Optimized operation method of reconfigurable soft switch-containing power distribution system based on reconstruction

Abstract

The invention discloses a reconfiguration-based operation optimization method for a power distribution system containing a reconfigurable soft switch. The method comprises the following steps of: 1, constructing a self-energy-storage type reconfigurable intelligent soft switch topology based on asymmetric voltage source converter capacity and feeder line selection; 2, modeling is carried out on the self-energy-storage recombined intelligent soft switch; and 3, modeling the reconfiguration-based flexible power distribution system containing the reconfigurable soft switch, and obtaining an optimal operation strategy of the power distribution system by solving the optimization operation model. According to the invention, the multi-element controllable equipment of the power distribution system can be reasonably utilized to improve the utilization rate of the traditional multi-terminal intelligent soft switch to the capacity of the voltage source converter, and the maximum transmission power of the traditional multi-terminal intelligent soft switch can be improved, so that the operation loss of the power distribution system can be reduced, and the new energy consumption can be improved.

Description

Technical Field

[0001] The invention belongs to the field of power distribution system operation optimization, and in particular to an optimization operation method of a power distribution system containing reconfigurable soft switches based on reconstruction. Background Art

[0002] In recent years, with the large-scale access of renewable energy to the distribution system and the development of power electronics technology, the distribution system has shown the characteristics of a high proportion of renewable energy access and a high proportion of power electronic equipment application. On the one hand, the output power of renewable energy, mainly wind and solar, has strong uncertainty and is easily affected by the external environment. It is unable to continuously output stable electricity, the problem of renewable energy absorption is becoming more and more serious, and the operating efficiency of the distribution system is reduced; on the other hand, the traditional radial distribution system has gradually evolved into a multi-power system, and the flow direction of the current has also changed from unidirectional flow to bidirectional flow, making the structure and control of the distribution system more complex; in addition, the development of power electronics technology has further promoted the evolution of the distribution system towards a flexible closed-loop structure. How to reasonably use the multi-controllable equipment (such as power electronic equipment) of the distribution system is an urgent challenge to be solved.

[0003] As a new type of power electronic device, the traditional multi-terminal intelligent soft switch has the advantages of precise power flow control, fast power transfer, active support of voltage and frequency, etc. It can realize the flexible closed-loop operation of the distribution network, provide active power flow control, reactive power compensation and voltage regulation functions, and has a wide range of applications in improving the consumption of new energy and reducing system operation losses. However, the symmetrical structure of the voltage source converter of the traditional multi-terminal intelligent soft switch is easy to limit the maximum transmission power of the multi-terminal intelligent soft switch, resulting in the lower utilization rate of the voltage source converter capacity of the traditional multi-terminal intelligent soft switch with more interconnected lines, which makes it difficult to apply the traditional multi-terminal intelligent soft switch in scenarios with large power transmission requirements (such as distribution systems with a high proportion of new energy). Summary of the invention

[0004] The present invention aims to solve the deficiencies of the above-mentioned prior art and proposes an optimization operation method for a distribution system containing a reconfigurable soft switch based on reconstruction, so as to reasonably utilize the multi-controllable equipment of the distribution system, so as to improve the utilization rate of the capacity of the voltage source converter of the traditional multi-terminal intelligent soft switch, and to increase the maximum transmission power of the traditional multi-terminal intelligent soft switch, thereby reducing the operating loss of the distribution system and improving the consumption of new energy.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme:

[0006] The invention provides a method for optimizing operation of a power distribution system containing a reconfigurable soft switch based on reconstruction, which comprises the following steps:

[0007] Step 1: The operation model of the energy storage system is obtained from equations (1) to (4):

[0008] (1)

[0009] (2)

[0010] (3)

[0011] (4)

[0012] In formula (1) to formula (4), , are the minimum and maximum charging power of the energy storage system, respectively. , are the minimum and maximum values ​​of the discharge power of the energy storage system respectively; , The energy storage system is Charging power and discharging power at the moment; For Auxiliary variables set at all times, when the energy storage system is When charging, =1, when discharging, let =0, , , are the minimum, maximum and initial values ​​of the state of charge of the energy storage system respectively; , are the total capacity of the energy storage system and Capacity at the moment; , The charging and discharging efficiency of the energy storage system; is the time step;

[0013] Step 2: Construct feeder selection constraints using equations (5) and (6):

[0014] (5)

[0015] (6)

[0016] In formula (5)-formula (6), , denote the set of nodes connected to the voltage source converter and the set of all voltage source converters respectively; for The node connected to the voltage source converter at all times Apparent power transmitted through the self-storage reorganized intelligent soft switch; Voltage Source Converter Capacity; is a binary variable representing the voltage source converter exist Whether the node is connected to the voltage source converter at the moment connected;

[0017] Step 3: Construct the operation model of the self-storage type recombinant intelligent soft switch R-SOP by equations (7) to (10):

[0018] (7)

[0019] (8)

[0020] (9)

[0021] (10)

[0022] In formula (7) to formula (10), , , They are Moments and Nodes Total losses of all connected voltage source converters, total DC side active power of all voltage source converters, total AC side active power of all voltage source converters; for Time Node Reactive power transmitted through the self-storage reorganization intelligent soft switch R-SOP; is the operating loss coefficient of the voltage source converter;

[0023] Step 4: Construct the controllable equipment operation model of the distribution system by using equations (11) to (14), including: the operation model of the on-load tap-changing transformer OLTC and the operation model of the switchable capacitor bank CBs:

[0024] Step 4.1: Construct the operation model of the on-load tap-changing transformer OLTC by equation (11)-(12):

[0025] (11)

[0026] (12)

[0027] In formula (11)-formula (12), for The node connected to the voltage source converter at all times The voltage is equal to the voltage on the secondary side of OLTC; for The node connected to the voltage source converter at all times With Node Between lines The tap position of OLTC; For Node exist Voltage at the moment; for Timeline Voltage regulation rate of OLTC; For line Initial voltage regulation rate of OLTC; For line The change in voltage regulation rate at adjacent tap positions;

[0028] The operation model of CBs is constructed by equation (13)-(14):

[0029] (13)

[0030] (14)

[0031] In formula (13)-formula (14), For Node exist The capacity of capacitors connected to the power distribution system at all times, yes The capacitor capacity, yes Time Node The number of capacitor banks put into operation, It is the maximum number of capacitor banks allowed to be put into operation;

[0032] Step 5: Construct the operation model of the distribution system containing the self-storage type reorganization type intelligent soft switch R-SOP from (15) to (22):

[0033] (15)

[0034] (16)

[0035] (17)

[0036] (18)

[0037] (19)

[0038] (20)

[0039] (twenty one)

[0040] (twenty two)

[0041] In formula (15) to formula (22), A collection of distribution lines; is the node of the distribution line, ; , They are Timeline Active power and reactive power transmitted on the network; yes Timeline The connectivity status, when Timeline When connected, =1, when Timeline When not connected, =0; and They are Timeline Active power and reactive power transmitted on the and Line resistance and reactance; for Timeline The current transmitted on , They are Time Node The amount of photovoltaic active power output and photovoltaic active power reduction; , They are Time Node The active output of the fan and the reduction of the active output of the fan; and They are Time Node Active power and reactive power consumed by the load; and are the upper and lower limits of node voltage in the power distribution system, respectively; It is the upper limit of line current in the power distribution system;

[0042] Step 6: Construct the objective function of the distribution system containing the self-storage type reorganized intelligent soft switch R-SOP from (23) to (24): :

[0043] (twenty three)

[0044] (twenty four)

[0045] In formula (23)-formula (24), , are the loss of the distribution system and the reduction of renewable energy output, , There are 2 weight coefficients respectively; , are the charging and discharging efficiency of the energy storage system respectively; represents the simulation time;

[0046] Step 7: Convert the operation constraints of the distribution system containing the self-storage reconfigurable intelligent soft switch R-SOP into mixed integer second-order cone programming constraints and solve them to obtain the operation plan of the distribution system including the reconfigurable soft switch action, the on-load tap-changing transformer action, the switchable capacitor action, and the energy storage system action.

[0047] The method for optimizing operation of a power distribution system containing a reconfigurable soft switch based on reconstruction according to the present invention is also characterized in that step 7 includes the following steps:

[0048] Step 7.1: Add two nonlinear variables and Replaced by two linear variables and , and use the big-M method to relax the power flow constraints (15)-(17) and (21)-(22) to transform them into linear constraints, and then use (25)-(29) to construct the converted constraints:

[0049] (25)

[0050] (26)

[0051] (27)

[0052] (28)

[0053] (29)

[0054] In formula (25) to formula (29), and respectively Time Node The square of the voltage, Time Node To Node The square of the branch current between them; and They are Timeline Active power and reactive power transmitted on the

[0055] Step 7.2: Use equation (30) to transform equation (18) into a second-order cone constraint:

[0056] (30)

[0057] Step 7.3: Modify equation (11) to construct the power flow constraint using equation (31);

[0058] (31)

[0059] In formula (31), Expressed as Time Node The square of the auxiliary node aux voltage at;

[0060] Step 7.4: Introduce auxiliary 0 / 1 variables and auxiliary voltage variables, and use equations (32) and (33) to obtain the constraints of the on-load tap changer OLTC after transformation:

[0061] (32)

[0062] (33)

[0063] In formula (32), For line The maximum value at the OLTC tap position;

[0064] Step 7.5: Use equations (34) and (35) to construct linear constraints:

[0065] (34)

[0066] (35)

[0067] In formula (34) and formula (35), for Time corresponding to the node To Node The tap of the on-load tap-changing transformer OLTC on the line The closed state of If it is turned on, is 0, for Time corresponding to the node To Node OLTC tap position of the line The square of the upper voltage;

[0068] Step 7.6: Use equation (36) to transform the capacity constraint of R-SOP (10) into a rotating cone constraint:

[0069] (36).

[0070] The present invention provides an electronic device, comprising a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the optimization operation method, and the processor is configured to execute the program stored in the memory.

[0071] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program executes the steps of the optimization operation method when executed by a processor.

[0072] Compared with the prior art, the present invention has the following beneficial effects:

[0073] 1. The present invention improves the utilization rate of the voltage source converter capacity by adopting a self-storage type reorganization type intelligent soft switch; the self-storage type reorganization type intelligent soft switch adopts an asymmetric design of the capacity of each voltage source converter, and realizes the dynamic connection between each voltage source converter and the feeder of the distribution system through a feeder selection switch, thereby improving the utilization efficiency of the voltage source converter capacity; this design enables the maximum transmission power of the self-storage type reorganization type intelligent soft switch to be increased to 50% of the total capacity of the voltage source converter;

[0074] 2. The present invention provides a method for implementing power scheduling, adaptive feeder selection, and port reorganization of a self-storage type reorganized intelligent soft switch, and can realize coordinated scheduling of the self-storage type reorganized intelligent soft switch and a variety of controllable devices; the self-storage type reorganized intelligent soft switch cooperates with the on-load tap-changing transformer and switchable capacitor group in the distribution system to jointly optimize the power flow distribution of the distribution system; thereby significantly improving energy utilization efficiency, enhancing system stability, and reducing operation and maintenance costs, providing strong support for the intelligent upgrade of the distribution system.

[0075] 3. The present invention realizes the optimal distribution of power flow through the coordination between the self-storage type reorganization type intelligent soft-open adaptive feeder selection and the dynamic network reconstruction of the distribution system, effectively reduces the operating loss of the distribution system, and improves the operating efficiency of the distribution system.

[0076] 4. The present invention can realize dynamic interconnection between multiple feeders in the distribution system, make full use of controllable resources between different distribution areas, and allow the self-storage type reorganization type intelligent soft-opening voltage source converter to be connected to any feeder, thereby realizing flexible linkage of power between feeders and improving the distribution system's ability to absorb renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1It is a traditional multi-terminal intelligent soft switch topology diagram;

[0078] Figure 2 It is a self-storage type reorganization type intelligent soft switch topology diagram;

[0079] Figure 3 It is the modified IEEE 33-node system topology diagram containing self-storage type reorganization type intelligent soft switch;

[0080] Figure 4 It is the active and reactive power diagram transmitted by feeders 12, 18, 22, and 33 through the self-storage type reorganization type intelligent soft switch;

[0081] Figure 5 It is an active power diagram transmitted by feeder 22 under the optimization operation method of the distribution system including the traditional multi-terminal soft switch (Case 1) and the optimization operation method of the distribution system including the reconfigurable soft switch based on reconstruction (Case 2);

[0082] Figure 6 It is the action strategy diagram of controllable equipment installed in the power distribution system (load-controlled voltage-changing transformer, switchable capacitor bank);

[0083] Figure 7 is a diagram of capacity variation of voltage source converters connected to feeders 12, 18, 22, and 33;

[0084] Figure 8 It is a flow chart for implementing the method of the invention. DETAILED DESCRIPTION

[0085] In this embodiment, a method for optimizing the operation of a power distribution system containing a reconfigurable soft switch based on reconstruction is provided. Figure 8 As shown, the specific steps are as follows:

[0086] Step 1: Self-storage type reorganization type intelligent soft switch topology construction based on asymmetric voltage source converter capacity and feeder selection:

[0087] Figure 1 For traditional intelligent soft switches, the capacity of each VSC is designed symmetrically. If the total capacity of the VSC of the SOP is 1 p.u. and contains 4 VSCs, the capacity of each VSC is 0.25 pu, which limits the power transmission. Figure 2 The figure shows the self-storage type reorganization intelligent soft switch topology. Figure 1The further improved design based on the traditional multi-terminal SOP topology shown in the figure: 1. Based on the intelligent soft switch and combined with energy storage, the combined self-storage reorganized intelligent soft switch can have energy storage capacity and accurately control the power flow in two dimensions of space and time; 2. After the VSC is reorganized, it is not directly connected to the distribution network feeder, but dynamically connected to the distribution network feeder through a group of feeder selection switches. Each VSC can be connected to any feeder to realize flexible power transfer between feeders; 3. The capacity of each VSC adopts an asymmetric design to improve the utilization rate of the VSC. Containing n voltage source converters, the specific voltage source converter capacity is designed as follows: voltage source converter 1 takes 0.5 times of the total capacity of the voltage source converters, voltage source converter 2 takes 0.618 times of the remaining capacity after removing voltage source converter 1, voltage source converter 3 takes 0.618 times of the remaining capacity after removing voltage source converter 1 and voltage source converter 2, and so on. The capacity of voltage source converter n-1 is 0.618 times of the remaining capacity after removing the capacity of the first n-2 voltage source converters, and the capacity of voltage source converter n is the remaining capacity after removing the capacity of the first n-1 voltage source converters; Figure 3 As shown in the figure, the self-storage type reorganization type intelligent soft switch including 4 VSCs is taken as an example. Assuming that the total capacity of the VSCs of the self-storage type reorganization type intelligent soft switch is 1 p.u., the capacity distribution of each VSC is shown in Figure 2 Specifically, VSC1 takes 0.5 times of the total capacity of VSC, VSC2 takes 0.618 times of the remaining capacity after removing VSC1, VSC3 takes 0.618 times of the remaining capacity after removing VSC1 and VSC2, and VSC4 takes the remaining capacity after removing VSC1, VSC2, and VSC3, that is, the capacities are 0.5pu, 0.309pu, 0.118pu, and 0.073pu respectively; this makes the self-energy storage type reorganization type intelligent soft switch have better performance than the intelligent soft switch in scenarios with high power transmission requirements.

[0088] Step 2: Build a self-storage reorganized intelligent soft-opening model:

[0089] Step 2-1: The operation model of the energy storage system is constructed by equations (1) to (4):

[0090] (1)

[0091] (2)

[0092] (3)

[0093] (4)

[0094] In formula (1) to formula (4), , are the minimum and maximum charging power of the energy storage system, respectively. , are the minimum and maximum values ​​of the discharge power of the energy storage system respectively; , The energy storage system is Charging power and discharging power at the moment; For Auxiliary variables set at all times, when the energy storage system is When charging, =1, when discharging, let =0, , , are the minimum, maximum and initial values ​​of the state of charge of the energy storage system respectively; , are the total capacity of the energy storage system and Capacity at the moment; , The charging and discharging efficiency of the energy storage system; is the time step.

[0095] Step 2-2, port reorganization:

[0096] The so-called port reorganization means that the voltage source converter port in the self-storage reorganization intelligent soft switch can be dynamically connected to different feeders through the feeder selection switch, thereby realizing flexible power transfer. This design allows the self-storage reorganization intelligent soft switch to adjust the feeders connected to each port according to actual needs, so that the voltage source converter capacity can be used more efficiently;

[0097] Step 2-3: From equation (5) to equation (6), we can get the feeder selection constraint:

[0098] (5)

[0099] (6)

[0100] In formula (5)-formula (6) , denote the set of nodes that can be connected to the voltage source converter and the set of all voltage source converters respectively; for Time Node Apparent power transmitted through the self-storage reorganized intelligent soft switch; For the The capacity of each voltage source converter; is a binary variable representing the A voltage source converter Whether the node is connected to the voltage source converter connected.

[0101] Step 2-4: The operation model of the self-storage type reorganization type intelligent soft switch R-SOP is obtained from equations (7) to (10):

[0102] (7)

[0103] (8)

[0104] (9)

[0105] (10)

[0106] In formula (7) to formula (10), , , They are Moments and Nodes the total losses of all connected voltage source converters, the total DC side active power of all voltage source converters and the total AC side active power of all voltage source converters; , for Time Node Apparent power and reactive power transmitted through the self-storage reorganization intelligent soft switch R-SOP; , For energy storage systems The charging and discharging power at each moment; is the operating loss coefficient of the voltage source converter.

[0107] Step 3: Model the flexible distribution system based on reconstruction with reconfigurable soft switches, and obtain the distribution system operation strategy by solving the optimization operation model:

[0108] Step 3.1: Construct the operation model of the controllable equipment of the distribution system, including the on-load tap-changing transformer model and the switchable capacitor bank model:

[0109] After introducing an auxiliary node aux on the secondary side of OLTC and ignoring the loss of OLTC itself, the operation model of on-load tap-changing transformer OLTC is constructed by equations (11) and (12):

[0110] (11)

[0111] (12)

[0112] In formula (11)-formula (12), for The node connected to the voltage source converter at all times The voltage is equal to the voltage on the secondary side of OLTC; for The node connected to the voltage source converter at all times With Node Between lines The tap position of OLTC; For Node exist Voltage at the moment; for Timeline Voltage regulation rate of OLTC; For line Initial voltage regulation rate of OLTC; For line The change in voltage regulation rate at adjacent tap positions;

[0113] The operation model of CBs is constructed by equation (13)-(14):

[0114] (13)

[0115] (14)

[0116] In formula (13)-formula (14), For Node exist The capacity of capacitors connected to the power distribution system at all times, yes The capacitor capacity, yes Time Node The number of capacitor banks put into operation, It is the maximum number of capacitor banks allowed to be put into operation;

[0117] Step 3.2: Construct the operation model of the distribution system containing the self-storage type reorganization type intelligent soft switch R-SOP from (15) to (22):

[0118] (15)

[0119] (16)

[0120] (17)

[0121] (18)

[0122] (19)

[0123] (20)

[0124] (twenty one)

[0125] (twenty two)

[0126] In formula (15) to formula (22), A collection of distribution lines; is the node of the distribution line, ; , They are Timeline Active power and reactive power transmitted on the network; yes Timeline The connectivity status, when Timeline When connected, =1, when Timeline When not connected, =0; and They are Timeline Active power and reactive power transmitted on the and Line resistance and reactance; for Timeline The current transmitted on , They are Time Node The amount of photovoltaic active power output and photovoltaic active power reduction; , They are Time Node The active output of the fan and the reduction of the active output of the fan; and They are Time Node Active power and reactive power consumed by the load; and are the upper and lower limits of node voltage in the power distribution system, respectively; It is the upper limit of line current in the power distribution system.

[0127] Step 3.3: Using two linear variables and Replace and Two nonlinear variables are added, and the power, current, and voltage constraints are relaxed using the big-M method. The mixed integer nonlinear programming model is then converted into a mixed integer second-order cone programming model using the second-order cone conversion method. Finally, the objective function of the distribution system containing the self-storage recombinant intelligent soft switch R-SOP is constructed by (23)-(24): :

[0128] (twenty three)

[0129] (twenty four)

[0130] In formula (23)-formula (24), , are the loss of the distribution system and the reduction of renewable energy output, , There are 2 weight coefficients respectively; , are the charging and discharging efficiency of the energy storage system respectively; represents the simulation time;

[0131] Step 4: Convert the operation constraints of the distribution system containing the self-storage reconfigurable intelligent soft switch R-SOP into mixed integer second-order cone programming constraints and solve them to obtain the operation plan of the distribution system including the reconfigurable soft switch action, the on-load tap-changing transformer action, the switchable capacitor action, and the energy storage system action;

[0132] Step 4.1: Add two nonlinear variables and Replaced by two linear variables and , and use the big-M method to relax the power flow constraints (15)-(17) and (21)-(22) to transform them into linear constraints, and then use (25)-(29) to construct the converted constraints:

[0133] (25)

[0134] (26)

[0135] (27)

[0136] (28)

[0137] (29)

[0138] In formula (25) to formula (29), and respectively Time Node The square of the voltage, Time Node To Node The square of the branch current between them;

[0139] Step 4.2: Use equation (30) to transform equation (18) into a second-order cone constraint:

[0140] (30)

[0141] Step 4.3: Modify equation (11) to construct the power flow constraint using equation (31);

[0142] (31)

[0143] In formula (31), Expressed as Time Node The square of the auxiliary node aux voltage at;

[0144] Step 4.4: Introduce auxiliary 0 / 1 variables and auxiliary voltage variables, and use equations (32) and (33) to obtain the constraints of the on-load tap changer OLTC after transformation:

[0145] (32)

[0146] (33)

[0147] In formula (32), For line The maximum value at the OLTC tap position;

[0148] Step 4.5: Use equations (34) and (35) to construct linear constraints:

[0149] (34)

[0150] (35)

[0151] In formula (34) and formula (35), for Time corresponding to the node To Node The tap of the on-load tap-changing transformer OLTC on the line The closed state of If it is turned on, is 0, for Time corresponding to the node To Node OLTC tap position of the line The square of the upper voltage;

[0152] Step 4.6: Use equation (36) to transform the capacity constraint of R-SOP (10) into a rotating cone constraint:

[0153] (36)

[0154] The above method is used to transform the operation constraints of the distribution system containing the self-energy storage reconfigurable intelligent soft switch R-SOP into a mixed integer second-order cone programming problem. With the help of the solver, the distribution system operation strategy including the reconfigurable intelligent soft switch action, the on-load tap-changing transformer action, the switchable capacitor action and the energy storage system action is obtained.

[0155] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0156] In this embodiment, a computer-readable storage medium stores a computer program on the computer-readable storage medium, and the computer program executes the steps of the above method when executed by a processor.

[0157] In order to enable those skilled in the art to better understand the present invention, the example analysis includes the following components:

[0158] 1. Case description and simulation results analysis

[0159] In order to verify its effectiveness, the present invention adopts Figure 3 The improved IEEE 33-node system with 4-port R-SOP shown in the figure is used as the test system, and a case analysis is performed. Figure 3 In the test system shown, the active and reactive power requirements of each node load are set the same as the standard IEEE 33-node test system, the system reference voltage is set to 12.66kV, and the voltage safety range is set to 0.95pu-1.05pu; the total capacity of the equipped 4-port R-SOP is 1MVA, and the capacity allocation ratio of each VSC port is, for example Figure 2 As shown in the figure, the nodes available for feeder selection switches are set at nodes 12, 18, 22, and 33. The system also has photovoltaic and wind turbine systems at nodes 16 and 31. CBs are set at node 28, and the capacity of each capacitor bank of CBs is 0.15MVar. A maximum of 4 groups of capacitors can be put into use. The simulation time scale is set to 1 hour, and the simulation time is 10:00-17:00.

[0160] In order to fully demonstrate the effectiveness of the reconstruction-based optimization operation method of the distribution system with reconfigurable soft switches, two schemes, Case 1 and Case 2, are set up in the example part for comparison. Case 1 uses a 4-port SOP with the same R-SOP capacity as Case 2 to replace the R-SOP in Case 2 for simulation analysis.

[0161] Case 1: Optimization operation method of distribution system with traditional multi-terminal soft switches.

[0162] Case 2: Reconfiguration-based optimization operation method for distribution system with reconfigurable soft switches.

[0163] All numerical simulations in the example part are carried out in MATLAB 2020a and solved using the YALMIP toolbox and Gurobi solver in a 64-bit Windows environment.

[0164] exist Figure 3 In the IEEE 33-node test system shown in FIG. 1 , the above two schemes are executed simultaneously. The system operation loss and new energy reduction of Case 1 and Case 2 are obtained, as shown in Table 1.

[0165] Table 1

[0166] plan System operation loss / MWh Reduction in renewable energy / MWh Case 1 0.6400 0.1803 Case 2 0.6245 0

[0167] From Table 1, we can see that the system operating loss of Case 2 is reduced by 0.0155MWh compared with Case 1. At the same time, there is no new energy abandonment in the distribution system under Case 2, which improves the distribution system's ability to absorb new energy compared with Case 1. It can be seen that the optimization operation method of the distribution system containing reconfigurable soft switches based on reconstruction proposed in the present invention has significant advantages over the optimization operation method based on traditional multi-terminal SOP, and can reasonably utilize the multi-controllable equipment of the distribution system to reduce the operating loss of the distribution system and improve the absorption of new energy.

[0168] Figure 5 is the active power transmitted by feeder 22 through R-SOP / traditional multi-terminal SOP under the two schemes, Figure 5 It can be seen that R-SOP can significantly improve the transmission power. This is because although the total VSC capacity of R-SOP / traditional multi-terminal SOP is the same under the two schemes, due to the structure of the traditional multi-terminal SOP in Case 1, its maximum transmission power is 0.25MVA, which is only 25% of the total VSC capacity. In contrast, the maximum transmission power of R-SOP in Case 2 is 0.5MVA, which is 50% of the total VSC capacity.

[0169] Figure 4is the active / reactive power transmitted by feeders 12, 18, 22, and 33 through R-SOP; Figure 6 It is the change of OLTC gear and the number of capacitor banks put into operation by CBs during the whole optimization process. Figure 7 is the capacity change of the VSCs connected to feeders 12, 18, 22, and 33, given by Figure 7 It can be seen that R-SOP can dynamically allocate the total capacity of VSCs connected to each feeder according to the actual power transmission demand of the feeder during operation.

[0170] In this specification, the schematic description of the present invention does not necessarily refer to the same embodiment or example, and those skilled in the art may combine and combine different embodiments or examples described in this specification. In addition, the contents of the embodiments of this specification are merely an enumeration of the implementation forms of the inventive concept, and the protection scope of the present invention should not be considered as limited to the specific forms described 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 based on the inventive concept.

Claims

1. A method for optimizing the operation of a power distribution system containing a reconfigurable soft switch based on reconstruction, characterized in that: The following steps are involved: Step 1: The operation model of the energy storage system is obtained from equations (1) to (4): (1) (2) (3) (4) In formula (1) to formula (4), , are the minimum and maximum charging power of the energy storage system, respectively. , are the minimum and maximum values ​​of the discharge power of the energy storage system respectively; , The energy storage system is Charging power and discharging power at the moment; For Auxiliary variables set at all times, when the energy storage system is When charging, =1, when discharging, let =0, , , are the minimum, maximum and initial values ​​of the state of charge of the energy storage system respectively; , are the total capacity of the energy storage system and Capacity at the moment; , The charging and discharging efficiency of the energy storage system; is the time step; Step 2: Construct feeder selection constraints using equations (5) and (6): (5) (6) In formula (5)-formula (6), , denote the set of nodes connected to the voltage source converter and the set of all voltage source converters respectively; for The node connected to the voltage source converter at all times Apparent power transmitted through the self-storage reorganized intelligent soft switch; Voltage Source Converter Capacity; is a binary variable representing the voltage source converter exist Whether the node is connected to the voltage source converter at the moment connected; Step 3: Construct the operation model of the self-storage type recombinant intelligent soft switch R-SOP by equations (7) to (10): (7) (8) (9) (10) In formula (7) to formula (10), , , They are Moments and Nodes Total losses of all connected voltage source converters, total DC side active power of all voltage source converters, total AC side active power of all voltage source converters; for Time Node Reactive power transmitted through the self-storage reorganization intelligent soft switch R-SOP; is the operating loss coefficient of the voltage source converter; Step 4: Construct the controllable equipment operation model of the distribution system by using equations (11) to (14), including: the operation model of the on-load tap-changing transformer OLTC and the operation model of the switchable capacitor bank CBs: Step 4.1: Construct the operation model of the on-load tap-changing transformer OLTC by equation (11)-(12): (11) (12) In formula (11)-formula (12), for The node connected to the voltage source converter at all times The voltage is equal to the voltage on the secondary side of OLTC; for The node connected to the voltage source converter at all times With Node Between lines The tap position of OLTC; For Node exist Voltage at the moment; for Timeline Voltage regulation rate of OLTC; For Line Initial voltage regulation rate of OLTC; For Line The change in voltage regulation rate at adjacent tap positions; The operation model of CBs is constructed by equation (13)-(14): (13) (14) In formula (13)-formula (14), For Node exist The capacity of capacitors connected to the power distribution system at all times, yes The capacitor capacity, yes Time Node The number of capacitor banks put into operation, It is the maximum number of capacitor banks allowed to be put into operation; Step 5: Construct the operation model of the distribution system containing the self-storage type reorganization type intelligent soft switch R-SOP from (15) to (22): (15) (16) (17) (18) (19) (20) (21) (22) In formula (15) to formula (22), A collection of distribution lines; is the node of the distribution line, ; , They are Timeline Active power and reactive power transmitted on the network; yes Timeline The connectivity status, when Timeline When connected, =1, when Timeline When not connected, =0; and They are Timeline Active power and reactive power transmitted on the and Line resistance and reactance; for Timeline The current transmitted on , They are Time Node The amount of photovoltaic active power output and photovoltaic active power reduction; , They are Time Node The active output of the fan and the reduction of the active output of the fan; and They are Time Node Active power and reactive power consumed by the load; and are the upper and lower limits of node voltage in the power distribution system, respectively; It is the upper limit of line current in the power distribution system; Step 6: Construct the objective function of the distribution system containing the self-storage type reorganized intelligent soft switch R-SOP from (23) to (24): : (23) (24) In formula (23)-formula (24), , are the loss of the power distribution system and the reduction of the output of new energy. , There are 2 weight coefficients respectively; , are the charging and discharging efficiency of the energy storage system respectively; represents the simulation time; Step 7: Convert the operation constraints of the distribution system containing the self-storage reconfigurable intelligent soft switch R-SOP into mixed integer second-order cone programming constraints and solve them to obtain the operation plan of the distribution system including the reconfigurable soft switch action, the on-load tap-changing transformer action, the switchable capacitor action, and the energy storage system action.

2. The optimization operation method of the power distribution system containing reconfigurable soft switches based on reconstruction according to claim 1 is characterized in that: The step 7 comprises the following steps: Step 7.1: Add two nonlinear variables and Replaced by two linear variables and , and use the big-M method to relax the power flow constraints (15)-(17) and (21)-(22) to transform them into linear constraints, and then use (25)-(29) to construct the converted constraints: (25) (26) (27) (28) (29) In formula (25) to formula (29), and respectively Time Node The square of the voltage, Time Node To Node The square of the branch current between them; and They are Timeline Active power and reactive power transmitted on the Step 7.2: Use equation (30) to transform equation (18) into a second-order cone constraint: (30) Step 7.3: Modify equation (11) to construct the power flow constraint using equation (31); (31) In formula (31), Expressed as Time Node The square of the auxiliary node aux voltage at; Step 7.4: Introduce auxiliary 0 / 1 variables and auxiliary voltage variables, and use equations (32) and (33) to obtain the constraints of the on-load tap changer OLTC after transformation: (32) (33) In formula (32), For Line The maximum value at the OLTC tap position; Step 7.5: Use equations (34) and (35) to construct linear constraints: (34) (35) In formula (34) and formula (35), for Time corresponding to the node To Node The tap of the on-load tap-changing transformer OLTC on the line The closed state of If it is turned on, is 0, for Time corresponding to the node To Node OLTC tap position of the line The square of the upper voltage; Step 7.6: Use equation (36) to transform the capacity constraint of R-SOP (10) into a rotating cone constraint: (36)。 3. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the optimization operation method according to claim 1 or 2, and the processor is configured to execute the program stored in the memory.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the optimization operation method according to claim 1 or 2 are executed.

Images