Household Photovoltaic Substation Reactive Power Control Method and Device

Abstract

The present invention provides a method and device for reactive power control in a household photovoltaic substation area, which relates to the technical field of reactive power control in a power system. The method includes establishing a network model of the household photovoltaic substation area, constructing an objective optimization model of the substation area network model according to at least one optimization objective such as no over-limit of the node voltage of the low-voltage substation area network, the lowest total network loss, the minimum three-phase unbalance degree, and reactive power self-balance, setting constraint conditions for the objective optimization model, obtaining the power adjustment value, and adjusting the reactive power of the household photovoltaic substation area, which can significantly improve the stability of the power grid operation under the condition of high penetration of household photovoltaic. Moreover, when it is not the initial adjustment, the optimization result of the reactive power at the previous time section is used as the input of the total objective optimization model at the current time section, and the optimization result of the reactive power at the current time section is output to realize the coherent control of the reactive power of the household photovoltaic substation area, which can ensure the stability of the adjustment at consecutive time sections.

Description

Technical Field

[0001] The present invention relates to the technical field of reactive power control in power systems, and particularly to a method and device for reactive power control in a household photovoltaic substation area. Background Art

[0002] In modern power systems, distributed power sources represented by distributed photovoltaics have developed rapidly, especially household photovoltaics, that is, photovoltaic power generation equipment installed in residential households has been rapidly popularized. Due to the randomness and volatility of distributed photovoltaics, many challenges have been brought to the stable operation of the power grid. Since the power generated by these photovoltaic devices is unstable, sometimes more and sometimes less, after a large amount is incorporated into the power grid, it may lead to a series of problems such as an increased risk of overvoltage in the substation area, a significant increase in line losses, and a sharp increase in the three-phase unbalance degree. These problems seriously threaten the safety and stable operation of the power grid.

[0003] To solve the above problems, traditional methods mainly achieve overvoltage control by adjusting the tap position of the substation area transformer. However, under high photovoltaic penetration, it is difficult to ensure qualified voltage at all times. Especially at night, since the photovoltaic devices do not generate electricity, low voltage is likely to occur. Moreover, the tap position of the substation area transformer usually needs to be adjusted without load, that is, power outage is required, which seriously affects residents' night-time electricity consumption.

[0004] Therefore, it is necessary to propose a method and device for reactive power control in a household photovoltaic substation area, which can improve the stability of power grid operation under the condition of high household photovoltaic penetration. Summary of the Invention

[0005] The object of the present invention is to provide a method and device for reactive power control in a household photovoltaic substation area, which can improve the stability of power grid operation under the condition of high household photovoltaic penetration.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides a method for controlling reactive power in a household photovoltaic substation area in a first aspect, including the following steps: establishing a network model of the household photovoltaic substation area, constructing an objective optimization model of the substation area network model according to the optimization objective, and setting constraint conditions for the objective optimization model. The optimization objective includes at least one of no over-limit of node voltage in the low-voltage substation area network, minimum total network loss, minimum three-phase unbalance degree, and reactive power self-balance; obtaining the adjustment value of reactive power as the optimization result according to the constraint conditions and the objective optimization model, and adopting a coherent control strategy to adjust the reactive power of the continuous time section of the household photovoltaic substation area, including the following: determining whether the reactive power of the household photovoltaic substation area is initially adjusted at the current time section; if it is the initial adjustment, obtaining each objective optimization model of each optimization objective, and obtaining the total objective optimization model of the current time section according to each objective optimization model, and outputting the optimization result of the reactive power of the current time section; if it is not the initial adjustment, obtaining the optimization result of the reactive power output by the total objective optimization model of the previous time section, using the optimization result of the reactive power of the previous time section as the input of the total objective optimization model of the current time section, and outputting the optimization result of the reactive power of the current time section; using the optimization result of the reactive power of the current time section as the input of the total objective optimization model of the next time section, and rolling updataing the total objective optimization model to realize the coherent control and optimization of the reactive power of the household photovoltaic substation area in the continuous time section.

[0008] To further improve the optimization effect, more reasonably balance the weights of each objective optimization, and obtain a more reliable total objective optimization model, the present invention provides a preferred solution in the first aspect. The adjustment value of reactive power is obtained according to the constraint conditions and the objective optimization model as the optimization result to adjust the reactive power of the household photovoltaic substation area. Among them, the variable weight principle is introduced, including: setting a weight rule according to different working conditions of the household photovoltaic substation area corresponding to different weights according to a preset rule, and determining the weights of the corresponding working conditions of each objective optimization model at the current time section according to the working conditions of the household photovoltaic substation area at the current time section. The working conditions of the household photovoltaic substation area include load demand and photovoltaic output; obtaining the total objective optimization model according to each objective optimization model and the weights of each objective optimization model.

[0009] In a second aspect, the present invention provides a reactive power control device for a household photovoltaic substation area, which executes the above control method, including: a model construction module, configured to establish a network model of the household photovoltaic substation area, construct an objective optimization model of the substation area network model according to an optimization objective, and set constraint conditions for the objective optimization model, where the optimization objective includes at least one of no over-limit of the node voltage of the low-voltage substation area network, the lowest total network loss, the minimum three-phase unbalance degree, and reactive power self-balance; an adjustment module, configured to obtain an adjustment value of reactive power according to the constraint conditions and the objective optimization model as an optimization result, and adopt a coherent control strategy to adjust the reactive power of the continuous time section of the household photovoltaic substation area, including the following: a judgment sub-module, configured to judge whether the reactive power of the household photovoltaic substation area is adjusted for the first time at the current time section; an optimization result output sub-module, configured to, if it is the first adjustment, obtain each objective optimization model of each optimization objective, and obtain a total objective optimization model of the current time section according to each objective optimization model, and output an optimization result of the reactive power of the current time section; if it is not the first adjustment, obtain an optimization result of the reactive power output by the total objective optimization model of the previous time section, use the optimization result of the reactive power of the previous time section as an input quantity of the total objective optimization model of the current time section, and output an optimization result of the reactive power of the current time section; a rolling update sub-module, configured to use the optimization result of the reactive power of the current time section as an input quantity of the total objective optimization model of the next time section, and rollingly update the total objective optimization model to realize coherent control optimization of the reactive power of the household photovoltaic substation area in a continuous time section.

[0010] Compared with the prior art, the above technical solution has the following advantages:

[0011] The reactive power control method and device for a household photovoltaic substation area of the present invention comprehensively consider multiple objectives such as voltage, total network loss, three-phase unbalance management, and reactive power self-balance, construct an objective optimization model, and adjust the reactive power of the household photovoltaic substation area according to the power adjustment value obtained based on the constraint conditions and the objective optimization model, which can significantly improve the stability of the power grid operation under the condition of high household photovoltaic penetration. Moreover, if it is not the first adjustment, obtain an optimization result of the reactive power output by the total objective optimization model of the previous time section, use the optimization result of the reactive power of the previous time section as an input quantity of the total objective optimization model of the current time section, and output an optimization result of the reactive power of the current time section, so as to realize coherent control of the reactive power of the household photovoltaic substation area and ensure the stability of the adjustment in a continuous time section. Description of the Drawings

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0013] Figure 1 It is a schematic diagram of the system structure of a household photovoltaic substation area of an implementation environment related to each embodiment of the present invention;

[0014] Figure 2 It is a flowchart of the reactive power control method for a household photovoltaic substation area provided in Embodiment 1 of the present invention;

[0015] Figure 3 It is a flowchart of S100 in the reactive power control method for a household photovoltaic substation area provided in Embodiment 1 of the present invention;

[0016] Figure 4 It is a flowchart of S200 in the reactive power control method for a household photovoltaic substation area provided in Embodiment 1 of the present invention;

[0017] Figure 5 It is a module schematic diagram of the reactive power control device for a household photovoltaic substation area provided in Embodiment 1 of the present invention;

[0018] Figure 6 It is a flowchart of S220 in the reactive power control method for a household photovoltaic substation area provided in Embodiment 2 of the present invention;

[0019] Figure 7 It is a flowchart of S220 in the reactive power control method for a household photovoltaic substation area provided in Embodiment 3 of the present invention:

[0020] Figure 8 It is a flowchart of the all-time reactive power control method for a high-penetration household photovoltaic substation area based on the NSGA-Ⅲ algorithm and the coherent control strategy provided in Embodiment 3 of the present invention;

[0021] Figure 9 It is a curve graph of the voltage of the remote node varying with time within a day before and after the control in Embodiment 3 of the present invention;

[0022] Figure 10 It is a curve graph of the total network loss of the substation area varying with time within a day before and after the control in Embodiment 3 of the present invention;

[0023] Figure 11 It is a curve graph of the three-phase unbalance degree varying with time within a day before and after the control in Embodiment 3 of the present invention;

[0024] Figure 12This is the curve graph of the reactive power output within the day before and after the control in the third embodiment of the present invention.

[0025] The reference signs are as follows: model construction module 100, adjustment module 200, judgment sub-module 210, first optimization result output sub-module 220, second optimization result output sub-module 230, rolling update sub-module 240. Detailed implementation manners

[0026] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0027] Please refer to Figure 1 , Figure 1 This is the schematic structural diagram of a household photovoltaic substation area of an implementation environment involved in each embodiment of the present invention. Among them, the household photovoltaic substation area is a power substation area equipped with household photovoltaic devices. The substation area is a basic unit in the power system, usually referring to the area powered by a transformer. In this area, many residential households may install photovoltaic power generation devices, and the electric energy generated by these devices can be incorporated into the power grid for use by other households or enterprises. Refer to Figure 1 As shown, the household photovoltaic substation area exemplified here includes 12 nodes. A substation transformer is installed at the outlet of the substation area. Among the 7 nodes in the system are load nodes, that is, nodes with loads mounted, and 4 nodes are installed with household photovoltaic units, and a total of 33 household photovoltaic units can be installed.

[0028] Embodiment 1:

[0029] Please refer to Figure 2 , first, this embodiment provides a method for controlling the reactive power of a household photovoltaic substation area, including S100 establishing a network model of the household photovoltaic substation area, constructing a target optimization model of the substation area network model according to the optimization objective, and setting constraint conditions for the target optimization model, where the optimization objective includes at least one of no voltage violation at the nodes of the low-voltage substation area network, the lowest total network loss, the minimum three-phase unbalance degree, and reactive power self-balance. S200 obtains the adjustment value of the reactive power as the optimization result according to the constraint conditions and the target optimization model, and adopts a coherent control strategy to adjust the reactive power of the continuous time section of the household photovoltaic substation area. The more detailed steps are as follows:

[0030] S100 establishes the network model of the residential photovoltaic substation area, constructs the target optimization model of the substation area network model according to the optimization objectives, and sets the constraint conditions for the target optimization model. As an example, this embodiment sets 4 optimization objectives, namely: no over-limit of the node voltage in the low-voltage substation area network, the lowest total network loss, the minimum three-phase unbalance degree, and reactive power self-balance. The target optimization model constructed thereby is a multi-objective optimization model. Here, the residential photovoltaic substation area specifically refers to the low-voltage substation area with high penetration of residential photovoltaic.

[0031] Please refer to Figure 3 , specifically, S100 has the following steps: S110 establishes the network model of the residential photovoltaic substation area, S120 constructs a multi-objective optimization model with the optimization objectives of no over-limit of the node voltage in the low-voltage substation area network, the lowest total network loss, the minimum three-phase unbalance degree, and reactive power self-balance, and S130 sets the constraint conditions of the multi-objective optimization model. The more detailed steps are as follows:

[0032] S110 establishes the network model of the residential photovoltaic substation area, and establishes a complete network model of the residential photovoltaic substation area based on information such as the network topology structure, line parameters, installation location and quantity of residential photovoltaic, and the access method of residential photovoltaic. The network topology structure can include different topological structures such as tree-shaped, star-shaped, and mesh-shaped, and each structure has different impacts on the performance of the network, such as reliability, transmission rate, and scalability. The line parameters can include the wire diameter, wire material, resistance, reactance, etc. of the wire, and these parameters directly affect the power loss and voltage distribution of the network. The installation location, quantity, and access method of residential photovoltaic will affect the power flow distribution and power balance of the network.

[0033] S120 constructs a multi-objective optimization model with the optimization objectives of no over-limit of the node voltage in the low-voltage substation area network, the lowest total network loss, the minimum three-phase unbalance degree, and reactive power self-balance. Among them:

[0034] No over-limit of the node voltage means ensuring that the voltage of all nodes in the low-voltage substation area network is within the allowable range and there is no situation of too high or too low voltage. Specifically, the target optimization model of no over-limit of the node voltage can be used to ensure that the voltage of all nodes is within the allowable range. The target optimization model of no over-limit of the node voltage can be realized by the following expression (1):

[0035] (1),

[0036] In formula (1) represents the node voltage at node i of phase p, represents the lowest allowable voltage at node i of phase p, represents the highest allowable voltage at node i of phase p, represents the set penalty function constant parameter.

[0037] The total network loss is minimized, that is, by adjusting the network structure and operation mode, the power loss of the entire network is reduced. Specifically, the minimization of the network loss can be achieved through the optimization model of the total network loss minimum target. The optimization model of the total network loss minimum target can be realized by the following expression (2):

[0038] (2),

[0039] In formula (2), N represents the total number of network nodes, 、 respectively represent the active and reactive power loads at node i of the corresponding phase p, represents the node voltage at node i of the corresponding phase p, 、 represent the resistance and reactance of the line between node i and node j.

[0040] The three-phase unbalance is minimized, that is, the three-phase load distribution is optimized to reduce the unbalance of the three-phase current and voltage. The three-phase load distribution can be optimized through the optimization model of the minimum three-phase unbalance target. The optimization model of the minimum three-phase unbalance target can be realized successively by the following expressions (3) and (4):

[0041] (3),

[0042] (4),

[0043] In formulas (3) and (4), 、 、 、 respectively represent the average current, phase A current, phase B current, and phase C current, Taking 1, 2, and 3 respectively represent phases A, B, and C, represents the corresponding phase current.

[0044] Reactive power self-balancing, that is, through means such as reactive power compensation devices, the reactive power balance in the network is achieved. The reactive power balance in the network is ensured through the optimization model of the reactive power self-balancing target. The optimization model of the reactive power self-balancing target can be realized by the following expression (5):

[0045] (5),

[0046] In formula (5), represents the reactive power injected into the superior power grid at the outlet of the corresponding phase substation area.

[0047] In this embodiment, after obtaining the above-mentioned various target optimization models, the final total target optimization model is the sum of the above-mentioned models. The total target optimization model can be implemented through the following expression (6):

[0048] (6),

[0049] The above-mentioned target optimization model is a function used in mathematics to describe the relationship between the optimization target and variables. By solving this function, the optimal solution to achieve the optimization target can be found.

[0050] S130 sets the constraint conditions of the multi-objective optimization model. The constraint conditions of this embodiment include the upper and lower limits of the voltage of each phase of each node in the photovoltaic substation area, the upper limit constraint of the current allowed to flow through each branch, the network power flow constraint, and the regulation constraint of the household photovoltaic inverter. Among them,

[0051] The voltage constraint of each phase of the node, that is, to ensure that the voltage of each node in the network is within a safe and stable range, and to avoid damage to equipment and users caused by too high or too low voltage. In the case of high-penetration household photovoltaic access, this constraint is particularly important because the volatility and intermittency of photovoltaic power sources may affect voltage stability. The voltage constraint of each phase of the node can be implemented through the following expression (7):

[0052] (7),

[0053] In formula (7), represents the node voltage at node i at time t, , , respectively represent the minimum allowable node voltage values of phases A, B, and C at node i, , , respectively represent the maximum allowable node voltage values of phases A, B, and C at node i.

[0054] The current constraint allowed to flow through the branch, that is, the current flowing through each branch should not exceed its maximum allowable value to prevent overheating, equipment damage or safety accidents. The current constraint allowed to flow through the branch can be implemented through the following expression (8):

[0055] (8),

[0056] In formula (8), , , respectively represent the current values of phases A, B, and C flowing through the line between nodes i and j at time t, represents the maximum current value allowed to flow through the line between nodes i and j.

[0057] Power flow constraints, that is, ensuring the balance between the input power and output power of each node in the network and satisfying the power flow equations of the network. This is a basic requirement for the stable operation of the power system. The power flow constraints can be achieved through the following expression (9):

[0058] (9),

[0059] In formula (9) 、 represent the active part of the load power and PV power at node i of phase p, 、 represent the reactive part of the load power and PV power at node i of phase p, 、 represent the conductance and susceptance of the line between node i and node j, represents the phase angle difference of the node voltages at node i and node j.

[0060] The adjustment constraints of household PV inverters are as follows: As the interface device between the PV system and the power grid, the adjustment ability of the PV inverter has an important impact on the stability and efficiency of the system. Therefore, during the optimization process, the adjustment constraints of the inverter need to be considered, such as maximum power point tracking, voltage and frequency adjustment ranges, etc. The adjustment constraints of household PV inverters can be achieved through the following expressions (10), (11) and (12):

[0061] (10),

[0062] (11),

[0063] (12),

[0064] In formulas (10) to (12) 、 respectively represent the active and reactive powers transmitted by the distributed PV at node i of phase p, represents the maximum apparent power of the distributed PV installed at node i connected to phase p, represents the maximum active power of the distributed PV installed at node i connected to phase p, 、 respectively represent the maximum and minimum reactive powers of the distributed PV installed at node i connected to phase p.

[0065] By comprehensively considering the above constraints and combining with the target optimization model, the corresponding power adjustment values can be calculated. Incorporating the above constraints into the model can ensure that the calculated power adjustment values can meet all the constraints.

[0066] S200 obtains the adjustment value of the reactive power according to the constraint conditions and the objective optimization model as the optimization result, and adopts a coherent control strategy to regulate the reactive power of the continuous time section of the household photovoltaic power distribution area.

[0067] Please refer to Figure 4 , and S200 is specifically implemented through the following steps:

[0068] S210 determines whether the reactive power of the household photovoltaic power distribution area is adjusted for the first time at the current time section. The time section can be a specific moment or time period in the power system. The first adjustment means that the reactive power of the household photovoltaic power distribution area has not been adjusted before the current time section.

[0069] S220 If it is the first adjustment, obtain the objective optimization models of each optimization objective, and according to each objective optimization model, obtain the total objective optimization model of the current time section, and output the optimization result of the reactive power of the current time section. Taking the optimization objectives as the node voltage of the low-voltage power distribution network without over-limit, the lowest total network loss, the minimum three-phase unbalance degree, and reactive power self-balance as examples, for each optimization objective to be optimized, an objective optimization model is established respectively, and the specific expressions refer to Formulas (1) to (5) of this embodiment.

[0070] S230 If it is not the first adjustment, obtain the optimization result of the reactive power output by the total objective optimization model of the previous time section, and use the optimization result of the reactive power of the previous time section as the input quantity of the total objective optimization model of the current time section, and output the optimization result of the reactive power of the current time section.

[0071] S240 Use the optimization result of the reactive power of the current time section as the input quantity of the total objective optimization model of the next time section, and rollingly update the total objective optimization model to realize the coherent control and optimization of the reactive power of the household photovoltaic power distribution area in the continuous time section, and ensure the stability of the adjustment in the continuous time period.

[0072] Specifically, Steps S230 and S240 are as follows: If the reactive power of the household photovoltaic power distribution area is not adjusted for the first time at the current time section, update the total objective optimization model according to the area state of the current time section to realize the coherent control of the reactive power of the household photovoltaic power distribution area. In other words, if it is not the first adjustment currently, the total objective optimization model of the previous time section can be used as the basis, and according to the optimization result of the previous time section and the actual situation of the current time section, update the total objective optimization model to realize the coherent control of the reactive power of the household photovoltaic power distribution area, and ensure the stability of the adjustment in the continuous time period.

[0073] The regulation of reactive power in this implementation can be specifically achieved by adjusting the input or cut-off of reactive power compensation equipment, adjusting the tap position of the transformer, etc. Specifically, it can be implemented according to the magnitude of the power adjustment value and in combination with the actual situation of the household photovoltaic substation area. By regulating reactive power, the voltage quality can be improved and the stability of the power system can be enhanced.

[0074] The reactive power control method for the household photovoltaic substation area in this embodiment comprehensively considers multiple objectives such as voltage, total network loss, three-phase imbalance control, and reactive power self-balance, constructs an objective optimization model, and adjusts the reactive power of the household photovoltaic substation area according to the power adjustment value obtained based on the constraint conditions and the objective optimization model, which can significantly improve the stability of the power grid operation under the condition of high household photovoltaic penetration.

[0075] Please refer to Figure 5 , secondly, this embodiment provides a reactive power control device for a household photovoltaic substation area, which executes the above control method and mainly consists of the following modules:

[0076] The model construction module 100 is used to establish the network model of the household photovoltaic substation area, construct the objective optimization model of the substation area network model according to the optimization objectives, and set constraint conditions for the objective optimization model. The optimization objectives include at least one of the node voltage of the low-voltage substation area network without over-limit, the lowest total network loss, the minimum three-phase imbalance degree, and reactive power self-balance;

[0077] The adjustment module 200 is used to obtain the adjustment value of reactive power according to the constraint conditions and the objective optimization model as the optimization result, and adopt a coherent control strategy to adjust the reactive power of the continuous time section of the household photovoltaic substation area, including the following:

[0078] The judgment sub-module 210 is used to judge whether the reactive power of the household photovoltaic substation area is adjusted for the first time at the current time section;

[0079] The first optimization result output sub-module 220 is used to, if it is the first adjustment, obtain the objective optimization models of each optimization objective, and obtain the total objective optimization model of the current time section according to each objective optimization model, and output the optimization result of the reactive power of the current time section;

[0080] The second optimization result output sub-module 230, if it is not the first adjustment, obtains the optimization result of the reactive power output by the total objective optimization model of the previous time section, uses the optimization result of the reactive power of the previous time section as the input quantity of the total objective optimization model of the current time section, and outputs the optimization result of the reactive power of the current time section;

[0081] The rolling update sub-module 240 is used to take the optimization result of the reactive power at the current time section as the input of the total target optimization model for the next time section, and rollingly update the total target optimization model to realize the coherent control and optimization of the reactive power of the household photovoltaic substation area at continuous time sections.

[0082] It should be noted here that the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the content disclosed in the methods of the above embodiments. It should be noted that the above modules, as part of the device, can run in an environment as shown in Figure 1 and can be implemented by software or by hardware.

[0083] In this embodiment, through the coherent control strategy, the multi-objective optimization model is reconstructed to realize the coherent control of the reactive power of the household photovoltaic substation area, which can ensure the stability of the adjustment at continuous time sections.

[0084] Embodiment 2:

[0085] On the basis of Embodiment 1, this embodiment provides a more preferable method for controlling the reactive power of a household photovoltaic substation area. Among them, S200 obtains the adjustment value of the reactive power according to the constraint conditions and the target optimization model as the optimization result to adjust the reactive power of the household photovoltaic substation area. Among them, the variable weight principle is introduced to reconstruct the multi-objective optimization model.

[0086] Please refer to Figure 6 , where S220 is mainly implemented through the following steps:

[0087] S221 sets weight rules according to different weights corresponding to different working conditions of the household photovoltaic power station area. The working conditions of the power station area refer to the operating conditions of the low-voltage power station area at a certain specific time section. The load demand reflects the power demand of users within this time section, which may vary with factors such as time of day, season, and weather. The photovoltaic output depends on the installed capacity of the photovoltaic system, lighting conditions, temperature, etc., and represents the power that the photovoltaic system can provide at the current time section. According to the preset working conditions, such as the typical daily load curve of the low-voltage power station area, photovoltaic output prediction, etc., weights are assigned to each optimization goal (such as no voltage violation at nodes, minimum total network loss, minimum three-phase unbalance degree, reactive power self-balance, etc.). These weights reflect the relative importance of each optimization goal under this preset working condition. The weights can be fixed weights or variable weights, that is, the weights can be determined according to the preset working conditions and variable weight principles. It should be noted that the variable weight principle is relative to the fixed weight principle. The variable weight principle takes into account the dynamic changes of the power station area state and adaptively adjusts the weights of each optimization goal. For example, during peak load periods, more attention is paid to voltage stability and minimum network loss; while during periods with large photovoltaic output, more attention is paid to reactive power self-balance and reduction of three-phase unbalance degree. As an example, this embodiment gives a variable weight rule established through the following steps:

[0088] First, the generated weight is as shown in expression (13):

[0089] (13),

[0090] Count the number of nodes in the household photovoltaic power station area where the photovoltaic output is greater than the load demand, which is achieved through the following expression (14):

[0091] (14),

[0092] Generate a judgment variable based on the counted number of nodes, which is achieved through the following expression (15):

[0093] (15),

[0094] Generate a weight rule based on the judgment variable, which is achieved through the following expression (16):

[0095] (16),

[0096] In formulas (13) to (16) represents the weight coefficient of the target of the voltage violation degree of the power station area, represents the weight coefficient of the target of the total network loss of the power station area, represents the weight coefficient of the target of the three-phase unbalance degree of the power station area, The weight coefficient representing the target of the reactive power self - balance degree of the sub - region. Represents the number of nodes in the sub - region where the photovoltaic output is greater than the load demand, Represents the total number of nodes installed with photovoltaics in the sub - region, 、 Respectively represent the active power output and active load demand of the photovoltaic at node i. Represents the weight combination of each optimization target at time t, Represents the weight combination of each optimization target at time t - 1.

[0097] S222 determines the weights corresponding to the working conditions of each target optimization model for the current time section according to the working conditions of the household photovoltaic sub - region at the current time section, where the working conditions of the household photovoltaic sub - region include load demand and photovoltaic output.

[0098] S223 obtains the total target optimization model according to each target optimization model and the weights of each target optimization model. Thus, the total target optimization model reconstructed by the variable - weight principle is obtained and realized through the following expression (6)’:

[0099] (6)’,

[0100] Exemplarily, first collect the sub - region state information of the current time section, including but not limited to key parameters such as sub - region load demand, photovoltaic output, node voltage, branch current, and three - phase unbalance degree. Compare the sub - region state information of the current time section with that of the previous time section, and analyze the change trend and amplitude of the state. According to the change of the sub - region state, dynamically adjust the weight coefficients of each optimization target to be optimized (such as no voltage violation, minimum total network loss, minimum three - phase unbalance degree, reactive power self - balance, etc.). For example, if the current voltage fluctuates greatly, the weight of the “no voltage violation” target can be appropriately increased. According to the new weight coefficients, adjust the form of the total target optimization function model. The total target optimization model is the weighted sum of each target optimization model. Therefore, the change of the weight coefficient will directly affect the shape of the function and the position of the optimal solution. Substitute the new weight coefficients and other relevant parameters into the total target optimization model to obtain the updated function expression.

[0101] In this embodiment, by introducing the variable - weight principle, the multi - target optimization model is reconstructed, and combined with the coherent control strategy, considering the dynamic change of the sub - region state, the weights of each optimization target can be adaptively adjusted according to the working conditions within a continuous time period, thereby improving the reliability and accuracy of the multi - target optimization model and further enhancing the reactive power regulation ability.

[0102] Embodiment Three:

[0103] Preferably, in this embodiment, S200 obtains the adjustment value of the reactive power according to the constraint conditions and the target optimization model as the optimization result. An objective function for regulating the penalty of the relative change of the reactive power in the household photovoltaic substation area at the continuous time section is introduced to update the total target optimization model. Specifically, please refer to Figure 7 , where S220, on the basis of S221 and S222 in Embodiment 2, is mainly implemented through the following steps:

[0104] S224 When the relative change of the reactive power in the household photovoltaic substation area at the continuous time section is greater than the preset value, a regulation penalty function is obtained to encourage the stable change of the reactive power, that is, the objective function for regulating the penalty of the relative change of the reactive power in the household photovoltaic continuous time section. Specifically, by monitoring the change of the reactive power of the photovoltaic system at the continuous time section and calculating its relative change. When this relative change exceeds the preset threshold, the regulation penalty function will be triggered. The specific form of this penalty function may be a mathematical expression related to the magnitude of the reactive power change, which is used to give a "penalty" to this sharp change in the subsequent optimization process, so as to encourage the system to find an operation strategy that makes the reactive power change more stable. The regulation penalty function in this embodiment is implemented through the following expression (17):

[0105] (17),

[0106] In formula (17), M represents the number of actions of the optimization algorithm, 、 respectively represent time, the reactive power action value of the i-th at time.

[0107] S225 According to the different weights corresponding to different working conditions of the household photovoltaic substation area preset, determine the weight of the regulation penalty function at the current time section according to the working condition of the household photovoltaic substation area at the current time section;

[0108] S226 According to each target optimization model, the regulation penalty function, and each weight, obtain the total target optimization model. That is, after introducing this objective function, it is added as an additional optimization target to the total target optimization model. According to the importance of each optimization target and penalty function, allocate the corresponding weights, and the final total target optimization model can be obtained by weighted summation. The final total target optimization model is updated to the following expression (18):

[0109] (18),

[0110] In formula (18), represents the weight coefficient of the target of the over-limit degree of the substation area voltage; The weight coefficient representing the total network loss target of the substation area; The weight coefficient representing the three-phase unbalance degree target of the substation area; The weight coefficient representing the reactive power self-balancing degree target of the substation area; The weight coefficient representing the continuous control target; Represents the total objective function of this multi-objective optimization problem, 、 、 、 、 Represents each sub-objective function.

[0111] In this embodiment, by introducing a regulation penalty function and considering the weight of the regulation penalty function, a multi-objective optimization model is reconstructed as one of the objective functions, further improving the reliability and accuracy of the multi-objective optimization model and further enhancing the reactive power regulation ability.

[0112] Embodiment 4:

[0113] This embodiment provides an algorithm for solving the total objective optimization models of Embodiment 1 and Embodiment 2 above. That is, for S200, according to the constraint conditions and the objective optimization model, the adjustment value of the reactive power is obtained. As the optimization result, based on the constraint conditions, the NSGA-Ⅲ algorithm is used to solve the total objective optimization model to obtain the power adjustment value as the optimization result.

[0114] This model usually includes one or more optimization objectives, such as minimizing network loss, maximizing voltage stability, minimizing the investment in reactive power compensation equipment, etc. Then, the above-mentioned constraint conditions are incorporated into the model to ensure that the obtained power adjustment value can satisfy all constraint conditions.

[0115] An optimization algorithm called NSGA-Ⅲ (Non-dominated Sorting Genetic Algorithm Ⅲ, that is, the third-generation non-dominated sorting genetic algorithm) can be used to solve this model. The NSGA-Ⅲ algorithm is a multi-objective optimization algorithm that can find a balance among multiple optimization objectives, so that the obtained power adjustment value can not only satisfy the constraint conditions but also be as close as possible to the optimization objectives. Specifically, the NSGA-Ⅲ algorithm can search for and evaluate possible power adjustment values according to the constraint conditions and the objective optimization model. During the search process, the algorithm will continuously generate new candidate solutions and retain the excellent solutions and eliminate the inferior solutions by comparing their advantages and disadvantages. Finally, when the algorithm reaches the stop condition, such as reaching the maximum number of iterations or finding a solution that meets the requirements, the optimal power adjustment value can be selected from the retained valid solutions.

[0116] Please refer to Figure 8, this embodiment presents a full-time reactive power control method for a high-penetration household photovoltaic substation area based on the NSGA-Ⅲ algorithm and the coherent control strategy, which is mainly divided into two major steps: initialization and multi-objective optimization under coherent control. The specific implementation is as follows:

[0117] S11 Input the parameters of the distribution network and photovoltaic equipment. The parameters in this step can refer to the network model of the household photovoltaic substation area established in step S110 of the above embodiment. A complete network model of the household photovoltaic substation area can be obtained based on information such as the network topology structure of the household photovoltaic low-voltage substation area, line parameters, installation location and quantity of household photovoltaics, and the access method of household photovoltaics.

[0118] S12 Input the population size and iteration parameters. The population size determines the number of individuals included in each generation of the genetic algorithm. A larger population size helps to better explore the search space, increasing the search ability and diversity of the algorithm, but it will also correspondingly increase the computational cost and time. A smaller population size may lead to insufficient coverage of the search space and is prone to falling into local optimal solutions. Usually, the population size is set according to the complexity of the problem and computational resources. For complex multi-objective optimization problems, a larger population size may be required to ensure finding high-quality solutions. The number of iterations determines how many generations the algorithm will run. In each generation, crossover, mutation, and selection operations are performed to gradually optimize the individuals in the population. A larger number of iterations helps the algorithm to more fully search for the optimal solution, but it will also increase the computational cost and time. A smaller number of iterations may cause the algorithm to fail to converge sufficiently and unable to find high-quality solutions. It is necessary to set an appropriate number of iterations according to the complexity of the problem and the convergence speed. For complex problems, more iterations may be required to ensure that the algorithm can fully search for the optimal solution. At the same time, the limitation of computational resources also needs to be considered to avoid unnecessary computational waste. Here, it can be set as needed according to the actual situation, such as comprehensively considering aspects such as the number of optimization objectives, chip computing power, speed requirements, and cost.

[0119] S13 Determine whether it is the initial optimization control. This step can be understood as determining whether to initially adjust the reactive power of the household photovoltaic substation area at the current time section in step S210 of the above embodiment.

[0120] S14 When it is the initial optimization control, the control strategy is initialized to 0, and then S16 is executed.

[0121] When it is not the initial optimization control, the input control strategy, that is, the result output by the total target optimization model at the previous time section, can refer to step S230 of the above-mentioned embodiment. If it is not the initial adjustment, obtain the optimization result of the reactive power output by the total target optimization model at the previous time section, and use the optimization result of the reactive power at the previous time section as the input of the total target optimization model at the current time section, which is the control strategy described in step S15. In this way, coherent control within continuous time sections is achieved.

[0122] S16 Adaptively select weights according to the working conditions. This step indicates the start of multi-objective optimization. This step can be understood as step S222 of the above-mentioned embodiment 2.

[0123] S17 Calculate the fitness values of each objective function. The fitness value refers to the degree of superiority or inferiority of an individual in a specific environment or problem, and it is usually closely related to the value of the objective function. In the NSGA-Ⅲ algorithm, the fitness value is used to guide the evolutionary direction of the population, that is, to select which individuals to perform operations such as crossover and mutation to generate new individuals. The calculation of the fitness value is usually based on the value of the objective function. In the NSGA-Ⅲ algorithm, the fitness value is usually determined through non-dominated sorting and reference point selection mechanisms.

[0124] S18 ENS non-dominated sorting. In the NSGA-Ⅲ algorithm, non-dominated sorting is used to determine the rank of each individual, so as to preferentially select individuals with higher ranks in subsequent selection operations. Preferential selection is carried out according to the rank of the individual. Usually, individuals with higher ranks have a higher probability of being selected to ensure that excellent individuals can be retained in the next generation.

[0125] S19 Judge whether the convergence condition is satisfied. When the convergence condition is not satisfied, execute S20. When the convergence condition is satisfied, directly jump to S23. According to the preset convergence condition, such as the number of iterations input in step S12, judge whether the algorithm converges. If the total objective function converges, output the optimal solution in the current population, and this optimal solution is used as the household photovoltaic output control strategy, that is, the reactive power adjustment value. Otherwise, continue iterative evolution.

[0126] S20 Select individuals based on the reference point mapping distance.

[0127] S21 Simulated binary crossover algorithm.

[0128] S22 Polynomial mutation method, and then return to S17.

[0129] The NSGA-Ⅲ algorithm iteratively evolves the population through three key steps: S20 selecting individuals based on the reference point mapping distance, S21 the simulated binary crossover algorithm, and S22 the polynomial mutation method, to find a uniformly distributed Pareto front solution. These steps together constitute the core framework of the NSGA-Ⅲ algorithm, enabling it to have excellent performance in dealing with multi-objective optimization problems.

[0130] S23 Household photovoltaic output control strategy. When the total objective function converges and the iteration is completed, the optimal solution in the current population is output as the household photovoltaic output control strategy for the current time section. Thus, the reactive power adjustment for the current time section is completed.

[0131] S24 Control strategy replacement. Replace the control strategy of the previous time section, and the control strategy of the current time section can be used for the reactive power adjustment of the next time section.

[0132] This embodiment uses the NSGA-Ⅲ algorithm to solve the total objective optimization model to obtain the power adjustment value, which can achieve multiple optimization objectives such as no over-limit of node voltage, minimum total network loss, minimum three-phase unbalance degree, and reactive power self-balance, thereby more accurately realizing the regulation of reactive power in the household photovoltaic area.

[0133] To further compare the advantages of the NSGA-Ⅲ coherent control in the full-time reactive power control method for the high-penetration household photovoltaic area in this embodiment, the control results of different control strategies (NSGA-Ⅲ traditional control, PSO traditional control, and NSGA-Ⅲ coherent control) are given, mainly compared in four parameters: average voltage over-limit rate, average reactive power output of the area (kW), average total network loss of the area (kW), and average three-phase unbalance degree, as shown in Table 1 below.

[0134] Table 1: Comparison of control results of different control strategies

[0135]

[0136] In addition, the intra-day voltage-time variation curve of the remote node voltage before and after the control of the full-time reactive power control method for the high-penetration household photovoltaic area based on the NSGA-Ⅲ algorithm and the coherent control strategy in this embodiment is given (reference Figure 9 ), the intra-day total network loss-time variation curve before and after the control (reference Figure 10 ), the intra-day three-phase unbalance degree-time variation curve before and after the control (reference Figure 11 ), and the intra-day reactive power output-time variation curve of the area before and after the control (reference Figure 12 ).

[0137] It can be seen that after the NSGA-Ⅲ coherent control strategy in this embodiment optimizes and adjusts the reactive power control of a high-penetration household photovoltaic substation area, the result parameters of the average voltage over-limit rate, the average reactive power output (kW) of the substation area, the average total network loss (kW) of the substation area, and the average three-phase unbalance degree have great advantages. Therefore, the NSGA-Ⅲ coherent control strategy in this embodiment has very good self-reactive power regulation ability, which reduces the total network loss of the substation area and improves the stability and economy of the operation of the low-voltage distribution network.

[0138] Combining the above embodiments, an all-time reactive power control optimization method for a high-penetration household photovoltaic substation area with coherent control disclosed by the present invention aims to solve problems such as strong hysteresis of traditional adjustment methods, high degree of manual intervention, and large equipment investment cost in low-voltage substation areas under the condition of high penetration of household photovoltaics. This method takes single-phase and three-phase connected household photovoltaic inverters as the objects of reactive power regulation, takes substation area voltage control, reduction of total network loss, three-phase unbalance control, and reactive self-balance as the goals, constructs a variable-weight multi-objective reactive power optimization model for the substation area facing each time section, and further comprehensively considers the economy, stability, and calculation efficiency of regulation within a continuous time period. The proposed method can fully exploit the self-reactive power regulation potential of high-penetration household photovoltaics in the substation area, realize comprehensive power quality management, reduce the total network loss of the substation area, and improve the stability and economy of the operation of the low-voltage distribution network.

[0139] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0140] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0141] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. A reactive power control method for a household photovoltaic power area, characterized in that, It includes the following steps: Establish a network model of a household photovoltaic substation area, construct an objective optimization model of the substation area network model according to the optimization objectives, and set constraint conditions for the objective optimization model. The optimization objectives include no over-limit of node voltages in the low-voltage substation area network, minimum total network loss, minimum three-phase unbalance degree, and reactive power self-balance; Obtain the adjustment value of reactive power as the optimization result according to the constraint conditions and the objective optimization model; introduce the variable weight principle: set the weight rule according to different weights corresponding to different operating conditions of the household photovoltaic substation area as preset, and determine the weights of the corresponding operating conditions of each objective optimization model at the current time section according to the operating conditions of the household photovoltaic substation area at the current time section to obtain the total objective optimization model; introduce the objective function of regulating the penalty for the relative change in reactive power of the household photovoltaic substation area in consecutive time sections as an optimization objective and add the corresponding weight to the total objective optimization model to update the total objective optimization model; And adopt a coherent control strategy to regulate the reactive power of the household photovoltaic substation area in consecutive time sections, including the following: Judge whether it is the first time to regulate the reactive power of the household photovoltaic substation area at the current time section; If it is the first time to regulate, obtain each objective optimization model of each optimization objective, and obtain the total objective optimization model at the current time section according to each objective optimization model, and output the optimization result of the reactive power at the current time section; If it is not the first time to regulate, obtain the optimization result of the reactive power output by the total objective optimization model of the previous time section, use the optimization result of the reactive power of the previous time section as the input quantity of the total objective optimization model at the current time section, and output the optimization result of the reactive power at the current time section; Use the optimization result of the reactive power at the current time section as the input quantity of the total objective optimization model of the next time section, and rollingly update the total objective optimization model to realize the coherent control and optimization of the reactive power of the household photovoltaic substation area in consecutive time sections.

2. The reactive power control method for household photovoltaic substations according to claim 1, characterized in that The operating conditions of the household photovoltaic substation area include load demand and photovoltaic power output; obtain the total objective optimization model according to each objective optimization model and the weights of each objective optimization model.

3. The reactive power control method for household photovoltaic substation area according to claim 1, wherein, The constraint conditions include at least one of the upper and lower limits of the voltages of each phase of each node in the photovoltaic substation area, the upper limit constraint of the current allowed to flow through each branch, the network power flow constraint, and the regulation constraint of the household photovoltaic inverter.

4. The method for controlling the reactive power of a household photovoltaic substation area according to claim 3, characterized in that, The optimization objectives include no over-limit of node voltages in the low-voltage substation area network, minimum total network loss, minimum three-phase unbalance degree, and reactive power self-balance. The objective optimization model constructed based on this is a multi-objective optimization model.

5. The method for controlling reactive power of a household photovoltaic substation area according to claim 4, wherein, The constraint conditions include the upper and lower limits of the voltages of each phase of each node in the photovoltaic substation area, the upper limit constraint of the current allowed to flow through each branch, the network power flow constraint, and the regulation constraint of the household photovoltaic inverter.

6. The reactive power control method for a household photovoltaic substation area according to claim 1, wherein, The network model of the household photovoltaic substation area is established based on the network topology structure of the household photovoltaic low-voltage substation area, line parameters, the installation location and quantity of household photovoltaics, and the access method of household photovoltaics.

7. The reactive power control method for a household photovoltaic substation area according to claim 1, wherein The adjustment value of reactive power obtained according to the constraint conditions and the objective optimization model is used as the optimization result. Based on the constraint conditions, solve the total objective optimization model through the NSGA-Ⅲ algorithm to obtain the power adjustment value as the optimization result.

8. The reactive power control method for household photovoltaic substation areas according to claim 1, characterized in that, Introduce the objective function for regulating the penalty of the relative change in reactive power in the household PV power area at consecutive time sections, and update the overall objective optimization model, including: When the relative change in reactive power at consecutive time sections in the household PV power area is greater than the preset value, obtain the regulation penalty function to encourage the smooth change of reactive power; According to the different weights corresponding to different working conditions of the household PV power area preset, determine the weight of the regulation penalty function at the current time section according to the working condition of the household PV power area at the current time section; Obtain the overall objective optimization model according to each objective optimization model, the regulation penalty function, and each weight.

9. The method for controlling the reactive power of a household photovoltaic substation area according to claim 1, characterized in that, The weight rule is established by the following steps: Count the number of nodes in the household PV power area where the PV output is greater than the load demand; Generate a judgment variable according to the counted number of nodes; Generate the weight rule according to the judgment variable.

10. A reactive power control device for a household photovoltaic substation area, which executes the control method according to any one of claims 1 to 9 above, characterized in that, Including: The model construction module is used to establish the network model of the household PV power area, construct the objective optimization model of the power area network according to the optimization objectives, and set the constraint conditions for the objective optimization model. The optimization objectives include no over-limit of the node voltage in the low-voltage power area network, the lowest total network loss, the minimum three-phase unbalance degree, and reactive power self-balance; The regulation module is used to obtain the adjustment value of reactive power as the optimization result according to the constraint conditions and the objective optimization model, and adopt a coherent control strategy to regulate the reactive power at consecutive time sections of the household PV power area, including the following: The judgment sub-module is used to judge whether the reactive power of the household PV power area is regulated for the first time at the current time section; The first optimization result output sub-module is used to, if it is the first regulation, obtain each objective optimization model of each of the above optimization objectives, and obtain the overall objective optimization model at the current time section according to each objective optimization model, and output the optimization result of the reactive power at the current time section; The second optimization result output sub-module, if it is not the first regulation, obtains the optimization result of the reactive power output by the overall objective optimization model at the previous time section, uses the optimization result of the reactive power at the previous time section as the input quantity of the overall objective optimization model at the current time section, and outputs the optimization result of the reactive power at the current time section; The rolling update sub-module is used to use the optimization result of the reactive power at the current time section as the input quantity of the overall objective optimization model at the next time section, and rollingly update the overall objective optimization model to realize the coherent control and optimization of the reactive power of the household PV power area at consecutive time sections.

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