Operation control method, device and computer equipment based on distributed photovoltaic cluster

Abstract

The present application discloses an operation control method, device and computer equipment based on a distributed photovoltaic cluster. The method includes: obtaining the operation cost parameters and power parameters of the distributed photovoltaic cluster; adjusting the operation strategies of the day-ahead stage, the intraday stage and the real-time stage according to the operation cost parameters and the power parameters according to three predetermined objective functions, wherein the three objective functions are a first objective function with the lowest operation cost of the distributed photovoltaic cluster as the adjustment target, a second objective function with the lowest power loss of the distributed photovoltaic cluster as the adjustment target, and a third objective function with the minimum voltage deviation value of each node in the distributed photovoltaic cluster as the adjustment target; controlling the operation of the distributed photovoltaic cluster according to the adjusted operation strategy, wherein the day-ahead stage is before the intraday stage, and the distributed photovoltaic cluster includes: a distribution network connected to the distributed photovoltaic power generation system.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network optimization and control, and specifically, to an operation control method, device and computer equipment based on a distributed photovoltaic cluster. Background Art

[0002] With the large-scale construction of distributed power sources, the high proportion of distributed power sources in various forms, including distributed photovoltaics, distributed wind power, distributed fuel cells, etc., connected to the distribution network will become a future development trend. The access of a high proportion of distributed power sources in local areas has brought multiple challenges to the control and operation of the distribution network. Due to the random output, different scales and wide distribution of distributed power sources, the structure of the distribution network has become more complex, changing from the original single-point radial to a multi-power interwoven network. This change causes a large fluctuation in voltage in the network and increases network losses, which in turn leads to voltage limit violations caused by the high proportion of distributed photovoltaics connected to the distribution network. Summary of the invention

[0003] The embodiments of the present application provide an operation control method, device and computer equipment based on a distributed photovoltaic cluster to at least solve the technical problem in the related art of voltage exceeding the limit due to a high proportion of distributed photovoltaic power sources being connected to the distribution network.

[0004] According to one aspect of an embodiment of the present application, there is provided an operation control method based on a distributed photovoltaic cluster, comprising: obtaining operation cost parameters and power parameters of the distributed photovoltaic cluster; adjusting the operation strategies of the day-ahead stage, the intraday stage and the real-time stage respectively according to the operation cost parameters and the power parameters according to three predetermined objective functions, wherein the three objective functions are respectively a first objective function with the lowest operation cost of the distributed photovoltaic cluster as an adjustment target, a second objective function with the lowest power loss of the distributed photovoltaic cluster as an adjustment target, and a third objective function with the minimum voltage deviation value of each node in the distributed photovoltaic cluster as an adjustment target; controlling the operation of the distributed photovoltaic cluster according to the adjusted operation strategy, wherein the day-ahead stage is before the intraday stage, and the distributed photovoltaic cluster comprises: a distribution network connected to a distributed photovoltaic power generation system.

[0005] Optionally, the operation strategies of the day-ahead stage, the intraday stage and the real-time stage are adjusted respectively according to the operation cost parameters and the power parameters according to three predetermined objective functions, including: respectively obtaining the first objective function, the second objective function and the third objective function and the constraints corresponding to the three objective functions; adjusting the operation strategies of the day-ahead stage, the intraday stage and the real-time stage according to the three objective functions within the constraints.

[0006] Optionally, the first objective function includes:

[0007] ;

[0008] Among them, f represents the value of the first objective function, t represents the time, and n t represents the total number of nodes contained in the distributed photovoltaic cluster, represents the amount of electricity purchased by node i at time t, represents the amount of electricity sold by node i at time t, and They represent the electricity purchase price and electricity sales price at time t, and They represent the total charging power and total discharging power of energy storage at time t, δ represents the energy storage charging and discharging cost, and They represent the shiftable load and interruptible load respectively, α1 and β1 represent the shiftable load cost and interruptible load cost respectively.

[0009] Optionally, before obtaining the second objective function, the method further includes: determining each node in the distributed photovoltaic cluster as an initial community; traversing all nodes in the distributed photovoltaic cluster, calculating the modularity gain brought about by moving all nodes to adjacent communities respectively, and forming adjacent nodes whose modularity gain is greater than zero into a new community; traversing the new community, repeatedly moving the adjacent communities until the modularity gain is no greater than zero, and obtaining multiple sub-photovoltaic clusters.

[0010] Optionally, the second objective function includes:

[0011] ;

[0012] in, represents the value of the second objective function, represents the active power of line ij at time t, represents the reactive power of line ij at time t, represents the voltage of node i at time t, represents the resistance of line ij, line ij represents the line between node i and node j, represents the i-th sub-PV cluster.

[0013] Optionally, before obtaining the third objective function, the method also includes: obtaining a preset penalty item; determining the sum of the second objective function and the preset penalty item as an updated second objective function; and adjusting the operating strategy of the intraday stage based on the updated second objective function.

[0014] Optionally, the third objective function includes:

[0015] ;

[0016] In the formula, represents the value of the third objective function, Indicates the reference voltage.

[0017] According to another aspect of an embodiment of the present application, an operation control device based on a distributed photovoltaic cluster is also provided, including: a receiving module, used to obtain operation cost parameters and power parameters of the distributed photovoltaic cluster; an adjustment module, used to adjust the operation strategies of the day-ahead stage, the intraday stage and the real-time stage according to the operation cost parameters and the power parameters according to three predetermined objective functions, wherein the three objective functions are respectively a first objective function with the lowest operation cost of the distributed photovoltaic cluster as an adjustment target, a second objective function with the lowest power loss of the distributed photovoltaic cluster as an adjustment target, and a third objective function with the minimum voltage deviation value of each node in the distributed photovoltaic cluster as an adjustment target; a control module, used to control the operation of the distributed photovoltaic cluster according to the adjusted operation strategy, wherein the day-ahead stage is before the intraday stage, and the distributed photovoltaic cluster includes: a distribution network connected to a distributed photovoltaic power generation system.

[0018] According to another aspect of the embodiment of the present application, a computer device is provided, including: a memory and a processor, wherein the memory is used to store program instructions; and the processor is connected to the memory and is used to execute the above-mentioned operation control method based on the distributed photovoltaic cluster.

[0019] According to another aspect of the embodiments of the present application, a non-volatile storage medium is provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned operation control method based on the distributed photovoltaic cluster by running the computer program.

[0020] According to another aspect of the embodiments of the present application, a computer program product is further provided, including computer instructions, which, when executed by a processor, implement the above-mentioned operation control method based on a distributed photovoltaic cluster.

[0021] In an embodiment of the present application, by obtaining the operating cost parameters and power parameters of the distributed photovoltaic cluster; adjusting the operating strategies of the day-ahead stage, the intraday stage and the real-time stage according to the operating cost parameters and the power parameters according to three predetermined objective functions, wherein the three objective functions are respectively a first objective function with the lowest operating cost of the distributed photovoltaic cluster as an adjustment target, a second objective function with the lowest power loss of the distributed photovoltaic cluster as an adjustment target, and a third objective function with the minimum voltage deviation value of each node in the distributed photovoltaic cluster as an adjustment target; controlling the operation of the distributed photovoltaic cluster according to the adjusted operating strategy, wherein the day-ahead stage is before the intraday stage, and the distributed photovoltaic cluster includes: a distribution network connected to a distributed photovoltaic power generation system, achieving the purpose of multiple regulation through multiple time scales, thereby achieving the technical effect of avoiding voltage over-limit caused by the distributed photovoltaic power source being connected to the distribution network, thereby solving the technical problem of voltage over-limit caused by a high proportion of distributed photovoltaic power sources being connected to the distribution network in the related technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1 It is a hardware structure block diagram of a computer terminal for implementing an operation control method based on a distributed photovoltaic cluster according to an embodiment of the present application;

[0024] Figure 2 is a flow chart of an operation control method based on a distributed photovoltaic cluster according to an embodiment of the present application;

[0025] Figure 3 is a schematic diagram of node topology of a distributed photovoltaic cluster according to an embodiment of the present application;

[0026] Figure 4 is a schematic diagram of node voltage distribution of a distributed photovoltaic cluster in different scenarios according to an embodiment of the present application;

[0027] Figure 5 is a schematic diagram of node voltage distribution of a distributed photovoltaic cluster in another different scenario according to an embodiment of the present application;

[0028] Figure 6 is a schematic diagram of node voltage distribution of a distributed photovoltaic cluster in another different scenario according to an embodiment of the present application;

[0029] Figure 7 It is a structural diagram of an operation control device based on a distributed photovoltaic cluster according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application 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 interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] In order to solve the problems existing in the related art, the embodiment of the present application provides an operation control method based on a distributed photovoltaic cluster, which can be operated on Figure 1 In the computer terminal shown, the computer terminal is explained below.

[0033] The operation control method embodiment based on the distributed photovoltaic cluster provided in the embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal for implementing an operation control method based on a distributed photovoltaic cluster is shown. Figure 1 As shown, the computer terminal 10 may include one or more (102a, 102b, ..., 102n are used to illustrate) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions connected via a wired and / or wireless network. In addition, it may also include: a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, and a BUS bus. Those skilled in the art will understand that Figure 1The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0034] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0035] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the operation control method based on the distributed photovoltaic cluster in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the above-mentioned operation control method based on the distributed photovoltaic cluster. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0036] The transmission module 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission module 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission module 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0037] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0038] It should be noted that, in some optional embodiments, the above Figure 1The computer terminal shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. It should be noted that Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computer terminal described above.

[0039] In the above operating environment, an embodiment of the present application provides an operation control method embodiment based on a distributed photovoltaic cluster. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0040] Figure 2 is a flow chart of an operation control method based on a distributed photovoltaic cluster according to an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps:

[0041] Step S202, obtaining operating cost parameters and power parameters of the distributed photovoltaic cluster.

[0042] In the above step S202, the distributed photovoltaic cluster is connected to the distribution network, and the operating cost parameters include at least: the total number of nodes included in the distributed photovoltaic cluster, the amount of electricity purchased by the nodes in the main network, the amount of electricity sold by the nodes in the main network, the purchase price and the sales price of electricity, the total charging power and the total discharging power of the energy storage, the charging and discharging cost of the energy storage, the cost of the shiftable load and the cost of the interruptible load, etc.

[0043] It should be noted that the total charging power and total discharging power of energy storage refer to the total charging power and total discharging power corresponding to the energy storage system connected to the distributed photovoltaic cluster.

[0044] Step S204, adjusting the operation strategies of the day-ahead stage, the intraday stage and the real-time stage respectively according to the operation cost parameters and the power parameters according to three predetermined objective functions, wherein the three objective functions are respectively a first objective function with the lowest operation cost of the distributed photovoltaic cluster as an adjustment target, a second objective function with the lowest power loss of the distributed photovoltaic cluster as an adjustment target, and a third objective function with the minimum voltage deviation value of each node in the distributed photovoltaic cluster as an adjustment target.

[0045] It should be noted that the day-ahead stage refers to the operation preparation stage of the distributed photovoltaic cluster before the target date, the intraday stage refers to the operation stage of the distributed photovoltaic cluster within the target date, and the real-time stage refers to any time of operation of the distributed photovoltaic cluster within the target date.

[0046] It is understandable that the target date can be set according to the actual application scenario, and the node is a distribution network node.

[0047] Step S206, controlling the operation of the distributed photovoltaic cluster according to the adjusted operation strategy, wherein the day-ahead phase is before the intraday phase, and the distributed photovoltaic cluster includes: a distribution network connected to a distributed photovoltaic power generation system.

[0048] Through the above steps S202 to S206, by obtaining the operating cost parameters and power parameters of the distributed photovoltaic cluster; adjusting the operating strategies of the day-ahead stage, the intraday stage and the real-time stage according to the operating cost parameters and the power parameters according to the three predetermined objective functions, wherein the three objective functions are respectively a first objective function with the lowest operating cost of the distributed photovoltaic cluster as the adjustment target, a second objective function with the lowest power loss of the distributed photovoltaic cluster as the adjustment target, and a third objective function with the minimum voltage deviation value of each node in the distributed photovoltaic cluster as the adjustment target; controlling the operation of the distributed photovoltaic cluster according to the adjusted operating strategy, wherein the day-ahead stage is before the intraday stage, achieving the purpose of multiple regulation through multiple time scales, thereby achieving the technical effect of avoiding the voltage over-limit caused by the distributed photovoltaic power source connected to the distribution network, thereby solving the technical problem of the voltage over-limit caused by the high proportion of distributed photovoltaic power sources connected to the distribution network in the related technology. The following is a detailed description.

[0049] In step S204 of the above-mentioned operation control method based on distributed photovoltaic cluster, the operation strategies of the day-ahead stage, the intraday stage and the real-time stage are adjusted respectively according to the operation cost parameters and the power parameters according to three predetermined objective functions, including: respectively obtaining the first objective function, the second objective function and the third objective function and the constraints corresponding to the three objective functions; adjusting the operation strategies of the day-ahead stage, the intraday stage and the real-time stage according to the three objective functions within the constraints.

[0050] Among them, the first objective function includes:

[0051]

[0052] In the formula, f represents the value of the first objective function, t represents the time, and n t Indicates the total number of nodes contained in the distributed photovoltaic cluster, represents the amount of electricity purchased by node i at time t, represents the amount of electricity sold by node i at time t, and They represent the electricity purchase price and electricity sales price at time t, and They represent the total charging power and total discharging power of energy storage at time t, δ represents the energy storage charging and discharging cost, and They represent the shiftable load and interruptible load respectively, α1 and β1 represent the shiftable load cost and interruptible load cost respectively.

[0053] It can be understood that the product of the electricity purchase price and the electricity purchase quantity is the total electricity purchase price, and the product of the electricity selling price and the electricity sales quantity is the total electricity selling price.

[0054] It should be noted that the constraints corresponding to the first objective function, the second objective function and the third objective function include:

[0055] 1. Line flow constraints, as shown in the following formula:

[0056]

[0057] In the formula, represents the square of the voltage at node i at time t, represents the square of the current of line ij at time t, Ui,t represents the voltage of node i at time t, Iij,t represents the current of line ij at time t, rij and xij represent the resistance and reactance of line ij respectively, Pij,t and Qij,t represent the active power and reactive power of line ij at time t respectively, k:j→k represents the set of branch end nodes with node j as the head node, i:i→j represents the set of branch head nodes with node j as the end node, Pj,t and Qj,t are the active power and reactive power injected into node j at time t respectively.

[0058] 2. Node voltage constraints, as shown in the following formula:

[0059]

[0060] Where Ui,max and Ui,min represent the upper and lower limits of the voltage at node i, respectively.

[0061] 3. Discrete reactive power compensation equipment constraints are as shown in the following formula:

[0062]

[0063]

[0064]

[0065] In the formula, represents the output of the capacitor (CB) connected to node i at time t, represents the power of a single group of CBs connected to node i, Indicates the number of CB groups that node i accesses at time t, and the value is an integer. represents the maximum number of CB switching groups connected to node i, and Z represents an integer set.

[0066] 4. Continuous reactive power compensation equipment constraints are as shown in the following formula:

[0067]

[0068] In the formula, represents the output of the SVC connected to node i at time t, , They respectively represent the upper and lower limits of the output of the SVC connected to node i.

[0069] It should be noted that SVC stands for Static Var Compensator.

[0070] 5. Constraints of on-load tap-changing transformers are as follows:

[0071]

[0072]

[0073]

[0074] In the formula, represents the transformation ratio of the OLTC connected between nodes i and j at time t, Indicates the OLTC gear position that nodes i and j access at time t, and the value is an integer. To adjust the step size, , They represent the upper and lower limits of the gear for OLTC access respectively, K0 is the initial gear, and is set to 0.95.

[0075] It should be noted that OLTC stands for On-Load Tap Changer.

[0076] 6. Distributed energy storage constraints are as follows:

[0077]

[0078]

[0079]

[0080] In the formula, , They represent the charging and discharging power of the energy storage connected to node i at time t, , They represent the minimum and maximum values ​​of the energy storage charging power at node i, respectively. , They represent the minimum and maximum values ​​of the energy storage charging power at node i, respectively. , denote the energy storage charging and discharging efficiency at node i, respectively, represents the charge state of the energy storage connected to node i at time t, Δt represents the time granularity of day-ahead scheduling, , They represent the minimum and maximum energy storage SOC of node i respectively, , They represent the energy storage charging and discharging flags at node i at time t, respectively. When they are 0 and 1, they represent stop and start, respectively, and the two satisfy the mutually exclusive constraint.

[0081]

[0082] 7. Demand-side response constraints are as follows:

[0083]

[0084]

[0085] In the formula, and They represent the shiftable load call amount and interruptible load call amount at node i at time t, , They represent the upper and lower limits of the load that can be transferred to node i, , They respectively represent the upper and lower limits of the interruptible load call amount connected to node i.

[0086] It should be noted that SOC stands for State of Charge, that is, the state of charge.

[0087] Before obtaining the second objective function, the method also includes: determining each node in the distributed photovoltaic cluster as an initial community; traversing all nodes in the distributed photovoltaic cluster, calculating the modularity gain brought by moving all nodes to adjacent communities respectively, and forming new communities with adjacent nodes whose modularity gain is greater than zero; traversing the new community, repeatedly moving the adjacent communities until the modularity gain is not greater than zero, and obtaining multiple sub-photovoltaic clusters.

[0088] Specifically, in step 1, each node in the distributed photovoltaic cluster is regarded as an independent community, each independent community is regarded as an initial community structure, and the initial modularity value Q0 of the distribution network is calculated. The modularity Q can be determined by the following formula:

[0089]

[0090] In the formula, is the weight of the edge connecting node i and node j; is the sum of the weights of all edges connected to node i; is the community where node i belongs; is a 0-1 function, if ,but , otherwise it is 0; m is the sum of the number of network edges.

[0091] Step 2: Randomly select adjacent nodes i and j to form a new community and calculate the modularity increment value of the distributed photovoltaic cluster. ,like , then nodes i and j are merged into the same community, otherwise the positions of nodes i and j remain unchanged.

[0092] Step 3: Treat the divided communities as new nodes to participate in the next level of division, and repeat step 2 until the modularity value Q no longer increases.

[0093] In an optional manner, the second objective function includes:

[0094]

[0095] In the formula, represents the value of the second objective function, represents the active power of line ij at time t, represents the reactive power of line ij at time t, represents the voltage of node i at time t, represents the resistance of line ij, line ij represents the line between node i and node j, represents the i-th sub-PV cluster.

[0096] In some embodiments of the present application, before obtaining the third objective function, the method also includes: obtaining a preset penalty item; determining the sum of the second objective function and the preset penalty item as an updated second objective function; and adjusting the operating strategy of the intraday stage based on the updated second objective function.

[0097] Specifically, virtual nodes are introduced at the boundaries of two adjacent sub-PV clusters, virtual power transmission lines are constructed to decouple clusters, and the SADMM algorithm is used to optimize multiple sub-PV clusters. The specific steps are as follows:

[0098] Step 1: Get the preset penalty item. The preset penalty item is as follows:

[0099]

[0100] In the formula, n represents the number of iterations, τ represents the penalty coefficient to ensure data consistency, and λ U , P , Q They represent the Lagrange multipliers of the voltage, active power and reactive power of the sub-PV cluster respectively, represents the voltage of edge node a in the sub-PV cluster at the nth iteration, Represents a virtual node The voltage at the nth iteration, represents the active power between edge node a and edge node b, Represents a virtual node The active power of the line between and edge node b at the nth iteration, represents the reactive power of the line between edge node a and edge node b at the nth iteration, Represents a virtual node The reactive power of the line between edge node a and edge node b at the nth iteration, wherein edge node a and edge node b are edge nodes in two adjacent sub-PV clusters respectively, and edge node a and edge node b are adjacent.

[0101] Step 2: Add a preset penalty term based on the second objective function, and the updated second objective function is as follows:

[0102]

[0103] Each sub-photovoltaic cluster updates the virtual node data between each sub-photovoltaic cluster according to the second objective function, and interacts with the adjacent sub-photovoltaic cluster.

[0104] Step 3: Based on the received inter-cluster virtual node data, each cluster updates the Lagrange multiplier of the virtual node data locally.

[0105] Step 4: Repeat steps 2 and 3 until the sum of the absolute values ​​of the edge node voltage deviation and the inter-cluster line power deviation between the sub-PV cluster and its adjacent clusters meets the iteration accuracy requirement.

[0106] In some embodiments of the present application, the third objective function is shown as follows:

[0107]

[0108] In the formula, Indicates the reference voltage.

[0109] In actual application scenarios, the voltages of multiple sub-PV clusters are adjusted according to a preset time granularity, and the preset time granularity can be set according to actual needs, for example: 5 minutes.

[0110] The constraints of the second objective function and the third objective function are the same.

[0111] Figure 3 A distributed photovoltaic cluster topology diagram is shown. Figure 3 As shown, 17 nodes, including nodes 4, 6, 7, 9, 11, 15, 17, 18, 21, 22, 23, 24, 26, 27, 28, 30, and 31, are selected to access the distributed photovoltaic (Photovoltaic, photovoltaic system). Node 33 is the connection point between the system and the upper network through OLTC. The OLTC ratio range is 0.95-1.05, and the adjustment gear of its tap is 11 gears. Network nodes 3 and 8 are connected to two groups of CBs with 6 gears and an output range of 0-3MW. Network nodes 19 and 25 are connected to SVCs (Static Var Compensator) with rated capacities of 0.5MW and 1MW respectively. Network nodes 24 and 29 are connected to ESSs (Energy Storage System) with a maximum charge and discharge power of 0.2MW and a capacity of 3MW respectively. The upper and lower limits of the network voltage are set to 1.07pu and 0.93pu respectively. For ease of understanding, this application embodiment sets four adjustment scenarios for comparison:

[0112] Scenario 1: No adjustment;

[0113] Scenario 2: Only day-ahead adjustments are used;

[0114] Scenario 3: Use day-ahead and intraday adjustments;

[0115] Scenario 4: Use day-ahead, intraday, and real-time adjustments.

[0116] Table 1

[0117]

[0118] Table 1 shows the nodes included in the three sub-clusters. The voltage distribution results of each sub-cluster are as follows: Figure 4 , Figure 5 and Figure 6As shown in the figure, in cluster 1, since scenario 3 only considers economic goals, the overall voltage is high, while in scenario 4, the voltage value is significantly reduced, which also means that more distributed photovoltaic output can be absorbed. The voltage fluctuation amplitude in scenarios 3 and 4 is significantly lower than that in scenarios 1 and 2. In cluster 2, there is no voltage regulation in scenario 1, and the voltage of 10 nodes in the cluster exceeds the upper limit. After centralized optimization and regulation in scenario 2, the voltage of each node is within the limit, but since only economic goals are considered in the day-ahead stage, the voltage of multiple nodes is close to the upper limit of the voltage. After further cluster regulation in scenarios 3 and 4, the voltage offset of each node is further improved, and the maximum voltage offset of each node in the cluster does not exceed 3%. The situation in cluster 3 is similar to that in cluster 2, and the voltage quality is significantly improved after real-time regulation in scenario 4.

[0119] This application aims to solve the problem of voltage over-limit caused by high proportion of distributed photovoltaic access to the distribution network, and proposes an operation control based on distributed photovoltaic clusters, which consists of three parts: day-ahead, intraday, and real-time. The day-ahead stage optimization control performs global control with economic optimization as the objective function, and then divides the clusters according to the day-ahead control results. Then the intraday control performs internal cluster optimization with economic goals, and then realizes inter-cluster decoupling and inter-cluster collaborative optimization based on the SADMM algorithm. Finally, the real-time control performs internal cluster optimization with safety goals, and performs inter-cluster collaborative optimization based on SADMM. The proposed optimization strategy can keep the voltage of each node in the network near the reference voltage and significantly reduce the voltage fluctuation amplitude.

[0120] Figure 7 is a structural diagram of an operation control device based on a distributed photovoltaic cluster according to an embodiment of the present application, such as Figure 7 As shown, the device comprises:

[0121] A receiving module 40 is used to obtain operating cost parameters and power parameters of the distributed photovoltaic cluster;

[0122] An adjustment module 42, configured to adjust the operation strategies of the day-ahead stage, the intraday stage and the real-time stage according to the operation cost parameter and the power parameter according to three predetermined objective functions, wherein the three objective functions are respectively a first objective function with the lowest operation cost of the distributed photovoltaic cluster as an adjustment target, a second objective function with the lowest power loss of the distributed photovoltaic cluster as an adjustment target, and a third objective function with the minimum voltage deviation value of each node in the distributed photovoltaic cluster as an adjustment target;

[0123] The control module 44 is used to control the operation of the distributed photovoltaic cluster according to the adjusted operation strategy, wherein the day-ahead phase is before the intraday phase, and the distributed photovoltaic cluster includes: a distribution network connected to a distributed photovoltaic power generation system.

[0124] Through the above-mentioned operation control device based on distributed photovoltaic cluster, by obtaining the operation cost parameters and power parameters of the distributed photovoltaic cluster; according to the operation cost parameters and the power parameters, the operation strategies of the day-ahead stage, the intraday stage and the real-time stage are adjusted respectively according to three predetermined objective functions, wherein the three objective functions are respectively a first objective function with the lowest operation cost of the distributed photovoltaic cluster as the adjustment target, a second objective function with the lowest power loss of the distributed photovoltaic cluster as the adjustment target, and a third objective function with the minimum voltage deviation value of each node in the distributed photovoltaic cluster as the adjustment target; the operation of the distributed photovoltaic cluster is controlled according to the adjusted operation strategy, wherein the day-ahead stage is before the intraday stage, and the distributed photovoltaic cluster includes: a distribution network connected to a distributed photovoltaic power generation system, achieving the purpose of multiple regulation through multiple time scales, thereby achieving the technical effect of avoiding voltage over-limit caused by the distributed photovoltaic power source being connected to the distribution network, thereby solving the technical problem of voltage over-limit caused by a high proportion of distributed photovoltaic power sources being connected to the distribution network in the related technology.

[0125] In the above-mentioned adjustment module 42 of the operation control device based on the distributed photovoltaic cluster, it includes: an adjustment submodule, which is used to adjust the operation strategies of the day-ahead stage, the intraday stage and the real-time stage according to the operation cost parameters and the power parameters according to three predetermined objective functions, including: respectively obtaining the first objective function, the second objective function and the third objective function and the constraints corresponding to the three objective functions; adjusting the operation strategies of the day-ahead stage, the intraday stage and the real-time stage according to the three objective functions within the constraints.

[0126] The adjustment submodule includes: a clustering unit and an updating unit, wherein the clustering unit is used to determine each node in the distributed photovoltaic cluster as an initial community before obtaining the second objective function; traverse all nodes in the distributed photovoltaic cluster, calculate the modularity gain brought by moving all nodes to adjacent communities respectively, and form new communities with adjacent nodes whose modularity gain is greater than zero; traverse the new community, repeatedly move the adjacent communities until the modularity gain is not greater than zero, and obtain multiple sub-photovoltaic clusters.

[0127] An updating unit is used to obtain a preset penalty term before obtaining the third objective function; determine the sum of the second objective function and the preset penalty term as the updated second objective function; and adjust the operation strategy of the intraday stage based on the updated second objective function.

[0128] It should be noted that Figure 7The operation control device based on the distributed photovoltaic cluster is used to execute Figure 5 The operation control method based on the distributed photovoltaic cluster is shown, so the relevant explanations in the above operation control method based on the distributed photovoltaic cluster are also applicable to the operation control device based on the distributed photovoltaic cluster, which will not be repeated here.

[0129] The embodiment of the present application further provides a computer device, including: a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute the above-mentioned operation control method based on the distributed photovoltaic cluster.

[0130] An embodiment of the present application further provides a non-volatile storage medium, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned operation control method based on the distributed photovoltaic cluster by running the computer program.

[0131] The embodiment of the present application further provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the operation control method based on a distributed photovoltaic cluster in the present application.

[0132] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0133] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0135] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0136] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0137] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc., which can store program code.

[0138] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An operation control method based on a distributed photovoltaic cluster, characterized in that: include: Obtaining operating cost parameters and power parameters of the distributed photovoltaic cluster; According to the operating cost parameter and the power parameter, the operating strategies of the day-ahead stage, the intraday stage and the real-time stage are adjusted respectively according to three predetermined objective functions, wherein the three objective functions are respectively a first objective function with the lowest operating cost of the distributed photovoltaic cluster as an adjustment target, a second objective function with the lowest power loss of the distributed photovoltaic cluster as an adjustment target, and a third objective function with the minimum voltage deviation value of each node in the distributed photovoltaic cluster as an adjustment target; The distributed photovoltaic cluster is controlled to operate according to the adjusted operation strategy, wherein the day-ahead phase is before the intraday phase, the distributed photovoltaic cluster includes: a distribution network connected to a distributed photovoltaic power generation system, and the first objective function includes: ； Where f represents the value of the first objective function, t represents the time, Indicates the total number of nodes contained in the distributed photovoltaic cluster, represents the amount of electricity purchased by node i at time t, represents the amount of electricity sold by node i at time t, and They represent the electricity purchase price and electricity sales price at time t, and They represent the total charging power and total discharging power of energy storage at time t, δ represents the charging and discharging cost of energy storage, and They represent the load that can be translated and the load that can be interrupted, and denote shiftable load cost and interruptible load cost respectively; The second objective function comprises: ； in, represents the value of the second objective function, represents the active power of line ij at time t, represents the reactive power of line ij at time t, represents the voltage of node i at time t, represents the resistance of line ij, line ij represents the line between node i and node j, represents the i-th sub-PV cluster; The third objective function comprises: ； In the formula, represents the value of the third objective function, Represents the reference voltage, where the day-ahead stage represents the operation preparation stage of the distributed photovoltaic cluster before the target date, the intraday stage represents the operation stage of the distributed photovoltaic cluster within the target date, and the real-time stage represents any time of the operation of the distributed photovoltaic cluster within the target date.

2. The method according to claim 1, characterized in that According to the operating cost parameter and the power parameter, the operating strategies of the day-ahead phase, the intraday phase and the real-time phase are adjusted respectively according to three predetermined objective functions, including: Respectively obtain the first objective function, the second objective function, the third objective function and the constraint conditions corresponding to the three objective functions; The operation strategies of the day-ahead phase, the intraday phase and the real-time phase are adjusted according to the three objective functions within the constraints.

3. The method according to claim 2, characterized in that Before obtaining the second objective function, the method further includes: Determine each node in the distributed photovoltaic cluster as an initial community; Traversing all nodes in the distributed photovoltaic cluster, calculating the modularity gains brought by moving all nodes to adjacent communities respectively, and forming new communities with adjacent nodes whose modularity gains are greater than zero; The new community is traversed, and the adjacent communities are repeatedly moved until the modularity gain is not greater than zero, thereby obtaining a plurality of sub-photovoltaic clusters.

4. The method according to claim 3, characterized in that Before obtaining the third objective function, the method further includes: Get the preset penalty item; Determine the sum of the second objective function and the preset penalty term as the updated second objective function; The operation strategy of the intraday stage is adjusted based on the updated second objective function.

5. An operation control device based on a distributed photovoltaic cluster, characterized in that: include: A receiving module, used to obtain operating cost parameters and power parameters of the distributed photovoltaic cluster; An adjustment module, used to adjust the operation strategies of the day-ahead stage, the intraday stage and the real-time stage according to the operation cost parameter and the power parameter according to three predetermined objective functions, wherein the three objective functions are respectively a first objective function with the lowest operation cost of the distributed photovoltaic cluster as an adjustment target, a second objective function with the lowest power loss of the distributed photovoltaic cluster as an adjustment target, and a third objective function with the minimum voltage deviation value of each node in the distributed photovoltaic cluster as an adjustment target; A control module, used for controlling the operation of the distributed photovoltaic cluster according to the adjusted operation strategy, wherein the day-ahead phase is before the intraday phase, and the distributed photovoltaic cluster includes: a distribution network connected to a distributed photovoltaic power generation system; The first objective function comprises: ； Where f represents the value of the first objective function, t represents the time, Indicates the total number of nodes contained in the distributed photovoltaic cluster, represents the amount of electricity purchased by node i at time t, represents the amount of electricity sold by node i at time t, and They represent the electricity purchase price and electricity sales price at time t, and They represent the total charging power and total discharging power of energy storage at time t, δ represents the energy storage charging and discharging cost, and They represent the load that can be translated and the load that can be interrupted, and denote shiftable load cost and interruptible load cost respectively; The second objective function comprises: ； in, represents the value of the second objective function, represents the active power of line ij at time t, represents the reactive power of line ij at time t, represents the voltage of node i at time t, represents the resistance of line ij, line ij represents the line between node i and node j, represents the i-th sub-PV cluster; The third objective function comprises: ； In the formula, represents the value of the third objective function, Represents the reference voltage, where the day-ahead stage represents the operation preparation stage of the distributed photovoltaic cluster before the target date, the intraday stage represents the operation stage of the distributed photovoltaic cluster within the target date, and the real-time stage represents any time of the operation of the distributed photovoltaic cluster within the target date.

6. A computer device, characterized in that: include: A memory and a processor, wherein the memory is used to store program instructions; The processor is connected to the memory and is used to execute the operation control method based on a distributed photovoltaic cluster as described in any one of claims 1 to 4.

7. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by the processor, the operation control method based on the distributed photovoltaic cluster described in any one of claims 1 to 4 is implemented.

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