A distributed photovoltaic carrying capacity calculation method considering reverse power flow

Abstract

The present application relates to a kind of distributed photovoltaic carrying capacity calculation method considering reverse power flow, belong to distribution network evaluation field, solve the problem of voltage out of limit, transformer capacity out of limit and distributed photovoltaic capacity evaluation safety due to reverse power flow.It includes generating typical day uncertainty scenario, calculating the probability of uncertainty scenario under each typical day of season;Establish transformer model and reversible sending capacity model under bidirectional power flow mode;Based on the probability of uncertainty scenario under each typical day of season, and transformer model and reversible sending capacity model, build distributed photovoltaic carrying capacity calculation model including with the minimum of distribution network annual comprehensive resource loss as objective function, and the constraint condition of corresponding ESS and SOP collaborative planning considering reverse power flow;Based on typical day uncertainty scenario data, one-dimensional search is used to solve distributed photovoltaic carrying capacity calculation model, and the distribution network distributed photovoltaic carrying capacity calculation scheme is obtained.The accurate calculation of distributed photovoltaic carrying capacity is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power distribution network evaluation and digital data processing technology, and particularly relates to a distributed photovoltaic carrying capacity calculation method considering reverse power flow. BACKGROUND

[0002] The access of a large number of distributed photovoltaic (PV, Photovoltaic) brings many power generation, power transformation or power distribution energy problems to the power distribution network, among which the reverse power flow and voltage out-of-limit risk are currently the problems that should be focused on solving. When the reverse power flow occurs, the reverse operation of the transformer will be caused, which not only causes the voltage on the low-voltage side to rise and causes the voltage out-of-limit risk, but also causes the capacity margin of the transformer capable of reverse sending to be less than the capacity margin in the forward operation, thereby causing the out-of-limit of the reverse sending capacity of the transformer. SUMMARY

[0003] In view of the above analysis, the embodiments of the present application aim to provide a distributed photovoltaic carrying capacity calculation method considering reverse power flow, so as to solve the technical problems of voltage out-of-limit, transformer capacity out-of-limit and distributed photovoltaic capacity evaluation safety caused by reverse power flow.

[0004] The main purpose of the present application is achieved by the following technical solutions:

[0005] The present application provides a distributed photovoltaic carrying capacity calculation method considering reverse power flow, comprising the following steps:

[0006] generating a typical day uncertainty scenario of a power distribution network power system, and calculating the probability of the uncertainty scenario under each typical day of the season;

[0007] establishing a transformer model and a reverse sending capacity model under a bidirectional power flow mode;

[0008] based on the probability of the uncertainty scenario under each typical day of the season, and the transformer model and the reverse sending capacity model, constructing a distributed photovoltaic carrying capacity calculation model including a target function of minimizing the annual comprehensive resource loss of the power distribution network, and corresponding constraint conditions of ESS and SOP collaborative planning considering reverse power flow;

[0009] based on the typical day uncertainty scenario data, solving the distributed photovoltaic carrying capacity calculation model by using one-dimensional search to obtain a power distribution network distributed photovoltaic carrying capacity calculation scheme.

[0010] Further, the generation of the typical day uncertainty scenario of the power distribution network power system and the calculation of the probability of the uncertainty scenario under each typical day include:

[0011] based on the historical photovoltaic output data and load data of the power distribution network power system within a certain time, clustering to obtain four typical day scenarios of spring, summer, autumn and winter;

[0012] In each seasonal typical day scenario, the probability density of distributed photovoltaic output is determined based on the photovoltaic output data of the typical day scenario by using a Beta distribution, and the most optimistic value, the most likely value, the most pessimistic value and the corresponding weight coefficient are calculated by using a three-point estimation method;

[0013] Based on the most optimistic value, the most likely value, the most pessimistic value and the corresponding weight coefficient, the uncertainty scenario probability of each seasonal typical day is calculated.

[0014] Further, the transformer model and the reversible sending capacity model in the two-way power flow mode include: establishing the transformer model in the step-down operation mode, the transformer model in the step-up operation mode and the transformer reversible sending capacity model.

[0015] Further, the target function is as follows:

[0016] minC=min(C loss +C int +C ess +C sop +C pv )

[0017] Wherein, C is the annual comprehensive resource consumption; C loss is the annual power supply loss of the distribution network converted into the amount of coal; C int is the annual power supply amount of the main network converted into the amount of coal; C ess is the energy storage operation loss converted into the amount of coal; C sop is the SOP operation loss converted into the amount of coal; C pv is the annual resource consumption required for constructing distributed photovoltaic.

[0018] Further, the energy storage operation loss converted into the amount of coal C ess is as follows:

[0019]

[0020] Wherein, τ is the annual operation and maintenance loss coefficient of energy storage; c om is the energy storage unit power capacity coal consumption coefficient; is the energy storage unit energy coefficient of node i in the distribution network; P i is the energy storage configuration capacity of node i in the distribution network; N ess is the total number of elements in the energy storage site set; p m,s is the probability of the uncertainty scenario under each seasonal typical day; S is the number of scenarios; M is the number of estimation points.

[0021] Further, the SOP operation loss converted into the amount of coal Csop As follows:

[0022]

[0023] Wherein, c g,SOP , S g,SOP , respectively, the gth SOP unit capacity coal consumption coefficient and SOP configuration capacity; η is the SOP annual operation and maintenance loss coefficient; N SOP The total number of elements in the SOP site set.

[0024] Further, the required annual resource consumption C pv As follows:

[0025]

[0026] Wherein, c r,PV , , respectively, the rth photovoltaic unit capacity required resource consumption coefficient and its kth distributed photovoltaic installed capacity; σ is the annual coefficient of distributed photovoltaic construction; N PV The total number of elements in the photovoltaic candidate node set.

[0027] Further, the constraint condition of ESS and SOP collaborative planning considering reverse power flow includes transformer bidirectional operation mode constraint, SOP power constraint, SOP installation constraint, ESS operation constraint, ESS installation constraint, power balance constraint, transmission power constraint of transmission and distribution network and distributed photovoltaic output constraint.

[0028] Further, the distributed photovoltaic carrying capacity calculation model is solved by one-dimensional search, including:

[0029] Set the initial value of distributed photovoltaic installed capacity And search step ΔS i,PV ;

[0030] Using Gurobi solver, it is judged whether the objective function has a solution under the constraint condition;

[0031] If there is a solution, the one-dimensional search method is used to update the distributed photovoltaic installed capacity;

[0032] Iteratively increase the search step until the distributed photovoltaic carrying capacity calculation model has no solution;

[0033] When there is no solution, back to the last feasible solution, and output the distribution network distributed photovoltaic carrying capacity calculation scheme corresponding to the last feasible solution;

[0034] Wherein, the one-dimensional search method is used to update the distributed photovoltaic installed capacity, as follows:

[0035]

[0036] wherein, is the (k+1)th distributed photovoltaic installed capacity of the ith node; is the kth distributed photovoltaic installed capacity of the ith node;ΔS i,PV is the search step length of the ith node.

[0037] Further, the power distribution network distributed photovoltaic carrying capacity calculation scheme comprises a distributed photovoltaic installed capacity, an energy storage configuration capacity and an SOP configuration capacity that meet the constraint condition and minimize the target function operation loss.

[0038] Compared with the prior art, the present application can at least achieve one of the following beneficial effects:

[0039] 1. The present application realizes the transfer of power flow between the main network and the distribution network by using SOP to open up the power flow mutual aid channel, optimizes the power flow and improves the system stability. It helps to solve the problems of reverse power flow and voltage out-of-limit risk caused by a large number of distributed photovoltaic access to the distribution network, thereby enhancing the reliability and stability of the power grid.

[0040] 2. The present application quantifies the transmission margin of transformer reverse power flow, improves the stability of transformer and system. By establishing a transformer model and a reversible sending capacity model under the bidirectional power flow mode, the voltage model of the transformer under the bidirectional mode is refined, the capacity margin of the transformer under different operation directions is more accurately evaluated, and the problems caused by the out-of-limit of the transformer reverse sending capacity are avoided.

[0041] 3. The present application considers the influence of coal consumption and loss by using one-dimensional search method under the premise of ensuring stability, evaluates the limit of distributed photovoltaic access capacity. This method not only considers the synergistic effect between energy storage and SOP, but also promotes new energy consumption, optimizes power flow, reduces network loss, improves distributed photovoltaic access capacity, accurately evaluates the limit of distributed photovoltaic access capacity, and thus provides guidance for the accurate calculation of the distributed photovoltaic carrying capacity of the power system.

[0042] In the present application, the above technical solutions can be combined with each other to realize more preferred combination schemes. Other features and advantages of the present application will be described in the subsequent specification, and some advantages will become apparent from the specification or be understood by implementing the present application. The purpose and other advantages of the present application can be achieved and obtained from the contents specifically pointed out in the specification and the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0043] The accompanying drawings are included to provide a further understanding of embodiments and are incorporated in and constitute a part of this application. The drawings illustrate embodiments for purposes of exemplification. These embodiments do not limit the scope of the application, and the patent is not limited to what has been illustrated and described.

[0044] Figure 1 A flow chart of a distributed photovoltaic carrying capacity calculation method considering reverse power flow in an embodiment of the present application. DETAILED DESCRIPTION

[0045] The preferred embodiments of the present application will be described in detail with reference to the drawings, wherein the drawings form a part of this application. The drawings illustrate the principles of the present application and the application should not be limited to the preferred embodiments used to explain the principles of the present application.

[0046] To solve the above problems, rational planning of energy storage and SOP (Soft Open Point) is the key to solving the problem of distributed photovoltaic grid connection by using computer-aided design, information and communication technology.

[0047] One specific embodiment of the present application discloses a distributed photovoltaic carrying capacity calculation method considering reverse power flow, as shown in Figure 1 The method comprises the following steps:

[0048] Step S1, generating a typical day uncertainty scenario of a power system of a distribution network, and calculating the probability of the uncertainty scenario under each typical day;

[0049] Step S2, establishing a transformer model and a reversible sending capacity model under a bidirectional power flow mode;

[0050] Step S3, based on the probability of the uncertainty scenario under each typical day, the transformer model and the reversible sending capacity model, constructing a distributed photovoltaic carrying capacity calculation model including a target function of minimizing the operation loss of the distribution network and corresponding constraint conditions of the ESS and SOP collaborative planning considering reverse power flow;

[0051] Step S4, based on the typical day uncertainty scenario data, solving the distributed photovoltaic carrying capacity calculation model by using one-dimensional search to obtain a distributed photovoltaic carrying capacity calculation scheme of the distribution network.

[0052] Step S1, specifically.

[0053] Step 1, generating a typical day uncertainty scenario of a power system, specifically comprising:

[0054] The generation of a typical day uncertainty scenario of a power system of a distribution network, the calculation of the probability of the uncertainty scenario under each typical day, comprises:

[0055] Based on the historical photovoltaic output data and load data of the power system of the distribution network within a certain time, four typical day scenarios of spring, summer, autumn and winter are obtained by clustering.

[0056] In each typical day scenario, the probability density of the distributed photovoltaic output is determined based on the photovoltaic output data of the typical day scenario using the Beta distribution, and the most optimistic value, the most likely value, and the most pessimistic value and the corresponding weight coefficient are calculated using the three-point estimation method;

[0057] Based on the most optimistic value, the most likely value, and the most pessimistic value and the corresponding weight coefficient, the uncertainty scenario probability of each typical day is calculated.

[0058] A typical day is a representative day that can reflect the general characteristics of a power system in a certain period or scenario.

[0059] According to the existing historical data, four typical day scenarios of spring, summer, autumn and winter are selected, and the base weight coefficients w(s) of the four scenarios are all set to 0.25. Each typical day data includes a photovoltaic daily output curve and a daily load curve.

[0060] The photovoltaic output is subject to Beta distribution, and the probability density function of the distributed photovoltaic output (i.e. power generation) is as follows:

[0061]

[0062] where P pv is the distributed photovoltaic output; P max is the maximum output power of the photovoltaic array; Γ(·) is the Gamma function; α and β are the shape parameter and distribution parameter of the photovoltaic intensity, respectively.

[0063] P pv is the real-time output of the distributed photovoltaic, which is the actual power generation, 0 pv max ; P max is the maximum theoretical output of the distributed photovoltaic.

[0064] The shape parameter α controls the left or right deviation, the distribution parameter controls the peak position, and the distribution parameter and the shape parameter jointly determine the steepness of the output distribution; the Gamma function ensures that the probability density integral is equal to 1.

[0065] Exemplarily, α = 3.5; β = 2.33.

[0066] The relationship between the input variables and the output variables of the power system satisfies the following power flow equation:

[0067] G = F(X) Formula (2)

[0068] where X = [x1, x2, …, x n ] is an n-dimensional input random variable, and the input data includes: photovoltaic output curve, daily load curve; G = [g1, g2, …, g​m ] is the m-dimensional uncertainty output variable, and the output data includes: node voltage, current, phase angle; F(·) is the power flow equation.

[0069] The daily fluctuation range of the electricity consumption curve of the conventional load is small, the deterministic load model is adopted in the application to generate the uncertainty scenario of photovoltaic output; the mean value δ n and the standard deviation σ n of the uncertainty variable are calculated according to the probability density function as follows:

[0070]

[0071] wherein f(x n ) is the probability density function of the input variable x n ;

[0072] The three-point estimation method is adopted to calculate three estimation points of the distributed photovoltaic output in each typical day scenario, i.e., the most optimistic value x1, the most likely value x2 and the most pessimistic value x3;

[0073] x m = δ + ξ m σ, m = 1, 2, 3 Formula (4)

[0074] wherein ξ m is the position measure, and the expression is as follows:

[0075]

[0076] wherein υ3 is the third-order central moment of the random variable x i , called skewness coefficient, representing the degree of distribution of x i deviating from the standard normal distribution; and υ4 is the fourth-order central moment of the random variable x i , called kurtosis coefficient.

[0077] The weight coefficients corresponding to the most optimistic value x1, the most likely value x2 and the most pessimistic value x3 are calculated as follows:

[0078]

[0079] wherein w m,s is the weight coefficient of the mth (m = 1, 2, 3) estimation point of the distributed photovoltaic output in the s th scenario.

[0080] The uncertainty scenario probability under the typical day of each season is calculated as follows:

[0081]

[0082] Wherein, S is the number of four scenes of spring, summer and autumn; M is the number of estimation points; Exemplarily, S is 4 and M is 3 in the present application.

[0083] Step S1 provides a representative set of typical daily uncertainty scenarios for the distributed photovoltaic carrying capacity calculation of the power distribution network, and the uncertainty scenarios mainly include the most optimistic value, the most likely value and the most pessimistic value of the typical day in each season; both the fluctuation of photovoltaic output due to seasonal changes in a year and the possible special cases in each season, such as the possibility of large output in sunny weather and sudden drop in output in cloudy weather, are considered, the uncertainty of photovoltaic output is considered; It is helpful to more accurately evaluate and plan the distributed photovoltaic carrying capacity calculation of the power distribution network in the subsequent steps.

[0084] Step S2, in particular.

[0085] The transformer model and the reversible sending capacity model under the bidirectional power flow mode include: establishing the transformer model under the step-down operation mode, the transformer model under the step-up operation mode and the transformer reversible sending capacity model.

[0086] The existing transformer model is a T-shaped or π-shaped equivalent circuit, which is established by internal equivalent impedance, leakage impedance and excitation impedance parameters. When reverse power flow occurs, it will not be affected by the power direction itself, but because its actual model is not an ideal model, when it runs in reverse, it will cause the voltage on the low-voltage side to rise, so the influence of the internal impedance of the transformer on the reverse operation needs to be considered.

[0087] The transformer internal impedance voltage is as follows:

[0088]

[0089] The transformer model under the step-down operation mode is established as follows:

[0090] KU2=U1(1-u%) Formula (9)

[0091] Wherein, u% is the impedance coefficient of the transformer converted to the high-voltage side; is the total impedance of the transformer converted to the high-voltage side; is the reference impedance of the transformer converted to the high-voltage side; is the rated current of the high-voltage side; is the rated voltage of the high-voltage side; Z1 and Z2' are the high-voltage side impedance and the impedance of the low-voltage side converted to the high-voltage side, respectively; K is the transformer ratio; U1 is the high-voltage side voltage; U2 is the low-voltage side voltage;

[0092] The transformer internal impedance voltage is as follows:

[0093]

[0094] The transformer model in the step-up operation mode is established as follows:

[0095] U2' = KU1'(1-u%) Formula (11)

[0096] wherein u% is the impedance coefficient of the transformer converted to the low-voltage side; is the total impedance of the transformer converted to the low-voltage side; is the reference impedance of the transformer converted to the low-voltage side; is the rated current of the high-voltage side; is the rated voltage of the high-voltage side; Z1' and Z2 are the impedance of the high-voltage side converted to the low-voltage side and the impedance of the low-voltage side, respectively; U1' and U'2 are the bus voltages of the low-voltage side and the high-voltage side, respectively;

[0097] When the reverse power flow occurs, the voltage rise of the low-voltage side will cause the magnetic circuit saturation, and then result in the decrease of the reverse sending capacity margin of the transformer. Therefore, in order to ensure that the transformer is within the safety margin range when the reverse power flow occurs, the relationship between the voltage and the magnetic flux is established by using the Faraday's law of electromagnetic induction:

[0098]

[0099] wherein Φ N and Φ ren are the magnetic flux value designed for the step-down transformer and the magnetic flux value when the reverse operation is performed, respectively; f is the frequency of the power system during operation;

[0100] The reverse sending coefficient is defined by the ratio of the magnetic flux as follows:

[0101]

[0102] The reverse sending capacity model of the transformer is established as follows:

[0103] S ren,max = εS N Formula (14)

[0104] wherein ε is the reverse sending coefficient; S N is the rated capacity of the transformer; S ren,max is the maximum value of the reverse sending capacity of the transformer.

[0105] The function of the step S2 is to establish and refine the transformer model considering the influence of the reverse power flow, including the models in the step-down and step-up operation modes and the reverse sending capacity model of the transformer, so as to accurately evaluate the performance and limitation of the transformer under the condition of the reverse power flow, thereby providing the basis for building the distributed photovoltaic carrying capacity calculation model in the step S3.

[0106] The step S3 is specifically.

[0107] Based on the probability of uncertainty scenarios of each typical day in each season, and the transformer model and the reversible capacity model, a target function is constructed with the minimum annual comprehensive resource loss of the distribution network as the target.

[0108] The target function is as follows:

[0109] minC=min(C loss +C int +C ess +C sop +C pv ) Formula (15)

[0110] Wherein, C is the annual comprehensive resource loss; C loss is the annual power supply loss of the distribution network converted into the amount of coal; C int is the annual power supply amount of the main network converted into the amount of coal; C ess is the energy storage operation loss converted into the amount of coal; C sop is the SOP operation loss converted into the amount of coal; C pv is the annual resource consumption required for constructing distributed photovoltaic.

[0111] The main network, also known as the transmission network, is mainly responsible for long-distance and large-capacity power transmission;

[0112] The distribution network is responsible for further distributing the power transmitted by the main network to specific users, such as residential, commercial and industrial users.

[0113] The annual power supply loss of the distribution network C loss refers to the power loss caused by factors such as resistance loss and transformer loss in the distribution network within a year.

[0114] The annual power supply loss of the distribution network converted into the amount of coal C loss is as follows:

[0115]

[0116] Wherein, λ is the coal consumption per degree of electricity; T takes 24 to represent the power supply time of one day; S is the number of scenarios; N is the number of nodes of the example system (in this invention, based on IEEE-33 nodes); P i,t,s is the sum of active power injected at node i at time t in the s-th scenario; is the power loss of node i and node j of SOP at time t under scenario s; p m,s is the uncertainty scenario probability corresponding to the scenario. At present, the main power supply is thermal power, that is, the loss of electricity is described by the coal consumption.

[0117] The annual power supply amount of the main network converted into the amount of coal C int refers to the amount of coal consumed by the main network during power supply.

[0118] The amount of coal C converted from the annual power supply of the main grid int As follows:

[0119]

[0120] Wherein, φ is the amount of coal saved per kilowatt-hour at time t under scenario s when it is fed back to the main grid; Qi is the interaction power between the main grid and the distribution grid at node i.

[0121] The amount of coal C converted from the operation loss of the energy storage ess , which refers to the loss of the energy storage system (such as battery energy storage) during operation, including charge and discharge efficiency loss, equipment depreciation, etc.

[0122] The amount of coal C converted from the operation loss of the energy storage ess As follows:

[0123]

[0124] Wherein, τ is the annual operation and maintenance loss coefficient of the energy storage; c om is the coal consumption coefficient of the energy storage unit power capacity; Pi is the energy storage unit energy coefficient of node i in the distribution grid; P i is the energy storage configuration capacity of node i in the distribution grid; N ess is the total number of elements in the energy storage site selection point set; p m,s is the probability of each season's typical day under uncertainty scenario; S is the number of scenarios; M is the number of estimation points.

[0125] Exemplarily, τ takes the value of 0.15; c om takes the value of 120; takes the value of 0.002.

[0126] The amount of coal C converted from the operation loss of the soft open point (SOP) sop , which refers to the loss generated when the soft open point is introduced in the distribution grid to optimize the power flow distribution.

[0127] The amount of coal C converted from the operation loss of the soft open point (SOP) sop As follows:

[0128]

[0129] Wherein, c g,SOP , S g,SOP are the coal consumption coefficient of the gth SOP unit capacity and the SOP configuration capacity, respectively; η is the annual operation and maintenance loss coefficient of the SOP; N SOP is the total number of elements in the SOP site selection point set.

[0130] Exemplarily, c g,SOP η takes a value of 0.10; η takes a value of 300.

[0131] The annual resource consumption C required for the construction of the distributed photovoltaic pv As follows:

[0132]

[0133] wherein, c r,PV , is the resource consumption coefficient required for the unit capacity of the rth photovoltaic and the kth distributed photovoltaic installed capacity, respectively; σ is the annualization coefficient of the distributed photovoltaic construction; N PV is the total number of elements in the set of photovoltaic nodes to be selected.

[0134] Exemplarily, σ takes a value of 0.04; c r,PV The value can be given according to "converting 1 kW of photovoltaic installed capacity to the standard coal consumed by one-time construction", which takes a value of 0.04 tce / kW, meaning that the one-time energy or standard coal consumption required for installing 1 kW of distributed photovoltaic components and supporting inverters, supports, cables, etc. is converted to 0.04 tons of standard coal; this coefficient can be fine-tuned according to the specific region, component type, transportation distance, installation method and life cycle assessment results, and the typical value range is 0.03-0.06 tce / kW.

[0135] N PV is the set of photovoltaic nodes to be selected, i.e. the number of distribution nodes that can install photovoltaics.

[0136] The constraint conditions of the ESS and SOP collaborative planning considering reverse power flow include transformer bidirectional operation mode constraint, SOP power constraint, SOP installation constraint, ESS operation constraint, ESS installation constraint, power balance constraint, transmission and distribution network transmission power constraint and distributed photovoltaic output constraint.

[0137] The constraint conditions of the ESS and SOP collaborative planning considering reverse power flow are constructed, combined with the transformer model in step S2 in the step-down operation mode, the transformer model in the step-up operation mode and the transformer reversible sending capacity model, and the constraint conditions corresponding to the objective function are constructed.

[0138] (1) Transformer bidirectional operation mode constraint, as follows:

[0139]

[0140] It is required to be true at each time and in each uncertainty scenario, according to the fourth and fifth inequalities in formula (26), the influence variables U i,t,s and U j,t,s; According to the third equation in formula (26), further affecting variable P i,t,s .

[0141] (2) SOP power constraints, as follows:

[0142]

[0143] where i, j are the node numbers of the distribution system connected by the SOP; t, s are the time and scenario, respectively; and are the active power injected by the SOP, is the loss of the SOP, which affects variable P i,t,s according to the first equation in formula (26); and are the reactive power injected on both sides of the SOP, which affects variable and further affecting variable P i,t,s according to the third equation in formula (26); is the loss coefficient of the converter; is the absolute value of the power factor angle sine.

[0144] (3) SOP installation constraints, as follows:

[0145] 0≤S g,SOP ≤S max,SOP formula (23)

[0146] where S g,SOP is the installed SOP capacity at the gth location; S max,SOP is the maximum capacity allowed to be installed at the selected location.

[0147] (4) ESS operation constraints, as follows:

[0148]

[0149] where and represent the charging and discharging states of the ESS, respectively, which are 0-1 variables, affecting variable P i,t,s ; are the charging and discharging active power of the energy storage at i at time t under scenario s, which affect variable P i,t,s according to the first inequality in formula (24); are the charging and discharging reactive power of the energy storage at i at time t under scenario s, which affect the size of variables and according to the second and third inequalities in formula (24); Pi , E i are the capacity and power of the energy storage configuration of node i, respectively; E i,t,s and E i,t+1,s are the energy storage state of charge at time t and t+1, respectively, which affect variable E and and in turn variable P i,t,s ; are the charging and discharging efficiency coefficients, respectively, both of which are constants; S i,max , S i,min are the upper and lower limits of the energy storage state of charge, respectively; which affect variable E i,t,s and in turn variable P i,t,s ; E i,0,s , E i,T,s are the energy storage state of charge at the initial and end time of each day, respectively; S ini is the initial value of the energy storage state of charge; which affects variable E i,t,s and in turn variable P i,t,s ; N ess is the set of energy storage site selection points.

[0150] (5) ESS installation constraints, as follows:

[0151]

[0152] where γ i is a 0-1 decision variable indicating whether the battery energy storage is built at node i; and are the maximum and minimum installed capacity of the battery energy storage at node i, respectively; P i max is the maximum installed power of the battery energy storage at node i.

[0153] (6) Power balance constraints, as follows:

[0154]

[0155] where, are the active and reactive power of the load at node i at time t in the s-th scenario, respectively; are the active and reactive power of the distributed photovoltaic injection at node i at time t in the s-th scenario, respectively; are the charging and discharging active power of the energy storage at node i at time t in the s-th scenario, respectively; are the charging and discharging reactive power of the energy storage at node i at time t in the s-th scenario, respectively; are the active and reactive power of the load at node i at time t in the s-th scenario, respectively; are the active and reactive power injected by the s-th scenario node i at time t; U i,t,s is the voltage amplitude of the s-th scenario node i at time t; U j,t,s is the voltage amplitude of the s-th scenario node j at time t; U i,max , U i,min are the upper and lower limits of the voltage amplitude of node i; U j,max , U j,min are the upper and lower limits of the voltage amplitude of node j; I ij,t,s is the current amplitude of branch ij at time t in the s-th scenario; I ij,max is the upper limit of the current amplitude of branch ij; r ij and x ij are the resistance and reactance of branch ij, respectively; the above variables all have an influence on variable P i,t,s ; are the active and reactive power emitted by node i of the s-th scenario of SOP at time t.

[0156] (7) Transmission and distribution network transmission power constraints, as follows:

[0157]

[0158] wherein, are the active and reactive net loads of the distribution network at time t; is the interactive reactive power between the transmission network and the distribution network; εS N is the transformer reversible sending capacity model of formula (14).

[0159] (8) Distributed photovoltaic output constraints, as follows:

[0160]

[0161] wherein, is the distributed photovoltaic installed capacity of the i-th node in the k-th iteration; η i,t is the output coefficient of the distributed photovoltaic connected to node i at time t; it is assumed that the reactive power of the distributed photovoltaic is output according to a fixed power factor, is the power factor angle of the photovoltaic connected to node i, which has an influence on variable P i,t,s according to formula (24).

[0162] The role of step S3 is to use the uncertainty scenario probability of each typical day in each season and the transformer and reversible sending capacity model to construct an optimization model with the objective of minimizing the operation loss of the distribution network, including an objective function and corresponding constraint conditions, to calculate the distributed photovoltaic carrying capacity of the distribution network.

[0163] Step S4, in particular.

[0164] Determine the distributed photovoltaic carrying capacity calculation scheme of the power distribution network that meets the constraint condition and optimizes the objective function through one-dimensional search.

[0165] Solve the distributed photovoltaic carrying capacity calculation model by one-dimensional search, including:

[0166] Set the initial value of the distributed photovoltaic installed capacity And search step ΔS i,PV ;

[0167] Use Gurobi solver to determine whether the objective function has a solution under the constraint condition;

[0168] If there is a solution, update the distributed photovoltaic installed capacity by one-dimensional search;

[0169] Iteratively increase the search step until the distributed photovoltaic carrying capacity calculation model has no solution;

[0170] When there is no solution, back to the last feasible solution, and output the power distribution network distributed photovoltaic carrying capacity calculation scheme corresponding to the last feasible solution;

[0171] Wherein, the distributed photovoltaic installed capacity is updated by one-dimensional search as follows:

[0172]

[0173] Wherein, The (k+1)th distributed photovoltaic installed capacity of the ith node; The kth distributed photovoltaic installed capacity of the ith node; ΔS i,PV The search step of the ith node.

[0174] Set the initial value And search step ΔS i,PV , input it into the distributed photovoltaic carrying capacity calculation model, solve it by using the solver Gurobi, determine whether it has a solution under the constraint condition, if there is a solution, update the installed capacity according to the step, until the distributed photovoltaic carrying capacity calculation model has no solution, that is, it cannot meet the constraint condition, the last solution without solution is the final running result, that is, the energy storage configuration capacity, the SOP configuration capacity, and the distributed photovoltaic installed capacity at this time.

[0175] The distributed photovoltaic carrying capacity calculation scheme of the power distribution network includes the distributed photovoltaic installed capacity, the energy storage configuration capacity and the SOP configuration capacity that meet the constraint condition and minimize the objective function running loss.

[0176] Exemplarily, assume the initial value Step ΔS i,PV= 100 kW, and then the initial value is input into the distributed photovoltaic carrying capacity calculation model, if the model has a solution at this time, the input value is updated When the input value is input, the model has no solution at this time, which represents that the system has been unable to accommodate more distributed photovoltaics, the planning result at the time when is read, that is, the energy storage configuration capacity P i and the SOP configuration capacity S g,SOP At this time, the distributed photovoltaic installation is the maximum value that can be borne by the system, that is, the distributed photovoltaic carrying capacity is 3900 kW; if the model has no solution, the input value is reduced to until the model has a solution, at this time, the read energy storage configuration capacity P i and the SOP configuration capacity S g,SOP , and the distributed photovoltaic carrying capacity is the value at the time when the model has a solution.

[0177] The role of step S4 is to determine the distributed photovoltaic carrying capacity calculation scheme of the power distribution network that meets the constraint conditions and optimizes the objective function through a one-dimensional search method. It is ensured that the optimal distributed photovoltaic installation capacity, energy storage configuration capacity and SOP configuration capacity are found under the consideration of the influence of reverse power flow, so that the optimal operation state of the power distribution network is realized.

[0178] In summary, the distributed photovoltaic carrying capacity calculation method considering reverse power flow of the embodiment of the application has the following beneficial effects:

[0179] 1. The application realizes the power flow transmission between the main network and the power distribution network, optimizes the power flow and improves the system stability by using the SOP to open the power flow mutual aid channel. It helps to solve the problems of reverse power flow and voltage out-of-limit risk caused by the large number of distributed photovoltaics connected to the power distribution network, thereby enhancing the reliability and stability of the power grid;

[0180] 2. The application quantifies the reverse power flow transmission margin of the transformer, improves the stability of the transformer and the system. By establishing the transformer model and the reversible sending capacity model under the bidirectional power flow mode, the voltage model of the transformer under the bidirectional mode is refined, the capacity margin of the transformer under different operation directions is more accurately evaluated, and the problems that may be caused by the out-of-limit of the reverse sending capacity of the transformer are avoided;

[0181] 3. The application considers the influence of coal consumption and loss under the premise of ensuring stability by using a one-dimensional search method to evaluate the limit of distributed photovoltaic connection capacity. This method not only considers the synergistic effect between energy storage and SOP, but also promotes new energy consumption, optimizes power flow, reduces network loss, improves distributed photovoltaic connection capacity, accurately evaluates the limit of distributed photovoltaic connection capacity, and thus provides guidance for the accurate calculation of the distributed photovoltaic carrying capacity of the power system.

[0182] Those skilled in the art can understand that all or part of the processes of the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer readable storage medium. The computer readable storage medium is a disk, an optical disk, a read-only memory, a random access memory, etc.

[0183] The above description is only the preferred embodiment of the present application, but the protection scope of the present application is not limited to this. Any changes or replacements within the technical scope disclosed by the present application can be easily thought by those skilled in the art, and should be covered within the protection scope of the present application.

Claims

1.A method for distributed photovoltaic carrying capacity calculation considering reverse power flow, characterized in that, The method comprises the following steps: generating a typical day uncertainty scenario of a power distribution network power system, and calculating the probability of the uncertainty scenario under each typical day; establishing a transformer model and a reversible capacity model under a bidirectional power flow mode; the reversible capacity model is as follows: wherein is the maximum value of the transformer reverse sending ability; is the reverse sending coefficient; is the rated capacity of the transformer; , are the magnetic flux value for the design of the step-down transformer and the magnetic flux value when operating in reverse, respectively; is the low-voltage side voltage; is the low-voltage side bus voltage; is the operating power system frequency; based on the probability of the uncertainty scenario under each typical day, and the transformer model and the reversible capacity model, a distributed photovoltaic carrying capacity calculation model is constructed, which comprises a target function of minimizing the annual comprehensive resource loss of the power distribution network, and constraint conditions of ESS and SOP collaborative planning considering reverse power flow; the target function is as follows: Wherein, C is the annual comprehensive resource consumption; is the amount of coal converted from the annual power supply loss of the distribution network; is the amount of coal converted from the annual power supply of the main network; is the amount of coal converted from the energy storage operation loss; is the amount of coal converted from the SOP operation loss; is the annual resource consumption required for the construction of distributed photovoltaic. based on the typical day uncertainty scenario data, the one-dimensional search is used to solve the distributed photovoltaic carrying capacity calculation model, and a distributed photovoltaic carrying capacity calculation scheme of the power distribution network is obtained. 2.The method of claim 1, wherein, The method for generating a typical day uncertainty scenario of a power distribution network power system and calculating the probability of the uncertainty scenario under each typical day comprises the following steps: based on the historical photovoltaic output data and load data of the power distribution network power system within a certain time, four typical day scenarios of spring, summer, autumn and winter are clustered; in each seasonal typical day scenario, the probability density of the distributed photovoltaic output is determined based on the photovoltaic output data of the typical day scenario using Beta distribution, and the most optimistic value, the most likely value and the most pessimistic value and the corresponding weight coefficient are calculated using the three-point estimation method; based on the most optimistic value, the most likely value and the most pessimistic value and the corresponding weight coefficient, the uncertainty scenario probability of each seasonal typical day is calculated. 3.The method of claim 1, wherein, The method for establishing a transformer model and a reversible capacity model under a bidirectional power flow mode comprises the following steps: establishing a transformer model under a step-down operation mode, a transformer model under a step-up operation mode and a transformer reversible capacity model. 4.The method of claim 1, wherein, The energy storage operation loss is converted into the amount of coal As follows: wherein, is the energy storage annual operation and maintenance loss coefficient; is the energy storage unit power capacity coal consumption coefficient; is the energy storage unit energy coefficient of the node in the distribution network; is the energy storage configuration capacity of the node in the distribution network; is the total number of elements in the energy storage site set; is the probability of the uncertainty scenario under the typical day of each season; is the number of scenarios; is the number of estimation points. 5.The method of claim 1, wherein, The SOP operating loss is converted into the amount of coal As follows: in, , The first Coal consumption coefficient per unit capacity of each SOP and SOP configuration capacity; The annual operating and maintenance loss factor for SOP; This represents the total number of elements in the SOP site selection point set. 6.The method of claim 1, wherein, The annual resource consumption required for building distributed photovoltaics As follows: Wherein, , are the resource consumption coefficient required by the first and the distributed photovoltaic installed capacity of the first photovoltaic unit capacity, respectively; is the annualization coefficient of distributed photovoltaic construction; is the total number of elements in the photovoltaic candidate node set. 7.The method of claim 1-6, wherein, The constraint conditions of ESS and SOP collaborative planning considering reverse power flow comprise transformer bidirectional operation mode constraints, SOP power constraints, SOP installation constraints, ESS operation constraints, ESS installation constraints, power balance constraints, transmission network transmission power constraints and distributed photovoltaic output constraints. 8.The method of claim 7, wherein, The method for solving the distributed photovoltaic carrying capacity calculation model using one-dimensional search comprises the following steps: Setting initial value of distributed photovoltaic installed capacity and search step ; using a Gurobi solver to determine whether the target function has a solution under the constraint conditions; if there is a solution, the distributed photovoltaic installed capacity is updated using one-dimensional search; the search step is iteratively increased until the distributed photovoltaic carrying capacity calculation model has no solution; when there is no solution, the last feasible solution is returned, and the distributed photovoltaic carrying capacity calculation scheme corresponding to the last feasible solution is output; wherein the distributed photovoltaic installed capacity is updated using one-dimensional search as follows: wherein, is the first node the first distributed photovoltaic installed capacity; is the first node the first distributed photovoltaic installed capacity; is the first search step size for the node. 9.The method of claim 8, wherein, The distributed photovoltaic carrying capacity calculation scheme of the power distribution network comprises a distributed photovoltaic installed capacity, an energy storage configuration capacity and an SOP configuration capacity that satisfy the constraint conditions and minimize the target function operation loss.

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