A multi-time scale optimization control method for day-ahead and intra-day microgrid of charging and swapping stations

Abstract

The present invention discloses a day-ahead and intraday multi-time scale optimization control method for a microgrid of a charging and swapping station. The present invention combines the day-ahead optimization scheduling with the goal of minimizing the net power purchase cost, the intraday rolling optimization scheduling with the goal of minimizing the sum of the net power purchase cost and the penalty cost of the energy storage system and the charging and swapping station power deviation of "day-ahead scheduling plan and intraday rolling scheduling plan", and the real-time feedback adjustment control with the goal of minimizing the sum of the power deviations of the distribution transformer, the energy storage system and the charging and swapping station of "intraday rolling scheduling plan and real-time feedback control", which effectively solves the problem that the fixed control mode lacks planning, flexibility and adaptability and cannot guarantee economy when parameters such as new energy and charging load fluctuate randomly and the prediction error is large.

Description

Technical Field

[0001] The present invention relates to a control method for a microgrid of a charging and swapping station, and in particular to a day-ahead and intra-day multi-time scale optimization control method for a microgrid of a charging and swapping station. Background Art

[0002] Charging and swapping stations are generally connected to the 10kV distribution network feeder through distribution transformers. At the same time, distributed photovoltaic power generation is built on the roof of the parking space and the rest room in the charging and swapping station, and distributed energy storage equipment of a certain capacity is configured to form a charging and swapping station microgrid that integrates photovoltaic storage, charging and swapping. Through the coordinated control between photovoltaic storage, charging and swapping, the overall economic efficiency of the operation of the charging and swapping station microgrid can be improved. At present, through the fixed control strategies such as photovoltaic "self-generation and self-use, surplus power to the grid", energy storage "valley charging, peak power discharge", and charging piles "charging with light", although the operating costs of charging and swapping stations can be reduced to a certain extent, these control strategies lack planning, flexibility and adaptability, and the economic efficiency cannot be guaranteed under the background of random fluctuations in parameters such as photovoltaic power generation and charging load and large prediction errors. Summary of the invention

[0003] Purpose of the invention: In view of the above problems, the present invention proposes a multi-time scale optimization control method for a microgrid of a charging and swapping station on a day-ahead and day-intraday basis, which can enable the microgrid of a charging and swapping station to adaptively generate a control strategy with the best economic performance when parameters such as new energy and charging load fluctuate randomly and the prediction error is large.

[0004] Technical solution: The technical solution adopted by the present invention is a multi-time scale optimization control method for a microgrid of a charging and swapping station on a day-ahead and day-intraday basis, comprising the following steps:

[0005] Step 1, based on the day-ahead optimization dispatching model, solving and obtaining the day-ahead dispatching plan; the day-ahead optimization dispatching model comprises: one day before the dispatching control day, according to the short-term forecast data of the next day's new energy output and the short-term forecast data of the load, with the goal of minimizing the net power purchase cost, obtaining the day-ahead dispatching plan for the next day;

[0006] Step 2, based on the intra-day rolling optimization scheduling model, solving the intra-day rolling scheduling plan; the intra-day rolling optimization scheduling model includes: on the scheduling control day, according to the ultra-short-term forecast data of new energy output and the ultra-short-term forecast data of load, with the goal of minimizing the sum of the net power purchase cost, the energy storage energy adjustment penalty cost of the day-ahead scheduling plan and the intra-day rolling scheduling plan, and the charging and swapping station power deviation penalty cost of the day-ahead scheduling plan and the intra-day rolling scheduling plan, obtaining the intra-day rolling scheduling plan within the rolling time window;

[0007] Step 3, based on the real-time feedback adjustment control model, solve the real-time control instructions; the real-time feedback adjustment control model includes: on the dispatch control day, according to the real-time monitoring data of new energy output and load power, with the goal of minimizing the sum of the power deviations of the distribution transformer, energy storage system, and charging and swapping station of the daily rolling dispatch plan and the real-time control instructions, obtain the real-time control instructions of the controllable components in the current time period.

[0008] The objective function of the day-ahead optimization scheduling model is as follows:

[0009] ,

[0010] Where: represents the objective function of the day-ahead optimization scheduling model, DT and They represent the set of distribution transformers and the set of day-ahead dispatching time periods, respectively. i , t is the loop variable, indicating the t Time period, and Respectively represent t The purchase price and sales price of electricity per unit of electricity in the time period, and Respectively represent i The distribution transformer is t The off-grid power and reverse power in the time period, The length of the interval for each scheduled time.

[0011] The objective function of the intraday rolling optimization scheduling model is as follows:

[0012] ,

[0013] Where: Represents the objective function of the intraday rolling optimization scheduling model; represents a rolling time period set, ES represents an energy storage system set, b is a loop variable, and BUS represents a bus set; represents the energy storage energy adjustment penalty cost coefficient per unit of electricity, The penalty cost coefficient for charging and swapping station power adjustment per unit power; Indicates i The capacity of the energy storage system, Indicates i The time period of the energy storage system in the daily rolling dispatch plan t The remaining energy level after the end, Indicates i The energy storage system is scheduled in the day-ahead schedule. t The remaining energy level after the end, Indicates connection to busbarb The time period of the charging and swapping station in the daily rolling scheduling plan t The amount of charge after completion, Indicates connection to busbar b The charging and swapping stations are scheduled in the day-ahead time period t The amount of charge after completion.

[0014] The objective function of the real-time feedback adjustment control model is as follows:

[0015] ,

[0016] Where: represents the objective function of the real-time feedback adjustment control model, , , , They represent the first i Distribution transformer, i Energy storage system, connected to the bus b The charging and swapping stations on the t r The actual control values ​​of the flow power, charging power, discharging power and charging power; , , , They represent the first i Distribution transformer, i Energy storage system, connected to the bus b The charging and swapping stations on the t r The planned values ​​of the flow power, charging power, discharge power and charging power, For the i The maximum active power capacity of a distribution transformer, Indicates i The maximum charge and discharge power of an energy storage system, Indicates connection to busbar b The maximum charging power of the charging and swapping station.

[0017] In the intraday rolling optimization scheduling model, the constraints on renewable energy output are as follows:

[0018] ,

[0019] Where: Indicates i A new energy system in t The new energy output in the rolling optimization model within the time period, It indicates the ultra-short-term forecast value of new energy output;

[0020] In the intraday rolling optimization model, the constraints on the charging power load of the charging piles in the charging and swapping station are as follows:

[0021] ,

[0022] Where: represents the charging power load of the charging pile connected to bus b in the intraday rolling optimization model, It represents the daily ultra-short-term forecast value of the charging power of the charging pile connected to bus b;

[0023] In the intraday rolling optimization model, the self-use load power constraints are as follows:

[0024] ,

[0025] Where: represents the self-use load power connected to bus b in the intraday rolling optimization model, It represents the intraday ultra-short-term forecast value of the self-use load power connected to bus b;

[0026] The overall operation constraints of the microgrid of the charging and swapping station are as follows:

[0027] ,

[0028] Where: For the i The distribution transformer is t Active power for a time period, and Respectively i The energy storage system is t The charging and discharging power of the time period, Indicates the charging and swapping station connected to bus bar b in the time period t The charging power, represents the set of distribution transformers connected to bus b, represents the set of photovoltaic systems connected to bus bar b, Represents the set of energy storage systems connected to bus b.

[0029] In the intraday rolling optimization cycle model, the charging and discharging state constraints of the energy storage system are:

[0030] ,

[0031] ,

[0032] Where: and Respectively represent the firsti The energy storage system is t Charging and discharging status over time periods; and They represent the first i The energy storage system is t Charge and discharge status plan for time period;

[0033] In the daily rolling optimization cycle model, the SOC constraints of the energy storage system are as follows:

[0034] ,

[0035] ,

[0036] ,

[0037] Where: Indicates i The SOC value of the energy storage system at the initial moment of the first rolling optimization cycle, Indicates i The actual SOC value of each energy storage system at the beginning of the rolling optimization cycle, , indicating the i The energy storage system is at the rth i The SOC value at the initial moment of a rolling optimization cycle, Indicates the rth i The rolling optimization cycles are the remaining rolling optimization cycles except the first rolling optimization cycle. Indicates i Energy storage systems in the last rolling optimization cycle r i-1 The actual SOC value after the first time period ends, Indicates i The energy storage system includes the last moment t end Rolling optimization cycle { r i} end The ending SOC value.

[0038] In the real-time feedback adjustment control model, the constraints on the output of renewable energy are as follows:

[0039] ,

[0040] Where: Indicates i New energy systems in the current period t r of efforts, Indicates i New energy systems in the current periodt r Real-time monitoring value of output;

[0041] In the real-time feedback adjustment control model, the charging power constraint of the charging pile in the charging and swapping station is:

[0042] ,

[0043] Where: Indicates connection to busbar b All charging piles on the current time period t r The charging power, Indicates connection to busbar b All charging piles on the current time period t r Real-time monitoring value of charging power;

[0044] In the real-time feedback adjustment control model, the power constraints of the self-use load are as follows:

[0045] ,

[0046] Where: Indicates connection to busbar b The self-use electricity load on t r The power, Indicates connection to busbar b The self-use electricity load on t r Real-time monitoring of power values.

[0047] The present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the day-ahead and intra-day multi-time scale optimization control method for a microgrid in a charging and swapping station is implemented.

[0048] The present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the multi-time scale optimization control method for a microgrid of a charging and swapping station is implemented.

[0049] The present invention provides a computer program product, including a computer program and / or instructions, which, when executed by a processor, implements the multi-time scale optimization control method for a microgrid of a charging and swapping station both day-ahead and day-intraday.

[0050] Beneficial effects: Compared with the prior art, the present invention has the following advantages: First, based on the short-term forecast data of the day's new energy output and load, the operation of the grid-connected distribution transformer, energy storage system, charging and swapping station and other components in the microgrid of the charging and swapping station, as well as constraints such as power balance are modeled respectively, and then a day-ahead optimization scheduling model for the microgrid of the charging and swapping station with the goal of minimizing the net power purchase cost is proposed; secondly, based on the ultra-short-term forecast data of the intraday new energy output and load, the operation of the components and power balance constraints in the microgrid of the charging and swapping station in each rolling optimization cycle are modeled, and then a daily rolling optimization scheduling model for the microgrid of the charging and swapping station with the goal of minimizing the sum of the net power purchase cost and the penalty cost of the energy storage system and the charging and swapping station electricity deviation of the "day-ahead scheduling plan and the intraday rolling scheduling plan" is proposed; then, based on the measured renewable energy output and load data, the microgrid of the charging and swapping station in the real-time feedback adjustment period is optimized. The component operation and power balance constraints in the power grid are modeled, and then a real-time feedback adjustment control optimization model for the charging and swapping station microgrid is proposed with the goal of minimizing the sum of power deviations of the distribution transformer, energy storage system, and charging and swapping station of "intraday rolling scheduling plan and real-time feedback control"; finally, the present invention combines the day-ahead optimization scheduling with the goal of minimizing the net power purchase cost, the intraday rolling optimization scheduling with the goal of minimizing the sum of the net power purchase cost and the penalty cost of the energy storage system and the charging and swapping station power deviation of the "day-ahead scheduling plan and intraday rolling scheduling plan", and the real-time feedback adjustment control with the goal of minimizing the sum of power deviations of the distribution transformer, energy storage system, and charging and swapping station of "intraday rolling scheduling plan and real-time feedback control", which effectively solves the problem that the fixed control mode lacks planning, flexibility and adaptability and cannot guarantee economy when parameters such as new energy and charging load fluctuate randomly and the prediction error is large. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a typical structure diagram of a microgrid in a charging and swapping station;

[0052] Figure 2 It is a multi-time scale optimization control flow chart of the microgrid of the charging and swapping station according to the present invention, day-ahead and day-intraday;

[0053] Figure 3 is the measured photovoltaic power generation curve of a microgrid of a charging and swapping station in this embodiment;

[0054] Figure 4 This is the actual measured charging pile power curve of a microgrid of a charging and swapping station in this embodiment;

[0055] Figure 5 It is the measured self-use power curve of a microgrid of a charging and swapping station in this embodiment;

[0056] Figure 6 This is a comparison chart of the simulation results of the energy storage SOC example;

[0057] Figure 7 This is a comparison chart of the simulation results of the charging power calculation example of the charging and swapping station;

[0058] Figure 8 It is a comparison chart of simulation results of grid-connected distribution transformer power calculation example. DETAILED DESCRIPTION

[0059] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0060] The typical structure of a microgrid in a charging and swapping station is as follows: Figure 1 As shown, the components mainly include grid-connected distribution transformers, photovoltaics, energy storage systems, charging piles, charging and swapping stations, and all self-use power loads including air conditioning and lighting.

[0061] The day-ahead and intraday multi-time scale optimization control method in the present invention refers to optimization control methods under three different time scales: day-ahead optimization scheduling, intraday rolling optimization scheduling and real-time feedback adjustment control. In this embodiment, the day-ahead optimization dispatch refers to solving the day-ahead optimization dispatch model with the 24-hour time window of the next day as the time range and every 15 minutes as a time period based on the day-ahead short-term forecast values ​​of the new energy output, charging load, and self-use power load on the day before the dispatch control day, and obtaining the dispatch control plan for each time period of the next day; the intraday rolling optimization dispatch refers to continuously solving the intraday rolling optimization dispatch model with the rolling time window (for example, 4 hours) as the time range and every 15 minutes as a time period based on the intraday ultra-short-term forecast values ​​of the new energy output, charging load, and self-use power load 15 minutes before the actual control on the dispatch control day, and obtaining the dispatch control plan for each time period within the future time window; the real-time feedback adjustment control refers to solving the real-time feedback adjustment control model with the real-time power monitoring values ​​of the new energy output, charging load, and self-use power load as the feedback values ​​during the actual control, and obtaining the dispatch plan adjustment amount and actual control value of the power of controllable components such as the energy storage system, charging and swapping station, and distribution transformer in the current time period.

[0062] The multi-time scale optimization control method for the microgrid of the charging and swapping station described in the present invention is as follows: Figure 2 As shown, the following steps are included:

[0063] Step 1, based on the day-ahead optimization scheduling model, solving and obtaining the day-ahead scheduling plan; the day-ahead optimization scheduling model includes: one day before the scheduling control day, according to the short-term forecast data of photovoltaic output and the short-term forecast data of load for the next day, with the goal of economic optimization, obtaining the day-ahead scheduling plan for 15 minutes every 24 hours of the next day;

[0064] Step 2, based on the intraday rolling optimization scheduling model, solving the intraday rolling scheduling plan; the intraday rolling optimization scheduling model includes: on the scheduling control day, according to the ultra-short-term forecast data of photovoltaic output and the ultra-short-term forecast data of load, with the goal of minimizing the sum of the net power purchase cost and the energy storage and charging and swapping station power deviation penalty costs of the day-ahead scheduling plan and the intraday rolling scheduling plan, obtaining the intraday rolling scheduling plan for every 15 minutes in the rolling time window;

[0065] Step 3, based on the real-time feedback adjustment control model, solve the real-time control instructions; the real-time feedback adjustment control model includes: on the scheduling control day, according to the real-time monitoring data of photovoltaic output and load power, with the goal of minimizing the sum of the power deviations of the distribution transformer, energy storage system, and charging and swapping station of the daily rolling scheduling plan and the real-time control instructions, obtain the real-time control instructions of the controllable elements in the current time period.

[0066] The following is a detailed introduction to the technical solution of the day-ahead and intraday multi-time scale optimization control method for the microgrid of the charging and swapping station, which includes day-ahead scheduling, intraday rolling optimization scheduling and real-time feedback adjustment control.

[0067] (1) Optimized scheduling of microgrids at charging and swapping stations.

[0068] Firstly, taking the typical microgrid structure of a charging and swapping station as the object, the component operation constraints and power balance constraints of the day-ahead optimal scheduling of the microgrid of the charging and swapping station are introduced.

[0069] The operation constraints of the grid-connected distribution transformer are modeled as follows:

[0070] (A1)

[0071] (A2)

[0072] (A3)

[0073] (A4)

[0074] Where: For the The distribution transformer is t Active power in the time period; t A time period refers to a period of time after discretization. For example, for the next 24 hours, each time period is 15 minutes, which corresponds to 96 time periods. and They represent the grid-connected power and reverse power of the distribution transformer respectively; and are 0 / 1 indicator variables, representing the The distribution transformer is tThe power direction in the time period is off-grid and reverse; For the The maximum active power capacity of a distribution transformer. Formula (A1~A2) indicates that at any time, the power flow direction of the distribution transformer is unique and the power outflow is positive and the reverse flow is negative; Formula (A3~A4) indicates that the power flowing through the distribution transformer does not exceed the maximum power capacity allowed by the distribution transformer.

[0075] The photovoltaic power generation power constraints are as follows:

[0076] (A5)

[0077] Where: For the The photovoltaic system in t The active output of the time period, For the The photovoltaic system in t The photovoltaic power output predicted for the time period. Formula (A5) indicates that the photovoltaic power output in the day-ahead optimization scheduling model is equal to the photovoltaic power output predicted for the day-ahead period (without considering the photovoltaic power cut-off).

[0078] The operating constraints of the energy storage system are as follows:

[0079] (A6)

[0080] (A7)

[0081] (A8)

[0082] (A9)

[0083] (A10)

[0084] (A11)

[0085] (A12)

[0086] (A13)

[0087] (A14)

[0088] (A15)

[0089] (A16)

[0090] (A17)

[0091] (A18)

[0092] (A19)

[0093] Where: and Respectively i The energy storage system is t The charging and discharging power of the time period, and Respectively i The energy storage system is t charging and discharging status of time periods (0 / 1 variables), Indicates i The maximum charge and discharge power of an energy storage system, For the i Energy storage system in time period t The remaining power level, For the i The energy storage system is t -1 remaining battery level for a time period, and Respectively i The charging and discharging efficiency of the energy storage system is Indicates i The capacity of the energy storage system, The length of time for each scheduling interval (usually 15 minutes), and Respectively i The minimum and maximum remaining power levels of the energy storage system are calculated to make Formula A9 valid for all time periods. t =1,2,…~,NT is applicable, using To indicate the i The initial remaining power level of the energy storage system, and Respectively i The final and initial power levels of an energy storage system within a dispatch cycle (usually 24 hours). Indicates i A distribution transformer connected to an energy storage system d In time period t The state indicator variable of whether the power is reversed, ES is the energy storage system set, DT i For the i A collection of distribution transformers connected to an energy storage system, and For the i Energy storage system in time period tWhether to start charging and discharging (0 / 1 variable), T Indicates the entire scheduling time range (24 hours of the next day), and is the maximum number of charging and discharging times allowed for the energy storage system within the entire dispatch time range, and Respectively i Energy storage system in time period t -1 charging and discharging state (0 / 1 variable). Formula (A6~A7) indicates that the charging and discharging power of the energy storage system does not exceed its maximum charging and discharging power; Formula (A8) indicates that the charging and discharging states of the energy storage system in the same time period are mutually exclusive; Formula (A9) indicates the energy-power balance relationship between the remaining energy and the charging and discharging power of the energy storage system; Formula (A10) indicates that the remaining power level of the energy storage system must be maintained in a certain range, such as 10%~100% of the capacity; Formula (A11) defines the remaining power level of the energy storage system at the initial moment; Formula (A12) indicates that the remaining power level of the energy storage system at the end moment is not lower than the initial power level; Formula (A 13) is the reverse power protection constraint of the energy storage system, which constrains the energy storage system from discharging when the power of the grid-connected distribution transformer is reversed; Formula (A14~A15) represents the total charging and discharging times of the energy storage system in the entire scheduling cycle, the purpose of which is to limit the frequent charging and discharging of the energy storage system; Formula (A16~A17) defines the mutually exclusive constraints of the energy storage system's start charging and start discharging states; Formula (A18) describes the logical relationship between the energy storage system's start charging logic variable and the charging state variable, and Formula (A19) describes the logical relationship between the energy storage system's start discharging logic variable and the discharge state variable.

[0094] The charging power constraints of the charging pile are as follows:

[0095] (A20)

[0096] Where: For connection to busbar b All charging stations on t Active output in the time period (aggregate value), For connection to busbar b All charging stations on t The day-ahead predicted charging power value for the time period. Formula (A20) indicates that the charging power of the charging pile in the day-ahead optimization scheduling model is equal to the day-ahead short-term predicted value.

[0097] The charging constraints of the charging and swapping stations are as follows:

[0098] (A21)

[0099] (A22)

[0100] (A23)

[0101] (A24)

[0102] Where: Indicates the charging and swapping station connected to bus bar b in the time period t The charging power, Indicates the maximum charging power of the charging and swapping station connected to bus b, Indicates the charging and swapping station connected to bus bar b in the time period t The charged capacity, Indicates that the charging and swapping station connected to bus bar b is at time t -1 of the charged amount, represents the charging efficiency of the charging and swapping station connected to bus bar b, represents the amount of charge at the charging and swapping station connected to bus bar b at the initial moment, Indicates the amount of charge at the charging and swapping station connected to bus bar b at the end time, It indicates the minimum charging capacity that the charging and swapping station connected to bus b must meet. Formula (A21) indicates that the charging power of the charging and swapping station does not exceed the maximum charging power; Formula (A22) indicates the energy-power balance relationship between the charged capacity and the charging power of the charging and swapping station; Formula (A23) indicates that the charged capacity of the charging and swapping station at the initial moment of the entire scheduling cycle is 0; Formula (A24) indicates that the charged capacity of the charging and swapping station at the end of the scheduling cycle is not less than the minimum charging capacity.

[0103] The power constraints of self-use electricity load are as follows:

[0104] (A25)

[0105] Where: Indicates that the self-use load connected to busbar b is t Active power for a time period, is the day-ahead forecast value of the self-use load power connected to bus b. Formula (A25) indicates that the self-use load power in the day-ahead optimization dispatching model is equal to the day-ahead short-term forecast value.

[0106] The overall operation constraints of the microgrid of the charging and swapping station are as follows:

[0107] (A26)

[0108] Where: DT represents the grid-connected distribution transformer set, PV represents the photovoltaic set, ES represents the energy storage system set, and index b represents bus b. Formula (A26) indicates that power generation and power consumption should maintain power balance at any time, that is, the power balance constraint condition. For the i The distribution transformer is t Active power for a time period, and Respectively i The energy storage system is t The charging and discharging power of the time period, Indicates the charging and swapping station connected to bus bar b in the time period t The charging power, represents the set of distribution transformers connected to bus b, represents the set of photovoltaic systems connected to bus bar b, Represents the set of energy storage systems connected to bus b.

[0109] The goal of the day-ahead dispatch of the microgrid of the charging and swapping station is to minimize the net electricity purchase cost, which can be expressed by the following formula:

[0110] (1)

[0111] Where: represents the total cost of day-ahead scheduling optimization, DT and They represent the set of grid-connected distribution transformers and the set of day-ahead dispatching time periods, respectively. and They represent the purchase price and sales price of electricity per unit of electricity. Formula (1) indicates that the total cost of day-ahead dispatch, i.e., the net purchase cost, is equal to the difference between the purchase cost and the sales revenue.

[0112] The overall optimization model of the day-ahead optimal dispatch of the microgrid of the charging and swapping station is shown in (X1):

[0113] (X1)

[0114] (2) Daily rolling optimization scheduling of microgrids at charging and swapping stations.

[0115] Intraday rolling time period ( , Roll ={ r 1, r 2,…, r i ,… r n},in r 1, r 2,…, r i ,…r n Respectively represent the 1st~ n The constraints of the grid-connected distribution transformers are consistent with formulas (A1~A4).

[0116] During the intraday rolling time period, the photovoltaic output is equal to the ultra-short-term output forecast value, and the constraints are as follows:

[0117] (B1)

[0118] Where: Indicates The photovoltaic system in t The photovoltaic output in the rolling optimization model within the time period, Indicates the ultra-short-term photovoltaic output forecast value.

[0119] In the intraday rolling optimization cycle model, the charging and discharging status of the energy storage system is consistent with the day-ahead dispatch plan, with the following constraints:

[0120] (B2)

[0121] (B3)

[0122] Where: and Respectively represent the first i The energy storage system is t Charging and discharging status over time periods; and They represent the first i The energy storage system is t The charging and discharging status plan for the time period.

[0123] The SOC value of the energy storage system at the initial moment of the first rolling optimization cycle is equal to the initial SOC value of the energy storage system. The SOC value at the initial moment of the remaining rolling optimization cycles is equal to the actual SOC value after the end of the previous time period (the first 15 minutes). The SOC value at the end of the rolling optimization cycle including the last moment is not less than the initial SOC value. The constraints are as follows:

[0124] (B4)

[0125] (B5)

[0126] (B6)

[0127] Where: Indicates iThe SOC value of the energy storage system at the initial moment of the first rolling optimization cycle, Indicates i The actual SOC value of each energy storage system at the beginning of the rolling optimization cycle, , indicating the i The energy storage system is at the rth i The SOC value at the initial moment of a rolling optimization cycle, Indicates the rth i The rolling optimization cycles are the remaining rolling optimization cycles except the first rolling optimization cycle. Indicates i Energy storage systems in the last rolling optimization cycle r i-1 The actual SOC value after the first time period ends, Indicates i The energy storage system includes the last moment t end Rolling optimization cycle { r i} end The ending SOC value.

[0128] In addition, the energy storage system has a rolling time cycle The maximum charge and discharge power constraints (A6~A7), power energy balance equation (A9), energy level constraints (A10), and energy storage reverse power protection constraints (A13) must still be met.

[0129] In the intraday rolling optimization model, the charging power load of the charging pile is equal to the ultra-short-term charging power forecast value, and the constraints are as follows:

[0130] (B7)

[0131] Where: represents the charging power load of the charging pile connected to bus b in the intraday rolling optimization model, It represents the daily ultra-short-term forecast value of the charging power of the charging pile connected to bus b.

[0132] In the intraday rolling optimization model, the charging power of the charging and swapping station still needs to satisfy constraint (A21), and the power-energy balance equation between the charging power and the charged amount still needs to satisfy constraint (A22), but the time range becomes The charged capacity of the charging and swapping station is equal to 0 at the initial moment of the first rolling optimization cycle, and equal to the actual charged capacity at the end of the previous time period (the first 15 minutes) at the initial moment of each subsequent rolling optimization cycle. The constraints are as follows:

[0133] (B8)

[0134] (B9)

[0135] Where: represents the charged capacity of the charging and swapping station connected to bus b at the initial moment of the first rolling optimization cycle, Indicates that the charging and swapping station connected to bus b is in the rolling optimization cycle r i The initial charge level, Indicates that the charging and swapping station connected to bus b in the last rolling optimization cycle r i-1 The actual charge level after the first time period ends.

[0136] The charging capacity of the charging and swapping station after the last time period is not less than the minimum charging capacity, subject to the following constraints:

[0137] (B10)

[0138] Where: Indicates that the last moment is included t end Rolling optimization cycle { r i} end The amount of charge at the charging and swapping station connected to bus b at the end time.

[0139] In the intraday rolling optimization model, the self-use load power is equal to its ultra-short-term forecast value, and the constraints are as follows:

[0140] (B11)

[0141] Where: represents the self-use load power connected to bus b in the intraday rolling optimization model, It represents the intraday ultra-short-term forecast value of the self-use load power connected to bus b.

[0142] The microgrid of the charging and swapping station must satisfy the power balance constraint (A26) at any time, so any time period in the intraday rolling optimization model , the power balance constraint (A26) still needs to be satisfied.

[0143] The purpose of intraday rolling optimization is to formulate an intraday dispatch plan. The objective function of intraday rolling optimization dispatch is to minimize the sum of the net power purchase cost, photovoltaic reverse transmission penalty cost, and energy storage energy adjustment penalty cost within the rolling period:

[0144] (2)

[0145] Where: Represents the objective function of the intraday rolling optimization scheduling model; represents a rolling time period set, ES represents an energy storage system set, b is a loop variable, and BUS represents a bus set; represents the energy storage energy adjustment penalty cost coefficient per unit of electricity, The penalty cost coefficient for charging and swapping station power adjustment per unit power; Indicates i The capacity of the energy storage system, Indicates i The time period of the energy storage system in the daily rolling dispatch plan t The remaining energy level after the end, Indicates i The energy storage system is scheduled in the day-ahead schedule. t The remaining energy level after the end, Indicates connection to busbar b The time period of the charging and swapping station in the daily rolling scheduling plan t The amount of charge after completion, Indicates connection to busbar b The charging and swapping stations are scheduled in the day-ahead time period t The three terms on the right side of formula (2) represent the net electricity purchase cost, energy storage energy adjustment penalty cost, and charging and swapping station power adjustment penalty cost during the rolling time period.

[0146] The overall optimization model of the daily rolling optimization scheduling of the microgrid of the charging and swapping station is shown in (X2):

[0147] (X2)

[0148] (3) Real-time feedback adjustment and control of the microgrid of the charging and swapping station.

[0149] The real-time feedback adjustment control of the microgrid of the charging and swapping station is based on the real-time monitoring of renewable energy output and load power fluctuation information to adjust and control the current time period (using t r The time range of real-time feedback control is 24 hours a day, which is expressed as T RT ={1,2,…, t end}; The time interval of real-time feedback control is ∆ t r to express (for example, 1 minute / 5 minutes).

[0150] Adjust the current time period of control in real-time feedback , the distribution transformer still needs to meet the operating constraints (A1~A4).

[0151] Adjust the current time period of control in real-time feedback , the photovoltaic output is equal to its real-time monitoring value, and the constraints are as follows:

[0152] (C1)

[0153] Where: Indicates i PV systems in the current time period t r of efforts, Indicates i PV systems in the current time period t r Real-time monitoring value of output.

[0154] Adjust the current time period of control in real-time feedback , the charging and discharging status of the energy storage system is consistent with the scheduling plan of the day before / intraday, and the constraints are as follows:

[0155] (C2)

[0156] (C3)

[0157] Where: and They represent the first i Energy storage system in the current time period t r The charging and discharging status; and They represent the first i Energy storage system in the current time period t r The planned value of the charge and discharge status in the time period.

[0158] In real-time feedback control, the energy storage system is in the initial period ( t r =1) is equal to the initial SOC value of the energy storage system in the entire scheduling period (24 hours). The energy storage system is t r ≥2) is equal to the initial SOC value of the previous time period (the first 5 minutes / the first 1 minute, depending on the time interval ∆ of the real-time feedback control t r ) after the end of the actual SOC value, the energy storage system in the last time period ( t r =t end ) after the actual SOC value is not less than the initial SOC value, and the constraints are as follows:

[0159] (C4)

[0160] (C5)

[0161] (C6)

[0162] Where: Indicates i Energy storage system in the current time period t r The initial remaining power level, Indicates i Energy storage system in the current time period t r Last time period t r -1 The actual remaining power level after the end, Indicates i Energy storage system in the current time period t r The remaining power level after the end, t end Represents the last time period of real-time feedback control.

[0163] In addition, the energy storage system still needs to meet the maximum charge and discharge power constraint (C7), power-energy balance constraint (C8) and energy level constraint (C9) in real-time feedback adjustment control, as shown below:

[0164] (C7)

[0165] (C8)

[0166] (C9)

[0167] (C10)

[0168] Where: and They represent the first i The energy storage system is in the time period t r The charging and discharging power, and They represent the first i The energy storage system is in the time period t rThe charging and discharging status (0 / 1 variables), Indicates i The maximum charge and discharge power of an energy storage system, For the i The energy storage system is in the time period t r The remaining power level, For the i The energy storage system is in the time period t r The initial SOC value, and Respectively i The charging and discharging efficiency of the energy storage system is Indicates i The capacity of the energy storage system, is the time interval for real-time feedback control (usually 5 minutes / 1 minute), and Respectively i The minimum and maximum remaining charge levels of an energy storage system.

[0169] In real-time feedback control, the charging power of the charging pile is equal to its real-time monitoring value:

[0170] (C11)

[0171] Where: Indicates connection to busbar b All charging piles on the current time period t r The charging power, Indicates connection to busbar b All charging piles on the current time period t r Real-time monitoring value of charging power.

[0172] In real-time feedback control, the charging and swapping station is in the initial time period (t r =1) is equal to the initial power of the charging and swapping station in the entire scheduling period (24 hours) 0, and the charging and swapping station in the remaining time period (t r ≥2) is equal to the initial power of the previous time period (the first 5 minutes / the first 1 minute, depending on the time interval of real-time feedback control ) after the end of the actual charge amount, the charging and swapping station in the last time period ( ) after the actual charged capacity is not less than the minimum charging capacity of the charging and swapping station. The constraints are as follows:

[0173] (C12)

[0174] (C13)

[0175] (C14)

[0176] Where: Indicates connection to busbar b The charging and swapping stations on Initial charge capacity, Indicates connection to busbar b The charging and swapping stations on Last time period t r -1 The amount of charge after completion, Indicates connection to busbar b The charging and swapping stations on The amount of charge after completion.

[0177] In addition, the charging and swapping station still needs to meet the maximum charging power constraint (C15) and power-energy balance constraint (C16) in real-time feedback adjustment control, as shown below:

[0178] (C15)

[0179] (C16)

[0180] Where: Indicates connection to busbar b The charging and swapping station on the r The charging power, Indicates charging and swapping stations b The maximum charging power, Indicates connection to busbar b The charging and swapping stations on the t The charged capacity, Indicates connection to busbar b The charging and swapping stations on t -1 of the charged amount, Indicates the charging efficiency of the charging and swapping station, Indicates connection to busbar b The amount of charge at the charging and swapping station at the initial moment, Indicates connection to busbar b The amount of charge at the charging and swapping station at the end time, Indicates connection to busbar b The minimum charging capacity that the charging and swapping stations must meet during the entire scheduling cycle.

[0181] In real-time feedback control, the self-use load power is the real-time monitoring value, and the constraints are as follows:

[0182] (C17)

[0183] In the formula: In the formula: Indicates connection to busbar b The self-use electricity load on t r The power, Indicates connection to busbar b The self-use electricity load on t r Real-time monitoring of power values.

[0184] The overall operation of the microgrid of the charging and swapping station must meet power balance at any time. Therefore, in real-time feedback control, the power balance constraint (A26) must still be met.

[0185] The goal of the real-time feedback adjustment control of the microgrid of the charging and swapping station is to minimize the total deviation of the real-time control power of the distribution transformer, energy storage system, and charging and swapping station compared with the daily rolling scheduling plan power, which is expressed as follows:

[0186] (3)

[0187] Where: represents the objective function of the real-time feedback adjustment control of the microgrid of the charging and swapping station (i.e., the total power deviation), , / , They represent the first i Distribution transformer, i Energy storage system, connected to the bus b The charging and swapping stations on the The actual control value of the flow power, charge / discharge power and charging power; , / , They represent the first i Distribution transformer, i Energy storage system, connected to the bus b The charging and swapping stations on The flow power, charge / discharge power, and charging power plan value, For the i The maximum active power capacity of a distribution transformer, Indicates i The maximum charge and discharge power of an energy storage system, Indicates connection to busbar b The maximum charging power of the charging and swapping station.

[0188] The real-time feedback adjustment control optimization model of the charging and swapping station microgrid is shown in (X3):

[0189] (X3)

[0190] (4) Multi-time scale control method for microgrid of charging and swapping station on day-ahead and day-intraday basis.

[0191] Based on the day-ahead optimization dispatch model (X1), the intraday rolling optimization dispatch model (X2), and the real-time feedback adjustment control optimization model (X3) of the microgrid of the charging and swapping station, a day-ahead and intraday multi-time scale control method for the microgrid of the charging and swapping station is proposed. The specific process is as follows: Figure 2 shown.

[0192] exist Figure 2 In the forecast, artificial intelligence algorithms such as long short-term memory network (LSTM), support vector machine (SVM), random forest regression (RF) and other artificial intelligence algorithms are used to predict new energy (distributed photovoltaic output) and load (charging piles, self-use power) on a day-ahead and day-ahead basis. Through deep learning and mining of historical data, sample data training is used to continuously improve the prediction model, and external parameters such as irradiance, temperature, and price are combined to achieve accurate prediction of new energy and load.

[0193] exist Figure 2 In the figure, the optimization model (X1) is a mixed integer linear optimization model (MILP), which can be solved by algorithms such as branch and bound method, cutting plane method, Benders decomposition method, DW decomposition method, etc.; the optimization models (X2) and (X3) are linear optimization models (LP), which can be solved by the simplex method; these optimization solving algorithms are integrated in general solvers (such as Cplex and Gurobi), and the global optimal solution of the optimization model (X1~X3) can be easily obtained by modeling in programming languages ​​such as C++, Java, Python, Matlab, etc. and calling the solver.

[0194] The present invention combines the day-ahead optimization scheduling-intraday rolling optimization scheduling-real-time feedback adjustment control" of the day-ahead-intraday multi-time scale control process and implementation method. Through this method, the problem that the fixed control mode lacks planning, flexibility and adaptability, and the economy cannot be guaranteed when parameters such as new energy and charging load fluctuate randomly and the prediction error is large is solved.

[0195] Simulation example:

[0196] The effect of this method is demonstrated through simulation examples. Based on a real charging and swapping station microgrid, the topology and component configuration are as follows: Figure 1As shown in the figure, the specific parameters are: the maximum charging and discharging power of the energy storage system is 100kW, the capacity is 215kWh, the charging and discharging efficiency is 90%, the SOC value range is 0.1~1, the initial SOC value is 0.1, and the maximum charging and discharging times within the scheduling cycle (24 hours) are 2; the maximum charging power of the charging and swapping station is 320kW, the charging efficiency is 90%, and the minimum charging capacity is 300kWh; the capacity of the grid-connected distribution transformer is 2500kW; the real-time monitoring prediction curves of the power of photovoltaic, charging piles and self-use power loads are as follows Figure 3 , Figure 4 , Figure 5 As shown in the figure, the day-ahead / intraday forecast values ​​of photovoltaic, charging pile and self-use electricity load are randomly generated, and the random error ranges are ±10% / ±5%, ±15% / ±5%, and ±10% / ±5%, respectively; the purchase price is the industrial and commercial agency price, which is 1.1566 yuan / kWh in the peak period (8:00~11:00 and 17:00~22:00), 0.2815 yuan / kWh in the valley period (0:00~8:00), and 0.6726 yuan / kWh in the flat period (11:00~17:00 and 22:00~24:00); the electricity sales price is the photovoltaic surplus power grid price, that is, 0.391 yuan / kWh; the unit penalty cost in formula (2) is and The average charge is 0.1 yuan / kWh.

[0197] Design comparison examples: Example 1 and Example 2. Example 1 uses a fixed control strategy, specifically: the energy storage system is charged and discharged twice a day, and the two discharges are respectively in two peak electricity price time periods, one of the two charges is in the night electricity price valley period, and the other is in the daytime electricity price flat period. The charging and discharging are performed at the maximum charging and discharging power until the SOC reaches the maximum / minimum. The charging and swapping station starts charging at the maximum charging power at the night electricity price valley until it is fully charged (that is, the minimum charging power is met). Example 2 uses the day-ahead-intraday multi-time scale control method proposed in this application. The control results obtained by the two methods are compared as follows.

[0198] The net electricity purchase costs of Examples 1 and 2 are 3308.1 yuan and 3294.7 yuan respectively, which shows that the multi-time scale control method proposed in this application saves the operating cost of the charging and swapping station compared with the fixed control strategy. Analyze the specific reasons, such as Figure 6 , Figure 7 and Figure 8As shown in the figure, the two control strategies have different energy storage system charging and discharging powers and charging and swapping station charging powers (the main difference is energy storage). The day-ahead-day multi-time scale control method proposed in this application adjusts the off-grid power of the distribution transformer by flexibly controlling the energy storage system charging and discharging power, so that the maximum off-grid power and load rate of the distribution transformer are reduced, and the photovoltaic reverse transmission during the peak charging load in the morning is reduced, which generally reduces the net electricity purchase cost. The calculation results verify the effectiveness of the control method proposed in this invention.

[0199] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned day-ahead and day-intraday multi-time-scale optimization control method for a microgrid of a charging and swapping station when executing the computer program.

[0200] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned day-ahead and intra-day multi-time-scale optimization control method of a microgrid of a charging and swapping station is implemented.

[0201] In one embodiment, a computer program product is provided, including a computer program / instruction, which, when executed by a processor, implements the day-ahead and intra-day multi-time-scale optimization control method for a microgrid of a charging and swapping station.

[0202] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0203] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0204] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0205] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

Claims

1. A multi-time scale optimization control method for a microgrid of a charging and swapping station, characterized in that: The following steps are involved: Step 1: Based on the day-ahead optimization scheduling model, a day-ahead scheduling plan is obtained; The day-ahead optimization dispatch model includes: one day before the dispatch control day, according to the short-term forecast data of new energy output and load for the next day, with the goal of minimizing the net power purchase cost, obtaining the day-ahead dispatch plan for the next day; Step 2, based on the intra-day rolling optimization scheduling model, solving the intra-day rolling scheduling plan; the intra-day rolling optimization scheduling model includes: on the scheduling control day, according to the ultra-short-term forecast data of new energy output and the ultra-short-term forecast data of load, with the goal of minimizing the sum of the net power purchase cost, the energy storage energy adjustment penalty cost of the day-ahead scheduling plan and the intra-day rolling scheduling plan, and the charging and swapping station power deviation penalty cost of the day-ahead scheduling plan and the intra-day rolling scheduling plan, obtaining the intra-day rolling scheduling plan within the rolling time window; Step 3, based on the real-time feedback adjustment control model, solve and obtain the real-time control instructions; the real-time feedback adjustment control model includes: on the day of the dispatch control, according to the real-time monitoring data of the output of new energy and the load power, with the goal of minimizing the sum of the power deviations of the distribution transformer, energy storage system, and charging and swapping station of the daily rolling dispatch plan and the real-time control instructions, obtain the real-time control instructions of the controllable components in the current time period; The objective function of the day-ahead optimization scheduling model is as follows: ， Where: represents the objective function of the day-ahead optimization scheduling model, DT and They represent the set of distribution transformers and the set of day-ahead dispatching time periods, respectively. i , t is the loop variable, indicating the t Time period, and Respectively represent t The purchase price and sales price of electricity per unit of electricity in the time period, and Respectively represent i The distribution transformer is t The off-grid power and reverse power in the time period, The length of the interval for each scheduling time; The objective function of the intraday rolling optimization scheduling model is as follows: ， Where: Represents the objective function of the intraday rolling optimization scheduling model; represents a rolling time period set, ES represents an energy storage system set, b is a loop variable, and BUS represents a bus set; represents the energy storage energy adjustment penalty cost coefficient per unit of electricity, The penalty cost coefficient for charging and swapping station power adjustment per unit power; Indicates i The capacity of the energy storage system, Indicates i The time period of the energy storage system in the daily rolling dispatch plan t The remaining energy level after the end, Indicates i The energy storage system is scheduled in the day-ahead schedule. t The remaining energy level after the end, Indicates connection to busbar b The time period of the charging and swapping station in the daily rolling scheduling plan t The amount of charge after completion, Indicates connection to busbar b The charging and swapping stations are scheduled in the day-ahead time period t The amount of charge after completion; The objective function of the real-time feedback adjustment control model is as follows: ， Where: represents the objective function of the real-time feedback adjustment control model, Indicates the real-time feedback adjustment control i Distribution transformers in the time period t r The power flowing through Indicates the real-time feedback adjustment control i Energy storage system in time period t r The charging power, Indicates the real-time feedback adjustment control i Energy storage system in time period t r The discharge power, Indicates that the bus is connected in real-time feedback adjustment control b The charging and swapping stations on the t r The actual control value of the charging power; Indicates the number of times the intraday rolling optimization schedule is i Distribution transformers in the time period t r The power flowing through Indicates the number of times the intraday rolling optimization schedule is i Energy storage system in time period t r The charging power Indicates the number of times the intraday rolling optimization schedule is i Energy storage system in time period t r The discharge power, Indicates that the bus is connected in the daily rolling optimization scheduling b The charging and swapping stations on the t r The planned value of charging power, For the i The maximum active power capacity of a distribution transformer, Indicates i The maximum charge and discharge power of an energy storage system, Indicates connection to busbar b The maximum charging power of the charging and swapping station; In the intraday rolling optimization scheduling model, the constraints on renewable energy output are as follows: ， Where: Indicates i A new energy system in t The new energy output in the rolling optimization model within the time period, It indicates the ultra-short-term forecast value of new energy output; In the intraday rolling optimization model, the constraints on the charging power load of the charging piles in the charging and swapping station are as follows: ， Where: represents the charging power load of the charging pile connected to bus b in the intraday rolling optimization model, It represents the daily ultra-short-term forecast value of the charging power of the charging pile connected to bus b; In the intraday rolling optimization model, the self-use load power constraints are as follows: ， Where: represents the self-use load power connected to bus b in the intraday rolling optimization model, It represents the intraday ultra-short-term forecast value of the self-use load power connected to bus b; In the real-time feedback adjustment control model, the constraints on the output of renewable energy are as follows: ， Where: Indicates i New energy systems in the current period t r of effort, Indicates i New energy systems in the current period t r Real-time monitoring value of output; In the real-time feedback adjustment control model, the charging power constraint of the charging pile in the charging and swapping station is: ， Where: Indicates connection to busbar b All charging piles on the current time period t r The charging power, Indicates connection to busbar b All charging piles on the current time period t r Real-time monitoring value of charging power; In the real-time feedback adjustment control model, the power constraints of the self-use load are as follows: ， Where: Indicates connection to busbar b The self-use electricity load on t r The power, Indicates connection to busbar b The self-use electricity load on t r Real-time monitoring of power values.

2. The multi-time scale optimization control method for the day-ahead and intra-day microgrid of the charging and swapping station according to claim 1 is characterized by: The overall operation constraints of the microgrid of the charging and swapping station are as follows: ， Where: For the i The distribution transformer is t Active power in a time period, and Respectively i The energy storage system is t The charging and discharging power of the time period, Indicates the charging and swapping station connected to bus bar b in the time period t The charging power, represents the set of distribution transformers connected to bus b, represents the set of photovoltaic systems connected to bus bar b, Represents the set of energy storage systems connected to bus b.

3. The day-ahead and intra-day multi-time scale optimization control method for a microgrid in a charging and swapping station according to claim 1 is characterized in that: In the intraday rolling optimization cycle model, the charging and discharging state constraints of the energy storage system are: ， ， Where: and Respectively represent the first i The energy storage system is t Charging and discharging status over time periods; and They represent the first i The energy storage system is t Charge and discharge status plan for time period; In the daily rolling optimization cycle model, the SOC constraints of the energy storage system are as follows: ， ， ， Where: Indicates i The SOC value of the energy storage system at the initial moment of the first rolling optimization cycle, Indicates i The actual SOC value of each energy storage system at the beginning of the rolling optimization cycle, , indicating the i The energy storage system is at the rth i The SOC value at the initial moment of a rolling optimization cycle, Indicates the rth i The rolling optimization cycles are the remaining rolling optimization cycles except the first rolling optimization cycle. Indicates i Energy storage systems in the last rolling optimization cycle r i-1 The actual SOC value after the first time period ends, Indicates i The energy storage system includes the last moment t end Rolling optimization cycle { r i } end The ending SOC value.

4. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the day-ahead and day-intraday multi-time-scale optimization control method for a microgrid in a charging and swapping station described in any one of claims 1 to 3 is implemented.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the day-ahead and day-intraday multi-time-scale optimization control method for a microgrid in a charging and swapping station according to any one of claims 1 to 3 is implemented.

6. A computer program product comprising a computer program and / or instructions, characterized in that: When the computer program and / or instructions are executed by the processor, the day-ahead and day-intraday multi-time-scale optimization control method for the microgrid of the charging and swapping station described in any one of claims 1 to 3 is implemented.

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