Flexible resource distribution robust configuration optimization method considering time-space response characteristics

Abstract

The invention relates to a flexible resource distribution robust configuration optimization method considering time-space response characteristics, and belongs to the technical field of new energy. The method comprises the following steps: constructing a spatial response characteristic flexibility evaluation model based on flexibility vacancy of each node of a power grid system; solving based on the power balance constraint, the flexibility vacancy constraint, the power interaction network constraint, the spinning reserve constraint and the operation constraint of each unit of each node to obtain a reference output scheme and a flexibility evaluation result; constructing a flexibility resource distribution robust configuration optimization model based on the output, rated power and energy storage capacity of each to-be-configured flexibility resource of each node; and solving and obtaining a flexibility resource configuration optimization scheme based on the power balance constraint of each node, the time response characteristic flexibility index constraint, the operation constraint of each unit, the reference output scheme and the flexibility evaluation result. According to the method, space and time response characteristics are considered, and safe and reliable operation of a power system is ensured while new energy installed capacity planning is met.

Description

Technical Field

[0001] The present invention belongs to the field of new energy technology, and in particular relates to a flexible resource distribution and robust configuration optimization method taking into account time and space response characteristics. Background Art

[0002] As the proportion of new energy sources such as wind and light connected to the power grid continues to expand, their randomness and uncertainty put forward higher requirements for the flexibility of the power system. In the power system, due to the large differences in the regulation characteristics of flexibility resources at different scales, it is often necessary to consider how to complement the configuration of different types of flexibility resources to meet the flexibility needs of different dimensions of the new power system. However, existing research mainly considers the economic benefits after optimized configuration or the improvement of the flexibility of power system operation. There is still a lack of research and scientific solutions on how to optimize the configuration of the power system while taking into account the complementary characteristics of economy and flexibility resources and improving the utilization rate of flexibility resources. Summary of the invention

[0003] In view of the above analysis, the present invention aims to provide a flexible resource distributed robust configuration optimization method taking into account the time-space response characteristics, construct a fuzzy set for new energy and load uncertainty, construct a spatial response characteristic flexibility evaluation model to analyze the flexibility adequacy of each node, screen out nodes with insufficient flexibility in the power system, and construct a flexible resource distributed robust configuration optimization model. Considering the flexibility indicator constraints based on the time response characteristics, the flexibility resource configuration plan for each node with insufficient flexibility is solved, which can maximize the potential of flexibility resources and meet the future new energy installed capacity planning while ensuring the safe and reliable operation of the power system.

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

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

[0006] Construct a net load demand uncertainty set based on historical data of wind power, photovoltaic and load demand;

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

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

[0009] Based on the power balance constraints of each node, the flexibility index constraints of the time response characteristics, the operation constraints of each unit, the flexibility resource regulation capability model, the benchmark output plan and the flexibility evaluation results, the flexibility resource allocation optimization model is solved to obtain the flexibility resource configuration optimization plan.

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

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

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

[0013] The expected average flexibility shortage model is constructed based on the mean value of flexibility shortage at each node in the power grid system in each period and the mathematical expectation of net load probability distribution.

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

[0015] Based on the historical data of wind power, photovoltaic and load demand, the corresponding wind power, photovoltaic and load typical scenario sets are obtained respectively;

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

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

[0018] The net load demand uncertainty set is constructed based on the reference probability distribution density function of the net load.

[0019] Further, the robust configuration optimization model includes a flexibility resource configuration cost minimization objective function and a net load demand uncertainty set;

[0020] The flexibility resource allocation cost minimization objective function is expressed as:

[0021]

[0022] Among them, C total Cost of configuring the flexibility resources to be configured; C inv , C ope are the construction cost and operation cost of the flexible resources to be configured; E Pnet The net load distribution is p net The mathematical expectation of

[0023] N lack is the set of nodes with insufficient flexibility; Ω flex is the set of flexible resources to be configured for node m; r is the discount rate; y i is the life cycle of the i-th flexibility resource to be configured at node m; are the construction costs of unit rated power and unit energy storage capacity of the i-th flexibility resource to be configured at node m, respectively; are the rated power and energy storage capacity of the i-th flexible resource to be configured at node m, respectively; T is the total number of moments; is the cost per unit operating power of the i-th flexibility resource to be configured at node m; is the operating power of the i-th flexible resource to be configured at node m at time t; is the operating cost of the existing group k of node m at time t.

[0024] Furthermore, the net load demand uncertainty set is expressed as:

[0025]

[0026] Among them, W net represents the net load demand uncertainty set; P net Indicates the actual net load distribution; represents the reference net load distribution; is the distance threshold between the reference net load distribution and the actual net load distribution; is 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 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:

[0028]

[0029] Among them, S net represents the number of typical scenes, is the upper quantile of the chi-square distribution with N-1 degrees of freedom, which can ensure that the net load is not less than α * The probability of being included in the net load demand uncertainty set.

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

[0031]

[0032] Among them, minf is the minimum objective function of the total deficit of power grid system flexibility; The net load distribution is p net The mathematical expectation when N m It is the collection of all flexibility nodes in the power grid system; is the flexibility deficit of node m at time t. When it is positive, it indicates insufficient downward climbing ability, and when it is negative, it indicates insufficient upward climbing ability. is the expected average flexibility deficit of node m.

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

[0034]

[0035] in, and Thresholds set for the flexibility coverage index; and They are the flexibility coverage indexes for upward adjustment and downward adjustment, and when calculating upward adjustment and downward adjustment separately, they are:

[0036]

[0037] FCI m FS is the flexibility coverage index; m,t FR is the upward or downward flexibility adjustment capability of node m; m,t The upward or downward flexibility adjustment requirements for node m.

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

[0039]

[0040] Among them, N k is the set of existing generators of node m; is the planned output of generator set k at node m at time t; N m is the set of all flexibility nodes in the power grid system; P mn,t is the power interaction between node m and node n at time t; is the net load value of node m at time t; is the power transmission demand of node m at time t; is the flexibility deficit of node m at time t.

[0041] Furthermore, the power interaction network constraint of each node is expressed as:

[0042]

[0043] Among them, Pmn,t is the power interaction between node m and node n at time t; P mn,max is the upper limit of the power interaction between node m and node n; and They are respectively the uplink and downlink backups that can interact between node m and node n at time t.

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

[0045] Taking into account the uncertainty of renewable energy output and load, a net load demand uncertainty set is constructed. By building a spatial response characteristic flexibility evaluation model, the nodes with insufficient flexibility in the power system are screened. Under the premise of considering the regulation characteristics of multiple types of flexibility resources, a flexible resource distributed robust optimization configuration model is constructed. The optimal configuration plan is obtained by considering the constraints of flexibility indicators with time response characteristics, so as to realize the planning of future renewable energy installed capacity, improve the utilization rate of various types of flexibility resources, and ensure the safe and reliable operation of the power system.

[0046] By considering a variety of flexibility resources including thermal power units, solar thermal power stations and energy storage equipment, among which the energy storage equipment considers pumped storage power stations, hydrogen energy storage systems and electrochemical storage, and constructing corresponding flexibility resource regulation capability models for various flexibility resources, in the flexibility resource allocation plan, the regulation characteristics of various types of flexibility resources at different scales are brought into play to meet the flexibility needs of different dimensions of the new power system.

[0047] Other features and advantages of the present invention will be described in the following description, and some advantages may become apparent from the description, or may be understood by practicing the present invention. The purpose and other advantages of the present invention may be realized and obtained through the contents particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0049] Figure 1 is a flow chart of the method of the present invention;

[0050] Figure 2 This is a schematic diagram of the flexibility coverage index. DETAILED DESCRIPTION

[0051] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a 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 used to limit the scope of the present invention.

[0052] A specific embodiment of the present invention discloses a flexible resource distribution and robust configuration optimization method taking into account the spatiotemporal response characteristics, which specifically includes the following steps:

[0053] Step S01, constructing 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 a minimum objective function for the total flexibility deficit of the power grid system and an expected average flexibility deficit model;

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

[0055] Step S03, solving the flexibility evaluation model based on the power balance constraints of each node, the flexibility shortage constraints of each node, the power interaction network constraints of each node, the rotation reserve constraints of each node, the operation constraints of each unit and the net load demand uncertainty set, and obtaining the benchmark output scheme of each node and the flexibility evaluation result of each node, the benchmark 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, and the flexibility evaluation result includes whether the flexibility of each node is sufficient;

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

[0057] Step S05, based on the power balance constraints of each node, the flexibility index constraints of the time response characteristics, the operation constraints of each unit, the flexibility resource regulation capability model, the benchmark output plan and the flexibility evaluation results, the flexibility resource allocation optimization model is solved to obtain the flexibility resource configuration optimization plan.

[0058] It should be noted that the flexibility resources considered in the present invention include thermal power units, solar thermal power stations, hydrogen energy storage, electrochemical energy storage, pumped storage and other energy storage equipment. The units at each node of the power grid system in the present invention include the above-mentioned flexibility resources, as well as wind power, photovoltaic and other output units.

[0059] It should be noted that step S01 and step S02 are not limited in order and can be performed simultaneously.

[0060] In this embodiment, by constructing a net load demand uncertainty set and a spatial response characteristic flexibility evaluation model, the flexibility adequacy of each node is analyzed, and the nodes with insufficient flexibility in the power system are screened out; further, for each node with insufficient flexibility, flexibility resources need to be configured. The present invention constructs a flexible resource distribution and robust configuration optimization model, considers the flexibility index constraints of the time response characteristics, and solves the flexibility resource configuration plan 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 sum of the flexibility deficits of each node in the power grid system at each time period and the mathematical expectation of the net load probability distribution, the objective function of minimizing the total flexibility deficit of the power grid system is constructed;

[0063] The expected average flexibility shortage model is constructed based on the mean value of flexibility shortage at each node in the power grid system in each period and the mathematical expectation of net load probability distribution.

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

[0065]

[0066] Where minf is the minimum objective function of the total flexibility deficit of the power grid system; E Pnet The net load distribution is p net The mathematical expectation when N m It is the collection of all flexibility nodes in the power grid system; is the flexibility deficit of node m at time t. When it is positive, it indicates insufficient downward climbing ability, and when it is negative, it indicates insufficient upward climbing ability. is the expected average flexibility deficit of node m; T is the total number of time periods.

[0067] Specifically, in step S02, constructing a net load demand uncertainty set based on historical data of wind power, photovoltaic power, and load demand includes:

[0068] Based on the historical data of wind power, photovoltaic and load demand, the corresponding wind power, photovoltaic and load typical scenario sets are obtained respectively;

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

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

[0071] The net load demand uncertainty set is constructed based on the reference probability distribution density function of the net load.

[0072] Furthermore, based on the historical data of wind power, photovoltaic power and load demand, the corresponding wind power, photovoltaic power and load typical scenario sets are clustered respectively, including:

[0073] The historical data of wind power, photovoltaic power and load demand in the past year were selected respectively, and 365 / 366 objects were constructed in units of days. Each object included 24 moments of time, forming a matrix of 365 / 366 rows and 24 columns as a data set, which described the wind power, photovoltaic power and load demand respectively;

[0074] For each data set, the k-means++ clustering algorithm is used to cluster each data set to obtain multiple cluster centers as typical scenes to form a typical scene set, and the corresponding probability of each typical scene is calculated.

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

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

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

[0078]

[0079] D(x) 2 This data point is selected as the next cluster center c j+1 The probability weight of is:

[0080]

[0081] step3, repeat step2 until k cluster centers are selected;

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

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

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

[0085] Furthermore, Calinski-Harabasz (CH (+) ) indicators for clustering effectiveness analysis:

[0086]

[0087] Where: T k , P k They are the sum of squares of between-class deviations and within-class deviations when the number of clusters is k, respectively; n is the total number of samples in the data set.

[0088] Furthermore, when the clustering results are obtained, for each cluster, the number of original scenes N contained in it is counted. i , and calculate the probability of occurrence of the cluster, that is, the corresponding probability of each typical scene:

[0089]

[0090] Where: N is the total number of clustered scenes.

[0091] Furthermore, each typical scene set is expressed as:

[0092] Typical wind power output scenarios: The corresponding probability of occurrence is

[0093] Typical photovoltaic output scenarios: The corresponding probability of occurrence is

[0094] Typical load output scenario collection: The corresponding probability of occurrence is

[0095] Furthermore, the net load scenario set obtained after fusion based on the wind power, photovoltaic and load typical scenario set includes:

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

[0097]

[0098] S net =z×h×l;

[0099]

[0100] in, is the net load demand of scenario z, h, l; The wind power output for scenario z; is the photovoltaic output of scenario n; P l load is the load demand of scenario l; S net is the total number of net load scenarios; is the probability of the net load scenario.

[0101] Furthermore, the net load demand uncertainty set is constructed based on the reference probability distribution density function of the net load, including:

[0102] step11. Based on the constructed net load scenario, obtain the reference probability density function f0(ξ) of the net load;

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

[0104]

[0105] in, is 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.

[0106] Step 13: Considering that the actual net load distribution cannot be measured, in order to ensure the actual net load distribution P net With reference net load distribution The similarity of is constructed based on KL divergence to construct the net load demand uncertainty set W net :

[0107]

[0108] in, is the distance threshold between the reference net load distribution and the actual net load distribution.

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

[0110]

[0111] Among them, S net represents the number of typical scenes, is the upper quantile of the chi-square distribution with N-1 degrees of freedom, which can ensure that the net load is not less than α * The probability of being included in the net load demand uncertainty set.

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

[0113]

[0114] Among them, N k is the set of existing generators of node m; is the planned output of generator set k at node m at time t; N m is the set of all flexibility nodes in the power grid system; P mn,t is the power interaction between node m and node n at time t; is the net load value of node m at time t; is the power transmission demand of node m at time t; is the flexibility deficit of node m at time t.

[0115] Specifically, in step S03, in order to prevent the reserve output of all units from being concentrated on meeting the flexibility demand of a certain node, the flexibility shortage of each node needs to meet the upper limit constraint, and the flexibility shortage constraint of each node is expressed as:

[0116]

[0117] in, and They are the upper limits of the uplink and downlink flexibility shortages of each node respectively.

[0118] Specifically, in step S03, the power interaction network constraint of each node is expressed as:

[0119]

[0120] Among them, P mn,max is the upper limit of the power interaction between node m and node n; and They are respectively the uplink and downlink backups that can interact between node m and node n at time t.

[0121] Specifically, in step S03, the rotation standby constraint of each node is expressed as:

[0122]

[0123] in, and is the total upstream and downstream reserve output of node m at time t; and are the up and down reserve outputs of generator set k at time t respectively; are the up and down spinning reserve coefficients of wind power, photovoltaic power and load respectively;

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

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

[0126] Specifically, in step S03, the operating constraints of each unit include upper and lower output limit constraints of each unit, climbing constraints and energy storage capacity constraints of the energy storage equipment.

[0127] The upper and lower output limits and climbing constraints of each unit are expressed as:

[0128]

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

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

[0131]

[0132] in, is the equivalent amount of electricity stored by the flexibility resource i of node m at time t; is the charging and discharging efficiency of flexibility resource i; are the charging and discharging power of the flexibility resource i of node m at time t,

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

[0134] Further, in step S03, according to the expected average flexibility deficit of node m obtained by solving The flexibility evaluation result is compared with the set threshold to determine whether the flexibility of each node is sufficient, that is, the flexibility evaluation result. According to the flexibility evaluation result, the nodes with insufficient flexibility in the power grid system are obtained.

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

[0136] Specifically, in step S04, the flexibility resource allocation cost minimization objective function is expressed as:

[0137]

[0138] Among them, C total Cost of configuring the flexibility resources to be configured; C inv , C ope are the construction cost and operation cost of the flexibility resources to be configured, respectively; The net load distribution is p net The mathematical expectation of

[0139] N lack is the set of nodes with insufficient flexibility; Ω flex is the set of flexible resources to be configured for node m; r is the discount rate; y i is the life cycle of the i-th flexibility resource to be configured at node m; are the construction costs of unit rated power and unit energy storage capacity of the i-th flexibility resource to be configured at node m, respectively; are the rated power and energy storage capacity of the i-th flexible resource to be configured at node m, respectively; T is the total number of moments; is the cost per unit operating power of the i-th flexibility resource to be configured at node m; is the operating power of the i-th flexible resource to be configured at node m at time t; is the operating cost of the existing group k of node m at time t.

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

[0141]

[0142] in, and Thresholds set for the flexibility coverage index; and The flexibility coverage index for upward adjustment and downward adjustment (such as Figure 2 As shown in the figure, when calculating the upward adjustment and downward adjustment separately, we have:

[0143]

[0144] FCI m FS is the flexibility coverage index; m,t FR is the upward or downward flexibility adjustment capability of node m; m,t The upward or downward flexibility adjustment requirements for node m.

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

[0146]

[0147] in, are the net load demands of node m at time t in scenarios 1, 2, ..., s, respectively; are the maximum and minimum values ​​of the operating power of node m, respectively; are the maximum and minimum values ​​of the energy storage capacity of node m respectively; η ch , η dis is the charge and discharge efficiency; P t m , R m , They are the existing flexibility resources at node m, the output of thermal power units at time t, the ramp rate and the storage capacity of energy storage flexibility resources.

[0148] Specifically, in step S05, the flexibility resource regulation capability model includes a thermal power unit regulation capability model, a solar thermal power station regulation capability model, and an energy storage regulation capability model.

[0149] It should be noted that the present invention considers pumped storage power stations, hydrogen energy storage systems and electrochemical energy storage as the main configuration of energy storage equipment. Among them, pumped storage units have strong capacity efficiency, fast response rate, and great response depth, which can effectively smooth the net load; hydrogen energy storage systems use hydrogen energy as a medium to store electrical energy on a large scale, which can also smooth the net load and fill the valley, and can also quickly respond to the fluctuation of the net load, but the energy conversion efficiency is low at this stage; the response rate of electrochemical energy storage is extremely high, which can smooth the high-frequency random fluctuations of the net load, but its installed capacity is limited by technical characteristics and cost factors, resulting in limited response depth.

[0150] Furthermore, thermal power units usually exhibit stable operating characteristics, can provide continuous power output, and meet power demand over a long time scale, especially when dealing with large fluctuations within the system or long-term demand. However, thermal power units have a slow response speed when dealing with high-frequency but low-amplitude random fluctuations.

[0151] Furthermore, the climbing performance of the thermal power unit and the upper and lower limits of the output and output power at that time affect the flexibility adjustment capability of the unit. Therefore, the thermal power unit adjustment capability model describes the flexibility of the thermal power unit at the Δt time scale, which is expressed as:

[0152]

[0153] in, and are the flexibility of thermal power units to adjust upward and downward respectively; P G,max , PG,min and P G,t are the maximum operating output, minimum operating output and current output of the thermal power unit at time t respectively; R is the climbing rate of the unit.

[0154] Furthermore, CSP plants usually have controllable heat release characteristics, and can adjust the energy release rate and duration according to the needs of the power system. This controllability enables them to flexibly participate in the response dispatch of the power system and provide electricity according to demand.

[0155] Furthermore, the regulation capability model of the CSP power station is expressed as:

[0156]

[0157] in, and They are the flexibility of upward and downward regulation of CSP plants respectively; are the maximum power generation and heat storage power of the CSP station; E t 、E max 、E min are the heat storage and upper and lower limits of the heat storage of the CSP power station at time t; P SF-HTF is the thermal power collected by the CSP station; η EH and η RC For the efficiency of charging and dissipating heat.

[0158] Furthermore, energy storage equipment can not only serve as a high-performance responsive power source to meet large-scale, system-level applications on the grid side, but can also provide two-way adjustment flexibility for frequent conversion of electric energy in a short period of time, effectively and smoothly handling high-frequency but low-amplitude random fluctuations.

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

[0160]

[0161] in, and They are the flexibility to adjust up and down the energy storage equipment respectively; are the maximum discharge and charge powers of the energy storage device; P es,t is the power of the energy storage device at time t, positive for discharge and negative for charge; SOC t , SOC max , SOC min are the equivalent power of the energy storage device at time t and the upper and lower limits of the device capacity; η dis and η ch For the efficiency of discharge and charge.

[0162] Specifically, in step S05, the C&CG algorithm is used to solve the flexible resource distribution and robust configuration optimization model based on the power balance constraints of each node, the time response characteristic flexibility index constraints, the operation constraints of each unit, the flexibility resource regulation capability model, the benchmark output plan and the flexibility evaluation results.

[0163] Specifically, when using the C&CG algorithm to solve, since the scenario is discrete, the expectation is equivalent to the multiplication of the scenario probability and the function value, so the flexible resource distribution robust configuration optimization model can be converted to:

[0164]

[0165] Among them, x is the decision variable in the planning and construction stage; y s is the decision variable in the planning and construction phase under scenario s; p s is the probability corresponding to scene s.

[0166] Furthermore, the converted model is a min-max-min structure, and the middle and inner max-min structures are equivalent to After the inner-layer scheduling problem is solved, the worst probability search in the middle layer becomes a simple linear programming problem. The model is transformed into a min-max two-layer configuration optimization problem that combines the outer layer with the middle and inner layers. The C&CG algorithm is used to solve it and obtain the flexibility resource configuration optimization plan, including the rated power and / or energy storage capacity of the flexibility resources that need to be configured at each node, and the output or charging and discharging power of each flexibility resource during operation.

[0167] The present embodiment discloses a flexible resource distributed robust configuration optimization method taking into account the spatiotemporal response characteristics. The method constructs a net load demand uncertainty set by considering the uncertainty of new energy output and load, and constructs a spatial response characteristic flexibility evaluation model to screen out nodes with insufficient flexibility in the power system. The flexible resource distributed robust optimization configuration model is constructed under the premise of considering the regulation characteristics of multiple types of flexible resources. The flexibility indicator constraints of the time response characteristics are considered to solve the optimal configuration plan, realize the planning of future new energy installed capacity, improve the utilization rate of various types of flexible resources, and ensure the safe and reliable operation of the power system.

[0168] By considering a variety of flexibility resources including thermal power units, solar thermal power stations and energy storage equipment, among which the energy storage equipment considers pumped storage power stations, hydrogen energy storage systems and electrochemical storage, and constructing corresponding flexibility resource regulation capability models for various flexibility resources, in the flexibility resource allocation plan, the regulation characteristics of various types of flexibility resources at different scales are brought into play to meet the flexibility needs of different dimensions of the new power system.

[0169] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A flexible resource allocation optimization method taking into account the spatiotemporal response characteristics, characterized in that: The steps include: A flexibility evaluation model of spatial response characteristics is constructed based on the flexibility deficit of each node in the power grid system; Construct a net load demand uncertainty set based on historical data of wind power, photovoltaic and load demand; Based on the power balance constraints of each node, the flexibility shortage constraints of each node, the power interaction network constraints of each node, the spinning reserve constraints of each node, the operation constraints of each unit and the uncertainty set of net load demand, the flexibility evaluation model is solved to obtain the benchmark output plan of each node and the flexibility evaluation results of each node; Based on the output, rated power, energy storage capacity and net load demand uncertainty set of each flexible resource to be configured at each node, a flexible resource regulation capability model and a flexible resource distributed robust configuration optimization model are constructed; Based on the power balance constraints of each node, the flexibility index constraints of the time response characteristics, the operation constraints of each unit, the flexibility resource regulation capability model, the benchmark output plan and the flexibility evaluation results, the flexibility resource allocation optimization model is solved to obtain the flexibility resource configuration optimization plan.

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

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

4. The configuration optimization method according to claim 3, characterized in that: The robust configuration optimization model includes a flexibility resource configuration cost minimization objective function and a net load demand uncertainty set; The flexibility resource allocation cost minimization objective function is expressed as: Among them, C total Cost of configuring the flexibility resources to be configured; C inv , C ope are the construction cost and operation cost of the flexible resources to be configured; E Pnet The net load distribution is p net The mathematical expectation of N lack is the set of nodes with insufficient flexibility; Ω flex is the set of flexible resources to be configured for node m; r is the discount rate; y i is the life cycle of the i-th flexibility resource to be configured at node m; are the construction costs of unit rated power and unit energy storage capacity of the i-th flexibility resource to be configured at node m, respectively; are the rated power and energy storage capacity of the i-th flexible resource to be configured at node m, respectively; T is the total number of moments; is the cost per unit operating power of the i-th flexibility resource to be configured at node m; is the operating power of the i-th flexible resource to be configured at node m at time t; is the operating cost of the existing group k of node m at time t.

5. The configuration optimization method according to claim 4, characterized in that: The net load demand uncertainty set is expressed as: Among them, W net represents the net load demand uncertainty set; P net Indicates the actual net load distribution; represents the reference net load distribution; is the distance threshold between the reference net load distribution and the actual net load distribution; is 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 reference probability density function of the net load, respectively.

6. The configuration optimization method according to claim 5, characterized in that: The distance threshold between the reference net load distribution and the actual net load distribution The calculation method is: Among them, S net represents the number of typical scenes, is the upper quantile of the chi-square distribution with N-1 degrees of freedom, which can ensure that the net load is not less than α * The probability of being included in the net load demand uncertainty set.

7. The configuration optimization method according to any one of claims 3 to 6, characterized in that: The spatial response characteristic flexibility evaluation model is expressed as: Where minf is the minimum objective function of the total flexibility deficit of the power grid system; E Pnet The net load distribution is p net The mathematical expectation when N m It is the collection of all flexibility nodes in the power grid system; is the flexibility deficit of node m at time t. When it is positive, it indicates insufficient downward climbing ability, and when it is negative, it indicates insufficient upward climbing ability. is the expected average flexibility deficit of node m.

8. The configuration optimization method according to claim 7, characterized in that: The time response characteristic flexibility index constraint is expressed as: in, and Thresholds set for the flexibility coverage index; and They are the flexibility coverage indexes for upward adjustment and downward adjustment, and when calculating upward adjustment and downward adjustment separately, they are: FCI m FS is the flexibility coverage index; m,t FR is the upward or downward flexibility adjustment capability of node m; m,t The upward or downward flexibility adjustment requirements for node m.

9. The configuration optimization method according to claim 8, characterized in that: The balance constraints of each node are expressed as: Among them, N k is the set of existing generator sets of node m; is the planned output of generator set k at node m at time t; N m is the set of all flexibility nodes in the power grid system; P mn,t is the power interaction between node m and node n at time t; is the net load value of node m at time t; is the power transmission demand of node m at time t; is the flexibility deficit of node m at time t.

10. The configuration optimization method according to claim 9, characterized in that: The power interaction network constraint of each node is expressed as: Among them, P mn,t is the power interaction between node m and node n at time t; P mn,max is the upper limit of the power interaction between node m and node n; and They are respectively the uplink and downlink backups that can interact between node m and node n at time t.

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