Method and device for configuring the capacity of a network-forming energy storage combined with robust optimization

Abstract

The present invention relates to the technical field of renewable energy, and is a method and device for configuring the capacity of a network-forming energy storage combined with robust optimization, including constructing a robust uncertainty set of actual power deviations; constructing a calculation expression for spatial constraint parameters in the robust uncertainty set; quantifying the system robustness using the probability value of renewable energy output outside extreme cases; constructing an optimization configuration model for network-forming energy storage under extreme cases of power generation and consumption power deviations; and solving the optimization configuration model for network-forming energy storage using the whale algorithm. On the basis of predicting power generation and consumption power, the present invention constructs a robust set with flexible adjustable boundaries by taking into account the probability characteristics of actual power deviations, and introduces spatial constraint parameters of uncertainty to flexibly adjust the boundaries of the constructed uncertainty set. On the basis of scientifically characterizing power uncertainty, the capacity of the energy storage power station is configured to accurately match the volatility of usage requirements, thereby improving the utilization rate of the energy storage.

Description

Technical Field

[0001] The present invention relates to the technical field of renewable energy, and is a method and device for configuring the capacity of a network-forming energy storage combined with robust optimization. Background Art

[0002] Renewable energy has the characteristics of large randomness in power generation and difficulty in accurate prediction. After being connected to the grid, the flexible response ability of the system is insufficient. Network-forming energy storage has the ability of rapid peak shaving and frequency modulation, and is one of the excellent technical means to achieve dynamic and rapid matching of supply and demand and improve the flexible regulation ability of the power grid. It is widely used in coordinating power generation plans and meeting the frequency requirements of the power grid. However, there is a problem that energy storage is not fully utilized due to the volatility of usage demand. The main manifestations are that the investment cost is too large or the return period is too long during the preliminary planning of energy storage. Therefore, the scientific description of power generation uncertainty and then the accurate characterization of energy storage usage demand are the main challenges in energy storage configuration.

[0003] The methods for describing uncertainty in the power system are mainly divided into the reserve setting method and the stochastic programming method. However, in the context of a new power system with complex morphological evolution and multiple uncertainties, these two methods may lead to uneconomical or unreliable results. Subsequently, methods such as fuzzyization, scenario analysis, spectrum analysis, point estimation method, and stochastic chance-constrained programming have been continuously proposed. Although they can improve the feasibility of the results, there are still many problems. For example, the selection of the membership function in the fuzzy chance-constrained method is relatively subjective; methods such as scenario analysis, spectrum analysis, and stochastic chance-constrained programming all require a large amount of sample data, the results are restricted by the number of scenarios, the calculation of multi-scenario description of uncertainty is complex, and it is difficult to ensure the solution efficiency and accuracy; due to the differences caused by the diversity of statistical samples in the point estimation method, the inference results are affected by the quality of the samples. Robust optimization opens up a new idea for describing power uncertainty by setting the fluctuation range of uncertain variables and finding the decision-making scheme under the worst scenario. However, most of the current robust optimizations establish an uncertain set with fixed boundaries, ignoring economy when improving the system robustness, making the results too conservative; ignoring robustness when improving the system economy, making the results unreliable. Even when using the preference optimization method to determine the set boundary, there are still disadvantages such as the inability to quantify the system robustness and effectively balance the economy. Therefore, how to reasonably adjust the boundary of the uncertain set, quantify the system robustness, so as to scientifically balance the economy and robustness, and clarify the complex relationship between multiple uncertain variables and the system economy and robustness when they are coupled needs further research. Summary of the Invention

[0004] The present invention provides a method and device for configuring the capacity of a network-forming energy storage combined with robust optimization, which overcomes the above-mentioned deficiencies of the prior art and can effectively solve the problem that the existing uncertainty description method used in the capacity configuration of energy storage power stations cannot adjust the boundary of the constructed uncertain set.

[0005] One of the technical solutions of the present invention is achieved by the following measures: A method for configuring the capacity of a network-forming energy storage combined with robust optimization, including:

[0006] Based on the prediction of power generation and consumption power, a robust uncertainty set of actual power deviation is constructed by using infinity norm constraint and 1-norm constraint;

[0007] Using the Lindeberg-levy central limit theorem to construct the calculation expression of the spatial constraint parameters in the robust uncertainty set;

[0008] Using linear duality theory and constructing a Lagrangian function to construct the output of renewable energy power stations in the robust uncertainty set under extreme conditions;

[0009] Quantifying the system robustness by using the probability value of renewable energy output outside the extreme situation;

[0010] With the goal of minimizing the overall cost in a large-scale renewable energy access system, a network-forming energy storage optimization configuration model is constructed under extreme conditions of power generation and consumption power deviation;

[0011] Using the whale algorithm to solve the network-forming energy storage optimization configuration model and analyzing the influence of uncertainty factors on the results of the network-forming energy storage optimization configuration.

[0012] The following is a further optimization and / or improvement of the above-mentioned invention technical solution:

[0013] The robust uncertainty set of the actual power deviation of wind power output constructed by using infinity norm constraint and 1-norm constraint based on the predicted power is as follows:

[0014]

[0015] In the formula, is the actual output of wind farm i at time t, is the predicted output, are the upper and lower limits of the output deviation respectively; N w is the number of wind farms is the infinity norm; is the perturbation 1-norm constraint, corresponding to the spatial clustering effect of wind power output in reality; is the deviation coefficient of wind farm i at time t; Similarly, uncertainty sets can be constructed for other uncertain variables in the system.

[0016] The above-mentioned construction of the calculation expression of the spatial constraint parameters in the robust uncertainty set by using the Lindeberg-levy central limit theorem includes:

[0017] Determine the spatial constraint parameters of wind power output uncertainty, including:

[0018] (1) Suppose in the first step There is , and assume that the power deviation is an independent and identically distributed random variable, then Is also independent and identically distributed, and its expectation is denoted as The variance is

[0019] (2) The standard variable of Is as follows:

[0020]

[0021] In the formula, E(·) represents the expectation, and D(·) represents the variance;

[0022] (3) The standard variable Follows the standard Gaussian distribution, and its cumulative distribution function For any probability α w Satisfies the following equation relationship:

[0023]

[0024] In the formula, α w Is the confidence probability;

[0025] (4) Deduce the spatial constraint parameters Of wind power output uncertainty as follows:

[0026]

[0027] Through the steps of determining the spatial constraint parameters of wind power output uncertainty, determine the spatial constraint parameters of other uncertain variables.

[0028] The above uses linear duality theory and constructs a Lagrangian function to construct the output of renewable energy power plants in the robust uncertain set under extreme conditions, including:

[0029] Use linear duality theory and construct a Lagrangian function to construct the output power of wind power in the robust uncertain set under extreme conditions;

[0030] (1) Through linear duality theory, construct The Lagrangian function of:

[0031]

[0032] (2) Since It can be obtained that the power of wind power output under extreme conditions of output deviation is:

[0033]

[0034] Since the optimal solution of linear programming is at the vertex, simplify the above formula:

[0035]

[0036] (3) According to the above combination of the most extreme situation in the t period, only the deviation coefficient of the output of one wind farm is less than 1. Let this wind farm be j, and its total output is as follows:

[0037]

[0038] In the formula, is the floor function symbol;

[0039] Use the above steps to determine the output power of other uncertainties in the extreme situation.

[0040] The above takes the minimum overall cost in the large-scale renewable energy access system as the goal, and constructs an optimal configuration model of network-forming energy storage in the extreme situation of power deviation in power generation and power consumption, including:

[0041] Taking the minimum overall cost of the access system as the goal, construct the objective function of the optimal configuration model of energy storage as shown below, where the overall cost includes: the power generation cost of thermal power units in the extreme situation of renewable energy power generation prediction error Unit start-stop cost Annualized initial investment cost of energy storage Maintenance cost Frequency modulation cost C PFR ;

[0042]

[0043] Considering the unit operation and network security factors in the access system, determine the constraint conditions of the optimal configuration model of network-forming energy storage;

[0044] (1) Power balance constraint:

[0045]

[0046] In the formula, is the power shortage at time t, is the load, is the charge and discharge power of energy storage to meet the power supply and demand balance; are all; extreme power situations;

[0047] (2) Energy storage operation constraints include:

[0048] 1. Charge and discharge power constraint:

[0049]

[0050] 2. Charge state constraint:

[0051]

[0052] 3. Equation that the charging amount equals the discharging amount within the total scheduling period:

[0053]

[0054] (3) Dynamic frequency output constraint of thermal power units, including:

[0055] 1. Frequency modulation output constraint:

[0056]

[0057] In the formula, K i is the static characteristic coefficient of power-frequency of thermal power unit i, Δf max represents the maximum frequency deviation, is the dead zone of frequency modulation of generator set i;

[0058] 2. Participation in frequency modulation state constraint:

[0059]

[0060] 3. Frequency modulation capacity constraint:

[0061]

[0062] (4) Frequency modulation capacity demand constraint:

[0063]

[0064] In the formula, is the dynamic frequency modulation capacity demand, Δ PRN is the disturbance amount of the predicted output of renewable energy.

[0065] The above uses the whale algorithm to solve the configuration optimization model of the network-forming energy storage, including:

[0066] For the boundary constraints in the configuration optimization model of the network-forming energy storage, the out-of-bounds processing method in the heuristic algorithm is used, and the ramp constraint is transformed into a boundary constraint through dynamic update;

[0067] For the power balance constraint in the configuration optimization model of the network-forming energy storage, the dynamic relaxation constraint processing method is adopted;

[0068] For the charge state constraint of the energy storage system in the configuration optimization model of the network-forming energy storage, it is processed through the filter technology;

[0069] Using the optimization mechanism of the whale algorithm to obtain the optimal solution of the configuration model of the network-forming energy storage.

[0070] The second technical solution of the present invention is achieved by the following measures: A network-forming energy storage capacity configuration device combined with robust optimization, including:

[0071] A set construction unit that constructs a robust uncertainty set of the actual power deviation by using the infinity norm constraint and the 1-norm constraint on the basis of predicting the power generation and consumption power;

[0072] A constraint parameter determination unit that constructs an expression for calculating the spatial constraint parameters in the robust uncertainty set by using the Lindeberg-levy central limit theorem;

[0073] A processing and determination unit that constructs the output of the renewable energy power station in extreme cases in the robust uncertainty set by using the linear duality theory and constructing the Lagrangian function;

[0074] A quantization unit that quantifies the system robustness by using the probability value of the renewable energy output outside the extreme cases;

[0075] A model construction unit that constructs an optimization configuration model of the network-forming energy storage in extreme cases of power generation and consumption power deviation with the goal of minimizing the overall cost in a large-scale renewable energy access system;

[0076] A solution unit that uses the whale algorithm to solve the optimization configuration model of the network-forming energy storage and analyzes the influence of the influencing factors of uncertainty on the optimization configuration result of the network-forming energy storage.

[0077] Based on the robust optimization of the network-forming energy storage capacity configuration with accurate probability distribution information, on the basis of predicting the power generation and consumption power, a robust set with a flexible adjustable boundary is constructed by taking into account the probability characteristics of the actual power deviation, and the spatial constraint parameters of uncertainty are introduced to flexibly adjust the boundary of the constructed uncertainty set. The capacity of the energy storage power station is configured on the basis of scientifically characterizing the power uncertainty to accurately match the volatility of the usage demand, thereby improving the utilization rate of the energy storage. Description of the Drawings

[0078] Att Figure 1 is a schematic diagram of the method flow of the present invention.

[0079] Att Figure 2 is the predicted values of the daily load, wind speed, irradiation intensity and temperature of a typical day in the present invention.

[0080] Att Figure 3 is the relationship between the confidence probability, spatial constraint parameters and POE in the wind power uncertainty set of the present invention.

[0081] Att Figure 4It is the solution flowchart of the energy storage optimization configuration model in the present invention.

[0082] Appendix Figure 5 It is the influence of the confidence probability of the uncertain quantity on the economy of the energy storage configuration result in the present invention.

[0083] Appendix Figure 6 It is the influence of the spatial clustering effect of the uncertain quantity on the economy of the energy storage configuration result in the present invention.

[0084] Appendix Figure 7 It is the influence of the uncertain quantity on the frequency modulation output of the energy storage and thermal power unit in the present invention.

[0085] Appendix Figure 8 It is the schematic diagram of the device structure of the present invention. Specific implementation manners

[0086] The present invention is not limited by the following embodiments, and the specific implementation manners can be determined according to the technical solution of the present invention and the actual situation.

[0087] The present invention will be further described below in conjunction with the embodiments and the drawings:

[0088] Embodiment 1: As shown in Appendix Figure 1 The embodiment of the present invention discloses a method for configuring the capacity of a network-forming energy storage combined with robust optimization, including:

[0089] Step S11, on the basis of predicting the power generation and consumption, construct a robust uncertainty set of the actual power deviation by using the infinity norm constraint and the 1-norm constraint;

[0090] Step S12, construct the calculation expression of the spatial constraint parameter in the robust uncertainty set by using the Lindeberg-levy central limit theorem;

[0091] Step S13, construct the output of the renewable energy power station in the extreme case in the robust uncertainty set by using the linear duality theory and constructing the Lagrangian function;

[0092] Step S14, quantify the system robustness by using the probability value outside the extreme case of the renewable energy output;

[0093] Step S15, with the goal of minimizing the overall cost in the system with large-scale renewable energy access, construct an optimization configuration model of the network-forming energy storage in the extreme case of the power generation and consumption deviation;

[0094] Step S16, use the whale algorithm to solve the optimization configuration model of the network-forming energy storage, and analyze the influence of the uncertainty influencing factors on the optimization configuration result of the network-forming energy storage.

[0095] A method for configuring the capacity of a network-forming energy storage combined with robust optimization. It lies in constructing a robust uncertainty set with flexible adjustable boundaries based on the probability characteristics of the actual power deviation on the basis of predicting the power generation and consumption, scientifically describing and accurately analyzing the uncertainty of power generation and consumption in a large-scale renewable energy access system. The boundary of the uncertainty set is flexibly adjusted by using the space constraint parameters to make up for the defect that the interval robust optimization result cannot take into account both economy and robustness. On the basis of the interval robustness with flexible adjustable boundaries of the uncertainty set, the system robustness is quantified by using the probability value of operation outside the extreme situation, making up for the current inability to quantify the robustness, thus intuitively balancing the defects of the economy and robustness of the result. On the basis of scientifically describing and characterizing the power generation uncertainty, taking the large-scale renewable energy access system as the research object, taking the thermal power unit as the main rotating equipment of the access system, and the energy storage as a necessary supplement, a network-forming energy storage optimization configuration model considering the dynamic frequency demand of the access system is constructed, and the influence of the influencing factors of uncertainty on the result of the network-forming energy storage optimization configuration is analyzed.

[0096] Example 2: The embodiment of the present invention discloses a method for configuring the capacity of a network-forming energy storage combined with robust optimization, including:

[0097] Step S21, constructing a robust uncertainty set of the actual power deviation by using the infinity norm constraint and the 1-norm constraint on the basis of predicting the power generation and consumption;

[0098] In the above steps, taking the wind power output as an example, on the basis of predicting the power generation and consumption (as shown in the appendix Figure 2 ), the robust uncertainty set of the actual power deviation constructed by using the infinity norm constraint and the 1-norm constraint is:

[0099]

[0100] In the formula, is the actual output of wind farm i at time t, is the predicted output, are the upper and lower limits of the output deviation respectively. N w is the number of wind farms is the infinity norm; is the perturbation 1-norm constraint, corresponding to the spatial clustering effect of the wind power output in reality. The spatial clustering effect can be explained as that the deviations of the powers of each wind farm cannot reach the maximum value at the same time in the same scheduling period. Therefore, the uncertainty space constraint parameter of the wind farm i at time t is introduced. Similarly, the robust uncertainty set can be constructed for other uncertain variables in the system.

[0101] In this step, the 1-norm constraint corresponds to the spatial clustering effect of the renewable energy output in the actual problem, that is, the actual power deviations of all power stations in the renewable energy power station group cannot reach the maximum simultaneously at the same time section. Therefore, an uncertain spatial constraint parameter is introduced to flexibly adjust the boundary of the constructed uncertain set.

[0102] Step S22: Use the Lindeberg-levy central limit theorem to construct the calculation expression of the spatial constraint parameter in the robust uncertain set;

[0103] The above steps include:

[0104] Step S201: Taking the wind power output as an example, construct the calculation expression of the spatial constraint parameter in the robust uncertain set, including:

[0105] (1) Suppose in step S21 There is Suppose the power deviation is an independent and identically distributed random variable, then is also independent and identically distributed, and its expectation is The variance is

[0106] (2) The standard variable of is as follows:

[0107]

[0108] In the formula, E(·) represents the expectation, and D(·) represents the variance;

[0109] (3) The standard variable obeys the standard Gaussian distribution, and its cumulative distribution function For any probability α w Satisfies the following equation relationship:

[0110]

[0111] In the formula, α w Is the confidence probability; Obtained through prediction data and statistical analysis. If there is not enough historical data as a sample, it can be assumed that obeys the Gaussian distribution; Assume obeys the Gaussian distribution, and its expectation is 0, and the variance is

[0112] (4) According to the above, the uncertain spatial constraint parameter of the wind power output is as follows:

[0113]

[0114] In step S202, the process of determining the spatial constraint parameter segments of other uncertain variables is the same as that in step S201.

[0115] In step S23, using the linear duality theory, construct the Lagrangian function to construct the output of the renewable energy power station in the robust uncertain set under extreme conditions;

[0116] In step S301, taking the wind power output as an example, using the linear duality theory, construct the Lagrangian function to construct the output power of the wind farm in the robust uncertain set under extreme conditions;

[0117] (1) Through the linear duality theory, construct the Lagrangian function of:

[0118]

[0119] (2) Since it is obtained that the power of the wind power output under the extreme condition of the output deviation is:

[0120]

[0121] Since the optimal solution of the linear programming is at the vertex, simplify the above formula:

[0122]

[0123] (3) According to the above combination, in the most extreme case at time t, there is only one wind farm with an output deviation coefficient less than 1. Let this wind farm be j, and its total output is as follows:

[0124]

[0125] In the formula, is the floor symbol;

[0126] In step S302, use step S302 to determine the output power of other uncertain quantities under extreme conditions.

[0127] In step S24, use the probability value of the renewable energy output outside the extreme case to quantify the system robustness;

[0128] Taking the wind power output as an example, on the basis of S22 and S23, set event A as the wind power output outside the constructed uncertain set, then the probability of A occurring can be expressed as follows:

[0129]

[0130] Express the POE of the system when only considering the uncertainty of the wind power output deviation as follows:

[0131]

[0132] Total number N of different wind farms w Under different confidence probabilities α w The theoretical results of the POE of the system are as attached Figure 3 。

[0133] Step S25: With the goal of minimizing the overall cost in the system with large-scale renewable energy access, construct an optimized configuration model of network-forming energy storage under extreme cases of power generation and power consumption deviation;

[0134] The above steps include:

[0135] Step S501: With the goal of minimizing the overall cost of the access system, construct the objective function of the energy storage optimized configuration model as shown below, where the overall cost includes: the power generation cost of thermal power units under extreme cases of renewable energy power generation prediction error Unit start-stop cost Annualized initial investment cost of energy storage Maintenance cost Frequency regulation cost C PFR ;

[0136]

[0137] In the formula, N G is the number of thermal power units in the system, a i , b i , c i are fuel cost coefficients, d i , e i are threshold effect coefficients; represents the operating state of generator i ( is operating); is a Boolean variable indicating the start-stop state of the thermal power unit. When the unit changes from shutdown to startup is 1 otherwise 0. When the unit changes from startup to shutdown is 1 otherwise 0. represents whether unit i participates in frequency regulation at time t ( represents participation). is the total output power of thermal power unit i at time t, where is the output power to meet the power supply and demand balance, is the frequency regulation power; is the unit start-stop cost coefficient.

[0138]

[0139] In the formula, are respectively the unit power cost and unit capacity cost of the energy storage system; are respectively the rated power and capacity of the energy storage system; γ is the capital discount rate; Trt For the entire life cycle of the energy storage system;

[0140]

[0141] Where C maint is the annual average maintenance cost coefficient of the energy storage system.

[0142]

[0143] Where are the frequency regulation bids of the generator set and the energy storage system respectively; is the frequency regulation power of the energy storage at time t.

[0144] Step S502: Considering the operation of the units and network security factors in the access system, determine the constraint conditions of the grid-forming energy storage optimization configuration model; including: access system power balance constraint, energy storage operation constraint, thermal power unit dynamic frequency output constraint, and frequency regulation capacity demand constraint. Specifically as follows:

[0145] (1) Power balance constraint:

[0146]

[0147] Where is the power shortage at time t, is the load, is the charge and discharge power of the energy storage to meet the power supply and demand balance; are both; extreme power conditions;

[0148] (2) Energy storage operation constraints, including:

[0149] 1. Charge and discharge power constraint:

[0150]

[0151] 2. State of charge constraint, set the state of charge constraint to ensure the normal operation of the energy storage system:

[0152]

[0153] The state of charge of the energy storage system at the current moment is not only related to the state of charge at the previous moment but also closely related to the charge and discharge electricity at this moment. The specific calculation formula for the state of charge at the current moment is as follows:

[0154]

[0155]

[0156] Where η s 、η c 、ηd They are the self-discharge rate and charge / discharge rate of energy storage respectively.

[0157] 3. The charging amount is equal to the discharging amount within the total dispatching period, and the charging amount is equal to the discharging amount within the total dispatching period, and settings are made to ensure the sustainable cyclic use of the ESS:

[0158]

[0159] (3) Constraints on the dynamic frequency output of thermal power units, including:

[0160] 1. Frequency modulation output constraint:

[0161]

[0162] In the formula, K i is the static power-frequency characteristic coefficient of thermal power unit i, Δf max represents the maximum frequency deviation, is the dead band of frequency modulation of generator set i;

[0163] 2. State constraint for participating in frequency modulation:

[0164]

[0165] 3. Frequency modulation capacity constraint:

[0166]

[0167] (4) Frequency modulation capacity demand constraint means that thermal power units and energy storage participating in frequency modulation should meet the primary frequency modulation capacity demand. If the frequency modulation capacity demand exceeds the maximum frequency modulation power of thermal power units, energy storage participates in frequency modulation to make up for the frequency modulation shortage of thermal power units, as shown specifically below:

[0168]

[0169] In the formula, is the dynamic frequency modulation capacity demand, △P RN is the disturbance amount of the predicted output of renewable energy, corresponding to the disturbance amount in the extreme case of the set of generation and electricity consumption uncertainties constructed for S1.

[0170] Step S26, use the whale algorithm to solve the grid-forming energy storage optimal configuration model, and analyze the influence of uncertainty factors on the grid-forming energy storage optimal configuration results.

[0171] In the above steps, using the whale algorithm to solve the grid-forming energy storage optimal configuration model includes:

[0172] Step S601: For the boundary constraints in the configuration optimization model of network-forming energy storage, use the out-of-bounds handling method in the heuristic algorithm, and convert the ramp constraint into a boundary constraint through dynamic update;

[0173] Step S602: For the power balance constraint in the configuration optimization model of network-forming energy storage, adopt the dynamic relaxation constraint handling method;

[0174] Step S603: For the state of charge constraint of the energy storage system in the configuration optimization model of network-forming energy storage, process it through the filter technology;

[0175] Step S604: Use the optimization mechanism of the whale algorithm to obtain the optimal solution of the configuration optimization model of network-forming energy storage; the specific implementation steps are attached Figure 4 as shown.

[0176] In the above steps, analyze the influence of the influencing factors of uncertainty on the configuration optimization results of network-forming energy storage, that is, analyze the confidence probability of the uncertain quantity and the influence of the spatial clustering effect on the economy of the configuration optimization results of network-forming energy storage and the frequency modulation output of thermal power units, as attached Figures 5 to 7 as shown.

[0177] Example 3: As attached Figure 8 shown, the embodiment of the present invention discloses a network-forming energy storage capacity configuration device combined with robust optimization, including:

[0178] Aggregate construction unit, constructing a robust uncertainty aggregate of the actual power deviation by using the infinity norm constraint and the 1-norm constraint on the basis of predicting the power generation and consumption;

[0179] Constraint parameter determination unit, constructing an expression for calculating the spatial constraint parameters in the robust uncertainty aggregate by using the Lindeberg-levy central limit theorem;

[0180] Processing and determination unit, constructing the output of the renewable energy power station in the extreme case in the robust uncertainty aggregate by using the linear duality theory and constructing the Lagrangian function;

[0181] Quantification unit, quantifying the system robustness by using the probability value of the renewable energy output outside the extreme case;

[0182] Model construction unit, constructing a configuration optimization model of network-forming energy storage in the extreme case of power generation and consumption power deviation with the goal of minimizing the overall cost in the large-scale renewable energy access system;

[0183] Solution unit, solving the configuration optimization model of network-forming energy storage by using the whale algorithm, and analyzing the influence of the influencing factors of uncertainty on the configuration optimization results of network-forming energy storage.

[0184] Embodiment 4: An embodiment of the present invention discloses a storage medium, on which a computer program readable by a computer is stored, and the computer program is configured to execute a method for identifying weak links in a power grid based on extreme ice disasters when running.

[0185] The above storage medium may include but is not limited to: various media that can store computer programs such as USB flash drives, read-only memories, mobile hard disks, magnetic disks, or optical discs.

[0186] Embodiment 5: An embodiment of the present invention discloses an electronic device, including a processor and a memory, and a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement a method for identifying weak links in a power grid based on extreme ice disasters.

[0187] The above processor may be a central processing unit CPU, a general-purpose processor, a digital signal processor DSP, an ASIC, an FPGA, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in connection with the disclosure of the present application. It can also be a combination that implements computing functions, such as a combination including one or more microprocessors, a combination of DSP and microprocessors, and so on. The memory may include but is not limited to: various media that can store computer programs such as USB flash drives, read-only memories, mobile hard disks, magnetic disks, or optical discs.

[0188] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes. The solutions in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0189] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocksFigure 1 a device for the functions specified in one or more boxes

[0190] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more processes and / or boxes Figure 1 one process or more processes and / or boxes Figure 1 a box or more boxes

[0191] The above technical features constitute the best embodiment of the present invention, which has strong adaptability and the best implementation effect. Non-essential technical features can be added or subtracted according to actual needs to meet the requirements of different situations.

Claims

1. A method for configuring the capacity of a grid-forming energy storage combined with robust optimization, characterized in that Including: Based on the predicted power generation and consumption, a robust uncertainty set of the actual power deviation is constructed using the infinity norm constraint and the 1-norm constraint; Using the Lindeberg-levy central limit theorem to construct the calculation expression of the spatial constraint parameters in the robust uncertainty set; Using the linear duality theory and constructing the Lagrangian function to construct the output of the renewable energy power station in the extreme case in the robust uncertainty set; Quantifying the system robustness using the probability value of the renewable energy output outside the extreme case; With the goal of minimizing the overall cost in the large-scale renewable energy integrated system, a configuration optimization model of the network-forming energy storage is constructed under the extreme cases of power generation and consumption power deviation; Using the whale algorithm to solve the configuration optimization model of the network-forming energy storage and analyzing the influence of the uncertainty factors on the configuration optimization results of the network-forming energy storage; Among them, the robust uncertainty set of the actual power deviation of the wind power output constructed using the infinity norm constraint and the 1-norm constraint based on the predicted power is: Wherein, is the actual output of wind farm i at time t, is the predicted output, are the upper and lower limits of the output deviation respectively; N w is the number of wind farms; is the infinity norm; is the perturbation 1-norm constraint, corresponding to the spatial clustering effect of the actual wind power output; is the deviation coefficient of wind farm i at time t; similarly, an uncertainty set is constructed for other uncertain variables in the system; Among them, using the Lindeberg-levy central limit theorem to construct the calculation expression of the spatial constraint parameters in the robust uncertainty set, including: Determining the spatial constraint parameters of the wind power output uncertainty, including: (1) Assume that in the first step There is Assume that the power deviation is an independent and identically distributed random variable, then is also independent and identically distributed, and its expected value is The variance is (2) standard variable as follows: In the formula, E(·) represents the expectation, and D(·) represents the variance; (3) Standard variable obeys the standard Gaussian distribution, and its cumulative distribution function For any probability α w satisfies the following equation relationship: where α w is the confidence probability; (4) Derive the uncertainty space constraint parameters of wind power output As follows: Through the steps of determining the spatial constraint parameters of the wind power output uncertainty, the spatial constraint parameters of other uncertain variables are determined.

2. The method for configuring the capacity of a network-constructing energy storage in combination with robust optimization according to claim 1, wherein, The method of using the linear duality theory and constructing the Lagrangian function to construct the output of the renewable energy power station in the extreme case in the robust uncertainty set includes: Using the linear duality theory and constructing the Lagrangian function to construct the output power of the wind power in the extreme case in the robust uncertainty set; (1) Through the linear duality theory, construct 's Lagrangian function: (2) Due to The power of the wind power output under extreme conditions of output deviation is: Since the optimal solution of the linear programming is at the vertex, simplify the above formula: (3) According to the above combination in the most extreme case at time t, only the deviation coefficient of the output of one wind farm is less than 1. Let this wind farm be j, and its total output is as follows: In the formula, is the floor symbol; Using the above steps to determine the output power of other uncertain quantities in the extreme case.

3. The method for configuring the capacity of a network-forming energy storage combined with robust optimization according to claim 1 or 2, wherein The method of constructing a configuration optimization model of the network-forming energy storage under the extreme cases of power generation and consumption power deviation with the goal of minimizing the overall cost in the large-scale renewable energy integrated system includes: Taking the minimum overall cost of the access system as the goal, the objective function of the energy storage optimal configuration model is constructed as follows. The overall cost includes: the power generation cost of thermal power units under extreme cases of renewable energy power generation prediction errors Unit start-stop cost Annualized initial investment cost of energy storage Maintenance cost Frequency regulation cost C PFR ; Considering the operation of the units and network security factors in the integrated system, determining the constraint conditions of the configuration optimization model of the network-forming energy storage; (1) Power balance constraint: Wherein, is the power shortage at time t, is the load, is the charge and discharge power of the energy storage to meet the power supply and demand balance; (2) The energy storage operation constraints include: Charge and discharge power constraint: State of charge constraint: The charging amount is equal to the discharging amount within the total scheduling period, the formula is: (3) The dynamic frequency output constraint of the thermal power unit, including: Frequency modulation output constraint: where K i is the power-frequency static characteristic coefficient of thermal power unit i, and Δf max represents the maximum frequency deviation, is the frequency modulation dead zone of generator set i; Participation in frequency modulation state constraint: Frequency modulation capacity constraint: (4) Frequency modulation capacity demand constraint: In the formula, is the dynamic frequency modulation capacity requirement, and ΔP RN is the disturbance amount of the predicted output of renewable energy.

4. The method for configuring the capacity of a grid-forming energy storage in combination with robust optimization according to claim 1 or 2, wherein The method of using the whale algorithm to solve the configuration optimization model of the network-forming energy storage includes: For the boundary constraints in the configuration optimization model of the network-forming energy storage, using the out-of-bounds processing method in the heuristic algorithm, and the ramp constraint is dynamically updated to the boundary constraint; For the power balance constraint in the configuration optimization model of the network-forming energy storage, adopting the dynamic relaxation constraint processing method; For the state of charge constraint of the energy storage system in the configuration optimization model of the network-forming energy storage, processing it through the filter technology; Using the optimization mechanism of the whale algorithm, find the optimal solution of the configuration optimization model of the network-forming energy storage.

5. The method for configuring the capacity of the network-forming energy storage combined with robust optimization according to claim 3, characterized in that The solution of the configuration optimization model of the network-forming energy storage by using the whale algorithm includes: For the boundary constraints in the configuration optimization model of the network-forming energy storage, use the out-of-bounds processing method in the heuristic algorithm, and the ramp constraint is transformed into a boundary constraint by dynamic update; For the power balance constraint in the configuration optimization model of the network-forming energy storage, adopt the dynamic relaxation constraint processing method; For the state-of-charge constraint of the energy storage system in the configuration optimization model of the network-forming energy storage, process it through the filter technology; Using the optimization mechanism of the whale algorithm, find the optimal solution of the configuration optimization model of the network-forming energy storage.

6. A network-constructing energy storage capacity configuration device combined with robust optimization, which applies the method according to any one of claims 1 to 5, characterized in that, Including: The collection construction unit constructs a robust uncertainty collection of the actual power deviation by using the infinity norm constraint and the 1-norm constraint on the basis of predicting the power generation and consumption power; The constraint parameter determination unit constructs an expression for calculating the spatial constraint parameters in the robust uncertainty collection by using the Lindeberg-levy central limit theorem; The processing determination unit constructs the output of the renewable energy power station in the extreme case in the robust uncertainty collection by using the linear duality theory and constructing the Lagrangian function; The quantization unit quantifies the system robustness by using the probability value of the renewable energy output outside the extreme case; The model construction unit constructs a configuration optimization model of the network-forming energy storage in the extreme case of power generation and consumption power deviation with the goal of minimizing the overall cost in the large-scale renewable energy access system; The solution unit uses the whale algorithm to solve the configuration optimization model of the network-forming energy storage and analyzes the influence of the influencing factors of uncertainty on the configuration optimization result of the network-forming energy storage; Among them, the robust uncertainty collection of the actual power deviation of the wind power output constructed by using the infinity norm constraint and the 1-norm constraint on the basis of the predicted power is: Wherein, is the actual output of wind farm i at time t, is the predicted output, are the upper and lower limits of the output deviation respectively; N w is the number of wind farms is the infinity norm; is the perturbation 1-norm constraint, corresponding to the spatial clustering effect of the actual wind power output; is the deviation coefficient of wind farm i at time t; similarly, an uncertainty set is constructed for other uncertain variables in the system; Among them, constructing an expression for calculating the spatial constraint parameters in the robust uncertainty collection by using the Lindeberg-levy central limit theorem includes: Determining the spatial constraint parameters of the wind power output uncertainty, including: (1) Assume that in the first step there is Assume that the power deviation is an independent and identically distributed random variable, then is also independent and identically distributed. Denote its expectation as and the variance as (2) Standard variable As follows: In the formula, E(·) represents the expectation, and D(·) represents the variance; (3) Standard variable obeys the standard Gaussian distribution, and its cumulative distribution function For any probability α w satisfies the following equation relationship: where α w is the confidence probability; (4) Derive the uncertainty space constraint parameters of wind power output As follows: Through the steps of determining the spatial constraint parameters of the wind power output uncertainty, determine the spatial constraint parameters of other uncertain variables.

7. A storage medium, characterized in that, The computer program readable by a computer is stored on the storage medium, and the computer program is set to execute the network-forming energy storage capacity configuration device combined with robust optimization as described in any one of claims 1 to 5 when running.

8. An electronic device, characterized in that, Including a processor and a memory, a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the network-forming energy storage capacity configuration device combined with robust optimization as described in any one of claims 1 to 5.

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