Day-ahead and intra-day multi-time-scale optimal control method for charging and battery swapping station microgrid

Abstract

Disclosed in the present invention is a day-ahead and intra-day multi-time-scale optimal control method for a charging and battery swapping station microgrid. The present invention combines day-ahead optimal scheduling aiming at the minimum net electricity purchase cost, intra-day rolling optimal scheduling aiming at the minimum sum of the net electricity purchase cost and energy storage and battery swapping station power deviation penalty costs of "a day-ahead scheduling plan and an intra-day rolling scheduling plan", and real-time feedback regulation and control aiming at the minimum sum of distribution transformer, energy storage and battery swapping station power deviations of "the intra-day rolling scheduling plan and real-time feedback control", thereby effectively solving the problem that under the conditions of random fluctuations in parameters such as new energy and charging load and large prediction errors, a fixed control mode lacks planning ability, flexibility, and adaptability, and the economic efficiency cannot be guaranteed.

Description

A multi-timescale optimization control method for day-ahead and intraday charging and swapping station microgrids Technical Field

[0001] This invention relates to a control method for a charging and swapping station microgrid, and more particularly to a day-to-day multi-timescale optimization control method for a charging and swapping station microgrid. Background Technology

[0002] Charging and battery swapping stations are typically connected to the 10kV distribution network feeder via a distribution transformer. Distributed photovoltaic (PV) power generation is constructed within the station using the rooftops of parking spaces and rest areas, along with a certain capacity of distributed energy storage equipment, forming an integrated microgrid of PV, energy storage, charging, and battery swapping. Through coordinated control of these components, the overall economic efficiency of the microgrid operation can be improved. Currently, fixed control strategies such as "self-consumption of PV power with surplus power fed into the grid," "charging during off-peak hours and discharging during peak hours" for energy storage, and "charging with the solar load" for charging piles can reduce operating costs to some extent. However, given the random fluctuations and large prediction errors in parameters such as PV power generation and charging load, these strategies lack planning, flexibility, and adaptability, and their economic viability cannot be guaranteed. Summary of the Invention

[0003] Purpose of the invention: To address the above problems, this invention proposes a day-to-day multi-timescale optimization control method for charging and swapping station microgrids. This method enables the charging and swapping station microgrid to adaptively generate the most economically efficient control strategy under conditions of random fluctuations in parameters such as renewable energy and charging load, and large prediction errors.

[0004] Technical Solution: The technical solution adopted in this invention is a day-to-day multi-timescale optimization control method for microgrids in charging and swapping stations, comprising the following steps:

[0005] Step 1: Based on the day-ahead optimization scheduling model, the day-ahead scheduling plan is obtained. The day-ahead optimization scheduling model includes: on the day before the scheduling control date, based on the short-term forecast data of new energy output and the short-term forecast data of load for the next day, with the goal of minimizing the net electricity purchase cost, the day-ahead scheduling plan for the next day is obtained.

[0006] Step 2: Based on the intraday rolling optimization scheduling model, the intraday rolling scheduling plan is obtained. The intraday rolling optimization scheduling model includes: on the scheduling control day, based on the ultra-short-term forecast data of new energy output and the ultra-short-term forecast data of load, the intraday rolling scheduling plan is obtained within the rolling time window with the goal of minimizing the sum of net electricity purchase cost, the energy storage energy adjustment penalty cost of the day-ahead scheduling plan and the intraday rolling scheduling plan, and the battery swapping station power deviation penalty cost of the day-ahead scheduling plan and the intraday rolling scheduling plan.

[0007] Step 3: Based on the real-time feedback adjustment control model, solve for the real-time control command; the real-time feedback adjustment control model includes: on the scheduling control day, based on the real-time monitoring data of new energy output and load power, with the goal of minimizing the sum of the power deviations of distribution transformers, energy storage, and battery swapping stations between the daily rolling scheduling plan and the real-time control command, obtaining the real-time control command for the controllable components in the current time period.

[0008] The objective function for optimizing the scheduling model is as follows:

[0009] In the formula: C DA Let DT and T represent the objective function of the current optimization scheduling model. DA Let i and t represent the set of distribution transformers and the set of day-ahead scheduling time periods, respectively, where i and t are loop variables, representing the t-th time period. and Let these represent the purchase price and the sales price of electricity per unit of electricity in time period t, respectively. and Let represent the power output to the grid and the power transmitted back to the grid for the i-th distribution transformer in time period t, respectively, and Δt be the interval length of each dispatch time.

[0010] The objective function of the intraday rolling optimization scheduling model is as follows:

[0011] In the formula: C Roll T represents the objective function of the intraday rolling optimization scheduling model; Roll μ represents the rolling time period set, ES represents the energy storage set, b is the loop variable, and BUS represents the bus set; ESdev The energy storage adjustment penalty cost coefficient per unit of electricity, μ Swapdev The battery swapping station power adjustment penalty cost coefficient represents the unit power consumption. This represents the capacity of the i-th energy storage system. This represents the remaining energy level of the i-th energy storage system after time period t ends in the daily rolling dispatch plan. This represents the remaining energy level of the i-th energy storage system after the end of time period t in the day-ahead scheduling plan. This represents the amount of battery swapping station connected to bus b at the end of time period t in the daily rolling scheduling plan. This indicates the amount of charge already received by the battery swapping station connected to bus b at the end of time period t in the day-ahead scheduling plan.

[0012] The objective function of the real-time feedback adjustment control model is as follows:

[0013] Where: ΔP devThis represents the objective function of the real-time feedback adjustment control model. These represent the i-th distribution transformer, the i-th energy storage system, and the battery swapping station connected to bus b in the real-time feedback adjustment control during time period t. r Actual control values ​​of power flowing through, charging / discharging power, and charging power; These represent the i-th distribution transformer, the i-th energy storage system, and the battery swapping station connected to bus b in the current time period t during the intraday rolling optimization scheduling. r The planned values ​​for power flow, charging / discharging power, and charging power. Let i be the maximum active power capacity of the i-th distribution transformer. This represents the maximum charge and discharge power of the i-th energy storage system. This indicates the maximum charging power of the battery swapping station connected to bus b.

[0014] In the intraday rolling optimization scheduling model, the constraints on the output of new energy sources are as follows:

[0015] In the formula: This represents the renewable energy output of the i-th renewable energy system in the daily rolling optimization model during the t-th time period. This indicates the very short-term forecast value for new energy output;

[0016] In the intraday rolling optimization model, the constraints on the charging power load of the charging piles are as follows:

[0017] In the formula: This represents the charging power load of the charging piles connected to bus b in the intraday rolling optimization model. This represents the intraday ultra-short-term forecast value of the charging power of the charging piles connected to bus b;

[0018] The power constraints for self-consumed electrical loads are as follows:

[0019] In the formula: This represents the self-consumed electrical load power connected to bus b in the intraday rolling optimization model. This represents the intraday ultra-short-term forecast of the self-consumed electrical load power connected to bus b;

[0020] The overall operational constraints of the microgrid at the charging and battery swapping station are as follows:

[0021] In the formula: Let be the active power of the i-th distribution transformer in time period t. and Let be the charging and discharging power of the i-th energy storage system in time period t, respectively. Let DT∩{b} represent the charging power of the battery swapping station connected to bus b during time period t, DT∩{b} represent the set of distribution transformers connected to bus b, PV∩{b} represent the set of photovoltaic systems connected to bus b, and ES∩{b} represent the set of energy storage systems connected to bus b.

[0022] Within the intraday rolling optimization cycle model, the charge / discharge state constraints for energy storage are:

[0023] In the formula: and These represent the charging and discharging states of the i-th energy storage unit in the t-th time period within the rolling time cycle, respectively. and These represent the planned charging and discharging states of the i-th energy storage unit in the day-ahead scheduling plan during time period t;

[0024] The SOC constraints of the energy storage system are as follows:

[0025] In the formula: This represents the State of Charge (SOC) of the i-th energy storage system at the initial moment of the first rolling optimization cycle. This represents the actual SOC value of the i-th energy storage system at the start of the rolling optimization cycle. r i ∈Roll\{r1} represents the SOC of the i-th energy storage system at the initial moment of the remaining rolling optimization cycles. This indicates that the i-th energy storage system was in the previous rolling optimization cycle r. i-1 The actual SOC after the first time period ends This indicates that the i-th energy storage system is at time t (including the last moment). end Rolling optimization cycle {r i} end The end of SOC.

[0026] In the real-time feedback adjustment control model, the constraints on the output of new energy sources are as follows:

[0027] In the formula: This indicates that the i-th new energy system is in the current time period t. r of efforts, This indicates that the i-th new energy system is in the current time period t. r Real-time output monitoring value;

[0028] In the real-time feedback adjustment control model, the charging power constraint condition for the charging pile is:

[0029] In the formula: This indicates that all charging piles connected to bus b are in the current time period t. r The charging power, This indicates that all charging piles connected to bus b are in the current time period t. r Real-time monitoring value of charging power;

[0030] In the real-time feedback adjustment control model, the power constraints of the self-consumed electrical load are as follows:

[0031] In the formula: This indicates the self-owned electrical load connected to bus b at time t. r Real-time monitoring power values.

[0032] 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, it implements the day-to-day multi-timescale optimization control method for the charging and swapping station microgrid.

[0033] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the day-ahead-day multi-timescale optimization control method for the microgrid of the charging and swapping station.

[0034] This invention provides a computer program product, including a computer program and / or instructions, which, when executed by a processor, implement the day-to-day multi-timescale optimization control method for the microgrid of the charging and swapping station.

[0035] Beneficial Effects: Compared with existing technologies, this invention has the following advantages: First, based on day-ahead renewable energy output and short-term load forecast data, it models the operation of components such as grid-connected distribution transformers, energy storage, and battery swapping stations in the charging and battery swapping station microgrid, as well as constraints such as power balance, and proposes a day-ahead optimal scheduling model for the charging and battery swapping station microgrid with the goal of minimizing net electricity purchase cost; Second, based on intraday renewable energy output and ultra-short-term load forecast data, it models the operation of components and power balance constraints in the charging and battery swapping station microgrid within each rolling optimization cycle, and proposes an intraday rolling optimal scheduling model for the charging and battery swapping station microgrid with the goal of minimizing the sum of net electricity purchase cost and the penalty cost for energy storage and battery swapping station power deviation between the "day-ahead scheduling plan and intraday rolling scheduling plan"; Then, based on measured renewable energy output and load data, it models the charging and battery swapping stations during the real-time feedback adjustment period. This invention models the component operation and power balance constraints in a microgrid, and then proposes a real-time feedback adjustment control optimization model for charging and battery swapping station microgrids with the objective of minimizing the sum of power deviations of distribution transformers, energy storage, and battery swapping stations under the "intraday rolling scheduling plan and real-time feedback control". Finally, this invention combines day-ahead optimal scheduling with the objective of minimizing net electricity purchase cost, intraday rolling optimal scheduling with the objective of minimizing the sum of net electricity purchase cost and the penalty cost of power deviation of energy storage and battery swapping stations under the "intraday rolling scheduling plan and intraday rolling scheduling plan", and real-time feedback adjustment control with the objective of minimizing the sum of power deviations of distribution transformers, energy storage, and battery swapping stations under the "intraday rolling scheduling plan and real-time feedback control". This effectively solves the problem that fixed control modes lack planning, flexibility, and adaptability, and cannot guarantee economic efficiency when parameters such as new energy and charging load fluctuate randomly and have large prediction errors. Attached Figure Description

[0036] Figure 1 is a typical structure diagram of a microgrid for a charging and swapping station;

[0037] Figure 2 is a flowchart of the day-to-day multi-timescale optimization control of the microgrid of the charging and swapping station described in this invention.

[0038] Figure 3 shows the measured power curve of a microgrid at a charging and swapping station in this embodiment.

[0039] Figure 4 is a comparison chart of the simulation results of the example. Detailed Implementation

[0040] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0041] The typical structure of a charging and battery swapping station microgrid is shown in Figure 1. The main components include grid-connected distribution transformers, photovoltaics, energy storage, charging piles, battery swapping stations, and all self-consumed electrical loads including air conditioning and lighting.

[0042] The day-to-day multi-timescale optimization control method in this invention refers to three optimization control methods under different timescales: day-to-day optimization scheduling, intraday rolling optimization scheduling, and real-time feedback adjustment control. In this embodiment, day-ahead optimization scheduling refers to solving a day-ahead optimization scheduling model based on the day-ahead short-term forecast values ​​of renewable energy output, charging load, and self-consumption load, with the next day's 24-hour time window as the time range and each time period as 15 minutes, to obtain the scheduling control plan for each time period of the next day. Intraday rolling optimization scheduling refers to continuously solving an intraday rolling optimization scheduling model based on the intraday ultra-short-term forecast values ​​of renewable energy output, charging load, and self-consumption load, with the rolling time window (e.g., 4 hours) as the time range and each time period as 15 minutes, to obtain the scheduling control plan for each time period within the future time window range, 15 minutes before the actual control on the scheduling control day. Real-time feedback adjustment control refers to solving a real-time feedback adjustment control model based on the real-time power monitoring values ​​of renewable energy output, charging load, and self-consumption load during actual control, to obtain the scheduling plan adjustment amount and actual control value of the power of controllable components such as energy storage system, battery swapping station, and distribution transformer in the current time period.

[0043] The multi-timescale optimization control method for charging and swapping station microgrids described in this invention, as shown in Figure 2, includes the following steps:

[0044] Step 1: Based on the day-ahead optimization scheduling model, the day-ahead scheduling plan is obtained. The day-ahead optimization scheduling model includes: on the day before the scheduling control date, based on the short-term forecast data of photovoltaic output and the short-term forecast data of load for the next day, with the goal of optimal economic efficiency, the day-ahead scheduling plan for the next day is obtained in 15-minute increments for 24 hours.

[0045] Step 2: Based on the intraday rolling optimization scheduling model, the intraday rolling scheduling plan is obtained. The intraday rolling optimization scheduling model includes: on the scheduling control day, based on 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 net electricity purchase cost and the penalty cost of energy storage and battery swapping station power deviation between the day-ahead scheduling plan and the intraday rolling scheduling plan, the intraday rolling scheduling plan is obtained in 15-minute increments within the rolling time window.

[0046] Step 3: Based on the real-time feedback adjustment control model, solve for the real-time control command; the real-time feedback adjustment control model includes: on the scheduling control day, based on the real-time monitoring data of photovoltaic output and load power, with the goal of minimizing the sum of the power deviations of distribution transformers, energy storage, and battery swapping stations between the daily rolling scheduling plan and the real-time control command, obtaining the real-time control command for the controllable components in the current time period.

[0047] The following section details the technical solution for a day-ahead and intraday multi-timescale optimization control method for charging and swapping station microgrids, which includes day-ahead scheduling, intraday rolling optimization scheduling, and real-time feedback adjustment control.

[0048] (1) The microgrid of charging and swapping stations was optimized and dispatched recently.

[0049] First, taking a typical charging and swapping station microgrid structure as an example, this paper introduces the component operation constraints and power balance constraints for day-ahead optimization scheduling of charging and swapping station microgrids.

[0050] The operational constraints of grid-connected distribution transformers are modeled as follows:

[0051] In the formula: Let t be the active power of the i-th distribution transformer in the t-th time period; the t-th time period refers to a discretized time period, for example, for the next 24 hours, each time period is 15 minutes, which corresponds to 96 time periods. and These represent the grid-connected power and back-feeding power of the distribution transformer, respectively. and The variables are 0 and 1, respectively indicating whether the power direction of the i-th distribution transformer in the time period t is down to the grid or back to the grid. Let be the maximum active power capacity of the i-th distribution transformer. Formulas (A1~A2) indicate that at any given time, the power flow direction of the distribution transformer is unique, with positive power supply to the grid and negative power supply to the grid; Formulas (A3~A4) indicate that the power flowing through the distribution transformer does not exceed the maximum allowable power capacity of the distribution transformer.

[0052] The photovoltaic power generation constraints are as follows:

[0053] In the formula: Let be the active power output of the i-th photovoltaic system in time period t. Let be the predicted photovoltaic output of the i-th photovoltaic system during time period t. Formula (A5) indicates that the photovoltaic output in the day-ahead optimal scheduling model is equal to the short-term predicted photovoltaic output (without considering the case of photovoltaic system disconnection).

[0054] The constraints for energy storage operation are as follows:

[0055] In the formula: and Let be the charging and discharging power of the i-th energy storage system in time period t, respectively. and Let represent the charging and discharging states (0 / 1 variables) of the i-th energy storage system in time period t. This represents the maximum charge and discharge power of the i-th energy storage system. Let represent the remaining power level of the i-th energy storage system during time period t. Let represent the remaining power level of the i-th energy storage system during the (t-1)-th time period. and Let be the charging and discharging efficiencies of the i-th energy storage system, respectively. Let represent the capacity of the i-th energy storage system, and Δt be the duration of each scheduling time interval (typically taken as 15 minutes). and Let A and B be the minimum and maximum remaining energy levels of the i-th energy storage system, respectively. To ensure that formula A9 is applicable to all time periods t = 1, 2, ..., NT, the following is adopted: Let represent the initial remaining power level of the i-th energy storage system. and These represent the end and initial power levels of the i-th energy storage system within a scheduling cycle (typically 24 hours). This represents the state indicator variable indicating whether the power of the distribution transformer d connected to the i-th energy storage system is back-feeding during time period t, where ES is the set of energy storage systems, and DT is the value of DT. i Let i be the set of distribution transformers connected to the i-th energy storage system. and This represents the indication status (0 / 1 variable) of whether the i-th energy storage system starts charging and discharging during time period t, where T represents the entire scheduling time range (24 hours the following day). and This refers to the maximum number of charge and discharge cycles allowed for the energy storage system throughout the entire scheduling period. and Let A and B represent the charging and discharging states (0 / 1 variables) of the i-th energy storage system in time period t-1. Formulas (A6-A7) indicate 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 are mutually exclusive in the same time period; 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 energy level of the energy storage system needs to be maintained within a certain range, such as 10% to 100% of the capacity; Formula (A11) defines the remaining energy level of the energy storage system at the initial moment; Formula (A12) indicates that the remaining energy level of the energy storage system at the end moment is not lower than the initial energy level; Formula (A13) indicates that the remaining energy level of the energy storage system at the end moment is not lower than the initial energy level; Formula (A14) indicates that the remaining energy level of the energy storage system at the end moment is not lower than the initial energy level; Formula (A15) indicates that the remaining energy level of the energy storage system at the end moment is not lower than the initial energy level; Formula (A16 ... 3) is the energy storage reverse power protection constraint condition, which restricts the energy storage from discharging when the power of the grid-connected distribution transformer is reversed; Formulas (A14~A15) represent the total number of charging and discharging times of the energy storage system in the entire dispatch cycle, which aims to limit the frequent charging and discharging of the energy storage system; Formulas (A16~A17) define the mutual exclusion constraints of the energy storage system at the start of charging and the start of discharging; Formula (A18) describes the logical relationship between the logical variable of the energy storage system at the start of charging and the charging state variable; Formula (A19) describes the logical relationship between the logical variable of the energy storage system at the start of discharging and the discharging state variable.

[0056] The charging power constraints of the charging piles are as follows:

[0057] In the formula: The aggregated value represents the active power output of all charging piles connected to bus b during time period t. Let be the day-ahead predicted charging power value for all charging piles connected to bus b in time period t. Formula (A20) indicates that the charging power of the charging piles in the day-ahead optimized scheduling model is equal to the day-ahead short-term predicted value.

[0058] The charging constraints at the battery swapping station are as follows:

[0059] In the formula: This represents the charging power of the battery swapping station connected to bus b during time period t. This indicates the maximum charging power of the battery swapping station connected to bus b. This represents the amount of charge already received by the battery swapping station connected to bus b during time period t. η represents the amount of charge already applied at the battery swapping station connected to bus b at time t-1. SwapCh This indicates the charging efficiency of the battery swapping station connected to bus b. This indicates the initial charge level of the battery swapping station connected to bus b. This indicates the amount of charge already applied at the battery swapping station connected to bus b at the end of the cycle. This indicates the minimum charging capacity that a battery swapping station connected to bus b must meet. Formula (A21) indicates that the charging power of the battery swapping station does not exceed the maximum charging power; Formula (A22) indicates the energy-power balance relationship between the charged amount and the charging power of the battery swapping station; Formula (A23) indicates that the charged amount of the battery swapping station is 0 at the beginning of the entire scheduling cycle; Formula (A24) indicates that the charged amount of the battery swapping station is not less than the minimum charging capacity at the end of the scheduling cycle.

[0060] The power constraints for self-consumed electrical loads are as follows:

[0061] In the formula: This represents the active power of the self-consumed electrical load connected to bus b during time period t. Let be the day-ahead forecast of the self-consumed power connected to bus b. Formula (A25) indicates that in the day-ahead optimized scheduling model, the self-consumed power is equal to the day-ahead short-term forecast.

[0062] The overall operational constraints of the microgrid at the charging and battery swapping station are as follows:

[0063] In the formula: DT represents the set of grid-connected distribution transformers, PV represents the photovoltaic array, ES represents the energy storage array, and index b represents bus b. Formula (A26) represents the power balance between power generation and consumption at any given time, i.e., the power balance constraint condition.

[0064] The objective of day-ahead dispatching for microgrids at charging and battery swapping stations is to minimize net electricity purchase costs, expressed by the following formula:

[0065] In the formula: C DA DT and T represent the total cost of the current day's scheduling optimization. DA These represent the set of grid-connected distribution transformers and the set of day-ahead dispatching time periods, respectively. and These represent the electricity purchase price and the electricity sales price per unit of electricity, respectively. Formula (1) indicates that the total cost of day-ahead dispatch, i.e., the net cost of electricity purchase, is equal to the difference between the cost of electricity purchase and the revenue from electricity sales.

[0066] The overall optimization model for day-ahead scheduling of the microgrid at the charging and swapping station is shown in (X1):

[0067] (2) Daily rolling optimization scheduling of microgrids for charging and swapping stations.

[0068] Within the intraday rolling time period (t∈T) Roll Roll = {r1, r2, ..., r} i ,…r n}, where r1, r2, ..., r i,…r n The constraints of the grid-connected distribution transformer are consistent with the formulas (A1 to A4), which represent the first to nth rolling optimization cycles respectively.

[0069] Within the intraday rolling time period, photovoltaic power output equals the ultra-short-term power output forecast, subject to the following constraints:

[0070] In the formula: This represents the photovoltaic output of the i-th photovoltaic system in the daily rolling optimization model during the t-th time period. This represents the predicted value of photovoltaic power output in the very short term.

[0071] Within the intraday rolling optimization cycle model, the charging and discharging state of energy storage remains consistent with the day-ahead scheduling plan, subject to the following constraints:

[0072] In the formula: and These represent the charging and discharging states of energy storage system i during time period t within the rolling time cycle; and These represent the charging and discharging status plans of energy storage system i in time period t during the day-ahead dispatch plan.

[0073] At the beginning of the first rolling optimization cycle, the SOC of energy storage is equal to the initial SOC of energy storage. At the beginning of the remaining rolling optimization cycles, the SOC is equal to the actual SOC at the end of the previous time period (the first 15 minutes). At the end of the rolling optimization cycle, including the last moment, the SOC is not less than the initial SOC, subject to the following constraints:

[0074] In the formula: This represents the State of Charge (SOC) of the i-th energy storage system at the initial moment of the first rolling optimization cycle. This represents the actual SOC value of the i-th energy storage system at the start of the rolling optimization cycle. Let SOC represent the initial state of the i-th energy storage system in the remaining rolling optimization cycles (the other rolling optimization cycles after the first rolling optimization cycle r1). This indicates that the i-th energy storage system was in the previous rolling optimization cycle r. i-1 The actual SOC after the end of the first time period, where the first time period refers to the first 15-minute period (0-15min). This indicates that the i-th energy storage system is at time t (including the last moment). end Rolling optimization cycle {r i} end The end of SOC.

[0075] In addition, energy storage has a rolling time period t∈TRoll 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.

[0076] In the intraday rolling optimization model, the charging power load of the charging pile is equal to the ultra-short-term charging power prediction value, with the following constraints:

[0077] In the formula: This represents the charging power load of the charging piles connected to bus b in the intraday rolling optimization model. This represents the intraday ultra-short-term forecast of the charging power of the charging piles connected to bus b.

[0078] In the intraday rolling optimization model, the charging power of the battery swapping station still needs to satisfy constraint (A21), and the power-energy balance equation between the charging power and the amount of charge still needs to satisfy constraint (A22), but the time range becomes t∈T. Roll The charged amount at the battery swapping station is equal to 0 at the beginning of the first rolling optimization cycle, and equal to the actual charged amount at the end of the previous time period (the first 15 minutes) at the beginning of each subsequent rolling optimization cycle, with the following constraints:

[0079] In the formula: This represents the amount of charge already applied at the battery swapping station connected to bus b at the beginning of the first rolling optimization cycle. This indicates that the battery swapping station connected to bus b is in the rolling optimization cycle r. i Initial charge level, This indicates that the battery swapping station connected to bus b was in the previous rolling optimization cycle r. i-1 The actual amount of charge received after the first time period ends.

[0080] The amount of charge already applied at the end of the last time period at a battery swapping station must not be less than the minimum charging capacity, subject to the following constraints:

[0081] In the formula: This indicates that the last time t is included. end Rolling optimization cycle {r i} end The charge level of the battery swapping station connected to bus b at the end of the cycle.

[0082] In the intraday rolling optimization model, the self-consumption electricity load power is equal to its ultra-short-term forecast value, with the following constraints:

[0083] In the formula: This represents the self-consumed electrical load power connected to bus b in the intraday rolling optimization model. This represents the intraday ultra-short-term forecast of the self-consumed electrical load power connected to bus b.

[0084] The microgrid at the charging and swapping station must satisfy the power balance constraint (A26) at any given time. Therefore, in any time period t∈T in the intraday rolling optimization model... Roll The power balance constraint (A26) still needs to be met.

[0085] The purpose of intraday rolling optimization is to formulate an intraday scheduling plan. The objective function of intraday rolling optimization scheduling is to minimize the sum of net electricity purchase cost, photovoltaic backfeed penalty cost, and energy storage energy adjustment penalty cost within the rolling period.

[0086] In the formula: C Roll Represents the total cost over the rolling time period; μ ESdev The energy storage adjustment penalty cost coefficient per unit of electricity, μ Swapdev T represents the penalty cost coefficient for adjusting the electricity consumption of a battery swapping station per unit of electricity. Roll The term "rolling time period" represents the set of time periods, and "BUS" represents the set of all bus lines. This represents the capacity of the i-th energy storage system. This represents the remaining energy level of the i-th energy storage system after time period t ends in the daily rolling dispatch plan. This represents the remaining energy level of the i-th energy storage system after the end of time period t in the day-ahead scheduling plan. This represents the amount of battery swapping station connected to bus b at the end of time period t in the daily rolling scheduling plan. This represents the amount of electricity charged by the battery swapping station connected to bus b at the end of time period t in the day-ahead scheduling plan. The three terms summed on the right side of equation (2) represent the net electricity purchase cost, energy storage energy adjustment penalty cost, and battery swapping station power adjustment penalty cost during the rolling time period, respectively.

[0087] The overall optimization model for the intraday rolling optimization scheduling of the microgrid at the charging and swapping station is shown in (X2):

[0088] (3) Real-time feedback adjustment and control of microgrid in charging and swapping stations.

[0089] The real-time feedback adjustment control of the charging and swapping station microgrid is based on real-time monitoring of renewable energy output and load power fluctuations to adjust the control for the current time period (denoted as t). r This represents the power control commands for controllable components (including energy storage, battery swapping stations, and distribution transformers). Real-time feedback control operates within a 24-hour timeframe, denoted as T. RT={1,2,…,t end The time interval for real-time feedback control is Δt. r To indicate (e.g., 1 minute / 5 minutes).

[0090] During the current time period t of real-time feedback adjustment control r ∈T RT Distribution transformers still need to meet operating constraints (A1~A4).

[0091] During the current time period t of real-time feedback adjustment control r ∈T RT The photovoltaic output is equal to its real-time monitoring value, with the following constraints:

[0092] In the formula: This indicates that the i-th photovoltaic system is in the current time period t. r of efforts, This indicates that the i-th photovoltaic system is in the current time period t. r The real-time monitoring value of the output.

[0093] During the current time period t of real-time feedback adjustment control r ∈T RT The energy storage charging and discharging status must remain consistent with the day-ahead / intraday scheduling plan, subject to the following constraints:

[0094] In the formula: and These represent the current time period t of the i-th energy storage system in the real-time feedback adjustment control. r The charging and discharging states; and These represent the current time period t for the i-th energy storage system in the day-ahead dispatch plan. r The planned values ​​for charging and discharging status during the corresponding time period.

[0095] In real-time feedback control, energy storage is used in the initial time period (t). r =1) The initial SOC is equal to the initial SOC of energy storage during the entire scheduling cycle (24 hours), and the energy storage in the remaining time periods (t) r The initial SOC of ≥2) is equal to that of the previous time period (the first 5 minutes / the first 1 minute, depending on the real-time feedback control time interval Δt). r The actual SOC after the end of the period (t) is the energy storage in the last time period. r =t end The actual SOC after the end is not less than the initial SOC, and the constraints are as follows:

[0096] In the formula: This indicates that the i-th energy storage system is in the current time period t. r The initial remaining power level, This indicates that the i-th energy storage system is in the current time period t. r The previous time period t r The actual remaining battery level after -1 ends. This indicates that the i-th energy storage system is in the current time period t. r Remaining battery level after the event, t end This indicates the last time period of real-time feedback control.

[0097] In addition, energy storage must also meet the maximum charge / discharge power constraint (C7), power-energy balance constraint (C8), and energy level constraint (C9) in real-time feedback adjustment control, as shown below:

[0098] In the formula: and These represent the time interval t for the i-th energy storage system in the real-time feedback adjustment control. r The charging and discharging power, and These represent the time interval t for the i-th energy storage system in the real-time feedback adjustment control. r The charging and discharging states (0 / 1 variables), This represents the maximum charge and discharge power of the i-th energy storage system. For the i-th energy storage system in time period t r The remaining battery level, For the i-th energy storage system in time period t r The initial SOC, and Let be the charging and discharging efficiencies of the i-th energy storage system, respectively. Let Δt represent the capacity of the i-th energy storage system. r The time interval for real-time feedback control (typically 5 minutes / 1 minute). and These are the minimum and maximum remaining power levels of the i-th energy storage system, respectively.

[0099] In real-time feedback control, the charging power of the charging pile is equal to its real-time monitored value:

[0100] In the formula: This indicates that all charging piles connected to bus b are in the current time period t. r The charging power, This indicates that all charging piles connected to bus b are in the current time period t. rThe real-time monitoring value of the charging power.

[0101] In real-time feedback control, the battery swapping station operates within the initial time period (t). r =1) The initial power of the battery swapping station is equal to the initial power of 0 in the entire scheduling cycle (24 hours). The battery swapping station in the remaining time periods (t r The initial charge of ≥2) is equal to that of the previous time period (the first 5 minutes / the first 1 minute, depending on the real-time feedback control time interval Δt). r The actual amount of charge received by the battery swapping station at the end of the last time period (t) r =t end The actual amount of charge after the event is not less than the minimum charging capacity of the battery swapping station, with the following constraints:

[0102] In the formula: This indicates that the battery swapping station connected to bus b is in the current time period t. r The initial charge level, This indicates that the battery swapping station connected to bus b is in the current time period t. r The previous time period t r The amount of charge remaining after -1 ends. This indicates that the battery swapping station connected to bus b is in the current time period t. r The amount of charge remaining after the event ends.

[0103] In addition, the battery swapping station still needs to meet the maximum charging power constraint (C15) and the power-energy balance constraint (C16) in the real-time feedback adjustment control, as shown below:

[0104] In the formula: This indicates that the battery swapping station connected to bus b is in time period t. r The charging power, This indicates the maximum charging power of battery swapping station b. This represents the amount of charge already received by the battery swapping station connected to bus b during time period t. η represents the amount of charge already applied at the battery swapping station connected to bus b at time t-1. SwapCh This indicates the charging efficiency of the battery swapping station. This indicates the initial charge level of the battery swapping station connected to bus b. This indicates the amount of charge already applied at the battery swapping station connected to bus b at the end of the cycle. This represents the minimum charging capacity that the battery swapping station connected to bus b must meet during the entire scheduling cycle.

[0105] In real-time feedback control, the self-consumed electrical load power is the real-time monitored value, and the constraints are as follows:

[0106] In the formula: This indicates the self-owned electrical load connected to bus b at time t. r Real-time monitoring power values.

[0107] The overall operation of the charging and swapping station microgrid must meet the power balance requirement at any time. Therefore, in real-time feedback control, the power balance constraint (A26) still needs to be met.

[0108] The objective of real-time feedback adjustment control of the charging and swapping station microgrid is to minimize the total deviation between the real-time control power of the distribution transformer, energy storage, and swapping station and the daily rolling dispatch plan power, as expressed below:

[0109] Where: ΔP dev The objective function (i.e., total power deviation) for the real-time feedback adjustment control of the microgrid at the charging and swapping station is represented. These represent the i-th distribution transformer, the i-th energy storage system, and the battery swapping station connected to bus b in the real-time feedback adjustment control during time period t. r Actual control values ​​of power flowing through, charging / discharging power, and charging power; These represent the i-th distribution transformer, the i-th energy storage system, and the battery swapping station connected to bus b in the current time period t during the intraday rolling optimization scheduling. r The planned values ​​for power flow, charging / discharging power, and charging power. Let i be the maximum active power capacity of the i-th distribution transformer. This represents the maximum charge and discharge power of the i-th energy storage system. This indicates the maximum charging power of the battery swapping station connected to bus b.

[0110] The real-time feedback adjustment control optimization model of the microgrid for charging and swapping stations is shown in (X3):

[0111] (4) Multi-timescale control method for microgrids of charging and swapping stations within the day-to-day period.

[0112] Based on the day-ahead optimization scheduling model (X1), intraday rolling optimization scheduling model (X2), and real-time feedback adjustment control optimization model (X3) of the charging and swapping station microgrid, a day-ahead and intraday multi-timescale control method for the charging and swapping station microgrid is proposed, and the specific process is shown in Figure 2.

[0113] In Figure 2, the day-ahead and intraday forecasts of new energy (distributed photovoltaic power output) and load (charging piles, self-consumed power) adopt artificial intelligence algorithms such as Long Short-Term Memory Network (LSTM), Support Vector Machine (SVM), and Random Forest Regression (RF). Through deep learning and mining of historical data, the forecast model is continuously improved by training sample data, and combined with external parameters such as irradiance, temperature, and price, accurate forecasts of new energy and load are achieved.

[0114] In Figure 2, the optimization model (X1) is a mixed integer linear optimization model (MILP), which can be solved using algorithms such as branch and bound, cutting plane method, Benders decomposition method, and DW decomposition method; optimization models (X2) and (X3) are linear optimization models (LP), which can be solved using the simplex method. These optimization algorithms can be easily integrated into general solvers (such as C++, Gurburne, etc.) to achieve the global and optimal solutions of the model C++(X1, X2, X3, ..., X4).

[0115] This invention combines a day-ahead and intraday multi-timescale control process and implementation method, which integrates "day-ahead optimized scheduling - intraday rolling optimized scheduling - real-time feedback adjustment control". This method addresses the problems of fixed control modes lacking planning, flexibility, and adaptability, and also failing to guarantee economic efficiency, when parameters such as renewable energy and charging loads fluctuate randomly and have large prediction errors.

[0116] Simulation examples:

[0117] The effectiveness of this method is demonstrated through simulation examples. Based on a real-world charging and battery swapping station microgrid, the topology and component configuration are shown in Figure 1. Specific parameters are as follows: maximum energy storage charging / discharging power is 100kW, capacity is 215kWh, charging / discharging efficiency is 90%, SOC range is 0.1–1, initial SOC is 0.1, and the maximum number of charging / discharging cycles (24 hours) is 2; the battery swapping station has a maximum charging power of 320kW, charging efficiency of 90%, and a minimum charging capacity of 300kWh; the grid-connected distribution transformer capacity is 2500kW; the real-time monitoring and prediction curves of the power of photovoltaic, charging piles, and self-consumed electricity loads are shown in Figure 3. The day-ahead / intra-day load forecasts are randomly generated, with random error ranges of ±10% / ±5%, ±15% / ±5%, and ±10% / ±5%, respectively; the electricity purchase price is the industrial and commercial agency price, which is RMB 1.1566 / kWh during peak hours (8:00-11:00 and 17:00-22:00), RMB 0.2815 / kWh during off-peak hours (0:00-8:00), and RMB 0.6726 / kWh during flat hours (11:00-17:00 and 22:00-24:00); the electricity sales price is the photovoltaic surplus electricity grid connection price, which is RMB 0.391 / kWh; the unit penalty cost μ in formula (2) ESdev and μ Swapdev The price is set at 0.1 yuan / kWh.

[0118] Comparative calculation examples are provided for Example 1 and Example 2. Example 1 uses a fixed control strategy: the energy storage system is charged and discharged twice daily, with the two discharges occurring during two peak electricity price periods. One charge occurs during the nighttime off-peak electricity price period, and the other during the daytime flat electricity price period. Charge and discharge are performed at maximum power until the State of Charge (SOC) reaches its maximum / minimum. The battery swapping station begins charging at maximum power during the nighttime low electricity price period until fully charged (i.e., meeting the minimum charging capacity). Example 2 uses the day-ahead-intraday multi-timescale control method proposed in this patent. The control results obtained by the two methods are compared below.

[0119] The net electricity purchase costs for Examples 1 and 2 are RMB 3308.1 and RMB 3294.7 respectively, indicating that the day-ahead-intraday multi-timescale control method proposed in this patent saves on the operating costs of charging and battery swapping stations compared to a fixed control strategy. The specific reasons are analyzed in Figure 4. The two control strategies have different energy storage charging and discharging powers and battery swapping station charging powers (the main difference lies in energy storage). The day-ahead-intraday multi-timescale control method proposed in this patent adjusts the grid-connected power of the distribution transformer by flexibly controlling the energy storage charging and discharging power, thereby reducing the maximum grid-connected power and load rate of the distribution transformer. Simultaneously, it reduces photovoltaic backfeeding during the morning charging load peak, thus lowering the overall net electricity purchase cost. The example results verify the effectiveness of the control method proposed in this invention.

[0120] 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. When the processor executes the computer program, it implements the above-described multi-timescale optimization control method for a charging and swapping station microgrid.

[0121] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described day-to-day multi-timescale optimization control method for a charging and swapping station microgrid.

[0122] In one embodiment, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the day-ahead-day multi-timescale optimization control method for the charging and swapping station microgrid.

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

[0124] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams.

[0125] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0126] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

Claims

1. A day-ahead and day-ahead multi-time scale optimization control method for a micro-grid of a charging and swapping station, characterized in that, Includes the following steps: Step 1: Based on the day-ahead optimization scheduling model, solve for the day-ahead scheduling plan; The day-ahead optimization scheduling model includes: on the day before the scheduling control date, based on the short-term forecast data of new energy output and the short-term forecast data of load for the next day, with the goal of minimizing net electricity purchase cost, obtaining the day-ahead scheduling plan for the next day; Step 2: Based on the intraday rolling optimization scheduling model, the intraday rolling scheduling plan is obtained. The intraday rolling optimization scheduling model includes: on the scheduling control day, based on the ultra-short-term forecast data of new energy output and the ultra-short-term forecast data of load, the intraday rolling scheduling plan is obtained within the rolling time window with the goal of minimizing the sum of net electricity purchase cost, the energy storage energy adjustment penalty cost of the day-ahead scheduling plan and the intraday rolling scheduling plan, and the battery swapping station power deviation penalty cost of the day-ahead scheduling plan and the intraday rolling scheduling plan. Step 3: Based on the real-time feedback adjustment control model, solve for the real-time control command; the real-time feedback adjustment control model includes: on the scheduling control day, based on the real-time monitoring data of new energy output and load power, with the goal of minimizing the sum of the power deviations of distribution transformers, energy storage, and battery swapping stations between the daily rolling scheduling plan and the real-time control command, obtaining the real-time control command for the controllable components in the current time period.

2. The day-to-day multi-timescale optimization control method for microgrids in charging and swapping stations according to claim 1, characterized in that: The objective function of the day-ahead optimal dispatch model is as follows: In the formula: C DA Let DT and T represent the objective function of the current optimization scheduling model. DA Let i and t represent the set of distribution transformers and the set of day-ahead scheduling time periods, respectively, where i and t are loop variables, representing the t-th time period. and Let these represent the purchase price and the sales price of electricity per unit of electricity in time period t, respectively. and Let represent the power output to the grid and the power transmitted back to the grid for the i-th distribution transformer in time period t, respectively, and Δt be the interval length of each dispatch time.

3. The day-to-day multi-timescale optimization control method for microgrids in charging and swapping stations according to claim 2, characterized in that: The objective function of the intraday rolling optimization scheduling model is as follows: In the formula: C Roll T represents the objective function of the intraday rolling optimization scheduling model; Roll μ represents the rolling time period set, ES represents the energy storage set, b is the loop variable, and BUS represents the bus set; ESdev The energy storage adjustment penalty cost coefficient per unit of electricity, μ Swapdev The battery swapping station power adjustment penalty cost coefficient represents the unit power consumption. This represents the capacity of the i-th energy storage system. This represents the remaining energy level of the i-th energy storage system after time period t ends in the daily rolling dispatch plan. This represents the remaining energy level of the i-th energy storage system after the end of time period t in the day-ahead scheduling plan. This represents the amount of battery swapping station connected to bus b at the end of time period t in the daily rolling scheduling plan. This indicates the amount of charge already received by the battery swapping station connected to bus b at the end of time period t in the day-ahead scheduling plan.

4. The day-to-day multi-timescale optimization control method for microgrids in charging and swapping stations according to claim 3, characterized in that: The objective function of the real-time feedback adjustment control model is as follows: Where: ΔP dev This represents the objective function of the real-time feedback adjustment control model. These represent the i-th distribution transformer, the i-th energy storage system, and the battery swapping station connected to bus b in the real-time feedback adjustment control during time period t. r Actual control values ​​of power flowing through, charging / discharging power, and charging power; These represent the i-th distribution transformer, the i-th energy storage system, and the battery swapping station connected to bus b in the current time period t during the intraday rolling optimization scheduling. r The planned values ​​for power flow, charging / discharging power, and charging power. Let i be the maximum active power capacity of the i-th distribution transformer. This represents the maximum charge and discharge power of the i-th energy storage system. This indicates the maximum charging power of the battery swapping station connected to bus b.

5. The day-to-day multi-timescale optimization control method for microgrids in charging and swapping stations according to claim 1, characterized in that: In the intraday rolling optimization scheduling model, the constraints on the output of new energy sources are as follows: In the formula: This represents the renewable energy output of the i-th renewable energy system in the daily rolling optimization model during the t-th time period. This indicates the very short-term forecast value for new energy output; In the intraday rolling optimization model, the constraints on the charging power load of the charging piles are as follows: In the formula: This represents the charging power load of the charging piles connected to bus b in the intraday rolling optimization model. This represents the intraday ultra-short-term forecast value of the charging power of the charging piles connected to bus b; The power constraints for self-consumed electrical loads are as follows: In the formula: This represents the self-consumed electrical load power connected to bus b in the intraday rolling optimization model. This represents the intraday ultra-short-term forecast of the self-consumed electrical load power connected to bus b; The overall operational constraints of the microgrid at the charging and battery swapping station are as follows: In the formula: Let be the active power of the i-th distribution transformer in time period t. and Let be the charging and discharging power of the i-th energy storage system in time period t, respectively. Let DT∩{b} represent the charging power of the battery swapping station connected to bus b during time period t, DT∩{b} represent the set of distribution transformers connected to bus b, PV∩{b} represent the set of photovoltaic systems connected to bus b, and ES∩{b} represent the set of energy storage systems connected to bus b.

6. The day-to-day multi-timescale optimization control method for microgrids in charging and swapping stations according to claim 1, characterized in that: Within the intraday rolling optimization cycle model, the charge / discharge state constraints for energy storage are: In the formula: and These represent the charging and discharging states of the i-th energy storage unit in the t-th time period within the rolling time cycle, respectively. and These represent the planned charging and discharging states of the i-th energy storage unit in the day-ahead scheduling plan during time period t; The SOC constraints of the energy storage system are as follows: In the formula: This represents the State of Charge (SOC) of the i-th energy storage system at the initial moment of the first rolling optimization cycle. This represents the actual SOC value of the i-th energy storage system at the start of the rolling optimization cycle. r i ∈Roll\{r1} represents the SOC of the i-th energy storage system at the initial moment of the remaining rolling optimization cycles. This indicates that the i-th energy storage system was in the previous rolling optimization cycle r. i-1 The actual SOC after the first time period ends denotes the end SOC of the i-th energy storage system at the end of the rolling optimization period {r end} i} end of the i-th energy storage system.

7. The day-to-day multi-timescale optimization control method for microgrids in charging and swapping stations according to claim 1, characterized in that: In the real-time feedback adjustment control model, the constraints on the output of new energy sources are as follows: In the formula: This indicates that the i-th new energy system is in the current time period t. r of efforts, represents the output real-time monitoring value of the i-th new energy system at the current time period t r ; In the real-time feedback adjustment control model, the charging power constraint condition for the charging pile is: In the formula: This indicates that all charging piles connected to bus b are in the current time period t. r The charging power, represents the charging power real-time monitoring value of all charging piles connected to the bus b at the current time period t r . In the real-time feedback adjustment control model, the power constraints of the self-consumed electrical load are as follows: In the formula: denotes the real-time monitored power value of the self-consumption electric load connected to the bus b at time t r denotes the real-time monitored power value of the self-consumption electric load connected to the bus b at time t 8. 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, it implements the day-to-day multi-timescale optimization control method for the charging and swapping station microgrid as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the day-to-day multi-timescale optimization control method for the charging and swapping station microgrid as described in any one of claims 1 to 7.

10. 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, they implement the day-to-day multi-timescale optimization control method for the charging and swapping station microgrid as described in any one of claims 1 to 7.

Images