Power distribution network day-ahead active-reactive coordinated scheduling method considering scheduling flexibility

Abstract

The invention belongs to the technical field of power distribution network dispatching, and particularly relates to a power distribution network day-ahead active-reactive coordinated dispatching method considering dispatching flexibility. The method comprises the following steps: S1, based on DPV and load day-ahead prediction error historical data, fitting day-ahead prediction error probability models of DPV and loads in different power intervals by adopting a Gaussian mixture model; s2, obtaining a scheduling flexibility demand scene by adopting a Monte Carlo sampling method, and calculating to obtain a scheduling flexibility demand robust interval; s3, establishing a scheduling flexibility supply and demand robust balance evaluation index based on the interval coverage rate; s4, establishing a day-ahead scheduling model with minimum daily operation cost and scheduling flexibility supply and demand robust balance penalty cost as a target; according to the day-ahead active-reactive coordinated scheduling method of the power distribution network considering the scheduling flexibility, the EVCS is matched with each scheduling flexibility resource in the system in combination with the scheduling flexibility supply and demand robust balance penalty cost, and the scheduling flexibility supply and demand robust balance capability of the system is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network dispatching, and in particular relates to a day-ahead active-reactive coordinated dispatching method for a distribution network taking dispatching flexibility into consideration. Background Art

[0002] As an infrastructure for transmitting electricity from the power generation side to the user side, the power grid is generally divided into a transmission and transformation network and a distribution network according to the voltage level and function. In general, the transmission and transformation network includes a voltage level of 220KV and above, and its main function is to transmit electricity from the power generation side. High voltage can reduce losses; the distribution network includes a voltage level of 110KV and below, and its main function is to distribute electricity step by step to users of each voltage level; since the power generation output of photovoltaic and wind power clean energy is highly correlated with natural environment such as sunshine and wind speed, photovoltaic and wind power have the characteristics of large fluctuations, poor stability, and mismatch between the power generation curve and the load curve, which have an impact on the power grid's absorption capacity; the same is true for distributed clean energy, and distributed energy The main impact of the source is the distribution network, whose power output and fluctuations require the distribution network to have stronger adaptation, regulation and transmission capabilities; coordinated dispatching of distribution networks refers to the process of achieving distribution, supply and control of electric energy by coordinating and dispatching various nodes, lines and equipment in the distribution network. This process aims to improve the reliability, quality and efficiency of the distribution network to meet the electricity needs of users and ensure the smooth operation of the power grid; the increasing penetration rate of new energy in ADN has led to more uncertainties, making ADN have higher and higher requirements for dispatching flexibility. Traditional methods have not included the generalized energy storage characteristic model with EVCS as the main body into day-ahead dispatching, which can effectively improve the system dispatching flexibility and robust balance of supply and demand. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a day-ahead active-reactive coordinated dispatching method for a distribution network taking dispatch flexibility into consideration. The Monte Carlo algorithm is used to solve the robust interval of dispatch flexibility demand and to analyze the dispatch flexibility supply capacity interval of each dispatch flexibility resource in the distribution network. The method is economical, reliable and easy to operate. Combined with the penalty cost of robust balancing of dispatch flexibility supply and demand, the EVCS is coordinated with each dispatch flexibility resource in the system, which can effectively improve the robust balancing capability of the system dispatch flexibility supply and demand.

[0004] The object of the present invention is achieved by: a method for coordinated dispatching of active and reactive power of a distribution network considering dispatching flexibility, comprising the following steps:

[0005] Step S1, based on the historical data of DPV and load day-ahead forecast errors, a mixed Gaussian model is used to fit the day-ahead forecast error probability model of DPV and load in different power intervals;

[0006] Step S2: using the Monte Carlo sampling method to obtain the scheduling flexibility demand scenario and then calculate the scheduling flexibility demand robust interval;

[0007] Step S3: Establishing a dispatch flexibility supply and demand robust balance evaluation index based on the interval coverage rate;

[0008] Step S4: Establish a day-ahead scheduling model with the goal of minimizing the penalty cost of robust balance of daily operating cost and scheduling flexibility supply and demand.

[0009] The step S1 uses a mixed Gaussian model to fit the DPV and load day-ahead forecast error probability model for different power intervals based on the historical data of DPV and load day-ahead forecast errors, including:

[0010] The DPV output forecast value consists of two parts: the day-ahead forecast value and the forecast error rate. When the actual DPV output value is greater than the forecast value, there is an upward forecast error rate. On the contrary, there is a downward forecast error rate The same is true for load, as shown in formula (1):

[0011]

[0012] Where: is the historical day-ahead forecast value of the dpvi-th DPV and the li-th load node, which is a deterministic variable. are the upward / downward prediction error rates of the dpvi-th DPV and the li-th load node, respectively, and are uncertainty variables, which are calculated by formula (1):

[0013]

[0014] Where: is the actual historical output value of the dpvi-th DPV and the li-th load node;

[0015] The DPV output is divided into three power sections according to the rated value, and the third-order Gaussian mixture model is used to fit the prediction error rate distribution of each power section; the mixed Gaussian model is shown in formula (3):

[0016]

[0017] Where: are Gaussian mixture functions of the upward / downward prediction error rates of the pth power segment of the dpvith DPV and the lith load node, respectively. is the x-th Gaussian function.

[0018] The step S2 uses the Monte Carlo sampling method to obtain the scheduling flexibility demand scenario and then calculates the scheduling flexibility demand robust interval, including:

[0019] (1) Establish a probability model for the prediction error rate distribution: According to the load and DPV historical upward / downward prediction error rate, a mixed Gaussian fitting is performed to obtain the mixed Gaussian function of the DPV in different power segments and the load, as shown in formula (3);

[0020] (2) Establishing load and DPV forecast scenarios: For given load and DPV day-ahead forecast values, the forecast error rate scenario is obtained based on the forecast error rate distribution probability model obtained in step (1) and a given confidence level. The load and DPV forecast scenarios are then accumulated with the day-ahead forecast values ​​at each time point, as shown in equations (4)-(5):

[0021]

[0022]

[0023] Where: is the klth load forecast scenario at time t; is the kpth DPV prediction scenario at time t; is the day-ahead forecast value of the li-th load node at time t; is the day-ahead forecast value of the dpvith DPV at time t; They are the i-th upward / downward prediction error rate scenario of the li-th load node and the j-th upward / downward prediction error rate scenario of the dpvi-th DPV, respectively;

[0024] (3) Establishing the net load forecasting scenario: Based on the obtained load and DPV forecasting scenario curves, the net load forecasting scenario is calculated, as shown in formula (6);

[0025]

[0026] Where: is the kth net load forecasting scenario at time t; NL and NDPV are the load nodes and DPV sets in the distribution network;

[0027] (4) Establishing the dispatch flexibility demand scenario: First, calculate the day-ahead net load forecast value according to equation (7);

[0028]

[0029] Where: is the day-ahead forecast value of the net load of the distribution network at time t;

[0030] Secondly, the difference between the net load forecast scenario obtained in step (3) and the net load day-ahead forecast value is calculated according to formula (8) to obtain the dispatch flexibility demand scenario;

[0031]

[0032] Where: is the kth scheduling flexibility demand scenario at time t;

[0033] (5) Establishing a robust interval of scheduling flexibility demand: According to formula (9), the maximum and minimum scheduling flexibility demand at each moment in the scheduling flexibility demand scenario are calculated to obtain the robust interval of scheduling flexibility demand.

[0034]

[0035] Where: is the robust interval of scheduling flexibility requirements, is the upper bound of the robust interval of scheduling flexibility requirements, It is the lower bound of the robust interval of scheduling flexibility requirements.

[0036] The step S3 establishes the scheduling flexibility supply and demand robust balance evaluation index based on the interval coverage rate, including:

[0037] When adjusting the ESS output, considering its maximum charging and discharging power limit and capacity limit, the upper and lower bounds of its dispatch flexibility supply are shown in equations (10)-(11):

[0038]

[0039]

[0040] Where: E si (t) is the day-ahead dispatch capacity of the si-th ESS at time t; E si,max 、E si,min , η si are the maximum charge and discharge power, maximum and minimum capacity, and charge and discharge efficiency of the si-th ESS respectively;

[0041] The output of MGT is also limited by its ramp power. Therefore, the upper and lower bounds of its dispatch flexibility are shown in equations (12)-(13):

[0042]

[0043]

[0044] Where: P gi (t) is the day-ahead dispatch value of the gi-th MGT at time t; ΔP gi is the maximum climbing power of the gi-th MGT at time t; τ is the power adjustment time scale; They are the maximum and minimum outputs of the gi-th MGT respectively;

[0045] The dispatch flexibility supply capacity range is calculated by formula (14):

[0046]

[0047] Where: Provides intervals for scheduling flexibility, Provides an upper bound for the scheduling flexibility interval, Provide a lower bound for the scheduling flexibility interval;

[0048] The proportion of the dispatch flexibility supply and demand robust balance interval in the dispatch flexibility demand robust balance interval, that is, the dispatch flexibility supply and demand robust balance rate S bal , as an evaluation index of the robust balance capability of supply and demand for scheduling flexibility, as shown in formula (15),

[0049]

[0050] Where: are the upper and lower bounds of the robust interval of scheduling flexibility requirements, are the upper and lower bounds of the robust balance interval of dispatch flexibility supply and demand, respectively. The calculation method is shown in formula (16):

[0051]

[0052] S bal The calculated value is in the range [0,1].

[0053] The step S4 establishes a day-ahead scheduling model with the goal of minimizing the penalty cost of robust balance of daily operation cost and scheduling flexibility supply and demand, including:

[0054] Taking into account the situation that active and reactive power in the distribution network are not decoupled, active and reactive power are coordinated and optimized, and the voltage deviation cost and network loss cost considering the voltage deviation are added to the objective function. The objective function is as follows:

[0055]

[0056]

[0057] Where: C buy (t) is the cost of purchasing electricity from the external network at time t; c buy (t), P buy (t) is the cost coefficient and power of electricity purchased from the external network at time t; C G (t) is the MGT operating cost at time t; c gi,op 、c gi,of is the output cost parameter and start-stop cost parameter of the gi-th MGT; gi (t) is the output state of the gi-th MGT at time t; u gi(t),u gi (t-1) is the start / stop state 0-1 variable of the gi-th MGT at time t and time t-1. gi (t) = 1, indicating that the gi-th MGT is in the power-on state at time t; C S (t) is the ESS operating cost at time t; c si is the charge and discharge loss cost parameter of the si-th ESS; P si,char (t), P si,disc (t) is the charge and discharge power of the si-th ESS at time t; C E (t) is the EVCS operating cost at time t; c ei is the discharge compensation cost parameter of the ei-th EVCS; ei,disc (t) is the discharge power of the ei-th EVCS; NE is the set of EVCS in the distribution network; C LOSS (t) is the network loss cost at time t; ij represents the branch with i as the head end and j as the end end; I ij (t) is the current amplitude flowing through branch ij at time t; r ij is the resistance value of branch ij; c LOSS is the network loss cost coefficient; C dV (t) is the voltage deviation cost at time t; V i (t) is the voltage amplitude per unit value of node i at time t; V i,N is the nominal value of the voltage at node i; c dV is the voltage deviation cost coefficient; Nl and Ni represent the branch and node set respectively; C bal Robust balancing cost of supply and demand for distribution network dispatch flexibility; c bal Robust balancing cost coefficient for supply and demand of distribution network dispatch flexibility;

[0058] The constraints for day-ahead scheduling are as follows:

[0059] (1) Power flow constraints

[0060] The DistFlow model after second-order cone relaxation is used, as shown in equations (19)-(25):

[0061]

[0062] (V j (t)) 2 =(V i (t)) 2 -2(r ij P ij (t)+x ij Q ij (t))+(I ij (t)) 2 (rij 2 +x ij 2 ),ij∈Nl (20)

[0063]

[0064] (I ij (t)) 2 =((P ij (t)) 2 +(Q ij (t)) 2 ) / V i (t),ij∈Nl (22)

[0065]

[0066]

[0067] (P ij (t)) 2 +(Q ij (t)) 2 ≤(I ij (t)) 2 (V i (t)) 2 ,ij∈Nl (25)

[0068] Where: V i,min 、V i,max Respectively represent the minimum and maximum voltage amplitude of node i; V i (t) represents the voltage amplitude of node i at time t; r ij 、x ij Respectively represent the resistance and reactance of branch ij; P ij (t), Q ij (t) represent the active and reactive power flowing through branch ij at time t respectively; I ij,max Indicates the maximum current amplitude allowed to flow through branch ij; P j (t), Q j (t) represents the active and reactive power injected by node j; Nl represents the node set; m(j) and n(j) represent the end (head) node set of the branch with node j as the head (end) respectively;

[0069] (2) Power balance constraints

[0070]

[0071] Q Load,i (t) = Q buy,i (t)+Q CB,i (t)+QSVC,i (t)-(I ij (t)) 2 x ij (27)

[0072] Where: P Load,i (t), Q Load,i (t) respectively represent the active and reactive load power of node i at time t; P buy,i (t), Q buy,i (t) represents the active and reactive power purchased by node i from the upper power grid at time t. If the node is not connected to the upper power grid, its value is 0; P pv,i (t), P g,i (t) represents the output value of the DPV device and MGT device connected to node i at time t. If the node is not connected to the above devices, its value is 0; They represent the discharge and charging power of the ESS connected to node i at time t, respectively. If the node is not connected to the ESS, its value is 0; They represent the discharge and charging power of the EVCS connected to node i at time t. If the node is not connected to EVCS, its value is 0; Q CB,i (t), Q SVC,i (t) represents the reactive power output of the CB and SVC equipment connected to node i at time t. If the node is not connected to the above equipment, its value is 0;

[0073] (3) Power purchase power constraints

[0074]

[0075]

[0076] Where: Indicates the maximum value of active power purchase; Respectively represent the minimum and maximum values ​​of reactive power purchase;

[0077] (4) MGT operation constraints

[0078] The MGT operation constraints include upper and lower output limit constraints, see equation (30), maximum climbing / slipping constraints, see equation (31), and minimum start-stop time constraints, see equations (32)-(37);

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] Where: Ng is the MGT set in ADN; P gi (t) is the output value of the gi-th MGT at time t; are the maximum climbing and sliding powers of the gi-th MGT, respectively; are the maximum and minimum outputs of the gi-th MGT respectively; T is the dispatching period; T gi,on,min , T gi,off,min are the minimum start and stop time of the gi-th MGT respectively; u gi (1) is the initial start and stop state of the gi-th MGT; u gi (t) is the start / stop status of the gi-th MGT at time t;

[0088] (5) ESS operation constraints

[0089]

[0090]

[0091] u si,char (t)+u si,disc (t)≤1,si∈Ns (40)

[0092]

[0093] Where: Ns is the set of ESS in ADN; P si,char (t), P si,disc (t) are the charge and discharge power of the si-th ESS at time t; are the maximum charge and discharge power of the si-th ESS; u si,char (t),u si,disc (t) is the charge and discharge state 0-1 variable of the si-th ESS at time t, u si,disc (t) = 1, it is in the discharge state, u si,char (t) = 1, it is in charging state; are the maximum and minimum SOC of the si-th ESS respectively; E si , η si are the maximum capacity and charge / discharge efficiency of the si-th ESS respectively; E si (t) is the capacity and charge-discharge efficiency of the si-th ESS at time t;

[0094] (6) EVCS operation constraints

[0095]

[0096]

[0097]

[0098]

[0099] u ei,char (t)+u ei,disc (t)≤1 (46)

[0100]

[0101] Where: E ei (t+1), E ei (t) represents the capacity status of the ei-th EVCS at time t+1 and t respectively; P ei,char (t), P ei,disc (t) respectively represent the charge and discharge power of the ei-th EVCS at time t; They represent the total capacity of EVs connected to and off-grid in the ei-th EVCS at time t+1; η ei is the EVCS charging and discharging efficiency; u ei,char (t),u ei,disc (t) are 0-1 variables representing the charge and discharge state of the ei-th EVCS at time t. ei,char (t) = 1, indicating that the EVCS is in the charging state, u ei,disc (t) = 1 indicates that the EVCS is in the discharge state; They represent the sum of the maximum and minimum expected capacity values ​​of the off-grid EV in the ei-th EVCS at time t, respectively, and are set by the user;

[0102] (7) SVC operation constraints

[0103]

[0104] Where: Nsvc is the SVC set in ADN; and They represent the upper and lower limits of the SVC operation capacity of the svci station respectively;

[0105] (8) CB operation constraints

[0106]

[0107]

[0108] Where: Ncb is the CB set in ADN; Q cbi (t) represents the compensation power of the cbith CB at time t; cbi (t) represents the number of cbi-th CB input groups at time t; It represents the compensation power of each group of cbi-th CB; Indicates the maximum number of investment groups for the cbi-th CB.

[0109] Beneficial effects of the present invention: The present invention adopts the method for coordinated dispatching of active and reactive power in distribution network considering dispatching flexibility, based on the load and DPV historical forecast error data, to solve the robust interval of dispatching flexibility demand by using Monte Carlo algorithm, and analyze the dispatching flexibility supply capacity interval of each dispatching flexibility resource in the distribution network, which is economical, reliable and easy to operate;

[0110] Based on the EV travel chain, the generalized energy storage characteristics of EVCS as the main body are analyzed and modeled, and incorporated into the day-ahead dispatch. The results show that only considering the EVCS energy storage characteristics cannot effectively improve the level of system dispatch flexibility supply, but combining the dispatch flexibility supply and demand robust balance penalty cost, so that EVCS and various dispatch flexibility resources in the system cooperate with each other, can effectively improve the system dispatch flexibility supply and demand robust balance capability;

[0111] Active-reactive coordinated optimization scheduling is adopted. After the optimization scheduling, the node voltage is distributed around the voltage per unit value of 1.0, and the overall distribution trend is relatively stable. In addition, EVCS, as a generalized energy storage, participates in the day-ahead scheduling process, which can effectively improve the voltage rise problem caused by DPV. BRIEF DESCRIPTION OF THE DRAWINGS

[0112] Figure 1 It is a schematic diagram of the process of the present invention.

[0113] Figure 2a This is a schematic diagram of the scheduling results of the plan one day in advance.

[0114] Figure 2b This is a schematic diagram of the scheduling results of the plan two days ago.

[0115] Figure 3a Schematic diagram of the robust balance interval of supply and demand for scheduling flexibility in Scheme 1.

[0116] Figure 3b Schematic diagram of the robust balance interval of supply and demand for scheduling flexibility in Scheme 1.

[0117] Figure 4a This is a schematic diagram of reactive power compensation results of Scheme 1.

[0118] Figure 4b This is a schematic diagram of reactive power compensation results of Scheme 2.

[0119] Figure 5a Schematic diagram of voltage distribution at each node in scheme 1.

[0120] Figure 5b Schematic diagram of voltage distribution at each node in scheme 2.

[0121] Figure 6a This is a schematic diagram of the scheduling results of the plan three days ago.

[0122] Figure 6b This is a schematic diagram of the scheduling results of the plan four days ago.

[0123] Figure 7a Schematic diagram of the robust balance range of supply and demand for scheduling flexibility in Scheme 3.

[0124] Figure 7b Schematic diagram of the robust balance range of supply and demand for scheduling flexibility in Scheme 4.

[0125] Figure 8 Schematic diagram of the MGT and ESS scheduling flexibility supply capacity for each scheme.

[0126] Fig. 9 This is a schematic diagram of the reactive power compensation results of schemes three and four.

[0127] Fig.10a Schematic diagram of voltage distribution at each node in scheme three.

[0128] Fig.10b Schematic diagram of voltage distribution at each node in scheme 4. DETAILED DESCRIPTION

[0129] The present invention will be further described below in conjunction with the accompanying drawings.

[0130] The present invention provides a method for coordinated dispatching of active and reactive power of a distribution network in the day ahead considering dispatching flexibility, which comprises the following steps:

[0131] Step S1, based on the historical data of DPV and load day-ahead forecast errors, a mixed Gaussian model is used to fit the day-ahead forecast error probability model of DPV and load in different power intervals;

[0132] Step S2: using the Monte Carlo sampling method to obtain the scheduling flexibility demand scenario and then calculate the scheduling flexibility demand robust interval;

[0133] Step S3: Establishing a dispatch flexibility supply and demand robust balance evaluation index based on the interval coverage rate;

[0134] Step S4: Establish a day-ahead scheduling model with the goal of minimizing the penalty cost of robust balance of daily operating cost and scheduling flexibility supply and demand.

[0135] The step S1 uses a mixed Gaussian model to fit the DPV and load day-ahead forecast error probability model for different power intervals based on the historical data of DPV and load day-ahead forecast errors, including:

[0136] The DPV output forecast value consists of two parts: the day-ahead forecast value and the forecast error rate. When the actual DPV output value is greater than the forecast value, there is an upward forecast error rate. On the contrary, there is a downward forecast error rate The same is true for load, as shown in formula (1):

[0137]

[0138] Where: is the historical day-ahead forecast value of the dpvi-th DPV and the li-th load node, which is a deterministic variable. are the upward / downward prediction error rates of the dpvi-th DPV and the li-th load node, respectively, and are uncertainty variables, which are calculated by formula (1):

[0139]

[0140] Where: is the actual historical output value of the dpvi-th DPV and the li-th load node;

[0141] The DPV output is divided into three power sections according to the rated value, and the third-order Gaussian mixture model is used to fit the prediction error rate distribution of each power section; the mixed Gaussian model is shown in formula (3):

[0142]

[0143] Where: are Gaussian mixture functions of the upward / downward prediction error rates of the pth power segment of the dpvith DPV and the lith load node, respectively. is the x-th Gaussian function.

[0144] The step S2 uses the Monte Carlo sampling method to obtain the scheduling flexibility demand scenario and then calculates the scheduling flexibility demand robust interval, including:

[0145] (1) Establish a probability model for the prediction error rate distribution: According to the load and DPV historical upward / downward prediction error rate, a mixed Gaussian fitting is performed to obtain the mixed Gaussian function of the DPV in different power segments and the load, as shown in formula (3);

[0146] (2) Establishing load and DPV prediction scenarios: For given load and DPV day-ahead prediction values, according to the prediction error rate distribution probability model obtained in step (1), since the power range of DPV output at each moment is different, the upward prediction error rate sampling and downward prediction error rate sampling of the load and DPV power at each moment are performed respectively. It is obviously too conservative to take the maximum upward / downward prediction error rate scenario into consideration. Therefore, a given confidence level is obtained to obtain the prediction error rate scenario, which is accumulated with the day-ahead prediction value at each moment to obtain the load and DPV prediction scenario, as shown in equations (4)-(5):

[0147]

[0148]

[0149] Where: is the klth load forecast scenario at time t; is the kpth DPV prediction scenario at time t; is the day-ahead forecast value of the li-th load node at time t; is the day-ahead forecast value of the dpvith DPV at time t; They are the i-th upward / downward prediction error rate scenario of the li-th load node and the j-th upward / downward prediction error rate scenario of the dpvi-th DPV, respectively;

[0150] (3) Establishing the net load forecasting scenario: Based on the obtained load and DPV forecasting scenario curves, the net load forecasting scenario is calculated, as shown in formula (6);

[0151]

[0152] Where: is the kth net load forecasting scenario at time t; NL and NDPV are the load nodes and DPV sets in the distribution network;

[0153] (4) Establishing the dispatch flexibility demand scenario: The net load forecast error is the source of the dispatch flexibility demand. The net load forecast error scenario is the dispatch flexibility demand scenario. First, the net load day-ahead forecast value is calculated according to formula (7);

[0154]

[0155] Where: is the day-ahead forecast value of the net load of the distribution network at time t;

[0156] Secondly, the difference between the net load forecast scenario obtained in step (3) and the net load day-ahead forecast value is calculated according to formula (8) to obtain the dispatch flexibility demand scenario;

[0157]

[0158] Where: is the kth scheduling flexibility demand scenario at time t;

[0159] (5) Establishing a robust interval for scheduling flexibility demand: As an effective uncertainty analysis method, the interval method only requires the size of the distribution interval of the uncertainty variable to solve the model. According to formula (9), the maximum and minimum scheduling flexibility demands at each moment in the scheduling flexibility demand scenario are calculated to obtain the robust interval for scheduling flexibility demand. The confidence level in step (2) reflects the robustness of the robust interval for scheduling flexibility demand. The smaller the confidence level, the stronger the robustness of the obtained interval, but it tends to be more conservative.

[0160]

[0161] Where: is the robust interval of scheduling flexibility requirements, is the upper bound of the robust interval of scheduling flexibility requirements, It is the lower bound of the robust interval of scheduling flexibility requirements.

[0162] The step S3 establishes the scheduling flexibility supply and demand robust balance evaluation index based on the interval coverage rate, including:

[0163] ESS provides upward and downward scheduling flexibility through discharging and charging. When adjusting the ESS output, considering its maximum charging and discharging power limit and capacity limit, its scheduling flexibility supply upper and lower bounds are shown in equations (10)-(11):

[0164]

[0165]

[0166] Where: E si (t) is the day-ahead dispatch capacity of the si-th ESS at time t; E si,max 、E si,min , η si are the maximum charge and discharge power, maximum and minimum capacity, and charge and discharge efficiency of the si-th ESS respectively;

[0167] MGT can provide upward and downward scheduling flexibility by increasing or decreasing output (without changing the start and stop state of MGT). MGT output is also limited by its climbing power. Therefore, the upper and lower bounds of its scheduling flexibility supply are shown in equations (12)-(13):

[0168]

[0169]

[0170] Where: P gi (t) is the day-ahead dispatch value of the gi-th MGT at time t; ΔP gi is the maximum climbing power of the gi-th MGT at time t; τ is the power adjustment time scale, which is set to 15 minutes; They are the maximum and minimum outputs of the gi-th MGT respectively;

[0171] The dispatch flexibility supply capacity range is calculated by formula (14):

[0172]

[0173] Where: Provides intervals for scheduling flexibility, Provides an upper bound for the scheduling flexibility interval, Provide a lower bound for the scheduling flexibility interval;

[0174] The proportion of the dispatch flexibility supply and demand robust balance interval in the dispatch flexibility demand robust balance interval, that is, the dispatch flexibility supply and demand robust balance rate S bal , as an evaluation index of the robust balance capability of supply and demand for scheduling flexibility, as shown in formula (15),

[0175]

[0176] Where: are the upper and lower bounds of the robust interval of scheduling flexibility requirements, are the upper and lower bounds of the robust balance interval of dispatch flexibility supply and demand, respectively. The calculation method is shown in formula (16):

[0177]

[0178] S bal The calculated value is in the range of [0,1]. The closer the value is to 1, the stronger the scheduling flexibility and robust balance between supply and demand are.

[0179] The step S4 establishes a day-ahead scheduling model with the goal of minimizing the penalty cost of robust balance of daily operation cost and scheduling flexibility supply and demand, including:

[0180] Taking into account the situation that active and reactive power in the distribution network are not decoupled, active and reactive power are coordinated and optimized, and the voltage deviation cost and network loss cost considering the voltage deviation are added to the objective function. The objective function is as follows:

[0181]

[0182]

[0183] Where: C buy(t) is the cost of purchasing electricity from the external network at time t; c buy (t), P buy (t) is the cost coefficient and power of electricity purchased from the external network at time t; C G (t) is the MGT operating cost at time t; c gi,op 、c gi,of is the output cost parameter and start-stop cost parameter of the gi-th MGT; gi (t) is the output state of the gi-th MGT at time t; u gi (t),u gi (t-1) is the start / stop state 0-1 variable of the gi-th MGT at time t and time t-1. gi (t) = 1, indicating that the gi-th MGT is in the power-on state at time t; C S (t) is the ESS operating cost at time t; c si is the charge and discharge loss cost parameter of the si-th ESS; P si,char (t), P si,disc (t) is the charge and discharge power of the si-th ESS at time t; C E (t) is the EVCS operating cost at time t; c ei is the discharge compensation cost parameter of the ei-th EVCS; ei,disc (t) is the discharge power of the ei-th EVCS; NE is the set of EVCS in the distribution network; C LOSS (t) is the network loss cost at time t; ij represents the branch with i as the head end and j as the end end; I ij (t) is the current amplitude flowing through branch ij at time t; r ij is the resistance value of branch ij; c LOSS is the network loss cost coefficient, set to 100 yuan / MW; C dV (t) is the voltage deviation cost at time t; V i (t) is the voltage amplitude per unit value of node i at time t; V i,N is the nominal value of the voltage at node i; c dV is the voltage deviation cost coefficient, which is set to 100 yuan / pu; Nl and Ni represent the branch and node set respectively; C bal Robust balancing cost of supply and demand for distribution network dispatch flexibility; c bal is the cost coefficient of robust balancing supply and demand of distribution network dispatch flexibility, which is set to -1000;

[0184] The constraints for day-ahead scheduling are as follows:

[0185] (1) Power flow constraints

[0186] The DistFlow model after second-order cone relaxation is used, as shown in equations (19)-(25):

[0187]

[0188] (V j (t)) 2 =(V i (t)) 2 -2(r ij P ij (t)+x ij Q ij (t))+(I ij (t)) 2 (r ij 2 +x ij 2 ),ij∈Nl (20)

[0189]

[0190] (I ij (t)) 2 =((P ij (t)) 2 +(Q ij (t)) 2 ) / V i (t),ij∈Nl (22)

[0191]

[0192]

[0193] (P ij (t)) 2 +(Q ij (t)) 2 ≤(I ij (t)) 2 (V i (t)) 2 ,ij∈Nl (25)

[0194] Where: V i,min 、V i,max Respectively represent the minimum and maximum voltage amplitude of node i; V i (t) represents the voltage amplitude of node i at time t; r ij 、x ij Respectively represent the resistance and reactance of branch ij; P ij (t), Q ij (t) represent the active and reactive power flowing through branch ij at time t respectively; I ij,max Indicates the maximum current amplitude allowed to flow through branch ij; P j (t), Qj (t) represents the active and reactive power injected by node j; Nl represents the node set; m(j) and n(j) represent the end (head) node set of the branch with node j as the head (end) respectively;

[0195] (2) Power balance constraints

[0196]

[0197] Q Load,i (t) = Q buy,i (t)+Q CB,i (t)+Q SVC,i (t)-(I ij (t)) 2 x ij (27)

[0198] Where: P Load,i (t), Q Load,i (t) respectively represent the active and reactive load power of node i at time t; P buy,i (t), Q buy,i (t) represents the active and reactive power purchased by node i from the upper power grid at time t. If the node is not connected to the upper power grid, its value is 0; P pv,i (t), P g,i (t) represents the output value of the DPV device and MGT device connected to node i at time t. If the node is not connected to the above devices, its value is 0; They represent the discharge and charging power of the ESS connected to node i at time t, respectively. If the node is not connected to the ESS, its value is 0; They represent the discharge and charging power of the EVCS connected to node i at time t. If the node is not connected to EVCS, its value is 0; Q CB,i (t), Q SVC,i (t) represents the reactive power output of the CB and SVC equipment connected to node i at time t. If the node is not connected to the above equipment, its value is 0;

[0199] (3) Power purchase power constraints

[0200]

[0201]

[0202] Where: Indicates the maximum value of active power purchase; Respectively represent the minimum and maximum values ​​of reactive power purchase;

[0203] (4) MGT operation constraints

[0204] The MGT operation constraints include upper and lower output limit constraints, see equation (30), maximum climbing / slipping constraints, see equation (31), and minimum start-stop time constraints, see equations (32)-(37);

[0205]

[0206]

[0207]

[0208]

[0209]

[0210]

[0211]

[0212]

[0213] Where: Ng is the MGT set in ADN; P gi (t) is the output value of the gi-th MGT at time t; are the maximum climbing and sliding powers of the gi-th MGT, respectively; are the maximum and minimum outputs of the gi-th MGT respectively; T is the dispatching period; T gi,on,min , T gi,off,min are the minimum start and stop time of the gi-th MGT respectively; u gi (1) is the initial start and stop state of the gi-th MGT; u gi (t) is the start / stop status of the gi-th MGT at time t;

[0214] (5) ESS operation constraints

[0215]

[0216]

[0217] u si,char (t)+u si,disc (t)≤1,si∈Ns (40)

[0218]

[0219] Where: Ns is the set of ESS in ADN; P si,char (t), P si,disc (t) are the charge and discharge power of the si-th ESS at time t; are the maximum charge and discharge power of the si-th ESS; usi,char (t),u si,disc (t) is the charge and discharge state 0-1 variable of the si-th ESS at time t, u si,disc (t) = 1, it is in the discharge state, u si,char (t) = 1, it is in charging state; are the maximum and minimum SOC of the si-th ESS respectively; E si , η si are the maximum capacity and charge / discharge efficiency of the si-th ESS respectively; E si (t) is the capacity and charge-discharge efficiency of the si-th ESS at time t;

[0220] (6) EVCS operation constraints

[0221]

[0222]

[0223]

[0224]

[0225] u ei,char (t)+u ei,disc (t)≤1 (46)

[0226]

[0227] Where: E ei (t+1), E ei (t) represents the capacity status of the ei-th EVCS at time t+1 and t respectively; P ei,char (t), P ei,disc (t) respectively represent the charge and discharge power of the ei-th EVCS at time t; They represent the total capacity of EVs connected to and off-grid in the ei-th EVCS at time t+1; η ei is the EVCS charging and discharging efficiency; u ei,char (t),u ei,disc (t) are 0-1 variables representing the charge and discharge state of the ei-th EVCS at time t. ei,char (t) = 1, indicating that the EVCS is in the charging state, u ei,disc (t) = 1 indicates that the EVCS is in the discharge state; They represent the sum of the maximum and minimum expected capacity values ​​of the off-grid EV in the ei-th EVCS at time t, respectively, and are set by the user;

[0228] (7) SVC operation constraints

[0229]

[0230] Where: Nsvc is the SVC set in ADN; and They represent the upper and lower limits of the SVC operation capacity of the svci station respectively;

[0231] (8) CB operation constraints

[0232]

[0233]

[0234] Where: Ncb is the CB set in ADN; Q cbi (t) represents the compensation power of the cbith CB at time t; cbi (t) represents the number of cbi-th CB input groups at time t; It represents the compensation power of each group of cbi-th CB; Indicates the maximum number of investment groups for the cbi-th CB.

[0235] The following takes the IEEE 33-node system including EVCS and DG as an example to analyze the ADN day-ahead scheduling considering scheduling flexibility.

[0236] In order to analyze the impact of EVCS on the supply capacity of scheduling flexibility resources, two different day-ahead scheduling schemes are formulated, and neither considers the penalty cost of robust balance between scheduling flexibility supply and demand:

[0237] Option 1: Day-ahead dispatch does not consider the EVCS energy storage characteristics;

[0238] Option 2: Day-ahead dispatch taking into account the energy storage characteristics of EVCS.

[0239] The day-ahead dispatch results and dispatch flexibility supply capacity ranges of each scheme are shown in Figures 2 and 3 respectively.

[0240] In addition, the voltage distribution of the two schemes is analyzed, and the reactive power compensation results and node voltage distribution of each scheme are shown in Figure 4 and Figure 5 respectively.

[0241] According to the voltage distribution of each scheme in Figure 5, it can be found that the voltage of each node in both schemes is distributed around the voltage per unit value of 1.0 at each moment, and the overall distribution trend is relatively stable. The SVC device and CB device connected to the node without DPV provide capacitive reactive compensation to increase the node voltage. The SVC device connected to the node with DPV provides capacitive reactive compensation to increase the node voltage when the DPV output is small, and reduces the node voltage by providing inductive reactive power when the DPV output is large. The effectiveness of the active-reactive coordinated scheduling method of the present invention is verified.

[0242] In order to analyze the impact of the penalty cost of the robust balancing of the supply and demand of dispatching flexibility proposed in the present invention on the day-ahead dispatching and the dispatching flexibility supply capacity of the distribution network, the day-ahead dispatching schemes 3 and 4 are formulated based on schemes 1 and 2 respectively, taking into account the penalty cost of the robust balancing of the supply and demand of dispatching flexibility:

[0243] Option 3: Day-ahead dispatch does not consider the EVCS energy storage characteristics, but considers the penalty cost of dispatch flexibility and robust balance of supply and demand;

[0244] Option 4: Day-ahead dispatch takes into account the EVCS energy storage characteristics and dispatch flexibility to balance supply and demand robustly with penalty costs.

[0245] The scheduling results and scheduling flexibility supply capacity ranges of Scheme 3 and Scheme 4 are shown in Figures 6 and 7.

[0246] By comparing Figure 7 with Figure 3, it can be found that Scheme 3 and Scheme 4, which consider the penalty cost of robust balancing of dispatch flexibility supply and demand, have stronger dispatch flexibility supply and demand balancing capabilities than Scheme 1 and Scheme 2.

[0247] Further analysis of the balance level of downward dispatch flexibility supply and demand of each scheme system is conducted. According to Figure 7 and Figure 3, it can be found that both Scheme 1 and Scheme 3 have insufficient downward dispatch flexibility supply capacity at 12-17 o'clock, but Scheme 3 has a slightly better robust balance ability of downward dispatch flexibility supply and demand than Scheme 1. Figure 8 (a) with Figure 8 (c), as well as Figure 2(a) and Figure 6(a), it can be found that Scheme 3 increases the ESS discharge power at moments 3-9 when the upward dispatch flexibility supply capacity is relatively abundant, in exchange for more ESS downward dispatch flexibility supply capacity, thereby improving the overall system's robust balance of downward dispatch flexibility supply and demand. However, Scheme 2 has insufficient downward dispatch flexibility supply at moments 1 and 5-8 because the ESS is charged at maximum power. Scheme 4 reduces the ESS charging power at moment 1 when the ESS is in the charging state to increase the ESS downward dispatch flexibility supply capacity at moments 1-5. At moments 6-8, when the ESS is in the discharging state, the ESS discharge power is increased to increase the ESS downward dispatch flexibility supply capacity at moments 6-8.

[0248] In addition, the voltage distribution of each scheme is analyzed. The reactive power compensation results and node voltage distribution of schemes 3 and 4 are shown as follows: Fig. 9 , as shown in Figure 10.

[0249] According to the voltage distribution of each scheme in Figure 5 and Figure 10, it can be found that considering the penalty cost of robust balance of supply and demand with scheduling flexibility has little impact on the overall voltage level of the system, and the voltage distribution trend is relatively stable.

[0250] In summary, the present invention adopts the method for coordinated dispatching of active and reactive power in distribution network considering dispatching flexibility, based on the load and DPV historical forecast error data, to solve the robust interval of dispatching flexibility demand, and analyze the dispatching flexibility supply capacity interval of each dispatching flexibility resource in the distribution network. The method is economical, reliable and easy to operate.

[0251] Based on the EV travel chain, the generalized energy storage characteristics of EVCS as the main body are analyzed and modeled, and incorporated into the day-ahead dispatch. The results show that only considering the EVCS energy storage characteristics cannot effectively improve the level of system dispatch flexibility supply, but combining the dispatch flexibility supply and demand robust balance penalty cost, so that EVCS and various dispatch flexibility resources in the system cooperate with each other, can effectively improve the system dispatch flexibility supply and demand robust balance capability;

[0252] Active-reactive coordinated optimization scheduling is adopted. After the optimization scheduling, the node voltage is distributed around the voltage per unit value of 1.0, and the overall distribution trend is relatively stable. In addition, EVCS, as a generalized energy storage, participates in the day-ahead scheduling process, which can effectively improve the voltage rise problem caused by DPV.

Claims

1. A day-ahead active-reactive coordinated dispatching method for distribution network considering dispatching flexibility, characterized in that: It includes the following steps: Step S1, based on the historical data of DPV and load day-ahead forecast errors, a mixed Gaussian model is used to fit the day-ahead forecast error probability model of DPV and load in different power intervals; Step S2: using the Monte Carlo sampling method to obtain the scheduling flexibility demand scenario and then calculate the scheduling flexibility demand robust interval; Step S3: Establishing a dispatch flexibility supply and demand robust balance evaluation index based on the interval coverage rate; Step S4: Establish a day-ahead scheduling model with the goal of minimizing the penalty cost of daily operating cost and robust balance of supply and demand for scheduling flexibility.

2. The method for coordinated dispatching of active and reactive power of a distribution network considering dispatching flexibility according to claim 1, characterized in that: The step S1 uses a mixed Gaussian model to fit the DPV and load day-ahead forecast error probability model for different power intervals based on the historical data of DPV and load day-ahead forecast errors, including: The DPV output forecast value consists of two parts: the day-ahead forecast value and the forecast error rate. When the actual DPV output value is greater than the forecast value, there is an upward forecast error rate. On the contrary, there is a downward forecast error rate The same is true for load, as shown in formula (1): Where: is the historical day-ahead forecast value of the dpvi-th DPV and the li-th load node, which is a deterministic variable. are the upward / downward prediction error rates of the dpvi-th DPV and the li-th load node, respectively, and are uncertainty variables, which are calculated by formula (1): Where: is the actual historical output value of the dpvi-th DPV and the li-th load node; The DPV output is divided into three power sections according to the rated value, and the third-order Gaussian mixture model is used to fit the prediction error rate distribution of each power section; the mixed Gaussian model is shown in formula (3): Where: are Gaussian mixture functions of the upward / downward prediction error rates of the pth power segment of the dpvith DPV and the lith load node, respectively. is the x-th Gaussian function.

3. The method for coordinated dispatching of active and reactive power of distribution network considering dispatching flexibility according to claim 2, characterized in that: The step S2 uses the Monte Carlo sampling method to obtain the scheduling flexibility demand scenario and then calculates the scheduling flexibility demand robust interval, including: (1) Establish a probability model for the prediction error rate distribution: According to the load and DPV historical upward / downward prediction error rate, a mixed Gaussian fitting is performed to obtain the mixed Gaussian function of the DPV in different power segments and the load, as shown in formula (3); (2) Establishing load and DPV forecast scenarios: For given load and DPV day-ahead forecast values, the forecast error rate scenario is obtained based on the forecast error rate distribution probability model obtained in step (1) and a given confidence level. The load and DPV forecast scenarios are then accumulated with the day-ahead forecast values ​​at each time point, as shown in equations (4)-(5): Where: is the klth load forecast scenario at time t; is the kpth DPV prediction scenario at time t; is the day-ahead forecast value of the li-th load node at time t; is the day-ahead forecast value of the dpvith DPV at time t; They are the i-th upward / downward prediction error rate scenario of the li-th load node and the j-th upward / downward prediction error rate scenario of the dpvi-th DPV, respectively; (3) Establishing the net load forecasting scenario: Based on the obtained load and DPV forecasting scenario curves, the net load forecasting scenario is calculated, as shown in formula (6); Where: is the kth net load forecasting scenario at time t; NL and NDPV are the load nodes and DPV sets in the distribution network; (4) Establishing the dispatch flexibility demand scenario: First, calculate the day-ahead net load forecast value according to equation (7); Where: is the day-ahead forecast value of the net load of the distribution network at time t; Secondly, the difference between the net load forecast scenario obtained in step (3) and the net load day-ahead forecast value is calculated according to formula (8) to obtain the dispatch flexibility demand scenario; Where: is the kth scheduling flexibility demand scenario at time t; (5) Establishing a robust interval of scheduling flexibility demand: According to formula (9), the maximum and minimum scheduling flexibility demand at each moment in the scheduling flexibility demand scenario are calculated to obtain the robust interval of scheduling flexibility demand. Where: is the robust interval of scheduling flexibility requirements, is the upper bound of the robust interval of scheduling flexibility requirements, It is the lower bound of the robust interval of scheduling flexibility requirements.

4. The method for coordinated dispatching of active and reactive power of distribution network considering dispatching flexibility according to claim 3, characterized in that: The step S3 establishes the dispatching flexibility supply and demand robust balance evaluation index based on the interval coverage rate, including: When adjusting the ESS output, considering its maximum charging and discharging power limit and capacity limit, the upper and lower bounds of its dispatch flexibility supply are shown in equations (10)-(11): Where: E si (t) is the day-ahead dispatch capacity of the si-th ESS at time t; E si,max 、E si,min , η si are the maximum charge and discharge power, maximum and minimum capacity, and charge and discharge efficiency of the si-th ESS respectively; The output of MGT is also limited by its ramp power. Therefore, the upper and lower bounds of its dispatch flexibility are shown in equations (12)-(13): Where: P gi (t) is the day-ahead dispatch value of the gi-th MGT at time t; ΔP gi is the maximum climbing power of the gi-th MGT at time t; τ is the power adjustment time scale; They are the maximum and minimum outputs of the gi-th MGT respectively; The dispatch flexibility supply capacity range is calculated by formula (14): Where: Provides intervals for scheduling flexibility, Provides an upper bound for the scheduling flexibility interval, Provide a lower bound for the scheduling flexibility interval; The proportion of the dispatch flexibility supply and demand robust balance interval in the dispatch flexibility demand robust balance interval, that is, the dispatch flexibility supply and demand robust balance rate S bal , as an evaluation index of the robust balance capability of supply and demand for scheduling flexibility, as shown in formula (15), Where: are the upper and lower bounds of the robust interval of scheduling flexibility requirements, are the upper and lower bounds of the robust balance interval of dispatch flexibility supply and demand, respectively. The calculation method is shown in formula (16): S bal The calculated value is in the range [0,1].

5. The method for coordinated dispatching of active and reactive power of distribution network considering dispatching flexibility according to claim 4, characterized in that: The step S4 establishes a day-ahead scheduling model with the goal of minimizing the penalty cost of robust balance of daily operation cost and scheduling flexibility supply and demand, including: Taking into account the situation that active and reactive power in the distribution network are not decoupled, active and reactive power are coordinated and optimized, and the voltage deviation cost and network loss cost considering the voltage deviation are added to the objective function. The objective function is as follows: Where: C buy (t) is the cost of purchasing electricity from the external network at time t; c buy (t), P buy (t) is the cost coefficient and power of electricity purchased from the external network at time t; C G (t) is the MGT operating cost at time t; c gi,op 、c gi,of is the output cost parameter and start-stop cost parameter of the gi-th MGT; gi (t) is the output state of the gi-th MGT at time t; u gi (t),u gi (t-1) is the start / stop state 0-1 variable of the gi-th MGT at time t and time t-1. gi (t) = 1, indicating that the gi-th MGT is in the power-on state at time t; C S (t) is the ESS operating cost at time t; c si is the charge and discharge loss cost parameter of the si-th ESS; P si,char (t), P si,disc (t) is the charge and discharge power of the si-th ESS at time t; C E (t) is the EVCS operating cost at time t; c ei is the discharge compensation cost parameter of the ei-th EVCS; P ei,disc (t) is the discharge power of the ei-th EVCS; NE is the set of EVCS in the distribution network; C LOSS (t) is the network loss cost at time t; ij represents the branch with i as the head end and j as the end end; I ij (t) is the current amplitude flowing through branch ij at time t; r ij is the resistance value of branch ij; c LOSS is the network loss cost coefficient; C dV (t) is the voltage deviation cost at time t; V i (t) is the voltage amplitude per unit value of node i at time t; V i,N is the nominal value of the voltage at node i; c dV is the voltage deviation cost coefficient; Nl and Ni represent the branch and node set respectively; C bal Robust balancing cost of supply and demand for distribution network dispatch flexibility; c bal Robust balancing cost coefficient for supply and demand of distribution network dispatch flexibility; The constraints for day-ahead scheduling are as follows: (1) Power flow constraints The DistFlow model after second-order cone relaxation is used, as shown in equations (19)-(25): (V j (t)) 2 =(V i (t)) 2 -2(r ij P ij (t)+x ij Q ij (t))+(I ij (t)) 2 (r ij 2 +x ij 2 ),ij∈Nl (20) (I ij (t)) 2 =((P ij (t)) 2 +(Q ij (t)) 2 ) / V i (t),ij∈Nl (22) (P ij (t)) 2 +(Q ij (t)) 2 ≤(I ij (t)) 2 (V i (t)) 2 ,ij∈Nl (25) Where: V i,min 、V i,max Respectively represent the minimum and maximum voltage amplitude of node i; V i (t) represents the voltage amplitude of node i at time t; r ij 、x ij Respectively represent the resistance and reactance of branch ij; P ij (t), Q ij (t) represent the active and reactive power flowing through branch ij at time t respectively; I ij,max Indicates the maximum current amplitude allowed to flow through branch ij; P j (t), Q j (t) represents the active and reactive power injected by node j; Nl represents the node set; m(j) and n(j) represent the end (head) node set of the branch with node j as the head (end) respectively; (2) Power balance constraints Q Load,i (t)=Q buy,i (t)+Q CB,i (t)+Q SVC,i (t)-(I ij (t)) 2 x ij (27) Where: P Load,i (t), Q Load,i (t) respectively represent the active and reactive load power of node i at time t; P buy,i (t), Q buy,i (t) represents the active and reactive power purchased by node i from the upper power grid at time t. If the node is not connected to the upper power grid, its value is 0; P pv,i (t), P g,i (t) represents the output value of the DPV device and MGT device connected to node i at time t. If the node is not connected to the above devices, its value is 0; They represent the discharge and charging power of the ESS connected to node i at time t, respectively. If the node is not connected to the ESS, its value is 0; They represent the discharge and charging power of the EVCS connected to node i at time t. If the node is not connected to EVCS, its value is 0; Q CB,i (t), Q SVC,i (t) represents the reactive power output of the CB and SVC equipment connected to node i at time t. If the node is not connected to the above equipment, its value is 0; (3) Power purchase power constraints Where: Indicates the maximum value of active power purchase; Respectively represent the minimum and maximum values ​​of reactive power purchase; (4) MGT operation constraints The MGT operation constraints include upper and lower output limit constraints, see equation (30), maximum climbing / slipping constraints, see equation (31), and minimum start-stop time constraints, see equations (32)-(37); Where: Ng is the MGT set in ADN; P gi (t) is the output value of the gi-th MGT at time t; are the maximum climbing and sliding powers of the gi-th MGT, respectively; are the maximum and minimum outputs of the gi-th MGT respectively; T is the dispatching period; T gi,on,min , T gi,off,min are the minimum start and stop time of the gi-th MGT respectively; u gi (1) is the initial start and stop state of the gi-th MGT; u gi (t) is the start / stop status of the gi-th MGT at time t; (5) ESS operation constraints u si,char (t)+u si,disc (t)≤1,si∈Ns (40) Where: Ns is the set of ESS in ADN; P si,char (t), P si,disc (t) are the charge and discharge power of the si-th ESS at time t; are the maximum charge and discharge power of the si-th ESS; u si,char (t),u si,disc (t) is the charge and discharge state 0-1 variable of the si-th ESS at time t, u si,disc (t) = 1, it is in the discharge state, u si,char (t) = 1, it is in charging state; are the maximum and minimum SOC of the si-th ESS respectively; E si , η si are the maximum capacity and charge / discharge efficiency of the si-th ESS respectively; E si (t) is the capacity and charge-discharge efficiency of the si-th ESS at time t; (6) EVCS operation constraints u ei,char (t)+u ei,disc (t)≤1 (46) Where: E ei (t+1), E ei (t) represents the capacity status of the ei-th EVCS at time t+1 and t respectively; P ei,char (t), P ei,disc (t) respectively represent the charge and discharge power of the ei-th EVCS at time t; They represent the total capacity of EVs connected to and off-grid in the ei-th EVCS at time t+1; η ei is the EVCS charging and discharging efficiency; u ei,char (t),u ei,disc (t) are 0-1 variables representing the charge and discharge state of the ei-th EVCS at time t. ei,char (t) = 1, indicating that the EVCS is in the charging state, u ei,disc (t) = 1 indicates that the EVCS is in the discharge state; They represent the sum of the maximum and minimum expected capacity values ​​of the off-grid EV in the ei-th EVCS at time t, respectively, and are set by the user; (7) SVC operation constraints Where: Nsvc is the SVC set in ADN; and They represent the upper and lower limits of the SVC operation capacity of the svci station respectively; (8) CB operation constraints Where: Ncb is the CB set in ADN; Q cbi (t) represents the compensation power of the cbith CB at time t; cbi (t) represents the number of cbi-th CB input groups at time t; It represents the compensation power of each group of cbi-th CB; Indicates the maximum number of investment groups for the cbi-th CB.

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