Method for evaluating bearing capacity of distributed power supply of power distribution network

Abstract

The invention discloses a power distribution network distributed power supply bearing capacity evaluation method, and relates to the technical field of data evaluation, and the method comprises the steps: carrying out a plurality of regulation measures, and achieving the active optimization of a power distribution network; in combination with a multi-period constraint condition, optimizing an energy storage system regulation and control strategy; and based on the comprehensive regulation and control means, constructing a power distribution network optimization model for distributed power supply bearing capacity. According to the method, four regulation and control means of energy storage equipment, OLTC tap adjustment, reactive power regulation equipment and network reconstruction are considered, so that the distributed power supply bearing capacity of the power distribution network is improved; a more detailed and practical constraint condition is provided for the OLTC adjustment process; the bearing capacity of the distributed power supply can be effectively improved, and network loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data evaluation, and in particular to a method for evaluating the carrying capacity of distributed power sources in a distribution network. Background Art

[0002] At present, we need to establish a clean, low-carbon, safe and efficient energy system, implement renewable energy substitution actions, deepen power system reform, and build a new power system with new energy as the main body.

[0003] However, with the access of large-scale distributed power sources, many problems will occur in the operation of the distribution network, such as voltage rise, reverse flow, increased network loss, and decreased power quality. Therefore, the distribution network's carrying capacity for distributed power sources is limited. In order to make better use of distributed power sources, appropriate methods need to be used to evaluate the carrying capacity of distributed power sources in the distribution network to guide the planning and construction of distributed power sources in the distribution network. The carrying capacity of distributed power sources in the distribution network is defined as: the maximum distributed power capacity that can be connected to the system under the condition of meeting various constraints. The factors affecting the carrying capacity of distributed power sources in the distribution network can be summarized into four aspects: distribution network safety operation constraints, distributed power characteristics and load characteristics, distribution network structure parameters, and distribution network operation and management methods.

[0004] At present, the research on the evaluation of distributed power capacity of distribution network includes:

[0005] Taking into account multiple constraints such as voltage, line current, and single-node distributed power injection, a global optimization is performed based on the quantum particle swarm algorithm with the goal of minimizing active network losses. Taking into account multiple constraints such as voltage, current, and stable open-loop operation, a genetic algorithm is used to select the site and size the distributed power source with the minimum network construction cost and operating cost as the objective function. Taking into account the voltage, power flow, the upper limit of distributed power generation, and the N-1 criterion, the optimization is performed with the goal of minimizing network losses and investment and operating costs.

[0006] However, current research on the assessment of distributed power generation capacity mainly considers the economic feasibility of planning and operation, and fails to maximize the "input rate" of distributed energy. Summary of the invention

[0007] In view of the above problems existing in the current research on distributed power supply carrying capacity evaluation, the present invention is proposed.

[0008] Therefore, the problem to be solved by the present invention is how to provide a method for evaluating the carrying capacity of distributed power sources in a distribution network.

[0009] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0010] In the first aspect, an embodiment of the present invention provides a method for evaluating the carrying capacity of distributed power sources in a distribution network, which includes implementing a variety of control measures to achieve active optimization of the distribution network; optimizing the control strategy of the energy storage system in combination with multi-time period constraints; and constructing a distribution network optimization model for the carrying capacity of distributed power sources based on comprehensive control measures.

[0011] As a preferred solution of the method for evaluating the carrying capacity of distributed power sources in the distribution network described in the present invention, the control measures include constructing a reactive compensation device model, constructing an on-load tap-changing transformer model, and constructing a network reconstruction model.

[0012] As a preferred solution of the method for evaluating the carrying capacity of distributed power sources in the distribution network of the present invention, wherein: the construction of the reactive power compensation device model includes a discrete reactive power compensation device model and a continuous reactive power compensation device model; the discrete reactive power compensation device model includes: a model of a discrete reactive power compensation device constructed by switching capacitor groups in groups:

[0013]

[0014] Among them, B CB is the set of nodes where CB is installed; is the number of CB groups put into operation, which is a discrete variable; is the upper limit of the number of CB groups that node j can connect to; The reactive power compensated by each group of CB; The reactive power that can be compensated by a single CB group; the static reactive power compensation device builds a model of the continuous reactive power compensation device:

[0015]

[0016] in, and The lower and upper limits of the SVC compensation power; B is the reactive power actually compensated by SVC; SVC is a collection of nodes containing SVC.

[0017] As a preferred solution of the method for evaluating the carrying capacity of distributed power sources in the distribution network of the present invention, the construction of the on-load tap-changing transformer model includes: regulating the bus voltage by the OLTC low-voltage side voltage:

[0018]

[0019] Among them, t is the time; is the voltage value of the high voltage side of the transformer; V j,max 、V j,min B is the upper and lower limit of bus voltage on the low voltage side of the substation; OLTCis a collection of substations containing OLTC; The upper and lower limits of the OLTC adjustable ratio; r j,t is the actual transformation ratio between the secondary side and the primary side of the OLTC; it is expressed as the discrete identification variable of the OLTC tap position:

[0020]

[0021] Among them, r j,t Indicates the absolute value of the increment between the OLTC gear and the adjacent gear; is the lower limit of OLTC adjustable ratio; r j,s is the ratio of the secondary side voltage to the primary side voltage corresponding to the sth gear; Indicates whether to adjust the sth gear, which is a 0-1 discrete variable; Indicates the OLTC gear adjustment requirement. Considering the gear adjustment number limit and the gear-by-gear adjustment requirement, the adjustment requirement is expressed as:

[0022]

[0023] Among them, SR j It is the change range limit of OLTC gear position, representing the maximum adjustable range; and They are the identification variables for whether the 1st and 2nd gears are adjusted, respectively, and are used to represent the OLTC gear adjustment requirement of "when adjusting the gear, the knob should be turned to the lowest gear first, and then gradually increased to the required voltage level";

[0024] and Represents the gear change mark. If When , it indicates that the gear is adjusted to a higher gear, indicating that the OLTC gear in period t is larger than that in the previous period. This means that the gear position is adjusted to a lower position, and the OLTC gear position in period t is smaller than that in the previous period; It is the limit of the number of times the OLTC tap position can be adjusted within a specific period of time T.

[0025] As a preferred solution of the method for evaluating the carrying capacity of distributed power sources in the distribution network of the present invention, the optimization strategy of the energy storage system control strategy combined with multi-period constraint conditions includes: the energy storage state and power constraint are:

[0026] u_dch+u_ch≤1(u_dch,u_ch∈{0,1})

[0027] 0≤p_dch≤u_dch×α

[0028] 0≤p_ch≤u_ch×β

[0029] The energy storage capacity constraint is:

[0030] E_ess t+1 =E_ess t +λ×p_ch t -μ×p_dch t

[0031] Among them, u_dch and u_ch are energy storage discharge and charge states respectively. When u_dch is 1, it means discharge, and when u_ch is 1, it means charge. Therefore, there are three energy storage states: charge, discharge and neither charge nor discharge. p_dch is the discharge power, p_ch is the charge power, α and β are the discharge and charge limit coefficients, λ and μ are the charge and discharge coefficients, and E_ess t+1 is the energy storage capacity at time t+1, E_ess t is the energy storage capacity at time t; p_ch t 、p_dch t are the charging and discharging powers at time t respectively.

[0032] As a preferred solution of the method for evaluating the carrying capacity of distributed power sources in the distribution network of the present invention, the construction of the distribution network optimization model includes: assuming that the impedance of line ij is Z ij =r ij +jx ij , then the power flow equation is:

[0033]

[0034] Among them, P ij , Q ij is the active and reactive power flowing from node i to node j; P jk , Q jk are the active power and reactive power flowing from node j to node k respectively; P j , Q j is the active power and reactive power of the load at node j; V i 、V j are the voltages of nodes i and j respectively. If the injected power of the remaining nodes and the voltage of one of the nodes are known, the injected power of the remaining nodes and the voltage of one of the nodes can be brought into the power flow calculation program to calculate the voltages of each node. Knowing the node voltage, the power flow of any branch can be obtained through the above branch power balance equation; the objective function and constraints of the second-order cone solution are set based on the branch power flow method. The objective function is:

[0035]

[0036] Among them, P DG,iis the active power injected by the distributed generation into the connected node i, N is the number of nodes, and ploss is the network loss; the constraints include the reactive power constraint emitted by the reactive compensation device, the voltage constraint and tap adjustment constraint of the on-load tap-changing transformer, the topology constraint of the network reconstruction, the charging and discharging state and power constraint of the energy storage, the capacity constraint of the energy storage, and the power flow constraint of each node and branch.

[0037] As a preferred solution of the method for evaluating the carrying capacity of distributed power sources in the distribution network of the present invention, the construction of the distribution network optimization model also includes: using the second-order cone convex power flow constraint: The objective function is:

[0038]

[0039] The constraints are rewritten as:

[0040]

[0041]

[0042] The node power balance equation is:

[0043] P i +P load,i -P g,i -P DG,i =0

[0044] Q i +Q load,i -Q g,i -Q DG,i =0

[0045] The output constraints of distributed generation are:

[0046]

[0047] Among them, P i is the active power flowing out from node i; P load,i is the active power of the load at node i; P g,i is the active power injected into node i; P DG,i The active power injected by the distributed generation to the connected node i; Q i is the reactive power flowing out from node i; Q load,i is the reactive power of the load at node i; Q g,i is the reactive power injected into node i; Q DG,i is the reactive power injected by the distributed generation to the connected node i; is the minimum active power output by the distributed power source connected to node i; is the maximum active power output by the distributed power source connected to node i; is the minimum reactive power output by the distributed power source connected to node i; is the maximum reactive power output by the distributed generation connected to node i.

[0048] On the second aspect, in order to further solve the problems existing in the current research on the evaluation of distributed power supply carrying capacity, the present invention provides a distribution network distributed power supply carrying capacity evaluation system in an embodiment, which includes: an active optimization module for implementing various control measures to achieve active optimization of the distribution network; an energy storage system control module for optimizing the energy storage system control strategy in combination with multi-time period constraints; a distribution network optimization model evaluation module for constructing a distribution network optimization model for distributed power supply carrying capacity based on comprehensive control measures.

[0049] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for evaluating the carrying capacity of distributed power sources in a distribution network as described in the first aspect of the present invention is implemented.

[0050] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for evaluating the carrying capacity of distributed power sources in a distribution network as described in the first aspect of the present invention is implemented.

[0051] The beneficial effects of the present invention are as follows: the present invention considers four control means, namely, energy storage equipment, OLTC tap adjustment, reactive power regulation equipment and network reconstruction, to improve the distributed power supply carrying capacity of the distribution network; and proposes more detailed and practical constraints for the OLTC adjustment process, such as considering the gear adjustment number limit, adjustment sequence requirements, etc.; multi-time period constraints are considered in the energy storage system model, which better reflects the actual operation characteristics of the energy storage system; the present invention can effectively improve the distributed power supply carrying capacity and reduce network losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing 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. Among them:

[0053] Figure 1 This is the model diagram of the on-load tap-changing transformer.

[0054] Figure 2 This is a schematic diagram of a radial distribution network.

[0055] Figure 3This is the structural diagram of the node power distribution system. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0058] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0059] Example 1

[0060] Reference Figure 1 to Figure 3 , which is the first embodiment of the present invention, and provides a method for evaluating the carrying capacity of distributed power sources in a distribution network, comprising the following steps:

[0061] S1: Implement a variety of control measures to achieve active optimization of the distribution network.

[0062] Preferably, the control measures include constructing a reactive power compensation device model, constructing an on-load tap-changing transformer model, and constructing a network reconstruction model.

[0063] S1.1: Constructing a reactive power compensation device model includes a discrete reactive power compensation device model and a continuous reactive power compensation device model.

[0064] Specifically, the discrete reactive power compensation device model includes:

[0065] Taking capacitor banks (CB) as an example, a discrete reactive power compensation device model is constructed:

[0066]

[0067] Among them, B CB is the set of nodes where CB is installed; is the number of CB groups put into operation, which is a discrete variable; is the upper limit of the number of CB groups that node j can connect to; The reactive power compensated by each CB group is the product of a discrete variable and the reactive power compensated by switching on a single CB group; It is the reactive power that can be compensated by a single CB group.

[0068] Taking the static VAR compensation (SVC) device as an example, a model of the continuous reactive power compensation device is constructed:

[0069]

[0070] in, and The lower and upper limits of the SVC compensation power; B is the reactive power actually compensated by SVC; SVC is a collection of nodes containing SVC.

[0071] S1.2: Construct an on-load tap-changing transformer model.

[0072] The structure of on-load tap changer (OLTC) is as follows: Figure 1 As shown, i, m, j, and k are the numbers of each node respectively.

[0073] S1.2.1: The bus voltage is regulated by the OLTC low-voltage side voltage.

[0074] Preferably, the substation bus voltage can be adjusted by the OLTC low-voltage side voltage. In this case, the bus voltage can be expressed as:

[0075]

[0076] Among them, t is the time; is the voltage value of the high voltage side of the transformer; V j,max 、V j,min B is the upper and lower limit of bus voltage on the low voltage side of the substation; OLTC is a collection of substations containing OLTC; The upper and lower limits of the OLTC adjustable ratio; r j,t is the actual transformation ratio between the secondary side and the primary side of the OLTC, which is expressed as the discrete identification variable of the OLTC tap position:

[0077]

[0078] Among them, r j,t Indicates the absolute value of the increment between the OLTC gear and the adjacent gear; is the lower limit of OLTC adjustable ratio; r j,s is the ratio of the secondary side voltage to the primary side voltage corresponding to the sth gear; Indicates whether to adjust the sth gear, which is a 0-1 discrete variable.

[0079] S1.2.2: Characterize the OLTC gear adjustment requirements, considering the gear adjustment number limit and gear-by-gear adjustment requirements.

[0080] Considering that under actual operating conditions, the OLTC gear adjustment is limited in number, and the adjustment starts from the lowest gear for recharging, the adjustment requirement can be expressed as:

[0081]

[0082] Among them, SR j It is the change range limit of OLTC gear position, representing the maximum adjustable range; and They are the identification variables for whether the 1st and 2nd gears are adjusted, respectively, and are used to represent the OLTC gear adjustment requirement of "when adjusting the gear, the knob should be turned to the lowest gear first, and then gradually increased to the required voltage level"; and Represents the gear change mark. If When , it indicates that the gear is adjusted to a higher gear, indicating that the OLTC gear in period t is larger than that in the previous period. This means that the gear position is adjusted to a lower position, and the OLTC gear position in period t is smaller than that in the previous period; It is the limit of the number of times the OLTC tap position can be adjusted within a specific period of time T.

[0083] S1.3: Constructing the network reconstruction model includes adjusting the distribution network topology through section switches and tie switches to achieve network reconstruction and introduce isolated node and ring structure constraints.

[0084] Preferably, the line switches in the distribution network mainly include two types: section switches and tie switches.

[0085] The section switch is a normally closed switch used to connect two lines; the connecting switch is a normally open switch used to transfer power, backup power and optimize network structure.

[0086] By changing the states of these two switches, the topology can be adjusted, a process called network reconstruction.

[0087] When reconfiguring the network, for protection setting considerations, the ring network topology should be avoided as much as possible. Therefore, the distribution network is required to be radial, and the corresponding constraints are:

[0088]

[0089] Among them, z ij It is a 0-1 discrete variable. When the value is 0, it means the branch is disconnected, and when the value is 1, it means the branch is closed. b is the total number of nodes in the distribution network; n sis the number of root nodes in the distribution network; Φ l It is the collection of all branches of the distribution network.

[0090] The closing and opening of the switch will affect the branch current and power flow, so it is necessary to establish constraints on the switch and branch current and power flow:

[0091]

[0092] in, and They are respectively the lower limit and upper limit of the active power allowed to pass under the safe operation conditions of the branch; and They are the lower and upper limits of reactive power allowed to pass under safe operation conditions of the branch respectively; and are the lower and upper limits of the current allowed to pass through the branch under safe operating conditions; σ ij.t is the switch state of branch ij. When the value is 0, it means the switch is open, and when it is 1, it means the switch is closed. E is the set of all branches of the distribution network. E sw N is the collection of branches equipped with switches in the distribution network; bus is the number of all nodes in the distribution network; N sub It is the number of root nodes of the distribution network. Usually, the number of root nodes should not be set too much.

[0093] After the network is restructured, there may be solutions that meet the radial constraints and power flow constraints but have isolated nodes and ring structures coexisting. Therefore, the following constraints are introduced:

[0094]

[0095] Among them, P jk , Q jk are the active power and reactive power of the jk branch respectively; P ij , Q ij are the active power and reactive power of the ij line respectively; are the active power and reactive power of the load at node j respectively; δ is a very small positive number. It is artificially set that the active and reactive injection amounts of all nodes must be greater than or equal to δ. Since δ is very small, it has almost no effect on the actual solution.

[0096] S2: Optimize the energy storage system control strategy by combining multi-period constraints.

[0097] Preferably, the energy storage system usually needs to consider constraints in multiple time periods, mainly including charging and discharging states and charging and discharging power limits, as well as the capacity limits of the energy storage itself.

[0098] Energy storage state and power constraints are:

[0099] u_dch+u_ch≤1(u_dch,u_ch∈{0,1})

[0100] 0≤p_dch≤u_dch×α

[0101] 0≤p_ch≤u_ch×β

[0102] The energy storage capacity constraint is:

[0103] E_ess t+1 =E_ess t +λ×p_ch t -μ×p_dch t

[0104] Among them, u_dch and u_ch are energy storage discharge and charge states respectively. When u_dch is 1, it means discharge, and when u_ch is 1, it means charge. Therefore, there are three energy storage states: charge, discharge and neither charge nor discharge. p-dch is the discharge power, p_ch is the charge power, α and β are the discharge and charge limit coefficients. λ and μ are the charge and discharge coefficients, E_ess t+1 is the energy storage capacity at time t+1, E_ess t is the energy storage capacity at time t; p_ch t 、p_dch t are the charging and discharging powers at time t respectively.

[0105] S3: Based on comprehensive control measures, a distribution network optimization model is constructed for distributed power generation capacity.

[0106] Considering various control measures such as reactive power compensation, OLTC gear change, network reconstruction, energy storage, etc., an optimized operation model of the distribution network is established. The carrying capacity of distributed power sources is evaluated while satisfying the system flow constraints and the control requirements of various control measures.

[0107] The decision variables of the optimization model include the charging and discharging power and status of energy storage, the opening and closing status of the tie switch and section switch in network reconstruction, the reactive compensation power, the OLTC tap position, etc.

[0108] The structure of radial distribution network is as follows: Figure 2 As shown, I, j, k, l, m, and n are node numbers respectively.

[0109] Assume the impedance of line ij is Z ij =r ij +jx ij , then the power flow equation is:

[0110]

[0111] Among them, P ij , Q ij is the active and reactive power flowing from node i to node j; P jk , Q jk are the active power and reactive power flowing from node j to node k respectively; P j , Q j is the active power and reactive power of the load at node j; V i 、V j are the voltages of node i and node j respectively. If the injected power of the remaining nodes and the voltage of one of the nodes are known, the injected power of the remaining nodes and the voltage of one of the nodes can be brought into the power flow calculation program to calculate the voltage of each node. Once the node voltage is known, the power flow of any branch can be obtained through the above branch power balance equation.

[0112] Furthermore, the objective function and constraint conditions of the second-order cone solution are set based on the branch power flow method. The specific steps are as follows:

[0113] The objective function is:

[0114]

[0115] Among them, P DG,i is the active power injected by the distributed generation into the connected node i, N is the number of nodes, and ploss is the network loss.

[0116] The defined constraints include the reactive power constraints generated by the reactive compensation device, the voltage constraints and tap adjustment constraints of the on-load tap-changing transformer, the topology constraints of the network reconstruction, the charging and discharging state and power constraints of the energy storage, the capacity constraints of the energy storage, and the flow constraints of each node and branch.

[0117] Preferably, the constraints are:

[0118]

[0119] Among them, P j , Q j Inject active power and reactive power into the node; π(j) is the branch set with node j as the end node; v(j) is the branch set with node j as the head node; B and E are the node set and branch set respectively.

[0120] Utilize the second-order cone to convexify the power flow constraints.

[0121] Furthermore, a second-order cone relaxation convexification is performed:

[0122] make The objective function is:

[0123]

[0124] Furthermore, the reactive power of the reactive compensation device, the tap position of the on-load tap-changing transformer, the opening and closing status of the interconnecting switches and section switches involved in network reconstruction, the charging and discharging status and power of the energy storage, and the power injected into the distributed power generation system are optimized to maximize the total power injected into the system and minimize the system network loss.

[0125] The constraints are rewritten as:

[0126]

[0127] The node power balance equation is:

[0128] P i +P load,i -P g,i -P DG,i =0

[0129] Q i +Q load,i -Q g,i -Q DG,i =0

[0130] The output constraints of distributed generation are:

[0131]

[0132] Among them, P i is the active power flowing out from node i; P load,i is the active power of the load at node i; P g,i is the active power injected into node i (excluding distributed power sources); P DG,i The active power injected by the distributed generation to the connected node i; Q i is the reactive power flowing out from node i; Q load,i is the reactive power of the load at node i; Q g,i is the reactive power injected into node i (excluding distributed power sources); Q DG,i is the reactive power injected by the distributed generation to the connected node i; is the minimum active power output by the distributed power source connected to node i; is the maximum active power output by the distributed power source connected to node i; is the minimum reactive power output by the distributed power source connected to node i; is the maximum reactive power output by the distributed generation connected to node i.

[0133] This embodiment also provides a distribution network distributed power source carrying capacity assessment system, including: an active optimization module, used to implement a variety of control measures to achieve active optimization of the distribution network; an energy storage system control module, used to optimize the energy storage system control strategy in combination with multi-time period constraints; a distribution network optimization model assessment module, used to construct a distribution network optimization model for distributed power source carrying capacity based on comprehensive control measures.

[0134] This embodiment also provides a computer device, which is applicable to the case of a method for evaluating the carrying capacity of distributed power sources in a distribution network, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method for evaluating the carrying capacity of distributed power sources in a distribution network as proposed in the above embodiment.

[0135] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0136] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for evaluating the carrying capacity of distributed power sources in a distribution network proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0137] In summary, the present invention considers four control methods, namely, energy storage equipment, OLTC tap adjustment, reactive power regulation equipment and network reconstruction, to improve the distributed power supply carrying capacity of the distribution network; and proposes more detailed and practical constraints for the OLTC adjustment process, such as the gear adjustment number limit, adjustment sequence requirements, etc.; multi-time period constraints are considered in the energy storage system model, which better reflects the actual operation characteristics of the energy storage system; the present invention can effectively improve the distributed power supply carrying capacity and reduce network losses.

[0138] Example 2

[0139] Reference Figure 3 , which is the second embodiment of the present invention, in order to further verify the advancement of the present invention, experimental simulation of the distribution network distributed power source carrying capacity evaluation method / comparison data with the prior art are provided.

[0140] The structure diagram of the 33-node distribution system is as follows Figure 3 As shown, the dotted lines represent tie lines.

[0141] When reactive power regulation is considered, reactive power compensation devices are connected to nodes 6, 16, and 32. The results of distributed power generation capacity and network loss are shown in Table 1:

[0142] Table 1 Distributed power generation capacity and network loss of distribution network considering reactive power regulation

[0143]

[0144] It can be seen from the table that when there is no regulation measure, the system distributed power carrying capacity is 233.3MW, and when reactive power regulation measures are added, 219.7MW can be connected. The distributed power carrying capacity is slightly reduced, but the network loss consumed by the line is greatly reduced. Therefore, considering the distributed power carrying capacity evaluation of reactive power regulation measures, the reactive power regulation measures change the network loss value more obviously. The reason is that the objective function contains the distributed power capacity and the network loss value, and the algorithm first optimizes the part with a fast decline rate. At the same time, the calculation results show that under the condition of the same network loss value, reactive power regulation can increase the system's connectable distributed power capacity.

[0145] When considering network reconstruction, the results of distributed generation capacity and network loss are shown in Table 2:

[0146] Table 2 Distributed generation capacity and network loss of distribution network considering network reconstruction

[0147]

[0148] As can be seen from the table, by adjusting the network topology, the system's distributed power carrying capacity can be improved. In addition, after the network is reconstructed, the system network loss is not much different from the network loss before adjustment, but the optimized network loss value is slightly larger. The reason is that the rate of decrease of network loss is slower than the rate of increase of distributed power capacity, so the algorithm prioritizes the optimization of the part that makes the overall objective function value increase faster, and the distributed power capacity is optimized first.

[0149] When energy storage is considered, energy storage is connected to nodes 16 and 33. The results of distributed generation capacity and network loss are shown in Table 3:

[0150] Table 3 Distributed power generation capacity and network loss of distribution network considering energy storage

[0151]

[0152] It can be seen from the table that after adding energy storage, the system's distributed power supply carrying capacity increases and the network loss value decreases.

[0153] Taking reactive power regulation, OLTC regulation, network reconstruction and energy storage into consideration, distributed power sources are connected at nodes 4, 17, 18, 19, 22, 31 and 32. The results of distributed power source carrying capacity and network loss are shown in Table 4:

[0154] Table 4 Distributed power generation capacity and network loss of distribution network considering comprehensive regulation

[0155]

[0156] The distributed power generation capacity is 63.52MW when comprehensive control measures are not added, and the distributed power generation capacity is 93.12MW after the distributed power generation is put into use at the fixed point. The distributed power generation capacity is increased, and the network loss is lower than the network loss when the distributed power generation is not connected. This shows that comprehensive control is an effective measure to improve the distributed power generation capacity of the system.

[0157] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for evaluating the carrying capacity of distributed power sources in distribution networks. Features: include: Implement a variety of control measures to achieve active optimization of the distribution network; Combine multi-period constraints to optimize the energy storage system control strategy; Based on comprehensive control measures, a distribution network optimization model is constructed for the distributed power generation capacity.

2. The method for evaluating the carrying capacity of distributed power sources in a distribution network according to claim 1, Features: The control measures include constructing a reactive power compensation device model, constructing an on-load tap-changing transformer model, and constructing a network reconstruction model.

3. The method for evaluating the carrying capacity of distributed power sources in a distribution network according to claim 2, Features: The reactive power compensation device model constructed includes a discrete reactive power compensation device model and a continuous reactive power compensation device model; The discrete reactive power compensation device model includes: The model of discrete reactive power compensation device constructed by switching capacitor groups in groups: Among them, B CB is the set of nodes where CB is installed; is the number of CB groups put into operation, which is a discrete variable; is the upper limit of the number of CB groups that node j can connect to; The reactive power compensated by each group of CB; The reactive power that can be compensated by a single CB group; Static reactive power compensation device builds a model of continuous reactive power compensation device: in, and The lower and upper limits of the SVC compensation power; B is the reactive power actually compensated by SVC; SVC is a collection of nodes containing SVC.

4. The method for evaluating the carrying capacity of distributed power sources in a distribution network according to claim 3, Features: The on-load tap-changing transformer model is constructed as follows: Regulating bus voltage through OLTC low voltage side voltage: Among them, t is the time; is the voltage value of the high voltage side of the transformer; V j,max 、V j,min B is the upper and lower limit of bus voltage on the low voltage side of the substation; OLTC is a collection of substations containing OLTC; The upper and lower limits of the OLTC adjustable ratio; r j,t is the actual transformation ratio between the secondary side and the primary side of the OLTC; The discrete identification variable of the OLTC tap position is expressed as: Among them, r j,t Indicates the absolute value of the increment between the OLTC gear and the adjacent gear; is the lower limit of OLTC adjustable ratio; r j,s is the ratio of the secondary side voltage to the primary side voltage corresponding to the sth gear; Indicates whether to adjust the sth gear, which is a 0-1 discrete variable; Characterizing the OLTC gear adjustment requirements, considering the gear adjustment times limit and gear-by-gear adjustment requirements, the adjustment requirements are expressed as: Among them, SR j It is the change range limit of OLTC gear position, representing the maximum adjustable range; and They are the identification variables for whether the 1st and 2nd gears are adjusted, respectively, and are used to represent the OLTC gear adjustment requirement of "when adjusting the gear, the knob should be turned to the lowest gear first, and then gradually increased to the required voltage level"; and Represents the gear change mark. If When , it indicates that the gear is adjusted to a higher gear, indicating that the OLTC gear in period t is larger than that in the previous period. This means that the gear position is adjusted to a lower position, and the OLTC gear position in period t is smaller than that in the previous period; It is the limit of the number of times the OLTC tap position can be adjusted within a specific period of time T.

5. The method for evaluating the carrying capacity of distributed power sources in a distribution network according to claim 4, Features: The optimization of the energy storage system control strategy by combining the multi-period constraint conditions includes: Energy storage state and power constraints are: u_dch+u_ch≤1 (u_dch, u_ch∈{0,1}) 0≤p_dch≤u_dch×α 0≤p_ch≤u_ch×β The energy storage capacity constraint is: E_ess t+1 =E_ess t +λ×p_ch t -μ×p_dch t Among them, u_dch and u_ch are energy storage discharge and charge states respectively. When u_dch is 1, it means discharge, and when u_ch is 1, it means charge. Therefore, there are three energy storage states: charge, discharge and neither charge nor discharge. p-dch is the discharge power, p_ch is the charge power, α and β are the discharge and charge limit coefficients, λ and μ are the charge and discharge coefficients, and E_ess t+1 is the energy storage capacity at time t+1, E_ess t is the energy storage capacity at time t; p_ch t 、p_dch t are the charging and discharging powers at time t respectively.

6. The method for evaluating the carrying capacity of distributed power sources in a distribution network according to claim 5, Features: The construction of the distribution network optimization model comprises: Assume the impedance of line ij is Z ij =r ij +jx ij , then the power flow equation is: Among them, P ij , Q ij is the active and reactive power flowing from node i to node j; P jk , Q jk are the active power and reactive power flowing from node j to node k respectively; P j , Q j is the active power and reactive power of the load at node j; V i 、V j are the voltages of nodes i and j respectively. If the injected power of the remaining nodes and the voltage of one of the nodes are known, the injected power of the remaining nodes and the voltage of one of the nodes can be brought into the power flow calculation program to calculate the voltages of each node. Once the node voltage is known, the power flow of any branch can be obtained through the above branch power balance equation. The objective function and constraints of the second-order cone solution are set based on the branch power flow method. The objective function is: Among them, P DG,i is the active power injected by the distributed generation to the connected node i, N is the number of nodes, and ploss is the network loss; The constraints include the reactive power constraints emitted by the reactive compensation device, the voltage constraints and tap adjustment constraints of the on-load tap-changing transformer, the topology constraints of the network reconstruction, the charging and discharging state and power constraints of the energy storage, the capacity constraints of the energy storage, and the flow constraints of each node and branch.

7. The method for evaluating the carrying capacity of distributed power sources in a distribution network according to claim 6, Features: The construction of the distribution network optimization model also includes: Use the second-order cone to convexify the power flow constraint: make The objective function is: The constraints are rewritten as: The node power balance equation is: P i +P load,i -P g,i -P DG,i =0 Q i +Q load,i -Q g,i -Q DG,i =0 The output constraints of distributed generation are: Among them, P i is the active power flowing out from node i; P load,i is the active power of the load at node i; P g,i is the active power injected into node i; P DG,i The active power injected by the distributed generation to the connected node i; Q i is the reactive power flowing out from node i; Q load,i is the reactive power of the load at node i; Q g,i is the reactive power injected into node i; Q DG,i is the reactive power injected by the distributed generation to the connected node i; is the minimum active power output by the distributed power source connected to node i; is the maximum active power output by the distributed power source connected to node i; is the minimum reactive power output by the distributed power source connected to node i; is the maximum reactive power output by the distributed generation connected to node i.

8. A system for evaluating the carrying capacity of distributed power sources in a distribution network, based on the method for evaluating the carrying capacity of distributed power sources in a distribution network according to any one of claims 1 to 7, Features: include: Active optimization module, used to implement various control measures to achieve active optimization of the distribution network; Energy storage system control module, used to optimize the energy storage system control strategy by combining multi-period constraints; The distribution network optimization model evaluation module is used to build a distribution network optimization model for distributed power supply carrying capacity based on comprehensive control measures.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program. Features: When the processor executes the computer program, the steps of the method for evaluating the carrying capacity of distributed power sources in a distribution network according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, Features: When the computer program is executed by a processor, the steps of the method for evaluating the carrying capacity of distributed power sources in a distribution network according to any one of claims 1 to 7 are implemented.

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