A power distribution network collaborative scheduling method, system and device

Abstract

The application discloses a power distribution network cooperative scheduling method, system and equipment, relates to the power system distribution network scheduling optimization field, and comprises the following steps: game equilibrium analysis is carried out on the constructed multi-element emerging load participating in power market transaction model, wherein the multi-element emerging load participating in power market transaction model comprises a 5G base station aggregator bidding model, an EV aggregator bidding model and a power transaction center clearing model; according to the game equilibrium analysis result, the objective function of the multi-element emerging load participating in power market transaction model is transformed, and the 5G base station aggregator bidding model, the EV aggregator bidding model and the power transaction center clearing model are solved respectively; a multi-element emerging load aggregator market settlement model is constructed, comprising a 5G base station aggregator settlement model and an EV aggregator settlement model. The application solves the problems of the emerging load multi-subject participating in the power market transaction bidding and the market clearing considering the power distribution network safety and congestion.

Description

Technical Field

[0001] This invention relates to the field of power system distribution network dispatch optimization technology, specifically to a distribution network collaborative dispatch method, system, and equipment. Background Technology

[0002] With the accelerated construction of industries related to "new infrastructure," emerging loads, represented by 5G base stations and electric vehicles (EVs), are booming and increasingly becoming new growth points for electricity consumption, posing challenges to the safe operation of the power system and the green development of industries. Therefore, it is urgent to address this from the demand side, fully leveraging the decisive role of power grid companies and the market in resource allocation, revitalizing dormant emerging load resources, promoting the active participation and efficient utilization of power flexibility resources, and transforming the power system operation from a "source-following-load" model to a "source-load interaction" model.

[0003] Currently, issues such as bidding for emerging loads involving multiple entities in the electricity market and market clearing considering distribution network safety and congestion remain to be resolved. Summary of the Invention

[0004] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a method, system, and equipment for coordinated dispatching of power distribution networks, which solves the bidding problem for multiple entities participating in electricity market transactions involving emerging loads and the market clearing problem considering power distribution network security and congestion.

[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: Firstly, a method for coordinated dispatching of power distribution networks is provided, including: A game equilibrium analysis was conducted on the constructed multi-emerging load participation model in the electricity market, which includes a 5G base station aggregator bidding model, an EV aggregator bidding model, and an electricity trading center clearing model. Based on the game equilibrium analysis results, the objective function of the multi-emerging load participation electricity market trading model is transformed in order to solve the 5G base station aggregator bidding model, EV aggregator bidding model and electricity trading center clearing model; Construct a market settlement model for diversified emerging load aggregators, including a settlement model for 5G base station aggregators and a settlement model for EV aggregators; The bidding models of 5G base station aggregators, EV aggregators, power trading center clearing models, 5G base station aggregator settlement models, and EV aggregator settlement models are solved respectively, and the effectiveness of the scheduling strategy is verified by the solution results.

[0006] Preferably, the construction of the 5G base station aggregator bidding model is as follows: During the bidding process, the calculations for 5G base station aggregator bids are performed with the goal of minimizing electricity costs. In the formula: For the first Bidding targets for 5G base station aggregators; Indicates the first Individual base station aggregators in time The quote; Represents a node The clearing electricity price; 5G base station aggregator The set of base station clusters under management; , Indicates medium-voltage distribution network node The winning bid for the charging and discharging power of energy storage resources for 5G base station clusters. ; medium-voltage distribution network node 5G base station cluster backup energy storage time Electricity reserves; medium-voltage distribution network node Basic power load of 5G base station clusters; The bidding time interval is 1 hour in the day-ahead market. The bidding period is 24 hours in the day-ahead market. When bidding for 5G base station aggregators, both pricing constraints and schedulable domain constraints must be met: Quotation constraints: In the formula: , To protect healthy market competition, the maximum and minimum bid prices are included in the bidding model. For decision variables; 5G base station cluster schedulable domain constraints: In the formula: , The charging and discharging indicator for the 5G base station cluster indicates that the cluster's net power can only be in one state at any given time, although the cluster can actually be charging and discharging simultaneously. , Improve cluster charging and discharging efficiency; , , , These are the schedulable domain parameters for the base station cluster.

[0007] Preferably, the construction of the EV aggregator bidding model is as follows: Bidding is based on the distribution network node where the EV charging station is located. The current day's bidding target is represented by the distribution network node: In the formula: medium-voltage distribution network node Bidding targets for EV aggregators; Represents a node EV aggregator in time The quote; , Indicates medium-voltage distribution network node The winning charge and discharge power of EV aggregators; For nodes Charging station at any time Dispatchable power status; EV aggregators must satisfy both pricing constraints and schedulable domain constraints when bidding: Quotation constraints: In the formula: EV aggregator The quoted price in the bidding model For decision variables; EV charging station dispatchable domain constraints: In the formula: , The charging and discharging indicator for the EV cluster indicates that the cluster's net power can only be in one state at any given time, and the cluster can be charging and discharging simultaneously. , Improve the charging and discharging efficiency of EV clusters; , , , For schedulable domain parameters of EV clusters; The scheduling interval.

[0008] Preferably, the construction of the power trading center clearing model specifically includes: For the clearing of the power trading center, the objective is to maximize the day-ahead market social welfare. For ease of solution, this is changed to a minimization problem. In the formula: The targets for clearing the current targets are as follows: the first target represents the cost of purchasing electricity from the upper-level power grid, the second target represents the cost of purchasing electricity from photovoltaic power generators, the third target represents the cost of purchasing electricity that all 5G base station aggregators are willing to pay, and the fourth target represents the charging cost that all EV aggregators are willing to pay. For power generators, tiered electricity pricing For ladder numbering; For the tiered pricing set, for The clearing power of each tier of the power grid is constantly purchased from the upper-level power grid; For photovoltaic power purchase price, The number of nodes where the photovoltaic system is located. For nodes Photovoltaics Clearing power at any given moment; Aggregator of base station aggregators; This is a collection of EV aggregators; it should be noted that the clearing target includes... and For the quote, , , , , , These are decision variables.

[0009] Upon clearing, the following constraints must be met: power flow constraints, voltage safety constraints, line capacity constraints, upstream power purchase constraints, emerging load constraints, and photovoltaic output constraints. Distribution network linearization power flow constraints: In the formula: Equations 1 to 4, 5 to 6, and 7 are active power balance constraints, reactive power balance constraints, and voltage balance constraints, respectively. For ease of expression, active power balance constraints and reactive power balance constraints will be expressed as single formulas in the following text. , , These are, respectively, the set of nodes in the medium-voltage distribution network, the set of nodes where base station aggregators are located, and the set of nodes where photovoltaic power is located; , Branch roads , At any moment The branch road has a positive flow. Represents a node When the parent node is the set of all its child nodes, For distribution network nodes In Basic active load at any given time; , Branch roads , At any moment The reactive power flow of the branch, For distribution network nodes In The basic reactive load at any given time, For distribution network nodes Photovoltaics Effortless exertion at all times; , Representing child nodes respectively With parent node At any moment The square value of the voltage, , Branch roads The resistance and reactance values; Distribution network safety constraints: In the formula: , These represent the maximum and minimum limits for the square of the node voltage, respectively.

[0010] Distribution network congestion management: In the formula: branch road Maximum load capacity; This refers to the collection of all branches in a medium-voltage distribution network. Electricity purchase constraints from higher authorities: Where: nodes Represents the root node; Represents the set of nodes connected to the root node; Divide the quote into segments Maximum power at that time; Emerging load power constraints: Photovoltaic output constraints: In the formula: , For nodes Maximum regulation of active and reactive power of photovoltaic power.

[0011] Preferably, the game equilibrium analysis of the constructed multi-emerging load participation electricity market trading model specifically includes: Nash games are formed among emerging load aggregators, while Stackelberg games are formed between all emerging load aggregators and the power trading center. The Stackelberg game problem is transformed into a single-level mixed integer linear programming model using the KKT reconstruction method, the Big M method, and the strong duality theorem, and solved using a commercial solver. The bidding single-level models of emerging load aggregators form a generalized Nash game due to the power coupling constraints of the distribution network.

[0012] Preferably, the objective function of the multi-emerging load participation electricity market trading model is transformed based on the game equilibrium analysis results, as follows: Establish a KKT system: The formula to be solved is as follows: In the formula: The objective function is the optimization problem to be solved in the model of multiple emerging loads participating in the electricity market transaction. For equality constraints; The number of equality constraints; Inequality constraints; The number of inequality constraints; The KKT system is converted as follows: In the formula: In Lagrange form; The symbol is a complementary symbol, meaning that there is one and only one term in both expressions on the left and right sides of the symbol that is 0; In the KKT system, complementary constraints are nonlinear constraints, which are linearized using the Big M method. The Big M method transformation is as follows: In the formula: For the added Boolean variable; It is a very large constant; Transformation objective function: The final objective of the 5G base station aggregator bidding process is transformed into: In the formula: These are the dual variables of the equality constraints; , , , , , , , , , , , , , , , , , , , , , , , These are the dual variables of the inequality constraints; The final objective of the EV aggregator bidding solution is transformed into: .

[0013] Preferably, the construction of the 5G base station aggregator settlement model specifically includes:

[0014] After the clearing process, 5G base station aggregators will receive the total charging and discharging power and the clearing electricity price. The final energy cost for 5G base station aggregators will be settled by the following formula.

[0015] In the formula: For aggregators Total energy cost.

[0016] To meet system demands, the energy storage charging and discharging power of each 5G base station needs to be managed. The output of each base station is optimized to minimize the total power output deviation of the 5G base stations. Since linear power flow calculations are used, the objective function has a non-unique solution. To ensure consistent power supply reliability across different base stations, a base station power allocation algorithm based on the consistency between scheduling capacity and schedulable capacity is proposed. The model is as follows: In the formula: Base station aggregator Optimize objectives , For aggregators Internal base station The planned charge and discharge power, For aggregators Internal base station set; Auxiliary variables to ensure weak consistency It is a deviation constant that guarantees weak consistency; the first constraint guarantees consistent power supply reliability of the base station; the other constraints are self-regulation constraints of the 5G base station.

[0017] Preferably, the construction of the EV aggregator settlement model specifically includes:

[0018] The settlement formula for EV aggregators after clearing is as follows: In the formula: Total electricity cost for EV aggregators; To respond to system requirements, the charging and discharging power of each EV is managed; with the goal of minimizing the total EV output deviation, the output of each EV is optimized, and to balance the battery losses of the EVs participating in the interaction, a weak consistency constraint on the discharge charge is added; the model is as follows: In the formula: EV aggregator Optimize objectives , For aggregators China Vehicle exist A real-time charge and discharge schedule. For aggregators Inner EV set; Auxiliary variables to ensure weak consistency It is a deviation constant that guarantees weak consistency; the first constraint guarantees the consistency of vehicle discharge; the other constraints are EV self-regulation constraints. The planned charging cost per EV is the product of the clearing electricity price and the planned charging / discharging power: In the formula: for Electricity costs; The profit coefficient is set by aggregators for their own profitability. Aggregators determine this coefficient by predicting their own revenue when bidding. At the same time, aggregators also attract EVs to participate in grid interaction by adjusting the product of this coefficient and the predicted clearing price.

[0019] Secondly, a distribution network collaborative dispatching system is provided, including: The analysis module is used to perform game equilibrium analysis on the constructed multi-emerging load participation model in the electricity market, wherein the multi-emerging load participation model in the electricity market includes a 5G base station aggregator bidding model, an EV aggregator bidding model, and an electricity trading center clearing model. The transformation module is used to transform the objective function of the multi-emerging load participation electricity market trading model based on the game equilibrium analysis results, so as to solve the 5G base station aggregator bidding model, EV aggregator bidding model and electricity trading center clearing model; The settlement model construction module is used to build settlement models for the diversified emerging load aggregator market, including 5G base station aggregator settlement models and EV aggregator settlement models; The solution module is used to solve the 5G base station aggregator bidding model, EV aggregator bidding model, power trading center clearing model, 5G base station aggregator settlement model and EV aggregator settlement model respectively, and to verify the effectiveness of the scheduling strategy through the solution results.

[0020] Thirdly, a computing device is provided, comprising: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described.

[0021] (III) Beneficial Effects (1) The present invention provides a method, system and equipment for coordinated dispatching of power distribution networks, which solves the bidding problem of multiple entities participating in electricity market transactions for emerging loads and the market clearing problem considering the safety and congestion of power distribution networks. (2) The present invention provides a distribution network collaborative scheduling method, system and equipment, which uses linearized power flow equations for voltage management and linear external approximation constraints for congestion management, thereby ensuring the safety of distribution network operation during power trading. (3) The present invention provides a distribution network collaborative dispatching method, system and equipment, which utilizes a bidding model that minimizes the cost of commercial electricity aggregation for emerging loads and a power market clearing model that maximizes social benefits, thereby improving the utilization rate of emerging load resources and reducing the energy cost of emerging loads without requiring additional subsidies from distribution network operators. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a network diagram illustrating a method example provided in an embodiment of the present invention. Figure 3 This is a voltage distribution diagram of the power distribution network after use, provided in an embodiment of the present invention. Figure 4 A diagram showing the load status of the power distribution network lines after use, provided as an embodiment of the present invention; Figure 5 This is a diagram of the base station aggregator clearing structure provided in an embodiment of the present invention; Figure 6 This is a diagram showing the clearing results of EV aggregators provided in an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions in the embodiments of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. 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] Example like Figure 1 As shown, one embodiment of the present invention provides a distribution network coordinated dispatching method, including: A game equilibrium analysis was conducted on the constructed multi-emerging load participation model in the electricity market, which includes a 5G base station aggregator bidding model, an EV aggregator bidding model, and an electricity trading center clearing model. Based on the game equilibrium analysis results, the objective function of the multi-emerging load participation electricity market trading model is transformed in order to solve the 5G base station aggregator bidding model, EV aggregator bidding model and electricity trading center clearing model; Construct a market settlement model for diversified emerging load aggregators, including a settlement model for 5G base station aggregators and a settlement model for EV aggregators; The bidding models of 5G base station aggregators, EV aggregators, power trading center clearing models, 5G base station aggregator settlement models, and EV aggregator settlement models are solved respectively, and the effectiveness of the scheduling strategy is verified by the solution results.

[0025] Please see Figure 2-6 The specific implementation steps are as follows: (1) Construct models for the participation of diverse emerging loads in the electricity market, including a 5G base station aggregator bidding model, an EV aggregator bidding model, and an electricity trading center clearing model; (2) Conduct game equilibrium analysis on the constructed transaction model; (3) Transform the constructed transaction model to facilitate solving; (4) Construct a market settlement model for diversified emerging load aggregators, including a settlement model for 5G base station aggregators and a settlement model for EV aggregators; (5) The effectiveness of the distribution network coordinated dispatch strategy considering the participation of multiple entities in the electricity market transaction of emerging loads was verified by using an actual radial network structure.

[0026] The implementation method of the present invention will be described in detail below: Step 1: Construct a model for multiple participants in the electricity market trading of emerging loads. The main steps in this part are as follows: (1) Construct a bidding model for 5G base station aggregators.

[0027] During the bidding process, each emerging load aggregator aims to minimize its own electricity costs. Therefore, the bidding calculations for 5G base station aggregators are based on minimizing electricity costs. The objectives for 5G base station aggregators are as follows: In the formula: For the first Bidding targets for 5G base station aggregators; Indicates the first Individual base station aggregators in time The quote; Represents a node The clearing electricity price; 5G base station aggregator The set of base station clusters under management; , Indicates medium-voltage distribution network node Regarding the winning bid for the charging and discharging power of energy storage resources for 5G base station clusters, it's important to note that 5G base stations in China are primarily constructed by the three major telecom operators. Therefore, the bidding unit is not based on distribution network nodes. ; medium-voltage distribution network node 5G base station cluster backup energy storage time Electricity reserves; medium-voltage distribution network node Basic power load of 5G base station clusters; The bidding time interval is 1 hour in the day-ahead market. The bidding period is 24 hours in the day-ahead market.

[0028] When bidding for 5G base station aggregators, price constraints and schedulable domain constraints should be met.

[0029] a) Pricing constraints: In the formula: , To protect healthy market competition, the maximum and minimum bid prices are included in the bidding model. These are decision variables.

[0030] b) 5G base station cluster schedulable domain constraints: In the formula: , The charging and discharging indicator for the 5G base station cluster indicates that the cluster's net power can only be in one state at any given time, although the cluster can actually be charging and discharging simultaneously. , Improve cluster charging and discharging efficiency; , , , These are the schedulable domain parameters for the base station cluster.

[0031] (2) Construct an EV aggregator bidding model.

[0032] Similar to 5G base station aggregators, EV aggregators currently target minimum energy costs in their bids. However, unlike 5G base station aggregators, EV energy costs are already included in charging power. Furthermore, EV clusters are aggregated in the form of charging stations; therefore, bids are submitted at the distribution network node level where the EV charging station is located. Current bidding targets can be represented by these distribution network nodes. In the formula: medium-voltage distribution network node Bidding targets for EV aggregators; Represents a node EV aggregator in time The quote; , Indicates medium-voltage distribution network node The winning charge and discharge power of EV aggregators; For nodes Charging station at any time Dispatchable power status.

[0033] EV aggregators should meet both pricing constraints and schedulable domain constraints when bidding.

[0034] a) Pricing constraints: In the formula: EV aggregator The quoted price in the bidding model These are decision variables.

[0035] b) Scheduling domain constraints for EV charging stations: In the formula: , The charging and discharging indicator for the EV cluster indicates that the cluster's net power can only be in one state at any given time, and the cluster can be charging and discharging simultaneously. , Improve the charging and discharging efficiency of EV clusters; , , , For schedulable domain parameters of EV clusters; The scheduling interval.

[0036] (3) Construct a power trading center clearing model.

[0037] For the clearing of the power trading center, the goal is to maximize the social welfare of the day-ahead market. For ease of solution, it is changed to a minimization solution.

[0038] In the formula: The targets for clearing the current targets are as follows: the first target represents the cost of purchasing electricity from the upper-level power grid, the second target represents the cost of purchasing electricity from photovoltaic power generators, the third target represents the cost of purchasing electricity that all 5G base station aggregators are willing to pay, and the fourth target represents the charging cost that all EV aggregators are willing to pay. For power generators, tiered electricity pricing For ladder numbering; For the tiered pricing set, for The clearing power of each tier of the power grid is constantly purchased from the upper-level power grid; For photovoltaic power purchase price, The number of nodes where the photovoltaic system is located. For nodes Photovoltaics Clearing power at any given moment; Aggregator of base station aggregators; This is for EV aggregators. Note that the clearing target includes... and For the quote, , , , , , These are decision variables.

[0039] When clearing out power, the following constraints should be met: power flow constraints, voltage safety constraints, line capacity constraints, upstream power purchase constraints, emerging load constraints, and photovoltaic power output constraints.

[0040] a) Distribution network linearization power flow constraints: In solving the electricity market trading model, the power flow constraints based on Distflow theory are a set of non-convex and nonlinear equations, which are not convenient for market clearing. Therefore, a linearized Distflow model is used to approximate the constraints. In the formula: Equations 1 to 4, 5 to 6, and 7 are active power balance constraints, reactive power balance constraints, and voltage balance constraints, respectively. For ease of expression, active power balance constraints and reactive power balance constraints will be expressed as single formulas in the following text. , , These are, respectively, the set of nodes in the medium-voltage distribution network, the set of nodes where base station aggregators are located, and the set of nodes where photovoltaic power is located; , Branch roads , At any moment The branch road has a positive flow. Represents a node When the parent node is the set of all its child nodes, For distribution network nodes In Basic active load at any given time; , Branch roads , At any moment The reactive power flow of the branch, For distribution network nodes In The basic reactive load at any given time, For distribution network nodes Photovoltaics Effortless exertion at all times; , Representing child nodes respectively With parent node At any moment The square value of the voltage, , Branch roads The resistance and reactance values.

[0041] b) Distribution network security constraints: In the formula: , These represent the maximum and minimum limits for the square of the node voltage, respectively.

[0042] c) Distribution network congestion management: In the formula: branch road Maximum load capacity; This represents the set of all branches in the medium-voltage distribution network. Although the line capacity constraint is a convex quadratic constraint, it exhibits strong nonlinearity and nonconvexity when using KKT conditions in the clearing problem. Therefore, a linear external approximation constraint can be used for congestion management, simplifying the clearing calculation.

[0043] d) Power purchase constraints from higher authorities: Where: nodes Represents the root node; Represents the set of nodes connected to the root node; Divide the quote into segments The maximum power at that time.

[0044] e) Emerging load power constraints: f) Photovoltaic output constraints: In the formula: , For nodes Maximum regulation of active and reactive power of photovoltaic power.

[0045] Step Two: Perform game equilibrium analysis on the trading model constructed in Step One. The main steps in this part are as follows: The bidding clearing model between emerging load aggregators and the power trading center reveals that a Nash game exists among the emerging load aggregators (5G base station aggregator 1, ..., 5G base station aggregator k, ..., EV aggregator 1, ..., EV aggregator n), while a Stackelberg game exists between all emerging load aggregators and the power trading center. The Stackelberg game problem essentially involves solving a two-level mixed-integer nonlinear programming (BMINLP) model, which cannot be directly solved using a commercial solver. Therefore, the KKT reconstruction method, the Big M method, and the strong duality theorem are used to transform the BMINLP into a single-level mixed-integer linear programming (MILP) model, which is then solved using a commercial solver. At this point, a generalized Nash game is formed between the single-level bidding models of emerging load aggregators due to the power coupling constraints within the distribution network. Since equilibrium in the electricity retail market is universally present, an equilibrium solution exists for the problem to be solved.

[0046] Step 3: Perform model transformation on the constructed trading model. The main steps in this part are as follows: (1) Establish a KKT system.

[0047] To facilitate the establishment of the KKT system, the basic form of the KKT condition transformation is described below. The following equation is the one to be solved: In the formula: The objective function is the optimization problem to be solved in the model of multiple emerging loads participating in the electricity market transaction. For equality constraints; The number of equality constraints; Inequality constraints; denoted as the number of inequality constraints.

[0048] The transformed KKT system is as follows: In the formula: In Lagrange form; The symbol is a complementary symbol, meaning that there is exactly one term in both expressions on the left and right sides of the symbol that is 0.

[0049] In the KKT system, complementary constraints are nonlinear constraints, therefore they are linearized using the Big M method. The Big M method transformation is as follows: In the formula: For the added Boolean variable; It is a very large constant.

[0050] (2) Transform the objective function.

[0051] The final objective of the 5G base station aggregator bidding process is transformed into: In the formula: These are the dual variables of the equality constraints; , , , , , , , , , , , , , , , , , , , , , , , is the dual variable of the inequality constraint.

[0052] The final objective of the EV aggregator bidding solution is transformed into: Step 4: Construct a settlement model for multiple emerging load aggregators. The main steps in this part are as follows: (1) Construct a settlement model for 5G base station aggregators.

[0053] After the clearing process, 5G base station aggregators will receive the total charging and discharging power and the clearing electricity price. The final energy cost for 5G base station aggregators will be settled by the following formula.

[0054] In the formula: For aggregators Total energy cost.

[0055] To meet system demands, the energy storage charging and discharging power of each 5G base station needs to be managed. The output of each base station is optimized to minimize the total power output deviation of the 5G base stations. Since linear power flow calculations are used, the objective function has a non-unique solution. To ensure consistent power supply reliability across different base stations, a base station power allocation algorithm based on the consistency between scheduling capacity and schedulable capacity is proposed. The model is as follows: In the formula: Base station aggregator Optimize objectives , For aggregators Internal base station The planned charge and discharge power, For aggregators Internal base station set; Auxiliary variables to ensure weak consistency It is a deviation constant that guarantees weak consistency; the first constraint guarantees consistent power supply reliability of the base station; the other constraints are self-regulation constraints of the 5G base station.

[0056] (2) Construct an EV aggregator settlement model.

[0057] Similar to 5G base station aggregators, EV aggregators will settle accounts in the following manner after clearing out.

[0058] In the formula: This represents the total electricity cost for EV aggregators.

[0059] To respond to system demands, the charging and discharging power of each EV is managed. The goal is to minimize the total EV output deviation, optimizing the output of each EV. Simultaneously, to balance battery losses among participating EVs, a weak consistency constraint on charge / discharge capacity is added. The model is as follows: In the formula: EV aggregator Optimize objectives , For aggregators China Vehicle exist A real-time charge and discharge schedule. For aggregators Inner EV set; Auxiliary variables to ensure weak consistency It is a deviation constant that guarantees weak consistency; the first constraint guarantees the consistency of vehicle discharge; the other constraints are EV self-regulation constraints.

[0060] The planned charging cost per EV is the product of the clearing electricity price and the planned charging / discharging power: In the formula: for Electricity costs; The profit coefficient is set by aggregators for their own profitability. Aggregators determine this coefficient by predicting their own revenue when bidding. At the same time, aggregators also attract EVs to participate in grid interaction by adjusting the product of this coefficient and the predicted clearing price.

[0061] Step 5: Based on Steps 1, 2, 3, and 4, the effectiveness of the distribution network coordinated dispatch strategy considering the participation of multiple entities in the electricity market for emerging loads is verified using an actual radial network structure. The main steps in this part are as follows: (1) Calculate the bidding model for 5G base station aggregators and EV aggregators.

[0062] (2) Calculate the electricity market clearing model.

[0063] (3) Calculate the settlement model for 5G base station aggregators.

[0064] (4) Calculate the EV aggregator settlement model.

[0065] Table 1: Aggregator's Expected Electricity Costs and Actual Electricity Costs One embodiment of the present invention provides a power distribution network collaborative dispatching system, comprising: The analysis module is used to perform game equilibrium analysis on the constructed multi-emerging load participation model in the electricity market, wherein the multi-emerging load participation model in the electricity market includes a 5G base station aggregator bidding model, an EV aggregator bidding model, and an electricity trading center clearing model. The transformation module is used to transform the objective function of the multi-emerging load participation electricity market trading model based on the game equilibrium analysis results, so as to solve the 5G base station aggregator bidding model, EV aggregator bidding model and electricity trading center clearing model; The settlement model construction module is used to build settlement models for the diversified emerging load aggregator market, including 5G base station aggregator settlement models and EV aggregator settlement models; The solution module is used to solve the 5G base station aggregator bidding model, EV aggregator bidding model, power trading center clearing model, 5G base station aggregator settlement model and EV aggregator settlement model respectively, and to verify the effectiveness of the scheduling strategy through the solution results.

[0066] Embodiments of this application may be provided as methods or computer program products. Therefore, this application may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application may take the form of a computer program product implemented 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. The solutions in the embodiments of this application may be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0067] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0068] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0069] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0070] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for coordinated dispatching of a power distribution network, characterized in that, include: A game equilibrium analysis was conducted on the constructed multi-emerging load participation model in the electricity market, which includes a 5G base station aggregator bidding model, an EV aggregator bidding model, and an electricity trading center clearing model. Based on the game equilibrium analysis results, the objective function of the multi-emerging load participation electricity market trading model is transformed in order to solve the 5G base station aggregator bidding model, EV aggregator bidding model and electricity trading center clearing model; Construct a market settlement model for diversified emerging load aggregators, including a settlement model for 5G base station aggregators and a settlement model for EV aggregators; The bidding models for 5G base station aggregators, EV aggregators, power trading center clearing models, 5G base station aggregator settlement models, and EV aggregator settlement models are solved respectively, and the effectiveness of the scheduling strategy is verified by the solution results. The construction of the 5G base station aggregator bidding model is as follows: During the bidding process, the calculations for 5G base station aggregator bids are performed with the goal of minimizing electricity costs. In the formula: For the first Bidding targets for 5G base station aggregators; Indicates the first Individual base station aggregators in time The quote; Represents a node The clearing electricity price; 5G base station aggregator The set of base station clusters under management; , Indicates medium-voltage distribution network node The winning bid for the charging and discharging power of energy storage resources for 5G base station clusters. ; medium-voltage distribution network node 5G base station cluster backup energy storage time Electricity reserves; medium-voltage distribution network node Basic power load of 5G base station clusters; The bidding time interval is 1 hour in the day-ahead market. The bidding period is 24 hours in the day-ahead market. When bidding for 5G base station aggregators, both pricing constraints and schedulable domain constraints must be met: Quotation constraints: In the formula: , To protect healthy market competition, the maximum and minimum bid prices are included in the bidding model. For decision variables; 5G base station cluster schedulable domain constraints: In the formula: , The charging and discharging indicator for the 5G base station cluster indicates that the cluster's net power can only be in one state at any given time, and that the cluster actually engages in both charging and discharging. , Improve the charging and discharging efficiency of the cluster; , , , These are the schedulable domain parameters for the base station cluster.

2. The distribution network coordinated dispatching method according to claim 1, characterized in that: The construction of the EV aggregator bidding model is as follows: Bidding is based on the distribution network node where the EV charging station is located. The current day's bidding target is represented by the distribution network node: In the formula: medium-voltage distribution network node Bidding targets for EV aggregators; Represents a node EV aggregator in time The quote; , Indicates medium-voltage distribution network node The winning charge and discharge power of EV aggregators; For nodes Charging station at any time Scheduled power status; EV aggregators must satisfy both pricing constraints and schedulable domain constraints when bidding: Quotation constraints: In the formula: EV aggregator The quoted price in the bidding model For decision variables; EV charging station dispatchable domain constraints: In the formula: , The charging and discharging indicator for the EV cluster indicates that the cluster's net power can only be in one state at any given time, and that the cluster is both charging and discharging. , Improve the charging and discharging efficiency of EV clusters; , , , For schedulable domain parameters of EV clusters; The scheduling interval.

3. The distribution network coordinated dispatching method according to claim 2, characterized in that: The construction of the power trading center clearing model specifically includes: For the clearing of the power trading center, the objective is to maximize the day-ahead market social welfare. For ease of solution, this is changed to a minimization problem. In the formula: The targets for clearing the current targets are as follows: the first target represents the cost of purchasing electricity from the upper-level power grid, the second target represents the cost of purchasing electricity from photovoltaic power generators, the third target represents the cost of purchasing electricity that all 5G base station aggregators are willing to pay, and the fourth target represents the charging cost that all EV aggregators are willing to pay. For power generators, tiered electricity pricing For ladder numbering; For the tiered pricing set, for The clearing power of each tier of the power grid is constantly purchased from the upper-level power grid; For photovoltaic power purchase price, This represents the number of nodes where the photovoltaic system is located. For nodes Photovoltaics Clearing power at any given moment; Aggregator for base station providers; This is a collection of EV aggregators; it should be noted that the clearing target includes... and For the quote, , , , , , For decision variables; Upon clearing, the following constraints must be met: power flow constraints, voltage safety constraints, line capacity constraints, upstream power purchase constraints, emerging load constraints, and photovoltaic output constraints. Distribution network linearization power flow constraints: In the formula: Equations 1 to 4, 5 to 6, and 7 are active power balance constraints, reactive power balance constraints, and voltage balance constraints, respectively. For ease of expression, active power balance constraints and reactive power balance constraints will be expressed as single formulas in the following text. , , These are, respectively, the set of nodes in the medium-voltage distribution network, the set of nodes where base station aggregators are located, and the set of nodes where photovoltaic power is located; , Branch roads , At any moment The branch road has a positive flow, Represents a node When the parent node is the set of all its child nodes, For distribution network nodes In The basic active power load at any given time; , Branch roads , At any moment The reactive power flow of the branch, For distribution network nodes In The basic reactive load at any given time, For distribution network nodes Photovoltaics Effortlessly exerting oneself in moments of ineffectiveness; , Representing child nodes respectively With parent node At any moment The square value of the voltage, , Branch roads The resistance and reactance values; Distribution network safety constraints: In the formula: , These are the maximum and minimum limits for the square of the node voltage, respectively. Distribution network congestion management: In the formula: branch road Maximum load capacity; This refers to the collection of all branches in a medium-voltage distribution network. Electricity purchase constraints from higher authorities: Where: nodes Represents the root node; Represents the set of nodes connected to the root node; Segmenting the quote Maximum power at that time; Emerging load power constraints: Photovoltaic output constraints: In the formula: , For nodes The maximum regulation of active and reactive power of photovoltaic power.

4. The distribution network coordinated dispatching method according to claim 3, characterized in that: The game equilibrium analysis of the constructed multi-emerging load participation electricity market trading model specifically includes: Nash games are formed among emerging load aggregators, while Stackelberg games are formed between all emerging load aggregators and the power trading center. The Stackelberg game problem is transformed into a single-level mixed integer nonlinear programming model using the KKT reconstruction method, the Big M method, and the strong duality theorem, and solved using a commercial solver. The bidding single-level models of emerging load aggregators form a generalized Nash game due to the power coupling constraints of the distribution network.

5. The distribution network coordinated dispatching method according to claim 4, characterized in that: Based on the game equilibrium analysis results, the objective function of the multi-emerging load participation electricity market trading model is transformed as follows: Establish a KKT system: The formula to be solved is as follows: In the formula: The objective function is the optimization problem to be solved in the model of multiple emerging loads participating in the electricity market transaction. For equality constraints; The number of equality constraints; Inequality constraints; The number of inequality constraints; The KKT system is converted as follows: In the formula: In Lagrange form; The symbol is a complementary symbol, meaning that there is one and only one term in both expressions on the left and right sides of the symbol that is 0; In the KKT system, complementary constraints are nonlinear constraints, which are linearized using the Big M method. The Big M method transformation is as follows: In the formula: For the added Boolean variable; It is a constant; Transformation objective function: The final objective of the 5G base station aggregator bidding process is transformed into: In the formula: These are the dual variables of the equality constraints; , , , , , , , , , , , , , , , , , , , , , , , These are the dual variables of the inequality constraints; The final objective of the EV aggregator bidding solution is transformed into: 。 6. The distribution network coordinated dispatching method according to claim 5, characterized in that: The construction of the 5G base station aggregator settlement model specifically includes: After the clearing process, 5G base station aggregators will receive the total winning bid charging and discharging power and the clearing electricity price. The final energy cost for 5G base station aggregators will be calculated using the following formula: In the formula: For aggregators Total energy cost; To meet system demands, the energy storage charging and discharging power of each 5G base station needs to be managed. The goal is to minimize the total power output deviation of each 5G base station, optimizing its output. Since linear power flow calculations are used, the objective function has a non-unique solution. To ensure consistent power supply reliability across different base stations, a base station power allocation algorithm based on the consistency between scheduling capacity and schedulable capacity is proposed. The model is as follows: In the formula: Base station aggregator Optimize objectives , For aggregators Internal base station The planned charge and discharge power, For aggregators Internal base station set; Auxiliary variables to ensure weak consistency It is a deviation constant that guarantees weak consistency; the first constraint guarantees consistent power supply reliability of the base station; the other constraints are self-regulation constraints of the 5G base station.

7. The distribution network coordinated dispatching method according to claim 6, characterized in that: The construction of the EV aggregator settlement model specifically includes: The settlement formula for EV aggregators after clearing is as follows: In the formula: Total electricity cost for EV aggregators; To respond to system requirements, the charging and discharging power of each EV is managed; the output of each EV is optimized with the goal of minimizing the total EV output deviation, while a weak consistency constraint on the discharge charge is added to balance the battery losses of the EVs participating in the interaction; the model is as follows: In the formula: EV aggregator Optimize objectives , For aggregators China Vehicle exist A real-time charge and discharge schedule. For aggregators Inner EV set; Auxiliary variables to ensure weak consistency It is a deviation constant that guarantees weak consistency; the first constraint guarantees the consistency of vehicle discharge; the other constraints are EV self-regulation constraints. The planned charging cost per EV is the product of the clearing electricity price and the planned charging / discharging power: In the formula: for Electricity costs; The profit coefficient is set by aggregators for their own profitability. Aggregators determine this coefficient by predicting their own revenue when bidding. At the same time, aggregators also attract EVs to participate in grid interaction by adjusting the product of this coefficient and the predicted clearing price.

8. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods according to claims 1-7.

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