Distribution network reactive power support capability assessment method and system considering uncertainty new energy output

Abstract

The invention discloses a distribution network reactive power support capability evaluation method and system considering uncertainty new energy output, and belongs to the technical field of power grid power generation dispatching, and the method comprises the steps: constructing a distribution network reactive power deviation index considering active and reactive power coupling, and fusing the index into a distribution network reactive power support capability evaluation calculation process; constructing reactive power reserve models of various traditional energy sources and uncertain new energy sources, wherein the reactive power reserve models comprise a generator set, a capacitor bank, wind power generation, photovoltaic power generation, energy storage, a flexible load and an electric vehicle; constructing a typical scene set which comprehensively considers the output of all the models; constructing a distribution network reactive power support capability calculation model considering the uncertainty new energy output; and solving by adopting a genetic algorithm to obtain the reactive power support capability of the distribution network. According to the method, multiple types of reactive power resources on the power distribution side are fully transferred, the reactive power reserve potential evaluation model for the active power distribution network individuals is established, and quantitative evaluation of the reactive power voltage supporting capacity of the power distribution network is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid generation dispatching, and in particular relates to a method and system for evaluating reactive support capacity of a distribution network considering uncertain new energy output. Background Art

[0002] In recent years, as the penetration rate of new energy continues to increase, the installed capacity of thermal power units has decreased year by year, which has put forward higher requirements for reactive power and voltage control of power systems. Reactive power reserve is an important indicator to ensure the safe and stable operation of the power grid. Most of the existing reactive power reserve optimization methods are aimed at conventional reactive power sources on the transmission side, while there are relatively few studies on various types of reactive resources in the distribution network. How to effectively tap various reactive resources in the distribution network, provide reactive power support for the transmission side, reduce reactive equipment investment, and improve the stability and economic operation of the power grid is an urgent problem to be solved. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a method and system for evaluating the reactive support capacity of a distribution network taking into account the output of uncertain new energy sources. Considering the full mobilization of multiple types of reactive resources on the distribution side and the reactive voltage support capacity on the transmission side, a reactive reserve potential evaluation model for individual active distribution networks is established, and an improved genetic algorithm is used to solve the problem in typical scenarios to achieve a quantitative evaluation of the reactive voltage support capacity of the distribution network.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A method for evaluating reactive power support capability of a distribution network considering uncertain new energy output comprises the following steps:

[0006] Construct reactive power reserve models for a variety of traditional energy sources and uncertain new energy sources, including reactive power reserve models for generator sets, reactive power reserve models for capacitor banks, reactive power reserve models for wind power generation, reactive power reserve models for photovoltaic power generation, reactive power reserve models for energy storage, reactive power reserve models for flexible loads, and reactive power reserve models for electric vehicles;

[0007] Construct a typical scenario set for the output of generator sets and capacitor groups, a typical scenario set for the output of wind and solar power generation, a typical scenario set for the output of flexible loads and energy storage systems, and a typical scenario set for the output of electric vehicles;

[0008] The reactive power reserve models of various traditional energy sources and uncertain new energy sources are used to establish the objective function and constraints of the reactive power support capacity evaluation problem of the distribution network;

[0009] The typical scenario sets of generator set and capacitor group output, wind and solar power generation output, flexible load and energy storage system output and electric vehicle output are taken as the initial population of the genetic algorithm. The value of the objective function is taken as the fitness of the individuals in the population. The genetic algorithm is used to solve the problem of reactive support capacity evaluation of the distribution network, and the optimal solution is taken as the reactive support capacity of the distribution network.

[0010] To optimize the above technical solutions, the specific measures taken also include:

[0011] Furthermore, the reactive power reserve model of the generator set is specifically as follows: the reactive power generated by the generator is used as the decrement, and the reactive power limit that can be generated based on the active power generated by the generator is used as the decrement, so as to calculate the reactive power reserve of the generator set;

[0012] The reactive power reserve model of the capacitor bank is specifically: the reactive power reserve Q of the parallel capacitor bank Crpr The expression is as follows:

[0013] Q Crpr =2pfC C V C 2 (n Cm -n C )

[0014] Where n Cm is the maximum number of capacitor groups that can be put into use; n C is the number of capacitor banks currently in use; f is the current frequency of the entire system; C C is the capacitance of a single group of capacitors; V C The node voltage of the node to which it is connected;

[0015] The reactive power reserve model of wind power generation is:

[0016]

[0017] In the formula, Q Wrpr is the reactive power reserve of wind power generation, Q Wmax is the maximum value of reactive power output limit; Q W For real-time reactive power output; P W For real-time active power output; V s is the stator terminal voltage; I rmax is the maximum value of the rotor terminal current; X r is the equivalent self-inductance of the rotor segment; X s is the stator end leakage reactance.

[0018] Furthermore, the photovoltaic power generation reactive power reserve model is specifically:

[0019] Solar photovoltaic array active output PPV and reactive power output Q PV As shown below:

[0020]

[0021] Where: E is the light intensity; h is the photoelectric conversion efficiency; A is the effective illumination area, |Q PV | max is the maximum reactive power regulation capability of the photovoltaic array; S PV The capacity of the grid-connected inverter for the photovoltaic array;

[0022] Reactive power reserve of photovoltaic system The expression is as follows:

[0023]

[0024] Where: is the maximum reactive power regulation value of photovoltaic at node m; is the photovoltaic inverter capacity of node m; and are the photovoltaic active and reactive power injected into node m respectively.

[0025] Furthermore, the energy storage reactive reserve model is specifically:

[0026] The true voltage amplitude limit value V of the converter in the energy storage system is calculated according to the following formula ESSmax :

[0027]

[0028] Where: K PWMmax is the maximum amplitude of the linear modulation ratio; is the DC voltage of the converter;

[0029] The maximum reactive output Q of the energy storage system is calculated according to the following formula ESSmax for:

[0030]

[0031] Where: is the voltage of the converter on the grid side; ω 1 is the angular frequency of the energy storage system; K is the equivalent inductance of the converter; P ESS Provide real-time active power for the energy storage system;

[0032] The dynamic reactive power reserve Q of the energy storage system is calculated according to the following formula ESSrpr :

[0033] Q ESSrpr =Q ESSmax -QESS

[0034] Where: Q ESS Provide real-time reactive power for the energy storage system.

[0035] Furthermore, the flexible load reactive reserve model is specifically:

[0036] Total reactive response value DQ provided by flexible load FL The calculation expression is as follows:

[0037] DQ FL =(DQ IL +DQ LR +DQ TL )×Dt

[0038] Where: DQ IL Reactive power response value provided for interruptible load; DQ LR Reactive response value provided for load that can be reduced; DQ TL The reactive power response value provided for the transferable load; Dt is the time period during which flexible load response is required;

[0039] The electric vehicle reactive power reserve model is specifically:

[0040] Effective reactive power reserve Q of bidirectional topology charging pile EVrpr The calculation formula is as follows:

[0041]

[0042] In the formula, Q EVmax is the reactive power output limit value of the bidirectional topology charging pile, S EV is the apparent power; P EV The active power currently injected into the grid by the charging pile; ω 2 is the angular frequency in the bidirectional topology charging pile; X EV is the equivalent inductive reactance; V EV is the voltage; Q EV is the injected reactive power; S EVmax Its currently achievable apparent power limit.

[0043] Furthermore, the specific process of constructing a typical scenario set of generator set and capacitor group output, a typical scenario set of wind and solar power generation output, a typical scenario set of flexible load and energy storage system output, and a typical scenario set of electric vehicle output is as follows:

[0044] Based on the historical data of wind speed and light intensity and the probability distribution curve of historical data, a wind-solar full scene set is generated, and the feature vector in the wind-solar full scene set is extracted as N W,PV Known attribute data of the scenery;

[0045] Based on the historical data of electric vehicle charging and discharging and combined with the spatiotemporal characteristics of the historical data, a full scene set of electric vehicles is generated, and the feature vectors in the full scene set of electric vehicles are extracted as N EV Known attribute data of electric vehicles;

[0046] Based on the historical data of flexible load and energy storage system and combined with the probability distribution curve of historical data, the full scene set of flexible load and energy storage system is generated, and the feature vector of the full scene set of flexible load and energy storage system is extracted as N FL,ESS Known attribute data of various flexible loads and energy storage systems;

[0047] Based on the historical data of the power system flow section, the full scene set of the generator set and the capacitor bank is generated, and the feature vectors in the full scene set of the generator set and the capacitor bank are extracted as N G,C The known attribute data of the generator set and capacitor bank;

[0048] The improved K-means algorithm is used to perform cluster analysis on each known attribute, and the typical scenario sets of generator set and capacitor group output, wind and solar power generation output, flexible load and energy storage system output and electric vehicle output are obtained.

[0049] Furthermore, the objective function of the distribution network reactive support capability evaluation problem is specifically:

[0050]

[0051] Where: Q abi Reactive power support capability of the distribution network; is the reactive power injected by the nth reactive source; is the reactive power required by the mth load; Q loss is the reactive power loss; Q tr is the reactive power exchange value between the transmission and distribution networks in the initial state; N is the total number of reactive sources; M is the total number of loads;

[0052]

[0053] Where: Q G Reactive power injected into the generator set; Q C Reactive power injected into the capacitor bank; Q W Reactive power injected into the wind farm; Q PV Reactive power injected into the photovoltaic power station; Q ESS Reactive power injected into the energy storage system inverter; Q FL Reactive power injected into flexible load; Q EV Reactive power injected into electric vehicles; B kis the susceptance of branch k; K is the number of branches; V i 、V j are the voltage amplitudes of nodes i and j respectively; θ ij is the voltage phase angle difference between nodes i and j.

[0054] Furthermore, the constraint condition is:

[0055]

[0056] Where: j∈i represents node j is all nodes connected to node i; P Li , Q Li is the active and reactive load injected into node i; P Gi , Q Gi G is the active and reactive power of the generator set injected into node i; ij , B ij is the admittance on branch ij; θ ij is the voltage phase difference between nodes i and j, Q Gn , Q Gn max , Q Gn min are the reactive power output of the nth generator set and its maximum and minimum values ​​allowed; V imin The minimum value to maintain voltage stability; V i is the voltage amplitude of node i; Vj is the voltage amplitude of node j, V imax The maximum value to maintain voltage stability; N node is the number of distribution network nodes; n C is the number of capacitor groups currently in use; n Cm P is the maximum number of capacitor groups that can be put into use; ESSmax is the maximum active output of the energy storage system; P ESS It is the real-time active power output of the energy storage system; Q ESS is the real-time reactive power output of the energy storage system, S ESS is the capacity of the energy storage system;

[0057] h is the reactive power deviation limit value; is the sensitivity of DNRPE index to the voltage and phase angle of each node; V G is the voltage of the distribution network node, V refG is the voltage reference value of the distribution network node; θ G is the phase angle of the distribution network node; θ refG is the phase angle reference value of the distribution network node; DNRPE represents the reactive power deviation index of the distribution network; ε is the expanded part of the inequality limit, and the expression is as follows:

[0058]

[0059] In the formula, Q maxa , Pmaxa 、V maxa ,θ maxa is the maximum reactive power, active power, voltage and phase angle that the distribution network can provide when supporting the main network; a is the coupling coefficient between active and reactive power, β is the regional voltage characteristic, Q refa , P refa 、V refa ,θ refa is the reference value of reactive power, active power, voltage and phase angle in the ath area; d maxa The maximum allowable distribution network loss when the distribution network supports the main network; d refa is the network loss reference value of the ath area; τ is the phase angle characteristic, R 1 , R 2 , R 3 , R 4 is the weight factor.

[0060] Furthermore, the reactive power deviation index of the distribution network is obtained according to the voltage of the transmission and distribution network node and the coupling of active and reactive power. The specific expression is as follows:

[0061]

[0062] In the formula, DNRPE represents the reactive power deviation index of the distribution network, Q a , P a 、V a ,θ a is the actual value of reactive power, active power, voltage and phase angle in the ath area; Q refa , P refa 、V refa ,θ refa are the reference values ​​of reactive power, active power, voltage and phase angle of the ath region; a is the coupling coefficient between active power and reactive power, β is the regional voltage characteristic, and τ is the phase angle characteristic.

[0063] The present invention also proposes a distribution network reactive power support capability evaluation system considering the uncertain new energy output, comprising:

[0064] Reactive power reserve model building module, used to build reactive power reserve models for various traditional energy sources and uncertain new energy sources, including reactive power reserve model for generator sets, reactive power reserve model for capacitor banks, reactive power reserve model for wind power generation, reactive power reserve model for photovoltaic power generation, reactive power reserve model for energy storage, reactive power reserve model for flexible loads and reactive power reserve model for electric vehicles;

[0065] A scenario set construction module is used to construct a typical scenario set for the output of generator sets and capacitor groups, a typical scenario set for the output of wind and solar power generation, a typical scenario set for the output of flexible loads and energy storage systems, and a typical scenario set for the output of electric vehicles;

[0066] The objective function and constraint condition building module is used to establish the objective function and constraint conditions for the reactive power support capacity evaluation problem of the distribution network by using reactive power reserve models of various traditional energy sources and uncertain new energy sources;

[0067] The solution module is used to use the typical scenario sets of generator set and capacitor group output, the typical scenario sets of wind and solar power generation output, the typical scenario sets of flexible load and energy storage system output and the typical scenario sets of electric vehicle output as the initial population of the genetic algorithm, and the value of the objective function as the fitness of the individuals in the population. The genetic algorithm is used to solve the problem of reactive support capacity evaluation of the distribution network, and the optimal solution obtained is used as the reactive support capacity of the distribution network.

[0068] The beneficial effects of the present invention are:

[0069] Construct a distribution network reactive error (Distribution Network Reactive Power Error, DNRPE) indicator that considers active and reactive power coupling, and subsequently integrate it into the reactive support capacity evaluation and calculation process of the distribution network, so as to simplify the calculation and reduce the time consumed by the calculation; construct reactive reserve models for a variety of traditional energy sources and uncertain new energy sources, including generator sets, capacitor groups, wind power generation, photovoltaic power generation, energy storage, flexible loads and electric vehicles; construct a typical scenario set that comprehensively considers the output of all the above models; construct a distribution network reactive support capacity calculation model that considers the output of uncertain new energy sources; use genetic algorithms to solve and obtain the reactive support capacity of the distribution network. The present invention considers fully mobilizing multiple types of reactive resources on the distribution side, the reactive voltage support capacity on the transmission side, establishes a reactive reserve potential evaluation model for individual active distribution networks, and uses an improved genetic algorithm to solve in typical scenarios to achieve a quantitative evaluation of the reactive voltage support capacity of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a flow chart of a method for evaluating reactive power support capability of a distribution network considering uncertain new energy output according to the present invention;

[0071] Figure 2 It is the genetic algorithm solution flow chart of the present invention. DETAILED DESCRIPTION

[0072] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0073] Embodiment 1

[0074] The present invention proposes a method for evaluating reactive power support capability of a distribution network considering the output of uncertain new energy sources, comprising the following steps:

[0075] Construct reactive power reserve models for a variety of traditional energy sources and uncertain new energy sources, including reactive power reserve models for generator sets, reactive power reserve models for capacitor banks, reactive power reserve models for wind power generation, reactive power reserve models for photovoltaic power generation, reactive power reserve models for energy storage, reactive power reserve models for flexible loads, and reactive power reserve models for electric vehicles;

[0076] Construct a typical scenario set for the output of generator sets and capacitor groups, a typical scenario set for the output of wind and solar power generation, a typical scenario set for the output of flexible loads and energy storage systems, and a typical scenario set for the output of electric vehicles;

[0077] The reactive power reserve models of various traditional energy sources and uncertain new energy sources are used to establish the objective function and constraints of the reactive power support capacity evaluation problem of the distribution network;

[0078] The typical scenario sets of generator set and capacitor group output, wind and solar power generation output, flexible load and energy storage system output and electric vehicle output are taken as the initial population of the genetic algorithm. The value of the objective function is taken as the fitness of the individuals in the population. The genetic algorithm is used to solve the problem of reactive support capacity evaluation of the distribution network, and the optimal solution is taken as the reactive support capacity of the distribution network.

[0079] The reactive power reserve model of the generator set is specifically as follows: the reactive power generated by the generator is used as the reduction, and the reactive power limit that can be generated based on the active power generated is used as the reduction, so as to calculate the reactive power reserve of the generator set;

[0080] The reactive power reserve model of the capacitor bank is specifically: the reactive power reserve Q of the parallel capacitor bank Crpr The expression is as follows:

[0081] Q Crpr =2pfC C V C 2 (n Cm -n C )

[0082] Where n Cm is the maximum number of capacitor groups that can be put into use; n C is the number of capacitor banks currently in use; f is the current frequency of the entire system; C C is the capacitance of a single group of capacitors; V C The node voltage of the node to which it is connected;

[0083] The active output of the doubly-fed wind turbine in the active network device (AND) can be regarded as a constant, so the purpose of regulation can be achieved by adjusting its reactive output.

[0084] The reactive power reserve model of wind power generation is:

[0085]

[0086] In the formula, Q Wrpr is the reactive power reserve of wind power generation, Q Wmax is the maximum value of reactive power output limit; Q W For real-time reactive power output; P W For real-time active power output; V s is the stator terminal voltage; I rmax is the maximum value of the rotor terminal current; X r is the equivalent self-inductance of the rotor segment; X s is the stator end leakage reactance.

[0087] The specific reactive power reserve model of photovoltaic power generation is:

[0088] Solar photovoltaic (PV) array active output P PV and reactive power output Q PV As shown below:

[0089]

[0090] Where: E is the light intensity; h is the photoelectric conversion efficiency; A is the effective illumination area, |Q PV | max is the maximum reactive power regulation capability of the photovoltaic array; S PV The capacity of the grid-connected inverter for the photovoltaic array;

[0091] Usually, photovoltaic systems generate electricity at maximum power, and their power factor can be adjusted to participate in reactive power regulation. The expression is as follows:

[0092]

[0093] Where: is the maximum reactive power regulation value of photovoltaic at node m; is the photovoltaic inverter capacity of node m; and are the photovoltaic active and reactive power injected into node m respectively.

[0094] The specific energy storage reactive reserve model is:

[0095] The true voltage amplitude limit value V of the converter in the energy storage system is calculated according to the following formula ESSmax :

[0096]

[0097] Where: K PWMmax is the maximum amplitude of the linear modulation ratio; is the DC voltage of the converter;

[0098] The maximum reactive output Q of the energy storage system is calculated according to the following formula ESSmax for:

[0099]

[0100] Where: is the voltage of the converter on the grid side; ω 1 is the angular frequency of the energy storage system; K is the equivalent inductance of the converter; P ESS Provide real-time active power for the energy storage system;

[0101] The dynamic reactive power reserve Q of the energy storage system is calculated according to the following formula ESSrpr :

[0102] Q ESSrpr =Q ESSmax -Q ESS

[0103] Where: Q ESS Provide real-time reactive power for the energy storage system.

[0104] The specific model of flexible load reactive power reserve is:

[0105] Traditionally, it is generally believed that flexible load (FL) exists in the following three forms:

[0106] (1) Interruptible Load (IL);

[0107] (2) Load Reduction (LR);

[0108] (3) Transferable Load (TL)

[0109] Assume that the power grid contains a 1 、a 2 、a 3 Different types of FL, comprehensive compensation electricity price K FL The calculation expression is as follows:

[0110] K FL =a1 K IL +a 2 K LR +a 3 K TL

[0111] Where: K IL The electricity price compensation required for interruptible load; K LR The electricity price compensation required for load reduction; K TL The electricity price compensation required for transferable load.

[0112] Total reactive response value DQ provided by flexible load FL The calculation expression is as follows:

[0113] DQ FL =(DQ IL +DQ LR +DQ TL )×Dt

[0114] Where: DQ IL Reactive power response value provided for interruptible load; DQ LR Reactive response value provided for load that can be reduced; DQ TL The reactive power response value provided for the transferable load; Dt is the time period during which flexible load response is required;

[0115] The electric vehicle reactive power reserve model is specifically:

[0116] During the charging process of an electric vehicle, there is actually a series of uninterrupted power exchanges between the energy storage elements of the charging pile and the connected power grid. This power is called pulsating power. From this, it can be obtained that when the electric vehicle is performing charging work, the energy E in the energy storage element EV The expression is as follows:

[0117]

[0118] Where: 2 is the angular frequency in the bidirectional topology charging pile; S EV is the apparent power; X EV is the equivalent inductive reactance; V EV is the voltage; Q EV To inject reactive power.

[0119] From the above formula, we can see that if the charging pile is regulated when charging the electric vehicle to provide capacitive reactive energy to the power grid, it will inevitably cause the energy storage components in the equipment to bear more pulsating power. At this time, the charging current passing through the energy storage components in the charging pile should be limited. This current I EV The value of can be estimated by the following formula:

[0120]

[0121] Where: V EVDC This is the voltage currently borne by the DC part of the charging pile.

[0122] Obviously, the maximum amplitude of this current is Therefore, the reactive output limit value Q of this type of charging pile can be calculated. EVmax , the effective reactive power reserve Q of the bidirectional topology charging pile EVrpr The calculation formula is as follows:

[0123]

[0124] In the formula, Q EVmax is the reactive power output limit value of the bidirectional topology charging pile, S EV is the apparent power; P EV The active power currently injected into the grid by the charging pile; ω 2 is the angular frequency in the bidirectional topology charging pile; X EV is the equivalent inductive reactance; V EV is the voltage; Q EV is the injected reactive power; S EVmax Its currently achievable apparent power limit.

[0125] The specific process of constructing the typical scenario set of generator set and capacitor group output, the typical scenario set of wind and solar power generation output, the typical scenario set of flexible load and energy storage system output, and the typical scenario set of electric vehicle output is as follows:

[0126] Based on the historical data of wind speed and light intensity and the probability distribution curve of historical data, a wind-solar full scene set is generated, and the feature vector in the wind-solar full scene set is extracted as N W,PV Known attribute data of the scenery;

[0127] Based on the historical data of electric vehicle charging and discharging and combined with the spatiotemporal characteristics of the historical data, a full scene set of electric vehicles is generated, and the feature vectors in the full scene set of electric vehicles are extracted as N EV Known attribute data of electric vehicles;

[0128] Based on the historical data of flexible load and energy storage system and combined with the probability distribution curve of historical data, the full scene set of flexible load and energy storage system is generated, and the feature vector of the full scene set of flexible load and energy storage system is extracted as N FL,ESS Known attribute data of various flexible loads and energy storage systems;

[0129] Based on the historical data of the power system flow section, the full scene set of the generator set and the capacitor bank is generated, and the feature vectors in the full scene set of the generator set and the capacitor bank are extracted as N G,CThe known attribute data of the generator set and capacitor bank;

[0130] The improved K-means algorithm is used to perform cluster analysis on each known attribute, and the typical scenario sets of generator set and capacitor group output, wind and solar power generation output, flexible load and energy storage system output and electric vehicle output are obtained.

[0131] The objective function of the reactive power support capacity evaluation problem of the distribution network is as follows:

[0132]

[0133] Where: Q abi Reactive power support capability of the distribution network; is the reactive power injected by the nth reactive source; is the reactive power required by the mth load; Q loss is the reactive power loss; Q tr is the reactive power exchange value between the transmission and distribution networks in the initial state; N is the total number of reactive sources; M is the total number of loads;

[0134]

[0135] Where: Q G Reactive power injected into the generator set; Q C Reactive power injected into the capacitor bank; Q W Reactive power injected into the wind farm; Q PV Reactive power injected into the photovoltaic power station; Q ESS Reactive power injected into the energy storage system inverter; Q FL Reactive power injected into flexible load; Q EV Reactive power injected into electric vehicles; B k is the susceptance of branch k; K is the number of branches; V i 、V j are the voltage amplitudes of nodes i and j respectively; θ ij is the voltage phase angle difference between nodes i and j.

[0136] The constraints are:

[0137]

[0138] Where: j∈i represents node j is all nodes connected to node i; P Li , Q Li is the active and reactive load injected into node i; P Gi , Q Gi G is the active and reactive power of the generator set injected into node i; ij , B ijis the admittance on branch ij; θ ij is the voltage phase difference between nodes i and j, Q Gn , Q Gn max , Q Gn min are the reactive power output of the nth generator set and its maximum and minimum values ​​allowed; V i min The minimum value to maintain voltage stability; V i is the voltage amplitude of node i; V j is the voltage amplitude at node j, V i max The maximum value to maintain voltage stability; N node is the number of distribution network nodes; n C is the number of capacitor groups currently in use; n Cm P is the maximum number of capacitor groups that can be put into use; ESS max is the maximum active output of the energy storage system; P ESS It is the real-time active power output of the energy storage system; Q ESS is the real-time reactive power output of the energy storage system, S ESS is the capacity of the energy storage system;

[0139] h is the reactive power deviation limit value; is the sensitivity of DNRPE index to the voltage and phase angle of each node; V G is the voltage of the distribution network node, V refG is the voltage reference value of the distribution network node; θ G is the phase angle of the distribution network node; θ refG is the phase angle reference value of the distribution network node; DNRPE represents the reactive power deviation index of the distribution network; ε is the expanded part of the inequality limit, and the expression is as follows:

[0140]

[0141] In the formula, Q maxa , P maxa 、V maxa ,θ maxa is the maximum reactive power, active power, voltage and phase angle that the distribution network can provide when supporting the main network; a is the coupling coefficient between active and reactive power, β is the regional voltage characteristic, Q refa , P refa 、V refa ,θ refa is the reference value of reactive power, active power, voltage and phase angle in the ath area; d maxa The maximum allowable distribution network loss when the distribution network supports the main network; d refa is the network loss reference value of the ath area; τ is the phase angle characteristic, R 1 , R 2 , R 3 , R 4 is the weight factor.

[0142] The distribution network reactive power error (DNRPE) indicator is obtained based on the node voltage and active and reactive power coupling of the transmission and distribution network, as shown in the following formula:

[0143]

[0144] Where: a is the coupling coefficient between active and reactive power, Q a , P a 、V a ,θ a is the actual value of reactive power, active power, voltage and phase angle in the ath area; Q refa , P refa 、V refa ,θ refa are the reactive power, active power, voltage and phase angle reference values ​​of the ath region; β and τ are the regional voltage and phase angle characteristics, which can be obtained from the historical data of the power grid.

[0145] The prerequisite for the reactive power of the distribution network to support the main network is naturally to ensure the safe and stable operation of the distribution network, so the following constraints are constructed:

[0146]

[0147] Where: V G is the voltage of the distribution network node; V refG is the voltage reference value of the distribution network node; θ G is the phase angle of the distribution network node; θ refG is the phase angle reference value of the distribution network node; h is the reactive power deviation limit value; ε is the expanded part of the inequality limit value; is the sensitivity of DNRPE index to the voltage and phase angle of each node; Q maxa , P maxa 、V maxa ,θ maxa The maximum reactive power, active power, voltage and phase angle that the distribution network can provide when supporting the main network; d maxa The maximum allowable distribution network loss when the distribution network supports the main network; d refa is the reference value of network loss in the ath area; R 1 , R 2 , R 3 , R 4 is the weight factor, and its value can be determined by the entropy weight method.

[0148] The expression of network loss δ is:

[0149]

[0150] Where: G k is the conductance of branch k; K is the number of branches; V i 、V j are the voltage amplitudes of nodes i and j respectively; θ ij is the voltage phase angle difference between nodes ij.

[0151] Genetic Algorithms (GA) is an optimization algorithm based on natural selection and aimed at optimizing specific goals. Its essence is to use the principle of group optimization and the idea of ​​"survival of the fittest" to gradually evolve the optimal solution. The steps are as follows: First, the typical scenario set of generator set and capacitor group output, the typical scenario set of wind and solar power generation output, the typical scenario set of flexible load and energy storage system output, and the typical scenario set of electric vehicle output are used as the initial population of the genetic algorithm. The value of the objective function is used as the fitness of individuals in the population. According to the principle of survival of the fittest, individuals with higher fitness are selected, and then these individuals with higher fitness are combined in pairs, and the genes on the chromosomes of these individuals with higher fitness are randomly crossed to form a new population. In this way, the entire population will gradually evolve, and this cycle will repeat until the evolution ends. The optimal solution obtained is used as the reactive support capacity of the distribution network.

[0152] Embodiment 2

[0153] The present invention proposes a distribution network reactive power support capability evaluation system considering the uncertain new energy output corresponding to the method of the first embodiment, comprising:

[0154] Reactive power reserve model building module, used to build reactive power reserve models for various traditional energy sources and uncertain new energy sources, including reactive power reserve model for generator sets, reactive power reserve model for capacitor banks, reactive power reserve model for wind power generation, reactive power reserve model for photovoltaic power generation, reactive power reserve model for energy storage, reactive power reserve model for flexible loads and reactive power reserve model for electric vehicles;

[0155] A scenario set construction module is used to construct a typical scenario set for the output of generator sets and capacitor groups, a typical scenario set for the output of wind and solar power generation, a typical scenario set for the output of flexible loads and energy storage systems, and a typical scenario set for the output of electric vehicles;

[0156] The objective function and constraint condition building module is used to establish the objective function and constraint conditions for the reactive power support capacity evaluation problem of the distribution network by using reactive power reserve models of various traditional energy sources and uncertain new energy sources;

[0157] The solution module is used to use the typical scenario sets of generator set and capacitor group output, the typical scenario sets of wind and solar power generation output, the typical scenario sets of flexible load and energy storage system output and the typical scenario sets of electric vehicle output as the initial population of the genetic algorithm, and the value of the objective function as the fitness of the individuals in the population. The genetic algorithm is used to solve the problem of reactive support capacity evaluation of the distribution network, and the optimal solution obtained is used as the reactive support capacity of the distribution network.

[0158] The implementation methods of each module and module function in the system are completely consistent with the steps of the method in Example 1, so they will not be repeated here.

[0159] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0160] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A method for evaluating reactive power support capability of a distribution network considering the output of uncertain new energy sources, characterized in that: The steps include: Construct reactive power reserve models for a variety of traditional energy sources and uncertain new energy sources, including reactive power reserve models for generator sets, reactive power reserve models for capacitor banks, reactive power reserve models for wind power generation, reactive power reserve models for photovoltaic power generation, reactive power reserve models for energy storage, reactive power reserve models for flexible loads, and reactive power reserve models for electric vehicles; Construct a typical scenario set for the output of generator sets and capacitor groups, a typical scenario set for the output of wind and solar power generation, a typical scenario set for the output of flexible loads and energy storage systems, and a typical scenario set for the output of electric vehicles; The reactive power reserve models of various traditional energy sources and uncertain new energy sources are used to establish the objective function and constraints of the reactive power support capacity evaluation problem of the distribution network; The typical scenario sets of generator set and capacitor group output, wind and solar power generation output, flexible load and energy storage system output and electric vehicle output are taken as the initial population of the genetic algorithm. The value of the objective function is taken as the fitness of the individuals in the population. The genetic algorithm is used to solve the problem of reactive support capacity evaluation of the distribution network, and the optimal solution is taken as the reactive support capacity of the distribution network.

2. The method for evaluating reactive power support capability of a distribution network considering the output of uncertain new energy sources as claimed in claim 1, characterized in that: The reactive power reserve model of the generator set is specifically as follows: the reactive power generated by the generator is decremented, and the reactive power limit that can be generated based on the active power generated is decremented, thereby calculating the reactive power reserve of the generator set; the reactive power reserve model of the capacitor bank is specifically as follows: the reactive power reserve Q of the parallel capacitor bank Crpr The expression is as follows: Where n Cm is the maximum number of capacitor groups that can be put into use; n C is the number of capacitor banks currently in use; f is the current frequency of the entire system; C C is the capacitance of a single group of capacitors; V C The node voltage of the node to which it is connected; The reactive power reserve model of wind power generation is: In the formula, Q Wrpr is the reactive power reserve of wind power generation, Q Wmax is the maximum value of reactive power output limit; Q W For real-time reactive power output; P W For real-time active power output; V s is the stator terminal voltage; I rmax is the maximum value of the rotor terminal current; X r is the equivalent self-inductance of the rotor segment; X s is the stator end leakage reactance.

3. The method for evaluating reactive power support capability of a distribution network considering the output of uncertain new energy sources as claimed in claim 1, characterized in that: The photovoltaic power generation reactive power reserve model is specifically: Solar photovoltaic array active output P PV and reactive power output Q PV As shown below: Where: E is the light intensity; h is the photoelectric conversion efficiency; A is the effective illumination area, |Q PV | max is the maximum reactive power regulation capability of the photovoltaic array; S PV The capacity of the grid-connected inverter for the photovoltaic array; Reactive power reserve of photovoltaic system The expression is as follows: Where: is the maximum reactive power regulation value of photovoltaic at node m; is the photovoltaic inverter capacity of node m; and are the photovoltaic active and reactive power injected into node m respectively.

4. The method for evaluating reactive power support capability of a distribution network considering the output of uncertain new energy sources as claimed in claim 1, characterized in that: The energy storage reactive power reserve model is specifically: The true voltage amplitude limit value V of the converter in the energy storage system is calculated according to the following formula ESSmax : Where: K PWMmax is the maximum amplitude of the linear modulation ratio; is the DC voltage of the converter; The maximum reactive output Q of the energy storage system is calculated according to the following formula ESSmax for: Where: is the voltage of the converter on the grid side; ω1 is the angular frequency of the energy storage system; K is the equivalent inductance of the converter; P ESS Provide real-time active power for the energy storage system; The dynamic reactive power reserve Q of the energy storage system is calculated according to the following formula ESSrpr : Q ESSrpr =Q ESSmax -Q ESS Where: Q ESS Provide real-time reactive power for the energy storage system.

5. The method for evaluating reactive power support capability of a distribution network considering the output of uncertain new energy sources as claimed in claim 1, characterized in that: The flexible load reactive power reserve model is specifically: Total reactive response value DQ provided by flexible load FL The calculation expression is as follows: DQ FL =(DQ IL +DQ LR +DQ TL )×Dt Where: DQ IL Reactive response value provided for interruptible load; DQ LR Reactive response value provided for load that can be reduced; DQ TL The reactive power response value provided for the transferable load; Dt is the time period during which flexible load response is required; The electric vehicle reactive power reserve model is specifically: Effective reactive power reserve Q of bidirectional topology charging pile EVrpr The calculation formula is as follows: In the formula, Q EVmax is the reactive power output limit value of the bidirectional topology charging pile, S EV is the apparent power; P EV is the active power currently injected into the grid by the charging pile; ω2 is the angular frequency in the bidirectional topology charging pile; X EV is the equivalent inductive reactance; V EV is the voltage; Q EV is the injected reactive power; S EVmax Its currently achievable apparent power limit.

6. The method for evaluating reactive power support capability of a distribution network considering the output of uncertain new energy sources as claimed in claim 1, characterized in that: The specific process of constructing the typical scenario set of generator set and capacitor group output, the typical scenario set of wind and solar power generation output, the typical scenario set of flexible load and energy storage system output, and the typical scenario set of electric vehicle output is as follows: Based on the historical data of wind speed and light intensity and the probability distribution curve of historical data, a wind-solar full scene set is generated, and the feature vector in the wind-solar full scene set is extracted as N W,PV Known attribute data of the scenery; Based on the historical data of electric vehicle charging and discharging and combined with the spatiotemporal characteristics of the historical data, a full scene set of electric vehicles is generated, and the feature vectors in the full scene set of electric vehicles are extracted as N EV Known attribute data of electric vehicles; Based on the historical data of flexible load and energy storage system and combined with the probability distribution curve of historical data, the full scene set of flexible load and energy storage system is generated, and the feature vector of the full scene set of flexible load and energy storage system is extracted as N FL,ESS Known attribute data of various flexible loads and energy storage systems; Based on the historical data of the power system flow section, the full scene set of the generator set and the capacitor bank is generated, and the feature vectors in the full scene set of the generator set and the capacitor bank are extracted as N G,C The known attribute data of the generator set and capacitor bank; The improved K-means algorithm is used to perform cluster analysis on each known attribute, and the typical scenario sets of generator set and capacitor group output, wind and solar power generation output, flexible load and energy storage system output and electric vehicle output are obtained.

7. The method for evaluating reactive power support capability of a distribution network considering the output of uncertain new energy sources as claimed in claim 1, characterized in that: The objective function of the distribution network reactive support capability evaluation problem is specifically: Where: Q abi Reactive power support capability of the distribution network; is the reactive power injected by the nth reactive source; is the reactive power required by the mth load; Q loss is the reactive power loss; Q tr is the reactive power exchange value between the transmission and distribution networks in the initial state; N is the total number of reactive sources; M is the total number of loads; Where: Q G Reactive power injected into the generator set; Q C Reactive power injected into the capacitor bank; Q W Reactive power injected into the wind farm; Q PV Reactive power injected into the photovoltaic power station; Q ESS Reactive power injected into the energy storage system inverter; Q FL Reactive power injected into flexible load; Q EV Reactive power injected into electric vehicles; B k is the susceptance of branch k; K is the number of branches; V i 、V j are the voltage amplitudes of nodes i and j respectively; θ ij is the voltage phase angle difference between nodes i and j.

8. The method for evaluating reactive power support capability of a distribution network considering the output of uncertain new energy sources as claimed in claim 1, characterized in that: The constraints are: Where: j∈i represents that node j is all nodes connected to node i; P Li , Q Li is the active and reactive load injected into node i; P Gi , Q Gi G is the active and reactive power of the generator set injected into node i; ij , B ij is the admittance on branch ij; θ ij is the voltage phase difference between nodes i and j, Q Gn , Q Gnmax , Q Gnmin are the reactive power output of the nth generator set and its maximum and minimum values ​​allowed; V imin The minimum value to maintain voltage stability; V i is the voltage amplitude of node i; V j is the voltage amplitude at node j, V imax The maximum value to maintain voltage stability; N node is the number of distribution network nodes; n C is the number of capacitor groups currently in use; n Cm P is the maximum number of capacitor groups that can be put into use; ESSmax is the maximum active output of the energy storage system; P ESS It is the real-time active power output of the energy storage system; Q ESS is the real-time reactive power output of the energy storage system, S ESS is the capacity of the energy storage system; h is the reactive power deviation limit value; is the sensitivity of DNRPE index to the voltage and phase angle of each node; V G is the voltage of the distribution network node, V refG is the voltage reference value of the distribution network node; θ G is the phase angle of the distribution network node; θ refG is the phase angle reference value of the distribution network node; DNRPE represents the reactive power deviation index of the distribution network; ε is the expanded part of the inequality limit, and the expression is as follows: In the formula, Q maxa , P maxa 、V maxa ,θ maxa is the maximum reactive power, active power, voltage and phase angle that the distribution network can provide when supporting the main network; α is the coupling coefficient between active and reactive power, β is the regional voltage characteristic, Q refa , P refa 、V refa ,θ refa is the reactive power, active power, voltage and phase angle reference value of the ath area; d maxa The maximum allowable distribution network loss when the distribution network supports the main network; δ refa is the reference value of network loss in the ath area; τ is the phase angle characteristic, and R1, R2, R3, and R4 are weight factors.

9. The method for evaluating reactive power support capability of a distribution network considering the output of uncertain new energy sources as claimed in claim 8, characterized in that: The reactive power deviation index of the distribution network is obtained according to the node voltage of the transmission and distribution network and the coupling of active and reactive power. The specific expression is as follows: In the formula, DNRPE represents the reactive power deviation index of the distribution network, Q a , P a 、V a ,θ a is the actual value of reactive power, active power, voltage and phase angle in the ath area; Q refa , P refa 、V refa ,θ refa are the reference values ​​of reactive power, active power, voltage and phase angle of the ath region; α is the coupling coefficient between active power and reactive power, β is the regional voltage characteristic, and τ is the phase angle characteristic.

10. A distribution network reactive power support capability evaluation system considering the uncertainty of new energy output, characterized in that: include: Reactive power reserve model building module, used to build reactive power reserve models for various traditional energy sources and uncertain new energy sources, including reactive power reserve model for generator sets, reactive power reserve model for capacitor banks, reactive power reserve model for wind power generation, reactive power reserve model for photovoltaic power generation, reactive power reserve model for energy storage, reactive power reserve model for flexible loads and reactive power reserve model for electric vehicles; A scenario set construction module is used to construct a typical scenario set for the output of generator sets and capacitor groups, a typical scenario set for the output of wind and solar power generation, a typical scenario set for the output of flexible loads and energy storage systems, and a typical scenario set for the output of electric vehicles; The objective function and constraint condition building module is used to establish the objective function and constraint conditions for the reactive power support capacity evaluation problem of the distribution network by using reactive power reserve models of various traditional energy sources and uncertain new energy sources; The solution module is used to use the typical scenario sets of generator set and capacitor group output, the typical scenario sets of wind and solar power generation output, the typical scenario sets of flexible load and energy storage system output and the typical scenario sets of electric vehicle output as the initial population of the genetic algorithm, and the value of the objective function as the fitness of the individuals in the population. The genetic algorithm is used to solve the problem of reactive support capacity evaluation of the distribution network, and the optimal solution obtained is used as the reactive support capacity of the distribution network.

Images