Active power balancing method and device for source-load-storage in substation area based on dynamic control interval

Abstract

The present invention provides a method and device for active power balancing of sources, loads and storage in a substation based on dynamic control intervals, and relates to the technical field of low-voltage distribution network operation control. The present invention converts the problem of distributed energy consumption into a problem of source-load-storage power balancing in a power supply substation. The cross-time-scale power supply substation source-load-storage power balancing optimization problem and the cross-time-scale time series optimization control problem are decomposed into day-ahead optimization and intraday control problems; by dynamically intervalizing the constraints, the optimization control of distributed photovoltaic, load and electric vehicle access is realized for uncertainty, and the optimization of source-load-storage power balancing in a power supply substation within a day is realized under the conditions of uncertain photovoltaic, energy storage and electric vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network operation control, and in particular to a method and device for active power balancing of sources, loads and storage in a substation based on a dynamic control interval. Background Art

[0002] The widespread penetration of distributed photovoltaic power generation into power grids has limited the capacity of distribution networks, creating bottlenecks in the acceptance and absorption of distributed photovoltaic power. Power grids are the primary battlefield for the absorption of distributed photovoltaic power, utilizing distributed energy storage, flexible loads, and electric vehicles to absorb distributed photovoltaic power. Traditional power grids lack monitoring capabilities, limiting the effectiveness of resources like distributed energy storage. Summary of the Invention

[0003] In view of the above problems, the present invention aims at the problem of distributed photovoltaic absorption in the power supply area, expresses the distributed photovoltaic absorption problem as the source-load-storage power balance, and achieves the source-load-storage power balance within the day through the active control of distributed energy storage and electric vehicles in the power supply area.

[0004] In a first aspect, an embodiment of the present invention provides a method for active power balancing of a substation source, load, and storage based on a dynamic control interval, comprising:

[0005] Step 1) Obtain the total charging and discharging power of distributed energy storage and the total expected charging power of electric vehicles based on the day-ahead load historical trend curve and the photovoltaic predicted output curve:

[0006] Based on the day-ahead load historical trend curve and the photovoltaic predicted output curve, a benchmark SOC curve for distributed energy storage is established. With the goal of minimizing the expected access power of electric vehicles, an optimization model including distributed energy storage SOC constraints and distributed energy storage SOC daily clearance constraints is constructed to solve and obtain the total charging and discharging power of the distributed energy storage and the total expected charging power of electric vehicles;

[0007] Step 2) Obtain the distributed energy storage power adjustment range and the electric vehicle power adjustment range based on the total charging and discharging power of the distributed energy storage and the total expected charging power of the electric vehicle as constraints:

[0008] ① Real-time collection of actual photovoltaic output deviation, load deviation and real-time SOC value of energy storage;

[0009] ② Dynamically generate the distributed energy storage power adjustment range;

[0010] ③ Dynamically generate the electric vehicle power adjustment interval;

[0011] Step 3) Performing intraday dynamic balance control according to the distributed energy storage power regulation interval and the electric vehicle power regulation interval:

[0012] By dynamically adjusting the distributed energy storage power and electric vehicle power, a dynamic balance of source, load and storage power can be achieved within the station during the day.

[0013] In one possible implementation, achieving a dynamic balance of power sources, loads, and storage in a daily substation area specifically includes:

[0014]

[0015] Where, P tie (t) represents the exchange power between the power grid and the power grid at time t. A positive value indicates power is taken from the power grid, and a negative value indicates power is supplied to the power grid. t represents 96 time points within a day. LOAD is the load set within the power grid, P load (j, t) represents the power of load j at time t; BESS is the distributed energy storage system in the power supply area, P bess (k, t) represents the charging and discharging power of distributed energy storage k at time t, where charging is positive and discharging is negative; EV is the set of charging piles in the power supply area, P ev (l,t) represents the charging power of charging pile l at time t. When there is no electric vehicle charging at the charging pile, P=0; PV is the distributed photovoltaic power collection in the power supply area, P pv (i, t) represents the photovoltaic output of photovoltaic i at time t, and the dynamic balance of source, load and storage power on a daily time scale is represented by minimizing the sum of the exchange power between the substation and the grid at each time within the day.

[0016] In one possible implementation, a benchmark SOC curve for distributed energy storage is established based on the day-ahead load historical trend curve and the photovoltaic predicted output curve. With the goal of minimizing the expected access power of electric vehicles, an optimization model including distributed energy storage SOC constraints and distributed energy storage SOC daily clearance constraints is constructed to solve and obtain the total charge and discharge power of the distributed energy storage and the total expected charging power of electric vehicles, specifically including:

[0017]

[0018] Among them, the following conditions are met:

[0019] (1) Distributed energy storage SOC constraints:

[0020]

[0021] (2) Distributed energy storage SOC daily clearance constraints:

[0022]

[0023] Where, P bess,total (t) represents the total charging and discharging power of the distributed energy storage in the substation at time t (charging is positive and discharging is negative); S SOC(t) represents the total SOC value of distributed energy storage in the substation at time t; [S SOC,min ,S SOC,max ] represents the total charge and discharge capacity range of distributed energy storage; P ev,expert (t) represents the total expected charging power of electric vehicles in the substation at time t.

[0024] In a possible implementation, the dynamically generating energy storage power adjustment interval and the dynamically generating electric vehicle power adjustment interval specifically include:

[0025] The dynamic constraints of distributed energy storage and electric vehicle charging and discharging power in the substation area are as follows:

[0026]

[0027] Where, t represents 96 time points within a day; P bess,charge,max (t) represents the total charging power constraint of distributed energy storage in the substation at time t; P bess,discharge,max (t) represents the total discharge power constraint of distributed energy storage in the substation at time t, and P bess,charge,max (t), P bess,discharge,max (t) with the P bess,total (t) related to; P ev,charge,max (t) represents the total charging power constraint of electric vehicles in the area at time t, and P ev,charge,max (t) with the P ev,expert (t) Related; each distributed energy storage and electric vehicle is allocated weighted according to its charging or discharging power.

[0028] In a possible implementation, the weighted allocation of each distributed energy storage and electric vehicle according to its charging or discharging power includes:

[0029] The calculation process is as follows:

[0030] (1) Based on the collection and statistics of the total real-time charging and discharging demand in the substation area, when the charging and discharging power does not meet the power balance conditions, the power balance is achieved through substation communication;

[0031]

[0032]

[0033] Where ΔP rt (t) represents the total power state in the substation at time t. A positive value indicates that the power generation in the substation is greater than the load, and a negative value indicates that the load is greater than the power generation; [P bess,charge,max +P ev,charge,max ,P bess,discharge,max ] represents the total charge and discharge capacity in the area. This value is a static value and represents the capacity of the area.

[0034] (2) Calculate the total charging and discharging demand in the next moment in the substation based on the load and photovoltaic power change trends;

[0035]

[0036] Where ΔP forcast (t+1) represents the total power status in the substation at time t+1. A positive value indicates that the power generation in the substation is greater than the load, and a negative value indicates that the load is greater than the power generation.

[0037] (3) The control ranges of distributed energy storage and electric vehicles are calculated according to the following principles;

[0038] a) When the total power status in the area at time t and time t+1 satisfies:

[0039]

[0040] The charging and discharging intervals of distributed energy storage and electric vehicles are as follows:

[0041]

[0042] b) When the total power status in the area at time t and time t+1 satisfies:

[0043]

[0044] The charging and discharging intervals of distributed energy storage and electric vehicles are as follows:

[0045]

[0046] c) When the total power status in the area at time t and time t+1 satisfies:

[0047]

[0048] The charging and discharging intervals of distributed energy storage and electric vehicles are as follows:

[0049]

[0050] d) When the total power status in the area at time t and time t+1 satisfies:

[0051]

[0052] The charging and discharging intervals of distributed energy storage and electric vehicles are as follows:

[0053]

[0054] Where: P ev,max (j) represents the maximum output limit of charging pile j; [0,Pev,max (t,j)] represents the charging and discharging power range of the jth electric vehicle at time t; [-P bess,discharge,max (t,i),P bess,charge,max (t,i)] represents the charging and discharging power range of the i-th distributed energy storage at time t; charge_averdispatch represents the power value is evenly distributed among the distributed energy storage and electric vehicles in the form of charging power according to power weight; bess_averdispatch represents the power is evenly distributed among the distributed energy storage in the form of charging power according to power weight; discharge_averdispatch represents the power is evenly distributed among the distributed energy storage in the form of discharging power according to power weight.

[0055] In one possible implementation, charge_averdispactch is expressed as:

[0056]

[0057] Where, P charge,total (t) represents the total charging power value of the power supply area at time t obtained by the optimization calculation on the previous day; P charge,assign (t) represents the total charging power to be allocated in the power supply area at time t; BESS represents the distributed energy storage system; P bess,charge,assign (i, t) represents the power allocated by distributed energy storage i at time t; P bess,base (i, t) represents the day-ahead optimized computing power of distributed energy storage i at time t; EV represents the set of charging vehicles; P ev,charge,assign (j, t) represents the power allocated to electric vehicle j at time t; P ev,base (j, t) represents the day-ahead optimized power calculation of electric vehicle j at time t;

[0058] bess_averdispactch is expressed as:

[0059]

[0060] Where, P charge,total (t) represents the total charging power value of the power supply area at time t obtained by the optimization calculation on the previous day; P charge,assign (t) represents the total charging power to be allocated in the power supply area at time t; BESS represents the distributed energy storage system; P bess,charge,assign (i, t) represents the power allocated by distributed energy storage i at time t; P bess,base (i, t) represents the day-ahead optimized computing power of distributed energy storage i at time t;

[0061] discharge_averdispactch is expressed as:

[0062]

[0063] Where, P discharge,assign (t) represents the total discharge power to be distributed in the power supply area at time t; P bess,base (i,t) represents the day-ahead optimized computing power of distributed energy storage i at time t.

[0064] In a second aspect, an embodiment of the present invention provides a source-load-storage active power balancing device for a substation based on a dynamic control interval, comprising:

[0065] An acquisition module is used to: acquire the total charge and discharge power of distributed energy storage and the total expected charging power of electric vehicles according to the day-ahead load historical trend curve and the photovoltaic predicted output curve; establish a benchmark SOC curve of distributed energy storage based on the day-ahead load historical trend curve and the photovoltaic predicted output curve; construct an optimization model containing distributed energy storage SOC constraints and distributed energy storage SOC daily clearance constraints with the goal of minimizing the expected access power of electric vehicles; obtain the total charge and discharge power of distributed energy storage and the total expected charging power of electric vehicles by solving the problem; obtain the distributed energy storage power adjustment range and the electric vehicle power adjustment range based on the distributed energy storage total charge and discharge power and the total expected charging power of electric vehicles as constraints: ① real-time acquisition of photovoltaic actual output deviation, load deviation and energy storage real-time SOC value; ② dynamic generation of the distributed energy storage power adjustment range; ③ dynamic generation of the electric vehicle power adjustment range;

[0066] The balancing module is used to: perform intraday dynamic balancing control according to the distributed energy storage power regulation interval and the electric vehicle power regulation interval: achieve intraday dynamic balance of source, load and storage power in the substation by dynamically adjusting the distributed energy storage power and the electric vehicle power.

[0067] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor executes the machine-executable instructions to implement the method of the first aspect.

[0068] In a fourth aspect, an embodiment of the present invention provides a machine-readable storage medium, which stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the method of the first aspect.

[0069] In a fifth aspect, an embodiment of the present invention provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer device, the computer device executes the method of the first aspect.

[0070] The present invention has the following characteristics:

[0071] (1) Expressing the distributed photovoltaic consumption problem as a source-load-storage power balance;

[0072] (2) Decomposing a relatively complex cross-time scale optimization control problem into relatively simple day-ahead optimization and intraday control;

[0073] (3) The day-ahead optimization problem takes the source-load balance of the substation as the goal and the power balance as the constraint, forming the basic curve for distributed energy storage control;

[0074] (4) Intraday control is based on the day-ahead optimization curve. For uncertain loads, distributed photovoltaics, and randomly connected electric vehicles, a dynamic control range of distributed energy storage is established based on the power change trend, and a control method is established with the goal of maximizing the SOC margin of distributed energy storage.

[0075] (5) Based on the real-time distributed energy storage charging and discharging power, distributed photovoltaic real-time output, and load real-time power, combined with the distributed photovoltaic output change trend and load power change trend, the upper and lower limits of dynamic regulation of distributed energy storage at different times of the day are dynamically established to form a dynamic control range of distributed energy storage and electric vehicles. According to the dynamic control range of distributed energy storage and electric vehicles, each distributed energy storage and electric vehicle is weighted and allocated according to its charging or discharging power, so as to achieve further optimized control of the uncertain load and electric vehicle power changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0077] Figure 1 A flow chart of a method for active power balancing of a power distribution area based on source, load and storage in a dynamic control interval provided by an embodiment of the present invention;

[0078] Figure 2 A schematic diagram of an application scenario of a method for active power balancing of source, load and storage in a substation based on a dynamic control interval provided by an embodiment of the present invention;

[0079] Figure 3 A photovoltaic power generation normalization curve of a method for active power balancing of source, load and storage in a substation based on a dynamic control interval provided by an embodiment of the present invention;

[0080] Figure 4A load power normalization curve of an active power balancing method for a substation source, load and storage based on a dynamic control interval provided by an embodiment of the present invention;

[0081] Figure 5 A distributed energy storage SOC curve for an active power balancing method for source, load and storage in a substation based on a dynamic control interval provided by an embodiment of the present invention;

[0082] Figure 6 A schematic structural diagram of a source-load-storage active power balancing method based on a dynamic control interval provided by an embodiment of the present invention;

[0083] Figure 7 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0084] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0085] Figure 1 The whole control process is divided into two stages:

[0086] Phase 1: A day-ahead approach aims to meet the power balance within the power supply area and the daily clearing constraints of distributed energy storage SOC. Based on the load trend curve and distributed photovoltaic generation trends, a power balance within the power supply area is established to fully accommodate renewable energy. This control is calculated based on the minimum expected electric vehicle access.

[0087] The second stage: In response to the uncertainty of load power and electric vehicle access in the operating power supply area during the day, with the goal of maximizing the distributed energy storage SOC control margin, based on the real-time distributed energy storage charging and discharging power, distributed photovoltaic real-time output, and load real-time power, combined with the distributed photovoltaic output change trend and load power change trend, the upper and lower limits of distributed energy storage and electric vehicle regulation are dynamically established to form a dynamic control range to cope with the uncertain load and electric vehicle power changes.

[0088] like Figure 1 The process shown may include:

[0089] S110. Obtain the total charging and discharging power of the distributed energy storage and the total expected charging power of the electric vehicle based on the day-ahead load historical trend curve and the photovoltaic predicted output curve.

[0090] In this step, a benchmark SOC curve for distributed energy storage can be established based on the day-ahead load historical trend curve and the photovoltaic predicted output curve. With the goal of minimizing the expected access power of electric vehicles, an optimization model containing distributed energy storage SOC constraints and distributed energy storage SOC daily clearance constraints is constructed to obtain the total charging and discharging power of the distributed energy storage and the total expected charging power of the electric vehicles.

[0091] Among them, the SOC basic operating curve can also be called or understood as the SOC base state operating mode.

[0092] S120. Obtain a distributed energy storage power adjustment interval and an electric vehicle power adjustment interval based on the total charging and discharging power of the distributed energy storage and the total expected charging power of the electric vehicle as constraints.

[0093] In this step, you can perform the following steps:

[0094] ① Real-time collection of actual photovoltaic output deviation, load deviation and real-time SOC value of energy storage;

[0095] ② Dynamically generate the distributed energy storage power adjustment range;

[0096] ③ Dynamically generate the electric vehicle power adjustment range.

[0097] S130. Perform intraday dynamic balance control according to the distributed energy storage power adjustment interval and the electric vehicle power adjustment interval.

[0098] In this step, the distributed energy storage power and electric vehicle power can be dynamically adjusted to achieve a dynamic balance of source, load and storage power in the substation during the day.

[0099] The technical solution of the present invention is as follows: Figure 2 The 10kV power supply area is shown.

[0100] Phase 1: With the power supply area's source-load balance as the goal and power balance as the constraint, the distributed energy storage SOC curve is determined to represent the active power balance of the source, load, and storage in the area within the day as follows. In other words, achieving the dynamic balance of the source, load, and storage power in the area within the day specifically includes:

[0101]

[0102] Where, P tie (t) represents the off-grid power of the power supply area at time t; t represents 96 time points within a day; LOAD is the load set in the power supply area, P load (j, t) represents the power of load j at time t; BESS is the distributed energy storage system in the power supply area, P bess(k, t) represents the charging and discharging power of distributed energy storage k at time t, where charging is positive and discharging is negative; EV is the set of charging piles in the power supply area, P ev (l,t) represents the charging power of charging pile l at time t. When there is no electric vehicle charging at the charging pile, P=0; PV is the distributed photovoltaic power collection in the power supply area, P pv (i,t) represents the photovoltaic output of photovoltaic i at time t.

[0103] Active power balancing satisfies the following constraints:

[0104] (1) Power balance constraints

[0105]

[0106] (2) Distributed photovoltaic output upper limit constraints

[0107]

[0108] Where, P pv,max (i) represents the maximum output limit of distributed photovoltaic i.

[0109] (3) Distributed energy storage charging power upper limit constraint

[0110]

[0111] Where, P bess,charge,max (i) represents the maximum charging power limit of distributed energy storage i.

[0112] (4) Upper limit constraint on the discharge power of distributed energy storage

[0113]

[0114] Where, P bess,discharge,max (i) represents the maximum discharge power limit of distributed energy storage i.

[0115] (5) Distributed energy storage SOC constraints

[0116]

[0117] Where S SOC (i,t) represents the capacity value of distributed energy storage i at time t; S SOC,max (i) represents the upper limit of the capacity of distributed energy storage i; S SOC,min (i) represents the lower limit of the capacity of distributed energy storage i.

[0118] (6) Distributed energy storage SOC daily clearance constraints

[0119]

[0120] (7) Upper limit constraints on electric vehicle charging power

[0121]

[0122] Where, P ev, (i, t) represents the charging power value of electric vehicle i at time t; P ev,charge,max (i) represents the upper limit of the charging power allowed for distributed energy storage i.

[0123] (8) Restrictions on charging time for electric vehicles

[0124]

[0125] Where, T represents the maximum charging time allowed for a charging vehicle; E ev, (l) represents the charge level of the charging vehicle. Since the data is collected every 15 minutes, the calculated power level is multiplied by 0.25.

[0126] Phase 2: Calculation method for the control strategy of the distributed energy storage dynamic control interval with the goal of maximizing the distributed energy storage SOC margin and the source-load balance of the power supply area within the day

[0127] In the constraints of the above formulas (21) to (28), P bess,charge,max (i) P bess,discharge,max (i) P ev,charge,max The upper and lower limits of distributed energy storage and electric vehicle devices of type (i) are both fixed values.

[0128] Based on the SOC curve of the distributed energy storage on the day before, the dynamic control range of the distributed energy storage that maximizes the SOC margin of the distributed energy storage is calculated. According to the following calculation steps, that is, based on the historical trend curve of the load on the day before and the predicted photovoltaic output curve, a benchmark SOC curve of the distributed energy storage is established. With the goal of minimizing the expected access power of electric vehicles, an optimization model containing distributed energy storage SOC constraints and distributed energy storage SOC daily clearance constraints is constructed to obtain the total charge and discharge power of the distributed energy storage and the total expected charging power of electric vehicles. Specifically, it includes:

[0129] Step 1: Establish a baseline SOC curve for distributed energy storage based on the photovoltaic and load power trend curves.

[0130]

[0131] satisfy

[0132] (1) Distributed energy storage SOC constraints

[0133]

[0134] (2) Distributed energy storage SOC daily clearance constraints

[0135]

[0136] Where, P bess,total (t) represents the total charging and discharging power of the distributed energy storage in the substation at time t (charging is positive and discharging is negative); S SOC (t) represents the total SOC value of distributed energy storage in the substation at time t; [S SOC,min ,S SOC,max ] represents the total charge and discharge capacity range of distributed energy storage; P ev,expert (t) represents the total expected charging power of electric vehicles in the substation at time t.

[0137] Step 2: Based on the real-time distributed energy storage charging and discharging power, combined with the distributed photovoltaic output change trend and load power change trend, establish the upper and lower limits of dynamic adjustment of distributed energy storage at different times of the day, and form a dynamic control range for distributed energy storage and electric vehicles.

[0138] The dynamic constraints of distributed energy storage and electric vehicle charging and discharging power in the substation area are as follows, that is, the dynamically generated energy storage power adjustment interval and the dynamically generated electric vehicle power adjustment interval, specifically including:

[0139]

[0140] Where, t represents 96 time points within a day; P bess,charge,max (t) represents the total charging power constraint of distributed energy storage in the substation at time t; P bess,discharge,max (t) represents the total discharge power constraint of distributed energy storage in the substation at time t; P ev,charge,max (t) represents the total charging power constraint of electric vehicles in the area at time t. Each distributed energy storage and electric vehicle is weighted according to its charging or discharging power.

[0141] Step 3: Intraday real-time control is performed to maximize the SOC regulation margin of distributed energy storage under power trends.

[0142] The calculation process is as follows, that is, each distributed energy storage and electric vehicle is weighted according to its charging or discharging power, including:

[0143] (1) Based on the collection and statistics of the total real-time charging and discharging demand in the substation area, when the charging and discharging power does not meet the power balance conditions, the power balance is achieved through substation communication;

[0144]

[0145]

[0146] Where ΔP rt(t) represents the total power state in the substation at time t. A positive value indicates that the power generation in the substation is greater than the load, and a negative value indicates that the load is greater than the power generation; [P bess,charge,max +P ev,charge,max ,P bess,discharge,max ] represents the total charge and discharge capacity within the station area. This value is a static value and represents the capacity of the station area.

[0147] (2) Calculate the total charging and discharging demand in the next moment in the substation based on the load and photovoltaic power change trends;

[0148]

[0149] Where ΔP forcast (t+1) represents the total power status in the substation at time t+1. A positive value indicates that the power generation in the substation is greater than the load, and a negative value indicates that the load is greater than the power generation.

[0150] (3) The control ranges of distributed energy storage and electric vehicles are calculated according to the following principles;

[0151] a) When the total power status in the area at time t and time t+1 satisfies:

[0152]

[0153] The charging and discharging intervals of distributed energy storage and electric vehicles are as follows:

[0154]

[0155] b) When the total power status in the area at time t and time t+1 satisfies:

[0156]

[0157] The charging and discharging intervals of distributed energy storage and electric vehicles are as follows:

[0158]

[0159] c) When the total power status in the area at time t and time t+1 satisfies:

[0160]

[0161] The charging and discharging intervals of distributed energy storage and electric vehicles are as follows:

[0162]

[0163] d) When the total power status in the area at time t and time t+1 satisfies:

[0164]

[0165] The charging and discharging intervals of distributed energy storage and electric vehicles are as follows:

[0166]

[0167] Where: P ev,max (j) represents the maximum output limit of charging pile j; [0,P ev,max (t,j)] represents the charging and discharging power range of the jth electric vehicle at time t; [-P bess,discharge,max (t,i),P bess,charge,max (t,i)] represents the charging and discharging power range of the i-th distributed energy storage at time t; charge_averdispatch represents the power value is evenly distributed among the distributed energy storage and electric vehicles in the form of charging power according to power weight; bess_averdispatch represents the power is evenly distributed among the distributed energy storage in the form of charging power according to power weight; discharge_averdispatch represents the power is evenly distributed among the distributed energy storage in the form of discharging power according to power weight.

[0168] Among them, charge_averdispactch is expressed as:

[0169]

[0170] Where, P charge,total (t) represents the total charging power value of the power supply area at time t obtained by the optimization calculation on the previous day; P charge,assign (t) represents the total charging power to be allocated in the power supply area at time t; BESS represents the distributed energy storage system; P bess,charge,assign (i, t) represents the power allocated by distributed energy storage i at time t; P bess,base (i, t) represents the day-ahead optimized computing power of distributed energy storage i at time t; EV represents the set of charging vehicles; P ev,charge,assign (j, t) represents the power allocated to electric vehicle j at time t; P ev,base (j, t) represents the day-ahead optimized power calculation of electric vehicle j at time t;

[0171] It should be noted that:

[0172] 1) In the formula, the charging and discharging states of distributed energy storage within the grid area are different, but in actual control, the charging and discharging states of the entire power supply grid area are generally controlled synchronously;

[0173] 2) Charging power can be allocated according to capacity as follows:

[0174]

[0175] Where, P charge,total(t) represents the total charging power value of the power supply area at time t; P bess,rt (i, t) represents the real-time charging power value of distributed energy storage i at time t; P bess,charge,max (i) represents the maximum charging power of distributed energy storage i; P ev,max (j) represents the maximum charging power of electric vehicle j.

[0176] Among them, bess_averdispactch is expressed as:

[0177]

[0178] Where, P charge,total (t) represents the total charging power value of the power supply area at time t obtained by the optimization calculation on the previous day; P charge,assign (t) represents the total charging power to be allocated in the power supply area at time t; BESS represents the distributed energy storage system; P bess,charge,assign (i, t) represents the power allocated by distributed energy storage i at time t; P bess,base (i, t) represents the day-ahead optimized computing power of distributed energy storage i at time t;

[0179] It should be noted that:

[0180] 1) In the formula, the charging and discharging states of distributed energy storage within the grid area are different, but in actual control, the charging and discharging states of the entire power supply grid area are generally controlled synchronously;

[0181] 2) Charging power can be allocated according to capacity as follows:

[0182]

[0183] Where, P charge,total (t) represents the total charging power value of the power supply area at time t; P bess,rt (i, t) represents the real-time charging power value of distributed energy storage i at time t; P bess,charge,max (i) represents the maximum charging power of distributed energy storage i.

[0184] Among them, discharge_averdispactch is expressed as:

[0185]

[0186] Where, P discharge,assign (t) represents the total discharge power to be distributed in the power supply area at time t; P bess,base (i,t) represents the day-ahead optimized computing power of distributed energy storage i at time t.

[0187] It should be noted that:

[0188] 1) In the formula, the charging and discharging states of distributed energy storage within the grid area are different, but in actual control, the charging and discharging states of the entire power supply grid area are generally controlled synchronously;

[0189] 2) Discharge power can be allocated according to capacity as follows:

[0190]

[0191] Where, P discharge,assign (t) represents the total discharge power to be distributed in the power supply area at time t; P bess,rt (i, t) represents the real-time charging power value of distributed energy storage i at time t; P bess,discharge,max (i) represents the maximum discharge power of distributed energy storage i.

[0192] The embodiments of the present invention bring the following effects: (1) the maximum absorption target of new energy is converted into the source-load balance target of the power supply substation, so that the absorption of new energy at the power supply substation level can be achieved through clear power control; (2) the present invention decomposes a complex intraday optimization control problem into an optimization auxiliary decision-making problem and a traditional control problem, which not only reduces the computational complexity and time, but also establishes a control system strategy with relatively simple computation; (3) the present invention considers the time series characteristics of load, electric vehicle, and distributed photovoltaic in the optimization and control, thereby improving the overall optimization and control effect; (4) the present invention copes with the uncertainty of load, power generation, and electric vehicle in the power supply substation through dynamic control interval in the intraday control, so as to meet the target of source-load-storage power balance in the power supply substation; in short: through the present invention, the target of source-load balance in the power supply substation is established at the power supply substation level, and the maximum absorption of new energy within the power supply substation is achieved.

[0193] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings.

[0194] Phase 1: With the goal of meeting the power balance within the power supply area and the daily clearance constraints of distributed energy storage SOC, based on the load trend curve and distributed photovoltaic power generation trend, the power balance within the power supply area is established to fully absorb new energy. The minimum electric vehicle access expectation is used as the calculation basis for this control.

[0195] Figure 3-4 These are the normalized trend curves of photovoltaic power generation and load in different seasons. Photovoltaic power generation and load have time series characteristics.

[0196] This method collects and accumulates data to form a trend in the total load of the power supply area; this method also collects and accumulates data to form a trend in the total photovoltaic power generation of the power supply area. With the availability of micro-meteorological data, distributed photovoltaic forecasting can make photovoltaic power generation trends more accurate.

[0197] Based on the formula (1) and formula (2) of the present invention, the total SOC curve of distributed energy storage in the substation area with the source-load-storage power balance as the goal is to establish the day before. When all distributed energy storage in the substation area are synchronized (charged and discharged according to the same SOC curve), then Figure 5 The middle curve is the SOC curve of all distributed energy storage in the substation area.

[0198] See also Figure 6 , Figure 6 The present invention provides a schematic diagram of a structure of a station area source-load-storage active power balancing device based on a dynamic control interval. Figure 6 The apparatus shown may include:

[0199] The acquisition module 610 is used to: obtain the total charge and discharge power of the distributed energy storage and the total expected charging power of the electric vehicle based on the day-ahead load historical trend curve and the photovoltaic predicted output curve; establish a benchmark SOC curve for the distributed energy storage based on the day-ahead load historical trend curve and the photovoltaic predicted output curve, and construct an optimization model containing the distributed energy storage SOC constraint and the distributed energy storage SOC daily clearance constraint with the goal of minimizing the expected access power of the electric vehicle, and solve and obtain the total charge and discharge power of the distributed energy storage and the total expected charging power of the electric vehicle; obtain the distributed energy storage power adjustment range and the electric vehicle power adjustment range based on the distributed energy storage total charge and discharge power and the total expected charging power of the electric vehicle as constraints: ① real-time acquisition of the actual photovoltaic output deviation, load deviation and real-time SOC value of the energy storage; ② dynamic generation of the distributed energy storage power adjustment range; ③ dynamic generation of the electric vehicle power adjustment range;

[0200] The balancing module 620 is used to perform intraday dynamic balancing control according to the distributed energy storage power regulation interval and the electric vehicle power regulation interval: by dynamically adjusting the distributed energy storage power and the electric vehicle power, the intraday dynamic balance of source, load and storage power in the substation is achieved.

[0201] In one possible implementation, achieving a dynamic balance of power sources, loads, and storage in a daily substation area specifically includes:

[0202]

[0203] Where, P tie (t) represents the exchange power between the power grid and the power grid at time t. A positive value indicates power is taken from the power grid, and a negative value indicates power is supplied to the power grid. t represents 96 time points within a day. LOAD is the load set within the power grid, Pload (j, t) represents the power of load j at time t; BESS is the distributed energy storage system in the power supply area, P bess (k, t) represents the charging and discharging power of distributed energy storage k at time t, where charging is positive and discharging is negative; EV is the set of charging piles in the power supply area, P ev (l,t) represents the charging power of charging pile l at time t. When there is no electric vehicle charging at the charging pile, P=0; PV is the distributed photovoltaic power collection in the power supply area, P pv (i, t) represents the photovoltaic output of photovoltaic i at time t, and the dynamic balance of source, load and storage power on a daily time scale is represented by minimizing the sum of the exchange power between the substation and the grid at each time within the day.

[0204] In one possible implementation, a benchmark SOC curve for distributed energy storage is established based on the day-ahead load historical trend curve and the photovoltaic predicted output curve. With the goal of minimizing the expected access power of electric vehicles, an optimization model including distributed energy storage SOC constraints and distributed energy storage SOC daily clearance constraints is constructed to solve and obtain the total charge and discharge power of the distributed energy storage and the total expected charging power of electric vehicles, specifically including:

[0205]

[0206] Among them, the following conditions are met:

[0207] (1) Distributed energy storage SOC constraints:

[0208]

[0209] (2) Distributed energy storage SOC daily clearance constraints:

[0210]

[0211] Where, P bess,total (t) represents the total charging and discharging power of the distributed energy storage in the substation at time t (charging is positive and discharging is negative); S SOC (t) represents the total SOC value of distributed energy storage in the substation at time t; [S SOC,min ,S SOC,max ] represents the total charge and discharge capacity range of distributed energy storage; P ev,expert (t) represents the total expected charging power of electric vehicles in the substation at time t.

[0212] In a possible implementation, the dynamically generating energy storage power adjustment interval and the dynamically generating electric vehicle power adjustment interval specifically include:

[0213] The dynamic constraints of distributed energy storage and electric vehicle charging and discharging power in the substation area are as follows:

[0214]

[0215] Where, t represents 96 time points within a day; P bess,charge,max (t) represents the total charging power constraint of distributed energy storage in the substation at time t; P bess,discharge,max (t) represents the total discharge power constraint of distributed energy storage in the substation at time t, and P bess,charge,max (t), P bess,discharge,max (t) with the P bess,total (t) related to; P ev,charge,max (t) represents the total charging power constraint of electric vehicles in the area at time t, and P ev,charge,max (t) with the P ev,expert (t) Related; each distributed energy storage and electric vehicle is allocated weighted according to its charging or discharging power.

[0216] In a possible implementation, the weighted allocation of each distributed energy storage and electric vehicle according to its charging or discharging power includes:

[0217] The calculation process is as follows:

[0218] (1) Based on the collection and statistics of the total real-time charging and discharging demand in the substation area, when the charging and discharging power does not meet the power balance conditions, the power balance is achieved through substation communication;

[0219]

[0220]

[0221] Where ΔP rt (t) represents the total power state in the substation at time t. A positive value indicates that the power generation in the substation is greater than the load, and a negative value indicates that the load is greater than the power generation; [P bess,charge,max +P ev,charge,max ,P bess,discharge,max ] represents the total charge and discharge capacity in the area. This value is a static value and represents the capacity of the area.

[0222] (2) Calculate the total charging and discharging demand in the next moment in the substation based on the load and photovoltaic power change trends;

[0223]

[0224] Where ΔP forcast (t+1) represents the total power status in the substation at time t+1. A positive value indicates that the power generation in the substation is greater than the load, and a negative value indicates that the load is greater than the power generation.

[0225] (3) The control ranges of distributed energy storage and electric vehicles are calculated according to the following principles;

[0226] a) When the total power status in the area at time t and time t+1 satisfies:

[0227]

[0228] The charging and discharging intervals of distributed energy storage and electric vehicles are as follows:

[0229]

[0230] b) When the total power status in the area at time t and time t+1 satisfies:

[0231]

[0232] The charging and discharging intervals of distributed energy storage and electric vehicles are as follows:

[0233]

[0234] c) When the total power status in the area at time t and time t+1 satisfies:

[0235]

[0236] The charging and discharging intervals of distributed energy storage and electric vehicles are as follows:

[0237]

[0238] d) When the total power status in the area at time t and time t+1 satisfies:

[0239]

[0240] The charging and discharging intervals of distributed energy storage and electric vehicles are as follows:

[0241]

[0242] Where: P ev,max (j) represents the maximum output limit of charging pile j; [0,P ev,max (t,j)] represents the charging and discharging power range of the jth electric vehicle at time t; [-P bess,discharge,max (t,i),P bess,charge,max (t,i)] represents the charging and discharging power range of the i-th distributed energy storage at time t; charge_averdispatch represents the power value is evenly distributed among the distributed energy storage and electric vehicles in the form of charging power according to power weight; bess_averdispatch represents the power is evenly distributed among the distributed energy storage in the form of charging power according to power weight; discharge_averdispatch represents the power is evenly distributed among the distributed energy storage in the form of discharging power according to power weight.

[0243] In one possible implementation, charge_averdispactch is expressed as:

[0244]

[0245] Where, P charge,total (t) represents the total charging power value of the power supply area at time t obtained by the optimization calculation on the previous day; P charge,assign (t) represents the total charging power to be allocated in the power supply area at time t; BESS represents the distributed energy storage system; P bess,charge,assign (i, t) represents the power allocated by distributed energy storage i at time t; P bess,base (i, t) represents the day-ahead optimized computing power of distributed energy storage i at time t; EV represents the set of charging vehicles; P ev,charge,assign (j, t) represents the power allocated to electric vehicle j at time t; P ev,base (j, t) represents the day-ahead optimized power calculation of electric vehicle j at time t;

[0246] bess_averdispactch is expressed as:

[0247]

[0248] Where, P charge,total (t) represents the total charging power value of the power supply area at time t obtained by the optimization calculation on the previous day; P charge,assign (t) represents the total charging power to be allocated in the power supply area at time t; BESS represents the distributed energy storage system; P bess,charge,assign (i, t) represents the power allocated by distributed energy storage i at time t; P bess,base (i, t) represents the day-ahead optimized computing power of distributed energy storage i at time t;

[0249] discharge_averdispactch is expressed as:

[0250]

[0251] Where, P discharge,assign (t) represents the total discharge power to be distributed in the power supply area at time t; P bess,base (i,t) represents the day-ahead optimized computing power of distributed energy storage i at time t.

[0252] This embodiment also provides an electronic device, including a processor and a memory. The memory stores computer-executable instructions executable by the processor, and the processor executes the computer-executable instructions to implement the above-described active balancing method for photovoltaic storage and charging in a substation considering dynamic charging and discharging intervals. The electronic device can be a server or a terminal device.

[0253] See also Figure 7 As shown, the electronic device includes a processor 100 and a memory 101, wherein the memory 101 stores computer executable instructions that can be executed by the processor 100, and the processor 100 executes the computer executable instructions to implement the above-mentioned active balancing method of photovoltaic storage and charging in the station area considering the dynamic charging and discharging interval.

[0254] Further, Figure 7 The electronic device shown further includes a bus 102 and a communication interface 103 , and the processor 100 , the communication interface 103 and the memory 101 are connected via the bus 102 .

[0255] The processor in the above-mentioned electronic device can implement the steps in the above-mentioned active balancing method of photovoltaic storage and charging in the station area considering the dynamic charging and discharging interval by executing computer-executable instructions.

[0256] This embodiment also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the above-mentioned active balancing method of photovoltaic storage and charging in the substation considering the dynamic charging and discharging interval.

[0257] The computer executable instructions stored in the above-mentioned computer-readable storage medium can be executed to implement the steps in the above-mentioned active balancing method of photovoltaic storage and charging in the station area considering the dynamic charging and discharging interval.

[0258] This embodiment also provides a computer program product, including program code. The instructions included in the program code can be used to execute the method in the previous method embodiment. The specific implementation can be found in the method embodiment and will not be repeated here.

[0259] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems and devices can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0260] In addition, in the description of the embodiments of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0261] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0262] Finally, it should be noted that the above embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for active power balancing of source, load and storage in a substation area based on dynamic control interval, characterized in that: include: Step 1) Obtain the total charging and discharging power of distributed energy storage and the total expected charging power of electric vehicles based on the day-ahead load historical trend curve and the photovoltaic predicted output curve: Based on the day-ahead load historical trend curve and the photovoltaic predicted output curve, a benchmark SOC curve for distributed energy storage is established. With the goal of minimizing the expected access power of electric vehicles, an optimization model including distributed energy storage SOC constraints and distributed energy storage SOC daily clearance constraints is constructed to solve and obtain the total charging and discharging power of the distributed energy storage and the total expected charging power of electric vehicles; Step 2) Obtain the distributed energy storage power adjustment range and the electric vehicle power adjustment range based on the total charging and discharging power of the distributed energy storage and the total expected charging power of the electric vehicle as constraints: ① Real-time collection of actual photovoltaic output deviation, load deviation and real-time SOC value of energy storage; ② Dynamically generate the distributed energy storage power adjustment range; ③ Dynamically generate the electric vehicle power adjustment interval; Step 3) performing intraday dynamic balance control according to the distributed energy storage power regulation interval and the electric vehicle power regulation interval, and achieving intraday dynamic balance of source, load and storage power in the substation area by dynamically adjusting the distributed energy storage power and the electric vehicle power; The realization of the intraday dynamic balance of power source, load and storage in the substation area specifically includes: (1)； (2)； Where, P tie (t) represents the exchange power between the power grid and the power grid at time t. A positive value indicates power is taken from the power grid, and a negative value indicates power is supplied to the power grid. t represents 96 time points within a day. LOAD is the load set within the power grid, P load (j, t) represents the power of load j at time t; BESS is the distributed energy storage system in the power supply area, P bess (k, t) represents the charging and discharging power of distributed energy storage k at time t, where charging is positive and discharging is negative; EV is the set of charging piles in the power supply area, P ev (l,t) represents the charging power of charging pile l at time t. When there is no electric vehicle charging at the charging pile, P=0; PV is the distributed photovoltaic power collection in the power supply area, P pv (i, t) represents the photovoltaic output of photovoltaic i at time t, and the dynamic balance of source, load and storage power on a daily time scale is represented by minimizing the sum of the exchange power between the substation and the grid at each time within the day.

2. The method according to claim 1, characterized in that The method of establishing a benchmark SOC curve for distributed energy storage based on the day-ahead load historical trend curve and the photovoltaic predicted output curve, and constructing an optimization model including distributed energy storage SOC constraints and distributed energy storage SOC daily clearance constraints with the goal of minimizing the expected access power of electric vehicles, and solving the problem to obtain the total charge and discharge power of the distributed energy storage and the total expected charging power of electric vehicles, specifically includes: (3)； Among them, the following conditions are met: (1) Distributed energy storage SOC constraints: (4)； (2) Distributed energy storage SOC daily clearance constraints: (5)； Where, P bess,total (t) represents the total charging and discharging power of the distributed energy storage in the substation at time t (charging is positive and discharging is negative); S SOC (t) represents the total SOC value of distributed energy storage in the substation at time t; [S SOC,min ,S SOC,max ] represents the total charge and discharge capacity range of distributed energy storage; P ev,expert (t) represents the total expected charging power of electric vehicles in the substation at time t.

3. The method according to claim 1, characterized in that The dynamically generated energy storage power adjustment interval and the dynamically generated electric vehicle power adjustment interval specifically include: The dynamic constraints of distributed energy storage and electric vehicle charging and discharging power in the substation area are as follows: (6)； Where, t represents 96 time points within a day; P bess,charge,max (t) represents the total charging power constraint of distributed energy storage in the substation at time t; P bess,discharge,max (t) represents the total discharge power constraint of distributed energy storage in the substation at time t, and P bess,charge,max (t), P bess,discharge,max (t) with the P bess,total (t) related to; P ev,charge,max (t) represents the total charging power constraint of electric vehicles in the area at time t, and P ev,charge,max (t) with the P ev,expert (t) Related; each distributed energy storage and electric vehicle is allocated weighted according to its charging or discharging power.

4. The method according to claim 3, characterized in that The weighted allocation of each distributed energy storage and electric vehicle according to its charging or discharging power includes: The calculation process is as follows: (1) Based on the collection and statistics of the total real-time charging and discharging demand in the substation area, when the charging and discharging power does not meet the power balance conditions, the power balance is achieved through substation communication; (7)； (8)； Where ΔP rt (t) represents the total power state in the substation at time t. A positive value indicates that the power generation in the substation is greater than the load, and a negative value indicates that the load is greater than the power generation; [P bess,charge,max +P ev,charge,max ,P bess,discharge,max ] represents the total charge and discharge capacity in the area. This value is a static value and represents the capacity of the area. (2) Calculate the total charging and discharging demand in the next moment in the substation based on the load and photovoltaic power change trends; (9)； Where, ΔP forcast (t+1) represents the total power status in the substation at time t+1. A positive value indicates that the power generation in the substation is greater than the load, and a negative value indicates that the load is greater than the power generation. (3) The control ranges of distributed energy storage and electric vehicles are calculated according to the following principles; a) When the total power status in the area at time t and time t+1 satisfies: (10)； The charging and discharging intervals of distributed energy storage and electric vehicles are as follows: (11)； b) When the total power status in the area at time t and time t+1 satisfies: (12)； The charging and discharging intervals of distributed energy storage and electric vehicles are as follows: (13)； c) When the total power status in the area at time t and time t+1 satisfies: (14)； The charging and discharging intervals of distributed energy storage and electric vehicles are as follows: (15)； d) When the total power status in the area at time t and time t+1 satisfies: (16)； The charging and discharging intervals of distributed energy storage and electric vehicles are as follows: (17)； Where: P ev,max (j) represents the maximum output limit of charging pile j; [0,P ev,max (t,j)] represents the charging and discharging power range of the jth electric vehicle at time t; [-P bess,discharge,max (t,i),P bess,charge,max (t,i)] represents the charging and discharging power range of the i-th distributed energy storage at time t; charge_averdispatch represents the power value is evenly distributed among the distributed energy storage and electric vehicles in the form of charging power according to power weight; bess_averdispatch represents the power is evenly distributed among the distributed energy storage in the form of charging power according to power weight; discharge_averdispatch represents the power is evenly distributed among the distributed energy storage in the form of discharging power according to power weight.

5. The method according to claim 4, characterized in that charge_averdispactch is expressed as: (18)； Where, P charge,total (t) represents the total charging power value of the power supply area at time t obtained by the optimization calculation on the previous day; P charge,assign (t) represents the total charging power to be allocated in the power supply area at time t; BESS represents the distributed energy storage system; P bess,charge,assign (i, t) represents the power allocated by distributed energy storage i at time t; P bess,base (i, t) represents the day-ahead optimized computing power of distributed energy storage i at time t; EV represents the set of charging vehicles; P ev,charge,assign (j, t) represents the power allocated to electric vehicle j at time t; P ev,base (j, t) represents the day-ahead optimized power calculation of electric vehicle j at time t; bess_averdispactch is expressed as: (19)； Where, P charge,total (t) represents the total charging power value of the power supply area at time t obtained by the optimization calculation on the previous day; P charge,assign (t) represents the total charging power to be allocated in the power supply area at time t; BESS represents the distributed energy storage system; P bess,charge,assign (i, t) represents the power allocated by distributed energy storage i at time t; P bess,base (i, t) represents the day-ahead optimized computing power of distributed energy storage i at time t; discharge_averdispactch is expressed as: (20)； Where, P discharge,assign (t) represents the total discharge power to be distributed in the power supply area at time t; P bess,base (i,t) represents the day-ahead optimized computing power of distributed energy storage i at time t.

6. A source-load-storage active power balancing device based on dynamic control interval, characterized in that: The method according to any one of claims 1 to 5, wherein the device comprises: An acquisition module is used to: acquire the total charge and discharge power of distributed energy storage and the total expected charging power of electric vehicles based on the day-ahead load historical trend curve and the photovoltaic predicted output curve; establish a benchmark SOC curve for distributed energy storage based on the day-ahead load historical trend curve and the photovoltaic predicted output curve, and construct an optimization model containing distributed energy storage SOC constraints and distributed energy storage SOC daily clearance constraints with the goal of minimizing the expected access power of electric vehicles, and solve and acquire the total charge and discharge power of distributed energy storage and the total expected charging power of electric vehicles; acquire the distributed energy storage power adjustment range and the electric vehicle power adjustment range based on the distributed energy storage total charge and discharge power and the total expected charging power of electric vehicles as constraints: ① real-time acquisition of photovoltaic actual output deviation, load deviation and energy storage real-time SOC value; ② dynamic generation of the distributed energy storage power adjustment range; ③ dynamic generation of the electric vehicle power adjustment range; The balancing module is used to: perform intraday dynamic balancing control according to the distributed energy storage power regulation interval and the electric vehicle power regulation interval: achieve intraday dynamic balance of source, load and storage power in the substation by dynamically adjusting the distributed energy storage power and the electric vehicle power.

7. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor executes the machine-executable instructions to implement the method according to any one of claims 1 to 5.

8. A machine-readable storage medium, characterized in that The machine-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the method according to any one of claims 1 to 5.

9. A computer program product, characterized in that The computer program product comprises a computer program stored on a computer-readable storage medium, wherein the computer program comprises program instructions. When the program instructions are executed by a computer device, the computer device is caused to perform the method according to any one of claims 1 to 5.

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