Energy storage optimal planning and operation method and apparatus for high-uncertainty power system
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2025-02-25
- Publication Date
- 2026-08-13
Smart Images
Figure CN2025079125_13082026_PF_FP_ABST
Abstract
Description
Methods and devices for optimized planning and operation of energy storage in power systems with high uncertainty
[0001] Cross-references to related applications
[0002] This disclosure claims priority to Chinese Patent Application No. 202510146438.9, filed on February 10, 2025, entitled "Method and Device for Optimized Planning and Operation of Energy Storage in Power Systems with High Uncertainty". Technical Field
[0003] This disclosure relates to the field of energy storage system planning and operation technology, and in particular to a method and apparatus for optimizing the planning and operation of energy storage in a power system with high uncertainty. Background Technology
[0004] The increasing penetration of new energy sources and the diversification of loads have exacerbated the uncertainty of the power system, posing a severe challenge to maintaining the system's active power balance. Simultaneously, the decreasing proportion of synchronous power sources and the increased level of power electronics have resulted in low system inertia, leading to insufficient system disturbance immunity and increasing the risk of system frequency instability. Configuring energy storage can effectively improve the system's power regulation capability, and virtual inertia control can provide inertia support, reducing the risk of system frequency instability.
[0005] Current energy storage planning primarily focuses on system power balance, neglecting its inertia support role in dynamic frequency response, thus limiting the full utilization of energy storage functions. Furthermore, research shows that system inertia distribution directly affects node frequency variations; the type and location of energy storage configurations can improve inertia distribution, thereby influencing local system frequency stability. Therefore, to address the high uncertainty of systems while simultaneously optimizing frequency stability and inertia distribution, it is urgent to research energy storage optimization planning and operation methods that enhance system stability and fully leverage the effectiveness of energy storage.
[0006] Public content
[0007] This disclosure provides a method and apparatus for optimizing the planning and operation of energy storage in a power system with high uncertainty, in order to solve the problem that current energy storage planning mainly focuses on system power balance, neglecting its inertia support role in dynamic frequency response, which limits the full utilization of energy storage functions.
[0008] The first aspect of this disclosure provides a method for optimizing the planning and operation of energy storage in a power system with high uncertainty, applied in the model building stage, comprising the following steps: determining the energy storage demand capacity of the power system based on the net load uncertainty scenario set of the net load forecast power sequence and system operation simulation; establishing a mathematical model of the energy storage virtual inertia of the power system based on the energy storage demand capacity and virtual inertia control, and generating a node frequency change rate that satisfies a preset maximum condition based on the energy storage virtual inertia mathematical model; and constructing a two-layer optimization planning model for the energy storage optimization planning and operation of the power system based on the node frequency change rate, the node inertia balance and planning indicators of the power system, and operating costs.
[0009] In some embodiments, before determining the energy storage demand capacity of the power system based on the net load uncertainty scenario set and system operation simulation, the method further includes: generating a net load scenario set using a preset quantile regression analysis and a Gaussian mixture model; characterizing the net load uncertainty of the power system based on the net load scenario set to generate the net load uncertainty scenario set.
[0010] In some embodiments, determining the energy storage demand capacity of the power system based on the set of uncertain net load scenarios and system operation simulation includes: establishing an energy storage demand capacity calculation model based on the set of uncertain net load scenarios and typical scenario operation simulations in the system operation simulation; based on the energy storage demand capacity calculation model, correcting the energy storage power according to the degree of deviation of the energy storage capacity and the growth rate of operating costs of the power system to generate a correction result, and determining the energy storage demand capacity of the power system based on the correction result.
[0011] In some embodiments, the calculation model for the asynchronous rotating energy storage inertia in the virtual inertia control is as follows:
[0012] in ， K Nrt_p and K Nrt_i These are the proportional and integral coefficients of the PI controller, T. Nrt_f and K Nrt_df These are the filtering time constant and gain of the virtual inertia controller, H. Nrt_eq ω Nrt 和 Δω Nrt_0 These represent the inherent inertial time constant, rated angular velocity, and initial angular velocity of the asynchronous rotating energy storage motor, respectively, Δω. s_0 The initial angular velocity of the system;
[0013] The calculation model for asynchronous static energy storage inertia in the virtual inertia control is as follows:
[0014] Among them, X eqy0 is the equivalent capacitance of capacitive asynchronous static energy storage or the equivalent inductance of inductive asynchronous static energy storage, and y0 is the initial voltage of capacitive asynchronous static energy storage or the initial current of inductive asynchronous static energy storage. ， T St_f and K St_df These represent the filter time constant and gain of the asynchronous static energy storage virtual inertia controller, P. St Rated power for asynchronous static energy storage;
[0015] The formula for calculating the node frequency change rate that satisfies the preset maximum condition is as follows:
[0016] in ， v n,s Ω represents the voltage relationship elements between other nodes n and synchronous power source node s in vector V. S H is the set of synchronous power nodes. S,n For a unified representation of the inertial time of synchronous power sources and energy storage, ΔP O,o B is the disturbance power generated at node o. n ,o is the susceptance at the potential node within the power source n and the node o where the disturbance occurs, Ω O The set of numbers for other nodes, Ω S ∪Ω O =Ω N .
[0017] In some embodiments, the formula for calculating the node inertia equalization index is:
[0018] Where N is the total number of system nodes, Ω N Let υ1 and υ2 be the weights of the mean and standard deviation of the maximum frequency change rate of the nodes. This represents the maximum / minimum rate of change of node frequency when no energy storage is configured.
[0019] The upper-layer configuration model in the energy storage two-layer optimization planning model is:
[0020] Among them, F Total The average daily cost of energy storage configuration and system operation, including the average daily investment and construction cost of energy storage, F. cot Daily maintenance cost of energy storage F mat and typical operating cost F of a Nissan system opt Ω M Divided into a set of pre-configured energy storage types, S γ To meet the system's uncertainty requirements for a set of scenarios with a confidence level of γ, for P represents the probability of occurrence of the corresponding scenarios, ordered from smallest to largest.m,n and E m,n These represent the configured power and capacity of n-node m-type energy storage, respectively, and c P,m c E,m and c Mat,m These represent the unit power, capacity cost, and daily maintenance cost per unit power for type m energy storage, respectively. aue,n For the cost of other equipment, c r,m For type m energy storage, the annual value coefficient is used.
[0021] The lower-level operation model in the energy storage two-layer optimization planning model is:
[0022] in, and These are the cost functions for energy storage peak shaving and frequency regulation of the system after configuring energy storage, respectively, Ω E To configure energy storage collections, This represents the maximum and minimum rate of change of node frequency. and These are conventional generating units, energy storage, and net load power, respectively. These represent the line transmission power with node n as the input and output node, respectively. Let each be a set of nodes at the other end of a line where node n is both the input and output node. These represent the transmission power and capacity of the line between nodes l and n, respectively. This indicates the unit's start-up and shutdown status. These represent the maximum and minimum power of conventional units, respectively. These represent the maximum upward and downward ramp rates for conventional units, and SOC. s,e,m,t , soc max,m and soc min,m These are the state of charge (SOC), maximum and minimum SOC, for configuring node e-type m energy storage. These are the configuration of the charging and discharging power of the energy storage of type m at node e, respectively, P m,e , and These represent the power configuration and charging / discharging efficiency of node e-type m energy storage.
[0023] A second aspect of this disclosure provides a method for optimizing the planning and operation of energy storage in a power system with high uncertainty, applied in the model application stage. The method includes the following steps: obtaining the node inertia balance, planning indicators, and operating costs of the power system; inputting the node inertia balance, planning indicators, and operating costs into a pre-constructed two-layer energy storage optimization planning model; generating a model solving algorithm based on NSGA-II according to the two-layer energy storage optimization planning model; and solving the two-layer energy storage optimization planning model using the NSGA-II model solving algorithm to generate an energy storage configuration scheme that meets preset optimal planning and operation conditions. The two-layer energy storage optimization planning model is constructed from the node inertia balance, planning indicators, and operating costs.
[0024] This disclosure provides a high-uncertainty power system energy storage optimization planning and operation device, applied in the model application stage, comprising: a determination module, used to determine the energy storage demand capacity of the power system based on the net load uncertainty scenario set of the net load predicted power sequence and system operation simulation; an establishment module, used to establish a mathematical model of the energy storage virtual inertia of the power system based on the energy storage demand capacity and virtual inertia control, and generate a node frequency change rate that satisfies a preset maximum condition based on the energy storage virtual inertia mathematical model; and a construction module, used to construct a two-layer optimization planning model for power system energy storage optimization planning and operation based on the node frequency change rate, the node inertia balance and planning indicators of the power system, and operating costs.
[0025] In some embodiments, the apparatus of this disclosure further includes: a generation module, configured to generate a net load scenario set using a preset quantile regression analysis and a Gaussian mixture model before determining the energy storage demand capacity of the power system based on the net load uncertainty scenario set and system operation simulation; and a characterization module, configured to characterize the net load uncertainty of the power system based on the net load scenario set, so as to generate the net load uncertainty scenario set.
[0026] In some embodiments, the determining module includes: an establishing unit, configured to establish an energy storage demand capacity calculation model based on the set of uncertain net load scenarios and typical scenario operation simulations in the system operation simulation; and a determining unit, configured to, based on the energy storage demand capacity calculation model, correct the energy storage power according to the degree of deviation of the energy storage capacity of the power system and the growth rate of operating costs, so as to generate a correction result, and determine the energy storage demand capacity of the power system based on the correction result.
[0027] In some embodiments, the calculation model for the asynchronous rotating energy storage inertia in the virtual inertia control is as follows:
[0028] Among them, K Nrt_p and K Nrt_iThese are the proportional and integral coefficients of the PI controller, T. Nrt_f and K Nrt_df These are the filtering time constant and gain of the virtual inertia controller, H. Nrt_eq ω Nrt and Δω Nrt_0 These represent the inherent inertial time constant, rated angular velocity, and initial angular velocity of the asynchronous rotating energy storage motor, respectively, Δω. s_0 The initial angular velocity of the system;
[0029] The calculation model for asynchronous static energy storage inertia in the virtual inertia control is as follows:
[0030] Among them, X eq y0 is the equivalent capacitance of capacitive asynchronous static energy storage or the equivalent inductance of inductive asynchronous static energy storage, and y0 is the initial voltage of capacitive asynchronous static energy storage or the initial current of inductive asynchronous static energy storage. St_f and K St_df These represent the filter time constant and gain of the asynchronous static energy storage virtual inertia controller, P. St Rated power for asynchronous static energy storage;
[0031] The formula for calculating the node frequency change rate that satisfies the preset maximum condition is as follows:
[0032] Among them, v n,s Ω represents the voltage relationship elements between other nodes n and synchronous power source node s in vector V. S H is the set of synchronous power nodes. S,n For a unified representation of the inertial time of synchronous power sources and energy storage, ΔP O,o B is the disturbance power generated at node o. n,o To shrink the susceptance to the potential node within the power source n and the perturbation node o, Ω O The set of numbers for other nodes, Ω S ∪Ω O =Ω N .
[0033] In some embodiments, the formula for calculating the node inertia equalization index is:
[0034] Where N is the total number of system nodes, Ω N Let υ1 and υ2 be the weights of the mean and standard deviation of the maximum frequency change rate of the nodes. This represents the maximum / minimum rate of change of node frequency when no energy storage is configured.
[0035] The upper-layer configuration model in the energy storage two-layer optimization planning model is:
[0036] Among them, F Total The average daily cost of energy storage configuration and system operation, including the average daily investment and construction cost of energy storage, F. cot Daily maintenance cost of energy storage F mat and typical operating cost F of a Nissan system opt Ω M Divided into a set of pre-configured energy storage types, S γ To meet the system's uncertainty requirements for a set of scenarios with a confidence level of γ, for P represents the probability of occurrence of the corresponding scenarios, ordered from smallest to largest. m,n and E m,n These represent the configured power and capacity of n-node m-type energy storage, respectively, and c P,m c E,m and c Mat,m These represent the unit power, capacity cost, and daily maintenance cost per unit power for type m energy storage, respectively. aue,n For the cost of other equipment, c r,m For type m energy storage, the annual value coefficient is used.
[0037] The lower-level operation model in the energy storage two-layer optimization planning model is:
[0038] in, and These are the cost functions for energy storage peak shaving and frequency regulation of the system after configuring energy storage, respectively, Ω E To configure energy storage collections, This represents the maximum and minimum rate of change of node frequency. and These are conventional generating units, energy storage, and net load power, respectively. These represent the line transmission power with node n as the input and output node, respectively. Let each be a set of nodes at the other end of a line where node n is both the input and output node. These represent the transmission power and capacity of the line between nodes l and n, respectively. This indicates the unit's start-up and shutdown status. These represent the maximum and minimum power of conventional units, respectively. These represent the maximum upward and downward ramp rates for conventional units, and SOC. s,e,m,t , soc max,m and soc min,m These are the state of charge (SOC), maximum and minimum SOC, for configuring node e-type m energy storage. These are the configuration of the charging and discharging power of the energy storage of type m at node e, respectively, P m,e , and These represent the power configuration and charging / discharging efficiency of node e-type m energy storage.
[0039] This fourth aspect of the disclosure provides a high-uncertainty power system energy storage optimization planning and operation device, applied in the model application stage, comprising: an acquisition module for acquiring the nodal inertia balance, planning indicators, and operating costs of the power system; and a solution module for inputting the nodal inertia balance, planning indicators, and operating costs into a pre-constructed energy storage two-layer optimization planning model, generating an NSGA-II-based model solving algorithm based on the energy storage two-layer optimization planning model, and solving the energy storage two-layer optimization planning model using the NSGA-II model solving algorithm to generate an energy storage configuration scheme that meets preset optimal planning and operation conditions, wherein the energy storage two-layer optimization planning model is constructed from the nodal inertia balance, planning indicators, and operating costs.
[0040] A fifth aspect of this disclosure provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the high-uncertainty power system energy storage optimization planning and operation method as described in the above embodiments.
[0041] A sixth aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for optimizing the planning and operation of energy storage in a high-uncertainty power system.
[0042] This disclosure, based on the premise of meeting the uncertain power regulation requirements of the system, introduces node frequency change rate constraints and node inertia balance indicators. With the objectives of minimizing the difference in maximum node frequency change rate and minimizing planned operating costs, it establishes multiple energy storage optimization planning models to maximize energy storage effectiveness and improve system power balance and node frequency stability. This solves the problem that current energy storage planning mainly focuses on system power balance, neglecting its inertia support role in dynamic frequency response, thus limiting the comprehensive utilization of energy storage functions.
[0043] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description
[0044] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:
[0045] Figure 1 is a flowchart of a high-uncertainty power system energy storage optimization planning and operation method applied to the model building stage according to an embodiment of the present disclosure;
[0046] Figure 2 is a flowchart illustrating the high-uncertainty power system energy storage optimization planning and operation method according to an embodiment of the present disclosure;
[0047] Figure 3 is a schematic diagram of the optimization solution process of the energy storage optimization planning and operation method for high uncertainty power systems according to an embodiment of the present disclosure;
[0048] Figure 4 is a flowchart of a high-uncertainty power system energy storage optimization planning and operation method applied to the model application stage according to an embodiment of the present disclosure.
[0049] Figure 5 is a schematic diagram of the structure of a high-uncertainty power system energy storage optimization planning and operation device applied in the model building stage according to an embodiment of the present disclosure;
[0050] Figure 6 is a schematic diagram of the structure of a high-uncertainty power system energy storage optimization planning and operation device applied in the model application stage according to an embodiment of the present disclosure;
[0051] Figure 7 is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present disclosure.
[0052] Explanation of reference numerals in the attached figures:
[0053] Among them, 10-High uncertainty power system energy storage optimization planning and operation device; 100-Determination module, 200-Establishment module, 300-Construction module, 400-Acquisition module and 500-Solution module; 701-Memory, 702-Processor and 703-Communication interface. Detailed Implementation
[0054] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.
[0055] The following describes a method and apparatus for optimizing the planning and operation of energy storage in a high-uncertainty power system, based on embodiments of the present disclosure, with reference to the accompanying drawings. Addressing the issue mentioned in the background art that current energy storage planning primarily focuses on system power balance, neglecting its inertia support role in dynamic frequency response and limiting the comprehensive utilization of energy storage functions, and considering the increased system uncertainty and frequency instability caused by rising renewable energy penetration, this disclosure proposes a method for optimizing the planning and operation of energy storage in a high-uncertainty power system to address system uncertainty and improve node frequency stability. This method introduces a node inertia balancing index, aiming at node inertia balancing and planning and operation costs, and embeds constraints on energy storage demand capacity and node frequency change rate. It aims to address system uncertainty while simultaneously considering frequency stability and inertia distribution optimization. A case study based on an improved IEEE 9-node power system determines an energy storage configuration scheme that meets the requirements of a high-uncertainty system, verifying that the node inertia balancing index can effectively characterize the suppression effect and uniform distribution level of the maximum node frequency change rate. It also verifies that the proposed energy storage configuration method can significantly suppress the maximum node frequency change rate. This solves the problem that current energy storage planning mainly focuses on system power balance, neglecting its inertial support role in dynamic frequency response, which limits the full utilization of energy storage functions.
[0056] Figure 1 is a flowchart illustrating a high-uncertainty power system energy storage optimization planning and operation method provided in an embodiment of this disclosure.
[0057] As shown in Figure 1, the energy storage optimization planning and operation method for a high-uncertainty power system includes the following steps:
[0058] In step S101, the energy storage demand capacity of the power system is determined based on the set of uncertain net load scenarios of the net load predicted power sequence and the system operation simulation.
[0059] The net load scenario set generation steps in this embodiment are as follows:
[0060] 1) Determine the nonparametric probability prediction model based on quantile regression analysis, and obtain the historical net load actual power p through interpolation calculation. r The quantile {τ} corresponding to (i,j) i,j |i=1,2,…,I; j=1,2,…,J};
[0061] 2) Let T I,j =[τ i,j The probit function is used to divide T, which follows a uniform distribution. I,J Transform into I J-dimensional Gaussian distributions N J (μ,Σ), where μ and Σ are parameters of a multivariate Gaussian distribution, and its maximum likelihood estimate is the sample mean vector. and sample covariance matrix S;
[0062] 3) Based on the Gaussian mixture model clustering method, I net load scenarios are reduced to k typical net load scenarios, and their probability distribution ρ is obtained. k,typ ;
[0063] 4) Use the inverse probit function to transform k vectors following a J-dimensional Gaussian distribution into k vectors following a uniform distribution T. J =[τ s,1 τ s,2 …τ s,J ] T , s=1,2,…,k, where τ s,J This represents the quantile of the Jth sampling point in the s-th typical net load scenario;
[0064] 5) Obtain the quantile matrix P of the predicted power of the known net load using a nonparametric model. r,τ Then according to P r,τ For the obtained k quantile vectors T J Linear interpolation is performed on the matrix to obtain a set of k net load uncertainty scenarios P corresponding to the net load forecast power sequence. k,typ : ρ k,typ =[ρ1 ρ2 L ρ k ] T
[0065] Among them, [p r,i,1 p r,i,2 … p r,i,J ] T (i = 1, 2, ..., k) represents the net load power vector for the i-th net load scenario; ρ i Let be the probability distribution of the i-th net load scenario.
[0066] The optimal operating model that minimizes the overall system operating cost without considering energy storage capacity constraints is as follows:
[0067] Where s represents the sequence number of uncertain scenarios for a typical day; S is the set of all uncertain scenarios for a typical day; ρ s The probability of scenario s occurring is given; t is the time value with peak shaving time as the period; T is the entire set of operating time series; i is the conventional unit number; N G It is a set of regular unit numbers. and These are the cost functions for conventional unit operation, peak shaving, start-up and shutdown, and standby, respectively. and These are the cost functions for energy storage peak shaving and frequency regulation, respectively.
[0068] The energy storage operation power correction model is as follows:
[0069] Where β1 and β2 are the target weights; Variables for adjusting peak power of energy storage; and ε s,t These represent the maximum and minimum values of peak-shaving power during the peak-shaving period t in scenario s, respectively. and These represent the active power output and planned power output of conventional unit i during time period t in uncertain scenario s, respectively. and These are the net load power and the predicted net load power for scenario s, respectively. and When the energy storage peak-shaving power is respectively Indicators include the deviation of energy storage capacity and the growth rate of system operating costs.
[0070] During the entire typical scenario operation period, the required energy storage power can be determined based on the maximum value of the corrected energy storage peak shaving and frequency regulation power, and the required energy storage capacity should be determined based on the maximum value of the cumulative charging or discharging of energy storage during the entire operation period.
[0071] Among them, Γ s For the set of continuous charge and discharge times, Γ s ={M l |l=1,2,…,L s}, where L s M represents the number of continuous charging / discharging times for energy storage in scenario s. l This is the set of continuous charging / discharging times for the l-th energy storage unit; s,t ={N l |l=1,2,…,K s Let} be the set of continuous charging / discharging times of energy storage frequency regulation within the peak-shaving period t in scenario s, and N be the set of times of charging / discharging. l Let K be the set of continuous charge / discharge times for the l-th energy storage frequency regulation. s The number of sets for continuous charging / discharging time of energy storage frequency regulation; Λ t Let be the set of frequency modulation time series for the t-th peak modulation process; Δw is the frequency modulation time step; and These represent the total energy storage power and capacity required to address the uncertainties of scenario s, respectively.
[0072] In some embodiments, before determining the energy storage demand capacity of the power system based on the net load uncertainty scenario set and system operation simulation, the method further includes: generating a net load scenario set using preset quantile regression analysis and Gaussian mixture model; and characterizing the net load uncertainty of the power system based on the net load scenario set to generate the net load uncertainty scenario set.
[0073] In actual implementation, the embodiments of this disclosure can use quantile regression analysis and Gaussian mixture models to generate net load scenario sets to characterize net load uncertainty, thereby providing support for the subsequent realization of energy storage optimization planning and operation of high-uncertainty power systems that take into account node inertia balance.
[0074] In some embodiments, the energy storage demand capacity of the power system is determined based on the set of uncertain net load scenarios and system operation simulations, including: establishing an energy storage demand capacity calculation model based on the set of uncertain net load scenarios and typical scenario operation simulations in the system operation simulation; based on the energy storage demand capacity calculation model, correcting the energy storage power according to the degree of deviation of the energy storage capacity of the power system and the growth rate of operating costs, so as to generate a correction result, and determining the energy storage demand capacity of the power system based on the correction result.
[0075] In this embodiment, an energy storage demand capacity calculation model can be established based on the net load uncertainty scenario set and typical scenario operation simulation in the system operation simulation. Based on the energy storage demand capacity calculation model, the energy storage demand is determined on the basis of energy storage power correction with the goal of minimizing the deviation of the energy storage capacity and the growth rate of the operating cost of the power system. This provides support for coping with system uncertainty while taking into account frequency stability and inertia distribution optimization.
[0076] In step S102, based on the energy storage demand capacity and virtual inertia control, a mathematical model of the energy storage virtual inertia of the power system is established, and a node frequency change rate that satisfies the preset maximum condition is generated according to the energy storage virtual inertia mathematical model.
[0077] It is understood that the node frequency change rate of the preset maximum condition in this embodiment can be the maximum node frequency change rate.
[0078] In actual implementation, in order to reflect the energy storage inertia support capability and node dynamic frequency response characteristics, the embodiments of this disclosure can summarize the characteristics of various types of energy storage inertia support, and based on the power response characteristics of different types of energy storage, combined with the corresponding virtual inertia control technology, establish a general, flexible, and different types of power system energy storage virtual inertia mathematical model, and establish a node maximum frequency change rate calculation model based on the energy storage virtual inertia mathematical model, and derive the node maximum frequency change rate of the energy storage system.
[0079] In some embodiments, the calculation model for the asynchronous rotating energy storage inertia in virtual inertia control is as follows:
[0080] Among them, K Nrt_p and K Nrt_i These are the proportional and integral coefficients of the PI controller, T. Nrt_f and K Nrt_dfThese are the filtering time constant and gain of the virtual inertia controller, H. Nrt_eq ω Nrt and Δω Nrt_0 These represent the inherent inertial time constant, rated angular velocity, and initial angular velocity of the asynchronous rotating energy storage motor, respectively, Δω. s_0 The initial angular velocity of the system;
[0081] The calculation model for asynchronous static energy storage inertia in virtual inertia control is as follows:
[0082] Among them, X eq y0 is the equivalent capacitance of capacitive asynchronous static energy storage or the equivalent inductance of inductive asynchronous static energy storage, and y0 is the initial voltage of capacitive asynchronous static energy storage or the initial current of inductive asynchronous static energy storage. St_f and K St_df These represent the filter time constant and gain of the asynchronous static energy storage virtual inertia controller, P. St Rated power for asynchronous static energy storage;
[0083] Taking into account the synchronous power supply and other nodes, the formula for calculating the node frequency change rate that satisfies the preset maximum condition is as follows:
[0084] Among them, v n,s Ω represents the voltage relationship elements between other nodes n and synchronous power source node s in vector V. S H is the set of synchronous power nodes. S,n For a unified representation of the inertial time of synchronous power sources and energy storage, ΔP O,o B is the disturbance power generated at node o. n,o To shrink the susceptance to the potential node within the power source n and the perturbation node o, Ω O The set of numbers for other nodes, Ω S ∪Ω O =Ω N .
[0085] In step S103, based on the node frequency change rate, a two-layer optimization planning model for energy storage is constructed according to the node inertia balance and planning indicators and operating costs of the power system.
[0086] It is understood that, based on the premise of meeting the uncertain power regulation requirements of the system, this disclosure introduces node frequency change rate constraints and node inertia balance index, and aims to minimize the difference in maximum node frequency change rate and the planned operating cost, and establishes multiple energy storage optimization planning models.
[0087] In actual implementation, the embodiments of this disclosure can introduce a node inertia balance index based on the energy storage optimization planning method. Based on the node frequency change rate, and with the goal of minimizing the node inertia balance and planning indicators and operating costs of the power system, a two-layer optimization planning model for energy storage optimization planning and operation of the power system is constructed, which embeds constraints on energy storage demand capacity and node frequency change rate.
[0088] The establishment of the two-layer energy storage optimization planning model includes:
[0089] Node frequency change rate constraint;
[0090] The maximum rate of change of node frequency is calculated as follows:
[0091] in, This represents the maximum / minimum value of the rate of change of node frequency. For the instant of disturbance.
[0092] When the node inertia is insufficient, the node's frequency change rate increases when facing the disturbance power shared by the node. This can cause the node frequency to exceed the threshold for low-frequency load shedding and high-frequency tripping, leading to the activation of the protection device. To avoid this situation, the inertia configured for the node should be increased to ensure that the node's frequency change rate meets the following constraints:
[0093] in, This is the limit for the rate of change of node frequency.
[0094] In some embodiments, the formula for calculating the node inertia balance index is as follows:
[0095] Where κ is the system node inertia balance index, the smaller the value, the more balanced the node inertia distribution and the smaller the difference in the maximum frequency change rate of the nodes; conversely, the larger the value, the greater the difference in the maximum frequency change rate of the nodes; N is the total number of system nodes; Ω N υ1 and υ2 are the weights of the mean and standard deviation of the maximum frequency change rate of the nodes; This represents the maximum / minimum rate of change of node frequency when no energy storage is configured.
[0096] The upper-layer configuration model in the two-layer energy storage optimization planning model is as follows:
[0097] Among them, F Total The average daily cost of energy storage configuration and system operation, including the average daily investment and construction cost of energy storage, F. cot Daily maintenance cost of energy storage F mat and typical operating cost F of a Nissan system opt Ω M Divided into a set of pre-configured energy storage types, Sγ To meet the system's uncertainty requirements for a set of scenarios with a confidence level of γ, for P represents the probability of occurrence of the corresponding scenarios, ordered from smallest to largest. m,n and E m,n These represent the configured power and capacity of n-node m-type energy storage, respectively, and c P,m c E,m and c Mat,m These represent the unit power, capacity cost, and daily maintenance cost per unit power for type m energy storage, respectively. aue,n For the cost of other equipment, c r,m The annual value coefficient for type m energy storage is calculated using the following formula:
[0098] Where r is the investment discount rate calculated at an annual interest rate; T life,m The service life of type m energy storage.
[0099] The lower-level operation model in the two-layer energy storage optimization planning model is:
[0100] in, and These are the cost functions for energy storage peak shaving and frequency regulation of the system after configuring energy storage, respectively, Ω E To configure energy storage collections, This represents the maximum and minimum rate of change of node frequency. and These are conventional generating units, energy storage, and net load power, respectively. These represent the line transmission power with node n as the input and output node, respectively. Let each be a set of nodes at the other end of a line where node n is both the input and output node. These represent the transmission power and capacity of the line between nodes l and n, respectively. This indicates the unit's start-up and shutdown status. These represent the maximum and minimum power of conventional units, respectively. These represent the maximum upward and downward ramp rates for conventional units, and SOC. s,e,m,t , soc max,m and soc min,m These are the state of charge (SOC) of the energy storage of type e node m, and the maximum and minimum SOCs, respectively. ， These are the configuration of the charging and discharging power of the energy storage of type m at node e, respectively, P m,e , and These represent the power configuration and charging / discharging efficiency of node e-type m energy storage.
[0101] Specifically, the working principle of the energy storage optimization planning and operation method for high-uncertainty power systems in this disclosure can be described in detail with reference to Figures 2 and 3, using a specific embodiment.
[0102] Figures 2 and 3 may include the following steps:
[0103] Step S201: Based on the refined model of the partial load characteristics of the composite compressed air energy storage, analyze the electro-electric conversion and electro-thermal conversion performance of the system compression / expansion process.
[0104] Step S202: Propose a capacity planning method for all new energy supply systems that takes into account the wide operating conditions of composite compressed air energy storage.
[0105] Step S203: Propose a capacity planning method for all new energy supply systems that takes into account the wide operating conditions of composite compressed air energy storage.
[0106] Figure 4 illustrates a high-uncertainty power system energy storage optimization planning and operation method provided by an embodiment of this disclosure, applied in the model application stage, including the following steps:
[0107] In step S401, the nodal inertia balance and planning indicators and operating costs of the power system are obtained.
[0108] In this embodiment, the node inertia balance and planning indicators and operating costs of the power system can be obtained, thereby providing support for the subsequent establishment of a two-layer optimization planning model for energy storage.
[0109] In step S402, the node inertia balance, planning indicators, and operating costs are input into the pre-constructed energy storage two-layer optimization planning model. Based on the energy storage two-layer optimization planning model, a model solving algorithm based on NSGA-II is generated, and the energy storage two-layer optimization planning model is solved using the NSGA-II model solving algorithm to generate an energy storage configuration scheme that meets the preset optimal planning and operating conditions. The energy storage two-layer optimization planning model is constructed from the node inertia balance, planning indicators, and operating costs.
[0110] It is understood that the embodiments of this disclosure can design a model-solving algorithm based on NSGA-II based on a two-layer energy storage optimization planning model, thereby verifying the effectiveness of the energy storage optimization planning operation method in addressing system uncertainties and improving node inertia balance. In the embodiments of this disclosure, the upper layer of the two-layer energy storage optimization planning model is a multi-objective nonlinear programming model, and the lower layer is a mixed-integer programming model. Given the characteristics of the models, an improved NSGA-II algorithm is developed to solve the upper-layer model, and Gurobi is used to solve the lower-layer model. To improve the solution speed and accuracy, improved strategies are formulated for the initial population and effective solution selection of the NSGA-II algorithm.
[0111] In actual implementation, the embodiments of this disclosure can input the node inertia balance, planning indicators and operating costs into a pre-constructed energy storage two-layer optimization planning model, generate a model solving algorithm based on NSGA-II based on the energy storage two-layer optimization planning model, and use the model solving algorithm of NSGA-II to solve the energy storage two-layer optimization planning model to generate an energy storage configuration scheme that meets the preset optimal planning and operating conditions.
[0112] To improve the solution speed and accuracy, improved strategies were developed for the initial population and effective solution selection in the NSGA-II algorithm, including:
[0113] Optimize model solution process
[0114] (1) Initial population selection
[0115] 1) Input the number of energy storage nodes n to configure E Energy storage options, required power and capacity, system structure and operating parameters, etc.
[0116] 2) Generated through combinatorial theory a number with n E A combination of pre-configured energy storage nodes.
[0117] 3) Randomly generate D for each pre-configured node combination. E There are populations, and the total number of populations is . The target values for various groups are obtained based on equation (47);
[0118] 4) To Each population is non-dominated and ordered; the population with the highest Pareto front rank is selected as system n. E Each node initializes the population.
[0119] 5) Change the number of configuration nodes n E , then return to step 2) to make a selection.
[0120] (2) Selection of efficient solutions
[0121] 1) Select the solution with the highest Pareto front rank after iteration using the NSGA-II algorithm as the effective solution;
[0122] 2) Assuming an effective solution with a nodal inertia equalization index κ eff and average daily total cost F Totol,eff The constitutive vector is F eff =[κ eff ,F Totol,eff The objective value of the effective solution is standardized using the trigonometric membership function, resulting in the standardized objective value vector.
[0123] 3) Assume the decision weight vector is Where β κ and The weights correspond to the node inertia balance and cost objectives, respectively; the evaluation result is obtained by weighted summation of the objective values of the effective solutions.
[0124] 4) Sort the effective solutions from largest to smallest and select the effective solution with the highest ranking as the best energy storage planning scheme.
[0125] Among them, the power system calculation and analysis software MATLAB and the mixed integer linear programming solution software GUROBI can run computer programs on the software.
[0126] According to the energy storage optimization planning and operation method for high-uncertainty power systems proposed in this disclosure, firstly, quantile regression analysis and Gaussian mixture models are used to characterize the uncertainty of the system's net load. Based on the net load uncertainty scenario set and system operation simulation, the energy storage demand capacity is determined. Then, combining various energy storage characteristics and virtual inertia control, a calculation model for the maximum frequency change rate of nodes is established. On this basis, a node inertia balancing index is introduced to construct a two-layer optimization planning model for energy storage that minimizes the node inertia balancing index and the average daily planning and operation costs. A model solution method based on NSGA-II is designed, which determines an energy storage configuration scheme that meets the requirements of high-uncertainty systems for improving the IEEE 9-bus system. The results show that the node inertia balancing index can effectively characterize the suppression effect and uniform distribution level of the maximum frequency change rate of nodes, and can significantly suppress the maximum frequency change rate of nodes. Thus, it solves the problem that current energy storage planning mainly focuses on system power balance, neglecting its inertia support role in dynamic frequency response, which limits the comprehensive utilization of energy storage functions.
[0127] Next, with reference to the accompanying drawings, a high-uncertainty power system energy storage optimization planning and operation device proposed according to an embodiment of this disclosure is described.
[0128] Figure 5 is a schematic diagram of the structure of the high uncertainty power system energy storage optimization planning and operation device according to an embodiment of this disclosure.
[0129] As shown in Figure 5, the energy storage optimization planning and operation device 10 for a high-uncertainty power system includes: a determination module 100, an establishment module 200, and a construction module 300.
[0130] Specifically, module 100 is used to determine the energy storage demand capacity of the power system based on the set of uncertain net load scenarios and system operation simulations of the net load predicted power sequence.
[0131] Module 200 is established to build a mathematical model of the power system's energy storage virtual inertia based on energy storage demand capacity and virtual inertia control, and to generate a node frequency change rate that meets the preset maximum condition based on the energy storage virtual inertia mathematical model.
[0132] Module 300 is used to construct a two-layer optimization planning model for energy storage optimization planning and operation of the power system, based on the node frequency change rate, the node inertia balance and planning indicators of the power system, and the operating cost.
[0133] In some embodiments, the high-uncertainty power system energy storage optimization planning and operation device 10 further includes a generation module and a characterization module.
[0134] The generation module is used to generate a net load scenario set using preset quantile regression analysis and Gaussian mixture model before determining the energy storage demand capacity of the power system based on the net load uncertainty scenario set and system operation simulation.
[0135] The characterization module is used to characterize the net load uncertainty of the power system based on the net load scenario set, so as to generate the net load uncertainty scenario set.
[0136] In some embodiments, the determining module 100 includes an establishing unit and a determining unit.
[0137] The establishment unit is used to establish a calculation model for energy storage demand capacity based on the set of uncertain net load scenarios and typical scenario operation simulations in system operation simulation.
[0138] The determination unit is used to adjust the energy storage power based on the energy storage demand capacity calculation model, according to the deviation of the energy storage capacity of the power system and the growth rate of operating costs, so as to generate the adjustment result and determine the energy storage demand capacity of the power system based on the adjustment result.
[0139] In some embodiments, the calculation model for the asynchronous rotating energy storage inertia in virtual inertia control is as follows:
[0140] Among them, K Nrt_p and K Nrt_i These are the proportional and integral coefficients of the PI controller, T. Nrt_f and K Nrt_df These are the filtering time constant and gain of the virtual inertia controller, H. Nrt_eq ω Nrt and Δω Nrt_0 These represent the inherent inertial time constant, rated angular velocity, and initial angular velocity of the asynchronous rotating energy storage motor, respectively, Δω. s_0 The initial angular velocity of the system;
[0141] The calculation model for asynchronous static energy storage inertia in virtual inertia control is as follows:
[0142] Among them, X eqy0 is the equivalent capacitance of capacitive asynchronous static energy storage or the equivalent inductance of inductive asynchronous static energy storage, and y0 is the initial voltage of capacitive asynchronous static energy storage or the initial current of inductive asynchronous static energy storage. St_f and K St_df These represent the filter time constant and gain of the asynchronous static energy storage virtual inertia controller, P. St Rated power for asynchronous static energy storage;
[0143] The formula for calculating the rate of change of node frequency that satisfies the preset maximum condition is:
[0144] Among them, v n,s Ω represents the voltage relationship elements between other nodes n and synchronous power source node s in vector V. S H is the set of synchronous power nodes. S,n For a unified representation of the inertial time of synchronous power sources and energy storage, ΔP O,o B is the disturbance power generated at node o. n,o To shrink the susceptance to the potential node within the power source n and the perturbation node o, Ω O The set of numbers for other nodes, Ω S ∪Ω O =Ω N .
[0145] In some embodiments, the formula for calculating the node inertia balance index is as follows:
[0146] Where N is the total number of system nodes, Ω N Let υ1 and υ2 be the weights of the mean and standard deviation of the maximum frequency change rate of the nodes. This represents the maximum / minimum rate of change of node frequency when no energy storage is configured.
[0147] The upper-layer configuration model in the two-layer energy storage optimization planning model is as follows:
[0148] Among them, F Total The average daily cost of energy storage configuration and system operation, including the average daily investment and construction cost of energy storage, F. cot Daily maintenance cost of energy storage F mat and typical operating cost F of a Nissan system opt Ω M Divided into a set of pre-configured energy storage types, S γ To meet the system's uncertainty requirements for a set of scenarios with a confidence level of γ, for P represents the probability of occurrence of the corresponding scenarios, ordered from smallest to largest. m,n and E m,nThese represent the configured power and capacity of n-node m-type energy storage, respectively, and c P,m c E,m and c Mat,m These represent the unit power, capacity cost, and daily maintenance cost per unit power for type m energy storage, respectively. aue,n For the cost of other equipment, c r,m For type m energy storage, the annual value coefficient is used.
[0149] The lower-level operation model in the two-layer energy storage optimization planning model is:
[0150] in, and These are the cost functions for energy storage peak shaving and frequency regulation of the system after configuring energy storage, respectively, Ω E To configure energy storage collections, This represents the maximum and minimum rate of change of node frequency. and These are conventional generating units, energy storage, and net load power, respectively. These represent the line transmission power with node n as the input and output node, respectively. Let each be a set of nodes at the other end of a line where node n is both the input and output node. These represent the transmission power and capacity of the line between nodes l and n, respectively. This indicates the unit's start-up and shutdown status. These represent the maximum and minimum power of conventional units, respectively. These represent the maximum upward and downward ramp rates for conventional units, and SOC. s,e,m,t , soc max,m and soc min,m These are the state of charge (SOC), maximum and minimum SOC, for configuring node e-type m energy storage. These are the configuration of the charging and discharging power of the energy storage of type m at node e, respectively, P m,e , and These represent the power configuration and charging / discharging efficiency of node e-type m energy storage.
[0151] Figure 6 shows a high-uncertainty power system energy storage optimization planning and operation device 20 provided in an embodiment of this disclosure, applied in the model application stage, including: acquisition module 400 and solution module 500.
[0152] Specifically, module 400 is used to acquire nodal inertia balance and planning indicators and operating costs of the power system.
[0153] The solver module 500 is used to input the node inertia balancing, planning indicators, and operating costs into the pre-constructed energy storage two-layer optimization planning model. Based on the energy storage two-layer optimization planning model, it generates a model solving algorithm based on NSGA-II and uses the NSGA-II model solving algorithm to solve the energy storage two-layer optimization planning model to generate an energy storage configuration scheme that meets the preset optimal planning and operating conditions. The energy storage two-layer optimization planning model is constructed from the node inertia balancing, planning indicators, and operating costs.
[0154] It should be noted that the foregoing explanation of the embodiment of the energy storage optimization planning and operation method for high uncertainty power systems also applies to the energy storage optimization planning and operation device for high uncertainty power systems in this embodiment, and will not be repeated here.
[0155] The energy storage optimization planning and operation device for high-uncertainty power systems proposed in this disclosure addresses the variability and complexity of energy storage demands in highly uncertain systems. It considers both the power balance problem caused by high uncertainty and the inertia support requirements and their distribution. Under the premise of meeting the system's uncertain power regulation needs, it introduces node frequency change rate constraints and node inertia balance indices. With the objectives of minimizing the difference in maximum node change rate and minimizing planning and operation costs, it establishes multiple energy storage optimization planning models to maximize energy storage effectiveness and improve system power balance and node frequency stability. This solves the problem that current energy storage planning mainly focuses on system power balance, neglecting its inertia support role in dynamic frequency response, thus limiting the comprehensive utilization of energy storage functions.
[0156] Figure 7 is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. The electronic device may include:
[0157] The memory 701, the processor 702, and the computer program stored on the memory 701 and capable of running on the processor 702.
[0158] When the processor 702 executes the program, it implements the high-uncertainty power system energy storage optimization planning and operation method provided in the above embodiments.
[0159] In addition, electronic devices also include:
[0160] Communication interface 703 is used for communication between memory 701 and processor 702.
[0161] The memory 701 is used to store computer programs that can run on the processor 702.
[0162] The memory 701 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.
[0163] If the memory 701, processor 702, and communication interface 703 are implemented independently, they can be interconnected via a bus to communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one thick line is used in Figure 7, but this does not indicate that there is only one bus or one type of bus.
[0164] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.
[0165] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present disclosure.
[0166] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for optimizing the planning and operation of energy storage in a high-uncertainty power system.
[0167] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0168] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.
[0169] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this disclosure pertain.
[0170] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.
[0171] It should be understood that the various parts of this disclosure can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0172] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
[0173] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
[0174] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.
Claims
1. A method for optimal planning and operation of energy storage in a power system with high uncertainty, wherein, Applied to the model building phase, it includes the following steps: The energy storage demand capacity of the power system is determined based on the set of net load uncertainty scenarios and system operation simulations of the net load forecast power sequence. Based on the energy storage demand capacity and virtual inertia control, a mathematical model of the energy storage virtual inertia of the power system is established, and a node frequency change rate that satisfies the preset maximum condition is generated according to the mathematical model of the energy storage virtual inertia. Based on the node frequency change rate, a two-layer optimization planning model for energy storage is constructed according to the node inertia balance, planning indicators, and operating costs of the power system.
2. The method according to claim 1, wherein, Before determining the energy storage demand capacity of the power system based on the set of uncertain net load scenarios and system operation simulations, the following steps are also included: A set of net load scenarios is generated using preset quantile regression analysis and Gaussian mixture models; The net load uncertainty of the power system is characterized based on the net load scenario set to generate the net load uncertainty scenario set.
3. The method according to claim 1, wherein, The process of determining the energy storage demand capacity of the power system based on the set of uncertain net load scenarios and system operation simulations includes: A calculation model for energy storage demand capacity is established based on the set of uncertain net load scenarios and the typical scenario operation simulation in the system operation simulation. Based on the energy storage demand capacity calculation model, the energy storage power is corrected according to the deviation of the energy storage capacity and the growth rate of the operating cost of the power system to generate the correction result, and the energy storage demand capacity of the power system is determined according to the correction result.
4. The method according to claim 1, wherein, The calculation model for the asynchronous rotating energy storage inertia in the virtual inertia control is as follows: Among them, K Nrt_p and K Nrt_i These are the proportional and integral coefficients of the PI controller, T. Nrt_f and K Nrt_df These are the filtering time constant and gain of the virtual inertia controller, H. Nrt_eq ω Nrt and Δω Nrt_0 These represent the inherent inertial time constant, rated angular velocity, and initial angular velocity of the asynchronous rotating energy storage motor, respectively, Δω. s_0 The initial angular velocity of the system; The calculation model for asynchronous static energy storage inertia in the virtual inertia control is as follows: Among them, X eq y0 is the equivalent capacitance of capacitive asynchronous static energy storage or the equivalent inductance of inductive asynchronous static energy storage, and y0 is the initial voltage of capacitive asynchronous static energy storage or the initial current of inductive asynchronous static energy storage. St_f and K St_df These represent the filter time constant and gain of the asynchronous static energy storage virtual inertia controller, P. St Rated power for asynchronous static energy storage; The formula for calculating the node frequency change rate that satisfies the preset maximum condition is as follows: Among them, v n,s Ω represents the voltage relationship elements between other nodes n and synchronous power source node s in vector V. S H is the set of synchronous power nodes. S,n For a unified representation of the inertial time of synchronous power sources and energy storage, ΔP O,o B is the disturbance power generated at node o. n,o To shrink the susceptance to the potential node within the power source n and the perturbation node o, Ω O The set of numbers for other nodes, Ω S ∪Ω O =Ω N .
5. The method according to claim 1, wherein, The formula for calculating the node inertia equalization index is as follows: Where N is the total number of system nodes, Ω N Let υ1 and υ2 be the weights of the mean and standard deviation of the maximum frequency change rate of the nodes. This represents the maximum / minimum rate of change of node frequency when no energy storage is configured. The upper-layer configuration model in the energy storage two-layer optimization planning model is: Among them, F Total The average daily cost of energy storage configuration and system operation, including the average daily investment and construction cost of energy storage, F. cot Daily maintenance cost of energy storage F mat and typical operating cost F of a Nissan system opt Ω M Divided into a set of pre-configured energy storage types, S γ To meet the system's uncertainty requirements for a set of scenarios with a confidence level of γ, for P represents the probability of occurrence of the corresponding scenarios, ordered from smallest to largest. m,n and E m,n These represent the configured power and capacity of n-node m-type energy storage, respectively, and c P,m c E,m and c Mat,m These represent the unit power, capacity cost, and daily maintenance cost per unit power for type m energy storage, respectively. aue,n For the cost of other equipment, c r,m For type m energy storage, the annual value coefficient is used. The lower-level operation model in the energy storage two-layer optimization planning model is: in, and These are the cost functions for energy storage peak shaving and frequency regulation of the system after configuring energy storage, respectively, Ω E To configure energy storage collections, This represents the maximum and minimum rate of change of node frequency. and These are conventional generating units, energy storage, and net load power, respectively. These represent the line transmission power with node n as the input and output node, respectively. Let each be a set of nodes at the other end of a line where node n is both the input and output node. These represent the transmission power and capacity of the line between nodes l and n, respectively. This indicates the unit's start-up and shutdown status. These represent the maximum and minimum power of conventional units, respectively. These represent the maximum upward and downward ramp rates for conventional units, and SOC. s,e,m,t , soc max,m and soc min,m These are the state of charge (SOC), maximum and minimum SOC, for configuring node e-type m energy storage. These are the configuration of the charging and discharging power of the energy storage of type m at node e, respectively, P m,e , and These represent the power configuration and charging / discharging efficiency of node e-type m energy storage.
6. A method for optimal planning and operation of energy storage in a power system with high uncertainty, wherein, The high-uncertainty power system energy storage optimization planning and operation method described in any one of claims 1-5, applied to the model application stage, includes the following steps: Obtain nodal inertia balance and planning indicators and operating costs of the power system; The node inertia balancing and planning indicators, along with the operating cost, are input into a pre-constructed energy storage two-layer optimization planning model. A model solving algorithm based on NSGA-II is generated based on the energy storage two-layer optimization planning model, and the energy storage two-layer optimization planning model is solved using the NSGA-II model solving algorithm to generate an energy storage configuration scheme that meets the preset optimal planning and operating conditions. The energy storage two-layer optimization planning model is constructed from the node inertia balancing and planning indicators and the operating cost.
7. A device for optimizing the planning and operation of energy storage in a high-uncertainty power system, wherein, Applied to the model building phase, including: The determination module is used to determine the energy storage demand capacity of the power system based on the set of net load uncertainty scenarios and system operation simulations of the net load forecast power sequence. A module is established to build a mathematical model of the energy storage virtual inertia of the power system based on the energy storage demand capacity and virtual inertia control, and to generate a node frequency change rate that satisfies a preset maximum condition based on the energy storage virtual inertia mathematical model. A construction module is used to construct a two-layer optimization planning model for energy storage optimization planning and operation of the power system based on the node frequency change rate, the node inertia balance and planning indicators and operating costs of the power system.
8. A device for optimizing the planning and operation of energy storage in a power system with high uncertainty, wherein, The high-uncertainty power system energy storage optimization planning and operation device as described in claim 7 is applied to the model application stage, including: The acquisition module is used to acquire nodal inertia balance and planning indicators and operating costs of the power system. The solution module is used to input the node inertia balancing and planning indicators and the operating cost into a pre-constructed energy storage two-layer optimization planning model, generate a model solving algorithm based on NSGA-II according to the energy storage two-layer optimization planning model, and use the model solving algorithm of NSGA-II to solve the energy storage two-layer optimization planning model to generate an energy storage configuration scheme that meets the preset optimal planning and operating conditions. The energy storage two-layer optimization planning model is constructed from the node inertia balancing and planning indicators and the operating cost.
9. An electronic device, wherein, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the high-uncertainty power system energy storage optimization planning and operation method as described in any one of claims 1-5 or claim 6.
10. A computer-readable storage medium having a computer program stored thereon, wherein, The program is executed by the processor to implement the high uncertainty power system energy storage optimization planning and operation method as described in any one of claims 1-5 or claim 6.