Point-to-point energy transaction method and device for defining price curve based on virtual line

Abstract

The invention belongs to the technical field of smart power grids, and particularly relates to a point-to-point energy transaction method and device for defining a price curve based on a virtual line. The method comprises the following steps: giving relevant parameters required by the energy exchange, including a demand-price curve, a supply-price curve and power grid parameters, and giving parameters of an energy storage battery for a participant containing the energy storage battery; each participant is used as a computing node for distributed optimization, an optimization model of the distributed P2P energy trading market is constructed based on given related parameters, and constraint conditions of the optimization model are determined; and iteratively solving the optimization model to obtain energy transaction data including the final transaction price and transaction volume. According to the invention, autonomous cooperation of market efficiency and power grid safety of energy transaction is realized.

Description

Technical Field

[0001] This invention belongs to the technical field of smart grids, specifically relating to a point-to-point energy trading method and apparatus based on a virtual line-defined price curve. Background Technology

[0002] In recent years, with the popularization of distributed energy resources and the deepening of electricity market reform, peer-to-peer (P2P) energy trading has received widespread attention as an effective means to improve resource allocation efficiency and enhance user participation. This model allows energy producers and consumers to trade energy directly locally, which helps promote distributed energy consumption.

[0003] In related prior art, Chinese patent document CN120278814A discloses a peer-to-peer energy trading method and system based on virtual communities and distributed generation. The method includes: establishing a building model; aggregating the building models into a virtual community model based on their location within the power distribution network; establishing a distributed generation model and introducing distributed generation sources into the virtual community model; establishing a power distribution system operator model when the virtual community and distributed generation sources conduct peer-to-peer transactions; and integrating the building model, virtual community model, distributed generation model, and power distribution system operator model into a P2P energy trading framework model to obtain the peer-to-peer energy trading method. This method significantly reduces energy trading costs, improves system flexibility and scalability, effectively maintains the stability of the power distribution network, prevents network overload, and provides privacy protection and a reliable trading mechanism.

[0004] However, existing P2P energy trading technologies still have significant shortcomings, mainly in the following two aspects: First, at the market mechanism level, existing trading models mostly employ fixed prices, unified clearing, or simple bilateral negotiation mechanisms. These methods struggle to accurately depict traders' cost and benefit preferences under different trading volumes, resulting in an imperfect market price discovery function and low economic efficiency in trading outcomes. While some price curve-based negotiation models exist, these models often lack coordination with the physical operating state of the power grid. Their optimization process is solely driven by economic interests, representing a purely "commercial-level" optimization that is disconnected from the "physical-level" operation of the power system.

[0005] Secondly, regarding network security, the randomness and decentralization of P2P transactions can easily cause power flow on local lines in the distribution network to exceed safety limits, leading to line congestion. The mainstream solution to this problem using existing technologies is "transaction first, management later," where grid operators, upon detecting congestion, issue mandatory dispatch instructions (such as reducing transaction volume) or charge exorbitant congestion fees. This passive, centralized management approach severely dampens market participation and limits transaction volume; furthermore, it contradicts the core decentralized principle of P2P transactions.

[0006] Although some studies have proposed using energy storage batteries to alleviate congestion, they are usually treated as independent, passive regulation units, uniformly scheduled by system operators, and have not been deeply embedded in market mechanisms. As a result, they cannot effectively incentivize users to actively use their own energy storage resources to participate in congestion management.

[0007] Based on the above, there is a prominent contradiction in the existing technology: the P2P market mechanism, which aims to improve efficiency, is limited in practical application due to the potential for network congestion; while traditional congestion management methods, due to their centralized and coercive characteristics, stifle the vitality of the market and the advantages of decentralization.

[0008] To address these issues, this invention designs a P2P energy trading method that enables autonomous coordination between market efficiency and grid security. Summary of the Invention

[0009] The present invention aims to overcome at least one of the defects of the prior art and provide a point-to-point energy trading method based on virtual line-defined price curves, which can realize market efficiency of energy trading and autonomous coordination of power grid security.

[0010] The present invention also discloses an apparatus loaded with a peer-to-peer energy trading method based on a virtual line-defined price curve.

[0011] The detailed technical solution of this invention is as follows: A peer-to-peer energy trading method based on virtual line-defined price curves, the method comprising: S1. Given the relevant parameters required for energy trading, the relevant parameters include the demand-price curve, the supply-price curve, and the power grid parameters. The demand-price curve is determined by the participants as buyers, and the supply-price curve is determined by the participants as sellers. For participants whose energy storage batteries are included, the parameters of their energy storage batteries must also be provided. S2. Treat each participant as a computing node in the distributed optimization, construct an optimization model for the distributed P2P energy trading market based on the given relevant parameters, and determine its constraints. S3. Iteratively solve the optimization model of the distributed P2P energy trading market to obtain energy trading data, which includes the final trading price and transaction volume.

[0012] According to a preferred embodiment of the present invention, in step S1, the demand-price curve is:

[0013] In formula (1): Indicates a participant as a buyer exist Purchase volume within the time period; Indicates a participant as a buyer In Purchase price within the time period; , For participants as buyers exist The coefficients set for the time period; This represents the set of participants who act as buyers; Indicates the total number of time periods; And / or, the supply-price curve is:

[0014] In formula (2): Indicates a participant as a seller exist Sales volume during the period; Indicates a participant as a seller exist The selling price during the period; , For participants as sellers exist The coefficients set for the time period; This represents the set of participants who act as sellers.

[0015] According to a preferred embodiment of the present invention, in step S1, the power grid parameters include transmission costs, which are expressed as follows:

[0016] In formula (3): This represents the transmission line between the buses of participants i and j. exist Transmission fees during the specified time period Define the symbol; , Parameters selected for power transmission system operators; express Transmission lines during the time period Capacity utilization rate; constant term Used to correct this function.

[0017] According to a preferred embodiment of the present invention, in step S1, the parameters of the energy storage battery include energy loss cost:

[0018] In equation (4): Indicates charging efficiency. Indicates discharge efficiency; Indicates participants Energy storage batteries in The amount of charging during the period, Indicates participants Energy storage batteries in Discharge amount within a time period; Indicates participants Energy storage batteries in Fixed transaction prices within a specific time period; This represents the set of participants that contain energy storage batteries; And / or, the parameters of the energy storage battery include secondary degradation costs:

[0019] In equation (5): This represents the cost coefficient for secondary degradation.

[0020] According to a preferred embodiment of the present invention, in step S2, an auxiliary busbar is introduced. , Indicates with participants If the set of auxiliary buses adjacent to the main bus is given, then the objective function of the constructed optimization model is:

[0021] In equation (8): Indicates participants Decision variables; Indicates participants exist The definition of benefits generated during a given period varies depending on the participant's status; Indicates a participant as a buyer exist The benefits derived from purchasing electricity during specific time periods; Indicates a participant as a seller exist The benefits generated from selling electricity during certain time periods; Indicates the split transmission line The cost of energy loss generated; This indicates participants in the virtual circuit. The cost of virtual lines, Represents a set of virtual lines; Indicates transmission line exist Capacity occupancy rate during the time period; Indicates transmission line exist The amount of active power transmitted within a given time period. Indicates transmission line exist The amount of reactive power transmitted during the time period; , Parameters selected for power transmission system operators; Indicates transmission line Maximum capacity.

[0022] According to a preferred embodiment of the present invention, in step S2, the constraints of the optimization model include equality constraints for the participants. That is, when participants When acting as a buyer, its set of equality constraints ,and:

[0023] In equation (9): Indicates a participant as a buyer The energy storage battery in The time period is for the participant Energy injected into the busbar; , Participants who acted as buyers busbar and its auxiliary busbar The real and imaginary parts of the admittance; , These respectively represent the participants acting as buyers. Auxiliary bus The real and imaginary parts of the admittance between itself and its busbar; , Indicates a participant as a buyer busbars and their auxiliary busbars exist Voltage during the period , Indicates a participant as a buyer busbars and their auxiliary busbars exist Phase of a time period; Indicates a participant as a buyer The busbar at Reactive power output during a given period; Indicates a participant as a buyer The busbar at Reactive power demand during certain time periods; Indicates auxiliary busbar exist Active output during a given period; Indicates auxiliary busbar exist Reactive power output during a given period; in, : ; For participants That is, when participants When acting as the seller, the set of equality constraints ,and: .

[0024] According to a preferred embodiment of the present invention, in step S2, the constraints of the optimization model further include inequality constraints, specifically: For participants And auxiliary bus The inequality constraints for its transmission lines are:

[0025] In equation (12): , They represent transmission lines The active and reactive quantities transmitted. Indicates transmission line Maximum capacity; For participants with energy storage batteries It also includes inequality constraints related to battery state:

[0026] In equation (13): For participants The initial energy of the energy storage battery, This is the maximum capacity of the energy storage battery; Indicates the upper bound of the variable; and Each represents a participant The charging and discharging efficiency of the energy storage battery; And, for each participant It also includes inequality constraints on the boundaries of their respective variables:

[0027] In equation (14): Indicates the lower bound of a variable.

[0028] In another aspect of the invention, an apparatus is provided for implementing the peer-to-peer energy trading method based on a virtual line-defined price curve as described above, the apparatus comprising: The input module is used to provide relevant parameters required for energy trading, including demand-price curves, supply-price curves, and grid parameters. The demand-price curves are determined by participants acting as buyers, and the supply-price curves are determined by participants acting as sellers. For participants whose energy storage batteries are included, the parameters of their energy storage batteries must also be provided. The model building module is used to treat each participant as a computing node in the distributed optimization, and to build an optimization model for the distributed P2P energy trading market based on the given relevant parameters, and to determine its constraints. The calculation and output module is used to iteratively solve the optimization model of the distributed P2P energy trading market to obtain energy trading data, which includes the final transaction price and transaction volume.

[0029] In another aspect of the invention, an electronic device is also provided, comprising: At least one processor; and The memory stores instructions that, when executed by the at least one processor, cause the at least one processor to perform the peer-to-peer energy trading method based on a virtual line-defined price curve as described above.

[0030] In another aspect of the invention, a machine-readable storage medium is also provided, which stores executable instructions that, when executed, cause the machine to perform the peer-to-peer energy trading method based on a virtual line-defined price curve as described above.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention provides a point-to-point energy trading method based on virtual lines, which increases the upper limit of energy trading volume within a limited line capacity, thereby generating more social benefits; (2) This invention provides a P2P energy trading strategy based on a price curve method, which enables P2P energy trading to be combined with power dispatch and to conduct trading under the premise of ensuring power transmission security. Attached Figure Description

[0032] Figure 1 This is the power system topology diagram used in Embodiment 1 of the present invention.

[0033] Figure 2 This is a graph showing the changes in power generation / consumption and price of each busbar during 12 time periods throughout the day in Embodiment 1 of the present invention, under the scenario where there is no congestion on the transmission line.

[0034] Figure 3 This is a graph showing the changes in the power of two energy storage batteries during 12 time periods throughout the day in Embodiment 1 of the present invention, under the scenario where there is no congestion on the transmission line.

[0035] Figure 4 This is a graph showing the changes in power generation / consumption and price of each busbar during 12 time periods of a day in the scenario of congestion on transmission lines 1-2, as described in Embodiment 1 of the present invention.

[0036] Figure 5 This is a graph showing the changes in the power of two energy storage batteries during 12 time periods throughout the day in Embodiment 1 of the present invention, under the scenario of congestion on transmission line 1-2. Detailed Implementation

[0037] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0038] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0039] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0040] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0041] Example 1 This embodiment provides a peer-to-peer energy trading method based on virtual line-defined price curves, applicable to, for example... Figure 1 The power system shown. The method includes: S1: Given the relevant parameters required for energy trading, the relevant parameters include the demand-price curve, the supply-price curve, and grid parameters. The demand-price curve is determined by the participant as the buyer, and the supply-price curve is determined by the participant as the seller. For participants with energy storage batteries, the parameters of their energy storage batteries also need to be provided.

[0042] Specifically, participants in P2P energy trading engage in energy transactions, which include a bus. A participant can act as both a seller (setting up an electricity sales plan) and a buyer (setting up an electricity purchase plan), but cannot act as both a seller and a buyer simultaneously.

[0043] For the buyer, the specified demand-price curve reflects the quantity of electricity purchased. and purchase price The relationship between them. According to the principle of maximizing electricity utility in economics, electricity demand decreases as prices rise, that is... , It is a differential operator.

[0044] For the seller, the specified supply-price curve reflects the quantity of electricity sold. and selling price According to the principle of profit maximization in power generation in economics, electricity supply increases as prices rise, i.e. .

[0045] In this embodiment, the grid parameters include parameters such as transmission line connections, admittance, and maximum capacity. To ensure reliable operation of energy trading within the grid, calculations need to be performed on these grid parameters.

[0046] In addition, to maximize congestion control capabilities, grid parameters also include transmission cost curve parameters. When the bus... and Between transmission lines When the load is too high, the curve generates expensive transmission costs. Reducing the transaction volume achieves the purpose of congestion control, thereby limiting the transaction to a safe and reliable range for the physical line.

[0047] Furthermore, for participants with energy storage batteries, it is also necessary to determine the parameters of the energy storage batteries: the parameters of the energy storage batteries include their initial state, maximum charge and discharge, maximum capacity, charge and discharge efficiency, etc.

[0048] Energy storage batteries do not directly participate in energy trading. In this method, they serve only as virtual lines to increase the upper limit of energy trading volume. These virtual lines come into play when trading volume decreases due to transmission congestion.

[0049] A virtual power line consists of a pair of energy storage batteries. By controlling one end to charge and the other end to discharge, it achieves the function of a real power transmission line. Using a virtual power line also incurs certain costs, including the energy loss costs during the charging and discharging of the energy storage batteries and the battery degradation costs.

[0050] Specifically, this embodiment uses the IEEE 5 bus system as an example. A day is divided into... A time period, a group of participants Each participant contains a bus, where participants 2 and 4 form the buyer set. Participants 1, 3, and 5 form the seller group. .

[0051] In this embodiment, the demand / supply price curve is taken as a linear function.

[0052] For participants This indicates that the participant The buyer selects the following linear function as its demand-price curve:

[0053] In formula (1): Indicates a participant as a buyer exist Purchase volume within the time period; Indicates a participant as a buyer In Purchase price within the time period; , For participants as buyers exist The coefficients set for the time period.

[0054] For participants This indicates that the participant The seller chooses the following linear function as its supply-price curve:

[0055] In formula (2): Indicates a participant as a seller exist Sales volume during the period; Indicates a participant as a seller exist The selling price during the period; , For participants as sellers exist The coefficients set for the time period.

[0056] In the IEEE 5 bus system, the power grid's transmission lines The admittance of its circuit is achieved using a matrix. and It indicates that the participants and Admittance between buses used and express.

[0057] In this embodiment, the following inverse proportional function is used as the transmission cost function:

[0058] In formula (3): This represents the transmission line between the buses of participants i and j. exist Transmission fees during the specified time period Define the symbol; , The parameters selected for power transmission system operators are used to calculate power transmission costs; express Transmission lines during the time period The higher the capacity utilization rate, the closer the utilization rate is to the capacity utilization rate. This indicates that the higher the transmission cost, the better; constant term This is used to correct the function so that when the capacity utilization rate is 0, the transmission cost is 0.

[0059] The location of the energy storage batteries is not marked in the IEEE 5 bus system. Therefore, in this method, a pair of energy storage batteries are manually specified to be located on buses 2 and 5, respectively. , This represents the set of participants containing energy storage batteries, and defines a virtual circuit. .

[0060] For participants and Its energy storage battery has the largest capacity Maximum charging capacity Maximum discharge amount Charging efficiency Discharge efficiency and initial energy Parameters such as these.

[0061] Based on this, the energy loss cost in energy storage batteries can be determined by the following formula:

[0062] In equation (4): Indicates participants Energy storage batteries in The amount of charging during the period, Indicates participants Energy storage batteries in Discharge amount within a time period; Indicates participants Energy storage batteries in A fixed trading price within a specific time period.

[0063] For participants The secondary degradation cost of its energy storage battery can be expressed as:

[0064] In equation (5): This represents the cost coefficient for secondary degradation.

[0065] S2: Treat each participant as a computing node in the distributed optimization, construct an optimization model for the distributed P2P energy trading market based on the given parameters, and determine its constraints.

[0066] In this embodiment, the objective function of the energy trading market optimization model is:

[0067] In formula (6): Indicates the time period of the transaction. Represents the set of buyers. Represents the set of sellers. This represents the set of participants containing energy storage batteries. Represents a collection of transmission lines. Represents a set of virtual lines; Decision variables It includes price variables, reactive power supply, voltage, and phase for all buyers and sellers within each time period; For the participants containing energy storage batteries, their decision variables It should also include the charge and discharge capacity of the energy storage battery; During a certain trading period Inside, For the trading market The total social welfare generated during that period; For participants , Indicates a participant as a buyer exist The benefits arising from purchasing electricity during specific time periods are borne by the participants who act as buyers. quadratic utility function Subtracting the cost of purchasing electricity, and Participant As the buyer The coefficients of the time-period utility function; For participants , Indicates a participant as a seller exist The benefits generated from selling electricity during certain time periods Indicates the amount of electricity generated. The cost required at that time Participant as a seller The power generation cost coefficient; Indicates a participant as a buyer exist The purchase volume within a given time period is equal to its purchase price. The function that satisfies ; Indicates a participant as a seller exist The sales volume within a given period is equal to its selling price. The function that satisfies ; For participants and Transmission lines between and Transmission lines exist The time-period transmission cost curve is represented as follows: ,in It is a power transmission line exist The capacity utilization rate within a time period is calculated by dividing the current line capacity by the maximum capacity. The ratio of squares; and Indicates transmission line exist Active and reactive quantities transmitted within a time period; Grid operators control transmission cost curve parameters and This ensures that when the line capacity approaches its maximum capacity, i.e. When the value approaches 1, transmission costs increase dramatically, thus preventing the lines from being fully loaded. exist During the specified time period, for participants in the virtual route Virtual line cost The first item The second item represents the energy loss during charging and discharging of the energy storage battery. This indicates the cost of secondary degradation of energy storage batteries.

[0068] To break down the above optimization into distributed optimization, for Transmission lines in Add a pair of auxiliary buses Used to separate two busbars from the middle of a power transmission line, where To represent a set consisting of pairs of auxiliary buses, use This represents the set consisting of all auxiliary buses. For participants... ,use This represents the set of auxiliary buses adjacent to its main bus.

[0069] Exemplary, referring to Figure 1 As shown, a pair of auxiliary buses (16,17) are added between bus 1 and bus 5, a pair of auxiliary buses (6,7) are added between bus 1 and bus 2, and a pair of auxiliary buses (10,11) are added between bus 1 and bus 4. The other buses are similar and will not be described in detail here.

[0070] Each participant needs to include the data from its own bus and the data from adjacent auxiliary buses in its computation. Therefore, after transforming it into a distributed optimization problem, each participant includes not only its own bus but also several auxiliary buses adjacent to its bus, using a set of buses. These buses represent participant i

[0071] use Denotes the auxiliary busbar of any participant, then in During the time period, each pair of auxiliary buses It includes four pairs of decision variables, namely active power. Reactive power ,Voltage and phase .

[0072] In addition, the bus assembly Between the intermediate busbar and the auxiliary busbar, there is also a transmission line that has been split in half, using... It should be noted that because the transmission line is split in two, the inductance and reactance between each auxiliary bus and the connected bus will become half that of the original transmission line.

[0073] For virtual lines Using a single variable This represents the active power on the virtual line, with the sign indicating the direction of the virtual power flow. The participants at both ends of this virtual line... and Add a copy of this variable as a consensus variable, i.e. .

[0074] Based on the above, the charge and discharge capacity of the energy storage battery can be re-expressed as:

[0075] In equation (7): the smoothing term It is a positive parameter close to 0; participants and Data should be held separately. and ,express The time period is during the participants and Electrical energy transferred between them via virtual lines.

[0076] The above representation implies that a single energy storage battery cannot simultaneously possess charge and discharge constraints. And the constraint that the magnitude and direction of power transmission at both ends of the virtual line are the same. , .

[0077] In the above manner, for each participant and its auxiliary busbar The objective function can be rewritten as a distributed objective function as follows:

[0078] In equation (8): Indicates participants Decision variables; Indicates participants exist Benefits generated during the period; Indicates the split transmission line The cost of energy loss generated; Indicates transmission line exist Capacity occupancy rate during the time period; Indicates transmission line exist The amount of active power transmitted within a given time period. Indicates transmission line exist The amount of reactive power transmitted during the time period.

[0079] The constraints in this distributed optimization problem vary slightly depending on the role and type of bus, and can be specifically divided into equality constraints and inequality constraints.

[0080] For participants That is, when participants When it is the buyer Its set of equality constraints ,and:

[0081] In equation (9): The first two equations represent participants acting as buyers. The active and reactive power balance equations for the intermediate busbar. Indicates a participant as a buyer The middle busbar is For the buyer, the reactive power output during a given period should be limited to zero, hence the third equation is generated. Indicates a participant as a buyer The middle busbar is The reactive power demand during a given time period is a constant value; the latter two equations represent the participation of the buyer. The power balance equation satisfied by the auxiliary busbar. , Indicates a participant as a buyer busbars and their auxiliary busbars exist Voltage during the period , Indicates a participant as a buyer busbars and their auxiliary busbars exist Phase of a time period.

[0082] Furthermore, Indicates a participant as a buyer The energy storage battery in The time period is for the participant The energy injected into the busbar is calculated as follows:

[0083] For participants That is, when participants When as a seller Equality constraint set ,and: .

[0084] In the formula The calculation method is the same as in equation (10) above. If the participant has an energy storage battery, then this term is not 0.

[0085] Each participant also has inequality constraints.

[0086] For participants And auxiliary bus The inequality constraints for its transmission lines are:

[0087] In equation (12): , They represent transmission lines The active and reactive quantities transmitted. Indicates transmission line Maximum capacity.

[0088] For participants with energy storage batteries Additionally, inequality constraints related to battery state need to be added:

[0089] In equation (13): For participants The initial energy of the energy storage battery, This is the maximum capacity of the energy storage battery; Indicates the upper bound of the variable; and Each represents a participant The charging and discharging efficiency of energy storage batteries .

[0090] In addition, each participant There are also some inequality constraints with independent variable limits:

[0091] In equation (14): Indicates the lower bound of a variable.

[0092] S3: Iteratively solve the optimization model of the distributed P2P energy trading market to obtain energy trading data, which includes the final trading price and transaction volume.

[0093] After the original problem is broken down, each participant will receive their own constrained sub-optimization problem:

[0094] In equation (15): Indicates participants The objective function, Let represent the set of equality constraints contained in participant i. Let i represent the set of inequality constraints contained in participant i.

[0095] Furthermore, the decision variables satisfy:

[0096] .

[0097] For this optimization problem, all participants input the sub-optimization problems and initial values ​​of the variables into a distributed optimization solver. After iterative solving by the distributed optimization solver, all participants obtain the final transaction price and trading volume data in a distributed manner.

[0098] The effectiveness of the method of the present invention will be verified by specific experiments below.

[0099] The experiment was conducted on an IEEE 5 bus system, and the experimental environment was set up using MATPOWER, including the parameters of the three generators and the system transmission line limitations.

[0100] The transaction data uses processed price and load data from the NYISO public date.

[0101] Reactive power demand is calculated based on a power factor of 0.95.

[0102] In addition, the transmission cost parameters are set to a and b.

[0103] The VPL line is installed between the busbars of participants 2 and 5, and the energy storage batteries at both ends are set to have a maximum capacity of 1MWh and a maximum power of 0.5MW.

[0104] Each participant's trading strategy is determined by their highest and lowest transaction prices and maximum supply (or demand). In this experiment, each participant uses a linear function trading strategy for pricing.

[0105] For buyers, they will refuse to buy if the price exceeds the maximum price, and will buy everything if the price is at the minimum price. The demand-price curve exhibits a linear function relationship between the maximum and minimum prices.

[0106] For sellers, all sales occur at the highest price, while power generation is refused when prices fall below the lowest price. The supply-price curve exhibits a linear function relationship between the highest and lowest prices.

[0107] Their trading strategies and price curve parameters are now presented in a table.

[0108]

[0109] In Table 1, C2 and C4 represent participants 2 and 4, who are carrying loads, respectively, and they purchase electricity as buyers.

[0110]

[0111] In Table 2, P1, P3, and P5 represent participants 2, 3, and 5, who have generators and sell electricity as sellers.

[0112] The experiment tested the results in two scenarios.

[0113] Scenario 1 represents the outcome of energy trading under normal conditions with no congestion. The result is as follows: Figure 2 and Figure 3 As shown, trading volume is highest in the evening, and the corresponding trading price increases. Trading volume is lowest in the early morning, and the corresponding trading price is also lowest. When there is no congestion on the line, the virtual line is not activated, so the power of the two energy storage batteries remains unchanged.

[0114] Scenario 2 involves a blockage on transmission line 1-2. To simulate this blockage, the maximum capacity of transmission line 1-2 was reduced in the experiment. The final transaction result is as follows: Figure 4and Figure 5 As shown, when congestion occurs, the cost of energy transmission increases, thus reducing the total transaction volume compared to Scenario 1. With line 1-2 blocked, buyer 2's purchasing demand is increasingly met by seller 3, leading to an increase in participant 3's power generation. Simultaneously, a virtual line is activated, with participant 2 releasing energy through its energy storage battery and participant 5 absorbing energy through its own energy storage battery, causing the virtual line to transfer energy to participant 2.

[0115] In summary, the peer-to-peer energy trading method based on virtual line-defined price curves of this invention increases the upper limit of energy trading volume within limited line capacity, thereby generating greater social benefits. Furthermore, the P2P energy trading strategy provided by this invention enables P2P energy trading to be combined with power dispatch, allowing transactions to be conducted while ensuring transmission security.

[0116] Example 2 This embodiment provides an apparatus for implementing a peer-to-peer energy trading method based on a virtual line-defined price curve. The apparatus includes: The input module is used to provide relevant parameters required for energy trading, including demand-price curves, supply-price curves, and grid parameters. The demand-price curves are determined by participants acting as buyers, and the supply-price curves are determined by participants acting as sellers. For participants whose energy storage batteries are included, the parameters of their energy storage batteries must also be provided. The model building module is used to treat each participant as a computing node in the distributed optimization, and to build an optimization model for the distributed P2P energy trading market based on the given relevant parameters, and to determine its constraints. The calculation and output module is used to iteratively solve the optimization model of the distributed P2P energy trading market to obtain energy trading data, which includes the final transaction price and transaction volume.

[0117] Example 3 This embodiment also provides an electronic device, including: At least one processor; and The memory stores instructions that, when executed by the at least one processor, cause the at least one processor to perform the peer-to-peer energy trading method based on a virtual line-defined price curve as described above.

[0118] In this embodiment, the electronic device may include, but is not limited to: personal computer, server computer, workstation, desktop computer, laptop computer, notebook computer, mobile computing device, smartphone, tablet computer, cellular phone, personal digital assistant (PDA), handheld device, messaging device, wearable computing device, consumer electronic device, etc.

[0119] Example 4 This embodiment also provides a machine-readable storage medium storing executable instructions that, when executed, cause the machine to perform the peer-to-peer energy trading method based on a virtual line-defined price curve as described above.

[0120] Specifically, a system or apparatus equipped with a readable storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer or processor of the system or apparatus can read and execute the instructions stored in the readable storage medium.

[0121] In this case, the program code read from the readable medium itself can perform the functions of any of the above embodiments, and therefore the machine-readable code and the readable storage medium storing the machine-readable code constitute a part of this specification.

[0122] Examples of readable storage media include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD-RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer or the cloud via a communication network.

[0123] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solutions of the present invention, and are not intended to limit the specific implementation of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A point-to-point energy transaction method based on virtual line defining price curve, characterized in that, The method comprises: S1, giving the relevant parameters required for energy transaction, the relevant parameters including demand-price curve, supply-price curve and grid parameters, the demand-price curve being determined by participants as buyers, the supply-price curve being determined by participants as sellers; wherein for participants containing energy storage batteries, parameters of their energy storage batteries are also given; S2, regarding each participant as a calculation node of distributed optimization, constructing an optimization model of distributed P2P energy transaction market based on the given relevant parameters, and determining the constraint conditions thereof; S3, iteratively solving the optimization model of the distributed P2P energy transaction market to obtain energy transaction data, which includes the final transaction price and transaction volume.

2. The method of claim 1, wherein the price curve is defined by a virtual line. In the S1, the demand-price curve is: in formula (1): denotes a participant acting as a buyer in the amount of purchases in the period; denotes a participant acting as a buyer in this the purchase price in the period; , a participant acting as a buyer in the period; denotes a set consisting of participants acting as buyers; denotes the total number of time periods; And / or, the supply-price curve is: in formula (2) : denotes a participant acting as a seller at the volume of sales during the period; denotes a participant acting as a seller at the price of sales during the period; , a coefficient established for a participant acting as a seller at the period; denotes a set of participants acting as sellers.

3. The method of claim 2, wherein the price curve is defined by a virtual line. In the S1, the grid parameters include transmission cost, which is expressed as: in formula (3) represents the transmission line between the buses of participants i and j In the transmission cost over the period, is a defined symbol; , is a parameter chosen by the transmission system operator; represents the capacity occupation of the transmission line over the period; constant term used to correct the function.

4. The method of claim 3, wherein the price curve is defined by a virtual line. In the S1, the parameters of the energy storage battery include energy loss cost: In formula (4), represents the charging efficiency, represents the discharging efficiency; represents the participant 's charging amount of the energy storage battery in the time period, represents the participant 's discharging amount of the energy storage battery in the time period; represents the participant 's fixed transaction price of the energy storage battery in the time period; represents the participant set containing the energy storage battery; And / or, the parameters of the energy storage battery include secondary degradation cost: In formula (5): is a quadratic degradation cost coefficient.

5. The method of point-to-point energy transaction based on virtual line defined price curve according to claim 4, characterized in that, In the S2, the auxiliary bus is introduced , denotes the set of auxiliary buses adjacent to the bus of the participant , the objective function of the constructed optimization model is: in equation (8) is: a decision variable of the participant ; a welfare generated by the participant in the time period, which is defined differently depending on the identity of the participant; a welfare generated by the participant in the time period by purchasing electricity; a welfare generated by the participant in the time period by selling electricity; a cost of energy loss on the split transmission line ; a virtual line cost of the participant participating in the virtual line, a set of virtual lines; a capacity occupation rate of the transmission line in the time period; an active power transmitted by the transmission line in the time period, a reactive power transmitted by the transmission line in the time period; , a parameter selected by the transmission system operator; a maximum capacity of the transmission line .

6. The method of point-to-point energy transaction based on virtual line defined price curve according to claim 5, characterized in that, In S2, the constraint conditions of the optimization model include equality constraints, for the participants , i.e. when the participant , as a buyer, the set of equality constraints , and: in formula (9): denotes the participant acting as a buyer the energy storage battery in injects into the participant bus; , denote the real and imaginary parts of the admittance between the bus of the participant acting as a buyer and its auxiliary bus ; , denote the real and imaginary parts of the admittance between the auxiliary bus of the participant acting as a buyer and its bus ; , denote the voltage of the bus of the participant acting as a buyer and its auxiliary bus in the time interval , , denote the phase of the bus of the participant acting as a buyer and its auxiliary bus in the time interval ; denotes the reactive power production of the bus of the participant acting as a buyer in the time interval ; denotes the reactive power demand of the bus of the participant acting as a buyer in the time interval ; denotes the active power production of the auxiliary bus in the time interval ; denotes the reactive power production of the auxiliary bus in the time interval ; wherein : ； For the participant i.e. when the participant As a seller, the set of equality constraints and: . 7.The point-to-point energy transaction method based on virtual line defining price curve according to claim 5, wherein, In the S2, the constraint conditions of the optimization model further include inequality constraints, specifically: For the participants and the auxiliary bus with inequality constraints for the transmission lines: In formula (12), , represent the active and reactive power transmitted by the transmission line, represent the maximum capacity of the transmission line, ​​ For participants with energy storage batteries which also includes inequality constraints on the battery state: In equation (13): For participants The initial energy of the energy storage battery, This is the maximum capacity of the energy storage battery; Indicates the upper bound of the variable; and Each represents a participant The charging and discharging efficiency of the energy storage battery; and, for each participant which also contains inequality constraints of the respective variable bounds: In formula (14): denotes the lower bound of the variable.

8. An apparatus for implementing the point-to-point energy transaction method based on a virtual line defining a price curve according to any one of claims 1 to 7, characterized in that, The device comprises: An input module for giving the relevant parameters required for energy transaction, the relevant parameters including demand-price curve, supply-price curve and grid parameters, the demand-price curve being determined by participants as buyers, the supply-price curve being determined by participants as sellers; wherein for participants containing energy storage batteries, parameters of their energy storage batteries are also given; A model construction module for regarding each participant as a calculation node of distributed optimization, constructing an optimization model of distributed P2P energy transaction market based on the given relevant parameters, and determining the constraint conditions thereof; A calculation and output module for iteratively solving the optimization model of the distributed P2P energy transaction market to obtain energy transaction data, which includes the final transaction price and transaction volume.

9. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the point-to-point energy transaction method based on virtual line defined price curve according to any one of claims 1 to 7.

10. A machine-readable storage medium, characterized in that, The machine-readable storage medium has stored executable instructions that, when executed, cause the machine to perform the point-to-point energy transaction method based on virtual line defined price curve according to any one of claims 1 to 7.

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