A Method and System for Assessing the Reactive Power Support Capacity of Distribution Networks Considering Uncertainty in Renewable Energy Output

Abstract

This invention discloses a method and system for assessing the reactive power support capacity of distribution networks considering uncertain renewable energy output. Belonging to the field of power grid generation dispatching technology, the method includes: constructing a reactive power deviation index for distribution networks that considers the coupling of active and reactive power, subsequently integrating it into the calculation process for assessing the reactive power support capacity of distribution networks; constructing reactive power reserve models for various traditional energy sources and uncertain renewable energy sources, including generator sets, capacitor banks, wind power, photovoltaic power, energy storage, flexible loads, and electric vehicles; constructing a typical scenario set that comprehensively considers the output of all the above models; constructing a calculation model for the reactive power support capacity of distribution networks considering uncertain renewable energy output; and solving the model using a genetic algorithm to obtain the reactive power support capacity of the distribution network. This invention fully mobilizes multiple types of reactive power resources on the distribution side, establishes a reactive power reserve potential assessment model for individual active distribution networks, and achieves a quantitative assessment of the reactive power voltage support capacity of the distribution network.

Description

Technical Field

[0001] This invention belongs to the field of power grid power generation dispatching technology, specifically relating to a method and system for assessing the reactive power support capacity of distribution networks that takes into account uncertainties in renewable energy output. Background Technology

[0002] In recent years, due to the continuous increase in the penetration rate of new energy sources, the installed capacity of thermal power units has been declining year by year, placing higher demands on the reactive power and voltage control of the power system. Reactive power reserve is an important indicator for ensuring the safe and stable operation of the power grid. Most existing reactive power reserve optimization methods are aimed at conventional reactive power sources on the transmission side, while research on various types of reactive power resources in the distribution network is relatively limited. How to effectively tap into various reactive power resources in the distribution network, provide reactive power support to the transmission side, reduce investment in reactive power equipment, and improve the stable and economical operation of the power grid is an urgent problem to be solved. Summary of the Invention

[0003] This invention addresses the shortcomings of existing technologies by providing a method and system for assessing the reactive power support capacity of distribution networks that considers the uncertainty of renewable energy output. It fully utilizes various types of reactive power resources on the distribution side, establishes a reactive power reserve potential assessment model for individual active distribution networks, and uses an improved genetic algorithm to solve the model in typical scenarios, thereby achieving a quantitative assessment of the reactive power and voltage support capacity of the distribution network.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A method for assessing the reactive power support capacity of distribution networks considering uncertain renewable energy output includes the following steps:

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

[0007] Construct typical scenario sets for generator set and capacitor bank output, wind and solar power output, flexible load and energy storage system output, and electric vehicle output;

[0008] The objective function and constraints for assessing the reactive power support capacity of distribution networks are established using reactive power reserve models of various traditional energy sources and uncertain new energy sources.

[0009] Typical scenarios for generator and capacitor output, wind and solar power output, flexible load and energy storage system output, and electric vehicle output are used as the initial population for the genetic algorithm. The value of the objective function is used as the fitness of individuals in the population. The genetic algorithm is used to solve the problem of distribution network reactive power support capacity assessment, and the optimal solution is used as the distribution network reactive power support capacity.

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

[0011] Furthermore, the generator set reactive power reserve model is specifically as follows: the reactive power already generated by the generator is the reduction, and the reactive power limit that can be generated based on the active power already generated is the reduction, thereby calculating the generator set reactive power reserve.

[0012] The reactive power reserve model for the capacitor bank is specifically as follows: 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] In the formula, n Cm n is the maximum number of capacitor banks that can be deployed. C The current number of capacitor banks in operation; f is the current frequency of the entire system; C C The capacitance of a single capacitor bank; V C The node voltage of its connected node;

[0015] The reactive power reserve model for wind power generation is as follows:

[0016]

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

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

[0019] Active power output P of solar photovoltaic power generation arrayPV With no effort Q PV As shown in the following formula:

[0020]

[0021] In the formula: E is the light intensity; h is the photoelectric conversion efficiency; A is the effective illuminated area, |Q PV | max This represents the maximum reactive power regulation capability of the photovoltaic power generation array; S PV For the grid-connected inverter capacity of the photovoltaic power generation array;

[0022] Reactive power reserve of photovoltaic systems The expression is shown in the following formula:

[0023]

[0024] In the formula: This represents the maximum reactive power regulation value of the photovoltaic system at node m. Let m be the capacity of the photovoltaic inverter at node m; and These represent the active and reactive power injected into node m, respectively.

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

[0026] Calculate the non-distortion voltage amplitude limit V of the converter in the energy storage system using the following formula. ESSmax :

[0027]

[0028] In the formula: K PWMmax This represents the maximum amplitude of the linear modulation ratio; This is the DC voltage of the converter;

[0029] Calculate the maximum reactive power output Q of the energy storage system using the following formula. ESSmax for:

[0030]

[0031] In the formula: ω1 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 To provide real-time active power output for energy storage systems;

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

[0033] Q ESSrpr =Q ESSmax -Q ESS

[0034] In the formula: Q ESS This provides real-time reactive power output for the energy storage system.

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

[0036] The total reactive power response value DQ provided by the flexible load FL The calculation expression is shown in the following formula:

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

[0038] Where: DQ IL The reactive response value provided for interruptible loads; DQ LR The reactive response value provided for load reduction; DQ TL The reactive power response value provided for transferable loads; Dt is the period during which flexible load response is required;

[0039] The specific reactive power reserve model for electric vehicles is as follows:

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

[0041]

[0042] In the formula, Q EVmax S represents the maximum reactive power output of a bidirectional topology charging pile. EV P represents apparent power. EV ω2 represents the active power currently injected into the grid by the charging pile; ω2 represents the angular frequency in the bidirectional topology charging pile; X EV For equivalent inductive reactance; V EV For voltage; Q EV To inject reactive power; S EVmax This represents its current achievable apparent power limit.

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

[0044] Based on historical data of wind speed and light intensity, and combined with the probability distribution curves of historical data, a complete set of wind and light scenes is generated. Feature vectors from this complete set of wind and light scenes are then extracted as N. W,PV Known attribute data of the landscape;

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

[0046] Based on historical data of flexible loads and energy storage systems, and combined with the probability distribution curves of historical data, a full-scenario set of flexible loads and energy storage systems is generated. Feature vectors from this full-scenario set are then extracted as N. FL,ESS Known attribute data for flexible load and energy storage systems;

[0047] Based on historical data from power system power flow sections, a full-scenario set of generator sets and capacitor banks is generated, and feature vectors from the full-scenario set of generator sets and capacitor banks are extracted as N. G,C Known attribute data for various generator sets and capacitor banks;

[0048] An improved K-means algorithm was used to perform cluster analysis on each known attribute to obtain typical scenario sets for generator set and capacitor bank output, wind and solar power output, flexible load and energy storage system output, and electric vehicle output.

[0049] Furthermore, the objective function of the distribution network reactive power support capability assessment problem is specifically as follows:

[0050]

[0051] In the formula: Q abi For the reactive power support capacity of the distribution network; The reactive power injected into the nth reactive power source; Q is the reactive power required by the m-th load; loss Q represents the reactive power loss. tr This represents the reactive power exchange value between the transmission and distribution networks under the initial state; N is the total number of reactive power sources; and M is the total number of loads.

[0052]

[0053] In the formula: 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 photovoltaic power plants; Q ESS The reactive power injected into the inverter of the energy storage system; Q FL Reactive power injected into flexible loads; Q EV Reactive power injected into electric vehicles; B k V is the susceptance of branch k; K is the number of branches; Vi V j θ represents the voltage magnitudes at nodes i and j, respectively; ij Let be the voltage phase angle difference between nodes i and j.

[0054] Furthermore, the constraint conditions are as follows:

[0055]

[0056] In the formula: j∈i represents that node j is all nodes connected to node i; P Li Q Li The active and reactive loads injected into node i; P Gi Q Gi The active and reactive power of the generator set injected into node i; G ij B ij θ is the admittance on branch ij; ij Q represents the voltage phase angle difference between nodes i and j. Gn Q Gn max Q Gn min These represent the reactive power output of the nth generator unit and its maximum and minimum allowable values, respectively; V imin The minimum value required to maintain voltage stability; V i Vi represents the voltage magnitude at node i; Vj represents the voltage magnitude at node j. imax To maintain the maximum value of voltage stability; N node n represents the number of distribution network nodes. C n represents the number of capacitor banks currently in operation. Cm P represents the maximum number of capacitor banks that can be deployed. ESSmax P represents the maximum active power output of the energy storage system. ESS For the real-time active power output of the energy storage system; Q ESS For the real-time reactive power output of the energy storage system, S ESS The capacity of the energy storage system;

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

[0058]

[0059] In the formula, Q maxa P maxa Vmaxa θ maxa The maximum reactive power, active power, voltage, and phase angle that the distribution network can provide when supporting the main grid; 'a' is the coupling coefficient between active and reactive power, 'β' is the regional voltage characteristic, and 'Q' is the ... refa P refa V refa θ refa Here are the reference values ​​for reactive power, active power, voltage, and phase angle for region a; d maxa The maximum allowable distribution network loss when the distribution network supports the main grid; d refa τ is the reference value for network loss in region a; τ is the phase angle characteristic; and R1, R2, R3, and R4 are weighting factors.

[0060] Furthermore, the reactive power deviation index of the distribution network is obtained based on the coupling between the voltage and active and reactive power at the transmission and distribution network nodes, and its specific expression is as follows:

[0061]

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

[0063] This invention also proposes a reactive power support capability assessment system for distribution networks that considers uncertainties in renewable energy output, comprising:

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

[0065] The scenario set construction module is used to construct typical scenario sets for generator set and capacitor bank output, wind and solar power output, flexible load and energy storage system output, and electric vehicle output.

[0066] The objective function and constraint construction module is used to establish the objective function and constraints for the reactive power reserve model of various traditional energy sources and uncertain new energy sources to evaluate the reactive power support capacity of the distribution network.

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

[0068] The beneficial effects of this invention are:

[0069] This invention constructs a Distribution Network Reactive Power Error (DNRPE) index that considers the coupling of active and reactive power, and integrates it into the calculation process for evaluating the reactive power support capacity of the distribution network, thereby simplifying the calculation and reducing the calculation time. It also constructs reactive power reserve models for various traditional energy sources and uncertain renewable energy sources, including generator sets, capacitor banks, wind power, photovoltaic power, energy storage, flexible loads, and electric vehicles. A typical scenario set comprehensively considering the output of all the above models is constructed. Finally, a calculation model for the reactive power support capacity of the distribution network considering the output of uncertain renewable energy sources is developed. A genetic algorithm is used to solve the model to obtain the reactive power support capacity of the distribution network. This invention considers fully mobilizing multiple types of reactive power resources on the distribution side to assess the reactive power and voltage support capacity on the transmission side. It establishes a reactive power reserve potential assessment model for individual active distribution networks and uses an improved genetic algorithm to solve the model in typical scenarios, achieving a quantitative assessment of the reactive power and voltage support capacity of the distribution network. Attached Figure Description

[0070] Figure 1 This is a flowchart of the method for assessing the reactive power support capacity of distribution networks considering uncertainties in renewable energy output, as presented in this invention.

[0071] Figure 2 This is a flowchart of the genetic algorithm solution of the present invention. Detailed Implementation

[0072] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0073] Example 1

[0074] This invention proposes a method for assessing the reactive power support capacity of distribution networks considering uncertainties in renewable energy output, comprising the following steps:

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

[0076] Construct typical scenario sets for generator set and capacitor bank output, wind and solar power output, flexible load and energy storage system output, and electric vehicle output;

[0077] The objective function and constraints for assessing the reactive power support capacity of distribution networks are established using reactive power reserve models of various traditional energy sources and uncertain new energy sources.

[0078] Typical scenarios for generator and capacitor output, wind and solar power output, flexible load and energy storage system output, and electric vehicle output are used as the initial population for the genetic algorithm. The value of the objective function is used as the fitness of individuals in the population. The genetic algorithm is used to solve the problem of distribution network reactive power support capacity assessment, and the optimal solution is used as the distribution network reactive power support capacity.

[0079] The generator set reactive power reserve model is as follows: the reactive power already generated by the generator is the reduction, and the reactive power limit that can be generated based on the active power already generated is the reduction, thereby calculating the generator set reactive power reserve.

[0080] The reactive power reserve model for the capacitor bank is specifically as follows: 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] In the formula, n Cm n is the maximum number of capacitor banks that can be deployed. C The current number of capacitor banks in operation; f is the current frequency of the entire system; C C The capacitance of a single capacitor bank; V C The node voltage of its connected node;

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

[0084] The reactive power reserve model for wind power generation is as follows:

[0085]

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

[0087] The photovoltaic power generation reactive power reserve model is as follows:

[0088] Photovoltaic (PV) array active power output P PV With no effort Q PV As shown in the following formula:

[0089]

[0090] In the formula: E is the light intensity; h is the photoelectric conversion efficiency; A is the effective illuminated area, |Q PV | max This represents the maximum reactive power regulation capability of the photovoltaic power generation array; S PV For the grid-connected inverter capacity of the photovoltaic power generation array;

[0091] Photovoltaic systems typically generate electricity at maximum power, and their power factor can be adjusted to participate in reactive power regulation. Reactive power reserve of photovoltaic systems. The expression is shown in the following formula:

[0092]

[0093] In the formula: This represents the maximum reactive power regulation value of the photovoltaic system at node m. Let m be the capacity of the photovoltaic inverter at node m; and These represent the active and reactive power injected into node m, respectively.

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

[0095] Calculate the non-distortion voltage amplitude limit V of the converter in the energy storage system using the following formula. ESSmax :

[0096]

[0097] In the formula: K PWMmaxThis represents the maximum amplitude of the linear modulation ratio; This is the DC voltage of the converter;

[0098] Calculate the maximum reactive power output Q of the energy storage system using the following formula. ESSmax for:

[0099]

[0100] In the formula: ω1 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 To provide real-time active power output for energy storage systems;

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

[0102] Q ESSrpr =Q ESSmax -Q ESS

[0103] In the formula: Q ESS This provides real-time reactive power output for the energy storage system.

[0104] The flexible load reactive power reserve model is as follows:

[0105] Traditionally, flexible loads (FL) are generally considered to exist in the following three forms:

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

[0107] (2) Load reduction (LR) is possible;

[0108] (3) Transferable Load (TL).

[0109] Assuming the power grid contains different types of FLs with proportions of a1, a2, and a3, the comprehensive compensation price K FL The calculation expression is shown in the following formula:

[0110] K FL =a1K IL +a2K LR +a3K TL

[0111] In the formula: K IL Electricity price compensation required for interruptible loads; K LR To compensate for the electricity price required to reduce load; K TL This is to compensate for the electricity price required for transferable loads.

[0112] The total reactive power response value DQ provided by the flexible load FL The calculation expression is shown in the following formula:

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

[0114] Where: DQ IL The reactive response value provided for interruptible loads; DQ LR The reactive response value provided for load reduction; DQ TL The reactive power response value provided for transferable loads; Dt is the period during which flexible load response is required;

[0115] The specific reactive power reserve model for electric vehicles is as follows:

[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, we can obtain the energy E in the energy storage elements of the electric vehicle when it is performing charging. EV The expression is shown below:

[0117]

[0118] In the formula: ω2 is the angular frequency in the bidirectional topology charging pile; S EV X represents apparent power. EV For equivalent inductive reactance; V EV For voltage; Q EV To inject reactive power.

[0119] As shown in the above equation, if the charging pile is regulated to provide capacitive reactive power to the grid during the charging of electric vehicles, it will inevitably cause the energy storage elements inside the equipment to bear more pulsating power. In this case, the charging current I through the energy storage elements inside the charging pile should be limited. EV The value can be estimated by the following formula:

[0120]

[0121] In the formula: V EVDC This refers to the voltage currently borne by the DC portion of the charging station.

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

[0123]

[0124] In the formula, Q EVmax S represents the maximum reactive power output of a bidirectional topology charging pile. EV P represents apparent power. EV ω2 represents the active power currently injected into the grid by the charging pile; ω2 represents the angular frequency in the bidirectional topology charging pile; X EV For equivalent inductive reactance; V EV For voltage; Q EV To inject reactive power; S EVmax This represents its current achievable apparent power limit.

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

[0126] Based on historical data of wind speed and light intensity, and combined with the probability distribution curves of historical data, a complete set of wind and light scenes is generated. Feature vectors from this complete set of wind and light scenes are then extracted as N. W,PV Known attribute data of the landscape;

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

[0128] Based on historical data of flexible loads and energy storage systems, and combined with the probability distribution curves of historical data, a full-scenario set of flexible loads and energy storage systems is generated. Feature vectors from this full-scenario set are then extracted as N. FL,ESS Known attribute data for flexible load and energy storage systems;

[0129] Based on historical data from power system power flow sections, a full-scenario set of generator sets and capacitor banks is generated, and feature vectors from the full-scenario set of generator sets and capacitor banks are extracted as N. G,C Known attribute data for various generator sets and capacitor banks;

[0130] An improved K-means algorithm was used to perform cluster analysis on each known attribute to obtain typical scenario sets for generator set and capacitor bank output, wind and solar power output, flexible load and energy storage system output, and electric vehicle output.

[0131] The objective function for assessing the reactive power support capability of distribution networks is as follows:

[0132]

[0133] In the formula: Q abi For the reactive power support capacity of the distribution network; The reactive power injected into the nth reactive power source; Q is the reactive power required by the m-th load; loss Q represents the reactive power loss. tr This represents the reactive power exchange value between the transmission and distribution networks under the initial state; N is the total number of reactive power sources; and M is the total number of loads.

[0134]

[0135] In the formula: 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 photovoltaic power plants; Q ESS The reactive power injected into the inverter of the energy storage system; Q FL Reactive power injected into flexible loads; Q EV Reactive power injected into electric vehicles; B k V is the susceptance of branch k; K is the number of branches; V i V j θ represents the voltage magnitudes at nodes i and j, respectively; ij Let be the voltage phase angle difference between nodes i and j.

[0136] The constraints are:

[0137]

[0138] In the formula: j∈i represents that node j is all nodes connected to node i; P Li Q Li The active and reactive loads injected into node i; P Gi Q Gi The active and reactive power of the generator set injected into node i; G ij B ij θ is the admittance on branch ij; ij Q represents the voltage phase angle difference between nodes i and j. Gn Q Gn max Q Gn min These represent the reactive power output of the nth generator unit and its maximum and minimum allowable values, respectively; V i min The minimum value required to maintain voltage stability; V i V represents the voltage magnitude at node i. j Let V be the voltage magnitude at node j. i max To maintain the maximum value of voltage stability; N node n represents the number of distribution network nodes.C n represents the number of capacitor banks currently in operation. Cm P represents the maximum number of capacitor banks that can be deployed. ESS max P represents the maximum active power output of the energy storage system. ESS For the real-time active power output of the energy storage system; Q ESS For the real-time reactive power output of the energy storage system, S ESS The capacity of the energy storage system;

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

[0140]

[0141] In the formula, 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 grid; 'a' is the coupling coefficient between active and reactive power, 'β' is the regional voltage characteristic, and 'Q' is the ... refa P refa V refa θ refa Here are the reference values ​​for reactive power, active power, voltage, and phase angle for region a; d maxa The maximum allowable distribution network loss when the distribution network supports the main grid; d refa τ is the reference value for network loss in region a; τ is the phase angle characteristic; and R1, R2, R3, and R4 are weighting factors.

[0142] The Distribution Network Reactive Power Error (DNRPE) index is obtained based on the coupling between voltage and active and reactive power at transmission and distribution network nodes, as shown in the following formula:

[0143]

[0144] In the formula: a is the coupling coefficient between active and reactive power. Q a P a V a θ a Q represents the actual values ​​of reactive power, active power, voltage, and phase angle for region a;refa P refa V refa θ refa τ represents the reference values ​​for reactive power, active power, voltage, and phase angle of the a-th region; β and τ represent the region's voltage and phase angle characteristics, which can be obtained from historical power grid data.

[0145] The prerequisite for the reactive power of the distribution network to support the main grid is, of course, to ensure the safe and stable operation of the distribution network. Therefore, the following constraints are established:

[0146]

[0147] In the formula: V G The voltage at the distribution network node; V refG θ is the voltage reference value for the distribution network node. G θ is the phase angle of the distribution network node. refG ε is the phase angle reference value for the distribution network node; h is the reactive power deviation limit value; ε is the expanded part of the inequality limit value. This refers to the sensitivity of the DNRPE index to the voltage and phase angle at 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 grid; d maxa The maximum allowable distribution network loss when the distribution network supports the main grid; d refa R1 is the reference value for network loss in region a; R2, R3, and R4 are weighting factors, whose values ​​can be determined by the entropy weighting method.

[0148] The expression for network loss δ is:

[0149]

[0150] In the formula: G k V is the conductance of branch k; K is the number of branches; V i V j These represent the voltage magnitudes at nodes i and j, respectively; θ ij Let be the voltage phase angle difference between nodes ij.

[0151] Genetic Algorithms (GA) are optimization algorithms based on natural selection, aiming to achieve the optimal solution for a specific objective. Essentially, they utilize the principle of population optimization, employing a "survival of the fittest" approach to gradually evolve to the optimal solution. The steps are as follows: First, typical scenarios for generator and capacitor bank output, wind and solar power output, flexible load and energy storage system output, and electric vehicle output are used as the initial population for the genetic algorithm. The objective function value is used as the fitness of individuals in the population. Individuals with higher fitness are selected according to the survival-of-the-fittest principle. Then, these individuals with higher fitness are paired and their chromosomes are randomly crossbred to form a new population. This process is repeated until the evolution ends. The resulting optimal solution represents the reactive power support capacity of the distribution network.

[0152] Example 2

[0153] This invention proposes a reactive power support capability assessment system for distribution networks that considers uncertainties in renewable energy output, corresponding to the method in Embodiment 1, comprising:

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

[0155] The scenario set construction module is used to construct typical scenario sets for generator set and capacitor bank output, wind and solar power output, flexible load and energy storage system output, and electric vehicle output.

[0156] The objective function and constraint construction module is used to establish the objective function and constraints for the reactive power reserve model of various traditional energy sources and uncertain new energy sources to evaluate the reactive power support capacity of the distribution network.

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

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

[0159] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0160] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for assessing the reactive power support capacity of distribution networks considering uncertain renewable energy output, characterized in that, Includes the following steps: Construct reactive power reserve models for various traditional energy sources and uncertain new energy sources, including reactive power reserve models for generator sets, capacitor banks, wind power generation, photovoltaic power generation, energy storage, flexible loads, and electric vehicles. Construct typical scenario sets for generator set and capacitor bank output, wind and solar power output, flexible load and energy storage system output, and electric vehicle output; The objective function and constraints for assessing the reactive power support capacity of distribution networks are established using reactive power reserve models of various traditional energy sources and uncertain new energy sources. Typical scenarios for generator and capacitor output, wind and solar power output, flexible load and energy storage system output, and electric vehicle output are used as the initial population for the genetic algorithm. The value of the objective function is used as the fitness of individuals in the population. The genetic algorithm is used to solve the problem of distribution network reactive power support capacity assessment, and the optimal solution is used as the distribution network reactive power support capacity.

2. The method for assessing the reactive power support capacity of distribution networks considering uncertain renewable energy output as described in claim 1, characterized in that, The generator set reactive power reserve model specifically calculates the reactive power reserve by using the reactive power already generated by the generator as the deduction and the maximum reactive power that can be generated based on the already generated active power as the deduction limit. The capacitor bank reactive power reserve model specifically calculates the reactive power reserve Q of the parallel capacitor bank. Crpr The expression is as follows: In the formula, n Cm n is the maximum number of capacitor banks that can be deployed. C The current number of capacitor banks in operation; f is the current frequency of the entire system; C C The capacitance of a single capacitor bank; V C The node voltage of its connected node; The reactive power reserve model for wind power generation is as follows: In the formula, Q Wrpr For reactive power reserve in wind power generation, Q Wmax Q represents the maximum value of the reactive power output limit. W For real-time reactive power output; P W For real-time active power output; V s I is the stator terminal voltage; rmax X represents the maximum value of the rotor terminal current. r The equivalent self-inductance of the rotor section; X s For stator leakage reactance.

3. The method for assessing the reactive power support capacity of distribution networks considering uncertain renewable energy output as described in claim 1, characterized in that, The photovoltaic power generation reactive power reserve model is specifically as follows: Active power output P of solar photovoltaic power generation array PV With no effort Q PV As shown in the following formula: In the formula: E is the light intensity; h is the photoelectric conversion efficiency; A is the effective illuminated area, |Q PV | max This represents the maximum reactive power regulation capability of the photovoltaic power generation array; S PV For the grid-connected inverter capacity of the photovoltaic power generation array; Reactive power reserve of photovoltaic systems The expression is shown in the following formula: In the formula: This represents the maximum reactive power regulation value of the photovoltaic system at node m. Let m be the capacity of the photovoltaic inverter at node m; and These represent the active and reactive power injected into node m, respectively.

4. The method for assessing the reactive power support capacity of distribution networks considering uncertain renewable energy output as described in claim 1, characterized in that, The energy storage reactive power reserve model is specifically as follows: Calculate the non-distortion voltage amplitude limit V of the converter in the energy storage system using the following formula. ESSmax : In the formula: K PWMmax This represents the maximum amplitude of the linear modulation ratio; This is the DC voltage of the converter; Calculate the maximum reactive power output Q of the energy storage system using the following formula. ESSmax for: In the formula: ω1 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 To provide real-time active power output for energy storage systems; The dynamic reactive power reserve Q of the energy storage system is calculated using the following formula. ESSrpr : Q ESSrpr =Q ESSmax -Q ESS In the formula: Q ESS This provides real-time reactive power output for the energy storage system.

5. The method for assessing the reactive power support capacity of distribution networks considering uncertain renewable energy output as described in claim 1, characterized in that, The flexible load reactive power reserve model is specifically as follows: The total reactive power response value DQ provided by the flexible load FL The calculation expression is shown in the following formula: DQ FL =(DQ IL +DQ LR +DQ TL )×Dt Where: DQ IL The reactive response value provided for interruptible loads; DQ LR The reactive response value provided for load reduction; DQ TL The reactive power response value provided for transferable loads; Dt is the period during which flexible load response is required; The specific reactive power reserve model for electric vehicles is as follows: Effective reactive power reserve Q of bidirectional topology charging pile EVrpr The calculation formula is shown below: In the formula, Q EVmax S represents the maximum reactive power output of a bidirectional topology charging pile. EV P represents apparent power. EV ω2 represents the active power currently injected into the grid by the charging pile; ω2 represents the angular frequency in the bidirectional topology charging pile; X EV For equivalent inductive reactance; V EV For voltage; Q EV To inject reactive power; S EVmax This represents its current achievable apparent power limit.

6. The method for assessing the reactive power support capacity of distribution networks considering uncertain renewable energy output as described in claim 1, characterized in that, The specific process for constructing typical scenario sets for generator set and capacitor bank output, wind and solar power output, flexible load and energy storage system output, and electric vehicle output is as follows: Based on historical data of wind speed and light intensity, and combined with the probability distribution curves of historical data, a complete set of wind and light scenes is generated. Feature vectors from this complete set of wind and light scenes are then extracted as N. W,PV Known attribute data of the landscape; Based on historical charging and discharging data of electric vehicles and combined with the spatiotemporal characteristics of the historical data, a full-scene set of electric vehicles is generated, and the feature vectors of the full-scene set of electric vehicles are extracted as N. EV Known attribute data of electric vehicles; Based on historical data of flexible loads and energy storage systems, and combined with the probability distribution curves of historical data, a full-scenario set of flexible loads and energy storage systems is generated. Feature vectors from this full-scenario set are then extracted as N. FL,ESS Known attribute data for flexible load and energy storage systems; Based on historical data from power system power flow sections, a full-scenario set of generator sets and capacitor banks is generated, and feature vectors from the full-scenario set of generator sets and capacitor banks are extracted as N. G,C Known attribute data for various generator sets and capacitor banks; An improved K-means algorithm was used to perform cluster analysis on each known attribute to obtain typical scenario sets for generator set and capacitor bank output, wind and solar power output, flexible load and energy storage system output, and electric vehicle output.

7. The method for assessing the reactive power support capacity of distribution networks considering uncertain renewable energy output as described in claim 1, characterized in that, The objective function for the distribution network reactive power support capability assessment problem is as follows: In the formula: Q abi For the reactive power support capacity of the distribution network; The reactive power injected into the nth reactive power source; Q is the reactive power required by the m-th load; loss Q represents the reactive power loss. tr This represents the reactive power exchange value between the transmission and distribution networks under the initial state; N is the total number of reactive power sources; and M is the total number of loads. In the formula: 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 photovoltaic power plants; Q ESS The reactive power injected into the inverter of the energy storage system; Q FL Reactive power injected into flexible loads; Q EV Reactive power injected into electric vehicles; B k V is the susceptance of branch k; K is the number of branches; V i V j θ represents the voltage magnitudes at nodes i and j, respectively; ij Let be the voltage phase angle difference between nodes i and j.

8. The method for assessing the reactive power support capacity of distribution networks considering uncertain renewable energy output as described in claim 1, characterized in that, The constraints are as follows: In the formula: j∈i represents that node j is all nodes connected to node i; P Li Q Li The active and reactive loads injected into node i; P Gi Q Gi The active and reactive power of the generator set injected into node i; G ij B ij θ is the admittance on branch ij; ij Q represents the voltage phase angle difference between nodes i and j. Gn Q Gnmax Q Gnmin These represent the reactive power output of the nth generator unit and its maximum and minimum allowable values, respectively; V imin The minimum value required to maintain voltage stability; V i V represents the voltage magnitude at node i. j Let V be the voltage magnitude at node j. imax To maintain the maximum value of voltage stability; N node n represents the number of distribution network nodes. C n represents the number of capacitor banks currently in operation. Cm P represents the maximum number of capacitor banks that can be deployed. ESSmax P represents the maximum active power output of the energy storage system. ESS For the real-time active power output of the energy storage system; Q ESS For the real-time reactive power output of the energy storage system, S ESS The capacity of the energy storage system; h is the reactive power deviation limit value; This refers to the sensitivity of the DNRPE index to the voltage and phase angle at each node; V G V is the voltage at the distribution network node. refG θ is the voltage reference value for the distribution network node. G θ is the phase angle of the distribution network node. refG Here, represents the phase angle reference value for the distribution network node; DNRPE represents the reactive power deviation index of the distribution network; ε is the expanded part of the inequality limit, expressed as follows: In the formula, 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 grid; α is the coupling coefficient between active and reactive power, β is the regional voltage characteristic, and Q refa P refa V refa θ refa Here are the reference values ​​for reactive power, active power, voltage, and phase angle for region a; d maxa The maximum allowable distribution network loss when the distribution network supports the main network; δ refa τ is the reference value for network loss in region a; τ is the phase angle characteristic; and R1, R2, R3, and R4 are weighting factors.

9. The method for assessing the reactive power support capacity of distribution networks considering uncertain renewable energy output as described in claim 8, characterized in that, The reactive power deviation index of the distribution network is obtained based on the coupling between the voltage and active and reactive power at the transmission and distribution network nodes, and its specific expression is as follows: In the formula, DNRPE represents the reactive power deviation index of the distribution network, and Q a P a V a θ a Q represents the actual values ​​of reactive power, active power, voltage, and phase angle for region a; refa P refa V refa θ refa α represents the reference values ​​for reactive power, active power, voltage, and phase angle of the a-th region; α is the coupling coefficient between active and reactive power; β is the region voltage characteristic; and τ is the phase angle characteristic.

10. A power distribution network reactive power support capacity assessment system considering uncertain renewable energy output, characterized in that, include: The reactive power reserve model building module is used to build reactive power reserve models for various traditional energy sources and uncertain new energy sources, including reactive power reserve models for generator sets, capacitor banks, wind power generation, photovoltaic power generation, energy storage, flexible loads, and electric vehicles. The scenario set construction module is used to construct typical scenario sets for generator set and capacitor bank output, wind and solar power output, flexible load and energy storage system output, and electric vehicle output. The objective function and constraint construction module is used to establish the objective function and constraints for the reactive power reserve model of various traditional energy sources and uncertain new energy sources to evaluate the reactive power support capacity of the distribution network. The solution module uses typical scenarios of generator and capacitor output, wind and solar power output, flexible load and energy storage system output, and electric vehicle output as the initial population of the genetic algorithm. The value of the objective function is used as the fitness of individuals in the population. The genetic algorithm is used to solve the problem of distribution network reactive power support capacity assessment, and the optimal solution is used as the distribution network reactive power support capacity.

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