Two-stage partition optimal configuration method and device for voltage regulation resources containing high-density photovoltaic power distribution network, electronic equipment and storage medium

Abstract

The invention discloses a two-stage partition optimal configuration method for voltage regulation resources containing a high-density photovoltaic power distribution network, and belongs to the technical field of power coordinated configuration planning. Comprising the following steps: generating a high-precision typical photovoltaic scene and a high-precision limit photovoltaic scene based on historical big data; carrying out two-step site selection on voltage regulation resources by utilizing subarea roughing configuration areas and subarea fine selection configuration nodes; and constructing a two-stage power distribution network voltage regulation resource constant volume model. According to the configuration method, the photovoltaic reactive output potential can be fully mobilized, the OLTC action times and the configuration capacity are reduced, the calculation dimension can be greatly reduced, the algorithm convergence capability is improved, the configuration is more appropriate to the scene, and meanwhile the economical efficiency, robustness and applicability of the configuration are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power coordinated configuration planning, and in particular to a two-stage partition optimization configuration method, device, electronic equipment and storage medium for voltage regulation resources in a high-density photovoltaic distribution network. Background Art

[0002] With the vigorous development of new energy, wind, solar and other new energy sources are connected to the distribution network with high density and high penetration, which has led to overcapacity of new energy, and the carrying capacity of some regions is close to the limit. There are also problems such as voltage exceeding the limit of the distribution network, difficulty in peak regulation of the power grid, and large voltage fluctuations. How to coordinate the allocation of voltage regulation resources is the fundamental problem to solve the voltage exceeding the limit and fluctuation of the distribution network containing high-density photovoltaic power generation.

[0003] At present, the existing technology has carried out a lot of research on the coordinated configuration of voltage regulation resources. Chinese patent CN116316666A "Reactive compensation device and energy storage coordinated optimization configuration method and system" carried out research on the coordinated optimization configuration of reactive compensation devices and energy storage; CN118523395A "Energy storage site selection and capacity determination method for high-proportion distributed photovoltaic grid-connected system" carried out research on the zoning site selection and capacity determination of voltage management resources; CN117374999A "A two-layer optimization configuration method and system for voltage regulation resources in a distribution network" carried out research on the coordinated optimization configuration model and algorithm of voltage regulation resources. Most of these studies are limited to a single scenario or a typical day scenario, the location selection is based on experience, and the configuration adopts a centralized optimization algorithm. Therefore, a two-stage zoning optimization configuration method for voltage regulation resources containing a high-density photovoltaic distribution network is needed to solve the problems of low computing speed, poor convergence, poor robustness and poor applicability of the existing collaborative configuration method of voltage regulation resources. Summary of the invention

[0004] The purpose of the present invention is to propose a two-stage partition optimization configuration method for voltage regulation resources in a high-density photovoltaic distribution network to solve the current complex photovoltaic scene generation method, the selection of regulation resource locations based on experience or lack of pertinence, the single capacity configuration scenario, and the seldom consideration of extreme scenarios, to ensure the stable operation of the distribution network voltage. The method includes the following steps:

[0005] Generate high-precision typical photovoltaic scenarios and extreme photovoltaic scenarios based on historical big data;

[0006] According to typical photovoltaic scenarios and extreme photovoltaic scenarios, the voltage regulation resources are selected in two steps by using the rough selection of configuration areas in the partitions and the fine selection of configuration nodes within the partitions;

[0007] According to the selected address of the voltage regulation resource, a two-stage distribution network voltage regulation resource sizing model is constructed. In the first stage, the photovoltaic reactive output of each partition is optimized hourly with the voltage deviation as the target, and the distribution network flow is updated according to the photovoltaic reactive output. In the second stage, a multi-scenario double-layer capacity optimization configuration model is established considering the capacity / power demand, capacity / power consumption, voltage over-limit times and degree, network loss, voltage deviation and fluctuation. The upper model is a configuration model including on-load tap-changing transformers, energy storage, SVG and adjustable capacitors, and the lower model is a centralized operation optimization model considering multiple scenarios. The upper model transmits the voltage regulation resource capacity configuration information to the lower model, and the lower model performs optimization calculation according to the voltage regulation resource capacity configuration information to obtain the optimization result and feeds it back to the upper model.

[0008] Furthermore, typical photovoltaic scenarios include four-season photovoltaic large-scale and small-scale scenarios.

[0009] Furthermore, the constraint expression of photovoltaic reactive power is as follows:

[0010]

[0011] Among them, S inv,N Indicates the rated capacity of the photovoltaic inverter; P PV Indicates the active output of photovoltaic power; Q PV Indicates the reactive power output of photovoltaic.

[0012] Furthermore, the upper layer model includes: equipment capacity / power demand model, capacity / power consumption model and voltage over-limit times and degree model;

[0013] The calculation formula of the equipment capacity / power demand model is as follows:

[0014]

[0015] α1+α2+α3+α4=1

[0016] Among them, N ESS N is the number of energy storage installations; CB N is the number of adjustable capacitor CB submodules; SVG SVG installation number; M device is the capacity / power demand model; The energy storage power installed at node i; Install energy storage capacity for node i; Install the capacity of the adjustable capacitor for node i; is the SVG power installed at node i; α1, α2, α3, α4 are the energy storage installation power, energy storage installation capacity, adjustable capacitor installation capacity and SVG installation capacity coefficient;

[0017] The calculation formula of the capacity / power consumption model is as follows:

[0018]

[0019] β1+β2=1

[0020] Among them, M op is the capacity and power consumption model of the distribution network; p j N is the ratio of the number of days in each typical scenario to the total number of years; b 、N l N are the number of distribution network branches and the number of power purchase ports from the upper level respectively; sen1 and N sen2 are the number of typical scenarios and extreme scenarios, respectively; and are the network loss power of branch λ at time t in scenario j and the input power of node i connected to the upper power grid at time t in scenario j; β1 and β2 are the network loss power and the power coefficient of power purchased from the upper power grid;

[0021] The calculation formula of the voltage over-limit times and degree model is as follows:

[0022]

[0023] Among them, N ove,all Indicates the total number of voltage limit violations in n extreme scenarios; N ove,n Indicates the total number of voltage limit violations in the nth extreme scenario; U ove,all Indicates the voltage limit degree of n extreme scenarios; U ove,n Indicates the voltage limit level of the nth extreme scenario; N sen1 and N sen2 are the numbers of typical scenarios and extreme scenarios respectively.

[0024] Furthermore, the lower-level models include: an operating network loss model, an operating voltage deviation model, an operating voltage fluctuation model, and a voltage over-limit times and degree model;

[0025] The calculation formula of the operating network loss model is as follows:

[0026]

[0027] Among them, P loss is the daily network loss model; p j The proportion of representative days to the total number of years for each typical scenario; is the network loss power of branch i at time t in scenario j; N sen1 is the number of typical fields; Nb is the number of branches;

[0028] The calculation formula of the operating voltage deviation model is as follows:

[0029]

[0030] Among them, U Δ represents the daily voltage deviation; N is the number of distribution network nodes; T is the number of hours in the scenario; U i,j,t is the voltage of node i at time t in scenario j; U N is the rated voltage;

[0031] The calculation formula of the operating voltage fluctuation model is as follows:

[0032]

[0033] Among them, U FL Refers to daily voltage fluctuations; is the average voltage value of node i during time T; U i,j,t is the voltage of node i at time t in scenario j; N is the number of distribution network nodes; T is the number of hours in the scenario;

[0034] The calculation formula of the voltage over-limit times and degree model is as follows:

[0035]

[0036] Among them, a i,t Indicates the voltage is out of limit; N ove Indicates the number of times the voltage of each node exceeds the limit in one cycle; b i,t Indicates that the voltage exceeds the limit value; U ove Indicates the voltage limit of each node in a cycle, U i,t is the voltage of node i at time t; U max is the voltage upper limit; U min The voltage lower limit.

[0037] Furthermore, the constraints of the upper model are:

[0038]

[0039] Among them, P ESS,min , P ESS,max is the lower and upper limits of the energy storage installation rated power; E ESS,min 、E ESS,max are the lower and upper limits of the rated capacity of the energy storage installation; Q SVG,min , Q SVG,max is the lower and upper limits of SVG installation capacity; Q CB,min , Q CB,max are the lower and upper limits of the CB installation capacity; They refer to energy storage rated power, energy storage rated capacity, SVG rated capacity and CB rated capacity respectively.

[0040] Furthermore, the constraints of the lower model include: distribution network operation constraints, on-load tap-changing transformer operation constraints, energy storage operation constraints, SVG operation constraints, and adjustable capacitor operation constraints;

[0041] The calculation formula of distribution network operation constraints is as follows:

[0042]

[0043] Among them, P i,t , Q i,t are the active and reactive power injected into node i at time t; G ij and B ij are the conductance and susceptance of line ij respectively; U i,t is the voltage of node i at time t; U j,t is the voltage of node j at time t; θ ij is the voltage phase difference between node i and node j; U min and U max is the upper and lower limits of the node voltage; I ij,t is the current of branch ij at time t; I ij,min and I ij,max are the upper and lower limits of the allowable current of branch ij;

[0044] The calculation formula for the operating constraints of the on-load tap-changing transformer is as follows:

[0045]

[0046]

[0047] Among them, r t0 is the transformer ratio at time t0; r t is the transformer ratio at time t; Δr j is the ratio difference between adjacent gears of the transformer; and They are the up and down gear operation of the transformer. When the gear is up, otherwise When downshifting, otherwise is the maximum number of operations allowed by the transformer within T time; T is the adjustment time of the transformer;

[0048] The calculation formula of energy storage operation constraint is as follows:

[0049]

[0050]

[0051]

[0052]

[0053] SOC i,min E i,N ≤E i,t ≤SOC i,max E i,N

[0054] in, and represents the charging and discharging state of the energy storage of node i at time t; Indicates the discharge power of energy storage at time t; Indicates the charging power of energy storage at time t; and Represents the upper and lower limit constraints of the energy storage discharge power; and Represents the upper and lower limits of the energy storage charging power; E i,t represents the energy storage capacity at node i at time t; E i,t+1 represents the energy storage capacity at node i at time t+1; and Indicates the efficiency of energy storage charging and discharging; SOC i,min and SOC i,max Indicates the minimum and maximum charge states of energy storage; E i,N Indicates the rated capacity of energy storage;

[0055] The calculation formula of SVG operation constraints is as follows:

[0056]

[0057] in, represents the reactive power output of SVG at node i at time t; represents the rated capacity of the SVG at node i;

[0058] The operating constraints of the adjustable capacitor are calculated as follows:

[0059]

[0060]

[0061]

[0062] Among them, n i,t is the number of capacitors put into the submodule at node i at time t; is the maximum number of submodules; N op,max is the maximum number of switching operations of the capacitor bank; Indicates the unit capacity of the adjustable capacitor; Represents the connected capacity of the adjustable capacitor at node i at time t.

[0063] A two-stage partition optimization configuration device for voltage regulation resources of a high-density photovoltaic distribution network, comprising:

[0064] Scenario generation module, used to generate high-precision typical photovoltaic scenarios and extreme photovoltaic scenarios based on historical big data;

[0065] A two-step site selection module is used to perform two-step site selection of voltage regulation resources based on typical photovoltaic scenarios and extreme photovoltaic scenarios by using coarse selection of configuration areas in the partitions and fine selection of configuration nodes within the partitions;

[0066] The two-layer optimization module is used to construct a two-stage distribution network voltage regulation resource sizing model according to the selected address of the voltage regulation resource. In the first stage, the photovoltaic reactive output of each partition is optimized hourly with the voltage deviation as the target, and the distribution network flow is updated according to the photovoltaic reactive output. In the second stage, a multi-scenario two-layer capacity optimization configuration model is established considering the capacity / power demand, capacity / power consumption, the number and degree of voltage over-limit, network loss, voltage deviation and fluctuation. The upper model is a configuration model including on-load tap-changing transformers, energy storage, SVG and adjustable capacitors, and the lower model is a centralized operation optimization model considering multiple scenarios. The upper model transmits the voltage regulation resource capacity configuration information to the lower model, and the lower model performs optimization calculation based on the voltage regulation resource capacity configuration information to obtain the optimization result and feeds it back to the upper model.

[0067] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step in a two-stage partitioning optimization configuration method for voltage regulation resources of a high-density photovoltaic distribution network is implemented.

[0068] A storage medium stores a computer program, which, when executed by a processor, implements each step of a two-stage partition optimization configuration method for voltage regulation resources of a high-density photovoltaic distribution network.

[0069] The beneficial effects of the present invention are:

[0070] 1. The configuration model introduces the whole-year scenario for capacity configuration, making the configuration closer to the actual situation and improving the applicability of the model;

[0071] 2. Before configuring the capacity of dedicated voltage regulation resources, give priority to optimizing the reactive output of photovoltaic inverters, which can fully mobilize the potential of photovoltaic resources to participate in voltage regulation and reduce the configuration capacity of dedicated voltage regulation resources;

[0072] 3. Calculate photovoltaic reactive voltage regulation resources and dedicated voltage regulation resources in stages, greatly reducing the calculation dimension and improving the convergence ability of calculation;

[0073] 4. Fully mobilizing the potential of photovoltaic reactive output can reduce the number of OLTC operations and delay equipment loss;

[0074] 5. The lower-layer operation model of the two-layer configuration model optimizes typical scenarios and out-of-limit scenarios in parallel with different objectives, which enables the configuration to fully consider various voltage problems in the distribution network and improve the pertinence and robustness of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a flow chart of the two-stage partition optimization configuration method of the voltage regulation resource containing high-density photovoltaic distribution network of the present invention;

[0076] Figure 2 Flowchart for cluster structure correction and cluster center correction;

[0077] Figure 3 Schematic diagram of the upper model and the lower model. DETAILED DESCRIPTION

[0078] The present invention proposes a two-stage partition optimization configuration method for voltage regulation resources in a high-density photovoltaic distribution network, which is further described below in conjunction with the accompanying drawings and specific embodiments.

[0079] Figure 1 The present invention is a flow chart of a two-stage partition optimization configuration method for voltage regulation resources in a high-density photovoltaic distribution network, the method comprising the following steps:

[0080] Step S101: Establish high-precision typical photovoltaic scenario and extreme photovoltaic scenario models based on historical big data.

[0081] Step S102: According to typical photovoltaic scenarios and extreme photovoltaic scenarios, a two-step site selection model for voltage regulation resources based on distribution network configuration partitions is constructed. The first step is to use the partitions to roughly select the configuration area, and the second step is to finely select the configuration nodes within the partitions.

[0082] Step S103: According to the selected address of the voltage regulation resource, a two-stage distribution network voltage regulation resource sizing model is constructed. In the first stage, the photovoltaic reactive output of each partition is optimized hourly with the voltage deviation as the target, the output potential of photovoltaic spare resources is fully mobilized, the subsequent configuration investment is reduced, and the distribution network trend is updated according to the photovoltaic reactive output. In the second stage, a multi-scenario double-layer capacity optimization configuration model is proposed, which considers multiple objectives such as capacity / power demand, capacity / power consumption, voltage over-limit times and degree, network loss, voltage deviation and fluctuation. The upper layer is the configuration model of on-load tap-changing transformer (OLTC), energy storage, SVG (Static VarGenerator) and adjustable capacitor (CB), and the lower layer is a centralized operation optimization model considering multiple scenarios. The upper layer transmits the voltage regulation resource capacity configuration information to the lower layer model, and the lower layer model performs optimization calculation according to the capacity configuration information to obtain the optimization result and feeds it back to the upper layer model.

[0083] The typical scene generation method based on historical data in step S101 includes: a cluster structure correction part and a cluster center correction part. The typical scene includes four-season photovoltaic large-scale and small-scale scenes.

[0084] The cluster structure modification steps include:

[0085] First, collect the historical data of sunlight radiation in the required years and summarize them by quarter; second, use the fuzzy C-means clustering algorithm to cluster each season, and the number of clusters in each season is 2, representing large and small photovoltaic power generation respectively; after clustering, calculate the silhouette coefficient (SC) of each particle and set the margin ε. Among the particles with a silhouette coefficient less than the margin ε, select the particle with the smallest silhouette coefficient and assign it to the adjacent class, and then calculate the silhouette coefficient repeatedly until the silhouette coefficient is greater than the margin ε. The process of using clustering structure correction is as follows: Figure 2 As shown in S201.

[0086] The cluster center correction steps include:

[0087] Using the clustering results corrected by clustering structure, the improved particle swarm algorithm is used to revise the cluster center of each cluster with the minimum density within the cluster as the fitness, so that the cluster center can better represent the light radiation level of the cluster. The process of using the cluster center correction method is as follows: Figure 2 S202 represents the expression of intra-cluster compactness as follows:

[0088]

[0089] Where CP is the compactness of the cluster; n is the number of particles in the cluster; c is the cluster center; c i is the particle value.

[0090] In step S101, the extreme photovoltaic scenario generates a summary curve of light radiation for each season from historical data, and then takes the upper and lower envelopes of the summary curve of light radiation for each season to generate the ideal expression of converting light radiation into photovoltaic output as follows:

[0091]

[0092] Among them, P PV,N is the rated power of photovoltaic; S is the actual light intensity; S N is the rated light intensity; P PV The actual photovoltaic output.

[0093] The two-step site selection model based on the voltage regulation resources of the distribution network in step S102 includes: a rough selection configuration area model using partitions and a fine selection configuration node model based on partitions.

[0094] The partitions of the regional model configured by using the partition rough selection are divided into the distribution network reactive dynamic partition model based on typical and extreme scenarios and the comprehensive dynamic partition model. The reactive dynamic partition model only considers the reactive impact and is used to configure SVG. SVG gives priority to the reactive partition configuration with poor balance. The comprehensive dynamic partition model considers both reactive and active impacts and is used to configure energy storage. Due to the high-density distribution characteristics of photovoltaics, each comprehensive partition is configured with at least one energy storage.

[0095] The dynamic zoning model includes: comprehensive indicator construction considering structural indicators and functional indicators and an annual distribution network zoning model based on seasonal zoning.

[0096] The steps of constructing a comprehensive index considering structural and functional indicators in seasons include: constructing a modularity function based on voltage sensitivity index values ​​to construct structural indicators for the partition, and constructing functional indicators for the partition by considering the active and reactive balance within the region.

[0097] The structural and functional indicators are weighted by the hierarchical analysis method to generate weighted comprehensive indicators. Since each season contains two typical scenarios, each of which contains 24-hour photovoltaic and load output, considering the time-varying voltage sensitivity and regional balance and the different probabilities of each scenario, the seasonal comprehensive indicator should be given in an expected form, and the genetic algorithm is used to solve the partition results of each season.

[0098] Specifically, the above structural indicators, functional indicators and comprehensive indicators can be determined by formulas (3)-(14):

[0099]

[0100]

[0101]

[0102] In the formula, S Pδ , S Qδ , S PV , S QV Indicates power-phase sensitivity and power-voltage sensitivity; is the active power-voltage sensitivity of node i to node j within a quarter; T is a cycle, usually T = 24; n s is the number of scenes s; p s is the probability of scenario s; is the active power-voltage sensitivity of node i to node j at time t; is the sensitivity distance of node i to node j; is the electrical distance between i and j; γ P is the active voltage sensitivity index, and the reactive voltage sensitivity index is the same as the active voltage sensitivity index; m is the sum of all edge weights of the network; k i Represents the sum of edge weights of all nodes.

[0103]

[0104]

[0105] Where c is the partition category; n c is the number of nodes in region c; N c is the number of partitions; s is the typical scene category; n s is the number of typical scenarios in each quarter; is the active power balance degree in the c area s scenario; T is the period of the typical scenario, usually T = 24; is the active power of distributed photovoltaic at node i in area c at time t in scenario s; is the active power of the load of node i in area c at time t in scenario s; p s is the probability of the sth scenario within the quarter.

[0106]

[0107]

[0108]

[0109]

[0110]

[0111] Among them, t1 is the scenario where the voltage exceeds the upper limit most seriously in the extreme scenario; t2 is the scenario where the voltage exceeds the lower limit most seriously in the extreme scenario, and its model is the same as the t1 scenario model; i is the node in the c region where the voltage exceeds the upper limit most seriously; is the over-limit value of node i at time t1; is the voltage margin of all distributed photovoltaic reactive idle capacity management nodes i in region c at time t1; is the number of distributed photovoltaics in area c; S ij is the voltage-reactive sensitivity of node i to node j; Q j is the maximum reactive idle capacity of node j; is the reactive power margin index of region c at time t1; is the reactive power margin index of the distribution network at time t1; N c is the number of partitions; ρ Q It is the reactive power margin index of the distribution network.

[0112]

[0113] α1+α2+α3+α4=1 (14)

[0114] Among them, δ ESS and δ SVG are the comprehensive indicators of energy storage and SVG respectively; α1, α2, α3, α4 are the coefficients of each indicator, which are determined by the hierarchical analysis method.

[0115] The seasonally partitioned annual distribution network partition model in step S102 means that for boundary nodes whose partition affiliation changes with the seasons, the probability of belonging to each partition is calculated according to the number of days they belong to different partitions, and finally the distribution network partition is completed in units of years by assigning them to areas with high probability of belonging.

[0116] The partition-based fine-selection configuration node model in step S102 includes: the specific installation nodes of SVG are evaluated and determined by the three indicators of degree in construction graph theory, compactness of voltage sensitivity construction and voltage deviation, and the TOPSIS method based on game theory weighting is used for evaluation; the specific installation nodes of energy storage devices are evaluated and determined by the four indicators of construction degree, comprehensive compactness of comprehensive sensitivity construction, whether it is a photovoltaic node, and voltage deviation, and the TOPSIS method based on game theory weighting is used for evaluation.

[0117] Specifically, the above SVG positioning and ESS positioning can be determined by formulas (15)-(18):

[0118]

[0119]

[0120] in, Refers to the degree of the node. When node i is connected to node j, a ij =1, otherwise, a ij =0; N Bis the number of nodes in the region; It represents the node closeness; is the average spatial electrical distance between nodes i and j within one season; is the voltage deviation of the node; U i is the node voltage; U N is the nominal voltage of the node; POS SVG It is the basis for selecting specific nodes. The larger the POS value of a node, the more suitable it is for installing SVG. α1, α2, and α3 are weights obtained by the hierarchical analysis method.

[0121]

[0122]

[0123] in, Refers to the degree of the node. When node i is connected to node j, a ij =1, otherwise, a ij =0; N B is the number of nodes in the region; It represents the node closeness; and is the average value of active and reactive spatial electrical distances between nodes i and j in one season; U i is the node voltage; U N is the rated voltage of the node; Indicates node voltage deviation; Yes indicates whether photovoltaic installation is available; POS ESS It is the basis for selecting specific node locations. The larger the node POS value, the more suitable it is for installing energy storage. α1, α2, α3, and α4 are weights obtained based on the hierarchical analysis method.

[0124] The specific contents of a two-stage distribution network voltage regulation resource capacity determination method in step S103 are as follows: in the first stage, the transformer gear is kept in the initial state. In the first stage, the photovoltaic reactive output of each partition is optimized hourly with the voltage deviation as the target, the output potential of photovoltaic spare resources is fully mobilized, the subsequent configuration investment is reduced, and the distribution network trend is updated according to the photovoltaic reactive output; the second stage is to use the constructed multi-scenario double-layer capacity optimization configuration model to centrally optimize the active and reactive output of OLTC, adjustable capacitor CB, SVG and energy storage to solve network losses, voltage deviations and fluctuations, and voltage over-limit problems. Each partition in the first stage refers to the partition in hours that considers structural indicators and reactive functional indicators in hours in each typical scenario. The photovoltaic reactive output constraint expression is as follows:

[0125]

[0126] Among them, S inv,NIndicates the rated capacity of the photovoltaic inverter; P PV Indicates the active output of photovoltaic power; Q PV Indicates the reactive power output of photovoltaic.

[0127] The multi-scenario dual-layer capacity optimization configuration model in step S103 includes an upper layer target optimization model and a lower layer target optimization model. The detailed model is as follows: Figure 3 As shown in S401 and S402, the upper model adopts the particle swarm algorithm and the lower model adopts the genetic algorithm.

[0128] The upper optimization model targets include equipment capacity / power demand model, capacity / power consumption model, and voltage over-limit frequency and degree model. The smaller the equipment capacity / power demand, the smaller the capacity / power consumption, and the smaller the voltage over-limit frequency and degree, the better the configuration effect and economy.

[0129] Specifically, the above-mentioned capacity / power demand model, capacity / power consumption model and voltage over-limit times and degree model can be determined by formulas (20)-(24):

[0130]

[0131] α1+α2+α3+α4=1 (21)

[0132] Among them, N ESS N is the number of energy storage installations; CB N is the number of adjustable capacitor CB submodules; SVG SVG installation number; M device is the capacity / power demand model; The energy storage power installed at node i; Install energy storage capacity for node i; Install the capacity of the adjustable capacitor for node i; is the SVG power installed at node i; α1, α2, α3, α4 are the energy storage installation power, energy storage installation capacity, adjustable capacitor installation capacity and SVG installation capacity coefficient, which is determined by the cost of each installed equipment. The larger the unit capacity cost, the larger the corresponding coefficient.

[0133]

[0134] β1+β2=1 (23)

[0135] Among them, M op is the capacity and power consumption model of the distribution network; p j N is the ratio of the number of days in each typical scenario to the total number of years; b 、N l N are the number of distribution network branches and the number of power purchase ports from the upper level respectively;sen1 and N sen2 are the number of typical scenarios and extreme scenarios, respectively; and They are respectively the network loss power of branch λ at time t in scenario j and the input power of node i connected to the upper-level power grid at time t in scenario j; β1 and β2 are the network loss power and the power coefficient purchased from the upper-level grid, which is determined by the unit power cost. The larger the unit capacity cost, the larger the corresponding coefficient.

[0136]

[0137] Among them, N ove,all Indicates the total number of voltage limit violations in n extreme scenarios; N ove,n Indicates the total number of voltage limit violations in the nth extreme scenario; U ove,all Indicates the voltage limit degree of n extreme scenarios; U ove,n Indicates the voltage limit level of the nth extreme scenario; N sen1 and N sen2 are the numbers of typical scenarios and extreme scenarios respectively.

[0138] The lower-level optimization model objectives include the operating network loss model, the operating voltage deviation model, the operating voltage fluctuation model, and the voltage over-limit times and degree model.

[0139] Lower-level optimization refers to the parallel optimization of typical scenarios and extreme scenarios. The typical scenario is optimized based on the operating network loss model, operating voltage deviation model, and operating voltage fluctuation model. The extreme scenario is optimized based on the voltage over-limit times and degree model.

[0140] Specifically, the operation network loss model, operation voltage deviation model, operation voltage fluctuation model, and voltage over-limit times and degree model in the lower optimization model can be determined by (25)-(28):

[0141]

[0142] Among them, P loss is the daily network loss model; p j The proportion of representative days to the total number of years for each typical scenario; is the network loss power of branch i at time t in scenario j; N sen1 is the number of typical fields; Nb is the number of branches.

[0143]

[0144] Among them, U Δ represents the daily voltage deviation; N is the number of distribution network nodes; T is the number of hours in the scenario; U i,j,t is the voltage of node i at time t in scenario j; UN is the rated voltage.

[0145]

[0146] Among them, U FL Refers to daily voltage fluctuations; is the average voltage value of node i during time T; U i,j,t is the voltage of node i at time t in scenario j; N is the number of distribution network nodes; T is the number of hours in the scenario.

[0147]

[0148] Among them, a i,t Indicates the voltage is out of limit; N ove Indicates the number of times the voltage of each node exceeds the limit in one cycle; b i,t Indicates that the voltage exceeds the limit value; U ove Indicates the voltage limit level of each node in a cycle.

[0149] The upper model constraints include: energy storage rated power constraint, energy storage rated capacity constraint, SVG rated capacity constraint, and adjustable capacitor rated capacity constraint. Specifically, the upper constraint model can be determined using (29):

[0150]

[0151] Among them, P ESS,min , P ESS,max is the lower and upper limits of the energy storage installation rated power; E ESS,min 、E ESS,max are the lower and upper limits of the rated capacity of the energy storage installation; Q SVG,min , Q SVG,max is the lower and upper limits of SVG installation capacity; Q CB,min , Q CB,max are the lower and upper limits of the CB installation capacity; They refer to energy storage rated power, energy storage rated capacity, SVG rated capacity and CB rated capacity respectively.

[0152] The constraints of the lower model include: distribution network operation equality constraints and inequality constraints, OLTC operation constraints, energy storage operation constraints, SVG operation constraints and adjustable capacitor CB operation constraints.

[0153] Specifically, the distribution network operation equality constraints and inequality constraints, OLTC operation constraints, energy storage operation constraints, SVG operation constraints and adjustable capacitor CB operation constraint models can be determined using (30)-(34):

[0154]

[0155] Among them, P i,t , Q i,t are the active and reactive power injected into node i at time t; G ij and B ij are the conductance and susceptance of line ij respectively; U i,t is the voltage of node i at time t; U j,t is the voltage of node j at time t; θ ij is the voltage phase difference between node i and node j; U min and U max is the upper and lower limits of the node voltage; I ij,t is the current of branch ij at time t; I ij,min and I ij,max It is the upper and lower limits of the allowable current of branch ij.

[0156]

[0157] Among them, r t0 is the transformer ratio at time t0; r t is the transformer ratio at time t; Δr j is the ratio difference between adjacent gears of the transformer; and They are the up and down gear operation of the transformer. When the gear is up, otherwise When downshifting, otherwise is the maximum number of operations allowed by the transformer within time T; T is the adjustment time of the transformer.

[0158]

[0159] in, and represents the charging and discharging state of the energy storage of node i at time t; Indicates the discharge power of energy storage at time t; Indicates the charging power of energy storage at time t; and Represents the upper and lower limit constraints of the energy storage discharge power; and Represents the upper and lower limits of the energy storage charging power; E i,t represents the energy storage capacity at node i at time t; E i,t+1 represents the energy storage capacity at node i at time t+1; and Indicates the efficiency of energy storage charging and discharging; SOC i,min and SOC i,max Indicates the minimum and maximum charge states of energy storage; E i,NIndicates the rated capacity of energy storage.

[0160]

[0161] in, represents the reactive power output of SVG at node i at time t; Represents the rated capacity of the SVG at node i.

[0162]

[0163] Among them, n i,t is the number of capacitors put into the submodule at node i at time t; is the maximum number of submodules; N op,max is the maximum number of switching operations of the capacitor bank; Indicates the unit capacity of the adjustable capacitor; represents the CB access capacity at node i at time t.

[0164] This embodiment also includes a two-stage partition optimization configuration device for voltage regulation resources including a high-density photovoltaic distribution network, including:

[0165] Scenario generation module, used to generate high-precision typical photovoltaic scenarios and extreme photovoltaic scenarios based on historical big data;

[0166] A two-step site selection module is used to perform two-step site selection of voltage regulation resources based on typical photovoltaic scenarios and extreme photovoltaic scenarios by using coarse selection of configuration areas in the partitions and fine selection of configuration nodes within the partitions;

[0167] The two-layer optimization module is used to construct a two-stage distribution network voltage regulation resource sizing model according to the selected address of the voltage regulation resource. In the first stage, the photovoltaic reactive output of each partition is optimized hourly with the voltage deviation as the target, and the distribution network flow is updated according to the photovoltaic reactive output. In the second stage, a multi-scenario two-layer capacity optimization configuration model is established considering the capacity / power demand, capacity / power consumption, the number and degree of voltage over-limit, network loss, voltage deviation and fluctuation. The upper model is a configuration model including on-load tap-changing transformers, energy storage, SVG and adjustable capacitors, and the lower model is a centralized operation optimization model considering multiple scenarios. The upper model transmits the voltage regulation resource capacity configuration information to the lower model, and the lower model performs optimization calculation based on the voltage regulation resource capacity configuration information to obtain the optimization result and feeds it back to the upper model.

[0168] This embodiment also includes an electronic device and a storage medium. An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step in the two-stage partition optimization configuration method for voltage regulation resources including a high-density photovoltaic distribution network is implemented. A storage medium stores a computer program on it. When the computer program is executed by the processor, each step in the two-stage partition optimization configuration method for voltage regulation resources including a high-density photovoltaic distribution network is implemented.

[0169] In summary, the method for coordinated optimization configuration of voltage regulation resources in a high-density photovoltaic distribution network provided by the present invention can not only fully mobilize the reactive power output potential of photovoltaics, reduce the number of OLTC actions, and reduce the configuration capacity to improve economy, but also greatly reduce the calculation dimension, improve the convergence capability and make the configuration more appropriate to the scenario.

[0170] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0171] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0172] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0173] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0174] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0175] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A two-stage partition optimization configuration method for voltage regulation resources in a high-density photovoltaic distribution network, characterized in that: The following steps are involved: Generate high-precision typical photovoltaic scenarios and extreme photovoltaic scenarios based on historical big data; According to typical photovoltaic scenarios and extreme photovoltaic scenarios, the voltage regulation resources are selected in two steps by using the rough selection of configuration areas in the partitions and the fine selection of configuration nodes within the partitions; According to the selected address of the voltage regulation resource, a two-stage distribution network voltage regulation resource sizing model is constructed. In the first stage, the photovoltaic reactive output of each partition is optimized hourly with the voltage deviation as the target, and the distribution network flow is updated according to the photovoltaic reactive output. In the second stage, a multi-scenario double-layer capacity optimization configuration model is established considering the capacity / power demand, capacity / power consumption, voltage over-limit times and degree, network loss, voltage deviation and fluctuation. The upper model is a configuration model including on-load tap-changing transformers, energy storage, SVG and adjustable capacitors, and the lower model is a centralized operation optimization model considering multiple scenarios. The upper model transmits the voltage regulation resource capacity configuration information to the lower model, and the lower model performs optimization calculation according to the voltage regulation resource capacity configuration information to obtain the optimization result and feeds it back to the upper model.

2. The two-stage partition optimization configuration method for voltage regulation resources containing a high-density photovoltaic distribution network according to claim 1 is characterized in that: The typical photovoltaic scenarios include four-season photovoltaic large-scale and small-scale photovoltaic scenarios.

3. The two-stage partition optimization configuration method for voltage regulation resources containing a high-density photovoltaic distribution network according to claim 1 or 2 is characterized in that: The constraint expression of photovoltaic reactive output is as follows: Among them, S inv,N Indicates the rated capacity of the photovoltaic inverter; P PV Indicates the active power output of photovoltaic; Q PV Indicates the reactive power output of photovoltaic.

4. The two-stage partition optimization configuration method for voltage regulation resources containing a high-density photovoltaic distribution network according to claim 3 is characterized in that: The upper layer model includes: equipment capacity / power demand model, capacity / power consumption model and voltage over-limit times and degree model; The calculation formula of the equipment capacity / power demand model is as follows: α1+α2+α3+α4＝1 Among them, N ESS N is the number of energy storage installations; CB N is the number of adjustable capacitor CB submodules; SVG The number of SVG installations; M device is the capacity / power demand model; The energy storage power installed at node i; Install energy storage capacity for node i; Install the capacity of the adjustable capacitor for node i; is the SVG power installed at node i; α1, α2, α3, α4 are the energy storage installation power, energy storage installation capacity, adjustable capacitor installation capacity and SVG installation capacity coefficient; The calculation formula of the capacity / power consumption model is as follows: β1+β2＝1 Among them, M op is the capacity and power consumption model of the distribution network; p j N is the ratio of the number of days in each typical scenario to the total number of years; b 、N l N are the number of distribution network branches and the number of power purchase ports from the upper level respectively; sen1 and N sen2 are the number of typical scenarios and extreme scenarios, respectively; and are the network loss power of branch λ at time t in scenario j and the input power of node i connected to the upper power grid at time t in scenario j; β1 and β2 are the network loss power and the power coefficient of power purchased from the upper power grid; The calculation formula of the voltage over-limit times and degree model is as follows: Among them, N ove,all Indicates the total number of voltage limit violations in n extreme scenarios; N ove,n Indicates the total number of voltage limit violations in the nth extreme scenario; U ove,all Indicates the voltage limit degree of n extreme scenarios; U ove,n Indicates the voltage limit level of the nth extreme scenario; N sen1 and N sen2 are the numbers of typical scenarios and extreme scenarios respectively.

5. The two-stage partition optimization configuration method for voltage regulation resources containing a high-density photovoltaic distribution network according to claim 4 is characterized in that: The lower layer models include: an operating network loss model, an operating voltage deviation model, an operating voltage fluctuation model, and a voltage over-limit times and degree model; The calculation formula of the operating network loss model is as follows: Among them, P loss is the daily network loss model; p j The proportion of representative days to the total number of years for each typical scenario; is the network loss power of branch i at time t in scenario j; N sen1 is the number of typical fields; Nb is the number of branches; The calculation formula of the operating voltage deviation model is as follows: Among them, U Δ represents the daily voltage deviation; N is the number of distribution network nodes; T is the number of hours in the scenario; U i,j,t is the voltage of node i at time t in scenario j; U N is the rated voltage; The calculation formula of the operating voltage fluctuation model is as follows: Among them, U FL Refers to daily voltage fluctuations; is the average voltage value of node i during time T; U i,j,t is the voltage of node i at time t in scenario j; N is the number of distribution network nodes; T is the number of hours in the scenario; The calculation formula of the voltage over-limit times and degree model is as follows: Among them, a i,t Indicates the voltage is out of limit; N ove Indicates the number of times the voltage of each node exceeds the limit in one cycle; b i,t Indicates that the voltage exceeds the limit value; U ove Indicates the voltage limit of each node in a cycle; U i,t is the voltage of node i at time t; U max is the voltage upper limit; U min The voltage lower limit.

6. The two-stage partition optimization configuration method for voltage regulation resources containing a high-density photovoltaic distribution network according to claim 4 is characterized in that: The constraints of the upper model are: Among them, P ESS,min , P ESS,max is the lower and upper limits of the energy storage installation rated power; E ESS,min 、E ESS,max are the lower and upper limits of the rated capacity of the energy storage installation; Q SVG,min , Q SVG,max is the lower and upper limits of SVG installation capacity; Q CB,min , Q CB,max are the lower and upper limits of the CB installation capacity; They refer to energy storage rated power, energy storage rated capacity, SVG rated capacity and CB rated capacity respectively.

7. The two-stage partition optimization configuration method for voltage regulation resources containing a high-density photovoltaic distribution network according to claim 5 is characterized in that: The constraints of the lower model include: distribution network operation constraints, on-load tap-changing transformer operation constraints, energy storage operation constraints, SVG operation constraints, and adjustable capacitor operation constraints; The calculation formula for distribution network operation constraints is as follows: Among them, P i,t , Q i,t are the active and reactive power injected into node i at time t; G ij and B ij are the conductance and susceptance of line ij respectively; U i,t is the voltage of node i at time t; U j,t is the voltage of node j at time t; θ ij is the voltage phase difference between node i and node j; U min and U max is the upper and lower limits of the node voltage; I ij,t is the current of branch ij at time t; I ij,min and I ij,max are the upper and lower limits of the allowable current of branch ij; The calculation formula for the operating constraints of the on-load tap-changing transformer is as follows: Among them, r t0 is the transformer ratio at time t0; r t is the transformer ratio at time t; Δr j is the ratio difference between adjacent gears of the transformer; and They are the up and down gear operation of the transformer. When the gear is up, otherwise When downshifting, otherwise is the maximum number of operations allowed by the transformer within T time; T is the adjustment time of the transformer; The calculation formula of energy storage operation constraint is as follows: SOC i,min IN i,N ≤E i,t ≤SOC i,max IN i,N in, and represents the charging and discharging state of the energy storage of node i at time t; Indicates the discharge power of energy storage at time t; Indicates the charging power of energy storage at time t; and Represents the upper and lower limit constraints of the energy storage discharge power; and Represents the upper and lower limits of energy storage charging power; E i,t represents the energy storage capacity at node i at time t; E i,t+1 represents the energy storage capacity at node i at time t+1; and Indicates the efficiency of energy storage charging and discharging; SOC i,min and SOC i,max Indicates the minimum and maximum charge states of energy storage; E i,N Indicates the rated capacity of energy storage; The calculation formula of SVG running constraints is as follows: in, represents the reactive power output of SVG at node i at time t; represents the rated capacity of the SVG at node i; The operating constraints of the adjustable capacitor are calculated as follows: Among them, n i,t is the number of capacitors of the submodules put into use by node i at time t; is the maximum number of submodules; N op,max is the maximum number of switching operations of the capacitor bank; Indicates the unit capacity of the adjustable capacitor; Represents the access capacity of the adjustable capacitor at node i at time t.

8. A two-stage partition optimization configuration device for voltage regulation resources in a high-density photovoltaic distribution network, characterized in that: include: Scenario generation module, used to generate high-precision typical photovoltaic scenarios and extreme photovoltaic scenarios based on historical big data; A two-step site selection module is used to perform two-step site selection of voltage regulation resources based on typical photovoltaic scenarios and extreme photovoltaic scenarios by using coarse selection of configuration areas in the partitions and fine selection of configuration nodes within the partitions; The two-layer optimization module is used to construct a two-stage distribution network voltage regulation resource sizing model according to the selected address of the voltage regulation resource. In the first stage, the photovoltaic reactive output of each partition is optimized hourly with the voltage deviation as the target, and the distribution network flow is updated according to the photovoltaic reactive output. In the second stage, a multi-scenario two-layer capacity optimization configuration model is established considering the capacity / power demand, capacity / power consumption, the number and degree of voltage over-limit, network loss, voltage deviation and fluctuation. The upper model is a configuration model including on-load tap-changing transformers, energy storage, SVG and adjustable capacitors, and the lower model is a centralized operation optimization model considering multiple scenarios. The upper model transmits the voltage regulation resource capacity configuration information to the lower model, and the lower model performs optimization calculation based on the voltage regulation resource capacity configuration information to obtain the optimization result and feeds it back to the upper model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step of the two-stage partition optimization configuration method for voltage regulation resources containing a high-density photovoltaic distribution network as claimed in any one of claims 1 to 7 is implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the two-stage partition optimization configuration method for voltage regulation resources containing a high-density photovoltaic distribution network as claimed in any one of claims 1 to 7 is implemented.

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