A multi-level and multi-objective optimization access method for distributed optical storage considering uncertainty

Abstract

The present invention discloses a multi-level and multi-objective optimization access method for distributed photovoltaic storage taking into account uncertainty. First, an optimization model for distributed photovoltaic storage access scheme is established with the goal of maximizing distributed photovoltaic access capacity and minimizing distributed photovoltaic storage investment, construction and operation and maintenance costs, wherein the access scheme considers medium voltage and low voltage multi-levels, and the constraints include various indicators of the distribution network, distributed photovoltaic, and energy storage battery-related constraints; then, robust optimization is used to process the uncertain parameters in the constraints, wherein the uncertain parameters include photovoltaic output and load; then, the MOEAD algorithm is used to solve the established multi-objective optimization configuration model to obtain the standard Pareto frontier curve; finally, the distributed photovoltaic storage access scheme individuals of the Pareto solution set are sorted based on fuzzy theory to select the optimal access scheme.

Description

Technical Field

[0001] The present invention relates to the field of distributed photovoltaic storage planning, and in particular to a distributed photovoltaic storage multi-level and multi-objective optimization access method taking into account source-load uncertainty, which is suitable for the reasonable layout of distributed photovoltaic storage under the background of whole-county photovoltaic. Background Art

[0002] With the advancement of distributed photovoltaic development in the whole county, the development capacity of photovoltaic has increased significantly, but photovoltaic planning is prone to problems such as waste of grid investment and unreasonable operation plans. Therefore, achieving accurate grid investment, economic operation of distribution network and reasonable layout of photovoltaic are issues that need to be focused on. Therefore, for the planning of distributed photovoltaic in the whole county, it is necessary to consider the economy and access capacity as well as the access location of multiple voltage levels at the same time.

[0003] However, the high uncertainty of distributed photovoltaic output and load will bring great risks to the safe operation of the distribution network. Energy storage has the flexible characteristics of bidirectional power. If effective measures are implemented, it will help improve the distribution network's acceptance of photovoltaics. Therefore, it is very meaningful to carry out research on distributed photovoltaic storage access solutions. Summary of the invention

[0004] In order to make up for the defects of the existing technology, the present invention provides a multi-level and multi-objective optimization access method for distributed photovoltaic storage taking into account uncertainty, and simultaneously considers the two optimization goals of maximizing the distributed photovoltaic access capacity and minimizing the investment, construction and operation and maintenance costs of distributed photovoltaic storage. While meeting the sustainable development needs under large-scale distributed photovoltaic access, the economy of the planning scheme is improved.

[0005] The present invention provides a multi-level and multi-objective optimization access method for dispersed optical storage taking into account uncertainty. Specifically, it includes:

[0006] 1) Establish a distributed photovoltaic and energy storage multi-level and multi-objective access scheme optimization model, including: the maximum distributed photovoltaic access capacity and the minimum distributed photovoltaic and energy storage investment, construction and operation and maintenance cost objective functions, as well as various distribution network indicators, distributed photovoltaics, and energy storage battery related constraints;

[0007] 2) Use robust optimization to process the uncertain parameters in the constraints, including photovoltaic output and load, and obtain a distributed photovoltaic storage multi-level and multi-objective access scheme optimization model that takes into account source and load uncertainty;

[0008] 3) Model solution, including: using the MOEAD algorithm to obtain the Pareto frontier curve and selecting the optimal access solution based on the fuzzy theory sorting method.

[0009] The objective function described in step 1) includes:

[0010] (1) Distributed photovoltaic access capacity is the largest

[0011] The objective function is to maximize the sum of the photovoltaic access capacity of all nodes on the feeder, as shown in the following formula:

[0012]

[0013] Where P mv PV,i is the installed capacity of distributed photovoltaic at node i on the medium voltage level side of the feeder; P lv PV,j is the installed capacity of distributed photovoltaic power at node j on the low-voltage level side of the feeder, which is used as the maximum power generation power of the distributed photovoltaic power generation curve in the calculation; N is the number of medium-voltage level nodes of the feeder; M is the number of low-voltage level nodes of the feeder.

[0014] (2) Distributed PV storage investment, construction and operation and maintenance costs are minimal

[0015] The objective function is to minimize the investment, construction and operation and maintenance costs of distributed photovoltaic storage, as shown in the following formula:

[0016] minf2=C cost (2)

[0017] C cost =C inv +C ope (3)

[0018]

[0019] C ope =αC inv (5)

[0020] In the formula, C cost is the annual investment, construction and operation and maintenance cost of distributed solar energy storage; C inv is the annual investment and construction cost of distributed solar energy storage; C ope is the annual operation and maintenance cost of distributed solar energy storage; C PV inv is the annual investment and construction cost of distributed photovoltaics; C BESS inv is the annual investment and construction cost of the energy storage battery; α is the conversion ratio of the operation and maintenance cost, which is taken as 0.1 in this paper.

[0021] The investment and construction costs of distributed photovoltaics include the investment and construction costs of rooftop photovoltaics connected to the low-voltage grid and the investment and construction costs of concentrated photovoltaic power stations in open spaces connected to the medium-voltage grid. The construction costs of grid connection at different voltage levels are different, and the construction costs of grid connection at the low-voltage level are lower. The investment and construction costs of distributed photovoltaics and energy storage batteries are shown in the following formulas:

[0022]

[0023]

[0024] Where r0 is the discount rate; y is the applicable planning period. In this paper, distributed photovoltaics are taken as 20 years and energy storage batteries are taken as 10 years; δ PV,lv inv and δ BESS,lv inv are the unit capacity investment and construction costs of medium-voltage grid-connected photovoltaic and energy storage batteries; PV,mv inv and δ BESS,mv inv are the unit capacity investment and construction costs of low-voltage grid-connected photovoltaic and energy storage batteries respectively; mv BESS,i is the installed capacity of the energy storage battery at the medium voltage level side of the feeder node i; P lv BESS,j is the installed capacity of the energy storage battery at node j on the low-voltage level side of the feeder.

[0025] The constraints described in step 1) include:

[0026] (1) Constraints on various indicators of distribution network

[0027] ① Power flow equation constraints

[0028]

[0029] Where P j,t , Q j,t , U j,t is the active injection power, reactive injection power and voltage at node j at time t; h(j) and e(j) are the branch end node set with node j as the head node and the branch head node set with node j as the end node respectively; P ij,t , Q ij,t is the active power and reactive power flowing from node i to node j in branch ij at time t; r ij 、x ij 、b ij is the resistance, reactance and susceptance of branch ij; P G j,t , Q G j,t are respectively the active power and reactive power injected by the upper power grid at node j at time t; P PV j,t , Q PV j,t are the photovoltaic active power and reactive power injected at node j at time t; P dis j,t , Q ch j,tare the discharge power and charging power of the energy storage battery at node j at time t; Q s j,t P is the reactive power injected by the continuous reactive power compensation device at node j at time t; load j,t , Q load j,t are the active load and reactive load at node j at time t respectively.

[0030] ②Node voltage constraints

[0031] U imin <U i,t <U imax (9)

[0032] Where U imin is the lower limit of node voltage, which is 0.9, U imax The upper limit of node voltage is taken as 1.07.

[0033] ③ Branch flow constraints

[0034] P ij,t ≤P ijmax (10)

[0035] Where P ijmax is the limit of single-phase active power transmitted by line ij, which can be obtained from the maximum current carrying capacity in the line parameters. The calculation formula is as follows:

[0036]

[0037] Where U is the rated voltage of the line, I max is the maximum current carrying capacity of the line, cosΦ is the power factor, which is 0.9.

[0038] ④Short-circuit current constraint

[0039] The access of power sources to the grid will increase the short-circuit current level of the grid. After the distributed power sources are connected, the short-circuit current of the line should not exceed the limit value of the voltage level.

[0040] I SCL <I SCLmax (12)

[0041] In the formula, I SCL and I SCLmax They are the maximum short-circuit current of the line and the maximum short-circuit current of the circuit breaker respectively.

[0042] ⑤ Voltage three-phase unbalance constraint

[0043] Because the load in the low-voltage layer is single-phase powered, the distributed power sources connected to the low-voltage layer are single-phase connected, so a voltage three-phase unbalance constraint is added.

[0044]

[0045] Where U i,t Φ is the square value of the voltage amplitude at node i at time t; U i,t avg is the mean of the square of the voltage amplitude at node i at time t.

[0046] (2) Constraints on energy storage devices

[0047] ①Battery state of charge constraints

[0048] Overcharging or over-discharging will greatly damage the life of the battery. In order to improve the utilization rate of the battery and extend the service life of the battery, the energy storage device needs to meet the upper and lower limit constraints of the battery state of charge when charging and discharging.

[0049] S min ≤S i,t ≤S max (14)

[0050] In the formula, S min and S max They represent the minimum SOC and maximum SOC allowed by the energy storage battery; S i,t is the state of charge of the energy storage battery at node i at time t. In this paper, we assume that S min is 0.1, S max is 0.9.

[0051]

[0052] Where Δt is the time interval; S i,t+Δt is the state of charge of the energy storage battery at node i at time t+Δt. ch is the charging power; P dch is the discharge power; η ch is the charging efficiency; η dch is the discharge efficiency; E0 is the rated capacity of the battery.

[0053] ②Charge and discharge state constraints

[0054] The battery cannot be in the charging and discharging states at the same time, so the charging and discharging state constraints need to be met.

[0055] U i,t +V i,t =1 (16)

[0056]

[0057] Where U i,t and V i,tare the charging and discharging states of the energy storage device at node i at time t, respectively.

[0058] (3) Distributed photovoltaic constraints

[0059] ①Distributed photovoltaic node installation capacity constraints

[0060]

[0061]

[0062] Where P PVmin is the minimum limit of distributed photovoltaic access in the line; P PVmax It is the maximum limit of distributed photovoltaic access in the line.

[0063] ② Constraints on the total amount of distributed photovoltaic installations

[0064]

[0065] In the formula, α PV It is the ratio coefficient of distributed photovoltaic grid-connected to the total load.

[0066] The uncertain parameter processing and distributed photovoltaic storage multi-level and multi-objective access scheme optimization model taking into account source-load uncertainty described in step 2) includes:

[0067] The interval robust optimization method is used to represent the uncertain parameters.

[0068]

[0069] In the formula, are the load prediction value and the maximum allowed deviation of node j respectively; are the predicted value of distributed photovoltaic output and the maximum allowed deviation of node j respectively. The corresponding constraints are modified according to the interval of uncertain parameters.

[0070] Take the reciprocal of f1, that is, f1=1 / f1, so the objectives of f1 and f2 are minimized uniformly, that is, the total objective function F is minimized.

[0071] The optimization model of distributed photovoltaic storage multi-level and multi-objective access scheme considering source-load uncertainty can be expressed as:

[0072]

[0073] The step 3) of obtaining the Pareto frontier curve using the MOEAD algorithm and selecting the optimal access solution using the sorting method based on fuzzy theory includes:

[0074] (1) Normalization of objective function

[0075] The objective function is normalized to obtain the multi-objective optimization model:

[0076]

[0077]

[0078] In the formula, f i is the true value of the i-th objective function; f imax is the maximum value of the i-th objective function; f imin is the minimum value of the i-th objective function; ω1 and ω2 are the weights of the two objective functions, ω1+ω2=1; and They are the normalized functions of the objective functions f1 and f2 respectively.

[0079] (2) Use the decomposition-based multi-objective evolutionary method MOEAD to obtain the Pareto solution set

[0080] The decomposition-based multi-objective evolutionary method MOEAD is used to solve the optimization model of distributed photovoltaic storage multi-level multi-objective access scheme considering the uncertainty of distribution network source and load. First, the population is initialized, and a uniformly distributed weight vector is generated as a reference point. The Chebyshev method is used to decompose and obtain the sub-problems corresponding to the two objective functions, which are expressed as follows:

[0081]

[0082] In the formula, ω j 1 and ω j 2 are the weight vectors of the two target vectors; z*1 and z*2 are the coordinates of the minimum values ​​of the two target vectors, that is, z*1=min{f1(x)}, z*2=min{f2(x)}.

[0083] Then, the two targets are mated and mutated one by one, a mating parent is randomly selected from the neighborhood of the current parent weight vector for mating, and then simulated binary crossover and polynomial mutation are used to mutate to obtain a new generation of population; then the individuals that do not meet the constraints are replaced with individuals within the boundary; then the reference point z is updated according to the aggregation function values ​​of the two targets, and z takes the minimum value between the current z and the aggregation function; then the weight neighborhood and EP are updated, and the dominant vector is removed from the EP; until the set maximum number of iterations is reached, the loop ends, and the Pareto optimal solution set of the distributed optical storage access solution is obtained.

[0084] Among them, robust optimization is used to solve the uncertainty of photovoltaic output and load. The conservative parameter is introduced to establish a robust equation with uncertain parameter constraints, forming a min-max double-layer optimization model for solution.

[0085] (3) Use fuzzy theory to sort the obtained Pareto solution set

[0086]

[0087] In the formula, Γ j is the fuzzy membership degree of the jth optimal solution in the Pareto optimal solution set; N i is the number of objective functions, here it is 2; N j is the number of optimal solutions in the Pareto optimal solution set; M j,i is the membership value of the i-th objective function in the j-th Pareto optimal solution, calculated as follows:

[0088]

[0089] Where M i is the membership value of the i-th objective function; f i is the value of the i-th objective function; f i,max is the maximum value of the i-th objective function in the Pareto solution set; f i,min is the minimum value of the i-th objective function in the Pareto solution set.

[0090] The above-mentioned sorting method is used to sort and select the distributed photovoltaic storage access solutions.

[0091] The advantages of the present invention are: in the context of whole-county photovoltaics, by optimizing the access scheme of distributed photovoltaic storage, the reasonable layout of distributed photovoltaic storage and the precise investment in the power grid are achieved, while the acceptance capacity of distributed power sources is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, 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 labor.

[0093] Figure 1 This is a framework diagram of the distributed photovoltaic storage planning model of the present invention;

[0094] Figure 2 This is a technical framework diagram of the present invention. DETAILED DESCRIPTION

[0095] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0096] The method comprises the following steps:

[0097] 1) Establish a distribution system model based on branch flow.

[0098]

[0099] Where P j,t , Q j,t , U j,t is the active injection power, reactive injection power and voltage at node j at time t; h(j) and e(j) are the branch end node set with node j as the head node and the branch head node set with node j as the end node respectively; P ij,t , Q ij,t is the active power and reactive power flowing from node i to node j in branch ij at time t; r ij 、x ij , b ij is the resistance, reactance and susceptance of branch ij; P G j,t , Q G j,t are respectively the active power and reactive power injected by the upper power grid at node j at time t; P PV j,t , Q PV j,t are the photovoltaic active power and reactive power injected at node j at time t; P dis j,t , Q ch j,t are the discharge power and charging power of the energy storage battery at node j at time t; Q s j,t P is the reactive power injected by the continuous reactive power compensation device at node j at time t; load j,t , Q load j,t are the active load and reactive load at node j at time t respectively.

[0100] 2) Establish a multi-level and multi-objective access optimization model for distributed optical storage

[0101] Model diagram as attached Figure 1As shown in the figure, the optimization object of the model is the access scheme of distributed photovoltaic and energy storage batteries, including the access location of each distributed photovoltaic and its access capacity, and the access location of each energy storage battery and its access capacity. The access location of distributed photovoltaic includes multiple levels of low-voltage and medium-voltage levels, among which rooftop photovoltaic is connected to the grid from the low-voltage level, and photovoltaic power stations are built in concentrated areas of county vacant land and connected to the grid from the medium-voltage level. Because the cost of connecting distributed photovoltaic to the grid at different voltage levels is different, the investment, construction and operation and maintenance costs of distributed photovoltaic are calculated separately. The treatment of energy storage access locations is similar. Assume that all photovoltaic power stations work in unit power factor mode, the maximum output power of photovoltaic is equal to its installed capacity, and the minimum output power is 0. The constraints of the model are considered from three aspects: various indicators of safe operation of the distribution network, distributed photovoltaic, and energy storage batteries.

[0102] 3) Use robust optimization to deal with uncertain parameters.

[0103] The interval robust optimization method is used to represent the uncertain parameters of photovoltaic output and load.

[0104]

[0105]

[0106]

[0107] In the formula, are the load prediction value and the maximum allowed deviation of node j respectively; are the predicted value of distributed photovoltaic output at node j and the maximum allowable deviation respectively.

[0108] According to the interval of uncertain parameters, the corresponding constraints are modified to construct robust equations. In order to overcome the disadvantage of overly conservative methods such as robust optimization, a conservative parameter is introduced to adjust the conservativeness of the robust optimization solution. By changing the value of this parameter, the objective function can obtain different values. The larger the parameter, the more conservative the distributed photovoltaic storage access scheme obtained. In actual operation, the distributed photovoltaic storage access scheme under the most conservative condition is first obtained. In this case, the distributed photovoltaic storage is configured, and the system can operate safely even if the situation that poses the greatest threat to the security of the distribution network occurs. These variable values ​​obtained by robust optimization can ensure that the distribution system maintains its own safe operation under the output power and load level of distributed power sources with uncertainty. Until the value of the conservative parameter is reduced to a certain extent, the comprehensive effect of the maximum capacity and minimum cost of distributed photovoltaics allowed to be connected in the distribution network will be significantly improved. Distribution network planners can adjust the value of the conservative parameter to improve the effect brought by the objective function value. However, a smaller value will increase the risk of the distribution network violating safety constraints.

[0109] 4) The MOEAD algorithm is used to solve the established multi-objective optimization configuration model and obtain the standard Pareto frontier curve; then, the distributed optical storage access scheme individuals in the Pareto solution set are sorted based on fuzzy theory to select the optimal access scheme.

[0110] Because there are two goals, one is to maximize the access capacity of distributed photovoltaics, and the other is to minimize the investment, construction and operation and maintenance costs of distributed photovoltaic storage. In order to facilitate the solution, the reciprocal of f1 is taken, that is, f1 = 1 / f1, and the f1 and f2 goals are minimized uniformly, that is, the total objective function F is minimized.

[0111] (1) Normalization of objective function

[0112] Because the dimensions and meanings of the two objectives are different, the objective function needs to be normalized to obtain a multi-objective optimization model:

[0113]

[0114]

[0115] In the formula, f i is the true value of the i-th objective function; f imax is the maximum value of the i-th objective function; f imin is the minimum value of the i-th objective function; ω1 and ω2 are the weights of the two objective functions, ω1+ω2=1; and They are the normalized functions of the objective functions f1 and f2 respectively.

[0116] (2) Use the decomposition-based multi-objective evolutionary method MOEAD to obtain the Pareto solution set

[0117] The decomposition-based multi-objective evolutionary method MOEAD is used to solve the optimization model of distributed photovoltaic storage multi-level multi-objective access scheme considering the uncertainty of distribution network source and load. First, the population is initialized, and a uniformly distributed weight vector is generated as a reference point. The Chebyshev method is used to decompose and obtain the sub-problems corresponding to the two objective functions, which are expressed as follows:

[0118]

[0119]

[0120] In the formula, ω j 1 and ω j 2 are the weight vectors of the two target vectors; z*1 and z*2 are the coordinates of the minimum values ​​of the two target vectors, that is, z*1=min{f1(x)}, z*2=min{f2(x)}.

[0121] Then, the two targets are mated and mutated one by one, a mating parent is randomly selected from the neighborhood of the current parent weight vector for mating, and then simulated binary crossover and polynomial mutation are used to mutate to obtain a new generation of population; then the individuals that do not meet the constraints are replaced with individuals within the boundary; then the reference point z is updated according to the aggregation function values ​​of the two targets, and z takes the minimum value between the current z and the aggregation function; then the weight neighborhood and EP are updated, and the dominant vector is removed from the EP; until the set maximum number of iterations is reached, the loop ends, and the Pareto optimal solution set of the distributed optical storage access solution is obtained.

[0122] Among them, robust optimization is used to solve the uncertainty of photovoltaic output and load. The conservative parameter is introduced to establish a robust equation with uncertain parameter constraints, forming a min-max double-layer optimization model for solution.

[0123] (3) Use fuzzy theory to sort the obtained Pareto solution set

[0124]

[0125] In the formula, Γ j is the fuzzy membership degree of the jth optimal solution in the Pareto optimal solution set; N i is the number of objective functions, here it is 2; N j is the number of optimal solutions in the Pareto optimal solution set; M j,i is the membership value of the i-th objective function in the j-th Pareto optimal solution, calculated as follows:

[0126]

[0127] Where M i is the membership value of the i-th objective function; f i is the value of the i-th objective function; f i,max is the maximum value of the i-th objective function in the Pareto solution set; f i,min is the minimum value of the i-th objective function in the Pareto solution set.

[0128] The above-mentioned sorting method is used to sort and select the distributed photovoltaic storage access solutions.

Claims

1. A multi-level and multi-objective optimization access method for distributed optical storage taking into account uncertainty, characterized in that: The specific steps include: 1) Establish a distributed photovoltaic storage multi-level and multi-objective access scheme optimization model, including: the maximum distributed photovoltaic access capacity and the minimum distributed photovoltaic storage investment, construction and operation and maintenance cost objective functions, as well as various distribution network indicators, distributed photovoltaics, and energy storage battery related constraints; The objective function includes: (1) Distributed photovoltaic access capacity is the largest The objective function is to maximize the sum of the photovoltaic access capacity of all nodes on the feeder, as shown in the following formula: Where P mv PV,i is the installed capacity of distributed photovoltaic at node i on the medium voltage level side of the feeder; P lv PV,j is the installed capacity of distributed photovoltaic power at node j on the low-voltage level side of the feeder, which is used as the maximum power generation power of the distributed photovoltaic power generation curve in the calculation; N is the number of medium-voltage level nodes of the feeder; M is the number of low-voltage level nodes of the feeder; (2) Distributed PV storage investment, construction and operation and maintenance costs are minimal The objective function is to minimize the investment, construction and operation and maintenance costs of distributed photovoltaic storage, as shown in the following formula: minf2=C cost (2) C cost =C inv +C ope (3) C ope =αC inv (5) In the formula, C cost is the annual investment, construction and operation and maintenance cost of distributed solar energy storage; C inv is the annual investment and construction cost of distributed solar energy storage; C ope is the annual operation and maintenance cost of distributed solar energy storage; C PV inv is the annual investment and construction cost of distributed photovoltaics; C BESS inv is the annual investment and construction cost of the energy storage battery; α is the conversion ratio of the operation and maintenance cost, which is taken as 0.1 in this paper; The investment and construction costs of distributed photovoltaics include the investment and construction costs of rooftop photovoltaics connected to the low-voltage grid and the investment and construction costs of concentrated photovoltaic power stations in open spaces connected to the medium-voltage grid. The construction costs of grid connection at different voltage levels are different, and the construction costs of grid connection at the low-voltage level are lower. The investment and construction costs of distributed photovoltaics and energy storage batteries are shown in the following formulas: Where r0 is the discount rate; y is the applicable planning period. In this paper, distributed photovoltaics are taken as 20 years and energy storage batteries are taken as 10 years; δ PV,lv inv and δ BESS,lv inv are the unit capacity investment and construction costs of medium-voltage grid-connected photovoltaic and energy storage batteries; PV,mv inv and δ BESS,mv inv are the unit capacity investment and construction costs of low-voltage grid-connected photovoltaic and energy storage batteries; P mv BESS,i is the installed capacity of the energy storage battery at the medium voltage level side of the feeder node i; P lv BESS,j is the installed capacity of the energy storage battery at node j on the low-voltage level side of the feeder; 2) Use robust optimization to process the uncertain parameters in the constraints, including photovoltaic output and load, and obtain a distributed photovoltaic storage multi-level and multi-objective access scheme optimization model that considers source and load uncertainty; 3) Model solution, including: using the MOEAD algorithm to obtain the Pareto frontier curve and the fuzzy theory-based sorting method to select the optimal access solution, The use of the MOEAD algorithm to obtain the Pareto frontier curve and the sorting method based on fuzzy theory to select the optimal access solution include: (1) Normalization of objective function Normalize the objective function to obtain the multi-objective optimization model: In the formula, f i is the true value of the i-th objective function; f imax is the maximum value of the i-th objective function; f imin is the minimum value of the i-th objective function; ω1 and ω2 are the weights of the two objective functions, ω1+ω2=1; and They are the normalized functions of the objective functions f1 and f2 respectively; (2) Use the decomposition-based multi-objective evolutionary method MOEAD to obtain the Pareto solution set The decomposition-based multi-objective evolutionary method MOEAD is used to solve the optimization model of the multi-level and multi-objective access scheme of distributed optical storage taking into account the uncertainty of source and load. First, the population is initialized to generate a uniformly distributed weight vector as a reference point. The Chebyshev method is used to decompose and obtain the sub-problems corresponding to the two objective functions, which are expressed as follows: In the formula, ω j 1 and ω j 2 are weight vectors of the two target vectors; z*1 and z*2 are coordinates of the minimum values ​​of the two target vectors, i.e., z*1=min{f1(x)}, z*2=min{f2(x)}; Then, the two targets are mated and mutated one by one, a mating parent is randomly selected from the neighborhood of the current parent weight vector for mating, and then simulated binary crossover and polynomial mutation are used to mutate to obtain a new generation of population; then, individuals that do not meet the constraints are replaced with individuals within the boundary; then, the reference point z is updated according to the aggregation function values ​​of the two targets, and z takes the minimum value between the current z and the aggregation function; then, the weight neighborhood and EP are updated, and the dominant vector is removed from the EP; until the set maximum number of iterations is reached, the loop ends, and the Pareto optimal solution set of the distributed optical storage access solution is obtained; Among them, robust optimization is used to solve the uncertainty of photovoltaic output and load, and conservative parameters are introduced to establish robust equations containing uncertain parameter constraints, forming a min-max double-layer optimization model for solution. (3) Use fuzzy theory to sort the obtained Pareto solution set In the formula, Γ j is the fuzzy membership degree of the jth optimal solution in the Pareto optimal solution set; N i is the number of objective functions, here it is 2; N j is the number of optimal solutions in the Pareto optimal solution set; M j,i is the membership value of the i-th objective function in the j-th Pareto optimal solution, calculated as follows: Where M i is the membership value of the i-th objective function; f i is the value of the i-th objective function; f i,max is the maximum value of the i-th objective function in the Pareto solution set; f i,min is the minimum value of the i-th objective function in the Pareto solution set; The above-mentioned sorting method is used to sort and select the distributed photovoltaic storage access solutions.

2. The multi-level and multi-objective optimization access method for dispersed optical storage taking into account uncertainty according to claim 1 is characterized in that: The constraints described in step 1) include: (1) Constraints on various indicators of distribution network ① Power flow equation constraints Where P j,t , Q j,t , U j,t is the active injection power, reactive injection power and voltage at node j at time t; h(j) and e(j) are the branch end node set with node j as the head node and the branch head node set with node j as the end node respectively; P ij,t , Q ij,t is the active power and reactive power flowing from node i to node j in branch ij at time t; r ij 、x ij 、b ij is the resistance, reactance and susceptance of branch ij; are the active power and reactive power injected by the upper power grid at node j at time t, respectively; are the photovoltaic active power and reactive power injected at node j at time t, respectively; are the discharging power and charging power of the energy storage battery at node j at time t respectively; is the reactive power injected by the continuous reactive power compensation device at node j at time t; are the active load and reactive load at node j at time t, respectively, P jk,t , Q jk,t is the active load power and reactive load power flowing from node j to node k in the branch at time t; ②Node voltage constraints IN imin <In i,t <In imax (9) Where U imin is the lower limit of node voltage, which is 0.9, U imax The upper limit of node voltage is taken as 1.07; ③ Branch flow constraints P ij,t ≤P ijmax (10) Where P ijmax is the limit of single-phase active power transmitted by line ij, which can be obtained from the maximum current carrying capacity in the line parameters. The calculation formula is as follows: Where U is the rated voltage of the line, I max is the maximum current carrying capacity of the line, cosΦ is the power factor, which is 0.9; ④Short-circuit current constraint The access of power to the grid will increase the short-circuit current level of the grid. After the access of distributed power sources, the short-circuit current of the line should not exceed the limit value of the voltage level. I SCL <I SCLmax (12) In the formula, I SCL and I SCLmax are the maximum short-circuit current of the line and the maximum short-circuit current of the circuit breaker, respectively. ⑤ Voltage three-phase unbalance constraint Because the load in the low-voltage layer is single-phase powered, the distributed power source connected to the low-voltage layer is single-phase connected, so a voltage three-phase unbalance constraint is added. Where U i,t Φ is the square value of the voltage amplitude at node i at time t; U i,t avg is the mean value of the square of the voltage amplitude at node i at time t, (2) Constraints on energy storage devices ①Battery state of charge constraints Overcharging or over-discharging will greatly damage the life of the battery. In order to improve the utilization rate of the battery and extend the service life of the battery, the energy storage device needs to meet the upper and lower limit constraints of the battery state of charge when charging and discharging. S min ≤S i,t ≤S max (14) In the formula, S min and S max They represent the minimum SOC and maximum SOC allowed by the energy storage battery; S i,t is the state of charge of the energy storage battery at node i at time t. In this paper, S min is 0.1, S max is 0.9, Where Δt is the time interval; S i,t+Δt is the state of charge of the energy storage battery at node i at time t+Δt, P ch is the charging power; P dch is the discharge power; η ch is the charging efficiency; η dch is the discharge efficiency; E0 is the rated capacity of the battery; U i,t and V i,t are the charging and discharging states of the energy storage device at node i at time t, respectively; ②Charge and discharge state constraints The battery cannot be in the charging and discharging states at the same time, so the charging and discharging state constraints need to be met. U i,t +V i,t =1 (16) (3) Distributed photovoltaic constraints ①Distributed photovoltaic node installation capacity constraints Where P PVmin is the minimum limit of distributed photovoltaic access in the line; P PVmax is the maximum limit of distributed photovoltaic access in the line; ② Constraints on the total amount of distributed photovoltaic installations In the formula, α PV is the ratio coefficient of distributed photovoltaic grid-connected to the total load, P j load is the active load power at node j.

3. The multi-level and multi-objective optimization access method for dispersed optical storage taking into account uncertainty according to claim 1 is characterized in that: The uncertain parameter processing described in step 2) and the distributed photovoltaic storage multi-level multi-objective access scheme optimization model considering source-load uncertainty include: The uncertainty parameters are represented by interval robust optimization method. In the formula, are the load prediction value and the maximum allowed deviation of node j respectively; They are the predicted value of distributed photovoltaic output at node j and the maximum allowed deviation, and the corresponding constraints are modified according to the interval of uncertain parameters; Take the reciprocal of f1, so the objectives of f1 and f2 are minimized uniformly, that is, the total objective function F is minimized; The optimization model of the distributed optical storage multi-level and multi-objective access scheme considering the uncertainty of source and load can be expressed as:

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