Multi-objective optimization control method and system for new energy units

Abstract

This disclosure provides a multi-objective optimization control method and system for new energy generating units. The method includes: acquiring the operating status information of each new energy generating unit in real time when a grid fault is determined based on the grid voltage signal; the operating status information is a multi-dimensional vector; normalizing the operating status information of each new energy generating unit to obtain operating characteristic information; using a preset multi-objective optimization algorithm to perform optimization analysis on the weights of the operating characteristic information to determine the units to be cut off from each new energy generating unit; wherein the multi-objective optimization algorithm converges when the number of units to be cut off, the frequency of faults, and the remaining service life of each new energy generating unit are jointly optimal; generating a cut-off command and sending the cut-off command to the units to be cut off to perform the cut-off operation.

Description

Technical Field

[0001] This disclosure relates to the field of new energy, and more specifically, to a multi-objective optimization control method and system for new energy units. Background Technology

[0002] When traditional power grid stabilization systems take control measures, they often directly cut off the entire photovoltaic power station or part of its internal collection lines according to the over-cut principle. The shortcomings of this are twofold: first, it causes the new energy units to lose power and need to be reconnected to the grid; second, the matching of measures is not precise enough.

[0003] With the ability to quickly adjust power output, new energy generating units have enriched the control resources of the stability control system. This means that they have both continuous adjustment capabilities and direct disconnection capabilities. When a system failure occurs, the ability to simultaneously meet the grid demand and maintain the stable operation of the generating units has become an urgent technical problem to be solved. Summary of the Invention

[0004] The purpose of the embodiments disclosed herein is to provide a multi-objective optimization control method and system for new energy generating units, so as to simultaneously meet grid demand and maintain stable operation of the generating units during grid fault conditions.

[0005] In a first aspect, the present invention provides a multi-objective optimization control method for new energy generating units, comprising: when a grid fault is determined to occur based on a grid voltage signal, acquiring real-time operating status information of each new energy generating unit; wherein the operating status information is a multi-dimensional vector.

[0006] The operating status information of each new energy unit is normalized to obtain operating characteristic information;

[0007] A preset multi-objective optimization algorithm is used to perform optimization analysis on the weights of the operational feature information to determine the units to be cut off from each new energy unit. The multi-objective optimization algorithm converges when the number of units to be cut off, the frequency of failures, and the remaining service life of each new energy unit are jointly optimized. The constraint condition of the optimization algorithm is that the difference between the total real-time power generation capacity of the remaining operating units in each new energy unit and the real-time demand of the grid is less than a preset threshold. The weights of the operational feature information include 0 or 1. When the multi-objective optimization algorithm converges, the new energy unit to which the operational feature information with a weight of 0 belongs is the unit to be cut off, and the new energy unit to which the operational feature information with a weight of 1 belongs is the unit to be retained.

[0008] Generate a cut-out command and send the cut-out command to the cut-out unit to perform the cut-out operation.

[0009] Furthermore, the operating status information includes power generation capacity information, fault frequency information, and remaining service life information.

[0010] Furthermore, the preset optimization algorithm is a multi-objective genetic algorithm.

[0011] Furthermore, the step of using a preset multi-objective optimization algorithm to perform optimization analysis on the weights of the operating characteristic information and determine the units to be cut off from each new energy unit includes:

[0012] Generate individuals to be optimized. These individuals serve as weights for the operational characteristic information of each new energy unit. The dimension is 3 * the number of new energy units, and the value of each dimension is 0 or 1.

[0013] Calculate the number of new energy units to which the operational feature information corresponds to a weight of 0, and use the number as the first fitness function of the individual to be optimized;

[0014] Calculate the sum of the fault frequency information corresponding to a weight of 1, and use the sum as the second fitness function of the individual to be optimized;

[0015] Calculate the sum of the remaining lifespan information corresponding to a weight of 1, and use the reciprocal of the sum of the remaining lifespan information as the third fitness function of the individual to be optimized;

[0016] The first fitness function, the second fitness function, and the third fitness function are used to jointly optimize individuals. When the multi-objective genetic algorithm converges, the cut-off units among the new energy units are determined.

[0017] Furthermore, the new energy unit includes wind power generation equipment and photovoltaic power generation equipment.

[0018] Secondly, the present invention provides a multi-objective optimization control system for new energy generating units, comprising: a status information acquisition module, used to acquire the operating status information of each new energy generating unit in real time when a grid fault is determined to occur based on the grid voltage signal; the operating status information is a multi-dimensional vector;

[0019] The operation feature processing module is used to normalize the operation status information of each new energy unit to obtain operation feature information.

[0020] A multi-objective optimization module is used to perform optimization analysis on the weights of the operating characteristic information using a preset multi-objective optimization algorithm to determine the units to be cut off from each new energy unit. The multi-objective optimization algorithm converges when the number of units to be cut off, the frequency of failures, and the remaining service life of each new energy unit are jointly optimized. The constraint condition of the optimization algorithm is that the difference between the total real-time power generation capacity of the remaining operating units in each new energy unit and the real-time demand of the grid is less than a preset threshold. The weights of the operating characteristic information include 0 or 1. When the multi-objective optimization algorithm converges, the new energy unit to which the operating characteristic information with a weight of 0 belongs is the unit to be cut off, and the new energy unit to which the operating characteristic information with a weight of 1 belongs is the unit to be retained.

[0021] The cut-out control module is used to generate a cut-out command and send the cut-out command to the cut-out unit to perform the cut-out operation.

[0022] Furthermore, the operating status information includes power generation capacity information, fault frequency information, and remaining service life information.

[0023] Furthermore, the preset optimization algorithm is a multi-objective genetic algorithm.

[0024] Further, the multi-objective optimization module is specifically used to generate individuals to be optimized; calculate the number of new energy units to which the operating feature information corresponds to a weight of 0, and use the number as the first fitness function of the individual to be optimized; calculate the sum of the fault frequency information corresponding to a weight of 1, and use the sum as the second fitness function of the individual to be optimized; calculate the sum of the remaining service life information corresponding to a weight of 1, and use the reciprocal of the sum of the remaining service life information as the third fitness function of the individual to be optimized; use the first fitness function, the second fitness function, and the third fitness function to jointly optimize the individual; when the multi-objective genetic algorithm converges, determine the units to be cut off from each new energy unit.

[0025] The individual is used as the weight of the operating characteristic information of each new energy unit, with a dimension of 3 * the number of new energy units, and each dimension has a value of 0 or 1.

[0026] Furthermore, the new energy unit includes wind power generation equipment and photovoltaic power generation equipment.

[0027] The multi-objective optimization control method and system for new energy generating units of the present invention achieves convergence of the number of units to be cut off, the frequency of failures, and the remaining service life of each new energy generating unit through a multi-objective optimization algorithm. The new energy generating unit to which the operating characteristic information corresponding to the convergence weight of 0 belongs is the unit to be cut off. Since the constraint condition of the optimization algorithm is that the difference between the total real-time power generation capacity of the remaining operating units in each new energy generating unit and the real-time demand of the grid is less than a preset threshold, the grid demand can be met when the algorithm converges. At the same time, the convergence of the combined optimal time can ensure that the number of units to be cut off is minimized when the frequency of failures and the remaining service life are optimal. Thus, it is possible to simultaneously meet the grid demand and maintain the stable operation of the generating units when the grid is in a fault state. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a flowchart of a multi-objective optimization control method for a new energy unit according to an embodiment of the present disclosure.

[0030] Figure 2 This is a schematic diagram of the structure of a multi-objective optimization control system for a new energy unit according to an embodiment of the present disclosure. Detailed Implementation

[0031] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0032] It should be noted that, in the absence of conflict, the following embodiments and features can be combined with each other; and, based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0033] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0034] Figure 1 This is a flowchart of a multi-objective optimization control method for a new energy unit according to an embodiment of this disclosure. Figure 1 As shown, the multi-objective optimization control method for this new energy unit includes:

[0035] Step 101: When a grid fault is determined to have occurred based on the grid voltage signal, the operating status information of each new energy unit is acquired in real time; the operating status information is a multi-dimensional vector.

[0036] Step 102: Normalize the operating status information of each new energy unit to obtain operating characteristic information.

[0037] Step 103: Utilize a preset multi-objective optimization algorithm to perform optimization analysis on the weights of the operational feature information to determine the units to be cut off from each new energy unit. The multi-objective optimization algorithm converges when the number of units to be cut off, the frequency of failures, and the remaining service life of each new energy unit are jointly optimized. The constraint condition of the optimization algorithm is that the difference between the total real-time power generation capacity of the remaining operating units in each new energy unit and the real-time demand of the power grid is less than a preset threshold. The weights of the operational feature information include 0 or 1. When the multi-objective optimization algorithm converges, the new energy unit to which the operational feature information with a weight of 0 belongs is the unit to be cut off, and the new energy unit to which the operational feature information with a weight of 1 belongs is the unit to be retained.

[0038] Step 104: Generate a cut-out command and send the cut-out command to the cut-out unit to perform the cut-out operation.

[0039] In this embodiment, when a grid fault occurs, a multi-objective optimization control strategy is adopted to achieve combined control of new energy source switching and regulation, minimizing the number of control switch-out objects and minimizing the control measure quantity matching error (i.e., the difference between the control measure quantity and the grid demand). Minimizing the number of control objects means minimizing intervention in operating equipment to avoid escalating the fault. Minimizing the control measure quantity matching error ensures the safe and stable operation of the grid and avoids causing localized low-frequency or high-frequency problems after measures are taken. New energy units have different operating states (such as generation capacity, fault frequency, and service life). When adopting multi-objective optimization control, the characteristics of uninterrupted new energy supply must be fully considered to meet the requirements of emergency control of the large power grid while maximizing the benefits of new energy.

[0040] The aforementioned multi-objective optimization control method for the new energy unit also includes at least one of the following preferred embodiments:

[0041] The first type: The operating status information includes power generation capacity information, fault frequency information, and remaining service life information.

[0042] The second type: The preset optimization algorithm is a multi-objective genetic algorithm.

[0043] The third method: The step of using a preset multi-objective optimization algorithm to perform optimization analysis on the weights of the operational characteristic information and determine the units to be cut off from each new energy unit includes:

[0044] Generate individuals to be optimized. These individuals serve as weights for the operational characteristic information of each new energy unit. The dimension is 3 * the number of new energy units, and the value of each dimension is 0 or 1.

[0045] Calculate the number of new energy units to which the operational feature information corresponds to a weight of 0, and use the number as the first fitness function of the individual to be optimized;

[0046] Calculate the sum of the fault frequency information corresponding to a weight of 1, and use the sum as the second fitness function of the individual to be optimized;

[0047] Calculate the sum of the remaining lifespan information corresponding to a weight of 1, and use the reciprocal of the sum of the remaining lifespan information as the third fitness function of the individual to be optimized;

[0048] The first fitness function, the second fitness function, and the third fitness function are used to jointly optimize individuals. When the multi-objective genetic algorithm converges, the cut-off units among the new energy units are determined.

[0049] The fourth type: The new energy units include wind power generation equipment and photovoltaic power generation equipment.

[0050] When performing multi-objective optimization control, the regulation capacity of new energy units should be fully utilized. If the amount of control measures is less than the regulation capacity of all units, then power regulation only needs to be performed on the new energy units. If the amount of control measures exceeds the regulation capacity of all units, then multi-objective optimization control should be performed, and the number of objects to be cut off should be minimized. At the same time, the continuity of new energy output regulation should be utilized to minimize the matching error (theoretically, zero error can be achieved).

[0051] Figure 2 This is a structural diagram of a multi-objective optimization control system for a new energy unit according to an embodiment of the present disclosure. Figure 1 The illustrated embodiments can be used to explain this embodiment. For example... Figure 2 As shown: A multi-objective optimization control system for a new energy unit, comprising:

[0052] The status information acquisition module 201 is used to acquire the operating status information of each new energy unit in real time when a grid fault is determined to have occurred based on the grid voltage signal; the operating status information is a multi-dimensional vector.

[0053] The operation feature processing module 202 is used to normalize the operation status information of each new energy unit to obtain operation feature information.

[0054] The multi-objective optimization module 203 is used to perform optimization analysis on the weights of the operating characteristic information using a preset multi-objective optimization algorithm to determine the units to be cut off from each new energy unit. The multi-objective optimization algorithm converges when the number of units to be cut off, the frequency of failures, and the remaining service life of each new energy unit are jointly optimized. The constraint condition of the optimization algorithm is that the difference between the total real-time power generation capacity of the remaining operating units in each new energy unit and the real-time demand of the power grid is less than a preset threshold. The weights of the operating characteristic information include 0 or 1. When the multi-objective optimization algorithm converges, the new energy unit to which the operating characteristic information with a weight of 0 belongs is the unit to be cut off, and the new energy unit to which the operating characteristic information with a weight of 1 belongs is the unit to be retained.

[0055] The cut-out control module 204 is used to generate a cut-out command and send the cut-out command to the cut-out unit to perform the cut-out operation.

[0056] Preferably, the operating status information includes power generation capacity information, fault frequency information, and remaining service life information.

[0057] Preferably, the preset optimization algorithm is a multi-objective genetic algorithm.

[0058] Preferably, the multi-objective optimization module is specifically used to generate individuals to be optimized; calculate the number of new energy units to which the operating feature information corresponding to a weight of 0 belongs, and use the number as the first fitness function of the individual to be optimized; calculate the sum of the fault frequency information corresponding to a weight of 1, and use the sum as the second fitness function of the individual to be optimized; calculate the sum of the remaining service life information corresponding to a weight of 1, and use the reciprocal of the sum of the remaining service life information as the third fitness function of the individual to be optimized; use the first fitness function, the second fitness function, and the third fitness function to jointly optimize the individual; when the multi-objective genetic algorithm converges, determine the units to be cut off from each new energy unit.

[0059] The individual is used as the weight of the operating characteristic information of each new energy unit, with a dimension of 3 * the number of new energy units, and each dimension has a value of 0 or 1.

[0060] Preferably, the new energy unit includes wind power generation equipment and photovoltaic power generation equipment.

[0061] This embodiment achieves optimal convergence of the number of units to be cut off from each new energy generating unit, the frequency of failures, and the remaining service life in each new energy generating unit through a multi-objective optimization algorithm. The new energy generating unit to which the operating characteristic information corresponding to the weight of 0 at the time of convergence belongs is the unit to be cut off. Since the constraint of the optimization algorithm is that the difference between the total real-time power generation capacity of the remaining operating units in each new energy generating unit and the real-time demand of the grid is less than a preset threshold, the grid demand can be met when the algorithm converges. At the same time, the joint optimal convergence can ensure that the number of units to be cut off is minimized when the frequency of failures and the remaining service life are optimal. Thus, it is possible to simultaneously meet the grid demand and maintain the stable operation of the generating units when the grid is in a fault state.

[0062] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0063] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0064] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0065] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion unit connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion unit execute some and all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0066] It should be noted that not all steps and modules in the above processes and system structure diagrams are mandatory; some steps or modules can be omitted as needed. The execution order of each step is not fixed and can be adjusted as required. The system structure described in the above embodiments can be a physical structure or a logical structure. That is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or they may be jointly implemented by certain components in multiple independent devices.

[0067] In the above embodiments, the hardware units can be implemented mechanically or electrically. For example, a hardware unit may include permanent dedicated circuitry or logic (such as a dedicated processor, FPGA, or ASIC) to perform the corresponding operation. The hardware unit may also include programmable logic or circuitry (such as a general-purpose processor or other programmable processor), which can be temporarily configured by software to perform the corresponding operation. The specific implementation method (mechanical, dedicated permanent circuitry, or temporarily configured circuitry) can be determined based on cost and time considerations.

[0068] The present invention has been shown and described in detail above with reference to the accompanying drawings and preferred embodiments. However, the present invention is not limited to these disclosed embodiments. Based on the above embodiments, those skilled in the art will know that more embodiments of the present invention can be obtained by combining the code review methods in the different embodiments. These embodiments are also within the protection scope of the present invention.

Claims

1. A multi-objective optimization control method for a new energy unit, characterized in that, include: When a grid fault is determined based on the grid voltage signal, the operating status information of each new energy unit is obtained in real time. The operating status information is a multi-dimensional vector; The operating status information of each new energy unit is normalized to obtain operating characteristic information; A preset multi-objective optimization algorithm is used to perform optimization analysis on the weights of the operational feature information to determine the units to be cut off from each new energy unit. The multi-objective optimization algorithm converges when the number of units to be cut off, the frequency of failures, and the remaining service life of each new energy unit are jointly optimized. The constraint condition of the optimization algorithm is that the difference between the total real-time power generation capacity of the remaining operating units in each new energy unit and the real-time demand of the grid is less than a preset threshold. The weights of the operational feature information include 0 or 1. When the multi-objective optimization algorithm converges, the new energy unit to which the operational feature information with a weight of 0 belongs is the unit to be cut off, and the new energy unit to which the operational feature information with a weight of 1 belongs is the unit to be retained. A cut-off command is generated and sent to the unit to be cut off to perform the cut-off operation. The operating status information includes power generation capacity information, fault frequency information, and remaining service life information. The preset optimization algorithm is a multi-objective genetic algorithm. The step of using a preset multi-objective optimization algorithm to perform weight optimization analysis on the operational characteristic information and determine the units to be cut off from each new energy unit includes: Generate individuals to be optimized. These individuals serve as weights for the operational characteristic information of each new energy unit. The dimension is 3 * the number of new energy units, and the value of each dimension is 0 or 1. Calculate the number of new energy units to which the operational feature information corresponds to a weight of 0, and use the number as the first fitness function of the individual to be optimized; Calculate the sum of the fault frequency information corresponding to a weight of 1, and use the sum as the second fitness function of the individual to be optimized; Calculate the sum of the remaining lifespan information corresponding to a weight of 1, and use the reciprocal of the sum of the remaining lifespan information as the third fitness function of the individual to be optimized; The first fitness function, the second fitness function, and the third fitness function are used to jointly optimize individuals. When the multi-objective genetic algorithm converges, the cut-off units among the new energy units are determined.

2. The multi-objective optimization control method for new energy generating units according to claim 1, characterized in that, The new energy units include wind power generation equipment and photovoltaic power generation equipment.

3. A multi-objective optimization control system for a new energy unit, characterized in that, include: The status information acquisition module is used to acquire the real-time operating status information of each new energy unit when a grid fault is determined to have occurred based on the grid voltage signal. The operating status information is a multi-dimensional vector; The operation feature processing module is used to normalize the operation status information of each new energy unit to obtain operation feature information. A multi-objective optimization module is used to perform optimization analysis on the weights of the operating characteristic information using a preset multi-objective optimization algorithm to determine the units to be cut off from each new energy unit. The multi-objective optimization algorithm converges when the number of units to be cut off, the frequency of failures, and the remaining service life of each new energy unit are jointly optimized. The constraint condition of the optimization algorithm is that the difference between the total real-time power generation capacity of the remaining operating units in each new energy unit and the real-time demand of the grid is less than a preset threshold. The weights of the operating characteristic information include 0 or 1. When the multi-objective optimization algorithm converges, the new energy unit to which the operating characteristic information with a weight of 0 belongs is the unit to be cut off, and the new energy unit to which the operating characteristic information with a weight of 1 belongs is the unit to be retained. The cut-out control module is used to generate a cut-out command and send the cut-out command to the cut-out unit to perform the cut-out operation. The operating status information includes power generation capacity information, fault frequency information, and remaining service life information. The preset optimization algorithm is a multi-objective genetic algorithm. The multi-objective optimization module is specifically used to generate individuals to be optimized; calculate the number of new energy units to which the operating feature information corresponds to a weight of 0, and use the number as the first fitness function of the individual to be optimized; calculate the sum of the fault frequency information corresponding to a weight of 1, and use the sum as the second fitness function of the individual to be optimized; calculate the sum of the remaining service life information corresponding to a weight of 1, and use the reciprocal of the sum of the remaining service life information as the third fitness function of the individual to be optimized; and use the first fitness function, the second fitness function, and the third fitness function to jointly optimize the individual. When the multi-objective genetic algorithm converges, the cut-off units in each new energy unit are determined. The individual is used as the weight of the operating characteristic information of each new energy unit, with a dimension of 3 * the number of new energy units, and each dimension has a value of 0 or 1.

4. The multi-objective optimization control system for new energy generating units according to claim 3, characterized in that, The new energy units include wind power generation equipment and photovoltaic power generation equipment.

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