Multi-time scale multivariate flexible resource coordination optimization configuration method and related device

Abstract

The present application discloses a multi-time scale multivariate flexible resource coordinated optimization configuration method and related devices. Aiming at the characteristics and demand differences of multivariate active and reactive flexible resources, the present application proposes a multi-time scale multivariate flexible resource coordinated optimization configuration method considering active and reactive coordination by distinguishing resource response time scales. On the basis of traditional flexible resource configuration methods, the present application distinguishes the differences in time scales and regulation characteristics of active and reactive multivariate flexible resources, and proposes a multi-time scale hierarchical optimization configuration method. The method can perform differentiated optimization configuration for different action time scales of the same flexible resource, taking into account the economic efficiency of equipment investment and grid reliability, and giving full play to the regulation potential of flexible resources, providing strong support for the further development of new energy and the safe and reliable operation of power systems.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method for coordinated optimization configuration of multi-time-scale multivariate flexible resources and related devices. Background Art

[0002] Flexible resources refer to resources that can increase the flexibility of supply and demand in the power system and respond to dynamic changes in the system. Compared with single disordered configuration, the coordinated configuration and scheduling of multiple resources can take into account both economy and flexibility to the greatest extent and give full play to the regulation potential of resources. However, there are differences in regulation characteristics and demands for active and reactive flexible resources on the source, network, load, and storage sides: 1. Active flexible resources such as energy storage equipment can withstand short-term heavy overloads, so they can be configured on a longer time scale (hourly level) without considering short-term power fluctuations, otherwise it will lead to reduced economic benefits. 2. New energy fluctuations in a short period of time will cause the system voltage to exceed the limit, so the optimal configuration of reactive flexible resources needs to consider short-term new energy output fluctuations. Summary of the invention

[0003] The present application provides a method and related device for coordinated optimization configuration of multi-time-scale multiple flexible resources, which performs differentiated optimization configuration for different action time scales of the same flexible resources, takes into account the economic efficiency of equipment investment and grid reliability, and gives full play to the regulation potential of flexible resources.

[0004] In view of this, the first aspect of the present application provides a method for coordinated optimization configuration of multi-time scale multivariate flexible resources, the method comprising:

[0005] According to the source-load model taking into account uncertainty, a Monte Carlo method is used for random sampling to obtain source-load output scenarios taking into account uncertainty, and the corresponding scenario time scale is set according to the configuration requirements of each source-load output scenario;

[0006] Combined with the economic characteristics of the regulation resources, the objective function of the flexible resource capacity configuration optimization model is analyzed to set the objective function of the system operation total cost, including: the new construction cost of the flexible resources, the operation cost, the environmental cost, and the cost of the new energy power abandonment;

[0007] Determine the constraints of the power system and multiple flexible resources, and analyze the response characteristics of the multiple flexible resources, so as to construct an optimal configuration model of the power system and multiple flexible resources, wherein the constraints include: system operation constraints and equipment operation constraints;

[0008] Based on the scenario time scale, the objective function required for the optimal configuration is selected according to the source-load output scenario, and the position of the multi-element flexible resources to be developed is set. The optimal configuration model is solved with the flexible resource power that can be dispatched in each time period within the time scale to obtain the collaborative optimal configuration result of multiple types of active power regulation resources considering the large-scale new energy grid connection;

[0009] According to the collaborative optimization configuration results of the multiple types of active power regulation resources, the reactive flexibility resources are cyclically optimized in a short time scale to obtain the reactive flexible resource optimization configuration results.

[0010] Optionally, the step of performing random sampling using a Monte Carlo method according to the source-load model taking uncertainty into account to obtain a source-load output scenario taking uncertainty into account includes:

[0011] According to the source-load model taking into account uncertainty, the Monte Carlo method is used for random sampling to generate a random scenario set, and the random scenario set is reduced by synchronous back substitution to obtain a source-load output scenario taking into account uncertainty.

[0012] Optionally, the objective function is expressed as:

[0013] minf=C op +C bs +C rs +C cl +C ab ;

[0014] In the formula, C op is the operating cost of the traditional unit; C bs C is the new construction and operation cost of energy storage; rs The new construction and operation cost of reactive power compensation equipment; C cl is the controllable load operating cost; C ab The cost of curtailing wind and solar power.

[0015] Optionally, the system operation constraints include: power flow constraints, node voltage constraints, branch power flow constraints, and power balance constraints.

[0016] Optionally, the equipment operation constraints include: traditional generator set constraints, shiftable load constraints, reducible load constraints, and energy storage constraints.

[0017] Optionally, solving the optimization configuration model to obtain a collaborative optimization configuration result of multiple types of active power regulation resources considering large-scale new energy grid connection includes:

[0018] The commercial solver is called to solve the optimization configuration model, and the output curve of the multi-time scale multivariate flexible resource coordinated optimization configuration algorithm considering active and reactive power coordination in each configuration scenario is obtained. The upper limit of the capacity value of each flexible resource called within the time scale of each scenario is extracted, and the upper limit of the capacity value of each flexible resource in each scenario is compared, and the highest value is taken as the final configuration capacity of the equipment.

[0019] Optionally, the short-time scale cyclic optimization configuration of reactive flexibility resources is performed according to the collaborative optimization configuration result of the multi-type active power regulation resources to obtain the reactive flexible resource optimization configuration result, including:

[0020] Based on the active flexible resource scheduling cycle and scheduling curve in the collaborative optimization configuration results of the multiple types of active power regulation resources, the time period with the highest requirement for the reactive flexible resource configuration capacity is selected, and the long-time scale active resource configuration scheduling results and the short-time scale new energy fluctuation scenario of the time period are used as constraints to perform short-time scale cyclic optimization configuration of the reactive flexible resources to obtain the reactive flexible resource optimization configuration result.

[0021] The second aspect of the present application provides a multi-time scale multivariate flexible resource coordinated optimization configuration system, the system comprising:

[0022] A generating unit, configured to perform random sampling using a Monte Carlo method according to a source-load model taking uncertainty into account, to obtain a source-load output scenario taking uncertainty into account, and to set a corresponding scenario time scale according to configuration requirements of each source-load output scenario;

[0023] A setting unit is used to analyze the economic characteristics of the regulating resources to set the objective function of the flexible resource capacity configuration optimization model, where the objective function is the total cost of system operation, including: new construction cost, operation cost, environmental cost, and new energy power abandonment cost of the flexible resources;

[0024] A modeling unit, used for determining the constraints of the power system and the multiple flexible resources, and analyzing the response characteristics of the multiple flexible resources, so as to construct an optimal configuration model of the power system and the multiple flexible resources, wherein the constraints include: system operation constraints and equipment operation constraints;

[0025] A first calculation unit is used to select the objective function required for the optimal configuration based on the scenario time scale and the source-load output scenario, and set the position of the multi-element flexible resources to be developed, and solve the optimal configuration model with the flexible resource power that can be dispatched in each time period within the time scale to obtain the collaborative optimal configuration result of multiple types of active power regulation resources considering the large-scale new energy grid connection;

[0026] The second calculation unit is used to perform short-time scale cyclic optimization configuration of reactive flexibility resources according to the collaborative optimization configuration results of the multiple types of active power regulation resources to obtain the optimization configuration results of reactive flexible resources.

[0027] The third aspect of the present application provides a multi-time scale multivariate flexible resource coordinated optimization configuration device, the device comprising a processor and a memory:

[0028] The memory is used to store program code and transmit the program code to the processor;

[0029] The processor is used to execute the steps of the multi-time-scale multivariate flexible resource coordinated optimization configuration method as described in the first aspect above according to the instructions in the program code.

[0030] In a fourth aspect, the present application provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the multi-time scale multivariate flexible resource coordinated optimization configuration method described in the first aspect above.

[0031] It can be seen from the above technical solutions that this application has the following advantages:

[0032] The present application provides a method for coordinated optimization configuration of multi-time-scale multiple flexible resources. On the basis of the traditional flexible resource configuration method, it distinguishes the differences in time scales and regulation characteristics of active and reactive multiple flexible resources, and proposes a multi-time-scale hierarchical optimization configuration method. It can perform differentiated optimization configuration for different action time scales of the same flexible resources, taking into account the economic efficiency of equipment investment and grid reliability, and giving full play to the regulation potential of flexible resources, providing strong support for the further development of new energy and the safe and reliable operation of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A schematic diagram of a flow chart of a multi-time scale multivariate flexible resource coordination optimization configuration method provided in an embodiment of the present application;

[0034] Figure 2 A schematic diagram of a photovoltaic random generation scenario provided in an embodiment of the present application;

[0035] Figure 3 A schematic diagram of a random generation scenario of wind power provided in an embodiment of the present application;

[0036] Figure 4 A schematic diagram of a random load generation scenario provided in an embodiment of the present application;

[0037] Figure 5 A schematic diagram of a scheduling curve of a reactive compensation device in a long-time scale optimization configuration result provided in an embodiment of the present application;

[0038] Figure 6 This is a structural diagram of a multi-time scale multi-element flexible resource coordination and optimization configuration system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0040] See also Figure 1 A multi-time scale multivariate flexible resource coordination optimization configuration method provided in an embodiment of the present application includes:

[0041] Step 101: Based on the source-load model taking into account uncertainty, a Monte Carlo method is used for random sampling to obtain a source-load output scenario taking into account uncertainty, and a corresponding scenario time scale is set according to the configuration requirements of each source-load output scenario.

[0042] It should be noted that, based on the source-load model that takes into account uncertainty, the Monte Carlo method is used for random sampling, and the synchronous back substitution method is used for scenario reduction to obtain the source-load output scenario that takes into account uncertainty. According to the actual configuration requirements, the configuration scenario time scale is selected, including hours-days-weeks-months-quarters-years, etc.

[0043] Step 102: Analyze the economic characteristics of the regulating resources to set the objective function of the flexible resource capacity configuration optimization model. The objective function is the total cost of system operation, including: new construction cost of flexible resources, operating cost, environmental cost, and new energy power abandonment cost.

[0044] It should be noted that the optimization goal of the flexible resource capacity allocation optimization model is to combine the analysis of the economic characteristics of regulating resources, consider the investment costs of different types of flexible resources and the penalty costs of flexible capacity shortages, load shedding and new energy abandonment costs as system economic goals.

[0045] Step 103: determine the constraints of the power system and the multiple flexible resources, and analyze the response characteristics of the multiple flexible resources, so as to construct an optimal configuration model of the power system and the multiple flexible resources, wherein the constraints include: system operation constraints and equipment operation constraints.

[0046] It should be noted that the analysis of the characteristics of multiple flexible resources and the constraints of the power system: analysis of the differences in the response characteristics of the "source-grid-load-storage" multiple flexible resources, targeted modeling of the power system and multiple flexible resources, considering the grid constraints of active and reactive coordination, and the flexible regulation-related constraints of multiple flexible resources, such as flow constraints, power consumption constraints, source side constraints, load side constraints, storage side constraints, etc.

[0047] Step 104: Based on the scenario time scale, the objective function required for optimal configuration is selected according to the source-load output scenario, and the locations of multiple flexible resources to be developed are set. The optimal configuration model is solved with the flexible resource power that can be dispatched in each period within the time scale to obtain the collaborative optimization configuration results of multiple types of active power regulation resources considering large-scale new energy grid connection.

[0048] It should be noted that, for the generated uncertainty scenario, the objective function required for the optimal configuration is selected, the optimal configuration time scale that meets the specific needs is selected, the locations of multiple flexible resources to be developed are set, and the optimal configuration model is solved with the dispatchable flexible resource power in each time period t within the time scale T. The results of the coordinated optimal configuration of multiple types of active power regulation resources considering the large-scale grid connection of new energy are obtained to cope with the problem of heavy (over) load of some equipment in the system caused by the uncertainty and intermittency of new energy output over a longer time scale.

[0049] Step 105: According to the collaborative optimization configuration results of multiple types of active power regulation resources, the reactive flexibility resources are cyclically optimized in a short time scale to obtain the reactive flexible resource optimization configuration results.

[0050] It should be noted that based on the active flexible resource scheduling cycle and its scheduling curve in the long-time scale optimization configuration results, the time period with the highest configuration capacity requirement for reactive flexible resources (such as SVC, etc.) is selected, and the long-time scale active resource configuration scheduling results and the short-time scale new energy fluctuation scenario of this period are used as constraints. The reactive flexible resources are cyclically optimized in a short-time scale to obtain the reactive flexible resource optimization configuration results, which can deal with the problem of system node voltage exceeding the limit under the short-term fluctuation of new energy output.

[0051] In one embodiment, in step 101, according to the source-load model taking into account uncertainty, random sampling is performed using the Monte Carlo method to obtain a source-load output scenario taking into account uncertainty, including:

[0052] According to the source-load model taking into account uncertainty, the Monte Carlo method is used for random sampling to generate a random scenario set. The random scenario set is then reduced through the synchronous back substitution method to obtain the source-load output scenario taking into account uncertainty.

[0053] It should be noted that the description of the generation of the multi-flexible resource coordination optimization configuration scenario is as follows:

[0054] Sampling is performed based on the Monte Carlo method and the probability distribution of renewable energy output. Random samples of probability distribution are obtained through sampling. When random scenarios are generated based on the Monte Carlo method, when the number of generated scenarios N is large, it will bring greater pressure to the calculation and solution. In order to reduce the amount of calculation to alleviate the pressure of calculation and solution, and at the same time ensure the random quality of the generated scenarios, it is necessary to reduce the generated original scenarios so that the reduced scenarios can better reflect the characteristics of the original scenarios. The synchronous back-substitution reduction method is used to reduce the generated scenarios, and the Euclidean distance is used as the measurement scale for scenario reduction. Assuming that si (i = 1,..., N) represents the N generated scenarios, the probability corresponding to each scenario is p s (p s >0), and satisfy ∑p s =1, dk(si,sj) represents the Euclidean distance between scene si and scene sj, that is, the norm of the two scene vectors. Set S represents the number of scenes before scene reduction, that is, the set of all scenes; So far, the specific process of the synchronous back-generation scene reduction method is described as follows:

[0055] Step 1: Input the N generated scenarios, then the probability of each scenario generated by the Monte Carlo method is 1 / N, and the number of scenarios in the current scenario set S is N;

[0056] Step 2: For each scene si, calculate its Euclidean distance dk and probability distance P with each remaining scene s' (s'≠si) Dk , as shown below:

[0057]

[0058] Step 3: Calculate the sum of the probability distances P between si and all remaining scenes D (si):

[0059]

[0060] Step 4: Find a scenario d such that P D (d) = minP D (si),si∈S, and reduce it, and find the scene sj with the shortest Euclidean distance dk, and add the probability of scene si to scene sj, as shown in the following formula:

[0061] p sj =p sj +p si (3)

[0062] Step 5: Update the number of scenes and delete {d} from the set S, as shown below:

[0063] S=S-{d} (4)

[0064] Step 6: Repeat steps 2 to 5 above until the number of remaining scenes reaches the set required number. At this time, the set S is the reduced target scene set.

[0065] In one embodiment, the objective function in step 102 is expressed as:

[0066] minf=C op +C bs +C rs +C cl +C ab ;

[0067] In the formula, C op is the operating cost of the traditional unit; C bs C is the new construction and operation cost of energy storage; rs The new construction and operation cost of reactive power compensation equipment; C cl is the controllable load operating cost; C ab The cost of curtailing wind and solar power.

[0068] It should be noted that the economic decision-making goal of optimizing the allocation of system regulation resources is to achieve flexible operation of the system at the lowest cost. The total cost of system operation is the sum of the cost of new construction of flexible resources, operating costs, environmental costs, and the cost of abandoned new energy.

[0069] Traditional unit operating cost: Traditional unit operating cost C op It is equal to the nth time in the period 1 to T. op Unit operating capacity of conventional units Multiply by the unit operating cost c op The sum of the products of is calculated as follows:

[0070]

[0071] Where N op is the total number of traditional units.

[0072] Load regulation cost: Load regulation cost C cl It is equal to the unit operating capacity of all controllable loads participating in the regulation in the period 1 to T multiplied by the unit operating cost c l The sum of the products of :

[0073]

[0074] In the formula, For nth clt The power of a translatable load at time t is: For nthcls The load power that can be reduced at time t is c clt 、c cls are the load that can be shifted per unit time and the load that can be reduced, respectively. clt 、N cls They are the total number of loads that can be translated and the total number of loads that can be reduced, respectively.

[0075] Energy storage cost: Energy storage cost C bs Including capacity configuration cost C inv.bs , Operation and maintenance costs C mai.bs and energy storage degradation cost C deg.bs .

[0076]

[0077]

[0078] In the formula, r, Y bs are interest rate and battery life respectively; c invp.bs 、c inve.bs 、c mai.bs 、c deg.bs They are the unit capacity configuration cost, unit energy configuration cost, unit capacity operation cost per unit time, and unit capacity degradation cost per unit time of battery energy storage. The nth bs The charging power and discharging power of a storage at time t, E nbs,t For nth bs The state of charge of the energy storage at time t, N bs is the total number of energy storage devices.

[0079] Reactive power compensation cost: Reactive power compensation cost C rs Including capacity configuration cost C inv.rs and operation and maintenance costs C mai.rs The calculation formula is as follows:

[0080]

[0081]

[0082] Where Y rs and the service life of reactive power compensation equipment; c inv.rs 、c mai.rs They are the unit capacity configuration cost and unit capacity operation cost per unit time of battery energy storage respectively. For nth rs The reactive power compensation device outputs power at time t, N rs is the total number of reactive power compensation devices.

[0083] Wind and solar power abandonment penalty cost: When new energy is abandoned due to insufficient flexibility, it will cause wind and solar power abandonment penalty cost. Wind and solar power abandonment penalty cost of new energy with flexible resources C ab It is equal to the sum of the power abandonment penalty costs of all flexible resources in the period 1 to T.

[0084]

[0085] In the formula, The nth wt The maximum output and actual output of each wind turbine at time t, The nth pv The maximum output and actual output of a photovoltaic unit at time t, c ab is the unit wind and solar power curtailment energy cost, N wt 、N pv They are the total number of wind turbines and the total number of photovoltaic units, respectively.

[0086] Load shedding penalty cost: If the system load is shedding due to insufficient flexible regulation capacity, load shedding penalty cost will be incurred. The system load shedding penalty cost is equal to the sum of the load shedding penalty costs in the system during the period 1 to T.

[0087]

[0088] Where P t cl is the load shedding capacity at time t, c cl is the unit capacity load shedding cost, P t cl is the load power removed at time t.

[0089] It should be further explained that when constructing the optimal configuration model of the power system and multiple flexible resources, it is also necessary to consider the decision variables:

[0090] The decision variables of the multi-time scale multivariate flexible resource coordination optimization configuration algorithm considering active and reactive power coordination are shown in Table 1:

[0091] Table 1 Decision variables of optimization configuration algorithm

[0092]

[0093]

[0094] In one embodiment, the system operation constraints in step 103 include: power flow constraints, node voltage constraints, branch power flow constraints, and power balance constraints. Equipment operation constraints include: traditional generator set constraints, shiftable load constraints, curtailable load constraints, and energy storage constraints.

[0095] System operation constraints:

[0096] Power flow constraints:

[0097] The linear DistFlow power flow equation is used to describe the power flow constraints, and the Big-M method is used to relax the voltage equation. The quadratic terms with sufficiently small values ​​can be ignored.

[0098]

[0099] Where P ij , Q ij are the active power and reactive power of line ij respectively; R ij , X ij are the resistance and reactance of line ij respectively.

[0100] Since the above formula is a nonlinear constraint between power, voltage and current, in order to reduce the complexity and difficulty of solving the original model, the second order cone relaxation method (SOCR) is used to convert it into a second order cone constraint. As a result, the scheduling model is simplified from a mixed integer nonlinear programming model (MINLP) to a mixed integer second order cone programming model (MISOCP), which can be directly solved by commercial solvers.

[0101]

[0102] Node voltage constraints:

[0103] The voltage at each load node should meet the required amplitude range.

[0104]

[0105] Where U i,max As the upper limit of node voltage, this paper sets U i,max =1.05U n ; U i,min is the lower limit of the node voltage. This application sets U i,m i n =0.95U n , U n It is the voltage reference value, that is, the rated voltage of the distribution network system.

[0106] Branch flow constraints:

[0107] The power of each line in the distribution network should not exceed the line transmission capacity limit.

[0108]

[0109] In the formula, are the maximum active power and maximum reactive power of line ij, respectively. ij,t is the running status of the line at time t, which is a 0-1 variable.

[0110] Power balance constraints:

[0111] The balance between power supply and load consumption in the system should be maintained at all times.

[0112]

[0113] In the formula, They are respectively the active and reactive output of conventional load at time t.

[0114] Equipment operation constraints:

[0115] Constraints of traditional generator sets: Traditional generator sets are power sources with strong stability and high reliability in the power system, and are an important part of flexible resources. Traditional energy sources usually have large installed capacity and stable output, but most traditional resources have weak adjustment capabilities and long start-up times, so they are subject to certain restrictions when providing flexible resources. Traditional thermal power units should ensure their normal start-up and shutdown and operating status when providing flexible resources, and meet the following constraints:

[0116]

[0117] In the formula, They are the shortest start-up and shutdown times of traditional thermal power units; The upper and lower limits of the ramp rate of traditional thermal power units; are the duration of the start and stop states of the traditional thermal power unit at time t-1 respectively; is the operating status of the traditional thermal power unit at time t, which is a 0-1 variable; They are the start and stop states of the traditional thermal power unit at time t, which are 0-1 variables, and ΔT is the start and stop interval time.

[0118] Reactive power compensation device constraints:

[0119] If a static reactive power compensation device is connected, its reactive power compensation amount can be adjusted continuously, so the operation constraint is:

[0120]

[0121] In the formula, The nth rs The minimum and maximum values ​​of reactive power compensation of each SVC device.

[0122] Translational load constraints:

[0123] The regulation characteristics of the shiftable load require that the total load on the user side remains unchanged, only the power consumption time changes, and the transfer value of the shiftable load in time period t should be at the maximum value that can be transferred. and minimum value Between tdn is the set of time periods during which the load can be shifted and reduced, t tup The set of time periods that can be increased for the shiftable load, that is, the load is only tdn can be reduced within the time period and needs to be reduced at t∈t tup Increase inside, The sum of the transfer load of the movable load in each time period should be 0:

[0124]

[0125] Reducible load constraints:

[0126] The load that can be cut is the load that is directly interrupted and is greater than the lower limit of the load that can be cut in period j, t sdn is the set of load curtailment periods, i.e., the load is curtailed only at time t∈t sdn The inner part can be reduced, and the constraint expression is as follows:

[0127]

[0128] Energy storage constraints:

[0129] Energy storage flexibility resources have similar working characteristics. Their adjustable range is limited by charging and discharging power, efficiency and energy within a certain time scale. The power constraints of energy storage equipment, the relationship between storage capacity and charging / discharging power, and storage capacity constraints are as follows:

[0130]

[0131]

[0132] In the formula, The charging and discharging status flags of the energy storage system are 0-1 variables respectively; are the maximum charging and discharging power of the energy storage system respectively; is the reactive power output of the energy storage system at time t; is the state of charge of the energy storage system at time t; η cha , η dis are the charging and discharging efficiencies of the energy storage system respectively; E is the battery capacity of the energy storage system; are the upper and lower limits of the state of charge of the energy storage system respectively; Δt is the time change.

[0133] In one embodiment, the optimization configuration model solved in step 104 obtains the collaborative optimization configuration result of multiple types of active power regulation resources considering large-scale new energy grid connection, including:

[0134] The commercial solver is called to solve the optimization configuration model, and the output curve of the multi-time scale multivariate flexible resource coordinated optimization configuration algorithm considering active and reactive power coordination in each configuration scenario is obtained. The upper limit of the capacity value of each flexible resource called within the time scale of each scenario is extracted, and the upper limit of the capacity value of each flexible resource in each scenario is compared, and the highest value is taken as the final configuration capacity of the equipment.

[0135] It should be noted that, for the different operation scenarios generated, the optimization configuration calculation of multiple types of adjustable resources is performed, and the mixed integer second-order cone programming model is solved by calling a commercial solver, that is, the output curve of the multi-time scale multivariate flexible resource coordinated optimization configuration algorithm considering active and reactive power coordination in each configuration scenario is obtained, and the upper limit of the capacity value of each flexible resource called within the T time scale of each scenario is extracted:

[0136]

[0137] Compare the upper limits of the flexible resource capacity values ​​in each scenario, and take the highest value as the final configuration capacity of the device.

[0138] In one embodiment, step 105 performs short-time scale cyclic optimization configuration on reactive flexibility resources according to the collaborative optimization configuration results of multiple types of active power regulation resources to obtain the reactive flexible resource optimization configuration results, including:

[0139] Based on the active flexible resource scheduling cycle and scheduling curve in the collaborative optimization configuration results of multiple types of active power regulation resources, the time period with the highest requirement for reactive flexible resource configuration capacity is selected. The long-time scale active resource configuration scheduling results and the short-time scale new energy fluctuation scenario are used as constraints to perform short-time scale cyclic optimization configuration of reactive flexible resources and obtain the reactive flexible resource optimization configuration results.

[0140] The following is an analysis of an implementation example of multi-time scale multi-element flexible resource coordination optimization configuration considering active and reactive power coordination:

[0141] Based on the optimization configuration scenario generation method considering uncertainty proposed in this application, the following is generated: Figure 2-4 The 10 typical scenarios of photovoltaic, wind power and load shown are used to verify the subsequent optimization configuration algorithm.

[0142] The adjusted IEEE 33-BUS system (Matpower 7.0, "case33mg" instance) is used as the distribution network system for simulation analysis. Distribution network benchmark value selection: S B =1MVA,UB =12.66kV; the detailed variable simulation parameters of each device are shown in Table 2 and Table 3, and the economic parameters of the example are shown in Table 4. In this embodiment, the long time scale is selected within 0-24h for optimization in units of h, and the short time scale is selected within 0-1h for optimization in units of 10min.

[0143] Table 2 Generators and adjustable resource development locations

[0144] equipment symbol Install Node equipment symbol Configurable nodes Traditional unit OP 2 Energy Storage ESS 8 Photovoltaic PV 7 Reactive power compensation SVC 30 Wind power WT 11 Translable load CLT 23 Load reduction possible CLS 24

[0145] Table 3 Technical parameters of the example

[0146]

[0147] Table 4 Economic parameters of the example

[0148]

[0149]

[0150] The results of the active time scale optimization configuration are shown in Table 5 below:

[0151] Table 5 Active time scale optimization configuration results

[0152]

[0153] The dispatch curve of reactive power compensation device in the long-term optimization configuration result is as follows: Figure 5 As shown:

[0154] For each dispatching scenario, the time period with the highest requirement for reactive resource configuration capacity, i.e., the 18-20 hour period, is selected. The short-time scale new energy fluctuation scenario (new energy output has a 30% fluctuation) and the long-time scale active resource configuration results are used as constraints. The reactive flexibility resources are optimized in a short-time scale cycle with a time scale of 10 minutes. The updated reactive configuration results are shown in Table 6:

[0155] Table 6 Reactive time scale optimization configuration results

[0156] SVC capacity / kVar 781.27

[0157] The multi-time-scale multi-element flexible resource coordinated optimization configuration method provided in the embodiment of the present application, based on the traditional flexible resource configuration method, distinguishes the differences in time scales and regulation characteristics of active and reactive multi-element flexible resources, and proposes a multi-time-scale hierarchical optimization configuration method. It can perform differentiated optimization configuration for different action time scales of the same flexible resources, take into account the economic efficiency of equipment investment and grid reliability, and give full play to the regulation potential of flexible resources, providing strong support for the further development of new energy and the safe and reliable operation of the power system.

[0158] The above is a multi-time scale multi-flexible resource coordinated optimization configuration method provided in an embodiment of the present application, and the following is a multi-time scale multi-flexible resource coordinated optimization configuration system provided in an embodiment of the present application.

[0159] See also Figure 6 , a multi-time scale multivariate flexible resource coordination optimization configuration system provided in an embodiment of the present application includes:

[0160] A generating unit 201 is used to perform random sampling using a Monte Carlo method according to a source-load model taking uncertainty into account, to obtain a source-load output scenario taking uncertainty into account, and to set a corresponding scenario time scale according to configuration requirements of each source-load output scenario;

[0161] A setting unit 202 is used to analyze the economic characteristics of the adjustment resources to set the objective function of the flexible resource capacity configuration optimization model, where the objective function is the total system operation cost, including: the new construction cost, operation cost, environmental cost, and new energy abandonment cost of the flexible resources;

[0162] A modeling unit 203 is used to determine the constraints of the power system and the multiple flexible resources, and analyze the response characteristics of the multiple flexible resources, so as to construct an optimal configuration model of the power system and the multiple flexible resources, wherein the constraints include: system operation constraints and equipment operation constraints;

[0163] The first calculation unit 204 is used to select the objective function required for the optimization configuration based on the scenario time scale and the source-load output scenario, and set the location of the multi-element flexible resources to be developed, and solve the optimization configuration model with the flexible resource power that can be dispatched in each time period within the time scale to obtain the collaborative optimization configuration result of multiple types of active power regulation resources considering the large-scale new energy grid connection;

[0164] The second calculation unit 205 is used to perform short-time scale cyclic optimization configuration on reactive flexibility resources according to the collaborative optimization configuration results of multiple types of active power regulation resources to obtain the reactive flexible resource optimization configuration results.

[0165] Furthermore, in an embodiment of the present application, a multi-time scale multivariate flexible resource coordination optimization configuration device is provided, the device comprising a processor and a memory:

[0166] The memory is used to store program code and transmit the program code to the processor;

[0167] The processor is used to execute the steps of the multi-time-scale multivariate flexible resource coordinated optimization configuration method as described in the above method embodiment according to the instructions in the program code.

[0168] Furthermore, a computer-readable storage medium is provided in an embodiment of the present application, and the computer-readable storage medium is used to store program code, and the program code is used to execute the multi-time-scale multivariate flexible resource coordinated optimization configuration method described in the above method embodiment.

[0169] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0170] The terms "first", "second", "third", "fourth", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0171] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0172] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0173] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0174] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0175] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0176] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-time scale multivariate flexible resource coordination optimization configuration method, characterized in that: include: According to the source-load model taking into account uncertainty, the Monte Carlo method is used for random sampling to obtain source-load output scenarios taking into account uncertainty, and the corresponding scenario time scale is set according to the configuration requirements of each source-load output scenario; Combined with the economic characteristics of the regulating resources, the objective function of the flexible resource capacity configuration optimization model is analyzed to set the objective function of the system operation total cost, including: the new construction cost of the flexible resources, the operating cost, the environmental cost, and the cost of the new energy power abandonment; Determine the constraints of the power system and multiple flexible resources, and analyze the response characteristics of the multiple flexible resources, so as to construct an optimal configuration model of the power system and multiple flexible resources, wherein the constraints include: system operation constraints and equipment operation constraints; Based on the scenario time scale, the objective function required for the optimal configuration is selected according to the source-load output scenario, and the position of the multi-element flexible resources to be developed is set. The optimal configuration model is solved with the flexible resource power that can be dispatched in each time period within the time scale to obtain the collaborative optimal configuration result of multiple types of active power regulation resources considering the large-scale new energy grid connection; According to the collaborative optimization configuration results of the multiple types of active power regulation resources, the reactive flexibility resources are cyclically optimized in a short time scale to obtain the reactive flexible resource optimization configuration results.

2. The multi-time scale multivariate flexible resource coordinated optimization configuration method according to claim 1 is characterized in that: The source-load model taking into account uncertainty is randomly sampled using the Monte Carlo method to obtain a source-load output scenario taking into account uncertainty, including: According to the source-load model taking into account uncertainty, the Monte Carlo method is used for random sampling to generate a random scenario set, and the random scenario set is reduced by synchronous back substitution to obtain a source-load output scenario taking into account uncertainty.

3. The multi-time scale multivariate flexible resource coordinated optimization configuration method according to claim 1 is characterized in that: The expression of the objective function is: minf=C op +C bs +C rs +C cl +C ab ; In the formula, C op is the operating cost of the traditional unit; C bs C is the new construction and operation cost of energy storage; rs The new construction and operation cost of reactive power compensation equipment; C cl is the controllable load operating cost; C ab The cost of curtailing wind and solar power.

4. The multi-time scale multivariate flexible resource coordinated optimization configuration method according to claim 1 is characterized in that: The system operation constraints include: power flow constraints, node voltage constraints, branch power flow constraints, and power balance constraints.

5. The multi-time scale multivariate flexible resource coordinated optimization configuration method according to claim 1 is characterized in that: The equipment operation constraints include: traditional generator set constraints, shiftable load constraints, reducible load constraints, and energy storage constraints.

6. The multi-time scale multivariate flexible resource coordinated optimization configuration method according to claim 1 is characterized in that: The optimization configuration model is solved to obtain the collaborative optimization configuration results of multiple types of active power regulation resources considering large-scale new energy grid connection, including: The commercial solver is called to solve the optimization configuration model, and the output curve of the multi-time scale multivariate flexible resource coordinated optimization configuration algorithm considering active and reactive power coordination in each configuration scenario is obtained. The upper limit of the capacity value of each flexible resource called within the time scale of each scenario is extracted, and the upper limit of the capacity value of each flexible resource in each scenario is compared, and the highest value is taken as the final configuration capacity of the equipment.

7. The multi-time scale multivariate flexible resource coordinated optimization configuration method according to claim 1 is characterized in that: The method of performing short-time scale cyclic optimization configuration on reactive flexibility resources according to the collaborative optimization configuration results of the multi-type active power regulation resources to obtain the reactive flexible resource optimization configuration results includes: Based on the active flexible resource scheduling cycle and scheduling curve in the collaborative optimization configuration results of the multiple types of active power regulation resources, the time period with the highest requirement for the reactive flexible resource configuration capacity is selected, and the long-time scale active resource configuration scheduling results and the short-time scale new energy fluctuation scenario of the time period are used as constraints to perform short-time scale cyclic optimization configuration of the reactive flexible resources to obtain the reactive flexible resource optimization configuration result.

8. A multi-time scale multivariate flexible resource coordination optimization configuration system, characterized in that: include: A generating unit, configured to perform random sampling using a Monte Carlo method according to a source-load model taking uncertainty into account, to obtain a source-load output scenario taking uncertainty into account, and to set a corresponding scenario time scale according to configuration requirements of each source-load output scenario; A setting unit is used to analyze the economic characteristics of the regulating resources to set the objective function of the flexible resource capacity configuration optimization model, where the objective function is the total cost of system operation, including: new construction cost, operation cost, environmental cost, and new energy power abandonment cost of the flexible resources; A modeling unit, used for determining the constraints of the power system and the multiple flexible resources, and analyzing the response characteristics of the multiple flexible resources, so as to construct an optimal configuration model of the power system and the multiple flexible resources, wherein the constraints include: system operation constraints and equipment operation constraints; A first calculation unit is used to select the objective function required for the optimal configuration based on the scenario time scale and the source-load output scenario, and set the position of the multi-element flexible resources to be developed, and solve the optimal configuration model with the flexible resource power that can be dispatched in each time period within the time scale to obtain the collaborative optimal configuration result of multiple types of active power regulation resources considering the large-scale new energy grid connection; The second calculation unit is used to perform short-time scale cyclic optimization configuration of reactive flexibility resources according to the collaborative optimization configuration results of the multiple types of active power regulation resources to obtain the optimization configuration results of reactive flexible resources.

9. A multi-time scale multivariate flexible resource coordination and optimization configuration device, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the multi-time-scale multivariate flexible resource coordinated optimization configuration method described in any one of claims 1-7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the multi-time-scale multivariate flexible resource coordinated optimization configuration method according to any one of claims 1 to 7.

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