Robust configuration optimization method for flexible resource distribution considering spatio-temporal response characteristics

Abstract

The present application relates to a kind of flexible resource distribution robust configuration optimization method considering space-time response characteristics, belong to new energy technical field.The method includes: based on the space response characteristic flexibility evaluation model of each node flexibility shortage of power grid system is constructed;Based on each node power balance constraint, flexibility shortage constraint, power interaction network constraint, spinning reserve constraint and each unit operation constraint, the base output scheme and flexibility evaluation result are obtained;Based on the output of each flexibility resource to be configured in each node, rated power and energy storage capacity, the flexibility resource distribution robust configuration optimization model is constructed;Based on each node power balance constraint, time response characteristic flexibility index constraint, each unit operation constraint and base output scheme and flexibility evaluation result, the flexibility resource configuration optimization scheme is solved to obtain.The present application method considers space and time response characteristics, while meeting new energy installed capacity planning, guarantee the safe and reliable operation of power system.

Description

Technical Field

[0001] This invention belongs to the field of new energy technology, and in particular relates to a flexible resource allocation optimization method that considers spatiotemporal response characteristics. Background Technology

[0002] As the proportion of new energy sources such as wind and solar power connected to the power grid continues to expand, their randomness and uncertainty place higher demands on the flexibility of the power system. In power systems, due to the significant differences in the adjustment characteristics of flexibility resources at different scales, it is often necessary to consider how to complementarily allocate different types of flexibility resources to meet the diverse flexibility needs of the new power system. However, existing research mainly considers the economic benefits of optimized allocation or the improvement of power system operational flexibility. There is a lack of research and scientific solutions on how to optimize power system allocation and improve the utilization rate of flexibility resources while taking into account both economic efficiency and the complementary characteristics of flexibility resources. Summary of the Invention

[0003] Based on the above analysis, this invention aims to provide a flexible resource allocation optimization method that considers spatiotemporal response characteristics. It constructs fuzzy sets to address uncertainties in new energy sources and loads, builds a spatial response characteristic flexibility evaluation model to analyze the flexibility adequacy of each node, identifies nodes with insufficient power system flexibility, and constructs a flexible resource allocation optimization model that considers flexibility index constraints based on time response characteristics. This method solves for the flexibility resource allocation schemes of each node with insufficient flexibility, maximizing the potential of flexibility resources and ensuring the safe and reliable operation of the power system while meeting future new energy installed capacity planning.

[0004] The method of the present invention specifically includes the following steps:

[0005] A spatial response characteristic flexibility evaluation model is constructed based on the flexibility deficit of each node in the power grid system.

[0006] A set of uncertainties in net load demand is constructed based on historical data of wind power, solar power, and load demand.

[0007] Based on the power balance constraints, flexibility deficit constraints, power interaction network constraints, spinning reserve constraints, unit operation constraints, and net load demand uncertainty set of each node, the flexibility evaluation model is solved to obtain the baseline output scheme and flexibility evaluation results of each node.

[0008] Based on the uncertainty set of output, rated power, energy storage capacity and net load demand of each flexible resource to be configured at each node, a flexible resource regulation capacity model and a flexible resource sub-bar configuration optimization model are constructed.

[0009] Based on the power balance constraints of each node, the time response characteristic flexibility index constraints, the operating constraints of each unit, the flexibility resource adjustment capability model, the benchmark output scheme, and the flexibility evaluation results, the flexibility resource allocation optimization scheme is obtained by solving the flexibility resource allocation optimization model.

[0010] Furthermore, the spatial response characteristic flexibility evaluation model includes an objective function for minimizing the total flexibility deficit of the power grid system and an expected average flexibility deficit model;

[0011] The spatial response characteristic flexibility evaluation model based on the flexibility deficit of each node in the power grid system includes:

[0012] Based on the mathematical expectation of the sum of flexibility deficits of each node in the power grid system at each time period and the probability distribution of net load, a minimum objective function for the total flexibility deficit of the power grid system is constructed.

[0013] A model for expected average flexibility deficit is constructed based on the mathematical expectation of the mean of flexibility deficit at each node of the power grid system and the probability distribution of net load at each time period.

[0014] Furthermore, the set of net load demand uncertainties constructed based on historical data of wind power, photovoltaic power, and load demand includes:

[0015] Based on historical data of wind power, solar power, and load demand, corresponding typical scenarios for wind power, solar power, and load are clustered respectively.

[0016] Based on the aforementioned typical wind power, photovoltaic, and load scenario sets, a fused net load scenario set is obtained;

[0017] The reference probability distribution density function of the net load is obtained based on the set of net load scenarios;

[0018] Construct a set of uncertainties in net load demand based on the reference probability distribution density function of net load.

[0019] Furthermore, the robust configuration optimization model includes an objective function that minimizes the cost of flexible resource allocation and a set of uncertainties in net load requirements;

[0020] The objective function for minimizing the cost of flexible resource allocation is expressed as:

[0021]

[0022] Among them, C total Cost of configuring flexible resources to be configured; C inv C ope These are the construction and operating costs of the flexible resources to be configured, respectively; E Pnet The net load distribution is represented by p. net Mathematical expectation at time;

[0023] N lack For a set of nodes with insufficient flexibility; Ω flex Let be the set of flexible resources to be configured for node m; r be the discount rate; y be the set of flexible resources to be configured for node m. i Let be the lifespan of the i-th type of flexibility resource to be configured in node m; These represent the construction costs per unit rated power and per unit energy storage capacity of the i-th type of flexibility resource to be configured at node m, respectively. , respectively, represent the rated power and energy storage capacity of the i-th type of flexibility resource to be configured at node m; T is the total number of time points; The cost per unit operating power for the i-th type of flexibility resource to be configured at node m; Let be the operating power of the i-th type of flexibility resource to be configured in node m at time t; Let $t$ be the operating cost of the existing unit $k$ at node $m$.

[0024] Furthermore, the set of uncertainties in net load demand is represented as:

[0025]

[0026] Among them, W net P represents the set of uncertainties in net load demand; net Indicates the actual net load distribution; Indicates the reference net load distribution; This serves as a distance threshold between the reference net load distribution and the actual net load distribution. The KL divergence of the net load, ξ is a random variable, Ω is a random space; f(ξ) and f0(ξ) are the actual probability density function and the reference probability density function of the net load, respectively.

[0027] Furthermore, the distance threshold between the reference net load distribution and the actual net load distribution The calculation method is as follows:

[0028]

[0029] Among them, S net Represents the number of typical scenarios. The upper quantile of a chi-square distribution with N-1 degrees of freedom ensures that the net load is not less than α. * The probability is included in the set of uncertain net load demand.

[0030] Furthermore, the spatial response characteristic flexibility evaluation model is expressed as follows:

[0031]

[0032] Wherein, minf is the objective function for minimizing the total flexibility deficit of the power grid system; The net load distribution is represented by p. net The expected value of N at time; m It is the set of all flexible nodes in the power grid system; Let represent the flexibility deficit of node m at time t. A positive value indicates insufficient downward climbing ability, while a negative value indicates insufficient upward climbing ability. Let m be the expected average flexibility deficit.

[0033] Furthermore, the time response characteristic flexibility index constraint is expressed as follows:

[0034]

[0035] in, and The threshold set for the flexibility coverage index; and These are the flexibility coverage indices for upward and downward adjustments, respectively. When calculating upward and downward adjustments separately:

[0036]

[0037] FCI m For flexibility coverage index; FS m,t FR refers to the upward or downward flexibility of node m. m,t To meet the flexibility adjustment requirements of node m in terms of moving up or down.

[0038] Furthermore, the balance constraints of each node are expressed as follows:

[0039]

[0040] Where, N k The set of generator sets already existing at node m; The planned output of generator set k at node m at time t; N m P represents the set of all flexible nodes in the power grid system. mn,t The power interaction between node m and node n at time t; Let be the net load value of node m at time t; Let m be the power transmission demand of node m at time t; Let m be the flexibility deficit of node m at time t.

[0041] Furthermore, the power interaction network constraints of each node are expressed as follows:

[0042]

[0043] Among them, Pmn,t The power interaction between node m and node n at time t; P mn,max This represents the upper limit of power interaction between node m and node n; and These represent the uplink and downlink backups that nodes m and n can interact with at time t.

[0044] The present invention can achieve at least one of the following beneficial effects:

[0045] Considering the uncertainties in renewable energy output and load, a set of net load demand uncertainties is constructed. By building a spatial response characteristic flexibility evaluation model, nodes with insufficient power system flexibility are screened. Under the premise of considering the adjustment characteristics of multiple types of flexibility resources, a flexible resource distributed bar optimization configuration model is constructed. Considering the time response characteristic flexibility index constraint, the optimal configuration scheme is obtained, realizing the planning of future renewable energy installed capacity, improving the utilization rate of various types of flexibility resources, and ensuring the safe and reliable operation of the power system.

[0046] By considering various flexible resources, including thermal power units, solar thermal power plants, and energy storage devices, with pumped storage power plants, hydrogen energy storage systems, and electrochemical storage devices included, and constructing corresponding flexible resource regulation capacity models for each type of flexible resource, the flexibility resource allocation scheme leverages the regulation characteristics of different types of flexible resources at different scales to meet the flexibility requirements of the new power system in different dimensions.

[0047] Other features and advantages of the invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained from what is particularly pointed out in the description, claims, and drawings. Attached Figure Description

[0048] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0049] Figure 1 This is a flowchart of the method of the present invention;

[0050] Figure 2 This is a diagram illustrating the flexibility coverage index. Detailed Implementation

[0051] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0052] A specific embodiment of the present invention discloses a flexible resource allocation optimization method that considers spatiotemporal response characteristics, specifically including the following steps:

[0053] Step S01: Construct a spatial response characteristic flexibility evaluation model based on the flexibility deficit of each node in the power grid system; the spatial response characteristic flexibility evaluation model includes an objective function for minimizing the total flexibility deficit of the power grid system and an expected average flexibility deficit model;

[0054] Step S02: Construct a set of net load demand uncertainties based on historical data of wind power, photovoltaic power, and load demand;

[0055] Step S03: Solve the flexibility evaluation model based on the power balance constraints of each node, the flexibility deficit constraints of each node, the power interaction network constraints of each node, the spinning reserve constraints of each node, the operating constraints of each unit, and the uncertainty set of net load demand to obtain the baseline output scheme of each node and the flexibility evaluation results of each node. The baseline output scheme includes the output of each unit in each time period, the transmission power between each node, and the upward / downward reserve output of each node. The flexibility evaluation results include whether the flexibility of each node is sufficient.

[0056] Step S04: Based on the uncertainty set of output, rated power, energy storage capacity, and net load demand of each flexible resource to be configured at each node, construct a flexible resource adjustment capability model and a flexible resource robust configuration optimization model; the robust configuration optimization model includes a minimum flexible resource configuration cost objective function and a net load demand uncertainty set;

[0057] Step S05: Based on the power balance constraints of each node, the time response characteristic flexibility index constraints, the operating constraints of each unit, the flexibility resource adjustment capability model, the benchmark output scheme, and the flexibility evaluation results, the flexibility resource allocation optimization model is solved to obtain the flexibility resource allocation optimization scheme.

[0058] It should be noted that the flexible resources considered in this invention include energy storage devices such as thermal power units, solar thermal power plants, hydrogen energy storage, electrochemical energy storage, and pumped storage. The generating units at each node of the power grid system in this invention include the aforementioned flexible resources, as well as output units such as wind power and photovoltaic power.

[0059] It should be noted that the order of steps S01 and S02 is not limited, and they can be performed simultaneously.

[0060] In this embodiment, by constructing a set of uncertainties in net load demand and constructing a spatial response characteristic flexibility evaluation model, the flexibility adequacy of each node is analyzed, and nodes with insufficient flexibility in the power system are screened out. Furthermore, for each node with insufficient flexibility that needs to be configured with flexibility resources, this invention constructs a flexible resource allocation optimization model, considering the time response characteristic flexibility index constraint, to solve the flexibility resource allocation scheme for each node with insufficient flexibility, so as to meet the future new energy installed capacity planning while ensuring the safe and reliable operation of the power system.

[0061] Furthermore, in step S01, the construction of a spatial response characteristic flexibility evaluation model based on the flexibility deficit of each node in the power grid system includes:

[0062] Based on the mathematical expectation of the sum of flexibility deficits of each node in the power grid system at each time period and the probability distribution of net load, a minimum objective function for the total flexibility deficit of the power grid system is constructed.

[0063] A model for expected average flexibility deficit is constructed based on the mathematical expectation of the mean of flexibility deficit at each node of the power grid system and the probability distribution of net load at each time period.

[0064] The spatial response characteristic flexibility evaluation model is expressed as follows:

[0065]

[0066] Where minf is the objective function for minimizing the total flexibility deficit of the power grid system; E Pnet The net load distribution is represented by p. net The expected value of N at time; m It is the set of all flexible nodes in the power grid system; Let represent the flexibility deficit of node m at time t. A positive value indicates insufficient downward climbing ability, while a negative value indicates insufficient upward climbing ability. Let m be the expected average flexibility deficit for node m; T is the total number of time periods.

[0067] Specifically, in step S02, the net load demand uncertainty set is constructed based on historical data of wind power, photovoltaic power, and load demand, including:

[0068] Based on historical data of wind power, solar power, and load demand, corresponding typical scenarios for wind power, solar power, and load are clustered respectively.

[0069] Based on the aforementioned typical wind power, photovoltaic, and load scenario sets, a fused net load scenario set is obtained;

[0070] The reference probability distribution density function of the net load is obtained based on the set of net load scenarios;

[0071] Construct a set of uncertainties in net load demand based on the reference probability distribution density function of net load.

[0072] Furthermore, based on historical data of wind power, solar power, and load demand, corresponding typical scenarios for wind power, solar power, and load are clustered respectively, including:

[0073] Historical data from the past year for wind power, solar power, and load demand were selected and constructed in daily units, with 365 / 366 objects each. Each object included 24 time points, forming a matrix of 365 / 366 rows and 24 columns to describe wind power, solar power, and load demand respectively.

[0074] For each dataset, the k-means++ clustering algorithm is used to cluster the datasets to obtain multiple cluster centers for each dataset, which are then used as typical scenarios to form a set of typical scenarios. The corresponding probability of each typical scenario is then calculated.

[0075] Furthermore, for each dataset, the clustering process includes the following steps:

[0076] Step 1: Randomly select a point in the dataset as the first cluster center c1;

[0077] Step 2: Calculate the distance D(x) between each data point x in the dataset and the selected cluster centers.

[0078]

[0079] With D(x) 2 This data point was selected as the next cluster center. j+1 The probability weights are as follows:

[0080]

[0081] Step 3: Repeat step 2 until k cluster centers are selected;

[0082] Step 4: Assign each data point to the cluster center with the smallest distance to it to form an initial cluster;

[0083] Step 5: Calculate the new cluster center for each cluster until all cluster centers converge or the maximum number of iterations is reached, then end the clustering process.

[0084] Furthermore, the clustering criterion function aims to minimize the sum of squared errors within clusters, expressed as: Among them, C i It is the i-th cluster, c i It is the corresponding cluster center, x belongs to cluster C. i Any data point.

[0085] Furthermore, Calinski-Harabasz (CH) can be used. (+) Cluster validity analysis was performed on the indicators:

[0086]

[0087] In the formula: T k P k , respectively, represent the sum of squared deviations between clusters and the sum of squared deviations within clusters when the number of clusters is k; n is the total number of samples in the dataset.

[0088] Furthermore, when obtaining the clustering results, for each cluster, the number N of original scenes contained within it is counted. i And calculate the probability of the cluster's occurrence, that is, the corresponding probability of each typical scenario:

[0089]

[0090] In the formula: N is the total number of clustering scenarios.

[0091] Furthermore, the typical scenario sets are represented as follows:

[0092] A collection of typical wind power output scenarios: Its corresponding probability of occurrence is

[0093] Collection of typical photovoltaic power output scenarios: Its corresponding probability of occurrence is

[0094] Typical load output scenarios: Its corresponding probability of occurrence is

[0095] Furthermore, the fused net load scenario set, based on the aforementioned typical wind power, photovoltaic, and load scenario set, includes:

[0096] The load and wind / solar output are randomly arranged to obtain the merged net load scenario. Based on the set of net load scenarios, the reference probability distribution density function of the net load is obtained, as follows:

[0097]

[0098] S net = z × h × l;

[0099]

[0100] in, The net load requirements for scenarios z, h, and l; For wind power output in scenario z; Provide photovoltaic power for scenario n; P l load For the load requirements of scenario l; S net This represents the total number of net load scenarios; The probability of a net load scenario.

[0101] Furthermore, the set of uncertainties in net load demand, constructed based on the reference probability distribution density function of net load, includes:

[0102] Step 11: Based on the constructed net load scenario, obtain the reference probability density function f0(ξ) of the net load;

[0103] Step 12: Use Kullback-Leibler divergence (KLD) to describe the uncertainty of net load demand. The KL divergence of net load is expressed as:

[0104]

[0105] in, Let ξ be the KL divergence of the net load; ξ be a random variable; Ω be a random space; f(ξ) and f0(ξ) be the actual probability density function and reference probability density function of the net load, respectively.

[0106] Step 13: Considering that the actual net load distribution is not measurable, in order to ensure the actual net load distribution P net Compared with reference net load distribution The similarity is used to construct the net load demand uncertainty set W based on KL divergence. net :

[0107]

[0108] in, This serves as a distance threshold between the reference net load distribution and the actual net load distribution.

[0109] Furthermore, a distance threshold between the reference net load distribution and the actual net load distribution is used. The calculation method is as follows:

[0110]

[0111] Among them, S net Represents the number of typical scenarios. The upper quantile of a chi-square distribution with N-1 degrees of freedom ensures that the net load is not less than α. * The probability is included in the set of uncertain net load demand.

[0112] Specifically, in step S03, the power balance constraint of each node is expressed as follows:

[0113]

[0114] Where, N k The set of generator sets already existing at node m; The planned output of generator set k at node m at time t; N m P represents the set of all flexible nodes in the power grid system. mn,t The power interaction between node m and node n at time t; Let be the net load value of node m at time t; Let m be the power transmission demand of node m at time t; Let m be the flexibility deficit of node m at time t.

[0115] Specifically, in step S03, to prevent the reserve output of all units from being concentrated on meeting the flexibility requirements of a single node, the flexibility deficit of each node needs to meet an upper limit constraint. The flexibility deficit constraint of each node is expressed as follows:

[0116]

[0117] in, and These represent the upper limits of the uplink and downlink flexibility deficits for each node.

[0118] Specifically, in step S03, the power interaction network constraints of each node are expressed as follows:

[0119]

[0120] Among them, P mn,max This represents the upper limit of power interaction between node m and node n; and These represent the uplink and downlink backups that nodes m and n can interact with at time t.

[0121] Specifically, in step S03, the rotational spare constraints for each node are represented as follows:

[0122]

[0123] in, and It is the total uplink and downlink reserve power of node m at time t; and These represent the uplink and downlink standby outputs of generator set k at time t, respectively. These are the uplink and downlink spinning reserve factors for wind power, solar power, and load, respectively.

[0124] Among them, the uplink and downlink standby output of generator set k at time t must meet the upper limit constraint:

[0125] ΔP k,m This represents the ramp rate of unit k at node m.

[0126] Specifically, in step S03, the operating constraints of each unit include the upper and lower limits of the unit's output, the ramp-up constraint, and the energy storage capacity constraint of the energy storage equipment.

[0127] The upper and lower limits of output and the ramp-up constraints for each unit are expressed as follows:

[0128]

[0129] in, and These are the upper and lower limits of the output of unit k under node m, respectively; This represents the operating status of unit k under node m.

[0130] The energy storage capacity constraint of an energy storage device is expressed as:

[0131]

[0132] in, The equivalent amount of electricity stored by node m's flexibility resource i at time t; To improve the charging and discharging efficiency of flexible resource i; These represent the charging and discharging power of node m's flexibility resource i at time t.

[0133] Specifically, in step S03, the flexibility evaluation model is solved based on the uncertainty set of net load demand to obtain the baseline output scheme and flexibility evaluation results for each node. The baseline output scheme includes the output of each unit in each time period. Transmission power P between nodes mn,t Upward / downward reserve output of each node and

[0134] Furthermore, in step S03, based on the expected average flexibility deficit of node m obtained from the solution... The results are compared with a set threshold to determine whether each node has sufficient flexibility, i.e., the flexibility evaluation result. Based on the flexibility evaluation result, nodes in the power grid system that lack flexibility are identified.

[0135] Furthermore, in step S03, the Cplex solver can be called through the Yalmip toolbox to perform the solution.

[0136] Specifically, in step S04, the objective function for minimizing the cost of flexible resource allocation is expressed as:

[0137]

[0138] Among them, C total Cost of configuring flexible resources to be configured; C inv C ope These are the construction costs and operating costs of the flexible resources to be configured, respectively. The net load distribution is represented by p. net Mathematical expectation at time;

[0139] N lack For a set of nodes with insufficient flexibility; Ω flex Let be the set of flexible resources to be configured for node m; r be the discount rate; y be the set of flexible resources to be configured for node m. i Let be the lifespan of the i-th type of flexibility resource to be configured in node m; These represent the construction costs per unit rated power and per unit energy storage capacity of the i-th type of flexibility resource to be configured at node m, respectively. , respectively, represent the rated power and energy storage capacity of the i-th type of flexibility resource to be configured at node m; T is the total number of time points; The cost per unit operating power for the i-th type of flexibility resource to be configured at node m; Let be the operating power of the i-th type of flexibility resource to be configured in node m at time t; Let $t$ be the operating cost of the existing unit $k$ at node $m$.

[0140] Specifically, in step S05, the time response characteristic flexibility index constraint is expressed as follows:

[0141]

[0142] in, and The threshold set for the flexibility coverage index; and The flexibility coverage index for upward and downward adjustments (e.g.) Figure 2 As shown), when calculating the upward and downward adjustments separately, we have:

[0143]

[0144] FCI m For flexibility coverage index; FS m,t FR refers to the upward or downward flexibility of node m. m,t To meet the flexibility adjustment requirements of node m in terms of moving up or down.

[0145] Furthermore, the flexibility adjustment capability FS m,t and flexibility adjustment requirements FR m,tThe calculation method is as follows:

[0146]

[0147] in, These represent the net load requirements of node m at time t in scenarios 1, 2...s, respectively; These are the maximum and minimum operating power values ​​of node m, respectively. These represent the maximum and minimum energy storage capacity of node m, respectively; η ch η dis For charging and discharging efficiency; P t m R m , These represent the existing flexibility resources of node m, the output of the thermal power unit at time t, the ramp rate, and the energy stored in the energy storage flexibility resources.

[0148] Specifically, in step S05, the flexibility resource regulation capability model includes the regulation capability model of thermal power units, the regulation capability model of solar thermal power plants, and the regulation capability model of energy storage.

[0149] It should be noted that this invention considers pumped-storage power plants, hydrogen energy storage systems, and electrochemical energy storage as the main energy storage equipment configurations. Among them, pumped-storage units have high capacity efficiency, fast response rate, and extremely large response depth, and can effectively smooth out peak loads; hydrogen energy storage systems store electrical energy on a large scale using hydrogen as a medium, and can also smooth out peak loads and quickly respond to fluctuations in net load, but their energy conversion efficiency is currently low; electrochemical energy storage has an extremely high response rate and can smooth out high-frequency random fluctuations in net load, but its installed capacity is limited by technical characteristics and cost factors, resulting in a limited response depth.

[0150] Furthermore, thermal power units typically exhibit stable operating characteristics, providing continuous power output and meeting power demand over long timescales, with their advantages being particularly pronounced when dealing with large fluctuations in system-wide or long-term demand. However, thermal power units respond slowly when dealing with high-frequency but low-amplitude random fluctuations.

[0151] Furthermore, the ramp-up performance of thermal power units and the upper and lower limits of their output and power output at that time affect the unit's flexibility regulation capability. Therefore, the thermal power unit regulation capability model describes the flexibility of the thermal power unit on the Δt time scale, expressed as:

[0152]

[0153] in, and The flexibility of adjusting the power generation capacity upwards and downwards for thermal power units; P G,max PG,min and P G,t These represent the maximum operating output, minimum operating output, and current output at time t of the thermal power unit, respectively; R is the unit's ramp rate.

[0154] Furthermore, concentrated solar power (CSP) plants typically possess controllable heat release characteristics, allowing them to adjust the energy release rate and duration according to the power system's needs. This controllability enables them to flexibly participate in power system response scheduling, providing electricity as required.

[0155] Furthermore, the regulation capacity model of a solar thermal power plant is expressed as follows:

[0156]

[0157] in, and The flexibility of adjusting the power supply for concentrated solar power (CSP) plants is reflected in both upward and downward adjustments. These are the maximum power generation and thermal storage capacity of the solar thermal power plant, respectively; E t E max E min These represent the upper and lower limits of the heat storage and thermal energy storage of the solar thermal power plant at time t; P SF-HTF The thermal power collected by the solar thermal power plant; η EH and η RC For charging and releasing heat efficiency.

[0158] Furthermore, energy storage devices can not only serve as high-performance response power sources to meet the needs of large-scale, system-level applications on the grid side, but also provide bidirectional adjustment flexibility by frequently converting electrical energy in a short period of time, effectively and smoothly handling high-frequency but low-amplitude random fluctuations.

[0159] Furthermore, the energy storage regulation capacity model is expressed as:

[0160]

[0161] in, and These represent the flexibility to adjust the price up or down for energy storage equipment; These are the maximum discharge and charging power of the energy storage device, respectively; P es,t The SOC (State of Charge) represents the power of the energy storage device at time t, with positive values ​​for discharging and negative values ​​for charging. t SOC max SOC min η represents the upper and lower limits of the equivalent energy and device capacity of the energy storage device at time t, respectively; dis and η ch For the efficiency of discharging and charging.

[0162] Specifically, in step S05, the C&CG algorithm is used to solve the flexibility resource allocation optimization model based on the power balance constraints of each node, the flexibility index constraints of time response characteristics, the operating constraints of each unit, the flexibility resource adjustment capability model, the benchmark output scheme, and the flexibility evaluation results.

[0163] Specifically, when using the C&CG algorithm to solve the problem, since the scene is discrete, the expectation is equivalent to the product of the scene probability and the function value. Therefore, the flexible resource allocation optimization model can be transformed into:

[0164]

[0165] Where x represents the decision-making variable during the planning and construction phase; y s p represents the decision variables for the planning and construction phase in scenario s. s Let be the probability corresponding to scenario s.

[0166] Furthermore, the transformed model is a min-max-min structure, and the middle and inner max-min structures are equivalent to... Once the inner-layer scheduling problem is solved, the worst-case probability search in the middle layer becomes a simple linear programming problem. The model is then transformed into a min-max two-layer configuration optimization problem that combines the outer, middle, and inner layers. The C&CG algorithm is used to solve this problem, resulting in an optimized scheme for flexible resource configuration, including the rated power and / or energy storage capacity of the flexible resources that each node needs to configure, and the output or charging / discharging power of each flexible resource during operation.

[0167] This embodiment discloses a flexible resource allocation optimization method based on spatiotemporal response characteristics. It constructs a net load demand uncertainty set by considering the uncertainties of new energy output and load, and builds a spatial response characteristic flexibility evaluation model to screen nodes with insufficient power system flexibility. Under the premise of considering the adjustment characteristics of multiple types of flexible resources, it constructs a flexible resource allocation optimization model based on spatiotemporal response characteristic flexibility index constraints, and solves for the optimal allocation scheme. This enables future planning of new energy installed capacity, improves the utilization rate of various types of flexible resources, and ensures the safe and reliable operation of the power system.

[0168] By considering various flexible resources, including thermal power units, solar thermal power plants, and energy storage devices, with pumped storage power plants, hydrogen energy storage systems, and electrochemical storage devices included, and constructing corresponding flexible resource regulation capacity models for each type of flexible resource, the flexibility resource allocation scheme leverages the regulation characteristics of different types of flexible resources at different scales to meet the flexibility requirements of the new power system in different dimensions.

[0169] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A flexible resource allocation optimization method based on spatiotemporal response characteristics, characterized in that, Includes the following steps: A spatial response characteristic flexibility evaluation model is constructed based on the flexibility deficit of each node in the power grid system. A set of uncertainties in net load demand is constructed based on historical data of wind power, solar power, and load demand. Based on the power balance constraints, flexibility deficit constraints, power interaction network constraints, spinning reserve constraints, unit operation constraints, and net load demand uncertainty set of each node, the flexibility evaluation model is solved to obtain the baseline output scheme and flexibility evaluation results of each node. Based on the uncertainty set of output, rated power, energy storage capacity, and net load demand of each flexible resource to be configured at each node, a flexible resource regulation capacity model and a flexible resource sub-bar configuration optimization model are constructed. The sub-bar configuration optimization model includes a flexible resource configuration cost minimization objective function and a net load demand uncertainty set. The flexible resource configuration cost minimization objective function is expressed as: ;in, Cost of allocating flexible resources to be configured; , These are the construction costs and operating costs of the flexible resources to be configured, respectively. Indicates the net load distribution as Mathematical expectation at time; A set of nodes with insufficient flexibility; For nodes m The set of flexible resources to be configured; The discount rate; For nodes m The The lifecycle of flexible resources to be configured; , They are nodes m The The construction cost per unit rated power and per unit energy storage capacity of the flexible resources to be configured; , They are nodes No. The rated power and energy storage capacity of the flexible resources to be configured; T Total number of moments; For nodes m The The cost per unit operating power of flexible resources to be configured; For nodes The Flexible resources to be configured at any time Operating power; For nodes existing organic groups At any moment Operating costs; the set of uncertainties in net load demand is represented as: ;in, This represents the set of uncertainties in net load demand; Indicates the actual net load distribution; Indicates the reference net load distribution; This serves as a distance threshold between the reference net load distribution and the actual net load distribution. The KL divergence of the net load, , For random variables, It is a random space; and These are the actual probability density function and the reference probability density function of the net load, respectively; Based on the power balance constraints of each node, the time response characteristic flexibility index constraints, the operating constraints of each unit, the flexibility resource adjustment capability model, the benchmark output scheme, and the flexibility evaluation results, the flexibility resource allocation optimization scheme is obtained by solving the flexibility resource allocation optimization model.

2. The configuration optimization method according to claim 1, characterized in that, The spatial response characteristic flexibility evaluation model includes an objective function for minimizing the total flexibility deficit of the power grid system and an expected average flexibility deficit model; The spatial response characteristic flexibility evaluation model based on the flexibility deficit of each node in the power grid system includes: Based on the mathematical expectation of the sum of flexibility deficits of each node in the power grid system at each time period and the probability distribution of net load, a minimum objective function for the total flexibility deficit of the power grid system is constructed. A model for expected average flexibility deficit is constructed based on the mathematical expectation of the mean of flexibility deficit at each node of the power grid system and the probability distribution of net load at each time period.

3. The configuration optimization method according to claim 2, characterized in that, The set of net load demand uncertainties constructed based on historical data of wind power, photovoltaic power, and load demand includes: Based on historical data of wind power, solar power, and load demand, corresponding typical scenarios for wind power, solar power, and load are clustered respectively. Based on the aforementioned typical wind power, photovoltaic, and load scenario sets, a fused net load scenario set is obtained; The reference probability distribution density function of the net load is obtained based on the set of net load scenarios; Construct a set of uncertainties in net load demand based on the reference probability distribution density function of net load.

4. The configuration optimization method according to claim 3, characterized in that, The distance threshold between the reference net load distribution and the actual net load distribution The calculation method is as follows: ； in, Represents the number of typical scenarios. For degrees of freedom The upper quantile of the chi-square distribution ensures that the net load is not less than The probability is included in the set of uncertain net load demand.

5. The configuration optimization method according to any one of claims 3-4, characterized in that, The spatial response characteristic flexibility evaluation model is expressed as follows: ； in, The objective function is to minimize the total flexibility deficit of the power grid system. Indicates the net load distribution as Mathematical expectation at time; It is the set of all flexible nodes in the power grid system; For nodes At any moment The flexibility deficiency is indicated by a positive value, which represents insufficient ability to climb downhill, and by a negative value, which represents insufficient ability to climb uphill. For nodes The expected average flexibility deficit.

6. The configuration optimization method according to claim 5, characterized in that, The time response characteristic flexibility index constraint is expressed as follows: ； in, and The threshold set for the flexibility coverage index; and These are the flexibility coverage indices for upward and downward adjustments, respectively. When calculating upward and downward adjustments separately: ； For flexibility coverage index; For nodes Its ability to adjust flexibly upwards or downwards; For nodes The need for flexible adjustment, either upwards or downwards.

7. The configuration optimization method according to claim 6, characterized in that, The balance constraints of each node are expressed as follows: ； in, For nodes m The existing collection of generator sets; For nodes generator set At any moment The plan is to contribute; It is the set of all flexible nodes in the power grid system; For nodes With nodes At any moment Power interaction; For nodes At any moment Net load value; For nodes At any moment The demand for electricity transmission to other regions; For nodes At any moment The lack of flexibility.

8. The configuration optimization method according to claim 7, characterized in that, The power interaction network constraints of each node are expressed as follows: ； in, For nodes With nodes At any moment Power interaction; For nodes With nodes The upper limit of power interaction between them; and They are nodes With nodes At any moment Interactive uplink and downlink backup.

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