Design method, device and medium of self-optimizing droop control strategy

Abstract

A design method, device, equipment and medium of a self-optimizing droop control strategy are disclosed. The features include: obtaining operation parameters and power parameters of a distributed generator set, constructing a multi-objective optimization function according to the operation parameters and the power parameters; determining an optimal operation condition according to the multi-objective optimization function; and designing a self-optimizing droop control strategy for a target droop controller according to the optimal operation condition. By setting the control strategy of the self-optimizing droop controller, optimal distribution of active power and reactive power can be achieved, the accuracy of the distribution of active power and reactive power is improved, the droop controller achieves optimal control in terms of operation cost and effective power, and the distributed generator set of the micro-grid can maintain the optimal operation condition under different load conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of microgrid networking, and particularly relates to a design method, device and equipment of a self-optimization droop control strategy and a medium. BACKGROUND

[0002] Economic operation and energy efficiency of microgrids are important issues that have attracted much attention in recent years. In order to reduce the overall operating cost, a centralized control strategy is set to minimize the generation cost. However, the communication facilities increase the hardware cost of the microgrid and reduce the reliability of the microgrid. In order to reduce the communication cost and enhance the reliability of the microgrid, a power control strategy based on droop is developed to achieve economic scheduling without communication. For energy efficiency, an inverter scheduling strategy is proposed to improve system efficiency by optimizing the number of inverter operations. In direct current microgrids and alternating current microgrids, the operating cost and efficiency may be highly coupled, and separate optimization of the operating cost or efficiency cannot achieve the overall optimal performance. The prior art cannot optimize the operating cost and efficiency of the microgrid simultaneously. SUMMARY

[0003] The present application provides a design method, device, equipment and medium of a self-optimization droop control strategy to solve the technical problem that the prior art cannot optimize the operating cost and efficiency of the microgrid simultaneously.

[0004] According to an aspect of the present application, a design method of a self-optimization droop control strategy is provided, comprising:

[0005] obtaining operating parameters and power parameters of a distributed generator set, and constructing a multi-objective optimization function according to the operating parameters and the power parameters;

[0006] determining optimal operating conditions according to the multi-objective optimization function;

[0007] designing a self-optimization droop control strategy for a target droop controller according to the optimal operating conditions.

[0008] According to another aspect of the present application, a design device of a self-optimization droop control strategy is provided, comprising:

[0009] a multi-objective optimization module for obtaining operating parameters and power parameters of a distributed generator set, and constructing a multi-objective optimization function according to the operating parameters and the power parameters;

[0010] an optimal parameter calculation module for determining optimal operating conditions according to the multi-objective optimization function;

[0011] a control strategy design module for designing a self-optimization droop control strategy for a target droop controller according to the optimal operating conditions.

[0012] According to another aspect of the present application, there is provided an electronic device comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein

[0015] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the method for designing a self-optimizing droop control strategy according to any one of the embodiments of the present application.

[0016] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for enabling a processor to implement the method for designing a self-optimizing droop control strategy according to any one of the embodiments of the present application when executed by the processor.

[0017] The technical scheme of the embodiment of the present application, by obtaining the operation parameters and power parameters of the distributed generator set, constructing a multi-objective optimization function according to the operation parameters and the power parameters, taking the operation cost and efficiency of the distributed generator set as the optimization objects through the construction of the multi-objective optimization function, realizing the double optimization of the operation cost and efficiency, determining the optimal operation condition according to the multi-objective optimization function, being able to determine the extreme value condition and constraint condition that meet the multi-objective optimization function through the optimal operation condition, being able to improve the accuracy of optimization, designing a self-optimizing droop control strategy for the target droop controller according to the optimal operation condition, being able to realize the dynamic adjustment of the parameters of the droop controller through the self-optimizing droop control strategy, realizing the optimal distribution of active power and reactive power, solving the technical problem that the operation cost and efficiency of the micro-grid cannot be optimized at the same time in the prior art. The accuracy of the distribution of active power and reactive power is improved, and the droop controller is optimized in the operation cost and effective power, so that the distributed generator set of the micro-grid can maintain the optimal operation condition under different load conditions.

[0018] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0020] Figure 1 A flow chart of a design method of a self-optimizing droop control strategy is provided for the embodiment of the present application.

[0021] Figure 2 A flow chart of a design method of a self-optimizing droop control strategy is provided for the embodiment of the present application.

[0022] Figure 3 A structural schematic diagram of a design device of a self-optimizing droop control strategy is provided for the embodiment of the present application.

[0023] Figure 4 A structural schematic diagram of an electronic device 10 that can be used to implement the embodiment of the present application is shown. DETAILED DESCRIPTION

[0024] In order to make the personnel in the technical field better understand the present application scheme, the technical scheme in the embodiment of the present application will be described clearly and completely in combination with the drawings in the embodiment of the present application. Obviously, the described embodiment is only a part of the embodiment of the present application, not all. Based on the embodiment in the present application, all other embodiments obtained by the person skilled in the art without creative labor should belong to the protection scope of the present application.

[0025] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0026] Figure 1 A flow chart of a design method of a self-optimizing droop control strategy is provided for the embodiment of the present application. The embodiment can be applicable to the case, and the method can be executed by a design device of a self-optimizing droop control strategy. The design device of a self-optimizing droop control strategy can be realized in the form of hardware and / or software, and can be configured in an electronic device. As shown in the figure, the method comprises: Figure 1

[0027] ​S110, acquire an operation parameter and a power parameter of the distributed generator set, and construct a multi-objective optimization function according to the operation parameter and the power parameter.

[0028] The distributed generator set can be a power generation device distributed in different areas of the micro-grid. It should be noted that the distributed generator set is usually small in size, and the distributed generator set is connected to the micro-grid through an inverter. For example, the distributed generator set can be a photovoltaic generator set.

[0029] The operation parameter can be the operation cost of the distributed generator set connected to the grid. Taking the photovoltaic generator set as an example: during the operation of the photovoltaic generator set, due to equipment aging, the photovoltaic components, inverters, supports and other key equipment will experience natural aging, wear and performance degradation during long-term use. Regular inspection, cleaning and maintenance are required to ensure normal operation and prolong service life. In addition, due to external environmental factors (such as wind and sand, rain and snow, lightning, etc.) or internal circuit failure, the photovoltaic generator set may fail and need to be troubleshooting and repaired, resulting in maintenance costs. The energy storage equipment provided by the photovoltaic generator set will also age during long-term charging and discharging, and new energy storage equipment needs to be replaced to ensure stable operation, resulting in storage replacement costs; During the entire life cycle of the photovoltaic generator set, a certain amount of energy consumption and emissions will be generated during operation, which will be included in the emission cost of the photovoltaic generator set. These emission costs, storage replacement costs and maintenance costs jointly constitute the operation cost of the photovoltaic generator set.

[0030] The power parameter can be power data of the distributed generator set during operation. It should be noted that in the embodiment of the application, the power parameter can include a power loss coefficient, reactive power and active power. The power loss system can be the ratio of power loss to output power of the distributed generator set during operation. The reactive power can be the power required to establish a magnetic field and maintain the normal operation of electrical equipment. The active power can be the actual output power of the distributed generator set. The reactive power is mainly used for energy exchange between inductive and capacitive elements between the distributed generator set and the micro-grid. The active power can be the energy provided by the distributed generator set to the micro-grid.

[0031] The multi-objective optimization function can be an optimization function that optimizes the operation cost and efficiency of the distributed generator set. It should be noted that the multi-objective optimization function is an optimization function of a multi-objective function, and in the embodiment of the present application, the optimization objectives of the multi-objective optimization function are to control the operation cost and efficiency of the distributed generator set through the target droop controller after the distributed generator set is connected to the grid through the inverter, to balance the operation cost and efficiency of the distributed generator set, and to determine the control strategy of the target droop controller that satisfies the optimal operation cost and efficiency of the distributed generator set.

[0032] Specifically, for the distributed generator set, the operation parameters and power parameters of the distributed generator set are obtained, and a multi-objective optimization function for optimizing the operation cost and efficiency of the distributed generator set is constructed for the distributed generator set according to the operation parameters and power parameters.

[0033] S120, determining the optimal operation condition according to the multi-objective optimization function.

[0034] The optimal operation condition can be a necessary condition for the distributed generator set to achieve the optimal operation cost and efficiency.

[0035] Specifically, the multi-objective optimization function is solved to determine the optimal operation condition of the optimal solution of the multi-objective optimization function.

[0036] Optionally, in another optional embodiment of the present application, the determining the optimal operation condition according to the multi-objective optimization function comprises:

[0037] constructing a multi-objective Lagrange function based on the multi-objective optimization function;

[0038] solving the multi-objective Lagrange function by the Lagrange multiplier method to determine the optimal operation condition; wherein the optimal operation condition comprises a first optimal condition and a second optimal condition.

[0039] The first optimal condition can be used to determine the droop parameters of the target droop controller, and the second optimal condition can be used to determine the equivalent output impedance of the nonlinear impedance compensation loop of the target droop controller.

[0040] Optionally, the target droop controller is composed of a droop controller and a nonlinear impedance compensation loop, the droop controller is capable of controlling the output power of the distributed generator set based on droop control parameters, the droop control parameters can be the output frequency of the distributed generator set, the output voltage reference value, the frequency voltage rating and the droop gain of active and reactive power; the nonlinear impedance compensation loop is introduced to the target droop controller to compensate the impedance characteristics of the target droop controller, the parameters and compensation strategies of the target droop controller can be adjusted, the target droop controller can combine the droop controller and the nonlinear impedance compensation loop, effectively improve the accuracy and stability of the droop control, and ensure the flexibility and reliability of the micro-grid after the distributed generator set is connected to the micro-grid.

[0041] It should be noted that the multi-objective optimization function can set a corresponding Lagrange multiplier for each optimization objective, and construct a multi-objective Lagrange function based on the optimization objective and the Lagrange multiplier. The Lagrange multiplier method can be a method for solving the multi-objective optimization function.

[0042] Specifically, a multi-objective Lagrange function is constructed according to the multi-objective optimization function of the distributed generator set, the multi-objective Lagrange function is solved by the Lagrange multiplier method, and the optimal operating condition is determined.

[0043] S130, designing a self-optimizing droop control strategy for the target droop controller according to the optimal operating condition.

[0044] The self-optimizing droop control strategy can be the droop control parameters and the equivalent output impedance of the target droop controller, the equivalent output impedance of the nonlinear impedance compensation loop of the target droop controller can be the impedance characteristics presented by the nonlinear impedance compensation loop, and the equivalent output impedance can improve the controllability of the target droop controller and ensure the system stability of the micro-grid.

[0045] Specifically, after obtaining the optimal operating condition, the corresponding self-optimizing droop control strategy is set for the target droop controller based on the optimal operating condition.

[0046] The technical scheme of the embodiment of the application obtains the operation parameters and power parameters of the distributed generator set, constructs a multi-objective optimization function according to the operation parameters and the power parameters, takes the operation cost and efficiency of the distributed generator set as the optimization objects by constructing the multi-objective optimization function, realizes the double optimization of the operation cost and efficiency, determines the optimal operation condition according to the multi-objective optimization function, can determine the extreme condition and constraint condition meeting the multi-objective optimization function through the optimal operation condition, and can improve the accuracy of optimization, designs a self-optimization droop control strategy for the target droop controller according to the optimal operation condition, can realize the dynamic adjustment of the parameters of the droop controller through the self-optimization droop control strategy, realizes the optimal distribution of the active power and the reactive power, solves the technical problem that the operation cost and efficiency of the micro-grid cannot be simultaneously optimized in the prior art, improves the accuracy of the distribution of the active power and the reactive power, and makes the distributed generator set of the micro-grid maintain the optimal operation condition under different load conditions.

[0047] Figure 2 The flowchart of another design method of the self-optimization droop control strategy provided by the embodiment of the application, the relationship between the embodiment and the above-mentioned embodiment is that the embodiment is a specific method of designing the self-optimization droop control strategy for the target droop controller. As shown in the figure, the method comprises the following steps. Figure 2

[0048] S210, obtaining operation parameters and power parameters of a distributed generator set, and constructing a multi-objective optimization function according to the operation parameters and the power parameters.

[0049] Optionally, in another optional embodiment of the application, the step of constructing a multi-objective optimization function according to the operation parameters and the power parameters comprises:

[0050] defining a normalized performance factor of the distributed generator set according to the operation parameters and the power parameters, and establishing the multi-objective optimization function of the distributed generator set according to the normalized performance factor.

[0051] The normalized performance factor can be defined by considering the operation cost and power loss of the distributed generator set. It should be noted that the power loss can be the loss generated after the distributed generator set is connected to the micro-grid. For example, the power loss can be the power loss caused by the converter, mainly including the conduction loss and switching loss of the semiconductor and the loss of the filter inductor.

[0052] Optionally, in another optional embodiment of the application, the step of defining a normalized performance factor of the distributed generator set according to the operation parameters and the power parameters comprises:

[0053] ​The power loss value is determined according to the power loss coefficient of the distributed generator set and the reactive power, the operation cost is determined according to the operation parameter, the active power and the power loss value, and the normalized performance factor of the distributed generator set is defined according to the operation cost and the power loss value.

[0054] The power loss value can be used to represent the power loss, and the power loss value of the ith distributed generator set can be represented by P loss_i , for example. oi The active power of the ith distributed generator set is P oi , the function representation of the power loss value is:

[0055]

[0056] The power loss coefficient of the ith distributed generator set is a i , b i , c i , d i , e i , h i , and the power loss coefficient can be obtained by fitting the experimental data of the inverter power loss experiment.

[0057] The operation cost can be the cost value of the distributed generator set during operation. The operation cost of the ith distributed generator set can be represented by C i , for example. Ci The operation parameter of the ith distributed generator set can be represented by K i , and the function expression of the operation cost is:

[0058] C i = K Ci P i = K Ci (P oi + P loss_i )

[0059] Specifically, the power loss value is determined according to the power loss coefficient of the distributed generator set and the reactive power, the operation cost is determined according to the operation parameter, the active power and the power loss value, and the normalized performance factor of the distributed generator set is defined according to the operation cost and the power loss value. The normalized performance factor of the ith distributed generator set is defined as F i , and α iLet β be the weighting coefficient for the operating cost of the i-th distributed generator unit, and it is greater than or equal to 0 and less than or equal to 1. i Let α be the power loss weighting coefficient of the i-th distributed generator unit, and it is greater than or equal to 0 and less than or equal to 1. i +β i =1. The normalized performance factor is defined as F. i The function is represented as:

[0060]

[0061] Among them, P loss_max Let C be the power loss value of the i-th distributed generator unit under maximum load conditions. i Let be the operating cost of the i-th distributed generator set under maximum load conditions.

[0062] Optionally, a multi-objective optimization function is established for the distributed generator sets based on the normalized performance factor. If the load active power demand of multiple distributed generator sets connected in parallel to the microgrid is also required based on the normalized performance factor, the load active power demand of the microgrid connected in parallel with the distributed generator sets is determined by obtaining the system efficiency of the microgrid and the power loss of each distributed generator set in the microgrid. For example, N is the number of distributed generator sets, and the load active power demand can be represented by P. load To represent, through P loss_tot Let η represent the sum of power losses from multiple distributed generator sets, and let η represent the system efficiency. The system efficiency is expressed as a function:

[0063]

[0064] Specifically, a normalized performance factor for the distributed generator set is defined based on operating parameters and power parameters. A multi-objective optimization function for the distributed generator set is then established based on this normalized performance factor. For example, the multi-objective optimization function can be expressed as follows:

[0065]

[0066] Where F represents the sum of normalized performance factors in distributed generator sets within the microgrid; Q load represents the reactive power demand of the distributed generator sets connected in parallel with the microgrid load; st represents the constraints of the multi-objective optimization function, which are active power and reactive power.

[0067] S220. Determine the optimal operating conditions based on the multi-objective optimization function.

[0068] Optionally, a multi-objective Lagrange function is constructed based on the multi-objective optimization function, and the multi-objective Lagrange function is solved by the Lagrange multiplier method to determine the first optimal condition and the second optimal condition. Exemplarily, the Lagrange function constructed based on the multi-objective optimization function is as follows by taking λ1 and λ2 as Lagrange multipliers:

[0069]

[0070] The first necessary condition of the optimal solution obtained by solving the Lagrange function by the Lagrange multiplier method can be expressed as:

[0071]

[0072] wherein, is the partial derivative of the active power;

[0073] The second necessary condition can be expressed as:

[0074]

[0075] wherein, is the partial derivative of the reactive power;

[0076] In the solving process, a performance factor F is defined as: c

[0077]

[0078] S230, determining a self-optimizing droop strategy according to the first optimal condition.

[0079] wherein, the self-optimizing droop strategy can be used to optimize the optimization strategy of the droop controller of the target droop controller.

[0080] Specifically, the self-optimizing droop strategy is determined according to the first optimal condition. The self-optimizing droop strategy is composed of the distributed power angle frequency reference value generated by the droop control loop, the classical droop control rated angle frequency, the active power droop gain, the self-optimizing droop control rated angle frequency, and the coefficient group related to the cost coefficient, the power loss coefficient, and the weight coefficient of the distributed generator set.

[0081] S240, determining the output power relationship of the distributed generator set according to the self-optimizing droop strategy.

[0082] wherein, the output power relationship can be the relationship information between the distributed generator set and another distributed generator set in the microgrid. It should be noted that the output power relationship can be composed of the active power and the reactive power of the two distributed generator sets and the coefficient group related to the cost coefficient, the power loss coefficient, and the weight coefficient.

[0083] ​Specifically, the output power relationship of the distributed generator set is determined according to the self-optimization droop strategy.

[0084] S250, a nonlinear impedance compensation loop of the distributed generator set is established according to the second optimal condition.

[0085] Optionally, the optimal reactive power sharing is realized through the second optimal condition, the nonlinear impedance compensation loop of the distributed generator set is established to reshape the equivalent output impedance of the distributed generator set, and the equivalent output impedance is composed of a coefficient group related to the closed-loop output impedance, the equivalent fundamental impedance and the cost coefficient, the power loss coefficient and the weight coefficient.

[0086] S260, the self-optimization droop control strategy is determined according to the output power relationship and the nonlinear impedance compensation loop.

[0087] Optionally, after the nonlinear impedance compensation loop is constructed, the voltage reference of the droop controller and the output current in the dq coordinate system are obtained, the voltage reference of the nonlinear impedance compensation loop in the dq coordinate system is determined according to the voltage reference of the droop controller and the output current in the dq coordinate system (synchronous rotating coordinate system), the reactive power sharing ratio of the distributed generator set is composed of the closed-loop output impedance, the equivalent fundamental impedance and the coefficient group related to the cost coefficient, the power loss coefficient and the weight coefficient and the voltage reference based on the impedance compensation method proposed in advance according to the second optimal condition.

[0088] Specifically, the self-optimization droop control strategy is determined through the output power relationship and the nonlinear impedance compensation loop.

[0089] Optionally, in another optional embodiment of the present application, the self-optimization droop control strategy is determined according to the output power relationship and the nonlinear impedance compensation loop, comprising:

[0090] a target droop control strategy is determined according to the output power relationship and the normalized performance factor;

[0091] an impedance compensation strategy is determined according to the nonlinear impedance compensation loop and the normalized performance factor; in the case that the target droop control strategy meets the first optimal condition and the impedance compensation strategy meets the second optimal condition, a self-optimization droop control strategy is determined according to the target droop control strategy and the impedance compensation strategy.

[0092] Optionally, after obtaining the output power relationship of the distributed generator set, the output power relationship and the normalized performance factor are combined for verification, and when the combination of the output power relationship and the normalized performance factor meets the first optimal condition, it is indicated that the self-optimizing droop strategy meets the droop control parameter of the droop controller in the target droop control strategy; after obtaining the nonlinear impedance compensation loop, the reactive power sharing ratio obtained based on the nonlinear impedance compensation loop and the second optimal condition are combined for verification, and when the combination of the reactive power sharing ratio and the second optimal condition meets the second optimal condition, it is indicated that the impedance compensation method corresponding to the nonlinear impedance compensation loop meets the impedance compensation strategy of the nonlinear impedance compensation loop in the target droop control strategy, and the impedance compensation method of the nonlinear impedance compensation loop is taken as the impedance compensation strategy, and the self-optimizing droop control strategy is composed based on the target droop control strategy and the impedance compensation strategy.

[0093] The technical scheme of the embodiment of the application derives the optimal operating condition, designs the self-optimizing droop control strategy of the target droop controller based on the optimal operating condition, sets the target droop control strategy for the droop controller in the target droop controller, can realize the optimal distribution of the active power and the reactive power of the distributed generator set, designs the impedance compensation strategy for the nonlinear impedance compensation loop in the target droop controller, and can make the distributed generator set of the microgrid maintain the optimal operating condition under different load conditions, thereby solving the technical problem that the operating cost and the efficiency of the microgrid cannot be optimized simultaneously in the prior art. By adjusting the parameters and the compensation strategy of the controller, the microgrid can maintain the optimal operating state under different load conditions.

[0094] Figure 3 A structural schematic diagram of a design device of a self-optimizing droop control strategy provided by the embodiment of the application is shown in FIG. 1. Figure 3 As shown in the figure, the device comprises a multi-objective optimization module 310, an optimal parameter calculation module 320 and a control strategy design module 330; wherein,

[0095] The multi-objective optimization module 310 is configured to obtain the operating parameters and the power parameters of the distributed generator set, and construct a multi-objective optimization function according to the operating parameters and the power parameters.

[0096] The optimal parameter calculation module 320 is configured to determine the optimal operating condition according to the multi-objective optimization function.

[0097] The control strategy design module 330 is configured to design a self-optimizing droop control strategy for a target droop controller according to the optimal operating condition.

[0098] The technical scheme of the embodiment of the application obtains the operation parameters and power parameters of the distributed generator set, constructs a multi-objective optimization function according to the operation parameters and the power parameters, takes the operation cost and efficiency of the distributed generator set as the optimization objects by constructing the multi-objective optimization function, realizes the double optimization of the operation cost and efficiency, determines the optimal operation condition according to the multi-objective optimization function, can determine the extreme condition and constraint condition satisfying the multi-objective optimization function through the optimal operation condition, and can improve the accuracy of optimization, designs a self-optimizing droop control strategy for the target droop controller according to the optimal operation condition, can realize the dynamic adjustment of the parameters of the droop controller through the self-optimizing droop control strategy, realizes the optimal distribution of active power and reactive power, solves the technical problem that the operation cost and efficiency of the micro-grid cannot be simultaneously optimized in the prior art, improves the accuracy of the distribution of active power and reactive power, and makes the distributed generator set of the micro-grid maintain the optimal operation condition under different load conditions.

[0099] Optionally, the multi-objective optimization module is specifically used for:

[0100] defining a normalized performance factor of the distributed generator set according to the operation parameters and the power parameters;

[0101] establishing the multi-objective optimization function of the distributed generator set according to the normalized performance factor, wherein the power parameters include a power loss coefficient, reactive power and active power.

[0102] Optionally, the multi-objective optimization module is specifically used for:

[0103] calculating a power loss value according to the power loss coefficient and the reactive power of the distributed generator set;

[0104] determining an operation cost according to the operation parameters, the active power and the power loss value;

[0105] defining the normalized performance factor of the distributed generator set according to the operation cost and the power loss value.

[0106] Optionally, the optimal parameter calculation module is specifically used for:

[0107] constructing a multi-objective Lagrange function based on the multi-objective optimization function;

[0108] solving the multi-objective Lagrange function by a Lagrange multiplier method to determine an optimal operation condition, wherein the optimal operation condition includes a first optimal condition and a second optimal condition.

[0109] Optionally, the control strategy design module is specifically used for:

[0110] determining a self-optimization droop strategy according to the first optimal condition;

[0111] determining an output power relationship of the distributed generator set according to the self-optimization droop strategy;

[0112] establishing a nonlinear impedance compensation loop of the distributed generator set according to the second optimal condition;

[0113] determining a self-optimization droop control strategy according to the output power relationship and the nonlinear impedance compensation loop.

[0114] Optionally, the control strategy design module is specifically used for:

[0115] determining a target droop control strategy according to the output power relationship and the normalized performance factor;

[0116] determining an impedance compensation strategy according to the nonlinear impedance compensation loop and the normalized performance factor;

[0117] determining a self-optimization droop control strategy according to the target droop control strategy and the impedance compensation strategy in a case where the target droop control strategy satisfies a first optimization condition and the impedance compensation strategy satisfies a second optimization condition.

[0118] The self-optimization droop control strategy design device provided in the embodiment can execute the self-optimization droop control strategy design method provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0119] Figure 4 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their modes of operation, are meant to be examples only, and are not intended to limit the inventiveness in any way.

[0120] As Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., communicatively connected to the at least one processor 11, where the memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0121] Various components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, a speaker, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer grid, such as the Internet, and / or various telecommunication grids.

[0122] The processor 11 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the design method of the self-optimizing droop control strategy.

[0123] In some embodiments, the design method of the self-optimizing droop control strategy can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the design method of the self-optimizing droop control strategy described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the design method of the self-optimizing droop control strategy by any other appropriate means, such as by means of firmware.

[0124] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a load programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0125] Computer programs used to implement the processes of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, cause modes / operations specified in the flow charts and / or block diagrams to be implemented. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a standalone software package and partially on a remote machine, or entirely on a remote machine or server.

[0126] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0127] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0128] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0129] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0130] It should be understood that various forms of flow shown above can be used, with steps re-ordered, added, or removed. For example, various steps recited in the description of the present application can be performed in parallel, in series, or in different orders, as long as the desired results of the present application are achieved, and are not limited herein.

[0131] The embodiment provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement a design method step of a self-optimizing droop control strategy provided by any embodiment of the present application, and the method comprises the following steps of:

[0132] Obtaining operation parameters and power parameters of a distributed generator set, and constructing a multi-objective optimization function according to the operation parameters and the power parameters;

[0133] Determining optimal operation conditions according to the multi-objective optimization function;

[0134] Designing a self-optimizing droop control strategy for the target droop controller according to the optimal operation conditions. The computer storage medium of the embodiment of the present application can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0135] The computer readable signal medium can include a data signal propagating in a baseband or as part of a carrier wave propagating through a transmission medium, in which the computer readable program code is embodied. Such a propagating data signal can take many forms, including but not limited to electro-magnetic, optical, or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium that is not a computer readable storage medium and that can transmit, propagate or transport program for use by or in connection with an instruction execution system, apparatus or device.

[0136] The program code contained on the computer readable medium can be transmitted in any suitable medium, including but not limited to wireless, wire, cable, optical fiber, RF, etc., or any suitable combination thereof.

[0137] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0138] Those skilled in the art will appreciate that the modules or steps of the present application described above can be implemented in a general purpose computer, and they can be centralized on a single computing device or distributed over a network of multiple computing devices. Alternatively, they can be implemented by computer executable program codes, which can be stored in a storage device and executed by a computing device, or they can be implemented by individual integrated circuit modules, or a plurality of modules or steps can be implemented by a single integrated circuit module. Thus, the present application is not limited to any particular combination of hardware and software.

[0139] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present application. For example, the steps recited in the present application can be performed in parallel, in series, or in a different order, and the present application is not limited in this regard.

[0140] The specific embodiments described above are not intended to limit the scope of the present application. Those skilled in the art will understand that various modifications, combinations, sub-combinations, and alternatives of the present application can be practiced without departing from the spirit and scope of the application. Any modification, equivalent replacement, and improvement, etc. made within the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A design method for a self-optimizing droop control strategy, characterized in that, include; The operating parameters and power parameters of the distributed generator set are obtained, and a multi-objective optimization function is constructed based on the operating parameters and power parameters. The multi-objective optimization function is an optimization function that jointly considers the operating cost and efficiency of the distributed generator set, and the power parameters are the power data of the distributed generator set during operation. The optimal operating conditions are determined based on the multi-objective optimization function, wherein the optimal operating conditions include a first optimal condition and a second optimal condition; The Lagrangian function constructed based on the multi-objective optimization function is: ； in, and Let F be the sum of the Lagrange multipliers and F be the sum of the normalized performance factors. To meet the active power demand of distributed generator sets connected in parallel with microgrid loads, Let i be the active power of the i-th distributed generator set. For the reactive power demand of the distributed generator sets connected in parallel with the microgrid load, Let N be the reactive power of the i-th distributed generator set, and N be the number of the distributed generator sets. Solving the Lagrange function using the Lagrange multiplier method yields the first optimal condition as follows: ； in, This is the partial derivative of the active power; The second optimal condition is: ； in, This is the partial derivative of reactive power; The output power relationship of the distributed generator set is determined based on the first optimal condition; A nonlinear impedance compensation circuit for the distributed generator set is established based on the second optimal condition. The nonlinear impedance compensation circuit is used to reshape the equivalent output impedance of the distributed generator set. The equivalent output impedance is composed of the closed-loop output impedance obtained from the second optimal condition, the equivalent fundamental impedance, the cost coefficient, the power loss coefficient, and the weighting coefficient. The self-optimizing droop control strategy is determined based on the output power relationship and the nonlinear impedance compensation circuit.

2. The method according to claim 1, characterized in that, The step of constructing a multi-objective optimization function based on the operating parameters and the power parameters includes: The normalized performance factor of the distributed generator set is defined based on the operating parameters and the power parameters. The multi-objective optimization function of the distributed generator set is established based on the normalized performance factor.

3. The method according to claim 2, characterized in that, The power parameters include power loss coefficient, reactive power, and active power.

4. The method according to claim 3, characterized in that, The step of defining the normalized performance factor of the distributed generator set based on the operating parameters and the power parameters includes: The power loss value is determined by calculating the power loss coefficient and reactive power of the distributed generator set. The operating cost is determined based on the operating parameters, the active power, and the power loss value. The normalized performance factor of the distributed generator set is defined based on the operating cost and the power loss value.

5. The method according to claim 1, characterized in that, The step of determining the self-optimized droop control strategy based on the output power relationship and the nonlinear impedance compensation circuit includes: The target droop control strategy is determined based on the output power relationship and the normalized performance factor. The impedance compensation strategy is determined based on the nonlinear impedance compensation circuit and the normalized performance factor. If the target droop control strategy satisfies the first optimal condition and the impedance compensation strategy satisfies the second optimal condition, a self-optimizing droop control strategy is determined based on the target droop control strategy and the impedance compensation strategy.

6. A design device for a self-optimizing droop control strategy, characterized in that, include; A multi-objective optimization module is used to obtain the operating parameters and power parameters of the distributed generator set, and construct a multi-objective optimization function based on the operating parameters and power parameters. The multi-objective optimization function is an optimization function that jointly considers the operating cost and efficiency of the distributed generator set, and the power parameters are the power data of the distributed generator set during operation. The optimal parameter calculation module is used to determine the optimal operating conditions based on the multi-objective optimization function, wherein the optimal operating conditions include a first optimal condition and a second optimal condition. The optimal parameter calculation module is specifically used for: The Lagrangian function constructed based on the multi-objective optimization function is: ； in, and Let F be the sum of the Lagrange multipliers and F be the sum of the normalized performance factors. To meet the active power demand of distributed generator sets connected in parallel with microgrid loads, Let i be the active power of the i-th distributed generator set. For the reactive power demand of the distributed generator sets connected in parallel with the microgrid load, Let N be the reactive power of the i-th distributed generator set, and N be the number of the distributed generator sets. Solving the Lagrange function using the Lagrange multiplier method yields the first optimal condition as follows: ； in, This is the partial derivative of the active power; The second optimal condition is: ； in, This is the partial derivative of reactive power; The control strategy design module is used to design a self-optimizing droop control strategy for the target droop controller based on the optimal operating conditions. The control strategy design module is specifically used for: The output power relationship of the distributed generator set is determined based on the first optimal condition; A nonlinear impedance compensation circuit for the distributed generator set is established based on the second optimal condition. The nonlinear impedance compensation circuit is used to reshape the equivalent output impedance of the distributed generator set. The equivalent output impedance is composed of the closed-loop output impedance obtained from the second optimal condition, the equivalent fundamental impedance, the cost coefficient, the power loss coefficient, and the weighting coefficient. The self-optimizing droop control strategy is determined based on the output power relationship and the nonlinear impedance compensation circuit.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the design method of the self-optimizing droop control strategy according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the design method of the self-optimizing droop control strategy according to any one of claims 1-5.

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