Distribution network distributed energy reactive power optimization regulation and control method, device, equipment and medium

Abstract

The embodiment of the invention discloses a reactive power optimization regulation and control method, device and equipment for distributed energy of a power distribution network and a medium. The method comprises the following steps: determining an ideal voltage safety margin of a load node; determining ideal voltage safety margins of distributed nodes, wherein the distributed nodes comprise distributed photovoltaic nodes and distributed electric vehicle nodes; establishing a distribution network reactive power optimization regulation and control model considering the operation economy and the voltage safety margin by using the target function; optimizing the distribution network reactive power optimization regulation and control model based on a membership function; and obtaining the reactive power optimization regulation and control method for the distributed energy of the power distribution network according to the model. According to the scheme, the optimal result of each objective function can be ensured to the greatest extent, and the reactive power optimization method corresponding to the concerned target can be obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, device, equipment and medium for optimizing reactive power control of distributed energy in a distribution network. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy technology, the proportion of distributed energy systems (such as distributed photovoltaic power generation, wind power generation, etc.) in the power grid is increasing. These distributed energy sources not only provide a diversified energy supply for the power grid, but also improve energy utilization efficiency and reduce environmental pollution. However, the randomness and volatility of distributed energy have brought challenges to the stable operation of the power grid, especially in terms of reactive power management.

[0003] In the distribution network system, the balance of reactive power is crucial to maintain voltage stability, reduce energy loss and improve the economic efficiency of system operation. The node voltage safety margin of the distribution network refers to the relative distance or margin between the voltage of each node in the distribution network and its allowable operating range, which is used to evaluate the ability of the distribution network to maintain voltage stability under normal operation or fault conditions. Specifically, it reflects the degree of proximity of the node voltage to its upper or lower voltage limit, thereby measuring the safety of the voltage. In the distribution network, each node has an allowable operating voltage range. When the node voltage operates within this range, the distribution network can be considered safe; when the node voltage approaches or exceeds this range, problems such as voltage instability or equipment damage may occur, affecting the safe operation of the distribution network.

[0004] However, in the distribution network system with extensive access to distributed energy, how to simultaneously consider the economic operation of the system and the voltage safety margin and achieve the optimal regulation of reactive power is an urgent problem to be solved. On the one hand, the optimal regulation of reactive power needs to ensure the voltage stability of the power grid and reduce or eliminate the voltage over-limit problem caused by the frequent and rapid random fluctuations of the source and load in the distribution network with a high proportion of distributed resources; on the other hand, it is also necessary to consider the economic operation of the distribution network system and reduce the operating cost. Summary of the invention

[0005] Based on this, it is necessary to propose a method, device, equipment and medium for optimizing reactive power control of distributed energy in distribution network to address the above problems.

[0006] To achieve the above objectives, the present application provides a first aspect of a method for optimizing reactive power control of distributed energy in a distribution network, wherein the strategy includes:

[0007] Determine the ideal voltage safety margin at the load node;

[0008] Determining an ideal voltage safety margin of a distributed node, wherein the distributed node includes a distributed photovoltaic node and a distributed electric vehicle node;

[0009] Taking minimizing the operating cost of reactive power optimization and control of distribution network and minimizing the deviation value of the actual voltage margin of the node relative to the ideal voltage safety margin of the node as the objective function, a distribution network reactive power optimization and control model considering the operating economy and voltage safety margin is established, wherein the ideal voltage safety margin of the node includes the ideal voltage safety margin of the load node and the ideal voltage safety margin of the distributed node;

[0010] Determine a membership function according to the objective function components and the slackable variables, and optimize the distribution network reactive power optimization control model based on the membership function;

[0011] The reactive power optimization and control method of distributed energy in the distribution network is obtained according to the optimized reactive power optimization and control model of the distribution network.

[0012] In some implementations, determining the ideal voltage safety margin of the load node specifically includes:

[0013] Determine the ideal load margin index at each moment based on the difference between the load parameters of the critical load point and the ideal operating point;

[0014] The ideal voltage safety margin of the load node is determined according to the average value of the ideal load margin index of the load node within a period of time, that is, according to Determine, among which, is the ideal voltage safety margin of load node i, and T is the time period.

[0015] In some embodiments, the determining of the ideal voltage safety margin of the distributed node, wherein the distributed node includes a distributed photovoltaic node and a distributed electric vehicle node, specifically includes:

[0016] According to the difference between the distributed resource output parameters of the critical node and the ideal operating point Determine the ideal output margin index of distributed photovoltaic at each moment;

[0017] The ideal voltage safety margin of the distributed photovoltaic node is determined according to the average value of the distributed photovoltaic ideal output margin index within a period of time of the distributed photovoltaic node, that is, according to Determine, among which, is the ideal voltage safety margin of distributed photovoltaic node i, and T is the time period.

[0018] In some embodiments, the determining of the ideal voltage safety margin of the distributed node, wherein the distributed node includes a distributed photovoltaic node and a distributed electric vehicle node, specifically includes:

[0019] When the electric vehicle is in the charging state, the ideal power demand of the charging pile at each moment is determined according to the difference between the actual charging capacity at the current operating point of the charging pile and the charging demand at the critical operating point;

[0020] When the electric vehicle is in a discharging state, the ideal power output of the charging pile at each moment is determined based on the difference between the actual discharge amount at the current operating point of the charging pile and the discharge amount at the critical operating point;

[0021] according to Determine the ideal voltage safety margin of the distributed electric vehicle node, wherein: is the ideal power demand of the charging pile at time t, is the ideal power output of the charging pile at time t, and T is the time period.

[0022] In some embodiments, in the objective function of minimizing the distribution network reactive power optimization and control operation cost and minimizing the deviation of the actual voltage margin of the node relative to the ideal voltage safety margin of the node, in establishing a distribution network reactive power optimization and control model that considers operation economy and voltage safety margin, the objective function is:

[0023] min G = g1 + g2;

[0024]

[0025] Among them, g1 represents the economic objective function component, g2 represents the voltage safety margin objective function component, P loss,t Represents line loss, S SOP Indicates the SOP installed capacity in the system, represents the cost of combined active and reactive voltage regulation of photovoltaic n, c p is the photovoltaic grid-connected electricity price, α PV is the reactive power regulation capacity cost coefficient of the photovoltaic converter, represents the cost of combined active and reactive voltage regulation of energy storage m, the cost of reactive regulation on converter capacity occupation and the charging cost of energy storage, c ES is the price at which the energy storage system sells electricity to the grid, is the spot market electricity price, and α ES is the reactive power regulation capacity cost coefficient of the photovoltaic converter, c ES,cha is the charging loss cost coefficient of the energy storage system, c loss 、c SOP Respectively represent the unit line loss cost coefficient and unit capacity SOP maintenance cost coefficient, m j,t represents the actual voltage margin of node j at time t, M F,j represents the ideal voltage margin at node j, represents the square of the capacity of n photovoltaic grid-connected circulators, represents the square of the inverter capacity, PPV，n，t It represents the active output of photovoltaic power at time t. Represents the square of the initial reactive power output value of the photovoltaic power plant at time t, ΔQ PV，i，t ΔQ PV，i，t Indicates the photovoltaic output difference at time t after photovoltaic i is connected to the grid, ΔQ ES，m，t It indicates the output difference at time t after inverter m is connected to the grid.

[0026] In some embodiments, in the objective function of minimizing the distribution network reactive power optimization and control operation cost and minimizing the deviation of the actual voltage margin of the node relative to the ideal voltage safety margin of the node, in establishing a distribution network reactive power optimization and control model that considers operation economy and voltage safety margin, the constraint conditions of the distribution network reactive power optimization and control model that considers operation economy and voltage safety margin are:

[0027] AC power flow equation constraints, for each node i, U i,t represents the node voltage at time t, which is a slackable variable, P i,t With Q i,t represents the active and reactive power injected into the node at time t, represents the distributed resource active power injected by the node; for each line li-j, G i-j and B i-j Represent the line conductance and susceptance respectively, P i-j,t represents the active power flowing through the line at time t, θ i-j,t It represents the phase difference between the two ends of the line at time t, ∈i is the set of all nodes connected to node i, and the power flow calculation model is in polar coordinate form:

[0028] Among them, P PV,i,t is the photovoltaic output value of distributed photovoltaic node i at time t, P load,i,t is the user load of node i at time t;

[0029] Runtime safety constraints: Among them, U i,min , U i,max Respectively represent the voltage upper and lower limits of the distribution network node i; I i,min ,I i,max Respectively represent the upper and lower limits of the current of the distribution network node i; I ij,t , They represent the current on branch ij and the maximum value allowed respectively;

[0030] PV operation constraints: Among them, P PV,i,t is the predicted active power of photovoltaic at time t; Q 0,t is the initial reactive power before photovoltaic voltage regulation at time t, with absorption as positive; SPV is the capacity of the photovoltaic grid-connected converter, tanθ i is the photovoltaic power factor angle;

[0031]

[0032] Electric vehicle charging pile operation constraints: (P ev,i,t ) 2 +(Q ev,i,t ) 2 =(S ev,con ) 2 , where: ΔP ES,t is the discharge power of EV at time t, P c,max,t and P f,max,t are the upper limits of EV charging and discharging power at time t, P ES,t and Q ES,t are the active and reactive power emitted or absorbed by EV at time t, S ES is the rated capacity of the inverter, E c Indicates the maximum capacity of EV, SOC max and SOC min and represent the maximum and minimum state of charge of the EV, respectively;

[0033] SOP operation constraints, set SOP to inject power into the node in the positive direction, and establish SOP operation constraints as follows: in: and are the active power and reactive power emitted by SOP, and are the loss coefficients of the inverters at both ends of the SOP, and are the active power losses of the inverters at both ends of the SOP.

[0034] In some implementations, in determining the membership function according to the objective function components and the slackable variables, and optimizing the distribution network reactive power optimization control model based on the membership function, the membership function is expressed as a piecewise linear function:

[0035] Among them C 0 +σ 0 It represents the highest acceptable total voltage regulation cost, which is also the cost before optimization. The cost after optimization is C 0 , the maximum cost saving is σ 0 , obtained by taking the average value based on historical data;

[0036] Where: m i (i=1, 2, .....2l) is the i-th element in the vector M; m gi and Respectively, vector and M g The i-th element in ; σ g1i and σ g2i denote the load margin fuzzy parameters corresponding to the lower and upper limits of the ideal load margin respectively;

[0037] When U min i，t ≤U max When the actual system voltage is within the expected ideal load margin range, the membership function is 1, and the downward sloping parts on both sides indicate that the more the actual voltage margin exceeds the ideal load margin range, the more unacceptable it is.

[0038] In some implementations, determining a membership function according to an objective function component and a slackable variable, and optimizing the distribution network reactive power optimization control model based on the membership function includes:

[0039] Let η be the smallest membership variable in the membership function, called satisfaction: η=min{μ(g 1 ),μ(g 2 )};

[0040] The distribution network reactive power optimization control model is converted into a single-objective nonlinear programming model for maximizing satisfaction η, to obtain the optimized distribution network reactive power optimization control model, wherein the objective function of the optimized distribution network reactive power optimization control model is: Min-η;

[0041] The optimized distribution network reactive power optimization control model adds the following constraints:

[0042] f+(η-1)σ i ≤C 0 ,i=1……n;-u+(η-1)σ i ≤-U min ; -u+(η-1)σ i ≤-U min ;

[0043] 0≤η≤1.

[0044] To achieve the above-mentioned purpose, the second aspect of the present application provides a distribution network distributed energy reactive power optimization and control device, the device comprising:

[0045] A load node ideal voltage safety margin determination module, used to determine the ideal voltage safety margin of the load node;

[0046] A distributed node ideal voltage safety margin determination module, used to determine the ideal voltage safety margin of distributed nodes, wherein the distributed nodes include distributed photovoltaic nodes and distributed electric vehicle nodes;​

[0047] A distribution network reactive power optimization and control model determination module is used to establish a distribution network reactive power optimization and control model that takes into account operation economy and voltage safety margin, taking minimization of the distribution network reactive power optimization and control operation cost and the minimum deviation of the actual voltage margin of the node relative to the ideal voltage safety margin of the node as the objective function, wherein the ideal voltage safety margin of the node includes the ideal voltage safety margin of the load node and the ideal voltage safety margin of the distributed node;

[0048] A distribution network reactive power optimization control model optimization module, used to determine a membership function according to an objective function component and a slackable variable, and optimize the distribution network reactive power optimization control model based on the membership function;

[0049] The reactive power optimization and control strategy determination module is used to obtain the reactive power optimization and control method of distributed energy in the distribution network according to the optimized reactive power optimization and control model of the distribution network.

[0050] To achieve the above-mentioned object, the third aspect of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the following steps:

[0051] Determine the ideal voltage safety margin at the load node;

[0052] Determining an ideal voltage safety margin of a distributed node, wherein the distributed node includes a distributed photovoltaic node and a distributed electric vehicle node;

[0053] Taking minimizing the operating cost of reactive power optimization and control of distribution network and minimizing the deviation value of the actual voltage margin of the node relative to the ideal voltage safety margin of the node as the objective function, a distribution network reactive power optimization and control model considering the operating economy and voltage safety margin is established, wherein the ideal voltage safety margin of the node includes the ideal voltage safety margin of the load node and the ideal voltage safety margin of the distributed node;

[0054] Determine a membership function according to the objective function components and the slackable variables, and optimize the distribution network reactive power optimization control model based on the membership function;

[0055] The reactive power optimization and control method of distributed energy in the distribution network is obtained according to the optimized reactive power optimization and control model of the distribution network.

[0056] To achieve the above-mentioned object, the fourth aspect of the present application provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the following steps:

[0057] Determine the ideal voltage safety margin at the load node;

[0058] Determining an ideal voltage safety margin of a distributed node, wherein the distributed node includes a distributed photovoltaic node and a distributed electric vehicle node;

[0059] Taking minimizing the operating cost of reactive power optimization and control of distribution network and minimizing the deviation value of the actual voltage margin of the node relative to the ideal voltage safety margin of the node as the objective function, a distribution network reactive power optimization and control model considering the operating economy and voltage safety margin is established, wherein the ideal voltage safety margin of the node includes the ideal voltage safety margin of the load node and the ideal voltage safety margin of the distributed node;

[0060] Determine a membership function according to the objective function components and the slackable variables, and optimize the distribution network reactive power optimization control model based on the membership function;

[0061] The reactive power optimization and control method of distributed energy in the distribution network is obtained according to the optimized reactive power optimization and control model of the distribution network.

[0062] The embodiments of the present invention have the following beneficial effects:

[0063] The present invention proposes a method for optimizing the reactive power of distributed energy in a distribution network. First, the ideal voltage safety margins of the load nodes and distributed resource nodes of the distribution network are calculated. Then, a distribution network reactive power optimization and control model is established which takes into account both the economic performance of the distribution network operation and the voltage safety margin. Finally, the objective function and the membership function of the variables are summarized to establish a distribution network reactive power optimization and control model based on the membership function. The method ensures that the result of each objective function is optimal to the greatest extent possible, and thus a reactive power optimization strategy corresponding to the target of interest can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0065] in:

[0066] Figure 1 A schematic diagram of a flow chart of a method for optimizing reactive power control of distributed energy in a distribution network in one embodiment;

[0067] Figure 2 A structural diagram of a reactive power optimization and control device for distributed energy in a power distribution network in one embodiment;

[0068] Figure 3 is a schematic diagram of the structure of a computer device in one embodiment;

[0069] Figure 4 FIG. 4 is a schematic diagram of the structure of a computer-readable storage medium in one embodiment. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] The existing reactive power optimization and control strategies are mainly limited by the determination of economy and safety margin, including the reasonable allocation of branch loss costs of the distribution network, voltage deviation adjustment costs and various resource action costs, as well as the voltage fluctuations caused by the access of distributed power sources to the distribution network, avoiding the impact of excessively high or low voltage on the safety of the power grid, and determining a reasonable voltage safety margin. Optimization and control need to ensure that the voltage of each node remains within a reasonable range to achieve the optimization goal of voltage stability.

[0072] Most studies only consider the economics of operation, such as reducing the active power loss of the system to reduce operating costs, thereby optimizing the power flow distribution of the power grid and improving the voltage quality. The impact of node voltage safety margin is rarely taken into account in the reactive power optimization and control strategy of distributed resources in the distribution network. Although the node voltage safety margin is crucial to the stable operation of the power system, its impact is often overlooked in the reactive power optimization and control strategy. Voltage stability optimization is an important aspect of reactive power optimization in the distribution network. By controlling the reactive power output of distributed power sources, the voltage of each node in the distribution network is kept within a reasonable range to avoid excessive or low voltage from affecting the safety of the power grid. However, in actual research, there are few strategies that can simultaneously consider the economics of operation and the node voltage safety margin.

[0073] In the process of processing the constraints of distributed energy participating in reactive power optimization modeling of distribution network, all constraints are treated as hard constraints and cannot be violated, which greatly reduces the feasible domain. In the modeling of some complex problems, hard constraints may make the problem difficult to solve. In actual operation, there are often many dynamic uncertainties, which may cause the original hard constraints to be unsatisfactory. In order to quickly obtain the optimization solution, some constraints are scalable, that is, they are allowed to exceed the limit slightly, such as node voltage constraints. However, how to determine a reasonable ideal node voltage margin range is crucial.

[0074] In the traditional process of determining the ideal node voltage margin range, usually only the load node margin range is included. However, as the distribution network transforms from a passive distribution network to an active distribution network, especially the large-scale multi-point access of distributed photovoltaics and electric vehicles, their frequent and rapid fluctuations and randomness have brought severe challenges to the voltage stability of the distribution network. How to ensure that the distributed resource nodes remain within the ideal voltage margin range while maintaining the stability of the grid voltage. There are challenges in determining the ideal voltage margin range of distributed resource nodes such as photovoltaics and energy storage.

[0075] In summary, the distributed energy reactive power optimization process faces a series of challenges and problems in terms of economy and voltage safety margin. In order to overcome these challenges and solve the problems, in the embodiment of the present application, a distribution network distributed energy reactive power optimization control method is provided. Figure 1 , Figure 1 It is a flow chart of a method for optimizing reactive power control of distributed energy in a distribution network in an embodiment; the method for optimizing reactive power control of distributed energy in a distribution network includes steps S1 to S5.

[0076] Step S1, determining an ideal voltage safety margin of a load node;

[0077] In some implementations, determining the ideal voltage safety margin of the load node specifically includes:

[0078] Determine the ideal load margin index at each moment based on the difference between the load parameters of the critical load point and the ideal operating point;

[0079] The ideal voltage safety margin of the load node is determined according to the average value of the ideal load margin index of the load node within a period of time, that is, according to Determine, among which, is the ideal voltage safety margin of load node i, and T is the time period.

[0080] Specifically, the load of the power system changes randomly. Although the system operation always meets the load demand, there is a load carrying capacity, that is, the maximum load capacity of the system. When this limit is exceeded, the system will experience voltage instability or collapse. It is also undesirable for a power system with economies of scale to operate at a system load level far less than the system carrying capacity. The "distance" between the ideal load level and the maximum load level that the system can achieve is defined as the ideal load margin index of the system. This distance is expressed by the difference between the load parameters of the critical load point and the ideal operating point, that is:

[0081] M F，t =λ Fc，t -λ Fp，t (1)

[0082] Among them, M F,tis the ideal load margin index at time t, λ Fp，t represents the load usage of the current operating point at time t, λ Fc，t It represents the load usage at the critical operating point of the load at time t. In order to simplify the calculation, the system load is studied in a nonlinear and continuous growth mode. The critical load power λ of the system at the operating point at time t is Fc，t It is expressed as:

[0083] P c，t =aln(1+bP p,t )

[0084] Q c，t =aln(1+bQ p,t )(2)

[0085] Among them, a and b are the load growth parameters of the critical operating point; P p,t and Q p,t They represent the active power and reactive power of the load node at the current operating point of the system at time t, respectively. c,t and Q c,t They represent the active power and reactive power of the load node at the critical operating point of the system respectively.

[0086] The ideal voltage safety margin of load node i is defined as the average value of the ideal load margin index within a period of time, and the calculation formula is:

[0087]

[0088] In the formula, is the ideal voltage safety margin of load node i, and T is the time period.

[0089] Step S2, determining an ideal voltage safety margin of a distributed node, wherein the distributed node includes a distributed photovoltaic node and a distributed electric vehicle node;

[0090] In some embodiments, the determining of the ideal voltage safety margin of the distributed node, wherein the distributed node includes a distributed photovoltaic node and a distributed electric vehicle node, specifically includes:

[0091] Determine the ideal output margin index of distributed photovoltaic at each moment according to the difference between the distributed resource output parameters of the critical node and the ideal operating point;

[0092] The ideal voltage safety margin of the distributed photovoltaic node is determined according to the average value of the distributed photovoltaic ideal output margin index within a period of time of the distributed photovoltaic node, that is, according to Determine, among which, is the ideal voltage safety margin of distributed photovoltaic node i, and T is the time period.

[0093] In some embodiments, the determining of the ideal voltage safety margin of the distributed node, wherein the distributed node includes a distributed photovoltaic node and a distributed electric vehicle node, specifically includes:

[0094] When the electric vehicle is in the charging state, the ideal power demand of the charging pile at each moment is determined according to the difference between the actual charging capacity at the current operating point of the charging pile and the charging demand at the critical operating point;

[0095] When the electric vehicle is in a discharging state, the ideal power output of the charging pile at each moment is determined based on the difference between the actual discharge amount at the current operating point of the charging pile and the discharge amount at the critical operating point;

[0096] according to Determine the ideal voltage safety margin of the distributed electric vehicle node, wherein: is the ideal power demand of the charging pile at time t, is the ideal power output of the charging pile at time t, and T is the time period.

[0097] Specifically, considering that distributed photovoltaic and electric vehicle charging piles are connected to the distribution network on a large scale and at multiple points, their frequent and rapid fluctuations and randomness have a great impact on the voltage stability of the distribution network. Therefore, the distributed resource nodes used include two types of nodes: one is the distributed photovoltaic node, and the other is the charging pile node.

[0098] Distributed photovoltaic nodes:

[0099] The calculation method of the safety margin of distributed photovoltaic nodes is basically the same as that of the load node safety margin, and is also expressed by the difference between the distributed resource output parameters of the critical node and the ideal operating point, that is,

[0100]

[0101] Where: is the ideal output margin of distributed photovoltaic at time t; Dp,t represents the distributed photovoltaic output at the operating point at time t; Dc,t The critical operating point output of distributed photovoltaics at time t is represented by ΔPi and ΔQ. i , and the voltage fluctuation generated at node j is recorded as △Uj, which can be expressed as:

[0102]

[0103] Where a ij, b ij is the power-voltage sensitivity coefficient between node i and node j. According to the limit value of power supply voltage deviation in GB / T12325-2008, the three-phase power supply voltage deviation of 20kV and below is ±7% of the nominal voltage. Therefore, for node i, the voltage upper limit is taken, and ΔU j = +7% is substituted into the above formula to obtain the critical operating point of the voltage safety margin of the distributed photovoltaic node, which is expressed as: (i∈Ω n ), where Ω n A collection of nodes.

[0104] At the same time, the active power output of distributed photovoltaics is also subject to the maximum output limit.

[0105]

[0106] And the reactive power is subject to the following constraints:

[0107]

[0108] Where: is the maximum output of distributed photovoltaic connected to node i at time t.

[0109] Then the critical operating point λ of distributed photovoltaic i Dc,t It is expressed as:

[0110]

[0111] Similarly, the ideal voltage safety margin of distributed photovoltaic node i is defined as the average value of the ideal output margin index within a period of time, and the calculation formula is:

[0112]

[0113] In the formula, is the ideal voltage safety margin of PV node i, and T is the time period.

[0114] Distributed electric vehicle nodes:

[0115] With the increase in the number of electric vehicles in recent years, electric vehicle charging piles have also played an active role in voltage regulation and peak load transfer in the power distribution system. However, unlike distributed photovoltaic nodes and load nodes, electric vehicles can be charged and discharged, because when analyzing the output margin of EV charging pile nodes, it is divided into two states: electric vehicle charging and discharging.

[0116] When the electric vehicle is in the charging state, similar to the load node, the ideal power demand of the charging pile is expressed by the difference between the actual charging amount of the charging pile at the current operating point and the charging demand at the critical operating point:

[0117]

[0118] In the formula, is the ideal power demand of the charging pile at time t; It indicates the charging demand at the critical operating point of the charging pile and the actual charging amount of the electric vehicle at the current operating point.

[0119] Calculation method for actual charging demand at charging pile node at time t:

[0120] Assume that the charge state of electric vehicle i when it arrives at the charging pile at time t is expressed as The desired state of charge when leaving is expressed as The estimated duration of stay is expressed as Then the actual active charging demand of charging pile node i at time t can be expressed as:

[0121]

[0122] Where E C is the battery capacity of electric vehicles, is the upper limit of the electric vehicle charging capacity per unit time. Next, the calculation method of charging demand at the critical operating point of the charging pile node at time t is introduced:

[0123]

[0124] In the formula It is the upper limit of SOC of electric vehicle.

[0125] When the electric vehicle is in the discharging state, similar to the distributed photovoltaic node, the ideal power output of the charging pile is expressed by the difference between the actual discharge amount at the current operating point of the charging pile and the discharge amount at the critical operating point:

[0126]

[0127] In the formula, is the ideal discharge amount of the charging pile at time t, Indicates the discharge capacity of the charging pile at the critical operating point, Indicates the actual discharge amount of the electric vehicle at the current operating point.

[0128] Calculation method of discharge capacity at critical operation point of charging pile node:

[0129] Assume that the charge state of electric vehicle i when it arrives at the charging pile at time t is expressed as To meet the subsequent mileage requirements, the expected state of charge when leaving is not less than The estimated duration of stay is expressed as Then the actual active discharge capacity of charging pile node i at time t can be expressed as:

[0130]

[0131] In the formula, E C is the battery capacity of electric vehicles, It is the upper limit of electric vehicle discharge per unit time.

[0132] Calculation method of discharge capacity at critical operation point of charging pile node at time t:

[0133]

[0134] In the formula It is the lower limit of SOC of electric vehicles.

[0135] Similarly, the ideal voltage safety margin of EV charging pile node i is defined as the average value of the ideal charging or discharging margin index within a period of time, and the calculation formula is:

[0136]

[0137] In the formula, is the ideal voltage safety margin of EV charging pile node i, and T is the time period.

[0138] Step S3, taking minimizing the operation cost of reactive power optimization and control of the distribution network and minimizing the deviation value of the actual voltage margin of the node relative to the ideal voltage safety margin of the node as the objective function, establishing a distribution network reactive power optimization and control model that considers operation economy and voltage safety margin, wherein the ideal voltage safety margin of the node includes the ideal voltage safety margin of the load node and the ideal voltage safety margin of the distributed node;

[0139] In some embodiments, in the objective function of minimizing the distribution network reactive power optimization and control operation cost and minimizing the deviation of the actual voltage margin of the node relative to the ideal voltage safety margin of the node, in establishing a distribution network reactive power optimization and control model that considers operation economy and voltage safety margin, the objective function is:

[0140] min G = g1 + g2;

[0141]

[0142] Among them, g1 represents the economic objective function component, g2 represents the voltage safety margin objective function component, P loss,t Represents line loss, S SOP Indicates the SOP installed capacity in the system, represents the cost of combined active and reactive voltage regulation of photovoltaic n, c p is the photovoltaic grid-connected electricity price, α PV is the reactive power regulation capacity cost coefficient of the photovoltaic converter, represents the cost of combined active and reactive voltage regulation of energy storage m, the cost of reactive regulation on converter capacity occupation and the charging cost of energy storage, c ES is the price at which the energy storage system sells electricity to the grid, is the spot market electricity price, and α ES is the reactive power regulation capacity cost coefficient of the photovoltaic converter, c ES,cha is the charging loss cost coefficient of the energy storage system, c loss 、c SOP Respectively represent the unit line loss cost coefficient and unit capacity SOP maintenance cost coefficient, m j,t represents the actual voltage margin of node j at time t, M F,j represents the ideal voltage margin at node j, represents the square of the capacity of n photovoltaic grid-connected circulators, represents the square of the inverter capacity, P PV，n，t It represents the active output of photovoltaic power at time t. Represents the square of the initial reactive power output value of the photovoltaic power plant at time t, ΔQ PV，i，t Indicates the photovoltaic output difference at time t after photovoltaic i is connected to the grid, ΔQ ES，m，t It indicates the output difference at time t after inverter m is connected to the grid.

[0143] In some embodiments, in the objective function of minimizing the distribution network reactive power optimization and control operation cost and minimizing the deviation of the actual voltage margin of the node relative to the ideal voltage safety margin of the node, in establishing a distribution network reactive power optimization and control model that considers operation economy and voltage safety margin, the constraint conditions of the distribution network reactive power optimization and control model that considers operation economy and voltage safety margin are:

[0144] AC power flow equation constraints, for each node i, U i,t represents the node voltage at time t, which is a slackable variable, P i,t With Q i,t represents the active and reactive power injected into the node at time t, represents the distributed resource active power injected by the node; for each line li-j, G i-j and B i-j Represent the line conductance and susceptance respectively, P i-j,t represents the active power flowing through the line at time t, θ i-j,t It represents the phase difference between the two ends of the line at time t, ∈i is the set of all nodes connected to node i, and the power flow calculation model is in polar coordinate form:

[0145] Among them, P PV,i,t is the photovoltaic output value of distributed photovoltaic node i at time t, P load,i,t is the user load of node i at time t;

[0146] Runtime safety constraints: Among them, U i,min , U i,max Respectively represent the voltage upper and lower limits of the distribution network node i; I i,min ,I i,max Respectively represent the upper and lower limits of the current of the distribution network node i; I ij,t , They represent the current on branch ij and the maximum value allowed respectively;

[0147] PV operation constraints: Among them, P PV,i,t is the predicted active power of photovoltaic at time t; Q 0,t is the initial reactive power before photovoltaic voltage regulation at time t, with absorption as positive; S PV is the capacity of the photovoltaic grid-connected converter, tanθ i is the photovoltaic power factor angle;

[0148] Electric vehicle charging station operation constraints: Where: ΔP ES,t is the discharge power of EV at time t, P c,max,t and P f,max,t are the upper limits of EV charging and discharging power at time t, P ES,t and Q ES,t are the active and reactive power emitted or absorbed by EV at time t, S ES is the rated capacity of the inverter, E c Indicates the maximum capacity of EV, SOC max and SOC min and represent the maximum and minimum state of charge of the EV, respectively;

[0149] SOP operation constraints, set SOP to inject power into the node in the positive direction, and establish SOP operation constraints as follows:

[0150] in: and are the active power and reactive power emitted by SOP, and are the loss coefficients of the inverters at both ends of the SOP, and They are the active power losses of the inverters at both ends of the SOP.

[0151] Specifically, a distribution network reactive power optimization control model considering operation economy and voltage safety margin is established:

[0152] Objective function: In order to reduce the operating cost of the distribution network, the distribution network distributed resource reactive power optimization control model aims to minimize the distribution network reactive power optimization control operating cost and the deviation of the actual node voltage margin relative to the ideal node voltage margin range. The control operation cost mainly includes the branch loss cost of the distribution network and the action cost of each resource. j,t represents the actual voltage margin of node j at time t, calculated according to formulas (1)-(2), (4)-(8), (10)-(15); M F,j represents the ideal voltage margin at node j, calculated according to equations (3), (9) and (16) in step 1.

[0153] Step S4, determining a membership function according to the objective function components and the slackable variables, and optimizing the distribution network reactive power optimization control model based on the membership function;

[0154] In some implementations, in determining the membership function according to the objective function components and the slackable variables, and optimizing the distribution network reactive power optimization control model based on the membership function, the membership function is expressed as a piecewise linear function:

[0155] Among them C 0 +σ 0 It represents the highest acceptable total voltage regulation cost, which is also the cost before optimization. The cost after optimization is C 0 , the maximum cost saving is σ 0 , obtained by taking the average value based on historical data;

[0156] Where: m i (i=1, 2, .....2l) is the i-th element in the vector M; m gi and Respectively, vector and M g The i-th element in ; σ g1i and σ g2i denote the load margin fuzzy parameters corresponding to the lower and upper limits of the ideal load margin respectively;

[0157] WhenU min i，t ≤U max When the actual system voltage is within the expected ideal load margin range, the membership function is 1. The downward sloping parts on both sides indicate that the more the actual voltage margin exceeds the ideal load margin range, the more unacceptable it is. i ​When the actual load margin is much smaller than the minimum ideal load margin, the system operation is closer to its voltage stability limit, and voltage instability is likely to occur and fail to meet safety requirements. This is a phenomenon that must be avoided in system operation. When the actual load margin is much larger than the maximum ideal load margin, the system operation is safer, but the system utilization efficiency is reduced and cannot meet the economic requirements. However, in some special cases, the system is allowed to lose some economy for safe operation.

[0158] In some implementations, determining a membership function according to an objective function component and a slackable variable, and optimizing the distribution network reactive power optimization control model based on the membership function includes:

[0159] Let η be the smallest membership variable in the membership function, called satisfaction:

[0160] η=min{μ(g 1 ),μ(g 2 )};

[0161] The distribution network reactive power optimization control model is converted into a single-objective nonlinear programming model for maximizing satisfaction η, to obtain the optimized distribution network reactive power optimization control model, wherein the objective function of the optimized distribution network reactive power optimization control model is: Min-η;

[0162] The optimized distribution network reactive power optimization control model adds the following constraints:

[0163] f+(η-1)σ i ≤C 0 ,i=1……n;-u+(η-1)σ i ≤-U min ; -u+(η-1)σ i ≤-U min ;

[0164] 0≤η≤1.

[0165] Specifically, a distribution network distributed resource reactive power optimization control transformation model based on membership function is established:

[0166] In order to overcome the difficulties and challenges of selecting appropriate weight factors and constraints for the "weighted sum" objective function and uniformly processing and modeling them according to hard constraints, the distribution network reactive power optimization control model is transformed and solved based on the membership function. The key to the transformation modeling lies in the determination of the membership function. In theory, it is always hoped that the actual node voltage margin deviates from the ideal node voltage margin as little as possible while satisfying all hard constraints and maximizing the scalability constraints (node ​​voltage safety margin constraints).

[0167] Step S5, obtaining the reactive power optimization and control method of distributed energy in the distribution network according to the optimized reactive power optimization and control model of the distribution network.

[0168] By adopting the technical solution of this embodiment, a method for defining the voltage safety margin of distribution network nodes is given, and an ideal voltage safety margin calculation model for load nodes, photovoltaic nodes and electric vehicle charging pile nodes is established; based on the calculated ideal voltage safety margin indicators of each node, a distribution network reactive power optimization and control model considering the operating economy and voltage safety margin is established, with the goal of minimizing the distribution network reactive power optimization and control operating cost and the deviation value of the actual node voltage margin relative to the ideal node voltage margin range. The operating cost takes into account the branch loss cost SOP action of the distribution network, photovoltaic active and reactive voltage regulation, and EV active and reactive voltage regulation costs; the membership functions of the objective function components and the slackable variables are established, and a distribution network reactive power optimization and control model based on the membership function is established to maximize the guarantee that each objective function result is optimal.

[0169] In the embodiment of the present application, a distributed energy reactive power optimization control device for a power distribution network is provided. Figure 2 , Figure 2 In one embodiment, a distribution network distributed energy reactive power optimization and control device is provided, which includes a load node ideal voltage safety margin determination module 201, a distributed node ideal voltage safety margin determination module 202, a distribution network reactive power optimization and control model determination module 203, a distribution network reactive power optimization and control model optimization module 204 and a reactive power optimization and control strategy determination module 205.

[0170] The load node ideal voltage safety margin determination module 201 is configured to determine the ideal voltage safety margin of the load node;

[0171] A distributed node ideal voltage safety margin determination module 202 is configured to determine an ideal voltage safety margin of a distributed node, wherein the distributed node includes a distributed photovoltaic node and a distributed electric vehicle node;

[0172] The distribution network reactive power optimization and control model determination module 203 is configured to establish a distribution network reactive power optimization and control model that takes into account operation economy and voltage safety margin, taking minimization of the distribution network reactive power optimization and control operation cost and the minimum deviation of the actual voltage margin of the node relative to the ideal voltage safety margin of the node as the objective function, wherein the ideal voltage safety margin of the node includes the ideal voltage safety margin of the load node and the ideal voltage safety margin of the distributed node;

[0173] The distribution network reactive power optimization control model optimization module 204 is configured to determine a membership function according to the objective function components and the relaxable variables, and optimize the distribution network reactive power optimization control model based on the membership function;

[0174] The reactive power optimization and control strategy determination module 205 is configured to obtain the reactive power optimization and control strategy of the distributed energy in the distribution network according to the optimized reactive power optimization and control model of the distribution network.

[0175] In some embodiments, the load node ideal voltage safety margin determination module 201 is further configured to determine an ideal load margin index at each moment according to a difference between a load parameter at a critical load point and an ideal operating point;

[0176] The ideal voltage safety margin of the load node is determined according to the average value of the ideal load margin index of the load node within a period of time, that is, according to Sure.

[0177] In some embodiments, the distributed node ideal voltage safety margin determination module 202 is further configured to determine the distributed photovoltaic ideal output margin index at each moment according to the difference between the distributed resource output parameters of the critical node and the ideal operating point;

[0178] The ideal voltage safety margin of the distributed photovoltaic node is determined according to an average value of the distributed photovoltaic ideal output margin indicator within a period of time of the distributed photovoltaic node.

[0179] In some embodiments, the distributed node ideal voltage safety margin determination module 202 is further configured to determine the ideal power demand of the charging pile at each moment according to the difference between the actual charging amount of the current operating point of the charging pile and the charging demand of the critical operating point when the electric vehicle is in a charging state;

[0180] When the electric vehicle is in a discharging state, the ideal power output of the charging pile at each moment is determined based on the difference between the actual discharge amount at the current operating point of the charging pile and the discharge amount at the critical operating point;

[0181] according to Determine the ideal voltage safety margin for distributed electric vehicle nodes.

[0182] In some implementations, the distribution network reactive power optimization control model determination module 203 is further configured to determine the objective function as:

[0183] min G = g1 + g2;

[0184]

[0185]

[0186] In some embodiments, the distribution network reactive power optimization control model determination module 203 is further configured to determine the constraint conditions of the distribution network reactive power optimization control model considering the operation economy and voltage safety margin as follows:

[0187] AC power flow equation constraints, for each node i, U i,t represents the node voltage at time t, which is a slackable variable, P i,t With Q i,t represents the active and reactive power injected into the node at time t, represents the distributed resource active power injected by the node; for each line li-j, G i-j and B i-j Represent the line conductance and susceptance respectively, P i-j,t represents the active power flowing through the line at time t, θ i-j,t It represents the phase difference between the two ends of the line at time t, ∈i is the set of all nodes connected to node i, and the power flow calculation model is in polar coordinate form:

[0188]

[0189] Runtime safety constraints:

[0190] PV operation constraints:

[0191] Electric vehicle charging station operation constraints:

[0192] SOP operation constraints, set SOP to inject power into the node in the positive direction, and establish SOP operation constraints:

[0193]

[0194] In some implementations, the distribution network reactive power optimization control model optimization module 204 is further configured to determine that the membership function is expressed as a piecewise linear function:

[0195]

[0196]

[0197] In some embodiments, the distribution network reactive power optimization control model optimization module 204 is further configured to set η as the minimum membership variable in the membership function, which is called satisfaction: η = min{μ(g 1 ),μ(g 2 )};

[0198] The distribution network reactive power optimization control model is converted into a single-objective nonlinear programming model for maximizing satisfaction η, to obtain the optimized distribution network reactive power optimization control model, wherein the objective function of the optimized distribution network reactive power optimization control model is: Min-η;

[0199] The optimized distribution network reactive power optimization control model adds the following constraints: 0≤η≤1;

[0200] f+(η-1)σ i ≤C 0 ,i=1……n;-u+(η-1)σ i ≤-U min ; -u+(η-1)σ i ≤-U min .

[0201] For other details about how each module in the reactive power optimization and control device for distributed energy in the distribution network implements the above-mentioned technical solution, please refer to the description of the reactive power optimization and control method for distributed energy in the distribution network provided above, which will not be repeated here.

[0202] In an embodiment of the present application, a computer device is provided. Figure 3 , Figure 3 3 is a schematic diagram of the structure of a computer device in an embodiment. The device includes a memory 301 and a processor 302. The memory 301 stores a computer program. When the computer program is executed by the processor 302, the processor 302 performs the following steps:

[0203] Determine the ideal voltage safety margin at the load node;

[0204] Determining an ideal voltage safety margin of a distributed node, wherein the distributed node includes a distributed photovoltaic node and a distributed electric vehicle node;

[0205] Taking minimizing the operating cost of reactive power optimization and control of distribution network and minimizing the deviation value of the actual voltage margin of the node relative to the ideal voltage safety margin of the node as the objective function, a distribution network reactive power optimization and control model considering the operating economy and voltage safety margin is established, wherein the ideal voltage safety margin of the node includes the ideal voltage safety margin of the load node and the ideal voltage safety margin of the distributed node;

[0206] Determine a membership function according to the objective function components and the slackable variables, and optimize the distribution network reactive power optimization control model based on the membership function;

[0207] The reactive power optimization and control method of distributed energy in the distribution network is obtained according to the optimized reactive power optimization and control model of the distribution network.

[0208] In an embodiment of the present application, a computer readable storage medium is provided. Figure 4 , Figure 4 The structure diagram of a computer-readable storage medium in an embodiment is a schematic diagram, on which a readable computer program 401 is stored; wherein the computer program 401 may be stored in the above storage medium in the form of a software product, and includes a number of instructions for causing a computer device (which may be a personal computer, a server machine, or a network device, etc.) or a processor to perform the following steps:

[0209] Determine the ideal voltage safety margin at the load node;

[0210] Determining an ideal voltage safety margin of a distributed node, wherein the distributed node includes a distributed photovoltaic node and a distributed electric vehicle node;

[0211] Taking minimizing the operating cost of reactive power optimization and control of distribution network and minimizing the deviation value of the actual voltage margin of the node relative to the ideal voltage safety margin of the node as the objective function, a distribution network reactive power optimization and control model considering the operating economy and voltage safety margin is established, wherein the ideal voltage safety margin of the node includes the ideal voltage safety margin of the load node and the ideal voltage safety margin of the distributed node;

[0212] Determine a membership function according to the objective function components and the slackable variables, and optimize the distribution network reactive power optimization control model based on the membership function;

[0213] The reactive power optimization and control method of distributed energy in the distribution network is obtained according to the optimized reactive power optimization and control model of the distribution network.

[0214] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for optimizing reactive power control of distributed energy in a distribution network, characterized in that: The method comprises: Determine the ideal voltage safety margin at the load node; Determining an ideal voltage safety margin of a distributed node, wherein the distributed node includes a distributed photovoltaic node and a distributed electric vehicle node; Taking minimizing the operating cost of reactive power optimization and control of distribution network and minimizing the deviation value of the actual voltage margin of the node relative to the ideal voltage safety margin of the node as the objective function, a distribution network reactive power optimization and control model considering the operating economy and voltage safety margin is established, wherein the ideal voltage safety margin of the node includes the ideal voltage safety margin of the load node and the ideal voltage safety margin of the distributed node; Determine a membership function according to the objective function components and the slackable variables, and optimize the distribution network reactive power optimization control model based on the membership function; The reactive power optimization and control method of distributed energy in the distribution network is obtained according to the optimized reactive power optimization and control model of the distribution network.

2. The method for optimizing reactive power control of distributed energy in distribution network according to claim 1, characterized in that: The determining of the ideal voltage safety margin of the load node specifically includes: Determine the ideal load margin index at each moment based on the difference between the load parameters of the critical load point and the ideal operating point; The ideal voltage safety margin of the load node is determined according to the average value of the ideal load margin index of the load node within a period of time, that is, according to Determine, among which, is the ideal voltage safety margin of load node i, and T is the time period.

3. The method for optimizing reactive power control of distributed energy in distribution network according to claim 2, characterized in that: The determining of the ideal voltage safety margin of the distributed node, wherein the distributed node includes a distributed photovoltaic node and a distributed electric vehicle node, specifically includes: According to the difference between the distributed resource output parameters of the critical node and the ideal operating point Determine the ideal output margin index of distributed photovoltaic at each moment; The ideal voltage safety margin of the distributed photovoltaic node is determined according to the average value of the distributed photovoltaic ideal output margin index within a period of time of the distributed photovoltaic node, that is, according to Determine, among which, is the ideal voltage safety margin of distributed photovoltaic node i, and T is the time period.

4. The method for optimizing reactive power control of distributed energy in distribution network according to claim 3 is characterized in that: The determining of the ideal voltage safety margin of the distributed node, wherein the distributed node includes a distributed photovoltaic node and a distributed electric vehicle node, specifically includes: When the electric vehicle is in the charging state, the ideal power demand of the charging pile at each moment is determined according to the difference between the actual charging capacity at the current operating point of the charging pile and the charging demand at the critical operating point; When the electric vehicle is in a discharging state, the ideal power output of the charging pile at each moment is determined based on the difference between the actual discharge amount at the current operating point of the charging pile and the discharge amount at the critical operating point; according to Determine the ideal voltage safety margin of the distributed electric vehicle node, wherein: is the ideal power demand of the charging pile at time t, is the ideal power output of the charging pile at time t, and T is the time period.

5. The method for optimizing reactive power control of distributed energy in distribution network according to claim 4, characterized in that: In the distribution network reactive power optimization and control model considering operation economy and voltage safety margin, the objective function is to minimize the operation cost of the distribution network reactive power optimization and control and minimize the deviation of the actual voltage margin of the node relative to the ideal voltage safety margin of the node, and the objective function is: min G = g1 + g2; Among them, g1 represents the economic objective function component, g2 represents the voltage safety margin objective function component, P loss,t Represents line loss, S SOP Indicates the SOP installed capacity in the system, represents the cost of combined active and reactive voltage regulation of photovoltaic n, c p is the photovoltaic grid-connected electricity price, α PV is the reactive power regulation capacity cost coefficient of the photovoltaic converter, represents the cost of combined active and reactive voltage regulation of energy storage m, the cost of reactive regulation on converter capacity occupation and the charging cost of energy storage, c ES is the price at which the energy storage system sells electricity to the grid, is the spot market electricity price, and α ES is the reactive power regulation capacity cost coefficient of the photovoltaic converter, c ES,cha is the charging loss cost coefficient of the energy storage system, c loss 、c SOP Respectively represent the unit line loss cost coefficient and unit capacity SOP maintenance cost coefficient, m j,t represents the actual voltage margin of node j at time t, M F,j represents the ideal voltage margin at node j, represents the square of the capacity of n photovoltaic grid-connected circulators, represents the square of the inverter capacity, P PV，n，t It represents the active power output of photovoltaic power at time t. Represents the square of the initial reactive power output value of the photovoltaic power plant at time t, ΔQ PV，i，t ΔQ PV，i，t Indicates the photovoltaic output difference at time t after photovoltaic i is connected to the grid, ΔQ ES，m，t It indicates the output difference at time t after inverter m is connected to the grid.

6. The method for optimizing reactive power control of distributed energy in distribution network according to claim 5, characterized in that: In the distribution network reactive power optimization and control model considering operation economy and voltage safety margin, which takes minimization of the operation cost of distribution network reactive power optimization and control and the minimum deviation of the actual voltage margin of the node relative to the ideal voltage safety margin of the node as the objective function, the constraint conditions of the distribution network reactive power optimization and control model considering operation economy and voltage safety margin are: AC power flow equation constraints, for each node i, U i,t represents the node voltage at time t, which is a slackable variable, P i,t With Q i,t represents the active and reactive power injected into the node at time t, represents the distributed resource active power injected by the node; for each line li-j, G i-j and B i-j Represent the line conductance and susceptance, P i-j,t represents the active power flowing through the line at time t, θ i-j,t It represents the phase difference between the two ends of the line at time t, ∈i is the set of all nodes connected to node i, and the power flow calculation model is in polar coordinate form: Among them, P PV,i,t is the photovoltaic output value of distributed photovoltaic node i at time t, P load,i,t is the user load of node i at time t; Runtime safety constraints: Among them, U i,min , U i,max Respectively represent the upper and lower limits of the voltage of the distribution network node i; I i,min ,I i,max Respectively represent the upper and lower limits of the current of the distribution network node i; I ij,t , They represent the current on branch ij and the maximum value allowed respectively; PV operation constraints: Among them, P PV,i,t is the predicted active power of photovoltaic at time t; Q 0,t is the initial reactive power before photovoltaic voltage regulation at time t, with absorption as positive; S PV is the capacity of the photovoltaic grid-connected converter, tanθ i is the photovoltaic power factor angle; Electric vehicle charging station operation constraints: Where: ΔP ES,t is the discharge power of EV at time t, P c,max,t and P f,max,t are the upper limits of EV charging and discharging power at time t, P ES,t and Q ES,t are the active and reactive power emitted or absorbed by EV at time t, S ES is the rated capacity of the inverter, E c Indicates the maximum capacity of EV, SOC max and SOC min and represent the maximum and minimum state of charge of the EV, respectively; SOP operation constraints, set SOP to inject power into the node in the positive direction, and establish SOP operation constraints as follows: in: and are the active power and reactive power emitted by SOP, and are the loss coefficients of the inverters at both ends of the SOP, and are the active power losses of the inverters at both ends of the SOP.

7. The method for optimizing reactive power control of distributed energy in distribution network according to claim 6, characterized in that: In determining the membership function according to the objective function components and the slackable variables, and optimizing the distribution network reactive power optimization control model based on the membership function, the membership function is expressed as a piecewise linear function: Where C0+σ0 represents the highest acceptable total voltage regulation cost, which is also the cost before optimization. The cost after optimization is C0, and the maximum cost saving is σ0, which is obtained by taking the average value based on historical data; Where: m i (i=1, 2, .....2l) is the i-th element in the vector M; m gi and Respectively, vector and M g The i-th element in ; σ g1i and σ g2i denote the load margin fuzzy parameters corresponding to the lower and upper limits of the ideal load margin respectively; When U min i，t ≤U max When the actual system voltage is within the expected ideal load margin range, the membership function is 1, and the downward sloping parts on both sides indicate that the more the actual voltage margin exceeds the ideal load margin range, the more unacceptable it is.​ 8. The method for optimizing reactive power control of distributed energy in distribution network according to claim 7, characterized in that: The step of determining a membership function according to the objective function components and the slackable variables, and optimizing the distribution network reactive power optimization control model based on the membership function, comprises: Let η be the smallest membership variable in the membership function, called satisfaction: η=min{μ(g1),μ(g2)}; The distribution network reactive power optimization control model is converted into a single-objective nonlinear programming model for maximizing satisfaction η, to obtain the optimized distribution network reactive power optimization control model, wherein the objective function of the optimized distribution network reactive power optimization control model is: Min -η; The optimized distribution network reactive power optimization control model adds the following constraints: f+(η-1)σ i ≤C0,i=1……n; -u+(η-1)σ i ≤-U min ; -u+(η-1)σ i ≤-U min ; 0≤η≤1.

9. A distributed energy reactive power optimization and control device for a distribution network, characterized in that: The device comprises: A load node ideal voltage safety margin determination module, used to determine the ideal voltage safety margin of the load node; A distributed node ideal voltage safety margin determination module, used to determine the ideal voltage safety margin of distributed nodes, wherein the distributed nodes include distributed photovoltaic nodes and distributed electric vehicle nodes; A distribution network reactive power optimization and control model determination module is used to establish a distribution network reactive power optimization and control model that takes into account operation economy and voltage safety margin, taking minimization of the distribution network reactive power optimization and control operation cost and the minimum deviation of the actual voltage margin of the node relative to the ideal voltage safety margin of the node as the objective function, wherein the ideal voltage safety margin of the node includes the ideal voltage safety margin of the load node and the ideal voltage safety margin of the distributed node; A distribution network reactive power optimization control model optimization module, used to determine a membership function according to an objective function component and a slackable variable, and optimize the distribution network reactive power optimization control model based on the membership function; The reactive power optimization and control strategy determination module is used to obtain the reactive power optimization and control method of distributed energy in the distribution network according to the optimized reactive power optimization and control model of the distribution network.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 8.

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