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

Abstract

The invention discloses a design method and device of a self-optimization droop control strategy, equipment and a medium. The method is characterized by comprising the following steps: acquiring 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; determining an optimal operation condition according to the multi-objective optimization function; and designing a self-optimization droop control strategy for a target droop controller according to the optimal operation condition. By setting a control strategy of the self-optimization droop controller, optimal distribution of active power and reactive power can be realized, the distribution precision of the active power and the reactive power is improved, and optimal control is achieved for the droop controller in two targets of operation cost and effective power. Therefore, the distributed generator set of the micro-grid can maintain the optimal operation condition under different load conditions.

Description

Technical Field

[0001] The present invention relates to the field of microgrid networking technology, and in particular to a design method, device, equipment and medium for a self-optimizing droop control strategy. Background Art

[0002] The economic operation and energy efficiency of microgrids are important issues that have received much attention in recent years. In order to reduce the overall operating cost, a centralized control strategy is set to minimize the cost of power generation. However, 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 droop-based power control strategy is developed to achieve economic dispatch without communication. For energy efficiency, an inverter dispatch strategy is proposed to improve system efficiency by optimizing the number of inverter operations. In DC microgrids and AC microgrids, operating costs and efficiency may be highly coupled, and optimizing operating costs or efficiency alone cannot achieve overall optimal performance. It is not possible to dual optimize operating costs and efficiency in the existing technology. Summary of the invention

[0003] The present invention provides a design method, device, equipment and medium for a self-optimizing droop control strategy to solve the technical problem in the prior art that the operating cost and efficiency of a microgrid cannot be optimized simultaneously.

[0004] According to one aspect of the present invention, there is provided a design method for a self-optimizing droop control strategy, comprising:

[0005] Acquire operating parameters and power parameters of the distributed generator set, and construct 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] A self-optimizing droop control strategy is designed for a target droop controller according to the optimal operating conditions.

[0008] According to another aspect of the present invention, there is provided a design device for a self-optimizing droop control strategy, comprising:

[0009] A multi-objective optimization module, used to obtain operating parameters and power parameters of the distributed generator set, and to construct a multi-objective optimization function according to the operating parameters and the power parameters;

[0010] An optimal parameter calculation module, used to determine the optimal operating conditions according to the multi-objective optimization function;

[0011] A control strategy design module is used to design a self-optimizing droop control strategy for a target droop controller according to the optimal operating conditions.

[0012] According to another aspect of the present invention, there is provided an electronic device, the 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 so that the at least one processor can execute the design method of the self-optimizing droop control strategy described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the design method of the self-optimizing droop control strategy described in any embodiment of the present invention when executed.

[0017] The technical solution of the embodiment of the present invention obtains the operating parameters and power parameters of the distributed generator set, constructs a multi-objective optimization function according to the operating parameters and the power parameters, and takes the operating cost and efficiency of the distributed generator set as the target optimization object by constructing the multi-objective optimization function, thereby realizing the dual optimization of the operating cost and efficiency; determining the optimal operating conditions according to the multi-objective optimization function, and determining the extreme value conditions and constraint conditions that satisfy the multi-objective optimization function through the optimal operating conditions, thereby improving the accuracy of the optimization; designing a self-optimizing droop control strategy for the target droop controller according to the optimal operating conditions, and dynamically adjusting the parameters of the droop controller through the self-optimizing droop control strategy, thereby realizing the optimal allocation of active power and reactive power, and solving the technical problem that the operating cost and efficiency of the microgrid cannot be optimized at the same time in the prior art. The accuracy of the allocation of active power and reactive power is improved, and the droop controller achieves optimal control in terms of the two objectives of operating cost and effective power, so that the distributed generator set of the microgrid can maintain the optimal operating conditions under different load conditions.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

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

[0021] Figure 2 A flow chart of a design method for a self-optimizing droop control strategy provided by an embodiment of the present invention;

[0022] Figure 3 A schematic diagram of the structure of a design device for a self-optimizing droop control strategy provided by an embodiment of the present invention;

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

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] Figure 1 A flowchart of a design method for a self-optimizing droop control strategy is provided for an embodiment of the present invention. This embodiment is applicable to situations where the method can be executed by a design device for a self-optimizing droop control strategy. The design device for a self-optimizing droop control strategy can be implemented in the form of hardware and / or software. The design device for a self-optimizing droop control strategy can be configured in an electronic device. Figure 1 As shown, the method includes:

[0027] S110, obtaining operating parameters and power parameters of the distributed generator set, and constructing a multi-objective optimization function according to the operating parameters and the power parameters.

[0028] Among them, the distributed generator set can be a power generation device dispersed in different areas of the microgrid. It should be noted that the distributed generator set is usually small in scale, and the distributed generator set is connected to the microgrid through an inverter. Exemplarily, the distributed generator set can be a photovoltaic generator set.

[0029] Among them, the operating parameters can be the operating costs of distributed generator sets connected to the grid. Take photovoltaic generator sets as an example: during the operation of photovoltaic generator sets, due to equipment aging, key equipment such as photovoltaic modules, inverters, and brackets will experience natural aging, wear and performance degradation during long-term use, and require regular inspection, cleaning and maintenance to ensure their normal operation and extend their service life. In addition, due to external environmental factors (such as wind, sand, rain, snow, lightning, etc.) or internal circuit failures, photovoltaic generator sets may fail, requiring troubleshooting and repair, thereby incurring maintenance costs. The energy storage equipment equipped with photovoltaic generator sets will also age during the long-term charging and discharging process, and new energy storage equipment needs to be replaced to ensure stable operation, which will incur storage replacement costs; during the entire life cycle of the photovoltaic generator set, during operation, a certain amount of energy consumption and emissions will be generated, which will be included in the emission costs of the photovoltaic generator set. These emission costs, storage replacement costs and maintenance costs together constitute the operating costs of the photovoltaic generator set.

[0030] Among them, the power parameter may be the power data of the distributed generator set during operation; it should be noted that, in an embodiment of the present invention, the power parameter may include power loss coefficient, reactive power and active power, and the power loss system may be the ratio of the power loss generated by the distributed generator set during operation to the output power; reactive power may be the power required for the distributed generator set to establish a magnetic field and maintain the normal operation of electrical equipment, and active power may be the power actually output by the distributed generator set. Reactive power is mainly used for energy exchange between the inductance and capacitance elements between the distributed generator set and the microgrid, and active power may be the energy provided by the distributed generator set to the microgrid.

[0031] Among them, the multi-objective optimization function can be an optimization function that obtains the best between the operating cost and efficiency of the distributed generator set. It should be noted that the multi-objective optimization function is an optimization function of the multi-objective function. In the embodiment of the present invention, the optimization objectives of the multi-objective optimization function are respectively to control the operating 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 operating cost and efficiency of the distributed generator set, and to determine that the control strategy of the target droop controller satisfies the optimal operating cost and efficiency of the distributed generator set.

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

[0033] S120. Determine optimal operating conditions according to the multi-objective optimization function.

[0034] Among them, the optimal operating condition may be a necessary condition for achieving the optimal operating cost and efficiency of the distributed generating set.

[0035] Specifically, the multi-objective optimization function is solved to determine the optimal operating conditions for the optimal solution of the multi-objective optimization function.

[0036] Optionally, in another optional embodiment of the present invention, determining the optimal operating condition according to the multi-objective optimization function includes:

[0037] Construct a multi-objective Lagrangian function based on the multi-objective optimization function;

[0038] The multi-objective Lagrangian function is solved by Lagrangian multiplier method to determine the optimal operating condition; wherein the optimal operating condition includes a first optimal condition and a second optimal condition.

[0039] The first optimal condition may be used to determine the droop parameter of the target droop controller; and the second optimal condition may 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 consists of a droop controller and a nonlinear impedance compensation loop. The droop control can control the output power of the distributed generator set based on the droop control parameters. The droop control parameters can be the output frequency, output voltage reference value, frequency voltage rated value and active and reactive droop gain of the distributed generator set; the nonlinear impedance compensation loop introduces a nonlinear impedance element into the target droop controller to compensate for the impedance characteristics of the target droop controller, and can adjust the parameters and compensation strategy of the target droop controller. The target droop controller can combine the droop controller and the nonlinear impedance compensation loop to effectively improve the accuracy and stability of the droop control, and ensure the flexibility and reliability of the microgrid after the distributed generator set is connected to the microgrid.

[0041] The multi-objective Lagrangian function may be a function composed of an optimization objective and a Lagrangian multiplier based on the multi-objective optimization function. It should be noted that the multi-objective optimization function may be a function that sets a corresponding Lagrangian multiplier for each optimization objective and constructs a multi-objective Lagrangian function based on the optimization objective and the Lagrangian multiplier. The Lagrangian multiplier method may be a method for solving a multi-objective optimization function.

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

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

[0044] Among them, the self-optimizing droop control strategy can be the droop control parameters and 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 to the outside. The equivalent output impedance can improve the controllability of the target droop controller and ensure the system stability of the microgrid.

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

[0046] The technical solution of the embodiment of the present invention obtains the operating parameters and power parameters of the distributed generator set, constructs a multi-objective optimization function according to the operating parameters and the power parameters, and takes the operating cost and efficiency of the distributed generator set as the target optimization object by constructing the multi-objective optimization function, thereby realizing the dual optimization of the operating cost and efficiency; determining the optimal operating conditions according to the multi-objective optimization function, and determining the extreme value conditions and constraint conditions that satisfy the multi-objective optimization function through the optimal operating conditions, thereby improving the accuracy of the optimization; designing a self-optimizing droop control strategy for the target droop controller according to the optimal operating conditions, and dynamically adjusting the parameters of the droop controller through the self-optimizing droop control strategy, thereby realizing the optimal allocation of active power and reactive power, and solving the technical problem that the operating cost and efficiency of the microgrid cannot be optimized at the same time in the prior art. The accuracy of the allocation of active power and reactive power is improved, and the droop controller achieves optimal control in terms of the two objectives of operating cost and effective power, so that the distributed generator set of the microgrid can maintain the optimal operating conditions under different load conditions.

[0047] Figure 2 Flow chart of another method for designing a self-optimizing droop control strategy provided by an embodiment of the present invention. The relationship between this embodiment and the above embodiment is a specific method for designing a self-optimizing droop control strategy for a target droop controller. Figure 2 As shown, the method includes:

[0048] S210. Obtain operating parameters and power parameters of the distributed generator set, and construct a multi-objective optimization function according to the operating parameters and the power parameters.

[0049] Optionally, in another optional embodiment of the present invention, constructing a multi-objective optimization function according to the operating parameters and the power parameters includes:

[0050] A normalized performance factor of the distributed generator set is defined according to the operating parameter and the power parameter; and the multi-objective optimization function of the distributed generator set is established according to the normalized performance factor.

[0051] The normalized performance factor may be defined by considering the operating cost and power loss of the distributed generator set. It should be noted that the power loss may be the loss generated by the distributed generator set after being connected to the microgrid. Exemplarily, the power loss may 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 present invention, defining the normalized performance factor of the distributed generator set according to the operating parameter and the power parameter includes:

[0053] Calculation is performed based on the power loss coefficient of the distributed generator set and the reactive power to determine the power loss value; operating cost is determined based on the operating parameters, the active power and the power loss value; and a normalized performance factor of the distributed generator set is defined based on the operating cost and the power loss value.

[0054] The power loss value can be used to represent the power loss. For example, the power loss value of the i-th distributed generator set can be represented by P loss_i To express, through P oi is the active power of the ith distributed generator set, Q oi is the reactive power of the i-th distributed generator set, and the functional representation of the power loss value is:

[0055]

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

[0057] The operating cost can be the cost value of the distributed generator set during operation. For example, the operating cost of the i-th distributed generator set can be expressed as C i The operating parameters of the i-th distributed generator set can be expressed as K Ci To express, P i is the sum of the active power and power loss of the i-th distributed generator set, and the functional expression of the operating cost is:

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

[0059] Specifically, the power loss value is determined by calculating the power loss coefficient of the distributed generator set and the reactive power, the operating cost is determined by calculating the operating parameters, the active power and the power loss value, and the normalized performance factor of the distributed generator set is defined according to the operating cost and the power loss value. Exemplarily, the normalized performance factor of the i-th distributed generator set is defined as F i , define α iis the operating cost weight coefficient of the i-th distributed generator set, which is greater than or equal to 0 and less than or equal to 1. Define β i is the power loss weight coefficient of the i-th distributed generator set, and 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 expressed as:

[0060]

[0061] Among them, P loss_max is the power loss value of the ith distributed generator set under maximum load conditions, C i is 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 set based on the normalized performance factor. Based on the normalized performance factor, the load active power demand of the microgrid connected in parallel with multiple distributed generator sets is also required. By obtaining the system efficiency of the microgrid connected in parallel with the distributed generator sets, the load active power demand of the microgrid connected in parallel with the distributed generator sets is determined based on the system efficiency and the power loss of each distributed generator set in the microgrid. Exemplarily, N is the number of distributed generator sets, and the load active power demand can be expressed as P load To express, through P loss_tot represents the sum of the power losses of multiple distributed generators, and η represents the system efficiency. The function of system efficiency is expressed as:

[0063]

[0064] Specifically, the normalized performance factor of the distributed generator set is defined according to the operating parameters and the power parameters, and a multi-objective optimization function of the distributed generator set is established according to the normalized performance factor. Exemplarily, the multi-objective optimization function can be expressed as follows:

[0065]

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

[0067] S220. Determine optimal operating conditions according to the multi-objective optimization function.

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

[0069]

[0070] By solving the Lagrangian function through the Lagrangian multiplier method, the first necessary condition for obtaining the optimal solution can be expressed as:

[0071]

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

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

[0074]

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

[0076] In the solution process, define the performance factor F c for:

[0077]

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

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

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

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

[0082] The output power relationship may be the relationship information between a distributed generator set and another distributed generator set in the microgrid. It should be noted that the output power relationship may be composed of coefficients related to the active power and reactive power of the two distributed generator sets and 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-optimizing droop strategy.

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

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

[0086] S260: Determine a self-optimizing droop control strategy according to the output power relationship and the nonlinear impedance compensation loop.

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

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

[0089] Optionally, in another optional embodiment of the present invention, the determining the self-optimizing droop control strategy according to the output power relationship and the nonlinear impedance compensation loop includes:

[0090] determining a target droop control strategy 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; and a self-optimizing droop control strategy is determined according to the target droop control strategy and the impedance compensation strategy when the target droop control strategy satisfies a first optimization condition and the impedance compensation strategy satisfies a second optimization condition.

[0092] Optionally, after obtaining the output power relationship of the distributed generator sets, the output power relationship and the normalized performance factor are combined for verification. When the output power relationship and the normalized performance factor are combined to meet the first optimal condition, it means that the self-optimizing droop strategy meets the droop control parameters 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. When the reactive power sharing ratio and the second optimal condition are combined to meet the second optimal condition, it means that the impedance compensation method corresponding to the nonlinear impedance compensation loop meets the impedance compensation strategy for the nonlinear impedance compensation loop in the target droop control strategy, and the impedance compensation method of the nonlinear impedance compensation loop is used as the impedance compensation strategy. The self-optimizing droop control strategy is composed of the target droop control strategy and the impedance compensation strategy.

[0093] The technical solution of the embodiment of the present invention designs a self-optimizing droop control strategy of a target droop controller based on the optimal operating conditions after deriving the optimal operating conditions. By setting the target droop control strategy for the droop controller in the target droop controller, the optimal distribution of active power and reactive power of the distributed generator set can be achieved. By designing the impedance compensation strategy for the nonlinear impedance compensation loop in the target droop controller, the distributed generator set of the microgrid can maintain the optimal operating conditions under different load conditions, solving the technical problem in the prior art that the operating cost and efficiency of the microgrid cannot be optimized at the same time. By adjusting the parameters and compensation strategy of the controller, the microgrid can maintain the optimal operating state under different load conditions.

[0094] Figure 3 A schematic diagram of the structure of a design device for a self-optimizing droop control strategy provided by an embodiment of the present invention. Figure 3 As shown, the device includes: a multi-objective optimization module 310, an optimal parameter calculation module 320 and a control strategy design module 330; wherein,

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

[0096] An optimal parameter calculation module 320, used to determine the optimal operating conditions according to the multi-objective optimization function;

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

[0098] The technical solution of the embodiment of the present invention obtains the operating parameters and power parameters of the distributed generator set, constructs a multi-objective optimization function according to the operating parameters and the power parameters, and takes the operating cost and efficiency of the distributed generator set as the target optimization object by constructing the multi-objective optimization function, thereby realizing the dual optimization of the operating cost and efficiency; determining the optimal operating conditions according to the multi-objective optimization function, and determining the extreme value conditions and constraint conditions that satisfy the multi-objective optimization function through the optimal operating conditions, thereby improving the accuracy of the optimization; designing a self-optimizing droop control strategy for the target droop controller according to the optimal operating conditions, and dynamically adjusting the parameters of the droop controller through the self-optimizing droop control strategy, thereby realizing the optimal allocation of active power and reactive power, and solving the technical problem that the operating cost and efficiency of the microgrid cannot be optimized at the same time in the prior art. The accuracy of the allocation of active power and reactive power is improved, and the droop controller achieves optimal control in terms of the two objectives of operating cost and effective power, so that the distributed generator set of the microgrid can maintain the optimal operating conditions 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 operating parameter and the power parameter;

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

[0102] Optionally, the multi-objective optimization module is further used to:

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

[0104] Determining an operating cost according to the operating parameter, the active power and the power loss value;

[0105] A normalized performance factor of the distributed generator set is defined according to the operating cost and the power loss value.

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

[0107] Construct a multi-objective Lagrangian function based on the multi-objective optimization function;

[0108] The multi-objective Lagrangian function is solved by Lagrangian multiplier method to determine the optimal operating condition; wherein the optimal operating 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-optimizing droop strategy according to the first optimal condition;

[0111] Determining the output power relationship of the distributed generator set according to the self-optimizing droop strategy;

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

[0113] A self-optimizing droop control strategy is determined according to the output power relationship and the nonlinear impedance compensation loop.

[0114] Optionally, the control strategy design module is further used to:

[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] In a case where the target droop control strategy satisfies a first optimization condition and the impedance compensation strategy satisfies a second optimization condition, a self-optimizing droop control strategy is determined according to the target droop control strategy and the impedance compensation strategy.

[0118] The design device for the self-optimizing droop control strategy provided by the embodiment of the present invention can execute the design method for the self-optimizing droop control strategy provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0119] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their modes are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0120] like Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. 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] A number of 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, speakers, etc.; a storage unit 18, such as a 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 may be a variety of general and / or special 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 dedicated 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 executes the 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 may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on 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 may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the design method of the self-optimizing droop control strategy in any other appropriate manner (e.g., by means of firmware).

[0124] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0125] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the modes / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0126] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 foregoing.

[0127] To provide interaction with a user, the systems and techniques described herein may 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 trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0128] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a grid browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication grid). Examples of communication grids include: a local area network (LAN), a wide area network (WAN), a blockchain grid, and the Internet.

[0129] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication grid. The client and server relationship is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server may 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 difficult management and weak business scalability in traditional physical hosts and VPS services.

[0130] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0131] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of a design method for a self-optimizing droop control strategy provided by any embodiment of the present invention are implemented. The method includes:

[0132] Acquire operating parameters and power parameters of the distributed generator set, and construct a multi-objective optimization function according to the operating parameters and the power parameters;

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

[0134] According to the optimal operating conditions, a self-optimizing droop control strategy is designed for the target droop controller. The computer storage medium of the embodiment of the present invention 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 can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, 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, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, device or device.

[0135] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0136] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0137] Computer program code for performing the operation of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of grid, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0138] It should be understood by those skilled in the art that the above modules or steps of the present invention can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a grid composed of multiple computing devices, optionally, they can be implemented by a program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0139] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0140] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A design method for a self-optimizing droop control strategy, characterized in that: include; Acquire operating parameters and power parameters of the distributed generator set, and construct a multi-objective optimization function according to the operating parameters and the power parameters; Determining optimal operating conditions according to the multi-objective optimization function; A self-optimizing droop control strategy is designed for a target droop controller according to the optimal operating conditions.

2. The method according to claim 1, characterized in that The constructing a multi-objective optimization function according to the operating parameters and the power parameters comprises: defining a normalized performance factor of the distributed generator set according to the operating parameter and the power parameter; The multi-objective optimization function of the distributed generator set is established according to the normalized performance factor.

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

4. The method according to claim 3, characterized in that Defining the normalized performance factor of the distributed generator set according to the operating parameter and the power parameter comprises: Calculate the power loss value according to the power loss coefficient of the distributed generator set and the reactive power; Determining an operating cost according to the operating parameter, the active power and the power loss value; A normalized performance factor of the distributed generator set is defined according to the operating cost and the power loss value.

5. The method according to claim 4, characterized in that Determining the optimal operating condition according to the multi-objective optimization function includes: Construct a multi-objective Lagrangian function based on the multi-objective optimization function; The multi-objective Lagrangian function is solved by Lagrangian multiplier method to determine the optimal operating condition; wherein the optimal operating condition includes a first optimal condition and a second optimal condition.

6. The method according to claim 5, characterized in that The step of designing a self-optimizing droop control strategy for a target droop controller according to the optimal operating condition comprises: Determining a self-optimizing droop strategy according to the first optimal condition; Determining the output power relationship of the distributed generator set according to the self-optimizing droop strategy; Establishing a nonlinear impedance compensation loop of the distributed generator set according to the second optimal condition; A self-optimizing droop control strategy is determined according to the output power relationship and the nonlinear impedance compensation loop.

7. The method according to claim 6, characterized in that The determining of the self-optimizing droop control strategy according to the output power relationship and the nonlinear impedance compensation loop comprises: determining a target droop control strategy according to the output power relationship and the normalized performance factor; determining an impedance compensation strategy according to the nonlinear impedance compensation loop and the normalized performance factor; In a case where the target droop control strategy satisfies a first optimization condition and the impedance compensation strategy satisfies a second optimization condition, a self-optimizing droop control strategy is determined according to the target droop control strategy and the impedance compensation strategy.

8. A design device for a self-optimizing droop control strategy, characterized in that: include; A multi-objective optimization module, used to obtain operating parameters and power parameters of the distributed generator set, and to construct a multi-objective optimization function according to the operating parameters and the power parameters; An optimal parameter calculation module, used to determine the optimal operating conditions according to the multi-objective optimization function; A control strategy design module is used to design a self-optimizing droop control strategy for a target droop controller according to the optimal operating conditions.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, 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 so that the at least one processor can perform the design method of the self-optimizing droop control strategy according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the design method of the self-optimizing droop control strategy according to any one of claims 1 to 7 when executed.

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