A Storage-Transmission Joint Planning Method and System for Considering the Improvement of Power System Flexibility

Abstract

The present invention belongs to the technical field of power system planning, and provides a combined energy storage and transmission planning method and system considering the improvement of power system flexibility. The present invention establishes a power grid flexibility index to measure the flexibility of the power grid, and establishes a combined energy storage and transmission planning model with economy and flexibility as the goals. By using the characteristics that energy storage devices can optimize the power supply structure of the entire system in the application of renewable energy and power generation, and equipped energy storage devices can suppress the fluctuations brought by renewable energy power generation and provide more stable power for the entire system, the coordination with the expansion of transmission lines is realized. It is both economical and can effectively improve the flexibility of the power system, and effectively resist the uncertain impact brought by the integration of renewable energy into the grid.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system planning, and in particular relates to a storage-transmission joint planning method and system considering the improvement of power system flexibility. Background Art

[0002] With the acceleration of the industrialization process worldwide, the living standards of human beings have been increasing day by day, and the dependence on electric energy has become higher and higher. The production of electric energy mainly depends on the conversion of the chemical energy of fossil energy into electric energy, which will lead to the over-exploitation of fossil energy and the aggravation of environmental pollution problems. Compared with fossil energy, renewable energy has the characteristics of wide distribution, clean and pollution-free, and sustainable development. Due to the randomness and volatility of the output of renewable energy power generation, the grid connection of a large amount of renewable energy has sharply increased the uncertain factors in the future power grid, affecting the real-time balance of electricity and threatening the safety of the power grid. Therefore, it is extremely important to improve the flexibility of the system, that is, the ability to handle the volatility of power generation and load.

[0003] The inventors found that currently, the research on improving the flexibility of power systems is mainly distributed in four aspects: the power source, the grid, energy storage, and the load. On the power source side, due to the general lack of flexibility in thermal power units, the insufficient peak shaving space on the supply side has caused a large amount of wind and light abandonment. Therefore, exploring the flexibility potential of thermal power units and improving the peak shaving ability of thermal power units have become the development trend of improving system flexibility on the power source side. On the grid side, attention is paid to the transformation and expansion of transmission lines. By expanding the transmission lines, the ability of the system to transmit power is improved, and the accommodation of renewable energy power generation is realized. On the energy storage side, the combination of energy storage and renewable energy can improve the utilization rate of renewable energy. Energy storage technology can not only cut peaks and fill valleys, smooth the load, reduce the power supply cost, but also improve the stability of system operation, adjust the frequency, and compensate for load fluctuations. On the load side, management methods such as load management and load response are used to address the problem of insufficient flexibility of the power system. And the introduction of electric vehicles has a positive impact on improving the load on the grid side and enhancing the operation reliability and flexibility of the power system. However, in the current research on power system flexibility, there is still a lack of research on the improvement of transmission grid planning in terms of flexibility. Summary of the Invention

[0004] In order to solve the above problems, the present invention proposes a storage-transmission joint planning method and system considering the improvement of power system flexibility. First, considering the grid connection of renewable energy, the output scenarios of renewable energy and the load demand scenarios are analyzed, and research is carried out from scenario generation and scenario reduction to generate typical scenarios considering the time correlation of wind and light and a set of typical scenarios of load uncertainty. Secondly, a grid flexibility index is constructed and a storage-transmission joint planning model with economy and the improvement of power system flexibility as the planning objectives is established. The grid flexibility index is used to reflect the degree of improvement of grid flexibility by the planning.

[0005] To achieve the above object, the present invention is implemented through the following technical solutions:

[0006] In a first aspect, the present invention provides a combined storage and transmission planning method considering the improvement of power system flexibility, including:

[0007] Obtain relevant data of the power system;

[0008] According to the obtained relevant data, construct wind power output scenarios, photovoltaic power output scenarios, and load self-correlation demand scenarios, and reduce the constructed wind power output scenarios, photovoltaic power output scenarios, and load self-correlation demand scenarios to obtain typical scenarios;

[0009] According to the obtained typical scenarios and a preset combined storage and transmission planning model, obtain a planning scheme for the power system;

[0010] Among them, in the combined storage and transmission planning model, the flexibility of the transmission network is used as an index to reflect the degree of improvement of the grid flexibility by the planning, the economy and the improvement of the power system flexibility are used as planning objectives, and energy storage devices, node power flow balance, generator output, upper and lower limits of branch power flow, and spinning reserve are used as constraint conditions.

[0011] Furthermore, constructing wind power output scenarios, photovoltaic power output scenarios, and load self-correlation demand scenarios includes: obtaining renewable energy output data; constructing probability distribution models of wind power and photovoltaic power output according to the obtained renewable energy output data; constructing probability distribution models of wind power and photovoltaic power output considering time correlation; and using Monte Carlo sampling to obtain a large number of output scenarios considering the time correlation of wind power and photovoltaic power.

[0012] Furthermore, use the k-means clustering algorithm to reduce the wind power and photovoltaic power output scenarios and load demand scenarios to obtain a set of typical scenarios.

[0013] Furthermore, the construction of the transmission network flexibility index is carried out with the transmission line load rate, which measures the transmission capacity of the transmission line, as the analysis quantity.

[0014] Furthermore, the economic objective includes the total cost of the energy storage system and the total cost of transmission line expansion, and the flexibility objective is the constructed grid flexibility index. By setting the weights of the flexibility objective and the economic objective, the multi-objective function is transformed into a single-objective function.

[0015] Furthermore, the constraint conditions include equality constraint conditions and inequality constraint conditions; the equality constraint conditions include the equality constraints of the characteristics of energy storage devices and the equality constraints of node power flow balance, and the inequality constraint conditions include the power flow non-exceeding constraint, the energy storage device constraint, and the spinning reserve constraint.

[0016] Furthermore, a genetic algorithm is used to solve the storage - transmission joint planning model. In the calculation of the planning model, actual power flow calculation is added. With the help of PSAT, the power of each branch is solved, and then the line load rate is calculated to achieve the solution of the power grid flexibility index. The flexibility objective and the economic objective are used as the population fitness function, and the optimal fitness planning scheme is selected through continuous iteration.

[0017] In a second aspect, the present invention also provides a storage - transmission joint planning system considering the improvement of power system flexibility, including:

[0018] A data acquisition module, configured to: obtain relevant data of the power system;

[0019] A typical scenario construction module, configured to: construct a wind power output scenario, a photovoltaic power output scenario, and a load self - correlation demand scenario based on the obtained relevant data, and reduce the constructed wind power output scenario, photovoltaic power output scenario, and load self - correlation demand scenario to obtain typical scenarios;

[0020] A scheme planning module, configured to: obtain a planning scheme of the power system according to the obtained typical scenarios and a preset storage - transmission joint planning model;

[0021] Among them, in the storage - transmission joint planning model, the flexibility of the transmission network is used as an index to reflect the degree of improvement of the power grid flexibility by the planning. The economic and power system flexibility improvement are used as planning objectives, and energy storage devices, node power flow balance, generator output, upper and lower limits of branch power flow, and spinning reserve are used as constraint conditions.

[0022] In a third aspect, the present invention also provides a computer - readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the storage - transmission joint planning method for considering the improvement of power system flexibility described in the first aspect are implemented.

[0023] In a fourth aspect, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the storage - transmission joint planning method for considering the improvement of power system flexibility described in the first aspect are implemented.

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] 1. The present invention establishes a power grid flexibility index to measure the flexibility of the power grid, and constructs a joint energy storage - transmission planning model with economy and flexibility as the goals. By virtue of the characteristics that the energy storage device can optimize the power structure of the entire system in the application of renewable energy and power generation, and the equipped energy storage device can suppress the fluctuations brought by renewable energy power generation and provide more stable power for the entire system, it realizes coordination with the expansion of transmission lines, being both economical and effectively improving the flexibility of the power system, and effectively resisting the uncertain impacts brought about after the integration of renewable energy into the grid;

[0026] 2. In the present invention, based on the wind and light output data of renewable energy and load data, probability distribution models of wind and light output and load demand are respectively established. A probability distribution model of wind and light output considering the time correlation of wind and light is established based on the Copula function. A large number of wind and light output scenario sets are generated by the Monte Carlo simulation method, and scenario reduction is carried out through the k - means clustering algorithm to obtain representative wind and light output scenario sets and load demand scenario sets, thus laying a foundation for the access of renewable energy and the simulation of load volatility in the subsequent energy storage - transmission planning model. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings forming a part of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments and descriptions thereof of this embodiment are used to explain this embodiment and do not constitute an improper limitation to this embodiment.

[0028] Figure 1 is the flowchart for generating the typical scenario set of Embodiment 1 of the present invention;

[0029] Figure 2 is a large number of scenario sets considering the time correlation of wind and light and load demand generated by the Monte Carlo simulation in Embodiment 1 of the present invention;

[0030] Figure 3 The typical wind power output scenario obtained by scenario reduction through the k - means clustering algorithm in Embodiment 1 of the present invention;

[0031] Figure 4 The typical photovoltaic output scenario obtained by scenario reduction through the k - means clustering algorithm in Embodiment 1 of the present invention;

[0032] Figure 5 The typical load demand scenario obtained by scenario reduction through the k - means clustering algorithm in Embodiment 1 of the present invention;

[0033] Figure 6 is the flowchart of the genetic algorithm calculation for the joint energy storage - transmission planning to improve the system flexibility in Embodiment 1 of the present invention;

[0034] Figure 7Improved IEEE-RTS 24-node network structure diagram for Embodiment 1 of the present invention;

[0035] Figure 8 Expansion diagram of the combined energy storage and transmission planning for improving the flexibility of the power system in Embodiment 1 of the present invention for the IEEE-RTS 24-node system;

[0036] Figure 9 Output diagram of the energy storage device in the planning scheme for Embodiment 1 of the present invention;

[0037] Figure 10 Output diagram after building an energy storage device around the wind power in the planning scheme for Embodiment 1 of the present invention. Detailed implementation manners

[0038] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0039] It should be noted that the following detailed descriptions are all exemplary and are intended to provide further descriptions of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0040] Embodiment 1:

[0041] This embodiment provides a combined energy storage and transmission planning method considering improving the flexibility of the power system, including:

[0042] Obtaining relevant data of the power system;

[0043] According to the obtained relevant data, constructing a wind power output scenario, a photovoltaic power output scenario, and a load self-correlation demand scenario, and reducing the constructed wind power output scenario, photovoltaic power output scenario, and load self-correlation demand scenario to obtain a typical scenario;

[0044] According to the obtained typical scenario and a preset combined energy storage and transmission planning model, obtaining a planning scheme for the power system;

[0045] Among them, in the combined energy storage and transmission planning model, the flexibility of the transmission network is used as an index to reflect the degree of improvement in the flexibility of the power grid by the planning, the economy and the improvement of the flexibility of the power system are used as planning objectives, and the energy storage device, node power flow balance, generator output, upper and lower limits of branch power flow, and spinning reserve are used as constraint conditions.

[0046] The specific steps of this embodiment are:

[0047] S1. Generating a set of wind and light output scenarios and a set of load demand scenarios considering the time correlation of wind power and photovoltaic power output;

[0048] S1.1. The Copula function is a type of function that can connect the joint distribution of multivariate random variables with the marginal distributions of each random variable. Through the Copula function, a joint probability model between multiple variables can be established to analyze the correlation between multiple variables;

[0049] H(x1,x2,L,x N )=C(H1(x1),H2(x2),L,H N (x N );θ) (1)

[0050] Among them, H(·) is the joint distribution function of multivariate variables; H1(x1), H2(x2), …, H N (x N ) are the marginal distribution functions of random variables; C(·) is the selected Copula function; θ is the parameter of the selected Copula function; x1, x2, …, x N is the random variable vector of each marginal distribution function.

[0051] S1.2. Based on historical data, the probability density function of wind and solar power output can be obtained. Taking wind power as an example, F(x t ,x t-1 ) is the joint probability density distribution of the output of adjacent moments of a wind farm generated based on the Copula function. Among them, x t is the output of the wind farm at time t, and x t-1 is the output of the wind farm at time t - 1. It can be obtained that:

[0052] F(x t ,x t-1 )=H(x t ,x t-1 )=C(F(x t ),F(x t-1 )) (2)

[0053] Among them, F(x t ) is the output probability distribution of the wind farm at time t; F(x t-1 ) is the output probability distribution of the wind farm at time t - 1. The same applies to the time correlation analysis of the photovoltaic sequence.

[0054] S1.3. In the generation of typical scenarios of wind power, photovoltaic power, and load, the Monte Carlo sampling method can be used to simulate the uncertainties of wind power, photovoltaic power, and load, generating a large number of new scenarios of wind power, photovoltaic power, and load; a large number of scenarios of wind power and photovoltaic power output and load demand are simulated, as shown in Figure 2 、 Figure 3 and Figure 4 as follows. The specific steps are as follows:

[0055] S1.3.1. Set the simulation period as T, and the wind farm output in each period is P wind,t , and the output of the PV power station is P solar,t , and the load demand is P load,t ;

[0056] S1.3.2. Based on the historical data of wind and PV power generation and load demand, perform probability modeling to obtain the probability density f wind,t (P wind,t ) and the cumulative probability distribution F wind,t (P wind,t ) of wind power output in each period, the probability density f solar,t (P solar,t ) and the cumulative probability distribution F solar,t (P solar,t ) of PV power output, the probability density f load,t (P load,t ) and the cumulative probability distribution F load,t (P load,t ) of load demand, where t = 1, 2, …, T;

[0057] S1.3.3. Construct a Copula function, analyze the time correlation according to the output of wind and PV power generation at adjacent moments respectively, solve the correlation coefficient of the Copula function, and optimize the Copula function to obtain the time correlation formula of the wind power sequence and the time correlation formula of the PV power sequence;

[0058] S1.3.4. Through Monte Carlo simulation, sample to generate a large number of random values {u wind,t , u solar,t , u load,t |t = 1, 2, …, T} with time correlation and load self-correlation. The range of this random value is between 0 and 1, that is, the generation probability of the output value. Each generation probability corresponds to an output value. Take the inverse of this random value to obtain a large number of wind and PV power output scenarios considering the time correlation of wind and PV power generation and the load self-correlation demand scenarios;

[0059] S1.4. Through Monte Carlo method, simulate a large number of scenarios considering the uncertainties of wind and PV power generation and load, and there is enough data to reflect the renewable energy output and load demand. However, in the power system planning, a large number of output scenarios are not conducive to analysis, which not only reduces the calculation efficiency, but also cannot intuitively reflect the characteristics of the output scenarios. Therefore, it is necessary to reduce the generated large number of scenarios to obtain a small number of representative typical scenarios. When reducing the scenarios, the k-means algorithm is used, and the method is as follows:

[0060] S1.4.1. Use Monte Carlo sampling to generate M groups of wind and PV power output scenarios and load demand scenarios respectively, that is, there are M groups of data for wind and PV power output and load demand at each moment;

[0061] S1.4.2. Set the number k of typical scenarios, and randomly select k initial clustering centers for wind, light, and load scenarios respectively. In this paper, 4 typical scenarios are generated for wind, light, and load respectively. Use the k-means algorithm to cluster the M groups of data of wind power output, light output, and load scenarios into 4 categories;

[0062] S1.4.3. Based on the principle of minimum distance, assign all clustering individuals to the class with the closest distance; measure the similarity between each individual in the set, and introduce a distance function, which is a function used to measure the similarity between every two individuals in the data set to be clustered; taking wind power output as an example, assume two wind power outputs, represented by two N-dimensional vectors x n =(x n1 ,x n2 ,…,x nN ) T and x m =(x m1 ,x m2 ,…,x mN ) T . According to the definition of Euclidean distance:

[0063]

[0064] The distance of wind power output sequences in two scenarios can be analyzed according to the Euclidean distance, and then it can be judged whether they belong to the same class. The same method is used for the classification of photovoltaic output and load demand scenarios.

[0065] S1.4.4. Re-solve the centroid of each class of wind power output, light output, and load demand scenarios as the new clustering center for the next iteration;

[0066] S1.4.5. The objective function uses the minimum variance function, that is, the sum of the squares of the Euclidean distances between all individuals in the class and the clustering center individual. The definition of the function is as follows:

[0067]

[0068] Repeat steps S1.4.2 and S1.4.3 to minimize the objective function value.

[0069] Among them, E is the sum of the squared errors between all individuals in the data set to be clustered and their corresponding clustering centers; x is an individual belonging to cluster c i ; n is the total number of data in the data set to be clustered; k is the number of clustering types to be classified.

[0070] S1.4.6. After the classification of wind power, PV output, and load demand scenarios, to reduce the computational workload in the planning scheme, the classified scenario set is reduced to obtain a probability combination scenario set. Calculate the number of scenarios in each category respectively, and calculate the generation probability of each scenario in each category according to the number of scenarios. Reduce the scenario set in each category, calculate the sum of the outputs of all scenarios simulated at each moment in each category, and then solve for the mean value, which is the output of the reduced scenario at that moment. Calculate all moments to obtain the reduced scenario of this category, which can be used as the typical scenario of this category. The generation of typical scenarios for wind power, PV output, and load demand is the same. Combine according to the generation probability of each typical scenario to obtain a combined scenario set of wind, light, and load with similar probabilities;

[0071] S1.5. The typical scenario generation process is as Figure 1 shown. The typical wind power output scenarios obtained after reduction by the k-means clustering algorithm are as Figure 3 shown. The typical PV output scenarios are as Figure 4 , and the typical load demand scenarios are as Figure 5 shown; The generation probabilities of each scenario are shown in Table 1:

[0072] Table 1 Generation Probabilities of Typical Scenarios of Wind Power, PV, and Load Uncertainties

[0073]

[0074] Combining according to the generation probabilities of wind power, PV output, and load demand scenarios can simplify the computational workload of the planning model and generate 4 groups of combined scenarios as follows:

[0075] Wind power scenario one, PV scenario two, load scenario two;

[0076] Wind power scenario two, PV scenario four, load scenario three;

[0077] Wind power scenario three, PV scenario one, load scenario four;

[0078] Wind power scenario four, PV scenario three, load scenario one;

[0079] After the generation of the S1.6 typical scenario set, it is necessary to evaluate the output uncertainty of the scenario set, and the coverage rate and power interval width can be used to evaluate the accuracy of the scenario set's description of uncertainty; The calculation formulas for the coverage rate and power interval width are as follows:

[0080]

[0081] Among them, CR (1-α) is the coverage rate under the confidence level of 1-α; N is the total number of sampling points in the test set; N 1-α is the total number of measured wind power falling within the predicted confidence interval under the confidence level of 1-α.

[0082]

[0083] Among them, PAW 1-α is the average width of the power interval at a confidence level of 1 - α; is the maximum value of the power at the nth sampling point at a confidence level of 1 - α; is the minimum value of the power at the nth sampling point at a confidence level of 1 - α; represents the interval width of the power at the nth sampling point at a confidence level of 1 - α.

[0084] The coverage rate is used to evaluate the coverage of the confidence interval for the measured value, and the power interval width is used to evaluate the ability of the generated day-ahead scenario set to aggregate uncertain information. When the coverage rate is larger and the power interval width is smaller, the description of the power uncertainty is more accurate. When there are similar coverage rates, the smaller the power interval width, the better the accuracy of the model. The evaluation of the wind power, photovoltaic and load scenario sets is shown in Table 2.

[0085] Table 2 Evaluation indicators of wind power output, photovoltaic output and load demand scenario sets

[0086]

[0087] In step S1, the output scenarios of renewable energy and the load demand scenarios are simulated. All the work lays a foundation for considering the access of renewable energy and load uncertainty in the subsequent energy storage - transmission joint planning method to improve the flexibility of the power system. After generating the probability combination scenario set, it is combined with the energy storage - transmission joint planning method in step S2 to obtain an energy storage - transmission joint planning scheme for improving the flexibility of the power system under the grid connection of renewable energy, so as to resist the risks brought to the operation of the power system under the grid connection of renewable energy.

[0088] S2. Establish an energy storage - transmission joint planning model for improving the flexibility of the power system;

[0089] S2.1 Grid flexibility index. The load rate of the line can effectively measure the transmission capacity of the transmission line. The lower the line load rate, the larger the capacity margin of the line, the stronger the ability to cope with the volatility of renewable energy power generation, and the lower the probability of large-scale cascading failures in the power system. In other words, in the power system, the line load rate determines the ability of the system to withstand uncertain impacts. Using the line load rate to construct the grid flexibility index and further as a measure of the flexibility goal of the planning model can effectively reflect the ability of the planned grid to resist uncertain impacts.

[0090] S2.1.1. Different lines have different flexibility requirements for the power system. Therefore, it is necessary to introduce a grid flexibility weight coefficient. In order to characterize the grid flexibility index through the line load rate, the greater the line load rate, the worse the grid flexibility. In order to make the flexibility index greater, the grid flexibility is better, the line flexibility index at time t is defined as:

[0091]

[0092] Where Ω is the set of flexibility evaluation routes; T is the set of flexibility evaluation moments; μ l is the flexibility weight coefficient of line l; L l (t) is the load rate of line l at time t, and

[0093] L l (t) = P l (t) / P Lmax ,t∈T (8)

[0094] Among them, P l (t) and P Lmax They are the current transmission power and maximum transmission capacity of the line respectively.

[0095] The flexibility weight coefficient is equal to the proportion of the variance of load rate fluctuation of line i in the sum of the variances of load rate fluctuations of all lines in the T time period, that is,

[0096]

[0097] Among them, L i is the average load rate of line i in the time period T. When the node injection power changes, the more drastic the line flow change, μ i The larger the μ i It can reflect the ability of the line to resist power flow fluctuations, and then identify the lines that truly restrict the flexibility of the power grid.

[0098] S2.1.2. The physical meaning of Flex(t) is the weighted sum of the line load rates and the flexibility weight coefficients at time t, which can reflect the margin of the power grid's flow dispatching capability. The larger Flex(t), the better the flexibility of the power grid. Therefore, the minimum value of Flex(t) within the time period T is defined as the flexibility index of the system power grid.

[0099] FLEX=min{Flex(t1),Flex(t2),…Flex(t n )} (10)

[0100] S2.2. Establishment of storage-transportation joint planning model

[0101] S2.2.1. Objective function: In this model, the flexibility index and the planning cost are selected as the objective functions. The weights of the economic objective and the flexibility objective are set respectively, and the multi-objective function is transformed into a single-objective function by the weighted method. The setting of the weights is determined according to the planning requirements.

[0102] F = λ1F1 + λ2F2 (11)

[0103] Among them, λ1 and λ2 represent the weights of the economic objective and the flexibility objective respectively, F1 is the economic objective, and F2 is the flexibility objective.

[0104] The economic objective includes the total cost of the energy storage system f1 and the equivalent annual investment cost of the transmission line f2.

[0105] F1 = f1 + f2 (12)

[0106] The energy storage investment includes fixed costs and variable costs. The fixed costs include a series of costs such as the device cost of the energy storage device itself and the installation cost, and the fixed costs are considered as constants. The variable costs mainly include the operation and maintenance costs of the energy storage device, which are reflected by the power cost and the capacity cost in this embodiment.

[0107]

[0108]

[0109] Among them, and are the fixed cost and variable cost of the energy storage device at node a; x a is the 0-1 decision variable for configuring the energy storage device at node a; k de is the annual depreciation coefficient of the energy storage device; k tc is the cost coefficient of the energy storage device in operation and maintenance; c p and c e are the unit power cost and unit capacity cost of the energy storage device; and are the power and capacity of the energy storage device configured at node a; Ω ess is the set of nodes where the energy storage device is to be configured.

[0110] In the transmission line expansion cost, whether to expand the line between two nodes is used as a decision variable, and the discount rate and the service life are added for overall calculation.

[0111]

[0112] Among them, r is the discount rate; n is the economic service life of the line; c line is the unit price of the newly built line; l ab is the line length between the newly built line ab; xab The 0-1 decision variable newly built for line ab; x ab is the set of lines to be built.

[0113] Construct the flexibility objective function in the model according to the grid flexibility index. To make the grid flexibility meet the expected expectations as much as possible, the line load rate should be made as small as possible, so that the grid will have enough margin to face the uncertainty fluctuations brought by renewable energy generation. When the flexibility index of the transmission line is larger, the flexibility of the grid is better. According to Equations (7) and (10), the upper limit of the grid flexibility index is 1. If a minimum value objective function is established, the flexibility objective function can be set as Equation (16).

[0114] F2 = 1 - FLEX (16)

[0115] where FLEX is the grid flexibility index.

[0116] S2.2.2. Constraint conditions: There is a mutually related relationship between the output power and capacity of the energy storage device, and the output power is related to the charging and discharging power of the energy storage device.

[0117] P ess,a,t = P ess,a,t,dc - P ess,a,t,ch (17)

[0118] E ess,a,t = E ess,a,t-1 + P ess,a,t (18)

[0119] E ess,a,t＝0 = E ess,a,t＝T (19)

[0120] where P ess,a,t,dc and P ess,a,t,ch are the discharging power and charging power of the energy storage device at time t, respectively; P ess,a,t is the output power of the energy storage device at time t; E ess,a,t is the capacity of the energy storage device at time t. Equations (17) to (19) constitute the equality constraints of the energy storage device.

[0121] P G,t + P wind,t + P solar,t + P ess,t = P L,t + Bθ (20)

[0122] where P G,t is the active power output vector of the thermal power plant at time t; P wind,t and P solar,t are the active power output vectors of wind power and photovoltaic power at time t, respectively; P ess,tOutput power vector of the energy storage device at time t; P L,t Load vector of each node at time t; B and θ are the admittance matrix and the node voltage phase angle matrix of the network respectively.

[0123]

[0124]

[0125]

[0126]

[0127] V min ≤V a,t ≤V max (25)

[0128] θ min ≤θ a,t ≤θ max ,θ ref =0 (26)

[0129] In the formula: P G,a,t Active power output of the thermal power unit at node a at time t; P W,a,t Active power output of the wind power at node a at time t; P S,a,t Active power output of the photovoltaic at node a at time t; P ab,t Branch power in the ab line at time t; V a,t Voltage of node a at time t; θ a,t Phase angle of node a at time t; Ω G 、Ω W 、Ω S Respectively represent the set of thermal power units, the set of wind turbines, and the set of photovoltaic units. Equations (21) to (26) constitute the model power flow non-exceeding constraint.

[0130]

[0131]

[0132]

[0133]

[0134] Among them, Is the maximum number of energy storage devices allowed to be built in the power grid.

[0135]

[0136] Among them, P l,a,t Is the load demand of node a at time t; Ω lSet of load nodes; R t Is the system reserve capacity.

[0137] S3. Algorithm solution

[0138] As Figure 6 Shown is the flow chart for solving the combined storage - transmission planning to improve the flexibility of the power system using the genetic algorithm. After separately setting the weights of the flexibility objective and the economic objective, the multi - objective function is transformed into a single - objective function, and this model becomes a non - linear single - objective programming model. In this embodiment, the genetic algorithm is used to solve the planning problem. The genetic algorithm is a method of random search and optimization based on natural biological selection and genetic mechanism. At the beginning of the calculation, the population is randomly initialized, and the fitness function of each individual is calculated, and the initial generation is generated at this time. If the optimization criterion is not met, the calculation of the new generation starts. To generate the next generation, individuals are selected according to their fitness. The parent generation generates offspring through gene recombination and mutation, and then the fitness of the offspring is calculated. The offspring are incorporated into the population to replace the parent generation to form a new population through selection, and this loop process continues until the optimization criterion is met.

[0139] In the calculation of the planning model, the actual power flow calculation is added. With the help of the psat power flow solver, the power of each branch is solved to calculate the line load rate, so as to achieve the solution of the power grid flexibility index. The flexibility objective and the economic objective are used as the population fitness function, and the optimal fitness planning scheme is selected through continuous iteration, which is the storage - transmission planning scheme for improving the flexibility of the power system. The planning process is as Figure 6 Shown.

[0140] S4. Planning results

[0141] The IEEE RTS - 24 - node system is transformed. Wind turbines are connected to 23 nodes, photovoltaic units are connected to 22 nodes, and the remaining PV nodes are connected to thermal power units. The simulated load values are distributed among nodes 3, 4, 5, 8, and 20 according to the initial load ratio. 7 expandable transmission corridors are added to the network, and there are a total of 41 transmission corridors that can be expanded. Each transmission corridor can build at most 3 lines. The network structure before system planning is as Figure 7 Shown. According to the occurrence probability of the simulated scenarios, 4 combined scenarios are formed respectively, and the 4 combined scenarios are analyzed respectively, and the corresponding planning schemes are shown in Table 3.

[0142] Table 3 Flexibility resource planning scheme for IEEE RTS - 24 - node system

[0143]

[0144]

[0145] Taking the degree of improvement of power grid flexibility indicators as the main analysis object, the results are shown in Table 4.

[0146] Table 4 Results Analysis of the Flexibility Resource Planning Scheme for the IEEE RTS-24 Node System

[0147]

[0148]

[0149] The planning scheme in Scenario 4 is the same as Scheme 2 in Scenario 2. Therefore, this scheme has applicability under different scenarios, can meet the flexibility improvement requirements under both scenarios, and has good economy compared with the best planning scheme in Scenario 1. Selecting to expand the transmission channels 1-8(2), 6-7(1), and 13-14(1) and setting the energy storage device at Node 23 is a planning scheme that takes into account both economy and flexibility and can be used as the planning scheme for the IEEE RTS-24 node system. After calculation by the planning model, the improved IEEE RTS-24 node system diagram is as Figure 8 shown. The dotted lines in the figure only represent the expanded transmission corridors and do not indicate the number of lines. The output of the energy storage device is obtained, and building an energy storage power station around the wind farm significantly reduces the peak-valley difference of the output storage, as Figure 9 and Figure 10 shown.

[0150] Example 2:

[0151] This example provides a storage-transmission joint planning system considering the improvement of power system flexibility, including:

[0152] A data acquisition module, configured to: obtain relevant data of the power system;

[0153] A typical scenario construction module, configured to: construct a wind power output scenario, a photovoltaic power output scenario, and a load self-correlation demand scenario based on the obtained relevant data, and reduce the constructed wind power output scenario, photovoltaic power output scenario, and load self-correlation demand scenario to obtain a typical scenario;

[0154] A scheme planning module, configured to: obtain a planning scheme for the power system according to the obtained typical scenario and a preset storage-transmission joint planning model;

[0155] Among them, in the storage-transmission joint planning model, the flexibility of the transmission grid is used as an indicator to reflect the degree of improvement of the power grid flexibility by the planning, the economy and the improvement of the power system flexibility are used as the planning objectives, and the energy storage device, node power flow balance, generator output, upper and lower limits of branch power flow, and spinning reserve are used as constraint conditions.

[0156] The working method of the system is the same as that of the storage-transmission joint planning method for improving the flexibility of the power system in Embodiment 1, which will not be elaborated here.

[0157] Embodiment 3:

[0158] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the storage-transmission joint planning method for improving the flexibility of the power system described in Embodiment 1 are implemented.

[0159] Embodiment 4:

[0160] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the storage-transmission joint planning method for improving the flexibility of the power system described in Embodiment 1 are implemented.

[0161] The above are only the preferred embodiments of this embodiment and are not used to limit this embodiment. For those skilled in the art, this embodiment can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this embodiment shall be included within the protection scope of this embodiment.

Claims

1. A combined storage and transmission planning method for improving the flexibility of a power system, characterized in that Including: Obtaining relevant data of the power system; Based on the obtained relevant data, constructing wind power output scenarios, photovoltaic power output scenarios, and load self-correlation demand scenarios, and reducing the constructed wind power output scenarios, photovoltaic power output scenarios, and load self-correlation demand scenarios to obtain typical scenarios; According to the obtained typical scenarios and a preset energy storage - transmission joint planning model, obtaining a planning scheme for the power system; Among them, in the energy storage - transmission joint planning model, the flexibility of the transmission network is used as an index to reflect the degree of improvement of the grid flexibility by the planning, the economy and the improvement of the power system flexibility are used as planning objectives, and energy storage devices, nodal power flow balance, generator output, upper and lower limits of branch power flow, and spinning reserve are used as constraint conditions; The construction of the transmission network flexibility index is carried out by using the transmission line load rate, which measures the transmission capacity of the transmission line, as the analysis quantity; Using the genetic algorithm to solve the energy storage - transmission joint planning model, adding actual power flow calculation in the calculation of the planning model, and calculating the line load rate by solving the power of each branch with the help of psat to achieve the solution of the grid flexibility index; taking the flexibility objective and the economic objective as the population fitness function, and continuously iterating to select the planning scheme with the optimal fitness; Defining the line flexibility index at time t as: Among them, Ω is the set of flexibility evaluation lines; T is the set of flexibility evaluation moments; μ l is the flexibility weight coefficient of line l; L l (t) is the load rate of line l at moment t, and L l L(t) = P l L(t) / P Lmax , t ∈ T (8) Among them, P l (t) and P Lmax are the current transmission power and the maximum transmission capacity of the line, respectively; Selecting the flexibility index and the planning cost as the objective functions, respectively setting the weights of the economic objective and the flexibility objective, and transforming the multi-objective function into a single-objective function by the weighted method: F = λ1F1 + λ2F2 (11) Where λ1 and λ2 respectively represent the weights of the economic objective and the flexibility objective, F1 is the economic objective, and F2 is the flexibility objective.

2. The combined energy storage and transmission planning method for improving the flexibility of the power system according to claim 1, characterized in that Constructing wind power output scenarios, photovoltaic power output scenarios, and load self-correlation demand scenarios includes: obtaining renewable energy output data; constructing wind power and photovoltaic power output probability distribution models according to the obtained renewable energy output data; constructing wind power and photovoltaic power output probability distribution models considering time correlation; using Monte Carlo sampling to obtain a large number of output scenario sets considering the time correlation of wind power and photovoltaic power.

3. The combined storage and transmission planning method for considering improving the flexibility of the power system according to claim 1, characterized in that Using the k-means clustering algorithm to reduce the wind power and photovoltaic power output scenarios and the load demand scenarios to obtain a typical scenario set.

4. The combined storage and transmission planning method for considering improving the flexibility of the power system according to claim 1, characterized in that The economic objective includes the total cost of the energy storage system and the total cost of transmission line expansion, and the flexibility objective is the constructed grid flexibility index. By setting the weights of the flexibility objective and the economic objective, the multi-objective function is transformed into a single-objective function.

5. The joint energy storage and transmission planning method for improving the flexibility of the power system according to claim 1, wherein The constraint conditions include equality constraint conditions and inequality constraint conditions; the equality constraint conditions include the equality constraints of energy storage device characteristics and nodal power flow balance, and the inequality constraint conditions include the power flow non-exceeding constraint, energy storage device constraint, and spinning reserve constraint.

6. A joint energy storage and transmission planning system for improving the flexibility of a power system, which adopts the joint energy storage and transmission planning method for improving the flexibility of a power system according to any one of claims 1-5, characterized in that Including: A data acquisition module configured to: obtain relevant data of the power system; A typical scenario construction module configured to: based on the obtained relevant data, construct wind power output scenarios, photovoltaic power output scenarios, and load self-correlation demand scenarios, and reduce the constructed wind power output scenarios, photovoltaic power output scenarios, and load self-correlation demand scenarios to obtain typical scenarios; A solution planning module, configured to: obtain a planning solution for the power system according to the obtained typical scenarios and a preset storage-transmission joint planning model; Wherein, in the storage-transmission joint planning model, the flexibility improvement degree of the planning for the power grid is reflected by the flexibility of the transmission grid, the economic efficiency and the improvement of the power system flexibility are taken as the planning objectives, and the energy storage device, the node power flow balance, the generator output, the upper and lower limits of the branch power flow, and the spinning reserve are used as the constraint conditions.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, the steps of the storage-transmission joint planning method for considering the improvement of the power system flexibility as described in any one of claims 1-5 are implemented.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that When the processor executes the program, the steps of the storage-transmission joint planning method for considering the improvement of the power system flexibility as described in any one of claims 1-5 are implemented.

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