A series type low-voltage direct-current remote supply and multi-resource multi-time scale coordinated optimization method

Abstract

This application provides a method for coordinating and optimizing a series-type low-voltage DC remote power supply system with multiple resources and time scales. The method includes: constructing a mathematical model of the series-type low-voltage DC remote power supply system; acquiring various adjustable resources of the distribution network; based on the mathematical model of the series-type low-voltage DC remote power supply system and the various adjustable resources of the distribution network, constructing a day-ahead-intraday rolling-real-time optimization model for coordinating the series-type low-voltage DC remote power supply system with multiple flexible resources of the distribution network; and solving the day-ahead-intraday rolling-real-time optimization model to determine a multi-time-scale, multi-resource coordination optimization scheme. The method proposed in this application can effectively solve the low-voltage problem for end users by coordinating other flexible resources of the distribution network with the low-voltage DC remote power supply system, and effectively reduce network losses, ultimately achieving the goal of overall economic, safe, and reliable system operation.

Description

Technical Field

[0001] This application belongs to the field of power distribution network technology, and in particular relates to a series-type low-voltage DC remote power supply and multi-resource multi-timescale coordination optimization method. Background Technology

[0002] To curb energy shortages and environmental pollution, grid connection of new energy sources such as distributed photovoltaic (PV) has received widespread attention. In the promotion of specific energy-related measures, the construction of distributed PV in low-voltage distribution networks has accelerated further. Because distributed PV systems are located close to end users, they enable rapid absorption of PV power generation, helping to reduce transmission losses and improve energy efficiency. However, the output power of large-scale grid-connected distributed PV systems exhibits intermittent and fluctuating characteristics, causing voltage fluctuations in the distribution network and increasing system losses. This is particularly pronounced for remote users with long-distance AC transmission lines, where network losses and voltage exceedance issues are more significant. How to coordinate flexible resource optimization and adjustment of the distribution network to reduce network losses, improve system voltage quality, and enhance system economic performance has become a major research focus. Summary of the Invention

[0003] This application provides a series-type low-voltage DC remote power supply and multi-resource, multi-time-scale coordination optimization method. To solve the above-mentioned technical problems, this application adopts the following technical method: In a first aspect, this application provides a series-type low-voltage DC long-distance power supply and multi-resource, multi-time-scale coordination optimization method, characterized by comprising: Construct a mathematical model for a series-type low-voltage DC remote power supply system; Access to various adjustable resources in the power distribution network; Based on the mathematical model of the series-type low-voltage DC remote power supply system and the various adjustable resources of the distribution network, a day-ahead-intraday rolling-real-time optimization model for coordinating the series-type low-voltage DC remote power supply system with the various flexible resources of the distribution network is constructed. The day-to-day rolling-real-time optimization model is solved to determine a multi-timescale, multi-resource coordination optimization scheme.

[0004] Optionally, the distribution network may include various adjustable resources such as OLTC, CB, ESS and DG.

[0005] Optionally, the constraints of the mathematical model of the series-type low-voltage DC remote power supply system include the following: Rectifier-side VSC operation constraints, inverter-side VSC operation constraints, DC line operation constraints, and VSC control mode constraints.

[0006] Optionally, the constraints of the day-to-day rolling-real-time optimization model during the day-to-day optimization phase include the following: Constraints of the mathematical model of a series-type low-voltage DC remote power supply system, including power flow constraints, active power collaborative balance constraints, reactive power redundancy constraints of DG, active power constraints of DG, ESS charging and discharging constraints, OLTC operation constraints, and CB compensation power constraints.

[0007] Optionally, the constraints of the day-to-day rolling-real-time optimization model during the intraday optimization phase include the following: Constraints of the mathematical model of a series-type low-voltage DC remote power supply system include: power flow constraints, active power collaborative balance constraints, reactive power redundancy constraints of DG, continuity constraints of energy storage SOC between different windows during intraday rolling optimization, consistency constraints between the final SOC after intraday rolling optimization and the day-ahead constraint, active power constraints of DG, and charging and discharging constraints of ESS.

[0008] Optionally, solving the day-to-day rolling-real-time optimization model to determine a multi-timescale, multi-resource coordination optimization scheme includes: Based on variable substitution and convex relaxation, the day-to-day rolling-real-time optimization model is transformed to obtain a second-order cone programming model corresponding to the day-to-day rolling-real-time optimization model. The Cplex commercial solver was used to solve the second-order cone programming model and determine a multi-timescale, multi-resource coordination optimization scheme.

[0009] Optionally, the step of using the Cplex commercial solver to solve the second-order cone programming model and determine a multi-timescale, multi-resource coordination optimization scheme includes: Collect long-term distribution network source-load data; Substitute the daytime long-term distribution network source-load data into the second-order cone planning model, and input them into the Cplex commercial solver for solving, outputting the OLTC daytime action scheme and the CB daytime action scheme; Collect intraday short-timescale power distribution network source-load data; Substitute the daytime long-term distribution network source-load data into the second-order cone planning model, and input them into the Cplex commercial solver for solving, outputting the OLTC daytime action scheme and the CB daytime action scheme; Collect intraday short-timescale power distribution network source-load data; The OLTC day-ahead action plan, CB day-ahead action plan, and intraday short-timescale distribution network source-load data are substituted into the second-order cone programming model and input together into the Cplex commercial solver for solving, outputting the ESS intraday action plan, DG intraday action plan, and low-voltage DC remote power supply system intraday action plan. The intraday operation schemes of the ESS, DG, and low-voltage DC remote power supply system are corrected to obtain the corrected intraday operation schemes of the ESS, DG, and low-voltage DC remote power supply system. By integrating the OLTC day-ahead action plan, the CB day-ahead action plan, the low-voltage DC remote power supply system correction day-ahead action plan, the ESS correction day-ahead action plan, and the DG correction day-ahead action plan, a multi-time-scale, multi-resource coordination and optimization scheme is obtained.

[0010] Optionally, the intraday operating scheme of the ESS, the intraday operating scheme of the DG, and the intraday operating scheme of the low-voltage DC remote power supply system are corrected to obtain corrected intraday operating schemes for the ESS, DG, and low-voltage DC remote power supply systems; including: Acquire real-time node voltage measurements and day-to-day optimized node operating voltages; Based on the optimized node operating voltage from the previous day to the next day, a reactive power curve control strategy is generated. Based on the reactive power curve control strategy and the real-time node voltage measurement, the reactive power regulation amount of DG is determined; Based on the DG reactive power adjustment amount, the DG intraday action plan is adjusted to generate a DG correction intraday action plan. Based on the real-time node voltage measurement and the DG reactive power adjustment, the active power output parameters in the ESS correction intraday action scheme and the active power output parameters in the low-voltage DC remote power supply system intraday action scheme are coordinated and optimized to generate the ESS correction intraday action scheme and the low-voltage DC remote power supply system correction intraday action scheme.

[0011] Secondly, this application also provides a computer system, comprising: Memory is used to store instructions that can be executed by the processor; A processor for executing the instructions to implement the method as described in the first aspect.

[0012] Thirdly, this application also provides a computer-readable medium storing computer program code that, when executed by a processor, implements the method described in the first aspect.

[0013] This application has the following beneficial effects: The method proposed in this application can effectively reduce network losses in low-voltage distribution networks that include users in remote areas and solve the problem of voltage exceeding limits through multi-resource collaboration. Attached Figure Description

[0014] Figure 1A flowchart illustrating a series-type low-voltage DC remote power supply and multi-resource, multi-time-scale coordination optimization method provided in this application embodiment; Figure 2 This is a schematic diagram of the structure of a series-type low-voltage DC remote power supply system provided in an embodiment of this application; Figure 3 This is a low-voltage distribution network topology diagram provided in an embodiment of the present invention; Figure 4 This is a node voltage distribution diagram of Comparative Scheme 1 provided in the embodiments of the present invention; Figure 5 This is a node voltage distribution diagram of comparative scheme 2 in the embodiments of the present invention; Figure 6 This is a node voltage distribution diagram of the present invention in an embodiment of the present invention; Figure 7 This is a diagram showing the total system loss distribution of each scheme in the embodiments of the present invention; Figure 7 (a) is a diagram showing the total loss distribution of the system in Scheme 1; Figure 7 (b) is a diagram showing the total loss distribution of the system in Scheme 2; Figure 7 (c) is a diagram showing the total loss distribution of the Scheme 3 system. Detailed Implementation

[0015] To facilitate understanding by those skilled in the art, the present application will be further described below in conjunction with embodiments and accompanying drawings. The content mentioned in the embodiments is not intended to limit the present application.

[0016] To solve the above technical problems, such as Figure 1 As shown, this application proposes a series-type low-voltage DC remote power supply and multi-resource, multi-time-scale coordination optimization method, including: Step S101: Construct a mathematical model for a series-type low-voltage DC remote power supply system; A low-voltage DC remote power supply system consists of a voltage source converter (VSC) on the rectifier side, a VSC on the inverter side, and a DC remote power supply line. Low-voltage DC remote power supply systems are typically used to provide efficient and reliable power to remote equipment or users in remote areas. Its core principle is to rectify 380V alternating current (AC) into ±375V direct current (DC) at the beginning, transmit it over long distances through a dedicated low-voltage DC line, and then invert the DC power back to the AC voltage required by the load equipment at the end. A schematic diagram is shown below. Figure 2 As shown. Therefore, the operating constraints of each component in the mathematical model of the low-voltage DC remote power supply system can be established as follows: Rectifier-side VSC operating constraints: (1) (2) (3) (4) In the formula: The AC power flowing into the low-voltage DC remote rectifier side VSC from the grid; The DC power output of its rectifier-side VSC; , The VSC on the rectifier side of the low-voltage DC remote power supply equipment is respectively t DC voltage and current during the time period; and These are the upper and lower limits of the active power of the VSC on the DC remote power supply rectifier side, respectively. and These are the upper and lower limits of the reactive power of the VSC on the DC remote power supply rectifier side, respectively. , These are the power loss factor and active power loss of the rectifier-side VSC, respectively; This refers to the capacity of the rectifier-side VSC.

[0017] Inverter-side VSC operating constraints: (5) (6) (7) (8) In the formula: Provides AC power from the inverter side VSC to the load for low-voltage DC remote supply; This refers to the DC power output of the VSC on its inverter side; , The VSC on the inverter side of the low-voltage DC remote power supply equipment is respectively t DC voltage and current during the time period; and They are respectively t Upper and lower limits of active power of VSC on the inverter side of DC remote supply during the time period; and These are the upper and lower limits of the reactive power of the VSC on the DC remote inverter side, respectively. , These are the power loss factor and active power loss of the inverter-side VSC, respectively; The capacity of the DC remote inverter side VSC.

[0018] DC line operating constraints: (9) (10) In the formula; The current of the DC remote power supply line during time period t; This refers to the maximum current that a DC remote power supply line is allowed to pass through.

[0019] VSC control mode constraints: VSC's control strategies are mainly divided into U dc - Q , P - Q Control, when it adopts U dc - Q During control, its main function is to maintain the stability of the DC voltage, and it employs... P - Q During control, it is used to flexibly regulate active and reactive power. In a series-type low-voltage DC remote power supply system, at least one of the rectifier-side VSC and inverter-side VSC must be operating. U dc - Q The control mode aims to maintain DC voltage stability. The rectifier-side VSC and inverter-side VSC are denoted as VSC1 and VSC2, respectively. Therefore, the control mode process follows this scenario: ; In addition, VSC needs to switch modes according to the node voltage status, and the mode variables for VSC1 and VSC2 are defined respectively. m 1(t), m 2(t), all belong to {0,1} (a value of 1 corresponds to...). P - Q The mode, with a value of 0 corresponding to U dc - Q model); Therefore, the mode switching constraint can be modeled as: (11) (12) Step S102: Obtain various adjustable resources of the distribution network; The various adjustable resources of the distribution network include on-load tap changers (OLTC), capacitor banks (CB), energy storage systems (ESS), and distributed generators (DG).

[0020] Here, a power flow constraint model for the distribution network and an operation constraint model for various adjustable resources including OLTC, CB, ESS, and DG are constructed.

[0021] Current constraints: Power flow constraints for a low-voltage hybrid AC / DC distribution network containing low-voltage DC remote power supply equipment are established based on an improved distflow model. The power flow constraints for AC grid branches are as follows: (13) (14) (15) (16) In the formula: Ω AC For the collection of communication lines; and They are communication nodes j Flow to Node k The active and reactive power; and They are communication nodes i Flow to Node j The active and reactive power; and They are communication nodes j Injected active and reactive power; For flowing through the AC branch road i-j The current; For communication nodes i The voltage amplitude; and They are respectively AC branch lines i - j Resistance and reactance; and They are communication nodes i Upper and lower limits of voltage amplitude.

[0022] The power flow constraints of the DC subnetwork can be expressed as: (17) (18) (19) (20) In the formula: Ω DC A collection of DC lines; for t DC branch node during time period j Flow to Node k The active power; for t Time period nodes i Flow to Node j The active power; DC nodej Injected active power; for t DC node during time period i Voltage amplitude; and DC nodes i Upper and lower limits of voltage amplitude; DC branch i - j The resistance; DC branch i - j The upper limit of active power.

[0023] The power balance constraint of the AC network can be expressed as: (twenty one) In the formula: and Each is a communication node i The active and reactive power injected by the connected DG; and Each is a communication node i Active and reactive power injected by the connected VSC; and Each is a communication node i The active and reactive power of the connected loads.

[0024] The power balance constraint of a DC network is: (twenty two) In the formula: , and These are the active power of the DG connected to DC node i, the low-voltage DC remote power supply VSC, and the energy storage injection, respectively. For DC node i Connected load power.

[0025] Adjustable resource operation constraints: DG primarily ensures that active power remains within the predicted power range, and its active power constraint model is as follows: (twenty three) In the formula: , They are nodes i The scheduled power and predicted power of DG in time period t.

[0026] Energy storage devices (ESS) require setting charging and discharging modes, as well as charging and discharging power and state of charge. The charging and discharging constraints of ESS can be modeled as follows: (twenty four) In the formula: , They are nodes i ESS at the location t Charging 0-1 auxiliary variable and discharging 0-1 auxiliary variable within the time period; , ESS respectively t The charging power and discharging power at any given time; , These are the charge and discharge efficiency coefficients of the ESS, respectively. For ESS t Electricity consumption during the period; and These are the minimum and maximum values ​​for the ESS battery level, which can be set to 0.1 and 0.9 respectively. t 1 represents the initial time; T , △ t These are the length of the time period and the minimum optimization time interval, respectively. SOC 0 represents the initial ESS battery level.

[0027] OLTC adjusts the movement of taps up and down. The OLTC motion constraints can be modeled as follows: (25) In the formula: Let be the square of the OLTC transformation ratio at time t; , The squares of the upper and lower limits of the adjustable ratio of OLTC are respectively obtained; For OLTC s The range of gear adjustment; , and OLTC respectively t Time period s Gear position action variable, increase gear position 0-1 auxiliary variable, decrease gear position 0-1 auxiliary variable; , These are the maximum gear of the OLTC and the maximum number of adjustments allowed within the scheduling cycle, respectively.

[0028] CB-compensated power constraints: CB compensates for reactive power by switching capacitor banks, thereby adjusting the grid voltage. The compensation power is modeled as follows: (26) In the formula, Represents the number of CB units in operation; This represents the compensation power for each CB group.

[0029] Considering the limitations on the number of capacitor banks and the total number of adjustments, auxiliary variables are introduced. The change of CB in adjacent time periods can be modeled as follows: (27) In the formula, For connecting nodes i The maximum number of CB groups on the top; This represents the maximum number of operations for CB. Step S103: Based on the mathematical model of the series-type low-voltage DC remote power supply system and the various adjustable resources of the distribution network, construct a day-ahead-intraday rolling-real-time optimization model for coordinating the series-type low-voltage DC remote power supply system with the various flexible resources of the distribution network; During the day-ahead dispatch phase, global dispatch optimization is performed based on forecast data of photovoltaic output and load, aiming to minimize the sum of system losses and voltage deviations. The resulting operating schemes for the energy storage system, on-load tap-changing transformer, and low-voltage DC remote power supply system are then transferred to the intraday dispatch phase. In the intraday rolling optimization phase, more precise optimized dispatch is performed based on model predictive control, combining short-term forecast data and day-ahead dispatch results. In the real-time correction phase, real-time optimization is performed in conjunction with improved local control of the photovoltaic reactive power curve, in collaboration with energy storage and VSC.

[0030] Low-voltage DC remote power supply technology can convert long-distance AC line power supply to low-voltage DC line power supply. Under the same power, the line loss of a DC line is approximately 1 / 9 that of an AC line. Low-voltage DC remote power supply can effectively reduce network losses and improve voltage quality. Energy storage systems can achieve charging and discharging across time scales, influencing network losses and voltage quality from a temporal perspective. On-load tap-changing transformers can adjust tap positions to regulate voltage, while capacitor banks improve voltage through reactive power compensation. Therefore, this step optimizes network losses and voltage deviation by coordinating low-voltage DC remote power supply technology and the flexible resources of the distribution network. The established optimization objective is primarily to minimize network losses and voltage deviation, as shown below: (28) (29) (30) (31) In the formula: f loss and f U These are active power loss and voltage deviation, respectively. f loss,VSC , f loss,line These are the VSC active power loss of low-voltage DC remote power supply equipment and the active power loss of AC / DC lines in the distribution network, respectively. The active power loss of VSC in low-voltage DC remote power supply equipment;N Let be the total number of nodes in the distribution network. Equation (31) represents a threshold function reflecting the degree of voltage deviation. When the node voltage is outside the expected voltage range... , ]hour, f U This function works to reduce the deviation from the desired voltage range.

[0031] Since the constraints differ when solving the day-to-day rolling-real-time optimization model in the two different optimization stages of day-to-day and intraday, the constraints are explained differently: During the current optimization phase, the constraints include equations (1) to (27). In addition, since it is also necessary to coordinate the active power of photovoltaic, energy storage, and low-voltage DC remote power supply systems (VSCs) to obtain better regulation effects, the active power fluctuations of photovoltaic can be compensated by the charging and discharging of energy storage systems (ESSs), thereby utilizing the excess capacity for voltage regulation. Therefore, the active power coordinated balance constraint can be established as follows: (32) In the formula: For communication nodes i Active power injected by connected substations; In addition, the reactive power reference value of DG needs to be coordinated with the system voltage regulation target, and local correction redundancy should be reserved. The reactive power output redundancy constraint of DG can be calculated as follows: (33) In the formula: This is the maximum reactive power correction value for the local DG. , The lower and upper limits of DG reactive power capacity are respectively.

[0032] Intraday optimization phase: On actual operating days, at shorter time intervals △ t (e.g., 15 minutes) Rolling execution. Based on the latest ultra-short-term photovoltaic forecast data and real-time grid status, the day-ahead plan is revised and refined. The decision-making targets are fast-response devices, such as the real-time charging and discharging power of energy storage and the fine-tuning of the transmission power of low-voltage DC remote power supply systems.

[0033] In the intraday phase, first, we will focus on the next 4 hours (△). TThe photovoltaic and load data (within 4 hours) are used for prediction, and then short-term action plans are obtained based on the day-ahead scheduling results. Considering that OLTCs are discrete devices, and due to the limited number of switching operations, intraday scheduling is not suitable; therefore, scheduling information needs to be determined at the day-ahead stage. Thus, for intraday scheduling, the control plan for the OLTC obtained from the day-ahead scheduling is directly used. Furthermore, to ensure the continuity of the state of charge (SOC) between the two rolling optimization processes, a continuity constraint model for the energy storage SOC between different windows of the intraday rolling optimization can be established: (34) To ensure that the final state of charge (SOC) of each intraday rolling optimization is equal to the corresponding value of the day-ahead scheduling, the DG active power constraint and ESS charge / discharge constraint models can be established as follows: (35) In the formula: For the energy storage system at node i acquired in the previous phase, at time... T n +Δ T The state of charge (SOC).

[0034] In addition, during the intraday optimization phase, the constraints also include equations (1)-(24) and (32)-(33).

[0035] The specific method for intraday scheduling is as follows: Let the first... n The initial time for the next rolling optimization is... T n Then each scrolling optimization can obtain from T n arrive T n +Δ T The cycle of action plan. In each Δ T During the time period, based on the OLTC action plan obtained from the day-ahead solution, and then based on equations (1)-(24) and (28)-(35), the action plans for the ESS and low-voltage DC remote power supply equipment in the day-ahead optimization stage are obtained. The solution process is the same as that for the day-ahead scheduling. After each solution, only the first Δ is retained. t The optimization results for the time period are then used to repeat the rolling, prediction, and optimization process described above. When the last Δ... T When the time period can no longer be scrolled forward, retain the entire Δ. T The optimization results for each time period are then combined to obtain the intraday action plan.

[0036] Step S104: Solve the day-to-day rolling-real-time optimization model to determine a multi-timescale, multi-resource coordination optimization scheme.

[0037] Since the day-to-day rolling-real-time optimization model is a nonlinear, non-convex integer programming problem, it can be transformed into a second-order cone programming problem using quantity substitution and convex relaxation methods. Specifically, the day-to-day rolling-real-time optimization model is transformed to obtain a corresponding second-order cone programming model. Then, the Cplex commercial solver is used to solve the second-order cone programming model to determine a multi-timescale, multi-resource coordination optimization scheme. The transformation and solution processes are explained in detail below: The conversion process is as follows: make , , , In the power flow operation constraints of AC branches in the distribution network, equations (13)-(16) can be modified into equations (36)-(39) respectively through linearization: (36) (37) (38) (39) Similarly, the power flow constraint equations (17), (18), and (20) of the DC subnetwork can be linearized and modified into equations (40), (41), and (42), respectively: (40) (41) (42) After linearizing the DC current constraint equation (10) for DC remote power supply, we get: (43) Objective function transformation: Linearizing the network loss term in the objective function yields: (44) The original nonlinear nonconvex model can be transformed into a second-order cone programming model using the linearization and relaxation methods described above. Then, the Cplex commercial solver can be called using the MATLAB software with embedded YALMIP to solve the model efficiently.

[0038] Solution process: First, daytime long-term distribution network source and load data are collected. Then, the daytime long-term distribution network source and load data are substituted into a second-order cone programming model and input into the Cplex commercial solver for solving. The output is the daytime action plan for OLTC and the daytime action plan for CB. Next, daytime short-term distribution network source and load data are collected. The daytime short-term distribution network source and load data, the daytime action plan for OLTC, and the daytime action plan for CB are substituted into the second-order cone programming model and input into the Cplex commercial solver for solving. The output is the daytime action plan for the low-voltage DC remote power supply system, the daytime action plan for ESS, and the daytime action plan for DG.

[0039] To cope with rapid voltage fluctuations, the power output of photovoltaic (PV) and energy storage systems needs to be adjusted based on real-time voltage measurements. Therefore, it is necessary to calibrate the intraday operating schemes for the ESS (Emerging Power Supply), DG (Distributed Gas Generation), and low-voltage DC remote power supply systems to obtain calibrated intraday operating schemes for the ESS, DG, and low-voltage DC remote power supply systems. The specific calibration process is as follows:

[0040] A shorter timescale (e.g., 5 minutes) can be used for local control of photovoltaic reactive power, and fine-tuning of ESS and VSC active power can be performed. First, real-time node voltage measurements and optimized node operating voltages from the previous day to the current day are acquired. Then, based on the optimized node operating voltages from the previous day to the current day, a reactive power curve control strategy is generated, as shown in the following equation: (45) In the formula: This refers to the reactive power regulation of DG. The optimized node operating voltage for the day before today; , These are the minimum and maximum critical voltages corresponding to the reactive power adjustable range, respectively. This is the difference between the voltage point corresponding to zero reactive power and the operating point. , Both are slope coefficients. This represents the real-time node voltage measurement.

[0041] As can be seen from equation (45), the reactive power adjustment amount of DG can be determined based on the reactive power curve control strategy and the real-time node voltage measurement value. Then, based on the reactive power adjustment amount of DG, the intraday action scheme of DG is adjusted (the upper / lower limit of reactive power output amplitude is corrected) to generate the intraday action scheme of DG correction.

[0042] Furthermore, photovoltaic reactive power regulation can be coordinated with ESS and VSC active power fine-tuning to avoid secondary voltage fluctuations, i.e., by using real-time node voltage measurements and DG reactive power regulation. The active power output parameters in the ESS intraday correction action scheme and the low-voltage DC remote power supply system intraday correction action scheme are collaboratively optimized and adjusted to generate the ESS intraday correction action scheme and the low-voltage DC remote power supply system intraday correction action scheme, as shown in the following formula: (46) In the formula: , and These are the photovoltaic voltage-reactive power sensitivity coefficient, the energy storage voltage-active power sensitivity coefficient, and the VSC voltage-active power sensitivity coefficient, respectively.

[0043] Finally, the daytime action plan of OLTC, the daytime action plan of CB, the daytime action plan of low-voltage DC remote power supply system correction, the daytime action plan of ESS correction, and the daytime action plan of DG correction are integrated to obtain a multi-timescale multi-resource coordination and optimization scheme.

[0044] Simulation verification: This application uses a 14-node low-voltage distribution network with a reference voltage of 0.38kV as the test system for simulation analysis. The network topology is as follows: Figure 3 As shown in Table 1, the simulation parameters are as follows.

[0045] Table 1 Simulation Parameters ; To verify the effectiveness of the method proposed in this application, three schemes are set up for comparison in this valve implementation: Option 1: Optimize and regulate the low-voltage distribution network using only on-load tap changers (OLTC).

[0046] Option 2: Coordinate and optimize the low-voltage distribution network through the low-voltage DC remote power supply system and multiple resources such as ESS, OLTC, CB, and DG, but only perform long-term scale scheduling optimization.

[0047] Option 3: This application proposes a solution that coordinates and optimizes the low-voltage distribution network across multiple time scales by using a low-voltage DC remote power supply system in conjunction with multiple resources such as ESS, OLTC, CB, and DG.

[0048] The above schemes were solved using MATLAB and Cplex solver. The optimization results of each scheme were obtained, and the node voltage quality and network loss under different schemes were compared, as shown in Tables 2 and 3.

[0049] Table 2. Voltage quality index analysis of different schemes ; Table 3 Losses of different schemes ; Figure 4 , Figure 5 , Figure 6 Voltage distribution diagrams for Schemes 1, 2, and 3 are presented respectively. As shown in the diagrams, Scheme 1, relying solely on OLTC voltage regulation, leads to low voltage exceeding limits at most nodes, with the lowest node voltage reaching 0.898 pu. Scheme 2, through a low-voltage DC remote supply system and multi-resource long-term optimization, improves the voltage exceeding limit situation, but some nodes still experience low voltage issues, with the lowest node voltage reaching 0.93 pu. The scheme presented in this application, however, solves the low voltage exceeding limit problem. Table 2 shows the 24-hour average voltage compliance rate and average voltage deviation index results for different schemes. The results in Table 2 show that the traditional method (i.e., Scheme 1) has an average voltage compliance rate of only 91.1%. Scheme 2, through a low-voltage DC remote supply system and multi-resource long-term coordinated optimization, solves the low voltage problem at some nodes, achieving an average voltage compliance rate of 96.7%. The scheme presented in this application, through day-to-day multi-timescale coordinated optimization, achieves an average voltage compliance rate of 100%.

[0050] Table 3 shows the line loss, VSC loss, and total loss under different schemes. The results show that Scheme 2 reduces the total loss by 11.59% compared to the traditional method, while Scheme 3 reduces it by 44.59%. This indicates that the scheme in this application has carried out more detailed optimization of the VSC power and energy storage power of the low-voltage DC remote power supply system through multiple time scales. Compared with the traditional scheme and long-term time scale optimization, it can achieve better results in reducing network losses.

[0051] Figure 7 The total system loss and loss composition are shown for three schemes at different time periods. In scheme 1, as... Figure 7 As shown in (a), the OLTC bears the entire load, which leads to significant line losses and a large total loss; in scheme 2, as Figure 7 As shown in (b), after connecting multiple control devices, the line loss is reduced. The VSC power is relatively large in the long-term optimization scheme, leading to VSC loss, but the overall total loss is lower than in scheme 1; in scheme 3, as... Figure 7 As shown in (c), under the multi-timescale optimization of resources such as low-voltage DC remote power supply system, ESS, OLTC, and CB, line loss and VSC are significantly reduced, and the total loss is the smallest among the three schemes, with the best governance effect.

[0052] In summary, the method proposed in this application achieves multi-timescale coordinated management and control of multiple flexible resources in low-voltage distribution networks across day-day, intraday, and real-time time scales. Considering the impact of VSC control mode, operational constraints and control mode constraint models for the series-type low-voltage DC remote power supply system are established. Furthermore, local control of the photovoltaic reactive power curve is established in real-time control, coordinating with energy storage and the low-voltage DC remote power supply system. Utilizing the low-voltage DC remote power supply system to coordinate with other flexible resources in the distribution network can effectively solve the low-voltage problem for end users and effectively reduce network losses, ultimately achieving the overall economical, safe, and reliable operation goal of the system.

[0053] In some embodiments, this application also provides a computer system including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0054] This application also provides a computer-readable storage medium for storing a computer program. This computer-readable storage medium can be applied to a computer device, and the computer program causes the computer device to execute the corresponding processes in the methods described above in the embodiments of this application; for brevity, further details are omitted here.

[0055] The above embodiments are preferred implementations of this application. In addition, this application can be implemented in other ways. Any obvious substitutions without departing from the concept of this technical solution are within the protection scope of this application.

[0056] To facilitate understanding by those skilled in the art of the improvements made by this application compared to the prior art, some of the accompanying drawings and descriptions have been simplified, and for clarity, some other elements have been omitted from this application. Those skilled in the art should realize that these omitted elements may also constitute the content of this application.

Claims

1. A series-type low-voltage DC long-distance power supply and multi-resource, multi-time-scale coordination optimization method, characterized in that, include: Construct a mathematical model for a series-type low-voltage DC remote power supply system; Access to various adjustable resources in the power distribution network; Based on the mathematical model of the series-type low-voltage DC remote power supply system and the various adjustable resources of the distribution network, a day-ahead-intraday rolling-real-time optimization model for coordinating the series-type low-voltage DC remote power supply system with the various flexible resources of the distribution network is constructed. The day-to-day rolling-real-time optimization model is solved to determine a multi-timescale, multi-resource coordination optimization scheme.

2. The method according to claim 1, characterized in that, The distribution network has various adjustable resources, including OLTC, CB, ESS and DG.

3. The method according to claim 1, characterized in that, The constraints of the mathematical model for the series-type low-voltage DC remote power supply system include the following: Rectifier-side VSC operation constraints, inverter-side VSC operation constraints, DC line operation constraints, and VSC control mode constraints.

4. The method according to claim 3, characterized in that, The constraints of the day-ahead-intraday rolling-real-time optimization model during the day-ahead optimization phase include the following: Constraints of the mathematical model of a series-type low-voltage DC remote power supply system, including power flow constraints, active power collaborative balance constraints, reactive power redundancy constraints of DG, active power constraints of DG, ESS charging and discharging constraints, OLTC operation constraints, and CB compensation power constraints.

5. The method according to claim 3, characterized in that, The constraints of the day-to-day rolling-real-time optimization model during the intraday optimization phase include the following: Constraints of the mathematical model of a series-type low-voltage DC remote power supply system include: power flow constraints, active power collaborative balance constraints, reactive power redundancy constraints of DG, continuity constraints of energy storage SOC between different windows during intraday rolling optimization, consistency constraints between the final SOC after intraday rolling optimization and the day-ahead constraint, active power constraints of DG, and charging and discharging constraints of ESS.

6. The method according to claim 5, characterized in that, Solving the day-to-day rolling-real-time optimization model to determine a multi-timescale, multi-resource coordination optimization scheme includes: Based on variable substitution and convex relaxation, the day-to-day rolling-real-time optimization model is transformed to obtain a second-order cone programming model corresponding to the day-to-day rolling-real-time optimization model. The Cplex commercial solver was used to solve the second-order cone programming model and determine a multi-timescale, multi-resource coordination optimization scheme.

7. The method according to claim 6, characterized in that, The Cplex commercial solver is used to solve the second-order cone programming model to determine a multi-timescale, multi-resource coordination optimization scheme, including: Collect long-term distribution network source-load data; Substitute the daytime long-term distribution network source-load data into the second-order cone planning model, and input them into the Cplex commercial solver for solving, outputting the OLTC daytime action scheme and the CB daytime action scheme; Collect intraday short-timescale power distribution network source-load data; The OLTC day-ahead action plan, CB day-ahead action plan, and intraday short-timescale distribution network source-load data are substituted into the second-order cone programming model and input together into the Cplex commercial solver for solving, outputting the ESS intraday action plan, DG intraday action plan, and low-voltage DC remote power supply system intraday action plan. The intraday operation schemes of the ESS, DG, and low-voltage DC remote power supply system are corrected to obtain the corrected intraday operation schemes of the ESS, DG, and low-voltage DC remote power supply system. By integrating the OLTC day-ahead action plan, the CB day-ahead action plan, the low-voltage DC remote power supply system correction day-ahead action plan, the ESS correction day-ahead action plan, and the DG correction day-ahead action plan, a multi-time-scale, multi-resource coordination and optimization scheme is obtained.

8. The method according to claim 7, characterized in that, The intraday operating schemes of the ESS, DG, and low-voltage DC remote power supply system are corrected to obtain corrected intraday operating schemes for the ESS, DG, and low-voltage DC remote power supply systems; including: Acquire real-time node voltage measurements and day-to-day optimized node operating voltages; Based on the optimized node operating voltage from the previous day to the next day, a reactive power curve control strategy is generated. Based on the reactive power curve control strategy and the real-time node voltage measurement, the reactive power regulation amount of DG is determined; Based on the DG reactive power adjustment amount, the DG intraday action plan is adjusted to generate a DG correction intraday action plan. Based on the real-time node voltage measurement and the DG reactive power adjustment, the active power output parameters in the ESS correction intraday action scheme and the active power output parameters in the low-voltage DC remote power supply system intraday action scheme are coordinated and optimized to generate the ESS correction intraday action scheme and the low-voltage DC remote power supply system correction intraday action scheme.

9. A computer system, characterized in that, include: Memory is used to store instructions that can be executed by the processor; A processor for executing the instructions to implement the method as described in any one of claims 1 to 8.

10. A computer-readable medium, characterized in that, The system stores computer program code that, when executed by a processor, implements the method as described in any one of claims 1 to 8.

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