Method for evaluating power supply capability of power distribution network containing new energy storage

Abstract

The invention provides a power supply capability evaluation method for a power distribution network containing new energy storage, and relates to the technical field of power supply of the power distribution network, and the method comprises the steps: calculating a load growth vector according to a power grid voltage reactive iterative equation and a power flow change relation, and constructing a power supply capability mathematical model according to the load growth vector, a basic load and energy storage charging power; determining related parameters, and calculating a new load value by using the related parameters; carrying out load flow calculation, judging whether a calculation result is out of limit or not, if so, carrying out parameter replacement and recalculating a new load value, otherwise, judging whether the calculation result reaches convergence precision or not, if not, changing a load increase step length and continuing iteration, and otherwise, outputting an optimal value to obtain the maximum power supply capacity. According to the method, the load increase vector is introduced, so that the load increase is influenced by the existing node load, the traditional single increase speed mode is changed, and the load calculation is more accurate; the constructed power supply capacity mathematical model improves the local consumption capacity of new energy power generation, and further improves the power supply capacity of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply of distribution network, and in particular to a method for evaluating power supply capacity of distribution network. Background Art

[0002] As the terminal link of the power grid, the distribution network directly connects the power transmission and transformation system with the majority of power users, and undertakes the important task of power distribution. In recent years, with the rapid development of the economy and the continuous improvement of people's living standards, the demand for electricity has shown a rapid growth trend. Especially in urban areas, with the rise of high-rise buildings, the continuous emergence of industrial enterprises, and the popularization of new power-consuming equipment such as electric vehicles and smart homes, the power load has increased sharply. This growth trend poses a severe challenge to the power supply capacity of the distribution network, requiring the distribution network to have sufficient capacity and flexibility to meet the growing demand for electricity. On the other hand, the distribution network equipment is large in scale, the network wiring method is complex, and the types of equipment and facilities are rich, widely distributed in every corner of cities and towns. Due to aging equipment and unreasonable planning, some distribution networks have problems such as insufficient power supply capacity, large line loss, and low power supply reliability. Therefore, it has become an important task for the current power industry to update equipment, rectify the grid, and transform facilities in the distribution network to improve the power supply capacity and reliability of the distribution network. In the traditional distribution network evaluation method, reliability and economy are two independent evaluation contents. However, with the gradual opening of the power market and the increasing requirements of users for power supply quality, how to improve the economic efficiency of the distribution network while ensuring power supply reliability has become an urgent problem to be solved. The power supply capacity assessment not only needs to consider whether the power supply capacity of the distribution network meets user needs, but also needs to comprehensively consider factors such as the investment cost, operation and maintenance costs, and energy conservation and emission reduction of the distribution network to achieve a balance between power supply reliability and economy.

[0003] In addition, the rapid development of new energy has put forward higher requirements for distribution networks. The access of new energy has changed the power flow distribution of distribution networks. The volatility and uncertainty of new energy generation have increased the difficulty of regulating the power grid, and posed new challenges to the flexibility and response speed of distribution networks, the grid structure and equipment performance of power grids. The development of energy storage technology has provided a new solution for improving the power supply capacity of distribution networks. As an important support for new energy, energy storage technology has the characteristics of flexible regulation and rapid response. It can provide power support when new energy generation is insufficient or when the load is at peak, smooth out power grid fluctuations, and improve power supply reliability. With the continuous maturity of energy storage technology and the reduction of costs, its application prospects in distribution networks are becoming more and more broad. By evaluating the power supply capacity of distribution networks containing new energy storage, it is helpful to discover the weak links of the power grid, optimize resource allocation, and improve the adaptability and stability of the power grid.

[0004] The invention patent with application number 202211589794.0 involves the field of distribution network, specifically a power supply capacity evaluation system and method for the access of new energy to the distribution network, which includes a partition module, a line division module, a theoretical power supply calculation module, a historical power consumption statistics module, a power supply coefficient calculation module, a power supply margin module and a comparison module; the theoretical power supply calculation module is used to calculate the theoretical unit time power supply A on each line; the historical power consumption statistics module is used to count the maximum unit time power consumption B of different lines in different time periods; the power supply coefficient calculation module is used to calculate the power supply coefficient C, C = B / A; the power supply margin module is used to set the safe power supply margin D. The above invention divides time according to the characteristics of new energy power supply, and can evaluate the power supply capacity of each time period. By comparing the power supply coefficient C with the safe power supply margin D, it has guiding significance for judging whether the area can be put into new loads and the maximum amount of load that can be put into, and can provide an effective basis for line rectification. However, the above patent does not take into account the annual growth of loads, and the description of the calculation of power consumption is vague, and there is no specific calculation method; in addition, the above patent only mentions the access of new energy to the distribution network, does not specify the type of new energy, and does not consider energy storage issues. Compared with the above patents, the patent of the present invention has better adaptability. Summary of the invention

[0005] In view of the technical problem that the existing distribution network power supply capacity assessment method is difficult to effectively improve the power supply stability of the distribution network, the present invention proposes a distribution network power supply capacity assessment method containing new energy energy storage, which improves the local consumption capacity of new energy power generation and further improves the power supply capacity of the distribution network by introducing a load growth vector and constructing a mathematical model of power supply capacity.

[0006] In order to achieve the above object, the technical solution of the present invention is implemented as follows: a method for evaluating the power supply capacity of a distribution network containing new energy storage, the steps of which are as follows:

[0007] Step 1: Calculate the load growth vector based on the grid voltage-reactive iterative equation and the power flow change relationship, and build a mathematical model of power supply capacity based on the load growth vector, basic load and energy storage charging power;

[0008] Step 2: Determine relevant parameters and use them to calculate new load values;

[0009] Step 3: Perform power flow calculation to determine whether the calculation result exceeds the limit. If so, replace the parameters and recalculate the new load value. Otherwise, determine whether the calculation result reaches the convergence accuracy. If not, change the load growth step and continue to iterate. Otherwise, output the optimal value to obtain the maximum power supply capacity.

[0010] Preferably, the method for calculating the load growth vector according to the grid voltage-reactive iterative equation and the power flow change relationship is: the voltage-reactive iterative equation of the fast decoupled power flow and the influence relationship of the node active power change on the branch power flow are respectively:

[0011]

[0012] In the formula, ΔU i , ΔQ i and ΔP i They represent the voltage change, reactive power change and active power change of node i respectively, and a i,j represents the sensitivity factor between node i and node j, n represents the number of system nodes, ΔP i,z represents the power change of branch z at node i, m represents the number of branches, b z,i represents the transfer distribution factor of branch z of node i;

[0013] The sensitivity factor a i,j and the transfer distribution factor b z,i Take out the column vectors, calculate the inverse of the modulus of each column vector, normalize them, and then add them up to get the load growth vector.

[0014] Preferably, the load growth vector is

[0015]

[0016] Where λ represents the load growth vector, i represents the load growth factor of node i, ||A i || and ||B i || The modulus of the sensitivity factor and transfer distribution factor of the i-th column respectively; || A j || and ||B j || The modulus of the sensitivity factor and transfer distribution factor of the jth column, respectively.

[0017] Preferably, the objective function of the power supply capability mathematical model is:

[0018]

[0019] In the formula, f i,0 (t), f i,R (t) and f i,C (t) represent the basic load, load growth baseline value and energy storage charging power of node i at time t, Δt represents the load growth step, and F(t) is the load at time t;

[0020] The constraints of the power supply capability mathematical model include power flow constraints, node voltage constraints, power balance constraints, photovoltaic output constraints and energy storage charging and discharging constraints.

[0021] Preferably, the constraints of the power supply capability mathematical model are:

[0022]

[0023] Where P i , Q i and U i Represent the active power, reactive power and voltage amplitude of node i respectively, G ij , B ij and They represent the conductance, susceptance and voltage phase difference of the branch formed by node i and node j respectively, and They represent the upper and lower voltage limits of node i, respectively, i,0 , P i,R , P i,C , P i,D and P i,pv They represent the basic active load of node i, the active load growth benchmark value, the energy storage active charging power, the energy storage active discharging power and the photovoltaic active output power, respectively. i,0 , Q i,R , Q i,C , Q i,D and Q i,pv They represent the basic reactive load of node i, the reactive load growth benchmark value, the energy storage reactive charging power, the energy storage reactive discharge power and the photovoltaic reactive output power, respectively. and They represent the upper and lower limits of the photovoltaic active output power of node i, respectively. and They represent the upper and lower limits of the active charging power of the energy storage at node i, respectively. and They represent the upper and lower limits of the active discharge power of the energy storage at node i, SOC i is the energy storage state of node i, and They represent the upper limit and lower limit of the energy storage state of node i respectively.

[0024] Preferably, the relevant parameters include load growth step, convergence accuracy, node initial load, photovoltaic power generation power, energy storage charging and discharging power and load growth reference value.

[0025] Preferably, the method for calculating the new load value in step 2 is: selecting typical daily load data in units of hours, determining the load base value, load growth step and convergence accuracy for each period, and calculating the initial load using the base load and the initial charging power of the energy storage:

[0026] F i (0) = f i,0 (0)+f i,C (0)

[0027] In the formula, F i (0) represents the initial load of node i, f i,0 (0) represents the basic initial load of node i, f i,C (0) represents the initial charging power of the energy storage at node i;

[0028] Calculate the new load value using the initial load, load increase step, load increase vector, and load increase base value:

[0029] F i (t+1)=F i (t)+Δtλ i f i,R (t)

[0030] In the formula, F i (t) and f i,R (t) represent the load value and load growth baseline value of node i at time t, respectively. i (t+1) represents the load value of node i at time t+1.

[0031] Preferably, the method for performing power flow calculation in step three is: performing power flow calculation using a power flow equation, and the calculation result is a load value.

[0032] Preferably, the method for performing parameter replacement is:

[0033]

[0034] In the formula, It indicates the load value of node i at time t after the power flow calculation result exceeds the limit.

[0035] Preferably, the method for judging whether the calculation result reaches the convergence accuracy is: checking the inequality Δtλ i <ε is true; where ε represents the convergence accuracy;

[0036] The method of changing the load growth step and continuing iteration is: the load value at time t+1 is:

[0037]

[0038] Return to the power flow equation and continue iterating until the convergence constraints are met and the optimal value is output.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: by introducing the load growth vector, the load growth is affected by the existing node load, the traditional single growth rate mode is changed, and the load calculation is more accurate. The present invention constructs a mathematical model of power supply capacity considering new energy storage, which not only reduces dependence on traditional fossil energy, reduces carbon emissions, promotes environmental protection and sustainable development, but also promotes the local consumption of new energy power generation, improves the utilization rate of new energy by the power grid, and further improves the power supply capacity of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] 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.

[0041] Figure 1 It is a flow chart of the present invention.

[0042] Figure 2 This is a system diagram of IEEE33 nodes including new energy storage.

[0043] Figure 3 This is a typical load curve and photovoltaic output curve for a certain place.

[0044] Figure 4 This is a comparison curve of the maximum power supply capacity without and with new energy storage.

[0045] Figure 5 This is a comparison curve of the maximum power supply capacity under different load growth modes between the present invention and the existing method. DETAILED DESCRIPTION

[0046] 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.

[0047] like Figure 1 As shown, a method for evaluating the power supply capacity of a distribution network including new energy storage is provided, and the steps are as follows:

[0048] Step 1: Calculate the load growth vector based on the grid voltage-reactive iterative equation and the power flow change relationship, and build a mathematical model of power supply capacity based on the load growth vector, basic load and energy storage charging power.

[0049] The load growth vector is calculated based on the voltage-reactive iterative equation of the power grid and the relationship between power flow changes. The voltage-reactive iterative equation of the fast decoupled power flow and the relationship between the node active power change and the branch power flow are:

[0050]

[0051]

[0052] In the formula, ΔU i , ΔQ i and ΔP i They represent the voltage change, reactive power change and active power change of node i respectively, and a i,j represents the sensitivity factor between node i and node j, n represents the number of system nodes, ΔP i,z represents the power change of branch z at node i, m represents the number of branches, b z,i represents the transfer distribution factor of branch z of node i.

[0053] The sensitivity factor a i,j and the transfer distribution factor b z,i Take out the column vectors, calculate the inverse of the modulus of each column vector, normalize them and add them together to get the load growth vector:

[0054]

[0055] Where λ represents the load growth vector, i represents the load growth factor of node i, ||A i || and ||B i ||The modulus of the sensitivity factor and transfer distribution factor of the i-th column respectively. ||A j || and ||B j || The modulus of the sensitivity factor and transfer distribution factor of the jth column, respectively.

[0056] The mathematical model of power supply capacity is constructed based on the basic load, load growth vector and energy storage charging power. That is, the objective function of the mathematical model of power supply capacity is:

[0057]

[0058] In the formula, f i,0 (t), f i,R (t) and f i,C (t) represent the base load, load growth benchmark value and energy storage charging power of node i at time t, Δt represents the load growth step. F(t) is the objective function.

[0059] Constraints include power flow constraints, node voltage constraints, power balance constraints, photovoltaic output constraints, energy storage charging and discharging constraints, etc. That is:

[0060]

[0061] P i =P i,0 +Δtλ i P i,R +P i,C -P i,D -P i,pv

[0062] Q i =Q i,0 +Δtλ i Q i,R +Q i,C -Q i,D -Q i,pv

[0063]

[0064] Where P i , Q i and U i Represent the active power, reactive power and voltage amplitude of node i respectively, G ij , B ij and They represent the conductance, susceptance and voltage phase difference of the branch formed by node i and node j respectively, and They represent the upper and lower voltage limits of node i, respectively, i,0 , P i,R , P i,C , P i,D and P i,pv They represent the basic active load of node i, the active load growth benchmark value, the energy storage active charging power, the energy storage active discharging power and the photovoltaic active output power, respectively. i,0 , Q i,R , Q i,C , Q i,D and Q i,pv They represent the basic reactive load of node i, the reactive load growth benchmark value, the energy storage reactive charging power, the energy storage reactive discharge power and the photovoltaic reactive output power, respectively. and They represent the upper and lower limits of the photovoltaic active output power of node i, respectively. and They represent the upper and lower limits of the active charging power of the energy storage at node i, respectively. and They represent the upper and lower limits of the active discharge power of the energy storage at node i, SOC i is the energy storage state of node i, and Respectively represent the upper limit and lower limit of the energy storage energy state of node i. Basic load, load growth benchmark value, these quantities generally refer to active power. However, after the new energy is connected to the distribution network, for the distribution network, each node contains active power, reactive power, voltage and other quantities. Therefore, it is necessary to restrict the operating conditions of the node, that is, constraints. The new energy involved in the patent of this invention is photovoltaic power generation, so it is necessary to restrict the photovoltaic output value. On the other hand, in addition to considering the access of new energy photovoltaics, the present invention also considers the impact of energy storage, and the energy storage energy state SOC reflects the remaining capacity of the energy storage system to provide electrical energy, usually expressed as a percentage. For example, if the SOC is 50%, it means that the energy storage system currently has half of its capacity remaining. Considering the role of energy storage, the energy state of energy storage also needs to be restricted.

[0065] Step 2: Determine relevant parameter values, such as load growth step, convergence accuracy, node initial load, photovoltaic power generation power, energy storage charging and discharging power, load growth benchmark value, etc., and use relevant parameters to calculate the new load value.

[0066] Select typical daily load data in hours, determine the load base value, load growth step, convergence accuracy, etc. for each period, and calculate the initial load using the base load and the initial charging power of the energy storage, that is:

[0067] F i (0) = f i,0 (0)+f i,C (0)

[0068] In the formula, F i (0) represents the initial load of node i, f i,0 (0) represents the basic initial load of node i, f i,C (0) represents the initial charging power of the energy storage at node i.

[0069] The new load value is calculated using the initial load, load growth step, load growth vector and load growth reference value, namely:

[0070] F i (t+1)=F i (t)+Δtλ i f i,R (t)

[0071] In the formula, F i (t) and f i,R (t) represent the load value and load growth benchmark value of node i at time t respectively. i(t+1) represents the load value of node i at time t+1.

[0072] Step 3: Perform power flow calculation to determine whether the calculation result exceeds the limit. If so, replace the parameters and recalculate the new load value. Otherwise, determine whether the calculation result (a load value) reaches the convergence accuracy. If not, change the time value and continue to iterate. Otherwise, output the optimal value, that is, the maximum power supply capacity.

[0073] Use the power flow equation to calculate the power flow and check whether the calculation result, that is, a load value, exceeds the limit. If not, continue to iterate; if yes, perform parameter replacement, that is:

[0074]

[0075] In the formula, It indicates the load value of node i at time t after the power flow calculation result exceeds the limit.

[0076] The parameter replacement here means that if the calculation result of the power flow equation exceeds the limit (generally exceeds the maximum value), this value needs to be replaced, that is, the load value that exceeds the limit is reduced.

[0077] Also check that the following inequalities hold:

[0078] Δtλ i <ε

[0079] In the formula, ε represents the convergence accuracy. This is because the step size is gradually decreasing. If the above formula is not satisfied, the step size is doubled, and the load growth step Δt is replaced by Δt / 2. During the iteration process, the load growth amount gradually decreases. If yes, the iteration ends and the maximum power supply capacity value is output; if not, at time t=t+1, the load value is:

[0080]

[0081] Here, halving the load growth step can narrow the iteration range and get closer to the optimal value.

[0082] Return to the power flow equation and continue iterating until the convergence constraints are met and the optimal value is output.

[0083] The following is an explanation with specific examples. Figure 2 The IEEE33 node system diagram with new energy storage is shown in the example analysis, where nodes 9, 16, 21, and 30 are connected to the new energy storage power station (PVS) at the same time, the photovoltaic power generation system capacity is 200kW, the energy storage system rated capacity is 1000kWh, the rated power is 500kW, and the discharge depth is 0.8. Figure 3 The place shown is Figure 2The typical load curve and photovoltaic output curve of the region where the IEEE33 node system is located are compared and analyzed. The load data and photovoltaic output data of the region are obtained. Figure 3 It is only given in the form of per unit value, not the specific load value. Based on these data, the daily load distribution and daily photovoltaic output of the region can be analyzed. Using the method proposed in the present invention, a comparison chart of the maximum power supply capacity without and with new energy storage is obtained as shown in the figure below: Figure 4 As shown, it can be seen that when new energy storage is considered, the maximum power supply capacity of the distribution network in each period is significantly improved. Figure 5 This is a comparison chart of the maximum power supply capacity under different load growth modes. It can be seen that compared with the traditional load multiple method and the fixed load growth method, the method proposed in the present invention can greatly improve the power supply capacity of the distribution network.

[0084] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating the power supply capacity of a distribution network containing new energy storage, characterized in that: The steps are as follows: Step 1: Calculate the load growth vector based on the grid voltage-reactive iterative equation and the power flow change relationship, and build a mathematical model of power supply capacity based on the load growth vector, basic load and energy storage charging power; Step 2: Determine relevant parameters and use them to calculate new load values; Step 3: Perform power flow calculation to determine whether the calculation result exceeds the limit. If so, replace the parameters and recalculate the new load value. Otherwise, determine whether the calculation result reaches the convergence accuracy. If not, change the load growth step and continue to iterate. Otherwise, output the optimal value to obtain the maximum power supply capacity.

2. The method for evaluating the power supply capacity of a distribution network containing new energy storage according to claim 1, characterized in that: The method for calculating the load growth vector according to the voltage-reactive iterative equation of the power grid and the relationship between power flow changes is as follows: the voltage-reactive iterative equation of the fast decoupled power flow and the relationship between the influence of the node active power change on the branch power flow are respectively: In the formula, ΔU i , ΔQ i and ΔP i They represent the voltage change, reactive power change and active power change of node i respectively, and a i,j represents the sensitivity factor between node i and node j, n represents the number of system nodes, ΔP i,z represents the power change of branch z at node i, m represents the number of branches, b z,i represents the transfer distribution factor of branch z of node i; The sensitivity factor a i,j and the transfer distribution factor b z,i Take out the column vectors, calculate the inverse of the modulus of each column vector, normalize them, and then add them up to get the load growth vector.

3. The method for evaluating the power supply capacity of a distribution network containing new energy storage according to claim 2, characterized in that: The load growth vector is Where λ represents the load growth vector, i represents the load growth factor of node i, ||A i || and ||B i || The modulus of the sensitivity factor and transfer distribution factor of the i-th column respectively; || A j || and ||B j || The modulus of the sensitivity factor and transfer distribution factor of the jth column, respectively.

4. The method for evaluating the power supply capacity of a distribution network containing new energy storage according to any one of claims 1 to 3, characterized in that: The objective function of the power supply capability mathematical model is: In the formula, f i,0 (t), f i,R (t) and f i,C (t) represent the basic load, load growth baseline value and energy storage charging power of node i at time t, Δt represents the load growth step, and F(t) is the load at time t; The constraints of the power supply capability mathematical model include power flow constraints, node voltage constraints, power balance constraints, photovoltaic output constraints and energy storage charging and discharging constraints.

5. The method for evaluating the power supply capacity of a distribution network containing new energy storage according to claim 4 is characterized in that: The constraints of the power supply capability mathematical model are: P i =P i,0 +Δtλ i P i,R +P i,C -P i,D -P i,pv Q i =Q i,0 +Δtλ i Q i,R +Q i,C -Q i,D -Q i,pv Where P i , Q i and U i Represent the active power, reactive power and voltage amplitude of node i respectively, G ij , B ij and They represent the conductance, susceptance and voltage phase difference of the branch formed by node i and node j respectively, and They represent the upper and lower voltage limits of node i, respectively, i,0 , P i,R , P i,C , P i,D and P i,pv They represent the basic active load of node i, the active load growth benchmark value, the energy storage active charging power, the energy storage active discharging power and the photovoltaic active output power, respectively. i,0 , Q i,R , Q i,C , Q i,D and Q i,pv They represent the basic reactive load of node i, the reactive load growth benchmark value, the energy storage reactive charging power, the energy storage reactive discharge power and the photovoltaic reactive output power, respectively. and They represent the upper and lower limits of the photovoltaic active output power of node i, respectively. and They represent the upper and lower limits of the active charging power of the energy storage at node i, respectively. and They represent the upper and lower limits of the active discharge power of the energy storage at node i, SOC i is the energy storage state of node i, and They represent the upper limit and lower limit of the energy storage state of node i respectively.

6. The method for evaluating the power supply capacity of a distribution network containing new energy storage according to claim 5, characterized in that: The relevant parameters include load growth step, convergence accuracy, node initial load, photovoltaic power generation power, energy storage charging and discharging power and load growth reference value.

7. The method for evaluating the power supply capacity of a distribution network containing new energy storage according to claim 6, characterized in that: The method for calculating the new load value in step 2 is: select typical daily load data in units of hours, determine the load base value, load growth step and convergence accuracy for each period, and calculate the initial load using the base load and the initial charging power of the energy storage: F i (0)=f i,0 (0)+f i,C (0) In the formula, F i (0) represents the initial load of node i, f i,0 (0) represents the basic initial load of node i, f i,C (0) represents the initial charging power of the energy storage at node i; Calculate the new load value using the initial load, load increase step, load increase vector, and load increase base value: F i (t+1)=F i (t)+Δtλ i f i,R (t) In the formula, F i (t) and f i,R (t) represent the load value and load growth baseline value of node i at time t, respectively. i (t+1) represents the load value of node i at time t+1.

8. The method for evaluating the power supply capacity of a distribution network containing new energy storage according to any one of claims 5 to 7, characterized in that: The method for performing power flow calculation in step three is: performing power flow calculation using a power flow equation, and the calculation result is a load value.

9. The method for evaluating the power supply capacity of a distribution network containing new energy storage according to claim 8, characterized in that: The method for performing parameter replacement is: In the formula, It indicates the load value of node i at time t after the power flow calculation result exceeds the limit.

10. The method for evaluating the power supply capacity of a distribution network containing new energy storage according to claim 9, characterized in that: The method for judging whether the calculation result reaches the convergence accuracy is: checking the inequality Δtλ i <ε is true; where ε represents the convergence accuracy; The method of changing the load growth step and continuing iteration is: the load value at time t+1 is: Return to the power flow equation and continue iterating until the convergence constraints are met and the optimal value is output.

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