Voltage treatment and economic cooperative regulation and control method for new energy flexible power distribution system

Abstract

The invention discloses a voltage governance and economic coordinated regulation and control method for a new energy flexible power distribution system, relates to a new energy power distribution system, and establishes a day-ahead long-time scale optimization model and an intra-day short-time scale rolling optimization model in consideration of the difference of action characteristics of discrete equipment and continuous equipment. A flexible power distribution system optimization scheduling framework is designed on the basis of multiple time scales of model prediction control, a voltage treatment and economic cooperative regulation model containing the high-permeability new energy flexible power distribution system is constructed, the active power flow of the system is effectively adjusted, sufficient reactive power support is provided, and therefore the safety and economical efficiency of system operation are guaranteed.

Description

Technical Field

[0001] The present invention relates to a new energy distribution system, and specifically to a voltage governance and economic collaborative regulation method for a new energy flexible distribution system. Background Art

[0002] Driven by the "dual carbon" goal, distributed energy is connected to the grid on a large scale, and high-penetration distributed energy will become one of the important characteristics of the new distribution system. However, the strong uncertainty of distributed energy output is likely to cause problems such as two-way power flow, voltage over-limit, and line overload in the system. In the existing research on active distribution network regulation, on the one hand, there is still a lack of research on dealing with the uncertainty of distributed energy; on the other hand, although many studies have achieved good results in separately improving the system stability performance or economy, affected by the distribution network topology, it is impossible to ensure both the economy and stability of the new distribution system operation at the same time. Therefore, problems such as overuse of grid assets, voltage over-limit, and energy waste may occur during the regulation process of the distribution system with widespread access to distributed energy.

[0003] Model predictive control considers the state of the system in a finite future time period, uses the measured value at the current moment as the initial state, solves the optimal control problem through online rolling, and obtains the optimal control variables for the current and future time periods to adapt to the changes and uncertainties of the system. Model predictive control can effectively improve the system's ability to cope with the uncertainty of distributed energy. The purpose of flexible distribution system regulation is to promote the local consumption of distributed energy and minimize energy loss and voltage deviation. Flexible distribution system regulation is a regulation method that considers multiple regulation devices under the access of a wide range of flexible resources. Therefore, in the flexible distribution system, the multi-time scale regulation model based on model predictive control has achieved good results in separately improving the system stability performance or economy. However, problems such as voltage over-limit and increased network loss caused by distributed energy fluctuations and load transfer do not occur alone. However, voltage governance and economic collaborative regulation not only consider voltage problems or economic problems, but can also consider the voltage and economic problems of the system at the same time, so as to achieve the goals of reducing network loss and alleviating voltage overvoltage fluctuations at the same time. Summary of the Invention

[0004] In order to overcome the deficiencies of the prior art, the present invention proposes a voltage governance and economic collaborative regulation method for a new energy flexible distribution system, which considers the differences in the action characteristics of discrete devices and continuous devices and the multi-time scale based on model predictive control. The present invention comprehensively considers the regulation problems in the distribution system and takes into account factors such as photovoltaic output, on-load tap-changer of on-load tap-changing transformer, intelligent soft switch, and energy storage.

[0005] In order to achieve the above object, the technical solution of the present invention is as follows:

[0006] A voltage governance and economic collaborative regulation method for a new energy flexible power distribution system, the method comprising the following steps:

[0007] S1: Set up power distribution system regulation equipment, including on-load tap-changing transformers, energy storage systems, intelligent soft switches, and photovoltaics, and establish models for various types of equipment according to the characteristics and parameters of on-load tap-changing transformers, energy storage systems, intelligent soft switches, and photovoltaics;

[0008] S2: According to the differences in the action characteristics of the regulation equipment and the intra-day renewable energy output data of wind turbines and photovoltaics predicted in the short term, establish an optimized scheduling framework for a multi-time-scale flexible power distribution system based on model predictive control;

[0009] S3: Based on the models of various types of equipment established in step S1 and the optimized scheduling framework for the multi-time-scale flexible power distribution system established in step S2, considering the optimal power flow distribution, establish a voltage governance and economic collaborative regulation model for a flexible power distribution system with high-penetration new energy;

[0010] S4: Based on the voltage governance and economic collaborative regulation model established in step S3, transform the voltage governance and economic collaborative regulation model, thereby establishing a mixed-integer programming model of the voltage governance and economic collaborative regulation model, and by solving this model, output the optimized strategy of the flexible power distribution system, and conduct a comparative analysis on whether to consider the role of multiple time scales.

[0011] As a further solution of the present invention, in step S1, establishing models for various types of equipment includes the following steps:

[0012] S1-1: Establish an on-load tap-changing transformer model:

[0013] The on-load tap-changing transformer has a global regulating ability for the voltage of the entire distribution network. Its essence is a transformer. By adjusting the tap position, the secondary side voltage can be changed, thereby affecting the voltage level of the entire distribution network. Its mathematical model is as follows:

[0014]

[0015]

[0016]

[0017]

[0018] In the formula, is the tap position of the on-load tap-changing transformer at line ij at time t; is the voltage regulation rate of the on-load tap-changing transformer at line ij at time t; is the initial voltage regulation rate of the on-load tap-changing transformer at line ij; Δkij is the change in the voltage regulation rate for adjacent tap positions; N T is the total operating time of the on-load tap-changer transformer in a day; is the limit on the total number of tap position changes of the on-load tap-changer transformer within a day; is the maximum value of the tap position of the on-load tap-changer transformer at line ij.

[0019] S1-2: Establish a model of the energy storage system:

[0020] The energy storage operation constraints are as follows:

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027] In the formula: P i c,rat and P i d,rat are the upper and lower limits of the charging power and discharging power of the energy storage at node i respectively; is the charging or discharging state variable of the energy storage at node i at time t. When it takes 1, it means the energy storage is in the charging state. When it takes 0, it means the energy storage is in the discharging state; is the remaining energy of the energy storage at node i at time t; η ess,c and η ess,d are the charging efficiency and discharging efficiency of the energy storage at node i respectively; and are the upper and lower limits of the energy stored in the energy storage at node i; and are the initial stored energy and the final energy of the energy storage at node i respectively; is the rated capacity of the energy storage at node i.

[0028] S1-3: Establish a model of the intelligent soft switch:

[0029] The intelligent soft switch is a power electronic device installed at the traditional tie switch. It can accurately control the active and reactive powers of the feeders on both sides it is connected to. The intelligent soft switch consists of two converters. The DC side is connected by a DC capacitor, and the AC sides are connected to each feeder respectively. During normal operation, the main converter in the intelligent soft switch adopts a constant U dcQ control is used to stabilize the DC voltage. The converter adopts the fixed PQ control to achieve flexible control of the transmission power. The intelligent soft switch itself does not generate active power and is only used for feeder power transfer. The sum of the active powers output by each converter is zero, without considering the loss of the intelligent soft switch. The reactive power outputs of each converter are independent of each other and only need to meet their respective capacity constraints. Selecting the power injected by the intelligent soft switch into the node as the positive direction, the following operation constraints can be obtained:

[0030]

[0031]

[0032]

[0033] In the formula: and are the active powers output by the intelligent soft switch at node i and node j at time t, respectively; and are the reactive powers output by the intelligent soft switch at node i and node j at time t, respectively; and are the capacities of the converters connected to node i and node j, respectively.

[0034] S1-4: Establish a photovoltaic model:

[0035] The photovoltaic power generation connected to the distribution system should meet certain range constraints, that is, the actual photovoltaic power generation connected to the distribution system cannot exceed the upper and lower limits of its allowable output. Therefore, the photovoltaic output constraint can be expressed as:

[0036]

[0037] In the formula: is the rated capacity of the photovoltaic at node i.

[0038] As a further solution of the present invention, in step S2, establishing an optimal scheduling framework for a multi-time-scale flexible distribution system based on model predictive control includes the following steps:

[0039] S2-1: Establish a day-ahead optimal scheduling framework:

[0040] The day-ahead optimization is a long-time-scale optimization. According to the day-ahead load and renewable energy prediction information, with the active power loss as the optimization target, the action plans of the discrete devices within the day are determined. The discrete devices mainly include on-load tap-changing transformers, etc. They are limited by manufacturing technology and equipment life, and there are certain limitations on the switching speed and daily switching times. These discrete devices are limited by the adjustment speed and are not suitable for short-term rolling optimization. The scheduling plan needs to be determined in the day-ahead stage.

[0041] S2-2: Establish an intraday rolling optimization scheduling framework:

[0042] The intraday short-term rolling optimization is an optimization at a short time scale, which is started every time interval Δt. According to the load and renewable energy prediction information in the future MΔt period, with the active power loss in this period as the optimization goal, determine the action plans of the intelligent soft switches, photovoltaics, and energy storage of the continuous equipment within the day. However, only the output of each control resource at the first moment is executed. At the next moment t0+Δt, the time window moves backward by one time interval, and the above process is repeated. The rolling optimization takes into account the fluctuations of the intraday load and renewable energy, and is more accurate than the day-ahead optimization stage.

[0043] S2-3: Setting of the time step, start period, and solution duration in each time scale:

[0044] For the setting of the time step, start period, and solution duration in each of the above time scales, it is necessary to consider the spatio-temporal differences and regulation speeds of each control resource in the flexible distribution network, and make reasonable settings to achieve a reasonable matching of each control resource and each optimization stage, so as to ensure the safety and economy of the control strategy of the flexible distribution system.

[0045] The time interval of the day-ahead optimization scheduling stage is 1 hour, with the minimum active power loss as the optimization goal, considering power flow constraints, distribution network security constraints, active and reactive power constraints of adjustable resources, and voltage stability constraints.

[0046] The start period of the intraday rolling optimization scheduling stage is 15 minutes, and the solution duration is 4 hours. With the minimum active power loss as the optimization goal, and the active / reactive power of the intelligent soft switch, the active / reactive power of the energy storage, and the reactive power of the photovoltaic as decision variables, under the condition that the scheduling plan of the on-load tap-changer is determined day-ahead, the intraday optimization dynamically adjusts the control variables of the continuous equipment to minimize the active power loss at each moment.

[0047] As a further solution of the present invention, in S3, establishing a voltage governance and economic collaborative control model for a flexible distribution system with a high penetration of new energy includes the following steps:

[0048] S3-1: Use Equation (15) to construct the objective function of the voltage governance and economic collaborative control model for the day-ahead flexible distribution system:

[0049]

[0050] In the formula: f is the active power loss of the system; C = {C BN , C BL}, C is the normal situation C BN and the critical situation C BLThe set; N = {0, 1, …, n}, where N is the set of all nodes; T is the solution time; Δt is the optimized time interval.

[0051] S3-2: The constraints for constructing the voltage governance and economic coordinated control model of the day-ahead flexible distribution system include: distribution system power flow constraints, distribution network operation safety constraints, photovoltaic capacity constraints, intelligent soft switch operation constraints, on-load tap-changer operation constraints, and energy storage operation constraints:

[0052] Among them, the distribution system power flow constraints include:

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059] In the formula: Ω b is the set of system lines; r ij and x ij are the resistance and reactance of branch ij respectively; and are the active power and reactive power at the head of branch ij at time t respectively; and are the voltage amplitudes of nodes i and j at time t respectively; is the current amplitude flowing through branch ij at time t; and are the active power and reactive power injected at node i at time t respectively; and are the active power and reactive power injected by the photovoltaic at node i at time t respectively; and are the active power and reactive power output by the intelligent soft switch at node i at time t respectively; and are the active power and reactive power of the load at node i at time t respectively; and are the charging power and discharging power of the energy storage at node i at time t respectively; is the reactive power output by the energy storage at node i at time t.

[0060] The operation of the distribution network also needs to meet security constraints to ensure that the voltage and current do not exceed the limits. Therefore, the following constraints need to be satisfied:

[0061]

[0062]

[0063] In the formula: U_max and U_min are the upper and lower limits of the system operating voltage respectively; I_max is the upper limit of the current in the system line.

[0064] S3-3: Construct the voltage governance and economic coordinated control model of the intraday flexible distribution system:

[0065] The objective function is the same as in formula (15), and the constraint conditions include power flow constraints, distribution network operation safety constraints, intelligent soft switch operation constraints, energy storage operation constraints, and photovoltaic capacity constraints.

[0066] As a further solution of the present invention, in step S4, the transformation of the voltage governance and economic coordinated control model includes the following steps:

[0067] S4-1: The linearization process includes:

[0068] Adopt and Variable substitution for quadratic terms and To achieve linearization, after linearization, it is as follows:

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075] For the absolute value terms contained in the on-load tap-changer operation constraints of the transformer, by introducing auxiliary variables and The positive and negative change amounts of the on-load tap-changer of the branch ij at time t are respectively. In this way, formula (3) is linearized as:

[0076]

[0077]

[0078]

[0079] After the variable substitution in , constraint (1) can be re-expressed and transformed as follows:

[0080]

[0081]

[0082]

[0083] where: is the binary auxiliary variable associated with . Based on equations (2), (33)-(35), the following equation can be obtained through transformation:

[0084]

[0085] In equation (36) after substituting with the auxiliary variable , the linearization result and additional constraints are as follows:

[0086]

[0087]

[0088]

[0089] S4-2: The second-order cone transformation process includes:

[0090] After the variable substitution in equation (19), the quadratic term product is still non-linear. Continue with the second-order cone relaxation as shown in equation (40), and then transform it into the standard second-order cone form of equation (41) through equivalent transformation:

[0091]

[0092]

[0093] The operation constraints of the intelligent soft switch in equations (12)-(13), the capacity constraints of the energy storage in equations (9)-(10), and the capacity constraints of the photovoltaic in equation (14) are all quadratic non-linear constraints and can be transformed into rotational quadratic cone constraints as follows:

[0094]

[0095] Equation (27) is relaxed to inequality (41). To evaluate the accuracy of the second-order cone relaxation, the infinity norm of the relaxation deviation is defined:

[0096]

[0097] If the gap value is small enough, it is considered that the second-order cone relaxation is accurate for model conversion. Even if the gap value is relatively large, the gap value can be restricted within a predefined error range.

[0098] The beneficial effects of the present invention are as follows:

[0099] 1. Considering the multi-time scales of the flexible distribution system, it can effectively address problems such as two-way power flow, voltage over-limit, and line overload caused by the strong uncertainty of distributed energy output.

[0100] 2. The voltage governance and economic coordinated control method for the new energy flexible distribution system proposed by the present invention can simultaneously improve the economy and stability of the operation of the distribution system with high-penetration new energy, and better adapt to the changes and uncertainties of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Figure 1 is the flexible distribution system architecture.

[0102] Figure 2 is the intelligent soft-switch topology.

[0103] Figure 3 is the multi-time scale flexible distribution system optimal scheduling framework based on model predictive control.

[0104] Figure 4 is the single-line diagram of the radial distribution system.

[0105] Figure 5 is the improved IEEE-33 node system.

[0106] Figure 6 is the active power output of the intelligent soft-switch, the reactive power output of the intelligent soft-switch, the reactive power output of the photovoltaic, the reactive power output of the energy storage, the active power output of the energy storage, and the operating state of the on-load tap-changer.

[0107] Figure 7 is the three-dimensional diagram of the system voltage.

[0108] Figure 8 is the maximum Gap value at each moment.

[0109] Figure 9 is the flowchart of a voltage governance and economic coordinated control method for a new energy flexible distribution system.

[0110] SPECIFIC IMPLEMENTATION METHOD

[0111] The present invention will be further described below with reference to the accompanying drawings.

[0112] Refer toFigures 1 to 9 , a voltage governance and economic collaborative regulation method for a new energy flexible distribution system, establishes a voltage governance and economic collaborative regulation model for a flexible distribution system with high-penetration new energy. The method includes the following steps:

[0113] S1: Set the distribution system regulation equipment, including on-load tap-changing transformers, energy storage systems, intelligent soft switches, and photovoltaics, and establish models for various types of equipment according to the characteristics and parameters of on-load tap-changing transformers, energy storage systems, intelligent soft switches, and photovoltaics;

[0114] S2: According to the differences in the action characteristics of the regulation equipment and the intra-day renewable energy output data of wind turbines and photovoltaics predicted in the short term, establish an optimized scheduling framework for a multi-time-scale flexible distribution system based on model predictive control;

[0115] S3: Based on the models of various types of equipment established in S1 and the optimized scheduling framework for the multi-time-scale flexible distribution system established in S2, considering the optimal power flow distribution, establish a voltage governance and economic collaborative regulation model for a flexible distribution system with high-penetration new energy;

[0116] S4: Based on the voltage governance and economic collaborative regulation model established in step S3, transform the voltage governance and economic collaborative regulation model, thereby establishing a mixed-integer programming model of the voltage governance and economic collaborative regulation model, and by solving this model, output the optimized strategy of the flexible distribution system, and conduct a comparative analysis on whether to consider the role of multiple time scales.

[0117] Furthermore, in the step S1, establishing the models of various types of equipment includes the following steps:

[0118] S1-1: Establish an on-load tap-changing transformer model:

[0119] The on-load tap-changing transformer has a global regulation ability for the voltage of the entire distribution network. Its essence is a transformer. By adjusting the tap position, the secondary-side voltage can be changed, thereby affecting the voltage level of the entire distribution network. Its mathematical model is as follows:

[0120]

[0121]

[0122]

[0123]

[0124] In the formula, is the tap position of the on-load tap-changing transformer at line ij at time t; is the voltage regulation rate of the on-load tap-changing transformer at line ij at time t; is the initial voltage regulation rate of the on-load tap-changer at line ij; Δk ij is the change in the voltage regulation rate at adjacent tap positions; N T is the total operating time of the on-load tap-changer in a day; is the limit on the total number of changes in the tap position of the on-load tap-changer within a day; is the maximum value of the tap position of the on-load tap-changer at line ij.

[0125] S1-2: Establish the energy storage system model:

[0126] The energy storage operation constraints are as follows:

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133] In the formula: P i c,rat and P i d,rat are the upper and lower limits of the charging power and discharging power of the energy storage at node i, respectively; is the charging or discharging state variable of the energy storage at node i at time t. When taking 1, it means the energy storage is in the charging state, and when taking 0, it means the energy storage is in the discharging state; is the remaining energy of the energy storage at node i at time t; η ess,c and η ess,d are the charging efficiency and discharging efficiency of the energy storage at node i, respectively; and are the upper and lower limits of the energy stored in the energy storage at node i; and are the initial stored energy and the final energy of the energy storage at node i, respectively; is the rated capacity of the energy storage at node i.

[0134] S1-3: Establish the intelligent soft switch model:

[0135] The intelligent soft switch is a power electronic device installed at the traditional tie switch, which can accurately control the active and reactive power of the feeders connected on both sides. The intelligent soft switch consists of two converters, with the DC side connected by a DC capacitor and the AC sides respectively connected to each feeder. During normal operation, the main converter in the intelligent soft switch adopts constant U dc Q control to stabilize the DC voltage, and the slave converter adopts constant PQ control to achieve flexible control of the transmitted power. The intelligent soft switch itself does not generate active power and is only used for feeder power transfer. The sum of the active powers output by each converter is zero, without considering the losses of the intelligent soft switch; the reactive power outputs of each converter are independent of each other and only need to meet their respective capacity constraints. Selecting the power injected by the intelligent soft switch into the node as the positive direction, the following operating constraints can be obtained:

[0136]

[0137]

[0138]

[0139] In the formula: and are the active power outputs of the intelligent soft switches at nodes i and j at time t respectively; and are the reactive power outputs of the intelligent soft switches at nodes i and j at time t respectively; and are the capacities of the converters connected to nodes i and j respectively.

[0140] S1-4: Establish a photovoltaic model:

[0141] The photovoltaic power generation connected to the distribution system should meet certain range constraints, that is, the actual photovoltaic power generation connected to the distribution system cannot exceed the upper and lower limits of its allowable output. Therefore, the photovoltaic output constraint can be expressed as:

[0142]

[0143] In the formula: is the rated capacity of the photovoltaic at node i.

[0144] Specifically, in the above S2, establishing an optimal scheduling framework for a multi-time-scale flexible distribution system based on model predictive control includes the following steps:

[0145] S2-1: Establish a day-ahead optimal scheduling framework:

[0146] Previously optimized for long - time - scale optimization, based on the predicted information of daily load and renewable energy, with the active power loss as the optimization goal, determine the action plans of discrete devices within a day. Discrete devices mainly include on - load tap - changing transformers, etc. Limited by manufacturing technology and equipment life, there are certain limitations on the switching speed and daily switching times. Restricted by the adjustment speed, these discrete devices are not suitable for short - term rolling optimization and need to determine the dispatching plan at the day - ahead stage.

[0147] S2 - 2: Establish the intra - day rolling optimization dispatching framework:

[0148] Intra - day short - term rolling optimization is short - time - scale optimization, which is started every time interval Δt. According to the predicted information of load and renewable energy in the future MΔt period, with the active power loss in this period as the optimization goal, determine the action plans of intelligent soft switches, photovoltaics, and energy storage in continuous devices within a day. However, only the output of each control resource at the first moment is executed. At the next moment t0 + Δt, the time window moves backward by one time interval, and the above process is repeated. Rolling optimization takes into account the fluctuations of intra - day load and renewable energy and is more accurate than the day - ahead optimization stage.

[0149] S2 - 3: Setting of time step, start cycle, and solution duration in each time scale:

[0150] For the setting of time step, start cycle, and solution duration in the above - mentioned each time scale, it is necessary to consider the spatio - temporal differences and adjustment speeds of each control resource in the flexible distribution network, etc., and make reasonable settings to achieve a reasonable match between each control resource and each optimization stage, so as to ensure the safety and economy of the control strategy of the flexible distribution system.

[0151] The time interval in the day - ahead optimization dispatching stage is 1 hour, with the minimum active power loss as the optimization goal, considering power flow constraints, distribution network security constraints, active and reactive power constraints of adjustable resources, and voltage stability constraints.

[0152] The start cycle of the intra - day rolling optimization dispatching stage is 15 minutes, and the solution duration is 4 hours. With the minimum active power loss as the optimization goal, using the active / reactive power of intelligent soft switches, the active / reactive power of energy storage, and the reactive power of photovoltaics as decision variables, under the dispatching plan of the on - load tap - changing transformer determined at the day - ahead stage, the intra - day optimization dynamically adjusts the control variables of continuous devices to minimize the active power loss at each moment.

[0153] Specifically, in S3, establishing a voltage governance and economic collaborative control model for a flexible distribution system with high - penetration new energy includes the following steps:

[0154] S3 - 1: Use equation (15) to construct the objective function of the voltage governance and economic collaborative control model for the day - ahead flexible distribution system:

[0155]

[0156] In the formula: f is the active power loss of the system; C = {C BN , C BL}, C is the set of normal condition C BN and critical condition C BL ; N = {0, 1, … n}, N is the set of all nodes; T is the solving duration; Δt is the optimized time interval.

[0157] S3-2: The constraints for constructing the voltage governance and economic coordinated regulation model of the day-ahead flexible distribution system include: distribution system power flow constraints, distribution network operation safety constraints, photovoltaic capacity constraints, intelligent soft switch operation constraints, on-load tap-changer operation constraints, and energy storage operation constraints:

[0158] Among them, the distribution system power flow constraints include:

[0159]

[0160]

[0161]

[0162]

[0163]

[0164]

[0165] In the formula: Ω b is the set of system lines; r ij and x ij are the resistance and reactance of branch ij respectively; and are the active power and reactive power at the head of branch ij at time t respectively; and are the voltage amplitudes of node i and node j at time t respectively; is the current amplitude flowing through branch ij at time t; and are the active power and reactive power injected at node i at time t respectively; and are the active power and reactive power injected by the photovoltaic at node i at time t respectively; and are the active power and reactive power output by the intelligent soft switch of node i at time t respectively; and are the active power and reactive power of the load at node i at time t respectively; and The charging power and discharging power of the energy storage at node i at time t, respectively; is the reactive power output by the energy storage at node i at time t.

[0166] The operation of the distribution network also needs to meet the security constraints to ensure that the voltage and current do not exceed the limits. Therefore, the following constraints need to be satisfied:

[0167]

[0168]

[0169] In the formula: and U are the upper and lower limits of the system operating voltage, respectively; is the upper limit of the current of the system line.

[0170] S3-3: Construct a voltage governance and economic coordinated control model for the intraday flexible distribution system:

[0171] The objective function is the same as formula (15), and the constraint conditions include power flow constraints, distribution network operation safety constraints, intelligent soft switch operation constraints, energy storage operation constraints, and photovoltaic capacity constraints.

[0172] Specifically, in step S4, the transformation of the voltage governance and economic coordinated control model includes the following steps:

[0173] S4-1: The linearization process includes:

[0174] Adopt and Variable substitution of quadratic terms and to achieve linearization. After linearization, it is as follows:

[0175]

[0176]

[0177]

[0178]

[0179]

[0180]

[0181] For the absolute value terms contained in the on-load tap-changer operation constraints, auxiliary variables and are introduced, which are the positive and negative change amounts of the on-load tap-changer tap action at branch ij at time t, respectively. In this way, formula (3) is linearized as:

[0182]

[0183]

[0184]

[0185] After the variable substitution in , Constraint (1) can be re-expressed and transformed as follows:

[0186]

[0187]

[0188]

[0189] where: is the binary auxiliary variable associated with . Based on Equations (2), (33)-(35), the following equation can be obtained through transformation:

[0190]

[0191] In Equation (36) after substituting with the auxiliary variable , the linearized result and additional constraints are as follows:

[0192]

[0193]

[0194]

[0195] S4-2: The second-order cone conversion process includes:

[0196] After using variable substitution in Equation (19), the quadratic term product is still non-linear. Continue with the second-order cone relaxation as shown in Equation (40), and then transform it into the standard second-order cone form of Equation (41) through equivalent transformation:

[0197]

[0198]

[0199] The operation constraints of the intelligent soft switch in Equations (12)-(13), the capacity constraints of the energy storage in Equations (9)-(10), and the capacity constraints of the photovoltaic in Equation (14) are all quadratic non-linear constraints and can be transformed into rotational quadratic cone constraints as follows:

[0200]

[0201] Equation (27) is relaxed to inequality (41). To evaluate the accuracy of the second-order cone relaxation, the infinity norm of the relaxation deviation is defined:

[0202]

[0203] If the gap value is small enough, the second-order cone relaxation is considered accurate for model conversion. Even if the numerical value is large, the gap value can be limited within a predefined error range.

[0204] To enable those skilled in the art to better understand the present invention, the case study analysis includes the following components:

[0205] 1) Network model and parameter settings: The present invention takes the improved IEEE 33-node system as an example, and the structure is as Figure 5 shown. The entire system includes four photovoltaic units, two intelligent soft switches, one energy storage, and one on-load tap-changing transformer. The system base voltage is set to 12.66 kV, and the per-unit value range of the node voltages is set to [0.95, 1.05] pu. The connection locations and capacities of the photovoltaic units are shown in Table 1. The system is connected with 2 groups of intelligent soft switches, which are installed at nodes 12 and 22 and nodes 25 and 29 respectively, with a capacity of 800 kVA each. A group of energy storage is installed at node 33, with a capacity of 3 MWh, a rated charge-discharge power of 0.6 MW, an initial state of charge (SOC) of 2 MWh, the upper and lower limits of the SOC are 3 MWh and 1.2 MWh respectively, and the charge-discharge efficiency is 0.95. For the on-load tap-changing transformer, the maximum allowable number of daily operations is 4 times, and the initial tap position and increment are 1.0 and 1% respectively. The case study simulations are all carried out in MATLAB 2020b. In a 64-bit Windows environment, the Gurobi solver and the YALMIP toolbox are used to solve the model.

[0206] Table 1 Basic installation parameters of photovoltaic units

[0207]

[0208]

[0209] 2) Analysis of safety and economic indicators in the day-ahead long time scale and intraday rolling optimization stages: The present invention conducts simulations based on the multi-time scale method of model predictive control, and the safety and economic indicator values in the day-ahead long time scale and intraday rolling optimization stages can be obtained respectively, as shown in Table 2. By comparing the day-ahead and intraday data, it can be found that the intraday rolling optimization stage reduces the system losses and voltage deviations, and improves the safety and economy of the system.

[0210] Table 2 Day-ahead and intraday optimization results

[0211]

[0212] 3) Adjustable resource regulation analysis: The coordinated output of multiple resources in the flexible distribution system is as Figure 6 shown. First, analyze the coordination of active resources in the flexible distribution system. From Figure 6 (a) and (e), it can be seen that the active operation strategies of the intelligent soft switch and energy storage are consistent with the power supply and demand situation of the distribution network. The high penetration of distributed generation makes the power flow in the power system fluctuate greatly. During 0:00 - 8:00 and 16:00 - 24:00, distributed energy cannot supply the high power demand. Two groups of intelligent soft switches transmit active power to nodes 12 and 29 and the energy storage discharges to relieve the power demand of the system. However, due to the surplus output of distributed energy far exceeding the load demand during 9:00 - 15:00, two groups of intelligent soft switches are dispatched to reverse-transmit active power to nodes 22 and 25 and the energy storage is charged to smooth the power fluctuation as much as possible. Next is the analysis of the coordination of reactive / voltage resources in the flexible distribution system. From Figure 6 (b) and (d), it can be seen that the reactive output strategies of the intelligent soft switch and energy storage are consistent with the load demand situation, providing strong reactive and voltage support for the system. During 16:00 - 24:00, the load demand is too large. From Figure 6 (c) and (f), it can be seen that the photovoltaic and on-load tap-changing transformers act to provide sufficient reactive power for the system, and the voltage stability of the system is maintained through the above reactive power strategies.

[0213] 4) Voltage stability analysis: The 24-hour voltages of each node in the flexible distribution system with high-penetration new energy are as Figure 7 shown. It can be found that the per-unit values of the voltages of each node are mostly between the expected voltage of 0.96 - 1.04, meeting the safety index requirements of the flexible distribution system and reducing the impact of distributed energy fluctuations on the system.

[0214] 5) Model verification analysis: Through the defined gap value, the accuracy of the convex relaxation proposed in this paper is verified. The 24-hour gap values of the flexible distribution system with high-penetration new energy are as Figure 8 shown. It can be found that the overall error values of the flexible distribution system with high-penetration new energy are all less than 1×10 ―6 , so the convex relaxation is accurate.

[0215] In summary, the voltage governance and economic coordinated regulation strategy of the flexible distribution system with high-penetration new energy effectively adjusts the active power flow of the system, can effectively solve a series of problems brought about by the grid connection of distributed resources, and at the same time provides sufficient reactive power support, thus ensuring the safety and economy of the system operation.

[0216] In the description of this specification, the illustrative representations of the present invention do not necessarily refer to the same embodiments or examples. Those skilled in the art can combine and combine the different embodiments or examples described in this specification. In addition, the additional content described in the embodiments of this specification is only a list of the implementation forms of the inventive concept. The protection scope of the present invention should not be regarded as limited to the specific forms stated in the implementation cases. The protection scope of the present invention also includes equivalent technical means that those skilled in the art can think of according to the inventive concept.

Claims

1. A voltage governance and economic collaborative regulation method for a new energy flexible power distribution system, characterized in that The method includes the following steps: S1: Set up distribution system control equipment, including on-load tap-changing transformers, energy storage systems, intelligent soft switches, and photovoltaics, and establish models for various types of equipment according to the characteristics and parameters of on-load tap-changing transformers, energy storage systems, intelligent soft switches, and photovoltaics; S2: Based on the differences in the action characteristics of the control equipment and the intra-day renewable energy output data of wind turbines and photovoltaics predicted in the short term, establish an optimal dispatching framework for a multi-time-scale flexible distribution system based on model predictive control; S3: Considering the optimal power flow distribution, establish a voltage governance and economic collaborative control model for a flexible distribution system with a high penetration of new energy; S4: Transform the voltage governance and economic collaborative control model, establish a mixed-integer programming model of the voltage governance and economic collaborative control model, solve this model, output the optimal strategy for the flexible distribution system, and conduct a comparative analysis on whether the multi-time-scale effect is considered.

2. The voltage governance and economic collaborative regulation method of the new energy flexible power distribution system according to claim 1, characterized in that In step S1, establishing models for various types of equipment includes the following steps: S1-1: Establish an on-load tap-changing transformer model: The mathematical model of the on-load tap-changing transformer is as follows: Wherein, is the tap position of the on-load tap-changer at line ij at time t; is the voltage regulation rate of the on-load tap-changer at line ij at time t; is the initial voltage regulation rate of the on-load tap-changer at line ij; Δk ij is the change amount of the voltage regulation rate between adjacent tap positions; N T is the total operating time of the on-load tap-changer in one day; is the limit on the total number of changes in the tap position of the on-load tap-changer within one day; is the maximum value of the tap position of the on-load tap-changer at line ij; S1-2: Establish an energy storage system model: The operation constraints of the energy storage are as follows: Where: P i c,rat and P i d,rat are respectively the upper and lower limits of the energy storage charging power and discharging power at node i; is the state variable of the energy storage charging or discharging at node i at time t. When taking 1, it means the energy storage is in the charging state, and when taking 0, it means the energy storage is in the discharging state; is the remaining energy of the energy storage at node i at time t; η ess,c and η ess,d are respectively the charging efficiency and discharging efficiency of the energy storage at node i; and are the upper and lower limits of the energy stored in the energy storage at node i; and are respectively the initial stored energy and the final energy of the energy storage at node i; is the rated capacity of the energy storage at node i; S1-3: Establish an intelligent soft switch model: Select the positive direction of the power injected by the intelligent soft switch into the node, and the following operation constraints are obtained: Wherein: and are the active power outputs of the intelligent soft switches at node i and node j at time t, respectively; and are the reactive power outputs of the intelligent soft switches at node i and node j at time t, respectively; and are the capacities of the converters connected to node i and node j, respectively; S1-4: Establish a photovoltaic model: The photovoltaic output constraint is expressed as: In the formula: is the rated capacity of the photovoltaic at node i.

3. The voltage governance and economic collaborative regulation method of the new energy flexible power distribution system according to claim 2, characterized in that In step S2, establishing an optimal dispatching framework for a multi-time-scale flexible distribution system based on model predictive control includes the following steps: S2-1: Establish a day-ahead optimal dispatching framework: The day-ahead optimization is a long-time-scale optimization. According to the day-ahead load and renewable energy prediction information, with the minimum active power loss as the optimization goal, determine the action plan of the intra-day discrete equipment. The discrete equipment includes on-load tap-changing transformers; S2-2: Establish an intra-day rolling optimal dispatching framework: The intra-day short-term rolling optimization is a short-time-scale optimization. It is started every time interval Δt. According to the load and renewable energy prediction information in the future MΔt time period, with the active power loss in this time period as the optimization goal, determine the action plans of the intra-day continuous equipment, namely intelligent soft switches, photovoltaics, and energy storage. However, only the output of each control resource at the first moment is executed. At the next moment t0 + Δt, the time window moves backward by one time interval, and the above process is repeated; S2-3: Setting the time step, start period, and solution duration in each time scale: The time interval in the day-ahead optimal dispatching stage is 1 hour. With the minimum active power loss as the optimization goal, consider the power flow constraint, the safety constraint of the distribution network, the active and reactive power constraints of adjustable resources, and the voltage stability constraint; The start period of the intra-day rolling optimal dispatching stage is 15 minutes, and the solution duration is 4 hours. With the minimum active power loss as the optimization goal, using the active / reactive power of the intelligent soft switch, the active / reactive power of the energy storage, and the reactive power of the photovoltaic as decision variables, under the condition that the dispatching plan of the on-load tap-changing transformer is determined in the day-ahead, the intra-day optimization dynamically adjusts the control variables of the continuous equipment to minimize the active power loss at each moment.

4. The voltage governance and economic collaborative regulation method of the new energy flexible power distribution system according to claim 3, characterized in that, In the step S3, establishing a voltage governance and economic collaborative regulation model for a new energy flexible distribution system with high permeability includes the following steps: S3-1: Use Equation (15) to construct the objective function of the voltage governance and economic collaborative regulation model for the day-ahead flexible distribution system: where: f is the active power loss of the system; C = {C BN , C BL}, C is the set of normal condition C BN and critical condition C BL ; N = {0, 1, … n}, N is the set of all nodes; T is the solving duration; Δt is the optimized time interval; S3-2: The constraints for constructing the voltage governance and economic collaborative regulation model of the day-ahead flexible distribution system include: power flow constraints of the distribution system, operation safety constraints of the distribution network, photovoltaic capacity constraints, operation constraints of intelligent soft switches, operation constraints of on-load tap-changing transformers, and energy storage operation constraints: Among them, the power flow constraints of the distribution system include: Where: Ω b is the set of system lines; r ij and x ij are the resistance and reactance of branch ij, respectively; and are the active power and reactive power at the head of branch ij at time t, respectively; and are the voltage amplitudes of node i and node j at time t, respectively; is the current amplitude flowing through branch ij at time t; and are the active power and reactive power injected at node i at time t, respectively; and are the active power and reactive power injected by the photovoltaic at node i at time t, respectively; and are the active power and reactive power output by the intelligent soft switch at node i at time t, respectively; and are the active power and reactive power of the load at node i at time t, respectively; and are the charging power and discharging power of the energy storage at node i at time t, respectively; is the reactive power output by the energy storage at node i at time t; The power flow constraints of the distribution system also include: where: U and U are respectively the upper and lower limits of the system operating voltage; is the upper limit of the current in the system line; S3-3: Construct the voltage governance and economic collaborative regulation model for the intra-day flexible distribution system: The objective function is the same as Equation (15), and the constraint conditions include power flow constraints, operation safety constraints of the distribution network, operation constraints of intelligent soft switches, energy storage operation constraints, and photovoltaic capacity constraints.

5. The voltage governance and economic collaborative regulation method of the new energy flexible power distribution system according to claim 4, characterized in that, In the step S4, the transformation of the voltage governance and economic collaborative regulation model includes the following steps: S4-1: The linearization process includes: Adopt and Variable substitution quadratic term and To achieve linearization, the result after linearization is as follows: For the absolute value terms in the on-load tap-changer transformer operation constraints, auxiliary variables are introduced and are the positive and negative change amounts of the on-load tap-changer tap action at branch ij at time t, respectively. Equation (3) is linearized as: After the variable substitution, Constraint (1) is re-expressed and transformed as follows: Wherein: is a binary auxiliary variable associated with Based on Equation (2), Equations (33)-(35), the following equation is obtained through transformation: In Equation (36) After substituting with auxiliary variables The linearized result and additional constraints are as follows: S4-2: The second-order cone transformation process includes: After the variable substitution in Equation (19), the product of quadratic terms is still non-linear. Continue with the second-order cone relaxation as shown in Equation (40), and then transform it into the standard second-order cone form of Equation (41) through equivalent transformation: The operation constraints of the intelligent soft switch in Equations (12)-(13), the capacity constraints of the energy storage in Equations (9)-(10), and the capacity constraints of the photovoltaic in Equation (14) are all quadratic non-linear constraints, and are transformed into rotated second-order cone constraints as follows: Equation (27) is relaxed to Inequality (41), and the infinity norm of the relaxation deviation is defined: If the gap value is small enough, it is considered that the second-order cone relaxation for model transformation is accurate; if the gap value is large, the gap value is restricted within the predefined error range.

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