Photovoltaic and energy storage optimal configuration method and device based on snowflake net

Abstract

The invention relates to the technical field of power grid optimization, and provides a photovoltaic and energy storage optimization configuration method and device based on a snowflake net, and the method comprises the steps: determining typical scenes of photovoltaic and load correlation and the occurrence probability of each typical scene based on load demand historical data and photovoltaic output historical data; the built photovoltaic and energy storage double-layer optimization configuration model comprising a planning layer and an operation layer is utilized to determine the access position and capacity of photovoltaic and energy storage; the planning layer calculates a constraint condition and a target function of the planning layer based on the optimization result of the operation layer, and optimizes the access position and capacity of photovoltaic and energy storage; and the operation layer optimizes the photovoltaic and energy storage output conditions of each time period based on the load curve and the photovoltaic output curve corresponding to the typical scene of which the occurrence probability is greater than the set value and the optimized result of the planning layer on the basis of the target function and the constraint condition of the operation layer, and feeds back the optimization result to the planning layer. The adaptability and flexibility of the snowflake net can be improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power grid optimization, and in particular to a photovoltaic and energy storage optimization configuration method and device based on a snowflake network. Background Art

[0002] To adapt to the trend of global energy transformation, the power system is actively promoting new energy power generation technologies, especially the widespread application of renewable energy represented by photovoltaics.

[0003] Photovoltaic has the advantages of being clean, environmentally friendly, and having low maintenance costs. Photovoltaic access is an important trend in the development of distribution networks. As the proportion of photovoltaics in the power grid gradually increases, the strong volatility and uncertainty of its output due to factors such as meteorological influences have posed new challenges to the operation and stability of the power system. In order to alleviate the adverse effects of photovoltaic access, the access of energy storage systems has become key. The energy storage system can store excess photovoltaic power when power demand is low, and release it when power demand is peak, thereby achieving energy balance regulation and ensuring the stable operation of the power grid.

[0004] Snowflake network is a new active distribution network with 10kV ring network box as the core node. It is composed of 8 circuits of 4 substations, or 6 circuits of 10kV lines of 3 substations to form an independent feeder cluster. The different feeders inside are interconnected to give full play to their strong load transfer capacity.

[0005] With the large-scale access of photovoltaic power generation and the gradual opening of the power market, the Snowflake network faces the problem of strong fluctuations in output and demand. This fluctuation not only affects the stable operation of the power grid, but also increases the difficulty of power grid planning and management. Therefore, a photovoltaic and energy storage optimization configuration solution based on the Snowflake network is needed to improve the adaptability and flexibility of the Snowflake network. Summary of the invention

[0006] To solve the above problems, the present invention provides a method and device for optimizing the configuration of photovoltaics and energy storage based on a snowflake network, which can improve the adaptability and flexibility of the snowflake network.

[0007] In a first aspect, a photovoltaic and energy storage optimization configuration method based on a snowflake network is provided, the method comprising: Obtain historical load demand data and photovoltaic output data from Xuehua.com; Based on the historical data of load demand and PV output, determine the typical scenarios of PV-load correlation and the probability of occurrence of each typical scenario; Using the constructed photovoltaic and energy storage dual-layer optimization configuration model, determine the access location and capacity of photovoltaic and energy storage; The model includes a planning layer and an operation layer. The planning layer takes the system economic index as the objective function, and the operation layer takes the operation cost and the voltage deviation index as the objective function. The system economic index includes the operation cost and the investment cost of photovoltaic and energy storage. The planning layer is used to calculate the constraints and objective functions of the planning layer based on the optimization results of the operation layer, and optimize the access location and capacity of photovoltaic and energy storage; The operation layer is used to optimize the output of photovoltaic and energy storage in each time period based on the load curve and photovoltaic output curve corresponding to the typical scenario with a probability of occurrence greater than the set value, the optimized photovoltaic and energy storage access location and capacity, and the objective function and constraints of the operation layer, and feed back the optimization results to the planning layer.

[0008] Furthermore, the constructed photovoltaic and energy storage dual-layer optimization configuration model is used to determine the access location and capacity of photovoltaic and energy storage, including: The capacity and access location of photovoltaic power generation, and the capacity and access location of energy storage are used as particles of the improved particle swarm algorithm, and the particle positions and velocities are initialized and input into the operation layer. The access locations and capacities of photovoltaic power generation and energy storage optimized by the planning layer are used to update the position and velocity of the particle population, and the updated position and velocity of the particle population are input into the operation layer until the objective function of the planning layer reaches the minimum value under the constraints of the planning layer, or the set number of iterations is reached.

[0009] Furthermore, the system economic index F2 is:

[0010]

[0011] in, is the investment cost of photovoltaic and energy storage, For operating costs, represents the discount rate; is the service life of distributed photovoltaic and energy storage equipment; Represents the distributed photovoltaic investment cost per unit capacity; Represents the energy storage investment cost per unit capacity; Represents the total installed capacity of distributed photovoltaics; Represents the total installed capacity of energy storage.

[0012] Furthermore, the operating cost F1 and the voltage deviation index VDI are:

[0013]

[0014]

[0015]

[0016]

[0017]

[0018] in, Indicates the operating cost; represents the network loss cost; Indicates the cost of purchasing electricity from the main grid; represents the cost of abandoned light; T represents the total number of moments; N represents the number of photovoltaic power stations; represents the price of photovoltaic power generation; represents the power generation of the i-th photovoltaic power station at time t; Indicates the network loss price; represents the network loss at time t; Indicates the main grid electricity purchase price; represents the main network purchase power at time t; Indicates the penalty price for photovoltaic power abandonment; represents the amount of power abandoned by the ith photovoltaic power station at time t; U i,t is the actual voltage value of node i at time t obtained by the probability flow; U i,N is the rated voltage value of node i at time t.

[0019] Furthermore, the constraints of the operation layer include: system flow constraints, system operation constraints, energy storage system charging and discharging power constraints, photovoltaic power output constraints, upper and lower limit constraints of the battery charging state of energy storage, and energy storage location constraints, among which the energy storage location constraint is that the location variable of the energy storage belongs to the energy storage alternative location, and the energy storage alternative location is obtained based on the site selection of the energy storage according to the interconnection line distribution information of the Snowflake network.

[0020] Furthermore, the constraints at the planning layer include: system flow constraints, system operation constraints, upper and lower limit constraints on the number of photovoltaic power sources connected, and upper and lower limit constraints on the number of energy storage access groups.

[0021] Furthermore, based on the historical data of load demand and the historical data of photovoltaic output, the typical scenarios of photovoltaic and load correlation and the probability of occurrence of each typical scenario are determined, including: Based on the historical data of load demand and the historical data of photovoltaic output, the probability density function of load demand and the probability density function of photovoltaic output are determined; Combine Latin hypercube sampling with Nataf transformation to perform correlation sampling on the probability density function of load demand and the probability density function of photovoltaic output, and obtain several time series scenarios of load and photovoltaic. A clustering algorithm is used to reduce the time series scenarios of several loads and photovoltaics to obtain a typical scenario set; The occurrence probability of each typical scenario is calculated by using the number of time series scenarios participating in the typical scenario clustering and the total number of time series scenarios.

[0022] In the second aspect, a photovoltaic and energy storage optimization configuration device based on a snowflake network is provided, and the device includes: An acquisition unit is used to acquire historical load demand data and photovoltaic output data from the Snowflake Network; An occurrence probability determination unit, used to determine typical scenarios of photovoltaic and load correlation and the occurrence probability of each typical scenario based on historical load demand data and historical photovoltaic output data; The optimization configuration unit is used to determine the access location and capacity of photovoltaic and energy storage by using the constructed photovoltaic and energy storage double-layer optimization configuration model; wherein the model includes a planning layer and an operation layer, the planning layer takes the system economic index as the objective function, and the operation layer takes the operation cost and the voltage offset index as the objective function, and the system economic index includes the operation cost and the photovoltaic and energy storage investment cost; the planning layer is used to calculate the constraints and objective functions of the planning layer based on the optimization results of the operation layer, and optimize the access location and capacity of photovoltaic and energy storage; the operation layer is used to optimize the output of photovoltaic and energy storage in each time period based on the load curve corresponding to the typical scenario with a probability of occurrence greater than the set value and the photovoltaic output curve, the optimized photovoltaic and energy storage access location and capacity, and the objective function and constraint conditions of the operation layer, and feed back the optimization results to the planning layer.

[0023] Furthermore, the optimization configuration unit is specifically used to use the capacity and access position of photovoltaics, the capacity and access position of energy storage as particles of the improved particle swarm algorithm, initialize the particle position and speed, and input them into the operation layer; use the access position and capacity of photovoltaics and energy storage optimized by the planning layer to update the position and speed of the particle population, and input the updated position and speed of the particle population into the operation layer until the objective function of the planning layer reaches the minimum value under the constraints of the planning layer, or reaches the set number of iterations.

[0024] Furthermore, the system economic index F2 is:

[0025]

[0026] in, is the investment cost of photovoltaic and energy storage, For operating costs, represents the discount rate; is the service life of distributed photovoltaic and energy storage equipment; Represents the distributed photovoltaic investment cost per unit capacity; Represents the energy storage investment cost per unit capacity; Represents the total installed capacity of distributed photovoltaics; Represents the total installed capacity of energy storage.

[0027] Furthermore, the operating cost F1 and the voltage deviation index VDI are:

[0028]

[0029]

[0030]

[0031]

[0032]

[0033] in, Indicates the operating cost; represents the network loss cost; Indicates the cost of purchasing electricity from the main grid; represents the cost of abandoned light; T represents the total number of moments; N represents the number of photovoltaic power stations; represents the price of photovoltaic power generation; represents the power generation of the i-th photovoltaic power station at time t; Indicates the network loss price; represents the network loss at time t; Indicates the main grid electricity purchase price; represents the main network purchase power at time t; Indicates the penalty price for photovoltaic power abandonment; represents the amount of power abandoned by the ith photovoltaic power station at time t; U i,t is the actual voltage value of node i at time t obtained by the probability flow; U i,N is the rated voltage value of node i at time t.

[0034] Furthermore, the constraints of the operation layer include: system flow constraints, system operation constraints, energy storage system charging and discharging power constraints, photovoltaic power output constraints, upper and lower limit constraints of the battery charging state of energy storage, and energy storage location constraints, among which the energy storage location constraint is that the location variable of the energy storage belongs to the energy storage alternative location, and the energy storage alternative location is obtained based on the site selection of the energy storage according to the interconnection line distribution information of the Snowflake network.

[0035] Furthermore, the constraints at the planning layer include: system flow constraints, system operation constraints, upper and lower limit constraints on the number of photovoltaic power sources connected, and upper and lower limit constraints on the number of energy storage access groups.

[0036] Furthermore, the occurrence probability determination unit is specifically used to determine the probability density function of load demand and the probability density function of photovoltaic output based on the historical data of load demand and the historical data of photovoltaic output; Latin hypercube sampling is combined with Nataf transformation to perform correlation sampling on the probability density function of load demand and the probability density function of photovoltaic output to obtain several time series scenarios of load and photovoltaic; a clustering algorithm is used to reduce the obtained several time series scenarios of load and photovoltaic to obtain a typical scene set; the occurrence probability of each typical scene is calculated using the number of time series scenarios participating in the typical scene clustering and the total number of time series scenarios.

[0037] In a third aspect, an electronic device is provided, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, used to store computer programs; The processor is used to implement the steps of the above method when executing the program stored in the memory.

[0038] In a fourth aspect, a computer storage medium is provided, wherein the computer storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0039] Compared with the prior art, the present invention has the following advantages: The solution of the disclosed embodiment is based on typical scenario data obtained by probability statistics. Through reasonable planning and optimal configuration of photovoltaic systems and energy storage systems, the Snowflake Network can better integrate various types of distributed power sources and realize large-scale photovoltaic energy consumption, thereby improving the adaptability and flexibility of the Snowflake Network, optimizing the utilization of Snowflake Network resources, and enhancing users' electricity stability and power quality.

[0040] Other features and advantages of the present disclosure will be described in the following description, and partly become apparent from the description, or be understood by implementing the present disclosure. The purpose and other advantages of the present disclosure can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present disclosure 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 some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1A schematic diagram of a photovoltaic and energy storage optimization configuration method based on a snowflake network according to an embodiment of the present disclosure is shown; Figure 2 A schematic diagram of another photovoltaic and energy storage optimization configuration method based on a snowflake network according to an embodiment of the present disclosure is shown; Figure 3 A topology diagram showing a network topology diagram according to an embodiment of the present disclosure; Figure 4 The data of photovoltaic output in each time period in each scenario according to the embodiment of the present disclosure are shown; Figure 5 shows the data of load demand in each time period in each scenario according to an embodiment of the present disclosure; Figure 6 The photovoltaic output data in each time period of a typical operation scenario according to an embodiment of the present disclosure is shown; Figure 7 shows load demand data in each time period of a typical operation scenario according to an embodiment of the present disclosure; Figure 8 The occurrence probabilities of 20 typical operation scenarios according to the embodiments of the present disclosure are shown; Fig. 9 A diagram showing the intraday distribution network optimization result obtained according to an embodiment of the present disclosure is shown; Fig.10 shows an energy storage operation status diagram according to an embodiment of the present disclosure; Fig.11 A schematic diagram of an access location scheme for an optimized configuration of a photovoltaic power source and energy storage according to an embodiment of the present disclosure is shown; Fig.12 A schematic diagram of a fitness curve according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0044] Figure 1 and Figure 2 A schematic diagram of a photovoltaic and energy storage optimization configuration method based on a snowflake network according to an embodiment of the present disclosure is shown. Figure 1 As shown, the photovoltaic and energy storage optimization configuration method based on the snowflake network of the embodiment of the present disclosure includes the steps of: S1. Input the topological structure data, load demand historical data and photovoltaic output historical data of the Snowflake network.

[0045] Among them, the topological structure data of the Snowflake network includes: the line connection status of the Snowflake network, and the line connection information includes the distribution information of the connecting lines.

[0046] S2. Determine the probability density function of load demand based on the historical data of load demand, and determine the probability density function of photovoltaic output based on the historical data of photovoltaic output.

[0047] The probability density function of load demand can be described by the probability density of active power and reactive power: (1) (2) In formulas (1) and (2), and Represent the mean values ​​of active and reactive power respectively; and represent the variance of active and reactive power respectively.

[0048] The probability density function of photovoltaic output can be described by the probability density function of photovoltaic output active power: (3) (4) In formulas (3) and (4), Γ(·) represents the Gamma function; the shape parameters α and β of the Beta distribution are obtained by maximum likelihood fitting based on the historical data of light intensity; in formula (3), P is the photovoltaic active power output; is the maximum output of photovoltaic active power; A is the total area of ​​photovoltaic cells; η is the total conversion efficiency of photovoltaic power station; r max is the maximum light intensity during the study period.

[0049] S3: Combine Latin hypercube sampling with Nataf transformation to perform correlation sampling on the probability density function of load demand and the probability density function of photovoltaic output to obtain several time series scenarios of load and photovoltaic.

[0050] The basic operation process of Latin hypercube sampling is divided into two steps: sampling and sorting.

[0051] sampling: 1) S random variables are known , , …, , for , , …, The probability distribution function of any random variable in is: (5) Assume that the sampling size is N, and each random variable The probability space of is divided into N equally spaced and non-overlapping intervals, that is, , ,…, ; 2) In each subinterval, randomly select a value as Sample value; 3) Calculate X using the inverse function shown in formula (6) k The nth sampling value of , where rand represents a random number between 0 and 1; (6) Through the above steps, an initial sampling matrix B of order S×N is obtained which can fully reflect the overall distribution of the random variable.

[0052] arrangement: The sample values ​​of random variables in the matrix B obtained by sampling show a monotonically increasing trend. Therefore, there is a strong correlation between the random variables in the matrix B, which may not match the correlation between the actual random variables. In order to adjust this correlation, it is necessary to change the arrangement order of the random variable sampling values ​​to minimize the correlation of the independent random variable sampling values. In the arrangement process, a K×N order sequence matrix I is first created, in which each row element represents the position where the elements of the corresponding row in the initial sampling matrix B should be arranged. According to the instructions of the sequence matrix I, the elements of the matrix B are rearranged to form the final sampling matrix.

[0053] In the case where photovoltaic output and load demand do not conform to the normal distribution, the sample data that do not conform to the normal distribution are transformed into a space that conforms to the normal distribution through the Nataf inverse transformation, which facilitates the use of the Latin hypercube sampling LHS method to generate multiple scenarios that take into account correlation.

[0054] Assume that the input random variable is ,remember Probability density function of (i=1,…,n) and the cumulative distribution function , its correlation coefficient matrix D X It is expressed as: (7) In formula (7) Represents a random variable and The correlation coefficient between .

[0055] By equal probability transformation: (8) Apply the Nataf transformation formula: (9) To obtain the correlation coefficient matrix D of the random variable Y Y The non-diagonal elements of , Denote random variables , The mean of Indicates the correlation coefficient The standard normal random , The joint probability density function of .

[0056] Through the above steps, the random variables are transformed from the input variable space to the standard normal space, and then the transformation to the independent standard normal space is completed.

[0057] D Y Perform choleskey decomposition to obtain D Y =D Y =EE T , where E is a lower triangular matrix. Then we can get a random vector: (10) In formula (10), Z is a random vector in an independent standard normal space.

[0058] S4. A clustering algorithm is used to reduce the scenarios of several load and photovoltaic timing scenarios to obtain a typical scenario set, and the occurrence probability of each typical scenario is calculated.

[0059] By comparing the number of time series scenarios participating in the typical scenario clustering with the total number of time series scenarios, the occurrence probability of the typical scenario is obtained.

[0060] Specifically, the photovoltaic and load samples in each time period can be taken as a scene, that is, the time series scene of load and photovoltaic, and the improved k-means algorithm - k-means++ can be used to reduce the scene, so as to describe the randomness and volatility of source and load in a deterministic way.

[0061] The k-means++ clustering method is an iterative process that aims to minimize the sum of squared errors of the entire data set. The algorithm includes the following steps 1) - 4): 1) Specify the number of clusters K and select K data objects in the data set as the initial cluster centers. Each selected data object will serve as the center of K clusters. 2) For the remaining data objects in the data set, the closer the distance, the greater the similarity between the two. The data objects are assigned to the clusters represented by the cluster centers with the greatest similarity. 3) Calculate the mean of all data objects in each cluster and use the calculated mean as the new cluster center of the cluster. Then calculate the sum of squared distances from all data objects to the cluster center of their cluster, that is, the sum of squared errors (SEE) of the entire data set. 4) Determine whether the error sum of squares and cluster centers of the entire data set have changed. If so, return to step 2) and start the next iteration; if not, clustering ends and the clustering results are output.

[0062] The Euclidean distance calculation formula between data points is as follows: (11) Among them, x represents the data object; b i represents the i-th cluster center; m represents the dimension of the data; x j represents the j-th attribute value of data point x, b ij Indicates b i The j-th attribute value of .

[0063] The formula for calculating the sum of squared error (SEE) of the entire data set is as follows: (12) S4. Use the constructed photovoltaic and energy storage dual-layer optimization configuration model to determine the access location and capacity of photovoltaic and energy storage.

[0064] Among them, the two-layer optimization configuration model is as follows Figure 2 As shown, it includes a planning layer and an operation layer, wherein the planning layer takes the system economic index as the objective function, and the operation layer takes the operation cost and the voltage deviation index as the objective function, and the system economic index includes the operation cost and the photovoltaic and energy storage investment cost; The planning layer is used to calculate the constraints and objective functions of the planning layer based on the optimization results of the operation layer, and optimize the access location and capacity of photovoltaic and energy storage; The operation layer is used to optimize the output of photovoltaic and energy storage in each time period based on the load curve and photovoltaic output curve corresponding to the typical scenario with a probability of occurrence greater than the set value, the optimized photovoltaic and energy storage access location and capacity, and the objective function and constraints of the operation layer, and feed back the optimization results to the planning layer.

[0065] This step S4 specifically includes: S41, setting the capacity and access position of photovoltaic power generation, the capacity and access position of energy storage as particles of the improved particle swarm algorithm, initializing the particle position and speed, and inputting them into the operation layer; Initialize the particle positions and velocities in the improved particle swarm algorithm, set the access positions and capacities of photovoltaic and energy storage in the planning layer, and use them as inputs for the operation layer; S42: The operation layer optimizes the output of photovoltaic and energy storage in each period based on the load curve, photovoltaic output curve, position and speed of the particle population corresponding to the typical scenario with a probability greater than the set value, and the objective function and constraints of the operation layer, and feeds the optimization results back to the planning layer; S43, the planning layer calculates the constraints and objective functions of the planning layer based on the optimization results of the operation layer, and optimizes the access location and capacity of photovoltaic and energy storage; S44. Use the access location and capacity of photovoltaic and energy storage optimized by the planning layer to update the position and speed of the particle population, input the updated position and speed of the particle population into the operation layer, and repeat steps S42 to S43 until the objective function of the planning layer reaches the minimum value under the constraints of the planning layer, or reaches the set number of iterations.

[0066] Among them, the objective function of the operation layer model includes the operation cost F1 and the voltage deviation index VDI: (13) (14) (15) (16) (17) in, Indicates the operating cost; represents the network loss cost; Indicates the cost of purchasing electricity from the main grid; represents the cost of abandoned light; T represents the total number of moments; N represents the number of photovoltaic power stations; represents the price of photovoltaic power generation; represents the power generation of the i-th photovoltaic power station at time t; Indicates the network loss price; represents the network loss at time t; Indicates the main grid electricity purchase price; represents the main network purchase power at time t; Indicates the penalty price for photovoltaic power abandonment; It represents the amount of power abandoned by the i-th photovoltaic power station at time t.

[0067] The voltage deviation index is used to measure the voltage stability of the distribution network at a specific moment. It indicates the degree to which the node voltage deviates from the rated voltage. The higher the index, the worse the voltage stability. Its calculation formula is as follows: (18) In formula (18), VDI represents the voltage deviation index, U i,t is the actual voltage value of node i at time t obtained by the probability flow; U i,N is the rated voltage value of node i at time t.

[0068] The constraints of the operation layer include static safety constraints and other constraints. Static safety constraints specifically cover system flow constraints and system operation constraints. Other constraints include energy storage system charging and discharging power constraints, photovoltaic power output constraints, upper and lower limit constraints of battery charging state of energy storage, and energy storage location constraints.

[0069] The expression of system power flow constraint is: (19) (20) (twenty one) (twenty two) (twenty three) (twenty four) In formulas (19)~(24), represents the set of all branches in the snowflake network, k represents node k, P ik Indicates the active power on branch ik, Q ik Represents the reactive power on branch ik, and is the resistance and reactance of branch ij; is the current of branch ij; is the active power flowing from node i to node j on the branch, is the reactive power flowing from node i to node j on the branch; is the reactive power flowing from node j to node i on the branch; is the voltage at node i, is the voltage at node j, is the sum of active power injected into node i, , , are the active powers injected by energy storage, injected by photovoltaic power and consumed by load at node i respectively; is the sum of reactive power injected into node i, , , are the reactive powers injected by energy storage, injected by photovoltaic power and consumed by load at node i respectively.

[0070] The expression of system operation constraints is: (25) (26) In formulas (25)~(26), and They are the minimum and maximum allowable node voltage values ​​of the system respectively; It is the maximum allowable branch current value of the system.

[0071] The charging and discharging power constraints of the energy storage system are: (27) (28) Formula (27)~28) is in the middle , and They respectively represent the charging and discharging power and the upper and lower limits of the energy storage transmission power of the i-th energy storage in the t-th period.

[0072] The photovoltaic power output constraints are: (29) In formula (29), The upper limit of photovoltaic power output The upper and lower limits of the battery charging state of energy storage are: (30) In formula (30), SOC represents the battery charging state; is the battery charging state of the energy storage of the i-th energy storage in the t-th period; and They represent the upper and lower limits of the battery charging state of the energy storage respectively.

[0073] The location constraints of energy storage are: (31) In formula (31), is the position variable of energy storage, It is a set of candidate locations for energy storage determined according to the distribution of interconnection lines in the snowflake network.

[0074] Most of the existing research on optimization configuration methods is only applicable to microgrids or single feeders. The modeling is relatively simple and does not consider the interconnection between different feeders. It is not suitable for snowflake networks to exert their capabilities. The alternative locations of energy storage determined according to the distribution of the interconnection lines of the snowflake network in the embodiment of the present invention are used as constraints in the optimization, which is suitable for snowflake networks to exert their capabilities.

[0075] The planning function of the planning layer model is the system economic index F2, including the investment cost C2 of photovoltaic and energy storage and the operating cost F1: (32) (33) In formulas (32) and (33), represents the discount rate; is the service life of distributed photovoltaic and energy storage equipment; Represents the distributed photovoltaic investment cost per unit capacity; Represents the energy storage investment cost per unit capacity; Represents the total installed capacity of distributed photovoltaics; Represents the total installed capacity of energy storage.

[0076] It should be noted that F2 here is the fitness function of the improved particle swarm algorithm.

[0077] The constraints at the planning layer include static safety constraints and other constraints. Static safety constraints are consistent with those at the operation layer. Other constraints include upper and lower limits on the number of photovoltaic power sources connected and upper and lower limits on the number of energy storage groups connected.

[0078] The upper and lower limits of the number of distributed photovoltaic access are: (34) In formula (34), NPV represents the number of photovoltaic access groups; and They represent the upper and lower limits of the number of photovoltaic access groups respectively.

[0079] The upper and lower limits of the number of energy storage access groups are: (35) In formula (35), NESS represents the number of photovoltaic access groups; and They represent the upper and lower limits of the number of photovoltaic access groups respectively.

[0080] The embodiment of the present invention is described below by using a specific example.

[0081] This paper uses the Snowflake network for example verification. The network topology diagram is as follows Figure 3 shown.

[0082] The candidate nodes for photovoltaic power (PV) access are {2, 3, 4, 8, 12, 20, 27, 32}; the candidate nodes for energy storage (ESS) access are {3, 6, 9, 18, 20, 28, 30}; the rated capacity of a single photovoltaic power source is 100kW, the maximum number of photovoltaic installations per node is 20, and the minimum number of photovoltaic installations per node is 5; the rated capacity of each energy storage group is 50kW, the maximum number of energy storage installations per node is 20, and the minimum number of energy storage installations per node is 6. The output period of photovoltaic power is from 06:00 to 17:00 every day. The economical applicable years of photovoltaic power and energy storage are set to 10 years. The charging and discharging efficiency of energy storage is 0.9, the maximum value of energy storage SOC is 0.9, the minimum value of energy storage SOC is 0.1, and the initial SOC of energy storage is 0.5. Other parameters are shown in Table 1 below.

[0083] The battery state of charge (SOC) is the percentage of the remaining battery power to the rated capacity of the battery. It is used to reflect the remaining capacity of the battery and the ability of the battery to continue working. When the battery is fully discharged, the SOC is 0, and when it is fully charged, the SOC is 1. It is generally expressed in 0 to 100%.

[0084] Table 1 Planning related parameters

[0085] The correlation coefficient between the light intensity and the load can be calculated from the historical data of the light intensity and the load at the same access location, and the correlation coefficient between the load and the light intensity is taken as 0.3. Latin hypercube sampling and Nataf inverse transformation are used to form a scene with the sampled values ​​of the photovoltaic output and the load at the corresponding time, and a total of 500 time-series scenes are generated. The data of the photovoltaic output and the load demand in each scene and each time period are as follows: Figure 4 and Figure 5 shown.

[0086] A 24×2×500-order photovoltaic output and load correlation sample matrix was constructed by sampling, in which the data at each moment corresponds to a specific scenario. In order to reduce the computational burden, this paper uses a clustering algorithm to perform cluster analysis on 500 scene samples according to similarity. The number of clusters is set to 20, and the K-means clustering method is used to process the light-load 24×2×500 dimensional data. The photovoltaic power supply (single group 100kw) output and load demand data of the 20 typical operating scenarios obtained by clustering in each period are as follows Figure 6 and Figure 7 shown.

[0087] Depend on Figure 6 and Figure 7It can be seen that the data in each typical operation scenario clustered show obvious differences, which can effectively represent the original data. By analyzing the number of original vectors contained in the cluster center, the probability of each typical scenario can be calculated, thereby realizing the probability simulation and feature expression of diversified operation scenarios. Figure 8 Shown are the occurrence probabilities of 20 typical operating scenarios.

[0088] Based on the two-level probabilistic programming model proposed in the article, the optimization configuration is performed, and the obtained daily distribution network optimization results and energy storage operation status are as follows: Fig. 9 and Fig.10 As shown, the optimization configuration results are as follows Fig.11 shown.

[0089] Fig.10 The left vertical axis of the middle bar graph corresponds to the graph, indicating the charging and discharging power of the energy storage at different times of the day; the right vertical axis of the line graph corresponds to the graph, indicating the charge state of the energy storage at different times of the day.

[0090] The final optimal configuration capacity planning result is that the photovoltaic capacity is 1.1MW (11 units) and the energy storage capacity is 0.4MW (8 groups). The optimal configuration of the photovoltaic power source and energy storage access location plan is as follows: Fig.11 shown.

[0091] As the number of iterations increases, the particle swarm algorithm will usually gradually converge to or approach the optimal solution. This can be reflected by observing that the fitness value gradually improves with the number of iterations and finally reaches a stable level. Usually, a fitness curve is drawn during the execution of the algorithm, which shows the change of the current optimal fitness value of the swarm as the number of iterations increases. By analyzing this curve, it can be determined whether the algorithm has converged or whether parameters (such as the number of iterations, inertia weight, acceleration coefficient, etc.) need to be adjusted to obtain better results. If the fitness curve shows that the algorithm has converged before the maximum number of iterations is reached, the number of iterations can be reduced to save computing resources. On the contrary, if the fitness improves slowly, it may be necessary to increase the number of iterations or adjust other parameters to facilitate better search. The fitness curve of the example in this embodiment is shown in Figure 2. Fig.12 shown.

[0092] Depend on Fig.12 It can be seen that when the number of iterations is 15, the algorithm converges close to the optimal solution. The example in this paper sets the number of iterations to 50, and the optimization result obtained is good. The final solution meets the goal well.

[0093] In summary, the present invention aims at the stability problem caused by the access of large-scale photovoltaic power generation to the snowflake network, and proposes a photovoltaic and energy storage optimization configuration method based on the snowflake network. First, based on the historical data of load demand and the historical data of photovoltaic output, the Latin hypercube sampling method and the Nataf inverse transform are used to generate scenarios that consider the correlation between photovoltaic and load. These scenarios can more truly reflect the operating status of the actual system. Subsequently, the K-means++ clustering algorithm is used to reduce the generated scenarios, which effectively reduces the complexity and time cost of the calculation. In order to optimize the configuration of photovoltaic and energy storage systems, the present invention constructs a two-layer optimization model. The planning layer takes the system economic index as the optimization target, focusing on the economy and return on investment of the system; the operation layer takes the system operating cost and voltage offset index as the target, aiming to ensure the efficient operation and stability of the system and reduce the voltage offset problem that may occur during the operation process. The entire optimization process is solved by the particle swarm optimization algorithm PSO. Through the two-layer iteration of the operation layer and the planning layer, the access location and capacity configuration of photovoltaic and energy storage are finally determined, thereby improving the overall benefit of the system. The research results show that the proposed method can not only effectively deal with the volatility of photovoltaic output, but also significantly optimize the configuration of photovoltaics and energy storage, thereby improving the operating efficiency and reliability of the distribution network. The optimization configuration scheme disclosed in this disclosure is of great significance for achieving the access of a high proportion of photovoltaic and other renewable energy sources. Through the application of this method, the Snow Flower distribution network can operate more stably and efficiently when facing the volatility of photovoltaic power generation. This not only provides valuable experience and technical references for the development of future power systems, but also provides practical solutions for actual engineering applications.

[0094] Based on the above method, the embodiment of the present disclosure also provides a photovoltaic and energy storage optimization configuration device based on a snowflake network corresponding to the above method, the device includes an acquisition unit, an occurrence probability determination unit and an optimization configuration unit, wherein: An acquisition unit is used to acquire historical load demand data and photovoltaic output data from the Snowflake Network; An occurrence probability determination unit, used to determine typical scenarios of photovoltaic and load correlation and the occurrence probability of each typical scenario based on historical load demand data and historical photovoltaic output data; The optimization configuration unit is used to determine the access location and capacity of photovoltaic and energy storage by using the constructed photovoltaic and energy storage double-layer optimization configuration model; wherein the model includes a planning layer and an operation layer, the planning layer takes the system economic index as the objective function, and the operation layer takes the operation cost and the voltage offset index as the objective function, and the system economic index includes the operation cost and the photovoltaic and energy storage investment cost; the planning layer is used to calculate the constraints and objective functions of the planning layer based on the optimization results of the operation layer, and optimize the access location and capacity of photovoltaic and energy storage; the operation layer is used to optimize the output of photovoltaic and energy storage in each time period based on the load curve corresponding to the typical scenario with a probability of occurrence greater than the set value and the photovoltaic output curve, the optimized photovoltaic and energy storage access location and capacity, and the objective function and constraint conditions of the operation layer, and feed back the optimization results to the planning layer.

[0095] Furthermore, the optimization configuration unit is specifically used to use the capacity and access position of photovoltaics, the capacity and access position of energy storage as particles of the improved particle swarm algorithm, initialize the particle position and speed, and input them into the operation layer; use the access position and capacity of photovoltaics and energy storage optimized by the planning layer to update the position and speed of the particle population, and input the updated position and speed of the particle population into the operation layer until the objective function of the planning layer reaches the minimum value under the constraints of the planning layer, or reaches the set number of iterations.

[0096] Furthermore, the occurrence probability determination unit is specifically used to determine the probability density function of load demand and the probability density function of photovoltaic output based on the historical data of load demand and the historical data of photovoltaic output; Latin hypercube sampling is combined with Nataf transformation to perform correlation sampling on the probability density function of load demand and the probability density function of photovoltaic output to obtain several time series scenarios of load and photovoltaic; a clustering algorithm is used to reduce the obtained several time series scenarios of load and photovoltaic to obtain a typical scene set; the occurrence probability of each typical scene is calculated using the number of time series scenarios participating in the typical scene clustering and the total number of time series scenarios.

[0097] Based on the same inventive concept as the above disclosed content, the present disclosure also provides an electronic device accordingly. The electronic device of the present disclosure embodiment includes at least one processor and at least one memory electrically connected, the memory is electrically connected to the processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the method as described above.

[0098] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean the connection between lines. An indirect connection method can be applied to the embodiments of the present disclosure as long as the purpose of the present disclosure is achieved.

[0099] Based on the same inventive concept, the present disclosure also provides a computer storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0100] Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A photovoltaic and energy storage optimization configuration method based on snowflake network, characterized in that: The method comprises, Obtain historical load demand data and photovoltaic output data from Xuehua.com; Based on the historical data of load demand and PV output, determine the typical scenarios of PV-load correlation and the probability of occurrence of each typical scenario; Using the constructed photovoltaic and energy storage dual-layer optimization configuration model, determine the access location and capacity of photovoltaic and energy storage; The model includes a planning layer and an operation layer. The planning layer takes the system economic index as the objective function, and the operation layer takes the operation cost and the voltage deviation index as the objective function. The system economic index includes the operation cost and the investment cost of photovoltaic and energy storage. The planning layer is used to calculate the constraints and objective functions of the planning layer based on the optimization results of the operation layer, and optimize the access location and capacity of photovoltaic and energy storage; The operation layer is used to optimize the output of photovoltaic and energy storage in each time period based on the load curve and photovoltaic output curve corresponding to the typical scenario with a probability of occurrence greater than the set value, the optimized photovoltaic and energy storage access location and capacity, and the objective function and constraints of the operation layer, and feed back the optimization results to the planning layer.

2. The method according to claim 1, characterized in that: The constructed photovoltaic and energy storage dual-layer optimization configuration model is used to determine the access location and capacity of photovoltaic and energy storage, including: The capacity and access location of photovoltaic power generation, and the capacity and access location of energy storage are used as particles of the improved particle swarm algorithm, and the particle positions and velocities are initialized and input into the operation layer. The access locations and capacities of photovoltaic power generation and energy storage optimized by the planning layer are used to update the position and velocity of the particle population, and the updated position and velocity of the particle population are input into the operation layer until the objective function of the planning layer reaches the minimum value under the constraints of the planning layer, or the set number of iterations is reached.

3. The method according to claim 2, characterized in that The system economic index F2 is: in, The investment cost of photovoltaic and energy storage is For operating costs, represents the discount rate; is the service life of distributed photovoltaic and energy storage equipment; Represents the distributed photovoltaic investment cost per unit capacity; Represents the energy storage investment cost per unit capacity; Represents the total installed capacity of distributed photovoltaics; Represents the total installed capacity of energy storage.

4. The method according to any one of claims 1 to 3, characterized in that: The operating cost F1 and voltage deviation index VDI are: in, Indicates the operating cost; represents the network loss cost; Indicates the cost of purchasing electricity from the main grid; represents the cost of abandoned light; T represents the total number of moments; N represents the number of photovoltaic power stations; represents the price of photovoltaic power generation; represents the power generation of the i-th photovoltaic power station at time t; Indicates the network loss price; represents the network loss at time t; Indicates the main grid electricity purchase price; represents the main network purchase power at time t; Indicates the penalty price for photovoltaic power abandonment; represents the amount of power abandoned by the ith photovoltaic power station at time t; U i,t is the actual voltage value of node i at time t obtained by the probability flow; U i,N is the rated voltage value of node i at time t.

5. The method according to any one of claims 1 to 3, characterized in that: The constraints of the operation layer include: system flow constraints, system operation constraints, energy storage system charging and discharging power constraints, photovoltaic power output constraints, upper and lower limit constraints of the battery charging state of energy storage, and energy storage location constraints. Among them, the energy storage location constraint is that the location variable of the energy storage belongs to the energy storage alternative location, and the energy storage alternative location is obtained based on the site selection of the energy storage according to the interconnection line distribution information of the Snowflake Network.

6. The method according to any one of claims 1 to 3, characterized in that: The constraints at the planning level include: system flow constraints, system operation constraints, upper and lower limits on the number of photovoltaic power sources connected, and upper and lower limits on the number of energy storage groups connected.

7. The method according to any one of claims 1 to 3, characterized in that: Based on the historical data of load demand and PV output, determine the typical scenarios of PV and load correlation and the probability of occurrence of each typical scenario, including: Based on the historical data of load demand and the historical data of photovoltaic output, the probability density function of load demand and the probability density function of photovoltaic output are determined; Combine Latin hypercube sampling with Nataf transformation to perform correlation sampling on the probability density function of load demand and the probability density function of photovoltaic output, and obtain several time series scenarios of load and photovoltaic. A clustering algorithm is used to reduce the time series scenarios of several loads and photovoltaics to obtain a typical scenario set; The occurrence probability of each typical scenario is calculated by using the number of time series scenarios participating in the typical scenario clustering and the total number of time series scenarios.

8. A photovoltaic and energy storage optimization configuration device based on snowflake network, characterized in that: The device comprises an acquisition unit, an occurrence probability determination unit and an optimization configuration unit, wherein: An acquisition unit is used to acquire historical load demand data and photovoltaic output data from the Snowflake Network; An occurrence probability determination unit, used to determine typical scenarios of photovoltaic and load correlation and the occurrence probability of each typical scenario based on historical load demand data and historical photovoltaic output data; The optimization configuration unit is used to determine the access location and capacity of photovoltaic and energy storage by using the constructed photovoltaic and energy storage double-layer optimization configuration model; wherein the model includes a planning layer and an operation layer, the planning layer takes the system economic index as the objective function, and the operation layer takes the operation cost and the voltage offset index as the objective function, and the system economic index includes the operation cost and the photovoltaic and energy storage investment cost; the planning layer is used to calculate the constraints and objective functions of the planning layer based on the optimization results of the operation layer, and optimize the access location and capacity of photovoltaic and energy storage; the operation layer is used to optimize the output of photovoltaic and energy storage in each time period based on the load curve corresponding to the typical scenario with a probability of occurrence greater than the set value and the photovoltaic output curve, the optimized photovoltaic and energy storage access location and capacity, and the objective function and constraint conditions of the operation layer, and feed back the optimization results to the planning layer.

9. The device according to claim 8, characterized in that The optimization configuration unit is specifically used to use the capacity and access position of photovoltaic power generation, the capacity and access position of energy storage as particles of the improved particle swarm algorithm, initialize the particle position and speed, and input them into the operation layer; use the access position and capacity of photovoltaic power generation and energy storage optimized by the planning layer to update the position and speed of the particle population, and input the updated position and speed of the particle population into the operation layer until the objective function of the planning layer reaches the minimum value under the constraints of the planning layer, or reaches the set number of iterations.

10. The device according to claim 9, characterized in that The system economic index F2 is: in, The investment cost of photovoltaic and energy storage is For operating costs, represents the discount rate; is the service life of distributed photovoltaic and energy storage equipment; Represents the distributed photovoltaic investment cost per unit capacity; Represents the energy storage investment cost per unit capacity; Represents the total installed capacity of distributed photovoltaics; Represents the total installed capacity of energy storage.

11. The device according to any one of claims 8 to 10, characterized in that: The operating cost F1 and voltage deviation index VDI are: in, Indicates the operating cost; represents the network loss cost; Indicates the cost of purchasing electricity from the main grid; represents the cost of abandoned light; T represents the total number of moments; N represents the number of photovoltaic power stations; represents the price of photovoltaic power generation; represents the power generation of the i-th photovoltaic power station at time t; Indicates the network loss price; represents the network loss at time t; Indicates the main grid electricity purchase price; represents the main network purchase power at time t; Indicates the penalty price for photovoltaic power abandonment; represents the amount of power abandoned by the ith photovoltaic power station at time t; U i,t is the actual voltage value of node i at time t obtained by the probability flow; U i,N is the rated voltage value of node i at time t.

12. The device according to any one of claims 8 to 10, characterized in that: The constraints of the operation layer include: system flow constraints, system operation constraints, energy storage system charging and discharging power constraints, photovoltaic power output constraints, upper and lower limit constraints of the battery charging state of energy storage, and energy storage location constraints. Among them, the energy storage location constraint is that the location variable of the energy storage belongs to the energy storage alternative location, and the energy storage alternative location is obtained based on the site selection of the energy storage according to the interconnection line distribution information of the Snowflake Network.

13. The device according to any one of claims 8 to 10, characterized in that: The constraints at the planning level include: system flow constraints, system operation constraints, upper and lower limits on the number of photovoltaic power sources connected, and upper and lower limits on the number of energy storage groups connected.

14. The device according to any one of claims 8 to 10, characterized in that: The occurrence probability determination unit is specifically used to determine the probability density function of load demand and the probability density function of photovoltaic output based on the historical data of load demand and the historical data of photovoltaic output; Latin hypercube sampling is combined with Nataf transformation to perform correlation sampling on the probability density function of load demand and the probability density function of photovoltaic output to obtain several time series scenarios of load and photovoltaic; a clustering algorithm is used to reduce the obtained time series scenarios of several load and photovoltaic to obtain a typical scene set; the occurrence probability of each typical scene is calculated using the number of time series scenarios participating in the typical scene clustering and the total number of time series scenarios.

15. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, used to store computer programs; A processor, for implementing the steps of any method described in claims 1-7 when executing a program stored in a memory.

16. A computer storage medium, characterized in that: The computer storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any method of claims 1-7 are implemented.

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