A voltage adjustment strategy for high penetration rate electric vehicles accessing power grid based on situation awareness

Abstract

The present application relates to a kind of high penetration rate electric vehicle access power grid after voltage adjustment strategy based on situation awareness.The present application uses situation awareness technology, collects real-time electric vehicle and power grid information to establish large-scale electric vehicle access power grid scene in model-driven mode, completes situation awareness;Set up economic cost objective function, use situation awareness information to deeply understand distribution network and reactive power compensation device state, provide basic data for situation guidance link;Using Monte Carlo simulation method, simulate the state of different types of EV access power grid, obtain the voltage of each node of distribution network at the charging time of charging demand prediction, correct the predicted voltage based on power flow section, realize situation prediction perception;Voltage deviation index is constructed at system level, accurate control of distribution network voltage is realized, and two-stage voltage regulation mode is used for different voltage deviation index to make optimal allocation of reactive power compensation power in different grade voltage regulation mode, improve the economy and stability of distribution network.

Description

Technical Field

[0001] This invention patent relates to a power system stable operation strategy, and more particularly to a practical control strategy that uses situational awareness technology to coordinate and solve the problem of voltage exceeding limits after high-penetration electric vehicles are connected to the power grid. Background Technology

[0002] With rapid economic development, global energy demand and the energy gap are increasing year by year, and the over-exploitation and consumption of energy have also caused a series of ecological and environmental problems. Electric vehicles, due to their outstanding characteristics such as low pollution, low energy consumption, and low cost, have become an important development trend in the modern automotive industry. However, after high-penetration EVs are connected to the power grid, their randomness and complexity can cause the voltage at power grid nodes to deviate significantly from the standard voltage, threatening the stability of the distribution network.

[0003] To this end, this patent proposes a voltage adjustment strategy for high-penetration electric vehicles connected to the power grid based on situational awareness, so as to perceive the current voltage status of the distribution network in real time and predict the future node voltage status, and make voltage adjustment strategies in advance. (1) This patent uses situational awareness technology to collect real-time electric vehicle information, power grid structure parameters, and electric vehicle charging station data to establish a large-scale electric vehicle connection to the power grid scenario in a model-driven manner, and complete situational awareness; (2) An economic cost objective function is established, and the situational awareness information is used to further understand the status of the distribution network and reactive power compensation device, providing basic data for situational guidance; (3) The Monte Carlo simulation method is used to simulate the connection status of different types of EVs to the power grid, obtain the voltage of each node of the distribution network at the charging time of the predicted charging demand, and correct the predicted voltage based on the power flow section to realize situational prediction and awareness; (4) A voltage deviation index is constructed at the system level to realize precise control of the distribution network voltage. At the same time, a two-level voltage regulation method is used to optimize the allocation of reactive power compensation power for different voltage deviation indices, thereby improving the economy and stability of the distribution network. Summary of the Invention

[0004] The purpose of this invention is to provide relevant grid information for regional distribution networks with large voltage deviations after high-penetration electric vehicles are connected to the grid, based on situational awareness, through situational perception, situational understanding, and situational prediction. By leveraging situational guidance, appropriate voltage regulation strategies can be selected and optimized for different grid voltage deviation situations.

[0005] The above-mentioned technical problems of the present invention are mainly solved by the following technical solutions:

[0006] A voltage regulation strategy for high-penetration electric vehicles connected to the grid based on situational awareness, including

[0007] During the situation awareness phase, the distribution network status is obtained through technologies such as PMU, observability measurement, and advanced measurement. Based on the multi-source information real-time interaction model, the SOC and charging characteristics information of large-scale electric vehicles connected to the distribution network are collected, and the current charging pile power, remaining compensation capacity, and reactive power compensation equipment status in the current EVS are uploaded to deeply perceive the current distribution network status.

[0008] Situational understanding phase: Establish an objective function that minimizes grid loss cost, capacitor bank reactive power compensation cost, on-load transformer cost, and battery loss cost, as well as distribution network constraints. Use a joint voltage regulation strategy that optimizes the power factor angle of charging piles in charging stations to understand and evaluate the situation of high-penetration EVs connected to the grid, and output optimized electric vehicle information.

[0009] Situation prediction phase: Using the acquired electric vehicle information for prediction, based on the information collected in the situation understanding phase after the electric vehicle access, the current state information of the power grid is obtained, and the development trend of the subsequent time period is predicted. The power grid voltage deviation is assessed and the degree of power grid voltage deviation is corrected.

[0010] Situation guidance phase: Based on the degree of grid voltage deviation obtained in the situation prediction phase, determine whether it exceeds the set range, and based on the judgment result, choose whether to implement a secondary voltage regulation strategy or end the entire voltage adjustment process.

[0011] The objective function formula for the above voltage adjustment strategy is as follows:

[0012]

[0013] In the formula: α l α is the distribution network loss cost coefficient. T Cost of on-load voltage regulation per cycle; α d For battery wear and tear costs, Let be the active power loss between node i and node j at time t, and E be the set of all branches in the distribution network; Let E be the reactive power compensation capacity of the capacitor bank at node i at time t. C Place the set of nodes for the capacitor; N T P represents the number of times the capacitor is switched on and off. charge The charging power for EVs.

[0014] In the aforementioned voltage regulation strategy, the distribution network constraints include the power flow constraints of the distribution network:

[0015]

[0016] In the formula: P evi,t Q evi,tLet Q be the active power and reactive power injected into the grid by EV at node i at time t; Ci,t X represents the reactive power injected into the capacitor bank at node i at time t; ij,t and Y ij,t These are the conductance and susceptance values ​​between node i and node j at time t, respectively; m j,t and n j,t P represents the real and imaginary parts of the voltage at node j at time t; i,t and Q i,t These represent the active power and reactive power of the total load at node i in the distribution network at time t, respectively.

[0017] In the aforementioned voltage regulation strategy, the distribution network constraints, including line losses, are expressed as follows:

[0018]

[0019]

[0020] In the formula: R is the reference voltage amplitude at node i at time t. ij,t Let t be the resistance value between node i and node j at time t; combining equations (3) and (4) can convert the active power loss of the line into electrical conductivity susceptance, which is convenient for calculation.

[0021] In the aforementioned voltage regulation strategy, the distribution network constraints include line safety constraints.

[0022]

[0023] In the formula: I ij,max It is the maximum value of the branch current ij; u i,t Let u be the voltage at node i at time t. min and u max This represents the minimum and maximum allowable node voltage.

[0024] In the aforementioned voltage regulation strategy, the distribution network constraints include capacitor bank constraints.

[0025] In actual power grid operation, the number of times a capacitor can be operated within a single cycle is limited. Furthermore, most capacitor switching methods involve switching capacitors in groups. Therefore, capacitor operation must meet the constraints of capacitor capacity and the number of switching cycles.

[0026]

[0027] In the formula: and These represent the reactive power compensation capacity of the capacitor bank connected at times t and t-1, respectively. For the XOR operator, if and The output result is 1 if the result is different, t C,max Q is the maximum number of times the capacitor bank can be switched on and off within a single cycle. C,min and Q C,max Q represents the minimum and maximum reactive power compensation capacity of the capacitor bank in the distribution network. C,t Let t be the capacity of the capacitor connected to the distribution network at time t.

[0028] In the aforementioned voltage regulation strategy, the distribution network constraints include on-load transformer constraints.

[0029] On-load transformers in a power grid regulate voltage by changing their turns ratio through switching taps. However, the turns ratio range and tap options of on-load transformers are limited; therefore, their operation must meet certain constraints.

[0030]

[0031] In the formula: β ij,t β is the on-load transformer turns ratio at time t; T The increment is 0.01 per gear position; T ij,t Let T be the transformer tap position between nodes ij at time t; ij,(t-1) M represents the transformer tap position between nodes ij at time t-1; OLTC The maximum range of tap position variation for an on-load transformer; t OLTC This represents the maximum number of tap adjustments for an on-load transformer.

[0032] In the aforementioned voltage adjustment strategy, the current grid, EV, and EVS status information is collected during the situation awareness phase and then analyzed during the situation understanding phase. After the situation prediction phase, the information from the understanding phase is further predicted to obtain the comprehensive information for the next moment. Based on the predicted information, the power factor angle of reactive power equipment in the grid and charging piles in the EVS is adjusted to reduce the degree of grid voltage deviation.

[0033] Establish the voltage deviation index ζ of the distribution network dev As a prerequisite standard for the guiding method in the guiding process, the voltage at time t+1 is adjusted according to the voltage deviation index:

[0034]

[0035] And a two-stage voltage regulation strategy, specifically including

[0036] Level 1 voltage regulation: When the voltage deviation exceeds the specified range, the control center performs power flow calculations on the tap positions of on-load transformers and the number of capacitor banks switched on and off in the current distribution network to determine the recovery status of node voltage. If the voltage can be restored to a stable range at this time, a mathematical model is established based on the situation understanding stage to regulate the voltage of the distribution network.

[0037] Secondary voltage regulation: If the primary voltage regulation cannot alleviate the voltage deviation, consider optimizing the power factor angle of the charging unit in the EVS through the control center, and adjust the charging pile power by changing the power factor angle to improve the voltage deviation.

[0038] The two-stage voltage regulation strategy incorporates EVS into reactive power compensation, increasing the reactive power compensation capacity and enhancing its flexibility. At the same time, the two-stage voltage regulation strategy classifies the reactive power compensation method and selects the optimal reactive power allocation strategy according to different degrees of voltage deviation, which greatly reduces the excess losses in reactive power compensation.

[0039] In the above voltage regulation strategy, the first-level voltage regulation model is established based on the following steps:

[0040] Based on the EV's initial state of charge (Soc) i And the EV's power consumption per unit distance can be used to calculate the EV's state of charge (Soc) upon arrival at the charging station. d

[19] ;

[0041] Soc d =Soc i -ξf(d) / C ev (9)

[0042] In the formula: ξ represents the EV energy consumption level per unit mileage, C ev For EV battery capacity;

[0043] C ev,min ≤C ev ≤C ev,max (10)

[0044] In the formula: C ev,min and C ev,max These represent the minimum and maximum capacities for various EV batteries;

[0045] Battery safety constraints:

[0046] Soc min ≤Soc d ≤Soc max (11)

[0047] Where: Soc min and Soc max These are the minimum and maximum capacities of the SoC, respectively. To ensure battery safety, the SoC... min =0.1, Soc max =0.97

[20] ;

[0048] After the EV enters the charging station and connects to the charging pile, the current SoC will be... dInteract with the power grid to determine the charging mode; define the EV charging state variable λ at time t. ev,t ;

[0049]

[0050] Based on equations (8), (10), and (13), the charging demand of the EV at the next moment can be deduced.

[0051]

[0052] In the formula: Soc t+1 and Soc t The SOC of the EV at time t+1 and time t are respectively, and the charging efficiency is η = 0.9; EV charging time constraint:

[0053]

[0054] EVS total demand capacity and the number of EVs within the EVS and the SOc d related

[0055]

[0056] In the formula: S EVS,t Let N be the total demand capacity within EVS at time t. ev Let Soc be the set of EVs in this EVS. n,t Let the SOC of the nth vehicle be at time t;

[0057] If all charging piles in an EVS are of the same model, then the SOC of each charging pile at time t is...

[0058]

[0059] In the formula: S p,t Let N be the battery capacity of the charging station at time t. p This refers to the collection of charging stations within the EVS.

[0060]

[0061] In the formula: P p,t Let Q be the active power of the charging pile at time t. p,t Let t be the reactive power of the charging pile. Let be the power factor angle at time t, when Q p,t When the value is less than 0, EVS transmits reactive power to the grid;

[0062] Charging station operation characteristic constraints:

[0063] 1) Power factor angle constraint

[0064]

[0065] 2) Charging pile capacity constraints

[0066] S p,min ≤S p,t ≤S p,max (19)

[0067] In the formula: S p,min and S p,max These are the minimum and maximum battery capacities of the charging station, respectively.

[0068] The capacity constraint of charging stations can be derived from equations (17) and (20).

[0069]

[0070] In the formula: S p,n,min and S p,n,max Let these represent the minimum and maximum capacities of the nth charging pile, respectively.

[0071] For a distribution network with a defined topology, after high-penetration EVs are connected to the grid, based on the predicted daily charging demand data of EVs, the node voltage at time t+1 is predicted by collecting the EV charging power at time t, the reactive power of the charging piles within the EVS, and the status of the reactive power compensation equipment; then the control variables for the voltage at time t are:

[0072] ΔY(k)=[ΔP charge,t (k),ΔQ p,t (k),ΔQ C,t The voltage prediction model for (k) is:

[0073]

[0074] In the formula: Let be the node voltage sensitivity matrix of the voltage change at node i at time t to each control variable;

[0075]

[0076] In the formula: u i,t+1 Let be the voltage of node i at time t+1.

[0077] In the aforementioned voltage adjustment strategy, the classic Newton-Raphson algorithm is employed. First, a node admittance matrix is ​​established. Substituting the initial voltage values ​​of each node, the values ​​of each element in the Jacobian matrix are obtained. The corrected equations are then solved to obtain the voltage changes at each node, including:

[0078] Grid voltage deviation assessment:

[0079] When the line impedance parameters and network topology remain unchanged, the predicted voltages of each node in a typical power flow section are calculated by combining the predicted voltages of each node obtained during the situation prediction phase.

[0080]

[0081] In the formula: Let be the voltage at a certain power flow section of node i at time t+1; k represents the operating state of the kth power flow of the system.

[0082] Grid voltage deviation correction:

[0083] Based on the power flow operation status of the distribution network collected during the situation awareness phase, m typical power flow operation states are extracted during the situation understanding phase. Correction coefficients are then calculated by combining the statistical probability p of each power flow operation state.

[0084]

[0085] In the formula: p (k) Let be the statistical probability of the k-th power flow state. This is the correction coefficient for the k-th power flow state of node i;

[0086] Considering that a change in the voltage of one node in a distribution network system will affect other nodes, the predicted voltage sensitivity θ between connected nodes is obtained according to equation (22). ij,t+1 for

[0087]

[0088] Grid voltage deviation acquisition:

[0089] To reflect the mutual influence between nodes in the global distribution network, a global electrical distance matrix is ​​established, where the electrical distance between connected nodes is...

[0090]

[0091] By combining the electrical distance between connected nodes and the correction factor, the corrected global electrical distance matrix D is obtained.

[0092]

[0093] The predicted node voltages at each section represent the operating status of the distribution network under the situation prediction. The node voltages are related to the EV charging load and the status of each reactive power compensation device. Therefore, the correction coefficient of the distribution network power flow operating status can reflect the impact of EVS on the distribution network. Thus, by using the corrected global electrical distance matrix, a distribution network power flow model based on EV charging nodes can be obtained. Solving the power flow model yields the corrected predicted voltages of each node.

[0094] Therefore, this invention has the following advantages: by utilizing situational awareness technology, the power grid status can be predicted in advance and the current power grid status can be perceived in real time, avoiding voltage overruns and comprehensively improving the economy of the distribution network; when the power factor of the charging pile is changed, some reactive power compensation devices are alleviated, reducing the number of operations of capacitors and on-load transformers, and improving the voltage regulation flexibility of the distribution network; the two-stage voltage regulation strategy combining reactive power compensation devices and charging piles is suitable for large-scale EV access and has a positive impact on the resilience of the distribution network. Attached Figure Description

[0095] Figure 1 The adjustable capacity of the charging pile of the present invention.

[0096] Figure 2 This is the flow chart of the two-stage voltage regulation strategy of the present invention.

[0097] Figure 3 This is a schematic diagram of the method flow of the present invention.

[0098] Figure 4 This is the IEEE 30-node diagram of this embodiment.

[0099] Figure 5 This is a schematic diagram of the area's daily load at a permeability of 16% in this embodiment.

[0100] Figure 6 This is a schematic diagram of the area's daily load at a permeability of 33% in this embodiment.

[0101] Figure 7 This is a schematic diagram of the area's daily load under a 50% permeability in this embodiment.

[0102] Figure 8 This is a schematic diagram of the node voltage curve at a penetration rate of 16% in this embodiment.

[0103] Figure 9 This is a schematic diagram of the node voltage during the severe over-limit period at a penetration rate of 16% in this embodiment.

[0104] Figure 10 This is a schematic diagram of the severely over-limit node voltage at a penetration rate of 16% in this embodiment.

[0105] Figure 11 This is a schematic diagram of the on-load transformer tap position under a 16% penetration rate in this embodiment.

[0106] Figure 12 This is a schematic diagram of the number of capacitor switching groups under a 16% penetration rate in this embodiment. Detailed Implementation

[0107] The technical solution of the present invention will be further described below through embodiments and in conjunction with the accompanying drawings.

[0108] First, the principle of this invention will be introduced. This invention includes...

[0109] Step 1: Situational awareness phase. Through technologies such as PMU, observability measurement, and advanced measurement, the distribution network status is obtained. Based on the multi-source information real-time interaction model, information such as SOC and charging characteristics after large-scale electric vehicles are connected to the distribution network is collected. The current charging pile power, remaining compensation capacity, and reactive power compensation equipment status in the current EVS are uploaded to deeply perceive the current distribution network status.

[0110] Step 2: Research on improving voltage stability after high-penetration electric vehicles are connected to the grid. This paper understands and evaluates the situation of high-penetration EVs connected to the grid through a combined voltage regulation strategy of traditional voltage regulation devices and power factor angle optimization of charging piles in charging stations. In the situation understanding stage, an objective function is established to minimize grid loss cost, capacitor bank reactive power compensation cost, on-load transformer cost, and battery loss cost.

[0111]

[0112] In the formula: α l α is the distribution network loss cost coefficient. T Cost of on-load voltage regulation per cycle; α d For battery wear and tear costs, Let be the active power loss between node i and node j at time t, and E be the set of all branches in the distribution network; Let E be the reactive power compensation capacity of the capacitor bank at node i at time t. C Place the set of nodes for the capacitor; N T P represents the number of times the capacitor is switched on and off. charge The charging power for EVs.

[0113] 1) Power flow constraints in distribution networks:

[0114]

[0115] In the formula: P evi,t Q evi,t Let Q be the active power and reactive power injected into the grid by EV at node i at time t; Ci,t X represents the reactive power injected into the capacitor bank at node i at time t; ij,t and Y ij,t These are the conductance and susceptance values ​​between node i and node j at time t, respectively; m j,t and n j,t P represents the real and imaginary parts of the voltage at node j at time t; i,t and Q i,t These represent the active power and reactive power of the total load at node i in the distribution network at time t, respectively.

[0116] 2) Network loss is represented as:

[0117]

[0118]

[0119] In the formula: R is the reference voltage amplitude at node i at time t. ij,t Let be the resistance value between node i and node j at time t. Combining equations (3) and (4) can convert the active power loss of the line into electrical conductivity susceptance, which is convenient for calculation.

[0120] 3) Line safety constraints

[0121]

[0122] In the formula: I ij,max It is the maximum value of the branch current ij; u i,t Let u be the voltage at node i at time t. min and u max This represents the minimum and maximum allowable node voltage.

[0123] 4) Capacitor device constraints

[0124] In actual power grid operation, the number of times a capacitor can be operated within a single cycle is limited. Furthermore, most capacitor switching methods involve switching capacitors in groups. Therefore, capacitor operation must meet the constraints of capacitor capacity and the number of switching cycles.

[0125]

[0126] In the formula: and These represent the reactive power compensation capacity of the capacitor bank connected at times t and t-1, respectively. For the XOR operator, if and The output result is 1 if the result is different, t C,max Q is the maximum number of times the capacitor bank can be switched on and off within a single cycle. C,min and Q C,max Q represents the minimum and maximum reactive power compensation capacity of the capacitor bank in the distribution network. C,t Let t be the capacity of the capacitor connected to the distribution network at time t.

[0127] 5) On-load transformer constraints

[0128] On-load transformers in a power grid regulate voltage by changing their turns ratio through switching taps. However, the turns ratio range and tap options of on-load transformers are limited; therefore, their operation must meet certain constraints.

[0129]

[0130] In the formula: β ij,t β is the on-load transformer turns ratio at time t; T The increment is 0.01 per gear position; T ij,t Let T be the transformer tap position between nodes ij at time t; ij,(t-1) M represents the transformer tap position between nodes ij at time t-1; OLTC The maximum range of tap position variation for an on-load transformer; t OLTC This represents the maximum number of tap adjustments for an on-load transformer.

[0131] Step 3: Situation prediction utilizes the information collected during the situation awareness and situation understanding phases to make predictions. Based on the information collected during the situation understanding phase regarding the connection of electric vehicles, the current state information of the power grid is obtained, summarized, and the development trend for subsequent time periods is predicted. The results of situation prediction and the information obtained from situation understanding will serve as the basis for the voltage regulation strategy during the situation guidance phase.

[0132] Based on the EV's initial state of charge (Soc) i And the EV's power consumption per unit distance can be used to calculate the EV's state of charge (Soc) upon arrival at the charging station. d

[19] .

[0133] Soc d =Soc i -ξf(d) / C ev (35)

[0134] In the formula: ξ represents the EV energy consumption level per unit mileage, C ev This refers to the EV battery capacity.

[0135] C ev,min ≤C ev ≤C ev,max (36)

[0136] In the formula: C ev,min and C ev,max These represent the minimum and maximum capacities for various types of EV batteries.

[0137] Battery safety constraints:

[0138] Soc min ≤Soc d ≤Soc max (37)

[0139] Where: Soc min and Soc max These are the minimum and maximum capacities of the SoC, respectively. To ensure battery safety, the SoC... min =0.1, Socmax =0.97

[20] .

[0140] After the EV enters the charging station and connects to the charging pile, the current SoC will be... d The charging mode is determined by interacting with the power grid. The EV charging state variable λ is defined at time t. ev,t .

[0141]

[0142] Based on equations (8), (10), and (13), the charging demand of the EV at the next moment can be deduced.

[0143]

[0144] In the formula: Soc t+1 and Soc t The SOC of EV at time t+1 and time t are respectively, and the charging efficiency is η = 0.9.

[0145] EV charging time constraints:

[0146]

[0147] EVS total demand capacity and the number of EVs within the EVS and the SOc d related

[0148]

[0149] In the formula: S EVS,t Let N be the total demand capacity within EVS at time t. ev Let Soc be the set of EVs in this EVS. n,t Let SOC be the state of the nth vehicle at time t.

[0150] Assuming all charging stations within the EVS are of the same model, the SOC of each charging station at time t is...

[0151]

[0152] In the formula: S p,t Let N be the battery capacity of the charging station at time t. p This refers to the set of charging stations in this EVS.

[0153]

[0154] In the formula: P p,t Let Q be the active power of the charging pile at time t. p,t Let t be the reactive power of the charging pile. Let be the power factor angle at time t, when Q p,t When the value is less than 0, EVS transmits reactive power to the grid.

[0155] Because the charging times for different types of EVs vary, not all charging stations within the EVS participate in EV charging. When the distribution network voltage exceeds the limit, charging stations in idle states switch to capacitive operation mode by changing their power factor angle, feeding reactive power back to the grid. The adjustable capacity of the charging station's reactive power compensation is as follows: Figure 1 As shown:

[0156] Charging station operation characteristic constraints:

[0157] 1) Power factor angle constraint

[0158]

[0159] 2) Charging pile capacity constraints

[0160] S p,min ≤S p,t ≤S p,max (45)

[0161] In the formula: S p,min and S p,max These are the minimum and maximum battery capacities of the charging station, respectively.

[0162] The capacity constraint of charging stations can be derived from equations (17) and (20).

[0163]

[0164] In the formula: S p,n,min and S p,n,max These represent the minimum and maximum capacities of the nth charging pile, respectively.

[0165] For a distribution network with a defined topology, after high-penetration EVs are connected to the grid, based on the predicted daily charging demand data of EVs, the node voltage at time t+1 is predicted by collecting the EV charging power at time t, the reactive power of charging piles within the EVS, and the status of reactive power compensation equipment. The control variables for voltage at time t are:

[0166] ΔY(k)=[ΔP charge,t (k),ΔQ p,t (k),ΔQ C,t The voltage prediction model for (k) is:

[0167]

[0168] In the formula: Let be the node voltage sensitivity matrix of the change in node i at time t to each control variable.

[0169]

[0170] In the formula: u i,t+1 Let be the voltage of node i at time t+1.

[0171] Distribution network power flow forecasting

[0172] Power flow calculation is used to calculate the voltage of each node and the power distribution of each branch under a given operating mode of the power grid. It is used to determine whether the power equipment in the system is overloaded, whether the node voltage of each device is within the specified operating range, whether the power distribution of each branch is reasonable, and the active and reactive power losses. The power flow calculation method of this patent adopts the classic Newton-Raphson algorithm. First, the node admittance matrix is ​​established, the initial value of each node voltage is substituted, the value of each element of the Jacobian matrix is ​​obtained, and the correction equation is solved to obtain the voltage change of each node.

[0173] Grid voltage deviation assessment:

[0174] When the line impedance parameters and network topology remain unchanged, the predicted voltages of each node in a typical power flow section are calculated by combining the predicted voltages of each node obtained during the situation prediction phase.

[0175]

[0176] In the formula: t+1 represents the voltage at a certain power flow section of node i at time t+1; k represents the k-th power flow operating state of the system.

[0177] Grid voltage deviation correction:

[0178] Based on the power flow operation status of the distribution network collected during the situation awareness phase, m typical power flow operation states are extracted during the situation understanding phase. Correction coefficients are then calculated by combining the statistical probability p of each power flow operation state.

[0179]

[0180] In the formula: p (k) Let be the statistical probability of the k-th power flow state. It is the correction coefficient for the k-th power flow operation state of node i.

[0181] Considering that a change in the voltage of one node in a distribution network system will affect other nodes, the predicted voltage sensitivity θ between connected nodes is obtained according to equation (22). ij,t+1 for

[0182]

[0183] Grid voltage deviation acquisition:

[0184] To reflect the mutual influence between nodes in the global distribution network, a global electrical distance matrix is ​​established, where the electrical distance between connected nodes is...

[0185]

[0186] By combining the electrical distance between connected nodes and the correction factor, the corrected global electrical distance matrix D is obtained.

[0187]

[0188] The predicted node voltages at each cross-section represent the operating state of the distribution network under the situational prediction. Since the node voltages are related to the EV charging load and the status of each reactive power compensation device, the correction coefficient for the distribution network power flow operating state can reflect the impact of EVS on the distribution network. Therefore, by using the corrected global electrical distance matrix, a distribution network power flow model based on EV charging nodes can be obtained, and the corrected predicted voltages of each node can be obtained by solving the power flow model.

[0189] Step 4: Based on the situation awareness phase, the current status information of the power grid, EV, and EVS is collected and then analyzed in the situation understanding phase. Following the situation prediction phase, the information from the understanding phase is further predicted to obtain comprehensive information for the next time step. Based on the predicted information, the power factor angles of reactive power equipment in the power grid and charging piles in the EVS are adjusted to reduce the degree of power grid voltage deviation.

[0190] Establish the voltage deviation index ζ of the distribution network dev As a prerequisite standard for the guiding method in the guiding process, the voltage at time t+1 is adjusted according to the voltage deviation index:

[0191]

[0192] Voltage deviation index accurately reflects the degree of voltage deviation in the current distribution network. However, a single voltage deviation index cannot guarantee the reasonable allocation of reactive power. In order to further improve the effective utilization rate of reactive power compensation, this paper proposes a two-stage voltage regulation strategy.

[0193] Level 1 voltage regulation: When the voltage deviation exceeds the specified range, the control center performs power flow calculations on the tap positions of on-load transformers and the number of capacitor banks switched on and off in the current distribution network to determine the recovery status of node voltage. If the voltage can be restored to a stable range at this time, a mathematical model is established based on the situation understanding stage to regulate the voltage of the distribution network.

[0194] Secondary voltage regulation: If the primary voltage regulation cannot alleviate the voltage deviation, consider optimizing the power factor angle of the charging unit in the EVS through the control center, and adjust the charging pile power by changing the power factor angle to improve the voltage deviation.

[0195] The two-stage voltage regulation strategy incorporates EVS (Electronic Voltage Suppression) into reactive power compensation, increasing the capacity and flexibility of reactive power compensation. Simultaneously, the two-stage voltage regulation strategy categorizes reactive power compensation methods, selecting the optimal reactive power allocation strategy based on different levels of voltage deviation, significantly reducing redundant losses during reactive power compensation. The flowchart for the two-stage voltage regulation is as follows: Figure 2 As shown.

[0196] This invention relates to a situational awareness-based method for adjusting the voltage limits of regional distribution networks after high-penetration electric vehicles are connected to the grid. Currently, no specific solution has been proposed in domestic and international research literature to address the grid voltage adjustment problem after high-penetration electric vehicles are connected to the grid. Therefore, this strategy has good adaptability.

[0197] The technical solution of this invention mainly provides relevant grid information for regional distribution networks with large voltage deviations after high-penetration EV access through situational awareness, situational understanding, and situational prediction. By leveraging the situation, appropriate voltage regulation strategies are selected and optimized for different grid voltage deviation situations.

[0198] II. The practicality of the proposed scheme will be verified by using the IEEE 30-node system.

[0199] IEEE 30-node diagram as follows Figure 3 As shown, the battery charging loss cost is 0.04 yuan / kW·h. Nodes 7, 17, 23, 29, and 30 are electric vehicle charging stations, each capable of connecting up to 150 vehicles (charging piles within the electric vehicle charging station). Each charging pile is set to have a capacity of 7kW, corresponding to a minimum and maximum power factor angle of 18.2° and 161.8°, respectively.

[20] Nodes 10 and 24 are equipped with capacitor switching devices, with 5 groups per node and 5 Mvar per group. The stable voltage range of the nodes is [0.95, 1.05] pu. There are three groups of on-load transformers located between nodes 6 and 9, nodes 10, 4 and 12, and nodes 27 and 28, respectively. The initial tap is 0, and there are 9 adjustable taps with an adjustable range of [0.9, 1.25] pu.

[0200] The example considers a total of 1,200 vehicles in the region during the day. The EV penetration rate is defined as the ratio of the number of EVs to the total number of cars in the region, and is 16%, 33%, and 50%, respectively. Figure 5 The figures show the total daily load in the region at a penetration rate of 16% and the charging load of different types of EVs. For the total daily load in the region at other penetration rates, see [link to relevant data]. Figure 6 , Figure 7 Comparing the regional daily load under three different penetration rates, it can be seen that during the peak electricity consumption period from 16:00 to 20:00, as the penetration rate increases, the peak-valley difference of the distribution network gradually increases, placing a great burden on the distribution network.

[0201] To verify the effectiveness of the proposed method, this paper conducts simulation analysis on the following three adjustment strategy schemes:

[0202] 1) Option 1. No reactive power compensation equipment, no reactive power compensation power injection for the charging pile.

[0203] 2) Option 2. Activate reactive power compensation equipment, and inject no reactive power compensation power into the charging column.

[0204] 3) Option 3. Based on situational awareness technology, the status of the distribution network is monitored in real time. Considering factors such as economy and stability, the reactive power of reactive power compensation equipment and charging piles is adjusted.

[0205] When 200 EVs are connected, the voltage curve of the distribution network at the nodes in the first scheme is as follows: Figure 8 As shown,

[0206] After EVs were connected, some nodes experienced voltage drops below the lower limit. Specifically, during the peak charging period from 15:00 to 20:00, the voltage at node 30, located at the end of the distribution network, dropped to a minimum of 0.9465 pu, below the stable voltage range, threatening the stable operation of the distribution network. Comparing EV connections at three different penetration rates, the degree of voltage drops below the lower limit was positively correlated with the EV penetration rate.

[0207] Adjustments will be made to the periods and nodes with severe over-limit violations according to Scheme 2 and Scheme 3.

[0208] Depend on Figure 8 It can be seen that, under Scheme 2, during the severe over-limit period at 20:00, although most nodes are within the specified stable range by switching capacitor banks and adjusting on-load transformers, the voltage at node 30 is below the lower voltage limit, and over-limit nodes still exist in the distribution network. Under Scheme 3, during the severe over-limit period, the overall voltage is significantly improved compared to Scheme 2, and all nodes return to stability, with a more stable voltage fluctuation amplitude than Scheme 2.

[0209] Depend on Figure 9 It can be seen that in Scheme 1, no adjustment measures were taken. As the EV load increased, the voltage amplitude of node 30 was lower than the specified lower voltage limit during the peak charging periods of 08:00 and 20:00. In Scheme 2, the traditional reactive power compensation equipment was used to partially reduce the time when the node voltage was lower than the lower voltage limit during the peak EV load period, but it was difficult to ensure voltage stability throughout the day. Scheme 3, based on situational awareness, uses a two-stage voltage regulation method consisting of on-load transformers, capacitor banks, and charging piles to ensure that the voltage is kept within a safe range throughout the day and that voltage fluctuations are smaller.

[0210] Combination Figure 10 and Figure 11Under Scheme 2, the total number of taps of the on-load transformer is more than 10 most of the time, and the number of times and groups of capacitor banks are switched on and off is relatively large; under Scheme 3, the total number of taps of the on-load transformer is less than 10 most of the time, and the number of times and groups of capacitor banks are switched on and off is significantly reduced.

[0211] Table 1. Loss Costs at 13% Penetration Rate

[0212]

[0213] As shown in Table 1, the strategy proposed in this paper not only effectively reduces network loss costs, but also reduces the number of times traditional reactive power compensation equipment is used, thus improving the economy of the distribution network system.

[0214] Simulation results show that the voltage adjustment strategy based on situational awareness for high-penetration electric vehicles connected to the power grid proposed in this invention can effectively solve the problem of grid voltage exceeding the limit after high-penetration electric vehicles are connected to the power grid.

[0215] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Those skilled in the art can make various modifications or compensations to the specific examples described, but without departing from the scope defined by the appended claims.

Claims

1. A voltage regulation strategy for high-penetration electric vehicles connected to the power grid based on situational awareness, characterized in that, include During the situation awareness phase, the distribution network status is obtained through PMU, observability measurement, and advanced measurement technologies. Based on the multi-source information real-time interaction model, the SOC and charging characteristics information of large-scale electric vehicles connected to the distribution network are collected, and the current charging pile power, remaining compensation capacity, and reactive power compensation equipment status in the current EVS are uploaded to deeply perceive the current distribution network status. Situational understanding phase: Establish an objective function that minimizes grid loss cost, capacitor bank reactive power compensation cost, on-load transformer cost, and battery loss cost, as well as distribution network constraints. Use a joint voltage regulation strategy that optimizes the power factor angle of charging piles in charging stations to understand and evaluate the situation of high-penetration EVs connected to the grid, and output optimized electric vehicle information. Situation prediction phase: Using the acquired electric vehicle information for prediction, based on the information collected in the situation understanding phase after the electric vehicle access, the current state information of the power grid is obtained, and the development trend of the subsequent time period is predicted. The power grid voltage deviation is assessed and the degree of power grid voltage deviation is corrected. Situation guidance phase: Based on the degree of grid voltage deviation obtained in the situation prediction phase, determine whether it exceeds the set range, and based on the judgment result, choose whether to implement a secondary voltage regulation strategy or end the entire voltage adjustment process; The objective function formula is as follows: （1） In the formula: This is the distribution network loss cost coefficient; Cost of each on-load voltage regulation; For battery wear and tear costs, for t time i Nodes and j The active power loss between nodes, where E is the set of all branches in the distribution network; for t time i Reactive power compensation capacity of capacitor banks at nodes Place a set of nodes for the capacitor; This refers to the number of times the capacitor is switched on and off. The charging power for EVs.

2. The voltage adjustment strategy for high-penetration electric vehicles connected to the power grid based on situational awareness, as described in claim 1, is characterized in that... Distribution network constraints include power flow constraints in the distribution network: （2） In the formula: , They are respectively t Time Node i The active and reactive power injected into the grid by the EV; for t Time Node i The reactive power injected into the capacitor bank; and They are t Time Node i With nodes j The electrical conductivity and susceptance values ​​between them; and They are respectively Time Node The real and imaginary parts of the voltage; and Distribution network Time Node The active and reactive power of the total load.

3. The voltage adjustment strategy for high-penetration electric vehicles connected to the power grid based on situational awareness, as described in claim 2, is characterized in that... Distribution network constraints, including line losses, are expressed as follows: （3） （4） In the formula: for t time i Node reference voltage amplitude, for t Time Node i With nodes j The resistance value between; combining equations (3) and (4) to convert the active power loss of the line into electrical conductivity susceptance.

4. The voltage adjustment strategy for high-penetration electric vehicles connected to the power grid based on situational awareness, as described in claim 3, is characterized in that... Distribution network constraints include line safety constraints: （5） In the formula: It is a side road ij Maximum current; for t time i Voltage at the node and This represents the minimum and maximum allowable node voltage.

5. A voltage adjustment strategy for high-penetration electric vehicles connected to the power grid based on situational awareness, as described in claim 4, is characterized in that... Distribution network constraints include capacitor bank constraints: （6） In the formula: and They are respectively t and t-1 The reactive power compensation capacity of the capacitor bank connected at two different times. For the XOR operator, if and If the results are different, the output judgment result is 1. This represents the maximum number of times the capacitor banks can be switched on and off within a single cycle. and These represent the minimum and maximum values ​​of the reactive power compensation capacity of the capacitor bank in the distribution network. for t Capacitor capacity connected to the distribution network at all times.

6. A voltage adjustment strategy for high-penetration electric vehicles connected to the power grid based on situational awareness, as described in claim 5, is characterized in that... Distribution network constraints include on-load transformer constraints: （7） In the formula: for t On-load transformer turns ratio at all times; The increment is 0.01 per gear position. for t time ij Transformer tap positions between nodes; for t-1 time ij Transformer tap positions between nodes; This represents the maximum range of tap position variation for on-load transformers. This represents the maximum number of tap adjustments for an on-load transformer.

7. A voltage adjustment strategy for high-penetration electric vehicles connected to the power grid based on situational awareness, as described in claim 6, is characterized in that... Based on the situation awareness phase, the current status information of the power grid, EV, and EVS is collected and then analyzed in the situation understanding phase. After the situation prediction phase, the information from the understanding phase is further predicted to obtain the comprehensive information for the next moment. According to the predicted information, the power factor angle of reactive equipment in the power grid and charging piles in the EVS is adjusted to reduce the degree of power grid voltage deviation. Establish distribution network voltage deviation index As a prerequisite standard for the guiding method in the guiding process, the voltage at time t+1 is adjusted according to the voltage deviation index: （8） And a two-stage voltage regulation strategy, specifically including Level 1 voltage regulation: When the voltage deviation exceeds the specified range, the control center performs power flow calculations on the tap positions of on-load transformers and the number of capacitor banks switched on and off in the current distribution network to determine the recovery status of node voltage. If the voltage can be restored to a stable range at this time, a mathematical model is established based on the situation understanding stage to regulate the voltage of the distribution network. Secondary voltage regulation: If the primary voltage regulation cannot alleviate the voltage deviation, consider optimizing the power factor angle of the charging unit in the EVS through the control center, and adjust the charging pile power by changing the power factor angle to improve the voltage deviation. The two-stage voltage regulation strategy incorporates EVS into reactive power compensation, increasing the reactive power compensation capacity and enhancing its flexibility. At the same time, the two-stage voltage regulation strategy classifies the reactive power compensation method and selects the optimal reactive power allocation strategy according to different degrees of voltage deviation, which greatly reduces the excess losses in reactive power compensation.

8. A voltage adjustment strategy for high-penetration electric vehicles connected to the power grid based on situational awareness, as described in claim 7, is characterized in that... The first-level voltage regulation model is established based on the following steps: Based on the initial state of charge of the EV Given the EV's power consumption per unit distance, calculate the EV's state of charge upon arrival at the charging station. ; （9） In the formula: Energy consumption per unit distance of EV For EV battery capacity; （10） In the formula: and These represent the minimum and maximum capacities for various EV batteries; Battery safety constraints: （11） In the formula: and These represent the minimum and maximum capacities of the State of Charge (SOC), respectively, to ensure battery safety. , ; After the EV enters the charging station and connects to the charging pile, the current Interact with the power grid to determine the charging method; define t EV charging state variables at any time ; （12） Based on equations (8), (10), and (13), the charging demand of the EV at the next moment can be deduced: （13） In the formula: and They are respectively t+1 Time and t The EV's SOC and charging efficiency are... ; EV charging time constraints: （14） EVS total demand capacity and the number of EVs within EVS and related: （15） In the formula: for t Total capacity required within EVS at any given time This is the set of EVs in this EVS. for t Time of the first n Vehicle SOC; If all charging piles in an EVS are of the same model, then the SOC of each charging pile at time t is: （16） In the formula: for t The battery capacity of the charging station at all times. This refers to the collection of charging stations within the EVS. （17） In the formula: for t The active power of the charging station at all times. for t The reactive power of the charging station at all times. for t The power factor angle at time , when At that time, EVS transmits reactive power to the grid; Charging station operation characteristic constraints: 1) Power factor angle constraint （18） 2) Charging pile capacity constraints （19） In the formula: and These are the minimum and maximum battery capacities of the charging station, respectively. The charging station capacity constraint is derived from equations (17) and (20): （20） In the formula: and Represented as the first n Minimum and maximum capacity of each charging station; For a distribution network with a defined topology, after high-penetration EVs are connected to the grid, based on the predicted daily charging demand data of EVs, the node voltage at time t+1 is predicted by collecting the EV charging power at time t, the reactive power of the charging piles within the EVS, and the status of the reactive power compensation equipment; then the control variables for the voltage at time t are: Its voltage prediction model is as follows: （21） In the formula: for t Time Node i The node voltage sensitivity matrix of voltage change to each control variable; （22） In the formula: for i Node at t+1 Voltage at any given moment.

9. A voltage adjustment strategy for high-penetration electric vehicles connected to the power grid based on situational awareness, as described in claim 8, is characterized in that... Using the classic Newton-Raphson algorithm, the nodal admittance matrix is ​​first established. Substituting the initial voltage values ​​of each node, the elements of the Jacobian matrix are obtained. The corrected equations are then solved to obtain the voltage changes at each node, including: Grid voltage deviation assessment: When the line impedance parameters and network topology remain unchanged, the predicted voltages of each node in a typical power flow section are calculated by combining the predicted voltages of each node obtained during the situation prediction phase. （23） In the formula: for t+1 Time Node i The voltage at a certain power flow section; k Represents the system's first k The current flow status; Grid voltage deviation correction: Based on the power flow operation status of the distribution network collected during the situation awareness phase, m typical power flow operation states are extracted during the situation understanding phase, and the statistical probabilities of each power flow operation state are combined. Calculate the correction factor: （24） In the formula: For the first k Statistical probability of each power flow state For nodes i No. k Correction coefficients for each power flow operation state; Considering that a change in the voltage of one node in a distribution network system will affect other nodes, the predicted voltage sensitivity between connected nodes is obtained according to equation (22). for: （25） Grid voltage deviation acquisition: To reflect the mutual influence between nodes in the global distribution network, a global electrical distance matrix is ​​established, where the electrical distance between connected nodes is: （26） By combining the electrical distance between connected nodes and the correction factor, the corrected global electrical distance matrix is ​​obtained. D : （27） The predicted node voltages at each section represent the operating status of the distribution network under the situation prediction.

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