Power distribution method and device of urban power grid, medium and equipment

Abstract

The invention discloses a power distribution method and device for an urban power grid, a medium and equipment, and belongs to the field of power distribution.The method comprises the steps that firstly, electric heavy truck, air conditioner and resident load data of all parks in the urban power grid are collected in real time, and the charge state, power capacity and power grid time-of-use electricity price information of shared energy storage are synchronously obtained; and then inputting the data into a double-layer flexible resource-shared energy storage decision model obtained by'historical data + income maximization 'training, and cooperatively optimizing upper-layer energy storage economic benefits and lower-layer multi-park flexible resource utilization under power coupling constraint through mixed integer programming. Outputting a charging and discharging power distribution instruction of shared energy storage and a flexible resource regulation and control instruction of each park; finally, energy storage charging and discharging and park load adjustment are controlled in a closed-loop mode according to the instruction, accurate, real-time and economical distribution of urban power grid power is achieved, and the problem that in the prior art, optimal adjustment of a power grid cannot be achieved with economic maximization as the target is effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of power distribution, and in particular to a power distribution method, device, medium and equipment for a city power grid. Background Art

[0002] With accelerating urbanization and rapid economic development, urban power grids face growing electricity demand and increasingly complex supply-demand balance challenges. Driven by emerging factors like the integration of new energy sources and the widespread adoption of electric vehicles, urban power grid loads are experiencing increased volatility and uncertainty. Furthermore, urban power grids must cope with power surges caused by extreme weather and emergencies to ensure stable operation and secure power supply.

[0003] To address these challenges and improve the grid's regulation capabilities and economic efficiency, various flexible resources have been introduced into urban power grids, including energy storage devices, demand-side management resources (such as adjustable loads), and electric vehicles. These flexible resources can play a role in peak load shaving and demand response in the power system, improving the grid's operational flexibility and reliability. However, effectively integrating and managing these dispersed flexible resources to achieve optimal power allocation between shared energy storage and flexible resources across various industrial parks within the urban power grid remains a pressing technical challenge.

[0004] Existing urban grid power allocation methods generally fail to fully consider the coordinated optimization of flexible resources across multiple campuses and shared energy storage. These methods often focus on optimizing the management of a single resource or lack comprehensive consideration of economic factors such as time-of-use electricity prices. These shortcomings prevent existing technologies from achieving optimal grid regulation with the goal of maximizing economic efficiency. Summary of the Invention

[0005] The present invention provides a power distribution method, device, medium and equipment for a city power grid to solve the problem in the prior art that it is impossible to achieve optimal regulation of the power grid with the goal of economic maximization.

[0006] In a first aspect, the present application provides a power distribution method for a city power grid, comprising:

[0007] Acquire real-time operating data from the city power grid, including load data for each park, energy storage device parameters, and time-of-use electricity price information for the power grid;

[0008] Based on the real-time operating data and in combination with a preset mixed integer programming method, a preset flexible resource-shared energy storage decision model is solved, so that the flexible resource-shared energy storage decision model allocates shared energy storage and flexible resources of each park through power coupling constraints, and outputs charging and discharging power allocation instructions for the shared energy storage and control instructions for the flexible resources of each park;

[0009] The flexible resource-shared energy storage decision model is based on historical data from the city's power grid and is trained to maximize the economic benefits of shared energy storage and the comprehensive benefits of each park's flexible resources.

[0010] The power of the urban power grid is distributed according to the charging and discharging power distribution instructions and the control instructions of the flexible resources of each park.

[0011] This application provides accurate data support for power grid operators by obtaining real-time operating data, including load data, energy storage equipment parameters and grid time-of-use electricity price information. These data are the basis for effective power allocation and resource scheduling. Combined with the mixed integer programming method, this method solves the flexible resource-shared energy storage decision model obtained by training based on historical data, and uses power coupling constraints to achieve the optimal power allocation between shared energy storage and flexible resources in each park. This process not only ensures the stable operation of the power grid in the face of power surges, but also achieves accurate power allocation to the urban power grid by optimizing the charging and discharging power allocation instructions and the control instructions of the flexible resources of each park. Since the decision model aims to maximize the economic benefits of shared energy storage and the comprehensive benefits of the flexible resources of each park, it can be inferred that this method can effectively reduce the operating costs of the power grid, improve economic benefits, and enhance the power grid's response capability to power surges, thereby improving the operating efficiency and reliability of the entire power grid. The implementation of this method is expected to bring significant economic and operational benefits to power grid operators. This application effectively solves the problem in the existing technology that it is impossible to achieve optimal regulation of the power grid with economic maximization as the goal.

[0012] Furthermore, the real-time operation data of the urban power grid is obtained as follows:

[0013] Obtaining real-time load data from the power monitoring system of each park, including the charging load of electric heavy trucks in the industrial park, the air conditioning load in the commercial park, and the daily electricity load in the residential park;

[0014] Obtaining real-time parameters of the energy storage device from a preset energy storage device management system, wherein the real-time parameters include the current state of charge, maximum charge and discharge power, and energy storage capacity of the energy storage device;

[0015] The real-time time-of-use electricity price information of the power grid is obtained from a preset power grid dispatching system, wherein the real-time time-of-use electricity price information includes peak time electricity price, normal time electricity price and valley time electricity price.

[0016] This application first obtains a supply and demand portrait with "second-level" accuracy by synchronously obtaining three types of real-time data from the park-level power monitoring system, energy storage equipment management system and power grid dispatching system - electric heavy truck charging load, air conditioning load, household electricity load, energy storage charge state and capacity, and peak and valley electricity prices. Once these data are injected into the model, the model can prioritize energy storage discharge and reduce high-priced electricity purchases during peak price periods, reverse charge and absorb redundant new energy during valley price periods, and dynamically reduce the park peak according to the adjustable potential of heavy trucks and air conditioners. The more complete the real-time data, the shorter the decision-making window, the greater the energy storage arbitrage space, and the more fully the adjustment margin of the park's flexible resources can be released, ultimately bringing multiple benefits: reduced grid operating costs, increased new energy absorption rate, and reduced park electricity bills.

[0017] Furthermore, the preset flexible resource-shared energy storage decision model is solved based on the real-time operation data in combination with a preset mixed integer programming method, specifically as follows:

[0018] Initializing parameters of a mixed integer programming solver based on the real-time operation data and a preset mixed integer programming method, wherein the parameters of the mixed integer programming solver include the number of iterations, convergence accuracy, and preset constants;

[0019] Based on the parameters of the initialized mixed integer programming solver, the preset flexible resource-shared energy storage decision model is solved;

[0020] Among them, the flexible resource-shared energy storage decision-making model includes an upper-level model and a lower-level model. The upper-level model is used to optimize the power interaction between shared energy storage and the power grid and each park, and is used to maximize the economic benefits of shared energy storage; the lower-level model includes a flexible model for electric heavy trucks in industrial parks and a flexible model for variable-frequency air conditioners in commercial parks, which is used to maximize the utilization of flexible resources within each park.

[0021] After initializing the parameters of the mixed integer programming solver, this application inputs the real-time operating data into a two-layer flexible resource-shared energy storage decision model: the upper-layer model globally optimizes the power interaction between shared energy storage, the power grid, and each park based on the time-of-use electricity price of the power grid, with the goal of maximizing the economic benefits of shared energy storage; the lower-layer model synchronously calls the flexible model of electric heavy trucks in the industrial park and the flexible model of variable frequency air conditioners in the commercial park, and feeds back the adjustable capacity of each park's flexible resources to the upper layer in real time to ensure that all power instructions meet the physical constraints of the equipment. This collaborative solution mechanism achieves a precise balance between the goal of maximizing benefits and operational constraints. The optimization result makes the energy storage charge and discharge curve highly matched with the peak and valley of electricity prices, while fully tapping the adjustment potential of heavy trucks and air conditioners, thereby reducing the peak load of the power grid, improving energy storage benefits, and reducing the electricity cost of the park.

[0022] Furthermore, the flexible resource-shared energy storage decision model allocates shared energy storage and flexible resources of each park through power coupling constraints, specifically:

[0023] The upper layer model of the flexible resource-shared energy storage decision model is based on the grid time-of-use electricity price information, the load forecast data of each park, and the charging and discharging cost of the shared energy storage, to build an optimization model with the goal of maximizing the economic benefits of shared energy storage, and solve the power purchase and sales power instructions of the shared energy storage and the grid, as well as the charging and discharging power instructions of the shared energy storage to each park.

[0024] The lower-level model uses the shared energy storage's charge and discharge power instructions to each park based on the upper-level model. It calculates the charge and discharge power of electric heavy-duty trucks in the industrial park using a pre-set flexible model for electric heavy-duty trucks in the industrial park. It also calculates the power demand for air conditioners in the commercial park based on a pre-set flexible model for variable-frequency air conditioners in the commercial park.

[0025] Based on the power purchase and sales power instructions, charging and discharging power instructions, the charging and discharging power of electric heavy trucks in the industrial park, and the power requirements of air conditioners in the commercial park, the power allocation of shared energy storage and flexible resources in each park is optimized through power coupling constraints.

[0026] The upper-level model of this application uses the grid's time-of-use electricity price, load forecast, and energy storage cost as boundary conditions to solve the optimal power purchase and sales of shared energy storage and the grid, as well as the charging and discharging power instructions to each park; after receiving the instructions, the lower-level model calls the electric heavy-duty truck flexible model and the variable-frequency air-conditioning flexible model respectively to calculate the achievable charging and discharging power and air-conditioning power requirements of the park. Through power coupling constraints, the upper-level economic goals and the lower-level physical feasible domain are aligned in real time, so that the energy storage charging and discharging curves accurately match the peaks and valleys of electricity prices, and the regulation potential of heavy trucks and air conditioners is fully released. Peak loads are reduced, energy storage benefits are maximized, the park's electricity costs are simultaneously reduced, and the economic efficiency and stability of grid operation are significantly improved.

[0027] Furthermore, the power coupling constraint is used to optimize the power allocation of shared energy storage and flexible resources in each park, specifically:

[0028] Based on the lower-level model's outputs of the industrial park's electric heavy-duty truck charging and discharging power requirements and the commercial park's air conditioning power requirements, combined with the upper-level model's outputs of shared energy storage and the power purchase and sales instructions from the grid, as well as the charging and discharging power instructions to each park, a power balance equation is constructed using preset power coupling constraints.

[0029] The power coupling constraints include the charge and discharge balance constraints of the shared energy storage itself and the power balance constraints of each park;

[0030] Based on the power balance equation and combined with the flexible resource-shared energy storage decision model, the power allocation of shared energy storage and flexible resources in each park is optimized.

[0031] This application uses power coupling constraints to align the heavy-truck charging and discharging plans and air conditioning power requirements generated by the lower-level model with the energy storage power purchase and sales and park charging and discharging instructions generated by the upper-level model. First, the shared energy storage's own charge and discharge balance constraints ensure that its state of charge does not exceed the limit. Then, the power balance constraints of each park ensure real-time matching of source, load, and storage within the park. On this basis, iterative optimization is carried out to seamlessly connect the energy storage arbitrage window with the park's adjustable capacity. The coupling mechanism compresses peak-to-valley differences within the physically feasible domain, improving the economic benefits of energy storage while fully unleashing the potential for flexible resource regulation. Ultimately, this reduces peak load on the grid, reduces operating costs, and simultaneously increases the renewable energy consumption rate.

[0032] Furthermore, the power balance equation is specifically:

[0033]

[0034] Where, represents the power purchased by the shared energy storage from the grid at time t, represents the power sold by the shared energy storage to the grid at time t, represents the power discharged by the shared energy storage to park i at time t, represents the power of shared energy storage charging park i at time t, where i∈[I,C,R], indicating whether the park is an industrial park, a commercial park, or a residential park. represents the actual discharge power of the shared energy storage at time t, Indicates the actual charging power of the shared energy storage at time t.

[0035] In a second aspect, the present application provides a power distribution device for a city power grid. The power distribution device for the city power grid comprises:

[0036] An acquisition module is used to obtain real-time operating data from the urban power grid, including load data of each park, energy storage equipment parameters, and time-of-use electricity price information of the power grid;

[0037] A solution module is configured to solve a preset flexible resource-shared energy storage decision model based on the real-time operation data in combination with a preset mixed integer programming method, so that the flexible resource-shared energy storage decision model allocates shared energy storage and flexible resources of each park through power coupling constraints, and outputs charging and discharging power allocation instructions for the shared energy storage and control instructions for the flexible resources of each park;

[0038] The flexible resource-shared energy storage decision model is based on historical data from the city's power grid and is trained to maximize the economic benefits of shared energy storage and the comprehensive benefits of each park's flexible resources.

[0039] The distribution module is used to distribute the power of the urban power grid according to the charging and discharging power distribution instructions and the control instructions of the flexible resources of each park.

[0040] The power distribution device of the urban power grid of the present application collects three types of data, namely load, energy storage and electricity price, in real time through the acquisition module, providing millisecond-level accuracy for subsequent decision-making; the solution module uses a two-layer decision-making model trained based on historical data to simultaneously optimize the economic benefits of the upper-layer energy storage and the flexible resource utilization rate of the lower-layer park under the power coupling constraint, and outputs executable charging and discharging instructions and control instructions; the distribution module performs closed-loop control according to the instructions, so that the energy storage charging and discharging curves accurately fit the peaks and valleys of electricity prices, and the potential for heavy trucks and air conditioning regulation can be fully released. The power distribution device of the urban power grid of the present application can dynamically shave peaks and fill valleys without human intervention, reduce the peak load and operating costs of the power grid, and at the same time improve the energy storage yield rate and park income, achieving a coordinated improvement in economy and safety.

[0041] Furthermore, the flexible resource-shared energy storage decision model allocates shared energy storage and flexible resources of each park through power coupling constraints, specifically:

[0042] The upper layer model of the flexible resource-shared energy storage decision model is based on the grid time-of-use electricity price information, the load forecast data of each park, and the charging and discharging cost of the shared energy storage, to build an optimization model with the goal of maximizing the economic benefits of shared energy storage, and solve the power purchase and sales power instructions of the shared energy storage and the grid, as well as the charging and discharging power instructions of the shared energy storage to each park.

[0043] The lower-level model uses the shared energy storage's charge and discharge power instructions to each park based on the upper-level model. It calculates the charge and discharge power of electric heavy-duty trucks in the industrial park using a pre-set flexible model for electric heavy-duty trucks in the industrial park. It also calculates the power demand for air conditioners in the commercial park based on a pre-set flexible model for variable-frequency air conditioners in the commercial park.

[0044] Based on the power purchase and sales power instructions, charging and discharging power instructions, the charging and discharging power of electric heavy trucks in the industrial park, and the power requirements of air conditioners in the commercial park, the power allocation of shared energy storage and flexible resources in each park is optimized through power coupling constraints.

[0045] The upper-level model of this application uses the grid's time-of-use electricity price, load forecast, and energy storage cost as boundary conditions to solve the optimal power purchase and sales of shared energy storage and the grid, as well as the charging and discharging power instructions to each park; after receiving the instructions, the lower-level model calls the electric heavy-duty truck flexible model and the variable-frequency air-conditioning flexible model respectively to calculate the achievable charging and discharging power and air-conditioning power requirements of the park. Through power coupling constraints, the upper-level economic goals and the lower-level physical feasible domain are aligned in real time, so that the energy storage charging and discharging curves accurately match the peaks and valleys of electricity prices, and the regulation potential of heavy trucks and air conditioners is fully released. Peak loads are reduced, energy storage benefits are maximized, the park's electricity costs are simultaneously reduced, and the economic efficiency and stability of grid operation are significantly improved.

[0046] In a third aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program. When the computer program is executed, the device containing the computer-readable storage medium is controlled to execute the method for distributing power in a city power grid. The beneficial effects thereof are the same as those of the method for distributing power in a city power grid provided in the first aspect of the present application.

[0047] In a fourth aspect, the present application provides a terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the power distribution methods for the urban power grid as described in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 : A schematic flow chart of an embodiment of a power distribution method for an urban power grid provided in this application;

[0049] Figure 2 : A schematic diagram of the structure of an embodiment of the shared energy storage-urban multi-park topology provided by this application;

[0050] Figure 3 : A schematic structural diagram of an embodiment of the optimization results of power allocation of each flexible resource under the impact power of the industrial park provided by this application;

[0051] Figure 4 : A schematic structural diagram of an embodiment of a power distribution device for an urban power grid provided in this application. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] Example 1

[0054] Please refer to Figure 1 In order to solve the problem that the existing technology cannot achieve optimal regulation of the power grid with the goal of economic maximization, an embodiment of the present invention provides a power distribution method for a city power grid, including steps S01-S03.

[0055] S01: Acquire real-time operation data in the city power grid, wherein the real-time operation data includes load data of each park, energy storage equipment parameters, and time-of-use electricity price information of the power grid.

[0056] As a preferred embodiment of this embodiment, the real-time operation data of the urban power grid is obtained, and the real-time operation data includes load data of each park, energy storage equipment parameters, and time-of-use electricity price information of the power grid, specifically including:

[0057] The load data of each park is as follows:

[0058] – Industrial Park: Charging power, discharging power, state of charge (SOC), and park load power of electric heavy-duty trucks;

[0059] – Business Park: total power of variable frequency air conditioners, room temperature, outdoor temperature, solar radiation, and park load power;

[0060] –Residential complex: total household electricity load power;

[0061] The energy storage device parameters are:

[0062] – Shared energy storage: rated capacity, maximum charging power, maximum discharging power, initial state of charge, charging efficiency, and discharging efficiency.

[0063] – Batteries for electric heavy-duty trucks in industrial parks: rated capacity, battery acquisition cost, total number of electric heavy-duty trucks, and number of cycles at different states of charge.

[0064] The grid time-of-use electricity price information is:

[0065] – Transaction prices between the grid and shared energy storage: electricity prices and time periods corresponding to peak hours (e.g., 08:00-12:00, 17:00-21:00), normal hours (e.g., 12:00-17:00, 21:00-24:00), and off-peak hours (e.g., 00:00-08:00);

[0066] – Service electricity prices set by shared energy storage: service prices for charging / discharging to industrial parks, commercial parks, and residential parks;

[0067] – Direct transaction electricity prices between the power grid and each park: industrial electricity prices, commercial electricity prices and residential electricity prices.

[0068] S02: Solving a preset flexible resource-shared energy storage decision model based on the real-time operating data and in combination with a preset mixed integer programming method, so that the flexible resource-shared energy storage decision model allocates shared energy storage and flexible resources of each park through power coupling constraints, and outputs charging and discharging power allocation instructions for the shared energy storage and control instructions for the flexible resources of each park;

[0069] The flexible resource-shared energy storage decision model is based on historical data of the urban power grid and is trained with the goal of maximizing the economic benefits of shared energy storage and the comprehensive benefits of flexible resources in each park.

[0070] As a preferred implementation of this embodiment, the preset flexible resource-shared energy storage decision model is solved based on the real-time operation data in combination with a preset mixed integer programming method, specifically as follows:

[0071] Upper-level model: Shared energy storage is used as the regional energy aggregator, and the objective function is to maximize total revenue (from electricity transactions with the grid and revenue from providing services to the park), namely:

[0072] The objective function of the flexible resource-shared energy storage decision model is:

[0073]

[0074] Where T is the scheduling period set (such as 24 hours), t is the time period index, and i is the index of different parks. is the price of electricity sold to the grid at time t, is the price of electricity purchased from the grid at time t, is the power sold to the grid at time t, is the power purchased from the grid at time t, λ s,i,t is the charging and discharging service price of shared energy storage for park i at time t, is the charging power of shared energy storage to park i at time t, is the discharge power of shared energy storage to park i at time t;

[0075] The lower model includes industrial parks, commercial parks, and residential parks, all of which aim to maximize their total benefits. The overall benefit of the industrial park is described as follows:

[0076]

[0077] Where, F I Represents the overall benefit of the industrial park, P EV,n,t represents the charging power of the nth electric heavy truck at time t, C EV,n,t represents the real-time battery loss of the nth electric heavy truck at time t, represents the power of shared energy storage charging the industrial park at time t, P represents the power discharged from the shared energy storage to the industrial park at time t. g,I,t represents the power purchased by the industrial park from the grid at time t, The electricity price of shared energy storage charging the industrial park at time t, The electricity price of the shared energy storage discharging to the industrial park at time t, which is set by the shared energy storage; λ g,I,t It represents the industrial electricity price stipulated by the power grid company at time t.

[0078] The overall benefits of the business park are described as:

[0079]

[0080] Where, F C Represents the overall benefit of the business park, P AC,m,t represents the power of the mth air conditioner at time t, λ AC,t represents the real-time cost of the air conditioner participating in the power regulation service at time t, represents the power discharged from the shared energy storage to the business park at time t, P represents the power of shared energy storage charging the business park at time t. g,C,t represents the power purchased by the business park from the grid at time t, represents the price of the business park discharging service to the shared energy storage at time t, represents the price of charging service from the business park to the shared energy storage at time t, which is set by the shared energy storage, g,C,t Indicates the commercial electricity price stipulated by the power grid company;

[0081] The benefits of the residential park can be described as:

[0082]

[0083] Where, F R Indicates the overall benefits of the residential park, represents the power discharged from the shared energy storage to the residential park at time t, P represents the power of shared energy storage charging the residential park at time t, g,R,t represents the power purchased by the residential area from the grid at time t, represents the price of the shared energy storage discharging service to the residential park at time t, represents the price of shared energy storage charging service to residential park at time t, which is set by shared energy storage, g,R,t Indicates the residential electricity price stipulated by the power grid company;

[0084] The charging and discharging behavior and state of charge of the electric heavy trucks in the industrial park are described as follows:

[0085]

[0086] Where, Indicates the state of charge of the electric heavy truck at time t+1, Indicates the state of charge of the electric heavy truck at time t, represents the charging power of the electric heavy truck at time t+1, η ch Indicates the battery charging efficiency, η dis Indicates the discharge efficiency of the battery, represents the discharge power of the electric heavy truck at time t+1, S EV Indicates the rated capacity of the electric heavy truck.

[0087] The available cycle number of lithium-ion batteries at different discharge depths can be fitted by the following formula:

[0088] N bat (SOC) = 2151 * SOC -2.301 ,SOC∈[0,0.9]

[0089] Where N bat Indicates the number of cycles a battery can handle, expressed as a function of state of charge.

[0090] Since the charge states of different electric heavy trucks are different during charging and discharging, the average loss cost is used, which is described by the following formula:

[0091]

[0092] Where C EV is the battery loss cost caused by charging and discharging of electric heavy trucks, C R is the purchase cost of the battery in the electric heavy truck, N EV is the total number of electric heavy trucks, SOC i represents the state of charge of the i-th electric vehicle, N bat,i Indicates the number of cycles that the i-th electric vehicle can cycle at its current state of charge;

[0093] The commercial park variable frequency air conditioning flexible model utilizes the time lag effect of air conditioning temperature adjustment in the commercial park to participate in demand-side response without affecting the user experience. The linear function is used to describe the cooling and heating capacity of the variable frequency air conditioning:

[0094] P AC,t =kQ AC,t +b

[0095] Where, P AC,t is the operating power of the air conditioner at time t, Q AC,tis the cooling / heating capacity of the air conditioner at time t, k and b are linearization coefficients.

[0096] The temperature of each room in the business park is described as:

[0097]

[0098] Where C j represents the equivalent heat capacity of the jth room, ΔT j represents the temperature change of the jth room, χ j A represents the amount of sunlight radiation in the jth room at time t, j represents the effective window equivalent area of ​​the jth room, T out (t) represents the outdoor temperature at time t, T j (t) represents the indoor temperature of the jth room at time t, Q AC，j (t) represents the air conditioning cooling / heating of the jth room at time t.

[0099] The PMV (Predicted mean vote) index is used to measure the impact of indoor temperature on human comfort. The index is defined as follows:

[0100]

[0101] Where, T op Indicates the most comfortable temperature for the human body, generally taken as 26℃, and T represents the current temperature. PMV represents human comfort and is a non-negative number. The smaller its value, the higher the current temperature comfort. When the temperature is 26℃, PMV is 0. According to the GBJ19-87 standard "Design Code for Heating, Ventilation and Air Conditioning" in my country, the PMV value is between -1 and 1.

[0102] The power coupling constraint model between the upper and lower constraint models includes:

[0103]

[0104] Where, represents the power purchased by the shared energy storage from the grid at time t, represents the power sold by the shared energy storage to the grid at time t, represents the power discharged by the shared energy storage to park i at time t, represents the power of shared energy storage charging park i at time t, where i∈[I,C,R], indicating whether the park is an industrial park, a commercial park, or a residential park. represents the actual discharge power of the shared energy storage at time t, Indicates the actual charging power of the shared energy storage at time t.

[0105] In the lower-level model, each park uses its own flexible resources and shared energy storage services to meet its power needs. When necessary, it can purchase electricity directly from the grid. Its power coupling can be described as:

[0106]

[0107] Where, represents the charging power of the nth electric heavy truck at time t, P represents the discharge power of the nth electric heavy truck at time t, I,load,t Represents the load power of the industrial park at time t, N AC represents the number of air conditioners in the business park, P AC,m,t P represents the power of the mth air conditioner in the business park at time t, C,load,t represents the load power of the business park at time t, P R,load,t represents the load power of the residential park at time t;

[0108] The flexible resource-shared energy storage decision model is based on historical data from the city power grid and is trained to maximize the economic benefits of shared energy storage and the comprehensive benefits of flexible resources in each park. Specifically,

[0109] The two-layer model is trained offline using the hourly load curves, energy storage operation logs, and electricity price series from the past year:

[0110] The upper layer obtains the joint probability distribution of "electricity price-load" through regression and scenario clustering, and then calibrates the energy storage arbitrage profit coefficient;

[0111] The lower layer uses the electric heavy-duty truck SOC cycle curve and air conditioner PMV-power mapping to calibrate the adjustable capacity and response cost of flexible resources;

[0112] After training is completed, except for real-time variables such as electricity prices and SOC, all economic coefficients and constraint boundaries are solidified as model parameters, ensuring that online solutions can output feasible solutions in just milliseconds.

[0113] During the online phase, since the model contains a product term of electricity price and power (nonlinearity), a large M method is used for linearization (M = 10,000) and the model is converted into a mixed integer programming problem for solution. After real-time operation data is input, the solver obtains the control instructions for the shared energy storage charging and discharging power and the flexible resources of each park;

[0114] The Big M method is used to linearize the constraints. The core of this method is to transform infeasible constraints into feasible constraints by introducing artificial variables and penalty coefficients. By assigning extremely high costs or benefits to artificial variables, the algorithm is forced to prioritize excluding artificial variables from the basis during the optimization process, thereby ensuring that the feasible solution satisfies the original constraints. The auxiliary variables are introduced and defined as:

[0115] U t =λ t P t

[0116] Where λ t and P t Denote the electricity price and power at time t respectively. For the above constraints, add the following linearization constraints:

[0117]

[0118] Where z t is a 0-1 variable, M is a sufficiently large constant, Represents the upper and lower limits of the price value. The penalty of M forces the algorithm to prioritize satisfying actual physical constraints, which can convert the original nonlinear model into a mixed integer linear programming problem and be solved efficiently using commercial solvers.

[0119] In this embodiment, the Figure 2 In the topology shown, the shared energy storage has an initial state of charge of 0.5, a maximum charge and discharge power of 1MW, and a maximum storage capacity of 5MWh. The grid uses time-of-use pricing: peak electricity prices are 1.3 yuan / kWh from 8:00 AM to 12:00 PM and 5:00 PM to 9:00 PM, 0.6 yuan / kWh from 12:00 PM to 5:00 PM and 9:00 PM to 12:00 AM, and 0.3 yuan / kWh from 00:00 AM to 8:00 AM during off-peak hours.

[0120] After initializing the parameters of the mixed integer programming solver, this application inputs the real-time operating data into a two-layer flexible resource-shared energy storage decision model: the upper-layer model globally optimizes the power interaction between shared energy storage, the power grid, and each park based on the time-of-use electricity price of the power grid, with the goal of maximizing the economic benefits of shared energy storage; the lower-layer model synchronously calls the flexible model of electric heavy trucks in the industrial park and the flexible model of variable frequency air conditioners in the commercial park, and feeds back the adjustable capacity of each park's flexible resources to the upper layer in real time to ensure that all power instructions meet the physical constraints of the equipment. This collaborative solution mechanism achieves a precise balance between the goal of maximizing benefits and operational constraints. The optimization result makes the energy storage charge and discharge curve highly matched with the peak and valley of electricity prices, while fully tapping the adjustment potential of heavy trucks and air conditioners, thereby reducing the peak load of the power grid, improving energy storage benefits, and reducing the electricity cost of the park.

[0121] S03: Allocate power to the urban power grid according to the charging and discharging power allocation instructions and the control instructions of the flexible resources of each park.

[0122] As a preferred implementation of this embodiment, the power of the urban power grid is distributed according to the charging and discharging power allocation instructions and the control instructions of the flexible resources of each park, specifically as follows:

[0123] Shared energy storage adjusts charging and discharging power based on instructions (e.g., discharging during peak hours and charging during off-peak hours in this example). Electric heavy-duty trucks in industrial parks adjust charging and discharging power based on instructions (to meet production targets and minimize battery loss), and air conditioners in commercial parks adjust power based on instructions (to ensure qualified PMV indicators). Each park coordinates shared energy storage services with power purchases from the grid to meet load demands. When the grid experiences power surges, shared energy storage and flexible resources respond in a coordinated manner, absorbing power locally and ensuring stable grid operation.

[0124] In order to verify the power allocation method of this embodiment, a simulation verification is carried out in this embodiment. Due to the production plan arrangement and photovoltaic power generation in the industrial park, the net load power fluctuates greatly at 9 am. Figure 3 As shown, through the multi-park flexible resource-shared energy storage power allocation method of the urban power grid that responds to impact power, the flexible resources of electric heavy trucks in the industrial park are urgently called upon to discharge, and at the same time, the discharge power is called upon to the shared energy storage to avoid purchasing peak-time electricity from the urban power grid. Due to the increase in photovoltaic power generation in the park, the park has a power surplus. At this time, the flexible resources of electric heavy trucks are called upon to charge, and at the same time, the excess electricity is stored in the shared energy storage to maximize the benefits. For the power grid, since the large fluctuations in the net load in the park are absorbed by the flexible resources and shared energy storage of each park, the power impact during the peak load period of the system is effectively reduced;

[0125] When the net load power impact of the application park is applied, the method of this embodiment realizes the economically optimal power allocation of multiple resources by quickly coordinating and controlling multiple resources and sharing energy storage, thereby improving the continuity of power supply and the economy of the power system.

[0126] In summary, this application provides accurate data support for power grid operators by obtaining real-time operating data, including load data, energy storage equipment parameters, and grid time-of-use electricity price information. These data are the basis for effective power allocation and resource scheduling. Combined with the mixed integer programming method, this method solves the flexible resource-shared energy storage decision model obtained by training based on historical data, and uses power coupling constraints to achieve the optimal power allocation between shared energy storage and flexible resources in each park. This process not only ensures the stable operation of the power grid in the face of power surges, but also achieves accurate power allocation to the urban power grid by optimizing the charging and discharging power allocation instructions and the control instructions of the flexible resources of each park. Since the decision model aims to maximize the economic benefits of shared energy storage and the comprehensive benefits of the flexible resources of each park, it can be inferred that this method can effectively reduce the operating costs of the power grid, improve economic benefits, and enhance the power grid's response capability to power surges, thereby improving the operating efficiency and reliability of the entire power grid. The implementation of this method is expected to bring significant economic and operational benefits to power grid operators. This application effectively solves the problem in the existing technology that it is impossible to achieve optimal regulation of the power grid with economic maximization as the goal.

[0127] Example 2

[0128] Please refer to Figure 4 , which is a power distribution device for the urban power grid provided in an embodiment of the present application.

[0129] In this embodiment, the power distribution device of the urban power grid includes an acquisition module 10 , a solution module 20 and a distribution module 30 .

[0130] The acquisition module 10 is used to acquire real-time operation data in the urban power grid, and the real-time operation data includes load data of each park, energy storage equipment parameters and time-of-use electricity price information of the power grid.

[0131] As a preferred embodiment of this embodiment, the real-time operation data of the urban power grid is obtained, and the real-time operation data includes load data of each park, energy storage equipment parameters, and time-of-use electricity price information of the power grid, specifically including:

[0132] The load data of each park is as follows:

[0133] – Industrial Park: Charging power, discharging power, state of charge (SOC), and park load power of electric heavy-duty trucks;

[0134] – Business Park: total power of variable frequency air conditioners, room temperature, outdoor temperature, solar radiation, and park load power;

[0135] –Residential complex: total household electricity load power;

[0136] The energy storage device parameters are:

[0137] – Shared energy storage: rated capacity, maximum charging power, maximum discharging power, initial state of charge, charging efficiency, and discharging efficiency;

[0138] - Batteries for electric heavy-duty trucks in industrial parks: rated capacity, battery purchase cost, total number of electric heavy-duty trucks, and number of cycles at different states of charge;

[0139] The grid time-of-use electricity price information is:

[0140] – Transaction prices between the grid and shared energy storage: electricity prices and time periods corresponding to peak hours (e.g., 08:00-12:00, 17:00-21:00), normal hours (e.g., 12:00-17:00, 21:00-24:00), and off-peak hours (e.g., 00:00-08:00);

[0141] – Service electricity prices set by shared energy storage: service prices for charging / discharging to industrial parks, commercial parks, and residential parks;

[0142] – Direct transaction electricity prices between the power grid and each park: industrial electricity prices, commercial electricity prices and residential electricity prices.

[0143] The solution module 20 is configured to solve a preset flexible resource-shared energy storage decision model based on the real-time operating data in combination with a preset mixed integer programming method, so that the flexible resource-shared energy storage decision model allocates shared energy storage and flexible resources of each park through power coupling constraints, and outputs charging and discharging power allocation instructions for the shared energy storage and control instructions for the flexible resources of each park;

[0144] The flexible resource-shared energy storage decision model is based on historical data of the urban power grid and is trained with the goal of maximizing the economic benefits of shared energy storage and the comprehensive benefits of flexible resources in each park.

[0145] As a preferred implementation of this embodiment, the preset flexible resource-shared energy storage decision model is solved based on the real-time operation data in combination with a preset mixed integer programming method, specifically as follows:

[0146] Upper-level model: Shared energy storage is used as the regional energy aggregator, and the objective function is to maximize total revenue (from electricity transactions with the grid and revenue from providing services to the park), namely:

[0147] The objective function of the flexible resource-shared energy storage decision model is:

[0148]

[0149] Where T is the scheduling period set (such as 24 hours), t is the time period index, and i is the index of different parks. is the price of electricity sold to the grid at time t, is the price of electricity purchased from the grid at time t, is the power sold to the grid at time t, is the power purchased from the grid at time t, λ s,i,t is the charging and discharging service price of shared energy storage for park i at time t, is the charging power of shared energy storage to park i at time t, is the discharge power of shared energy storage to park i at time t;

[0150] The lower model includes industrial parks, commercial parks, and residential parks, all of which aim to maximize their total benefits. The overall benefit of the industrial park is described as follows:

[0151]

[0152] Where, F I Represents the overall benefit of the industrial park, P EV,n,t represents the charging power of the nth electric heavy truck at time t, C EV,n,t represents the real-time battery loss of the nth electric heavy truck at time t, represents the power of shared energy storage charging the industrial park at time t, P represents the power discharged from the shared energy storage to the industrial park at time t. g,I,t represents the power purchased by the industrial park from the grid at time t, The electricity price of shared energy storage charging the industrial park at time t, The electricity price of the shared energy storage discharging to the industrial park at time t, which is set by the shared energy storage; λ g,I,t It represents the industrial electricity price stipulated by the power grid company at time t.

[0153] The overall benefits of the business park are described as:

[0154]

[0155] Where, F C Represents the overall benefit of the business park, P AC,m,t represents the power of the mth air conditioner at time t, λ AC,t represents the real-time cost of the air conditioner participating in the power regulation service at time t, represents the power discharged from the shared energy storage to the business park at time t, P represents the power of shared energy storage charging the business park at time t. g,C,t represents the power purchased by the business park from the grid at time t, represents the price of the business park discharging service to the shared energy storage at time t, represents the price of charging service from the business park to the shared energy storage at time t, which is set by the shared energy storage, g,C,t Indicates the commercial electricity price stipulated by the power grid company;

[0156] The benefits of the residential park can be described as:

[0157]

[0158] Where, F R Indicates the overall benefits of the residential park, represents the power discharged from the shared energy storage to the residential park at time t, P represents the power of shared energy storage charging the residential park at time t, g,R,t represents the power purchased by the residential area from the grid at time t, represents the price of the shared energy storage discharging service to the residential park at time t, represents the price of shared energy storage charging service to residential park at time t, which is set by shared energy storage, g,R,t Indicates the residential electricity price stipulated by the power grid company;

[0159] The charging and discharging behavior and state of charge of the electric heavy trucks in the industrial park are described as follows:

[0160]

[0161] Where, Indicates the state of charge of the electric heavy truck at time t+1, Indicates the state of charge of the electric heavy truck at time t, represents the charging power of the electric heavy truck at time t+1, η ch Indicates the battery charging efficiency, η dis Indicates the discharge efficiency of the battery, represents the discharge power of the electric heavy truck at time t+1, S EV Indicates the rated capacity of the electric heavy truck.

[0162] The available cycle number of lithium-ion batteries at different discharge depths can be fitted by the following formula:

[0163] N bat (SOC) = 2151 * SOC -2.301 ,SOC∈[0,0.9]

[0164] Where N bat Indicates the number of cycles a battery can handle, expressed as a function of state of charge.

[0165] Since the charge states of different electric heavy trucks are different during charging and discharging, the average loss cost is used, which is described by the following formula:

[0166]

[0167] Where C EV is the battery loss cost caused by charging and discharging of electric heavy trucks, C R is the purchase cost of the battery in the electric heavy truck, N EV is the total number of electric heavy trucks, SOC i represents the state of charge of the i-th electric vehicle, N bat,i Indicates the number of cycles that the i-th electric vehicle can cycle at its current state of charge;

[0168] The commercial park variable frequency air conditioning flexible model utilizes the time lag effect of air conditioning temperature adjustment in the commercial park to participate in demand-side response without affecting the user experience. The linear function is used to describe the cooling and heating capacity of the variable frequency air conditioning:

[0169] P AC,t =kQ AC,t +b

[0170] Where, P AC,t is the operating power of the air conditioner at time t, Q AC,t is the cooling / heating capacity of the air conditioner at time t, k and b are linearization coefficients.

[0171] The temperature of each room in the business park is described as:

[0172]

[0173] Where C j represents the equivalent heat capacity of the jth room, ΔT j represents the temperature change of the jth room, χ j A represents the amount of sunlight radiation in the jth room at time t, j represents the effective window equivalent area of ​​the jth room, T out (t) represents the outdoor temperature at time t, T j (t) represents the indoor temperature of the jth room at time t, Q AC，j (t) represents the air conditioning cooling / heating of the jth room at time t.

[0174] The PMV (Predicted mean vote) index is used to measure the impact of indoor temperature on human comfort. The index is defined as follows:

[0175]

[0176] Where, T opIndicates the most comfortable temperature for the human body, generally taken as 26℃, and T represents the current temperature. PMV represents human comfort and is a non-negative number. The smaller its value, the higher the current temperature comfort. When the temperature is 26℃, PMV is 0. According to the GBJ19-87 standard "Design Code for Heating, Ventilation and Air Conditioning" in my country, the PMV value is between -1 and 1.

[0177] The power coupling constraint model between the upper and lower constraint models includes:

[0178]

[0179] Where, represents the power purchased by the shared energy storage from the grid at time t, represents the power sold by the shared energy storage to the grid at time t, represents the power discharged by the shared energy storage to park i at time t, represents the power of shared energy storage charging park i at time t, where i∈[I,C,R], indicating whether the park is an industrial park, a commercial park, or a residential park. represents the actual discharge power of the shared energy storage at time t, Indicates the actual charging power of the shared energy storage at time t.

[0180] In the lower-level model, each park uses its own flexible resources and shared energy storage services to meet its power needs. When necessary, it can purchase electricity directly from the grid. Its power coupling can be described as:

[0181]

[0182] Where, represents the charging power of the nth electric heavy truck at time t, P represents the discharge power of the nth electric heavy truck at time t, I,load,t Represents the load power of the industrial park at time t, N AC represents the number of air conditioners in the business park, P AC,m,t P represents the power of the mth air conditioner in the business park at time t, C,load,t represents the load power of the business park at time t, P R,load,t represents the load power of the residential park at time t;

[0183] The flexible resource-shared energy storage decision model is based on historical data from the city power grid and is trained to maximize the economic benefits of shared energy storage and the comprehensive benefits of flexible resources in each park. Specifically,

[0184] The two-layer model is trained offline using the hourly load curves, energy storage operation logs, and electricity price series from the past year:

[0185] The upper layer obtains the joint probability distribution of "electricity price-load" through regression and scenario clustering, and then calibrates the energy storage arbitrage profit coefficient;

[0186] The lower layer uses the electric heavy-duty truck SOC cycle curve and air conditioner PMV-power mapping to calibrate the adjustable capacity and response cost of flexible resources;

[0187] After training is completed, except for real-time variables such as electricity prices and SOC, all economic coefficients and constraint boundaries are solidified as model parameters, ensuring that online solutions can output feasible solutions in just milliseconds.

[0188] During the online phase, since the model contains a product term of electricity price and power (nonlinearity), a large M method is used for linearization (M = 10,000) and the model is converted into a mixed integer programming problem for solution. After real-time operation data is input, the solver obtains the control instructions for the shared energy storage charging and discharging power and the flexible resources of each park;

[0189] The Big M method is used to linearize the constraints. The core of this method is to transform infeasible constraints into feasible constraints by introducing artificial variables and penalty coefficients. By assigning extremely high costs or benefits to artificial variables, the algorithm is forced to prioritize excluding artificial variables from the basis during the optimization process, thereby ensuring that the feasible solution satisfies the original constraints. The auxiliary variables are introduced and defined as:

[0190] U t =λ t P t

[0191] Where λ t and P t Denote the electricity price and power at time t respectively. For the above constraints, add the following linearization constraints:

[0192]

[0193] Where z t is a 0-1 variable, M is a sufficiently large constant, Represents the upper and lower limits of the price value. The penalty of M forces the algorithm to prioritize satisfying actual physical constraints, which can convert the original nonlinear model into a mixed integer linear programming problem and be solved efficiently using commercial solvers.

[0194] In this embodiment, the Figure 2In the topology shown, the shared energy storage has an initial state of charge of 0.5, a maximum charge and discharge power of 1MW, and a maximum storage capacity of 5MWh. The grid uses time-of-use pricing: peak electricity prices are 1.3 yuan / kWh from 8:00 AM to 12:00 PM and 5:00 PM to 9:00 PM, 0.6 yuan / kWh from 12:00 PM to 5:00 PM and 9:00 PM to 12:00 AM, and 0.3 yuan / kWh from 00:00 AM to 8:00 AM during off-peak hours.

[0195] After initializing the parameters of the mixed integer programming solver, this application inputs the real-time operating data into a two-layer flexible resource-shared energy storage decision model: the upper-layer model globally optimizes the power interaction between shared energy storage, the power grid, and each park based on the time-of-use electricity price of the power grid, with the goal of maximizing the economic benefits of shared energy storage; the lower-layer model synchronously calls the flexible model of electric heavy trucks in the industrial park and the flexible model of variable frequency air conditioners in the commercial park, and feeds back the adjustable capacity of each park's flexible resources to the upper layer in real time to ensure that all power instructions meet the physical constraints of the equipment. This collaborative solution mechanism achieves a precise balance between the goal of maximizing benefits and operational constraints. The optimization result makes the energy storage charge and discharge curve highly matched with the peak and valley of electricity prices, while fully tapping the adjustment potential of heavy trucks and air conditioners, thereby reducing the peak load of the power grid, improving energy storage benefits, and reducing the electricity cost of the park.

[0196] The allocation module 30 is used to allocate the power of the urban power grid according to the charging and discharging power allocation instructions and the control instructions of the flexible resources of each park.

[0197] As a preferred implementation of this embodiment, the power of the urban power grid is distributed according to the charging and discharging power allocation instructions and the control instructions of the flexible resources of each park, specifically as follows:

[0198] Shared energy storage adjusts charging and discharging power based on instructions (e.g., discharging during peak hours and charging during off-peak hours in this example). Electric heavy-duty trucks in industrial parks adjust charging and discharging power based on instructions (to meet production targets and minimize battery loss), and air conditioners in commercial parks adjust power based on instructions (to ensure qualified PMV indicators). Each park coordinates shared energy storage services with power purchases from the grid to meet load demands. When the grid experiences power surges, shared energy storage and flexible resources respond in a coordinated manner, absorbing power locally and ensuring stable grid operation.

[0199] In order to verify the power allocation method of this embodiment, a simulation verification is carried out in this embodiment. Due to the production plan arrangement and photovoltaic power generation in the industrial park, the net load power fluctuates greatly at 9 am. Figure 3As shown, through the multi-park flexible resource-shared energy storage power allocation method of the urban power grid that responds to impact power, the flexible resources of electric heavy trucks in the industrial park are urgently called upon to discharge, and at the same time, the discharge power is called upon to the shared energy storage to avoid purchasing peak-time electricity from the urban power grid. Due to the increase in photovoltaic power generation in the park, the park has a power surplus. At this time, the flexible resources of electric heavy trucks are called upon to charge, and at the same time, the excess electricity is stored in the shared energy storage to maximize the benefits. For the power grid, since the large fluctuations in the net load in the park are absorbed by the flexible resources and shared energy storage of each park, the power impact during the peak load period of the system is effectively reduced;

[0200] When the net load power impact of the application park is applied, the method of this embodiment realizes the economically optimal power allocation of multiple resources by quickly coordinating and controlling multiple resources and sharing energy storage, thereby improving the continuity of power supply and the economy of the power system.

[0201] In summary, the power distribution device of the urban power grid of the present application collects three types of data, namely load, energy storage and electricity price, in real time through the acquisition module, providing millisecond-level accuracy for subsequent decision-making; the solution module uses a two-layer decision-making model trained based on historical data to simultaneously optimize the upper-layer energy storage economic benefits and the lower-layer park flexible resource utilization under power coupling constraints, and outputs executable charging and discharging instructions and control instructions; the distribution module performs closed-loop control according to the instructions, so that the energy storage charging and discharging curves accurately fit the peaks and valleys of electricity prices, and the potential for heavy trucks and air conditioning regulation can be fully released. The power distribution device of the urban power grid of the present application can dynamically shave peaks and fill valleys without human intervention, reduce the peak load and operating costs of the power grid, and at the same time improve the energy storage yield rate and park income, achieving a coordinated improvement in economy and safety.

[0202] Example 3:

[0203] An embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the power distribution method for a city power grid;

[0204] Wherein, if the power distribution method of a city power grid is implemented in the form of a software functional unit and used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0205] Example 4

[0206] The present application provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements any one of the power distribution methods for the urban power grid as described in Example 1.

[0207] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A power distribution method for a city power grid, characterized in that: include: Acquire real-time operating data from the city power grid, including load data for each park, energy storage device parameters, and time-of-use electricity price information for the power grid; Based on the real-time operating data and in combination with a preset mixed integer programming method, a preset flexible resource-shared energy storage decision model is solved, so that the flexible resource-shared energy storage decision model allocates shared energy storage and flexible resources of each park through power coupling constraints, and outputs charging and discharging power allocation instructions for the shared energy storage and control instructions for the flexible resources of each park; The flexible resource-shared energy storage decision model is based on historical data from the city's power grid and is trained to maximize the economic benefits of shared energy storage and the comprehensive benefits of each park's flexible resources. The power of the urban power grid is distributed according to the charging and discharging power distribution instructions and the control instructions of the flexible resources of each park.

2. The power distribution method of the urban power grid according to claim 1, characterized in that: The real-time operation data of the urban power grid is obtained as follows: Obtaining real-time load data from the power monitoring system of each park, including the charging load of electric heavy trucks in the industrial park, the air conditioning load in the commercial park, and the daily electricity load in the residential park; Obtaining real-time parameters of the energy storage device from a preset energy storage device management system, wherein the real-time parameters include the current state of charge, maximum charge and discharge power, and energy storage capacity of the energy storage device; The real-time time-of-use electricity price information of the power grid is obtained from a preset power grid dispatching system, wherein the real-time time-of-use electricity price information includes peak time electricity price, normal time electricity price and valley time electricity price.

3. The power distribution method of the urban power grid according to claim 1, characterized in that: The preset flexible resource-shared energy storage decision model is solved based on the real-time operation data and combined with a preset mixed integer programming method, specifically as follows: Initializing parameters of a mixed integer programming solver based on the real-time operation data and a preset mixed integer programming method, wherein the parameters of the mixed integer programming solver include the number of iterations, convergence accuracy, and preset constants; Based on the parameters of the initialized mixed integer programming solver, the preset flexible resource-shared energy storage decision model is solved; Among them, the flexible resource-shared energy storage decision-making model includes an upper-level model and a lower-level model. The upper-level model is used to optimize the power interaction between shared energy storage and the power grid and each park, and is used to maximize the economic benefits of shared energy storage; the lower-level model includes a flexible model for electric heavy trucks in industrial parks and a flexible model for variable-frequency air conditioners in commercial parks, which is used to maximize the utilization of flexible resources within each park.

4. The power distribution method of the urban power grid according to claim 1, characterized in that: The flexible resource-shared energy storage decision model allocates shared energy storage and flexible resources of each park through power coupling constraints as follows: The upper layer model of the flexible resource-shared energy storage decision model is based on the grid time-of-use electricity price information, the load forecast data of each park, and the charging and discharging cost of the shared energy storage, to build an optimization model with the goal of maximizing the economic benefits of shared energy storage, and solve the power purchase and sales power instructions of the shared energy storage and the grid, as well as the charging and discharging power instructions of the shared energy storage to each park. The lower-level model uses the shared energy storage's charge and discharge power instructions to each park based on the upper-level model. It calculates the charge and discharge power of electric heavy-duty trucks in the industrial park using a pre-set flexible model for electric heavy-duty trucks in the industrial park. It also calculates the power demand for air conditioners in the commercial park based on a pre-set flexible model for variable-frequency air conditioners in the commercial park. Based on the power purchase and sales power instructions, charging and discharging power instructions, the charging and discharging power of electric heavy trucks in the industrial park, and the power requirements of air conditioners in the commercial park, the power allocation of shared energy storage and flexible resources in each park is optimized through power coupling constraints.

5. The power distribution method of the urban power grid according to claim 4, characterized in that: The power coupling constraint is used to optimize the power allocation between shared energy storage and flexible resources in each park. Specifically, Based on the lower-level model's outputs of the industrial park's electric heavy-duty truck charging and discharging power requirements and the commercial park's air conditioning power requirements, combined with the upper-level model's outputs of shared energy storage and the power purchase and sales instructions from the grid, as well as the charging and discharging power instructions to each park, a power balance equation is constructed using preset power coupling constraints. The power coupling constraints include the charge and discharge balance constraints of the shared energy storage itself and the power balance constraints of each park; Based on the power balance equation and combined with the flexible resource-shared energy storage decision model, the power allocation of shared energy storage and flexible resources in each park is optimized.

6. The power distribution method of the urban power grid according to claim 5, characterized in that: The power balance equation is specifically: Where, represents the power purchased by the shared energy storage from the grid at time t, represents the power sold by the shared energy storage to the grid at time t, represents the power discharged by the shared energy storage to park i at time t, represents the power of shared energy storage charging park i at time t, where i∈[I,C,R], indicating whether the park is an industrial park, a commercial park, or a residential park. represents the actual discharge power of the shared energy storage at time t, Indicates the actual charging power of the shared energy storage at time t.

7. A power distribution device for a city power grid, characterized in that: include: An acquisition module is used to obtain real-time operating data from the urban power grid, including load data of each park, energy storage equipment parameters, and time-of-use electricity price information of the power grid; A solution module is configured to solve a preset flexible resource-shared energy storage decision model based on the real-time operation data in combination with a preset mixed integer programming method, so that the flexible resource-shared energy storage decision model allocates shared energy storage and flexible resources of each park through power coupling constraints, and outputs charging and discharging power allocation instructions for the shared energy storage and control instructions for the flexible resources of each park; The flexible resource-shared energy storage decision model is based on historical data from the city's power grid and is trained to maximize the economic benefits of shared energy storage and the comprehensive benefits of each park's flexible resources. The distribution module is used to distribute the power of the urban power grid according to the charging and discharging power distribution instructions and the control instructions of the flexible resources of each park.

8. The power distribution device for the urban power grid according to claim 7, characterized in that: The flexible resource-shared energy storage decision model allocates shared energy storage and flexible resources of each park through power coupling constraints as follows: The upper layer model of the flexible resource-shared energy storage decision model is based on the grid time-of-use electricity price information, the load forecast data of each park, and the charging and discharging cost of the shared energy storage, to build an optimization model with the goal of maximizing the economic benefits of shared energy storage, and solve the power purchase and sales power instructions of the shared energy storage and the grid, as well as the charging and discharging power instructions of the shared energy storage to each park. The lower-level model uses the shared energy storage's charge and discharge power instructions to each park based on the upper-level model. It calculates the charge and discharge power of electric heavy-duty trucks in the industrial park using a pre-set flexible model for electric heavy-duty trucks in the industrial park. It also calculates the power demand for air conditioners in the commercial park based on a pre-set flexible model for variable-frequency air conditioners in the commercial park. Based on the power purchase and sales power instructions, charging and discharging power instructions, the charging and discharging power of electric heavy trucks in the industrial park, and the power requirements of air conditioners in the commercial park, the power allocation of shared energy storage and flexible resources in each park is optimized through power coupling constraints.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the power distribution method for the urban power grid according to any one of claims 1 to 6.

10. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the power distribution method for the urban power grid according to any one of claims 1 to 6 is implemented.

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