusts the transaction plan using a peer-to-peer trading model until all users accept the transaction plan and the final transaction plan is determined.

[0059] Furthermore, the system identifies different types of energy users in the city, including temperature-controlled main users equipped with distributed renewable energy generators, manufacturing users, commercial users, and users equipped with distributed combined heat and power (CHP) power generation equipment. Temperature-controlled main users refer to buildings equipped with temperature control systems. For each type of user, the system constructs a utility function based on energy demand data, distributed energy output data, and energy storage device status. It collects energy demand data, distributed energy output data, energy storage device status, and market price information for each type of user. Temperature-controlled main users have a clear need for temperature control, and their utility depends on the effectiveness of temperature control. The utility function for temperature-controlled main users is... I represents the number of users controlling the temperature, t∈T, t is the time, u i The cooling efficiency value for the whole day, M i This represents the utility value when temperature control achieves optimal results across all time periods. For temperature control objectives, The temperature control result at time t is α. i For conversion factors, This is the cooling power load of the temperature control system; various manufacturing users need to comprehensively consider industrial production needs and energy costs, and its utility function is... J represents the number of manufacturing users, t∈T, t is the time, and ε j U is the conversion factor. j For the user's utility under actual load planning, M j This indicates the user's utility under the original plan. For the original load plan, For actual load planning; the utility function for commercial users is K represents the number of business users, and t∈T, where t is the time point. For behavioral parameters, For load, u k For utility;

[0060] By introducing the transaction costs between users and the distribution network and the usage costs of distributed energy storage devices, the utility values ​​obtained by temperature control users, manufacturing users, and commercial users without participating in point-to-point transactions can be obtained, i.e., the negotiation breakdown point. Setting a negotiation breakdown point provides clear negotiation bottom lines for both parties, helping to avoid unfair transactions. Simultaneously, the transaction model constructed using Nash bargaining theory ensures that the transaction outcome is optimal for both parties, thereby improving transaction satisfaction and stability. Through point-to-point transaction strategies, energy supply and demand are directly connected, reducing intermediate links, lowering transaction costs, and improving transaction efficiency. Personalized transaction strategies are formulated based on each user's energy demand and utility function, ensuring that energy is allocated optimally to each user to meet their diverse energy needs. This also considers the daily usage costs of energy storage devices borne by the users. in, These are charging efficiency and discharging efficiency, respectively. These are the charging power and discharging power, respectively, which enhance the flexibility and adaptability of the system's trading strategy, enabling it to better cope with fluctuations and uncertainties in energy supply and demand.

[0061] Example 2, please refer to Figure 1 and Figure 2 A peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, wherein step S2 further includes the pricing of network access fees, which includes the following steps:

[0062] A peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, characterized by the following steps:

[0063] S1. Identify the types of energy users in the city and establish the utility function and constraints for each type of user;

[0064] S2. Using the utility value obtained by each user when not participating in peer-to-peer transactions as the negotiation breakdown point, a peer-to-peer transaction model is constructed based on Nash bargaining theory. The power flow transmission factor model is used to calculate the electrical distance between users. Based on the electrical distance and the grid access fee price coefficient set by the power distribution company, the grid access fee for transactions between users is calculated. A grid access fee cost term is added to the user's utility function to correct the negotiation breakdown point. A grid access fee allocation constraint is added to the constraints of the peer-to-peer transaction model. Users are classified according to electrical distance, and the priority of matching by the transaction platform is set.

[0065] S3. Users report their trading plans to the trading platform. The trading platform proposes an initial trading plan based on the peer-to-peer trading model. Users evaluate their own utility based on the trading plan and provide feedback to the trading platform.

[0066] S4. Based on user feedback, the trading platform adjusts the trading plan using a peer-to-peer trading model until all users accept the trading plan and the final trading plan is determined.

[0067] Furthermore, the grid access fee is the cost paid by various types of users for using the power grid assets of the distribution company when conducting point-to-point electricity transactions. It reflects the physical characteristics of power transmission and the cost structure of the distribution network. As a power grid usage cost, the grid access fee helps recover power grid investment, maintain the safe and stable operation of the power grid, and incentivizes various types of users to include the grid access fee in their utility function. The grid access fee price is directly proportional to the electrical distance and the amount of electricity traded, encouraging users to trade when the electrical distance is short, thereby reducing network losses. At the same time, including the grid access fee as an additional cost in the utility function influences users' trading decisions, causing them to weigh the costs and benefits when considering a transaction. The model considers network costs and transaction benefits to help users make more rational transaction choices. It adds a network cost sharing constraint to the peer-to-peer (P2P) transaction model, ensuring that the transaction scheme satisfies the interests of all users while helping power distribution companies recover operating costs incurred from P2P transactions, including network losses and equipment maintenance. This, in turn, increases the incentive for power distribution companies to provide power distribution services for P2P transactions. Furthermore, it categorizes users based on electrical distance and sets a priority for matching transactions on the trading platform. Since transactions between users with shorter electrical distances have lower network costs, transactions between these users are prioritized, thereby reducing overall transaction costs and improving transaction efficiency.

[0068] Example 3, please refer to Figure 1 and Figure 3 A peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, wherein step S3 further includes multi-time-scale optimization scheduling, which includes the following steps:

[0069] S31. Submission of trading plans at multiple time scales: In addition to submitting a trading plan for a single point in time to the trading platform, users of all types can submit trading plans at multiple time scales, including day-to-day, intraday, and real-time.

[0070] S32. Integration of multi-timescale information: The trading platform collects and integrates the multi-timescale trading plans of all users to generate a trading information database that reflects the overall energy supply and demand situation and market price fluctuation trends of the system at different time scales.

[0071] Furthermore, in addition to submitting transaction plans for a single point in time to the trading platform, users of various types can submit transaction plans for multiple time scales, including day-ahead, intraday, and real-time. These transaction plans encompass different stages, from long-term forecasting to short-term real-time adjustments, thus helping the system to comprehensively understand users' energy demand or supply. By integrating these transaction plans, the trading platform forms a comprehensive transaction information database, thereby obtaining the overall energy supply and demand situation of users at different time scales, as well as market price fluctuation trends. Multi-time-scale optimized scheduling reduces information asymmetry between users, improves the transparency and fairness of transactions between users, promotes the efficient flow and allocation of energy, and provides a stable and reliable energy supply for various types of entities in the city.

[0072] Example 4, please refer to Figure 1 and Figure 4 A peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, wherein step S3 further includes user auxiliary services, which include the following steps:

[0073] Ancillary service contribution quantification: Collect data on flexibility resources provided by various types of users, evaluate the potential contribution of flexibility resources in providing ancillary services, including voltage regulation effect and standby capacity call rate, establish a contribution quantification model, and calculate the specific contribution of users;

[0074] Market Mechanism Design: Design the ancillary services market architecture, define the roles and responsibilities of market participants, establish trading instruments and trading cycles, and formulate ancillary services trading rules that include pricing mechanisms, clearing mechanisms, and settlement mechanisms;

[0075] Incentive and compensation mechanism design: Establish an incentive mechanism based on contribution, including direct subsidies, priority transaction rights, and tax incentives, and provide a compensation mechanism for users who incur additional costs due to providing auxiliary services, including cost subsidies and tax reductions.

[0076] Furthermore, the trading platform collects data on flexible resources provided by users, such as energy production capacity, storage capacity, and demand response capacity. Based on this data, the trading platform uses a contribution metric model to evaluate the potential contribution of various types of users in providing ancillary services. Evaluation indicators include voltage regulation effectiveness and reserve capacity call-up rate. Voltage regulation effectiveness refers to the contribution of users to grid voltage stability by adjusting their own energy use or production strategies, while reserve capacity call-up rate refers to the user's ability to provide additional energy in emergency situations. This calculates the user's specific contribution, which serves as the basis for subsequent market mechanisms and incentive compensation measures. In designing the ancillary services market architecture, it is necessary to clearly define the roles of market participants. The roles and responsibilities of various users, distribution companies, and ancillary service providers should be defined, and the types of ancillary services to be traded, such as reactive power regulation services and reserve capacity services, should be determined, along with the trading cycle. Incentive and compensation mechanisms such as direct subsidies, priority trading rights, and tax breaks should be provided to provide economic subsidies to users who provide ancillary services and to give priority to users with high contributions in subsequent transactions. By assessing users' potential in providing ancillary services, market mechanisms and incentive compensation measures can be designed to ensure users actively participate in ancillary services, thereby ensuring the safe and stable operation of the power grid and improving the overall operating efficiency and safety of the power grid. For example, voltage regulation services can maintain stable grid voltage and prevent voltage fluctuations from damaging equipment.

[0077] Example 5, please refer to Figure 1 and Figure 5 A peer-to-peer trading strategy based on Nash bargaining for various types of entities in a city, the trading strategy also includes the construction and application of a trading platform, the construction and application of the trading platform includes the following steps:

[0078] Setting up a blockchain network: Design the blockchain network architecture, establish a decentralized peer-to-peer trading platform, deploy smart contracts, and perform digital identity authentication for users joining the blockchain network;

[0079] Transaction plan broadcasting: The smart contract verifies the transaction plan submitted by the user, verifies the format and content of the transaction plan, and broadcasts the verified transaction plan to all nodes in the blockchain network.

[0080] Furthermore, the trading platform utilizes blockchain technology to achieve decentralized data management and transaction verification, ensuring transparency and security. The nodes in the blockchain network consist of various users and the trading platform itself. Each node holds a complete copy of the transaction records, writes smart contract code based on trading rules and the Nash bargaining model, and deploys it to the blockchain network for automatic transaction verification, matching, and settlement. Users submit their personal trading plans through the trading platform, including information such as transaction type, quantity, and price. The smart contract performs dual verification of the submitted trading plans in terms of format and content, ensuring they comply with the standards of the trading rules and the Nash bargaining model. Verified trading plans are broadcast to all nodes in the blockchain network for other users to view and match. Based on the peer-to-peer trading model and current market trading plans, the trading platform automatically generates preliminary trading schemes and publishes these schemes to all participating users for evaluation and feedback.

[0081] Example 6, please refer to Figure 1 and Figure 6 A peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, wherein step S2 further includes incomplete information processing, which includes the following steps:

[0082] Identify incomplete information due to data deviation, transmission delay, and user information protection needs, and transform the incomplete information into complete but imperfect information with a probability distribution according to the user type.

[0083] Based on the peer-to-peer transaction model, and combined with probability distribution, a Nash bargaining model that considers incomplete information is constructed.

[0084] Furthermore, the system identifies data collected from various user types, including energy demand data, distributed energy output data, and energy storage device status. It identifies sources of incomplete information, such as inaccurate energy usage data due to measurement equipment precision, data transmission errors, or human factors; outdated data due to information delays; and data where key information is hidden or obscured to protect user privacy. By introducing probability distributions, incomplete information is transformed into quantifiable risks, thereby reducing transaction errors caused by data bias and transmission delays, and improving the fairness and efficiency of transactions. Using probability distribution information, a more realistic Nash bargaining model is constructed, making transaction strategies more rational and effective.

[0085] Example 7, please refer to Figure 1 and Figure 7 A peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, wherein step S4 further includes adaptive state tree shaving, which includes the following steps:

[0086] S41. After receiving user feedback, the trading platform uses adaptive state tree pruning technology to calculate the transition probability between each state, filter out invalid and redundant states, and optimize the state space.

[0087] S42. Based on the optimized state space, the trading platform dynamically adjusts the trading plan in real time, taking into account user interactions and incomplete information.

[0088] Furthermore, the trading platform represents trading schemes as nodes in a state tree, with each node corresponding to a trading state. States are connected by transition probabilities, which reflect how the trading scheme changes under different conditions. Redundant states are those that have no or minimal impact on the final trading scheme. Redundant states are identified by calculating the transition probabilities between states. After identifying redundant states, the trading platform reduces them, thereby simplifying the state space, reducing the computational burden on the trading platform, and thus reducing the amount of computation, improving the convergence speed of trading schemes, and consequently increasing the generation speed of trading schemes. The optimization of the state space enables the trading platform to comprehensively consider various trading scenarios and conditions, improving overall trading efficiency.

[0089] Working principle: By setting a negotiation breakdown point, it establishes the bottom line for users in the transaction, preventing unequal transactions from occurring. Through the transaction model, it ensures the best benefits for both users. Through the point-to-point transaction strategy, it reduces intermediate links and lowers transaction costs.

[0090] By incorporating grid access fees as an additional cost into the utility function, the transaction scheme can reduce the operating costs of the power distribution company while satisfying the interests of users. By prioritizing the matching of transactions with users who are closer in distance, the overall transaction cost can be reduced and the transaction efficiency can be improved.

[0091] By providing incentive and compensation mechanisms for users who provide ancillary services, we ensure that users actively participate in ancillary services, guarantee the stability of the power grid's safe operation, improve the overall operating efficiency and security of the power grid, and provide stable and reliable energy supply to various types of entities in the city through multi-timescale optimized scheduling.

[0092] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, characterized by: Includes the following steps: S1. Data Collection: Identify the types of energy users in the city and establish the utility function and constraints for each type of user; S2. Construction of the transaction model: Taking the utility value obtained by each user when not participating in peer-to-peer transactions as the negotiation breakdown point, a peer-to-peer transaction model is constructed based on Nash bargaining theory. S3. Proposal of a Trading Plan: Users report their trading plans to the trading platform. Based on the peer-to-peer trading model, the trading platform proposes an initial trading plan. Users then evaluate the utility of the trading plan and provide feedback to the trading platform. S4. Determination of the transaction plan: Based on user feedback, the trading platform adjusts the transaction plan using a peer-to-peer trading model until all users accept the transaction plan and the final transaction plan is determined.

2. The peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, as described in claim 1, is characterized in that: Step S2 also includes pricing for network access fees, which includes the following steps: S21. Calculation of network access fees: Using the power flow transmission factor model, calculate the electrical distance between each user, and calculate the network access fees for transactions between users based on the electrical distance and the network access fee price coefficient set by the power distribution company. S22. Incorporating network access fees into the transaction model: Adding a network access fee cost item to the user's utility function, correcting the negotiation breakdown point, and adding network access fee sharing constraints to the constraints of the peer-to-peer transaction model. S23. Transaction matching priority optimization: Classify users based on electrical distance and set the priority for matching with transaction platforms.

3. The peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, as described in claim 1, is characterized in that: Step S3 further includes multi-time-scale optimization scheduling, which includes the following steps: S31. Submission of trading plans at multiple time scales: In addition to submitting a trading plan for a single point in time to the trading platform, users of all types can submit trading plans at multiple time scales, including day-to-day, intraday, and real-time. S32. Integration of multi-timescale information: The trading platform collects and integrates the multi-timescale trading plans of all users to generate a trading information database that reflects the overall energy supply and demand situation and market price fluctuation trends of the system at different time scales.

4. The peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, as described in claim 1, is characterized in that: Step S3 also includes user assistance services, which include the following steps: Ancillary service contribution quantification: Collect data on flexibility resources provided by various types of users, evaluate the potential contribution of flexibility resources in providing ancillary services, including voltage regulation effect and standby capacity call rate, establish a contribution quantification model, and calculate the specific contribution of users; Market Mechanism Design: Design the ancillary services market architecture, define the roles and responsibilities of market participants, establish trading instruments and trading cycles, and formulate ancillary services trading rules that include pricing mechanisms, clearing mechanisms, and settlement mechanisms; Incentive and compensation mechanism design: Establish an incentive mechanism based on contribution, including direct subsidies, priority transaction rights, and tax incentives, and provide a compensation mechanism for users who incur additional costs due to providing auxiliary services, including cost subsidies and tax reductions.

5. A peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, as described in claim 1, is characterized in that: The trading strategy also includes the construction and application of a trading platform, which includes the following steps: Setting up a blockchain network: Design the blockchain network architecture, establish a decentralized peer-to-peer trading platform, deploy smart contracts, and perform digital identity authentication for users joining the blockchain network; Transaction plan broadcasting: The smart contract verifies the transaction plan submitted by the user, verifies the format and content of the transaction plan, and broadcasts the verified transaction plan to all nodes in the blockchain network.

6. The peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, as described in claim 1, is characterized in that: Step S2 further includes incomplete information processing, which includes the following steps: Identify incomplete information due to data deviation, transmission delay, and user information protection needs, and transform the incomplete information into complete but imperfect information with a probability distribution according to the user type. Based on the peer-to-peer transaction model, and combined with probability distribution, a Nash bargaining model that considers incomplete information is constructed.

7. The peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, as described in claim 1, is characterized in that: Step S4 further includes adaptive state tree pruning, which includes the following steps: S41. After receiving user feedback, the trading platform uses adaptive state tree pruning technology to calculate the transition probability between each state, filter out invalid and redundant states, and optimize the state space. S42. Based on the optimized state space, the trading platform dynamically adjusts the trading plan in real time, taking into account user interactions and incomplete information.

8. A peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, as described in claim 1, is characterized in that: The types of energy users include: temperature-controlled main users equipped with distributed renewable energy generator sets, manufacturing users, commercial users, and users equipped with distributed combined heat and power (CHP) power generation equipment.

9. A peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, as described in claim 1, is characterized in that: The function for obtaining the negotiation breakdown point is: Where N is the number of users, u n , These represent the user's utility and workload, respectively. For transaction costs between users and the distribution network, The cost of using distributed energy storage devices for users.

10. A peer-to-peer transaction strategy based on Nash bargaining for multiple types of entities in a city, as described in claim 1, is characterized in that: The peer-to-peer transaction model is as follows: in, For the user's production costs, The price for transactions between users. Let n and m be the transaction volumes between any two users. A positive value indicates that n is purchasing energy from m. A negative value indicates that n sells energy to m.

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