Power system whole-process restoration optimization method based on dynamic evaluation of subject importance

Abstract

The application provides a power system whole-process recovery optimization method based on subject importance dynamic evaluation, and the global recovery state of each recovery subject at the mth step is determined in the following manner: based on the source and load power, flexibility resource balance degree and energy storage state of charge of the recovered power system at the m-1th step, the importance value of each recovery subject at the current step is determined; the wind power output and fluctuation power at the optimization time domain of the current step are determined; the recovery benefit objective function is solved to obtain the local recovery state of each recovery subject at the m-k+1th step and at each step; and the local recovery state of each recovery subject at the first step in the local recovery state of each recovery subject at the m-k+1th step and at each step is taken as the global recovery state of each recovery subject at the mth step, so as to improve the recovery efficiency of the power system.

Description

Technical Field

[0001] This application relates to the field of power technology, and more specifically, to a method for power system full-process recovery optimization based on dynamic assessment of principal importance. Background Technology

[0002] my country has entered a new stage of energy development, actively promoting the goals of "carbon peaking and carbon neutrality" and committing to a clean and low-carbon energy transition. Consequently, the power system is characterized by a high proportion of new energy sources and a high degree of power electronics integration. The inherent random fluctuations of large-scale wind and solar power, as well as the cumulative effects of occasional factors or extreme situations on the power generation, grid, and load sides, further increase the risk of widespread power outages. Therefore, improving the recovery efficiency of the power system to cope with the potential risk of large-scale power outages has become an urgent problem to be solved. Summary of the Invention

[0003] The purpose of this application is to provide a power system full-process recovery optimization method based on dynamic assessment of subject importance, so as to improve the recovery efficiency of the power system.

[0004] In a first aspect, the present invention provides an optimization method for the whole-process restoration of a power system based on dynamic assessment of the importance of the subject. The power restoration scheme includes n steps and the global restoration state of each restoration subject. The global restoration state of each restoration subject at the m-th step is determined by the following method:

[0005] Based on the source-load power, flexibility resource balance, and energy storage charge status of the restored power system at the (m-1)th step, the dynamic adjustment index is updated, and the importance value of each restoration entity at the current step is determined. The wind power output and its fluctuation power in the optimization time domain at the current step are determined. The objective function of restoration revenue is solved to obtain the local restoration state of each restoration entity at each of the m-k+1 steps. The local restoration state of each restoration entity at the first step in the local restoration state of each of the m-k+1 steps is taken as the global restoration state of each restoration entity at the mth step. Here, k is the step number.

[0006] In an optional implementation, the restoration entity includes thermal power units and new energy units, independent energy storage devices, and loads. The importance value ω of the restoration entity i at time t is determined by the following method. i (t):

[0007]

[0008] Where, δ i τ represents the node importance obtained through network topology evaluation. i (t) represents the dynamic adjustment index of the recovering subject i; k i With k RC,iThe maximum power P of unit i is respectively Gmax,i and maximum spinning reserve capacity P RC,max,i The percentage of total unit power and total spinning reserve; P i Let be the active power of load i; DS be the system supply and demand factor; and FLE be the system flexibility factor.

[0009] In an optional implementation, the system supply and demand coefficients are calculated in the following manner:

[0010]

[0011] in, Π represents the actual output of unit j in the system that has been restored at time t; Π represents the set of units in the system that have been restored. Let t be the load amount of load j to be restored at time t.

[0012] The system flexibility coefficient is calculated using the following methods:

[0013]

[0014] in, The amount of flexibility resources provided by thermal power unit j in the system has been restored at time t; P represents the amount of flexibility resources provided by the independent energy storage devices in the system that have been restored at time t. flu,j (t) represents the fluctuating power value of the new energy generating unit j that has been connected to the grid and is waiting to be connected at a future time.

[0015] In an optional implementation, the objective function for restoring revenue is:

[0016]

[0017] Among them, Ψ T For a collection of power grid loads or generating units; Θ T A collection of synchronous generator units or independent energy storage devices; I i (t) represents the recovery benefit of the load or unit; C i (t) represents the unit recovery cost or risk; R i (t) is the indirect benefit from the backup power output provided by synchronous generators or independent energy storage devices to mitigate power fluctuations.

[0018] In optional implementations, the recovery revenue objective function also includes constraints on component commissioning, continuous power supply, unit output, unit start-up time, energy storage, node power balance, line capacity, unit reserve, flexibility resources, voltage safety, and frequency.

[0019] Secondly, this invention provides an optimization system for the full-process restoration of a power system based on dynamic assessment of the importance of the subject. The power restoration scheme includes n steps and the global restoration state of each restoration subject. The optimization system determines the global restoration state of each restoration subject at the m-th step in the following way:

[0020] Based on the source-load power, flexibility resource balance, and energy storage charge status of the restored power system at the (m-1)th step, the dynamic adjustment index is updated to determine the importance value of each restoration entity at the current step.

[0021] Determine the wind power output and its fluctuating power in the optimized time domain at the current step size;

[0022] Solving the objective function for recovery yields m-k+1 step sizes and the local recovery state of each recovery subject corresponding to each step size;

[0023] The local recovery state of each recovery subject at the first step of the m-k+1 step sizes and the local recovery state of each recovery subject at each step size is taken as the global recovery state of each recovery subject at the m-th step size.

[0024] In an optional implementation, the recovery entity includes thermal power units and new energy units, independent energy storage devices, and loads. The optimization system determines the importance value ω of the recovery entity i at time t using the following method. i (t):

[0025]

[0026] Where, δ i τ represents the node importance obtained through network topology evaluation. i (t) represents the dynamic adjustment index of the recovering subject i; k i With k RC,i The maximum power P of unit i is respectively Gmax,i and maximum spinning reserve capacity P RC,max,i The percentage of total unit power and total spinning reserve; P i Let be the active power of load i; DS be the system supply and demand factor; and FLE be the system flexibility factor.

[0027] In an optional implementation, the optimization system calculates the system supply and demand coefficients in the following manner:

[0028]

[0029] in, Π represents the actual output of unit j in the system that has been restored at time t; Π represents the set of units in the system that have been restored. Let t be the load amount of load j to be restored at time t.

[0030] The system flexibility coefficient is calculated by optimizing the system in the following ways:

[0031]

[0032] in, The amount of flexibility resources provided by thermal power unit j in the system has been restored at time t; P represents the amount of flexibility resources provided by the independent energy storage devices in the system that have been restored at time t. flu,j (t) represents the fluctuating power value of the new energy generating unit j that has been connected to the grid and is waiting to be connected at a future time.

[0033] In an optional implementation, the objective function for restoring revenue is:

[0034]

[0035] Among them, Ψ T For a collection of power grid loads or generating units; Θ T A collection of synchronous generator units or independent energy storage devices; I i (t) represents the recovery benefit of the load or unit; C i (t) represents the unit recovery cost or risk; R i (t) is the indirect benefit from the backup power output provided by synchronous generators or independent energy storage devices to mitigate power fluctuations.

[0036] In optional implementations, the recovery revenue objective function also includes constraints on component commissioning, continuous power supply, unit output, unit start-up time, energy storage, node power balance, line capacity, unit reserve, flexibility resources, voltage safety, and frequency.

[0037] This application provides a power system full-process restoration optimization method based on dynamic assessment of subject importance. The power restoration scheme includes n steps and the global restoration state of each restoration subject. The global restoration state of each restoration subject in the m-th step is determined as follows: based on the source-load power, flexibility resource balance, and energy storage charge state of the restored power system in the (m-1)-th step, the dynamic adjustment index is updated to determine the importance value of each restoration subject in the current step; the wind power output and its fluctuation power in the optimization time domain of the current step are determined; the restoration benefit objective function is solved to obtain the local restoration state of each restoration subject in the m-k+1 steps; the local restoration state of each restoration subject in the first step of the m-k+1 steps is taken as the global restoration state of each restoration subject in the m-th step. By constructing a transmission network restoration model with restoration benefit as the optimization objective and solving it using a rolling optimization framework, an optimized power system restoration scheme is obtained, improving the power system restoration efficiency. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A flowchart illustrating an optimization method for power system full-process recovery based on dynamic assessment of principal importance, provided for embodiments of this application;

[0040] Figure 2 A schematic diagram illustrating a rolling optimization framework solution provided in an embodiment of this application;

[0041] Figure 3 This application provides a schematic diagram of a first power transmission network structure for embodiments of the present application;

[0042] Figure 4 A schematic diagram of wind power prediction output and fluctuation in a power transmission network provided in this application embodiment;

[0043] Figure 5 This is a schematic diagram illustrating the relationship between unit recovery sequence and unit importance, provided as an embodiment of this application. Detailed Implementation

[0044] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0045] Figure 1A flowchart illustrating an optimization method for power system full-process recovery based on principal importance assessment, provided as an embodiment of this application. Figure 1 As shown, the power restoration scheme provided in this application includes n step sizes and a global restoration state for each restoration entity. The global restoration state can be represented by 0 or 1. 0 represents a non-started state, and 1 represents a started state. The power system includes black-start power sources (hydropower units), thermal power units, new energy units (wind power units), independent energy storage devices, and loads.

[0046] Here, the global recovery state of each recovery subject at the m-th step size can be determined in the following way:

[0047] S1. Based on the source-load power, flexibility resource balance, and energy storage charge status of the restored power system at the (m-1)th step, determine the importance value of each restoration entity at the current step.

[0048] Here, the importance value ω of the recovered subject i at time t can be determined in the following way. i (t):

[0049]

[0050]

[0051] Where, δ i τ represents the node importance obtained through network topology evaluation. i (t) represents the dynamic adjustment index of the recovering subject i; k i With k RC,i The maximum power P of unit i is respectively Gmax,i and maximum spinning reserve capacity P G max,i The percentage of total unit power and total spinning reserve; P i Let be the active power of load i; DS be the system supply and demand factor; and FLE be the system flexibility factor.

[0052] The system supply and demand coefficients are calculated using the following method:

[0053]

[0054] in, Π represents the actual output of unit j in the system that has been restored at time t; Π represents the set of units in the system that have been restored. Ω represents the load quantity of load j to be restored at time t. load For load sets.

[0055] The system flexibility coefficient is calculated using the following methods:

[0056]

[0057] in, The amount of flexibility resources provided by thermal power unit j in the system has been restored at time t; P represents the amount of flexibility resources provided by the independent energy storage devices in the system that have been restored at time t. flu,j (t) represents the fluctuating power values ​​of the new energy generating units j that are already connected to the grid and those awaiting grid connection at future times; Ω G A collection of synchronous generator units; Ω BES For energy storage collection; Ω W It is a collection of new energy generating units.

[0058] S2. Determine the wind power output and its fluctuating power in the optimized time domain at the current step size.

[0059] The wind power output and its fluctuating power here can be predicted based on historical wind speed data.

[0060] S3. Solve the objective function of recovery benefits to obtain m-k+1 step sizes and the local recovery state of each recovery subject corresponding to each step size.

[0061] The objective function for restoring returns is:

[0062]

[0063] Among them, Ψ T For a collection of power grid loads or generating units; Θ T A collection of synchronous generator units or independent energy storage devices; I i (t) represents the recovery benefit of the load or unit; C i (t) represents the unit recovery cost or risk; R i (t) is the indirect benefit from the backup power output provided by synchronous generators or independent energy storage devices to mitigate power fluctuations.

[0064] The recovery revenue objective function also includes component commissioning constraints:

[0065]

[0066] u ij (t)≤u i (t-1)+u j (t-1);

[0067]

[0068] Among them, u G,g (t), u G,gen u i (t), u Load,Tl u ij (t) represents the recovery status of the non-black start unit, black start unit, node, transmission network load, and line at time step t, respectively; ΩNBS Ω BSG Ω bus Ω line Ω Load These are the collections of all non-black start generators, black start generators, nodes, lines, and transmission network loads.

[0069] The first two inequalities constrain the restoration of the generator unit and the transmission network load until their connected nodes are restored. The third inequality states that the line can only resume power supply after the end nodes of the line are restored. The fourth inequality indicates that a node can be restored by restoring its connected lines, or by restoring power to a node directly connected to a black-start generator unit.

[0070] To effectively ensure the continuity of power supply and improve the user's power experience, it is agreed that each component will not be disconnected after restoration, and continuous power supply constraints are also included:

[0071]

[0072] This also includes unit output constraints, which can be simplified from the conventional unit ramp-up output function to a piecewise linear function:

[0073]

[0074] Among them, T start,i R is the startup time of synchronous unit i; G,i T is the ramp rate of synchronous generator unit i; gc,i The time from startup to grid connection and operation of synchronous generator unit i.

[0075] And unit start-up time constraints:

[0076] After a power outage, the unit must be started within the hot start time; otherwise, it can only be started cold after the hot start has transitioned to a cold start state.

[0077]

[0078] Among them, T G,max Indicates the maximum warm-up time; T tran This indicates the time required for the transition between hot and cold states.

[0079] This also includes energy storage constraints. Considering that energy storage helps improve the efficiency of new energy utilization, new energy units will be equipped with a certain capacity of energy storage. The state of charge / discharge and power constraints of the energy storage are as follows:

[0080]

[0081] Among them, P c,e (t) and P f,e(t) represents the charging and discharging power of the stored energy e at time step t, respectively; P c,max and P f,max These are the maximum charging and discharging power of the energy storage, respectively; η c,e and η f,e These represent the charging and discharging efficiencies of energy storage e, respectively; u c,e (t) and u f,e (t) represent the charging and discharging states of the stored energy e, respectively; E e (t) represents the energy stored at time step t.

[0082] To effectively ensure the continuity of power supply and improve the user's power experience, it is agreed that all components will not be disconnected after restoration, and node power balance constraints are also included:

[0083]

[0084] Among them, P r,i (t) and Q G,i (t) represents the active and reactive power output of unit i at time step t; P start,i (t) represents the starting power of unit i at time step t; P Load,i (t) represents the power demand of the load connected to node i at time step t; P ij (t) and Q ij (t) represents the power flowing through the line.

[0085] Lines have a long-term allowable power capacity; therefore, to ensure that the power does not exceed this limit during restoration, line capacity constraints must be met. Thus, line capacity constraints are also included.

[0086]

[0087] In the formula, S max,ij This represents the maximum allowable power of the line.

[0088] To mitigate fluctuations in wind power output and to prevent load fluctuations or unit failures from impacting the recovery process, conventional units, in addition to the energy storage configured in renewable energy units, also need to maintain a certain reserve capacity. Therefore, this also includes unit reserve constraints:

[0089]

[0090] Among them, P RC,i (t) represents the spinning reserve provided by unit i at time step t to avoid load fluctuations; α represents the system reserve requirement; R G,i Ω represents the ramp rate of unit i; BES For energy storage sets; Δt is the step size of a single time step.

[0091] It also includes flexibility resource constraints:

[0092]

[0093] 0≤P fleG,i (t)≤0.2(P RC,max,i (t)-P RC,i (t))i∈Ω G ;

[0094] 0≤P fleB,e (t)≤P f，max -(P f，e (t)-p c，e (t))e∈Ω BES ;

[0095] P RC,max,i (t)=min(P Gmax,i -P G,i (t),R G,i Δt);

[0096] Among them, P fleG,i (t), P fleB,i (t) represents the actual flexibility resources provided by thermal power unit i and energy storage i at time step t, respectively. vol,g (t) represents the fluctuation of the wind turbine g at time step t.

[0097] It also includes voltage safety constraints:

[0098]

[0099] Among them, U min,i U max,o These are the upper and lower limits of the node voltage, respectively. The node voltage must be within the safe limit range.

[0100] It also includes frequency constraints:

[0101]

[0102] Among them, K G,j This refers to the unit regulating power of a conventional unit.

[0103] S4. Take the local recovery state of each recovery subject under the first step of the m-k+1 step sizes and the local recovery state of each recovery subject under each step size as the global recovery state of each recovery subject under the m-th step size.

[0104] Understandably, let k be the time step number, ΔT be the length of a single time step, n be the total number of time steps, and T be the total number of time steps. mLet m be the set of time steps under the m-th optimization. Taking the m-th importance dynamic assessment and model rolling optimization process as an example, firstly, the source load power, flexibility resource balance degree, and energy storage charge state of the restored system at time (m-1)ΔT are used as boundary conditions for the importance assessment of the restoration subject. At the same time, the updated system operation status is used as the initial condition for this optimization. Secondly, using historical wind power data, the wind power output and its fluctuation degree in the time domain of this optimization are obtained. Finally, the established model is used to optimize and solve the problem to obtain the restoration order and restoration benefit decision of the restoration subject to be restored. However, only the decision of the first time step in the optimization result is taken as the final restoration scheme of the k-th time step. The above process is repeated. As the restoration status of the unit, shared energy storage, and load changes, the importance of the restoration subject will also be adjusted accordingly, thus affecting the final restoration benefit decision.

[0105] This application provides an optimization method for the entire process of power system restoration based on dynamic assessment of the importance of the main entities. It considers the comprehensive importance of the restoration entities in the context of network topology and actual conditions, especially with the participation of large-scale wind power. It dynamically assesses the importance of new restoration entities by utilizing the flexibility resources of the already restored system and the source-load balance level, coordinating their own power generation resources and efficiently coordinating the restoration process. It uniformly measures the restoration benefits of the target entities, objectively guiding the entire process of grid restoration and avoiding the problem of delayed restoration of certain important loads caused by traditional stage-based division.

[0106] In one embodiment of this application, under the background of a new power system, the main contradiction during the recovery process is the imbalance between power source and load. Furthermore, it is necessary to ensure that flexibility support matches flexibility requirements. If this contradiction is not overcome, it may lead to insufficient support after new energy sources are integrated into the grid. This would not only fail to leverage the advantages of rapid startup and power output from new energy sources, but also pose potential risks to the power system and hinder the recovery process.

[0107] At the same time, in order to properly guide the power system recovery and efficiently utilize the available capacity in the power grid during the recovery process, it is necessary to effectively integrate the objectives and tasks of different recovery stages and achieve consistency between the assessment of the importance of the entities to be restored and the recovery optimization objectives.

[0108] To address these issues, the importance of new recovery entities should be assessed. The importance of generating units is influenced by system supply and demand and flexibility levels, while loads need to consider their own demand size and importance to ensure rapid recovery of certain critical loads. Fluctuations in renewable energy generating units constitute a major part of flexibility requirements, while energy storage can provide flexibility resources. Therefore, the assessment of their importance is closely related to the degree of matching between flexibility support and flexibility requirements in the recovered system.

[0109] Based on this, the supply-demand factor DS and the flexibility factor FLE are first defined. When calculating the supply-demand factor DS and the flexibility factor FLE, each thermal power unit can provide up to 20% of its available spinning reserve capacity.

[0110] Secondly, the importance value ω of the multiple recovery subjects is characterized. i (t):

[0111]

[0112] Where, τ i The first term in (t) represents the dynamic adjustment index of the synchronous generator unit. Since the unit simultaneously undertakes the roles of output and flexibility support, a higher-risk factor is selected to measure the change in importance. The second term represents the load dynamic adjustment index, which is related to the load level, the proportion of load recovery, and the output level of the restored system units. β represents the load level coefficient. The first item represents the total output of thermal and renewable energy units in the system that has been restored. The second item represents the dynamic adjustment index of renewable energy units, which is related to their own fluctuations in the future and the support level of the restored system. The third item represents the dynamic adjustment index of energy storage, whose dynamic adjustment index changes in the opposite direction to that of renewable energy units. b,i W represents the total capacity of energy storage node i; i (t) represents the capacity of energy storage node i at time t.

[0113] In one embodiment of this application, the power grid restoration model can be constructed in the following manner:

[0114] To efficiently utilize the available capacity in the transmission network during the restoration process, the transmission network restoration sub-problem takes the restoration benefits of a unified target entity as the optimization objective, guiding the restoration of the transmission system.

[0115] The objective function for restoring returns is constructed as follows:

[0116]

[0117] Among them, T s To optimize time periods; P r,g (t) represents the actual output of synchronous generator unit g in the power transmission network; P start,g (t) represents the starting power of the synchronous generator unit g; P is the expected output of the wind farm at time step t. vol,w (t) represents the fluctuating power of the wind farm w; n w,sum n represents the total number of wind turbines. w (t) represents the number of wind turbines that have been restored in the wind farm w at time t; P Load,i (t) represents the power demand of the load connected to node i at time step t; P fleG,i (t) and P fleB,i(t) represents the actual flexibility resources provided by thermal power unit i and energy storage i at time step t; Ω G Ω NBSG Ω NBSWG Ω Load Ω BES These are, respectively, all synchronous generator units in the power transmission network, non-black start synchronous generator units, non-black start wind turbine units, load and energy storage collections.

[0118] Furthermore, the prediction error for wind power increases with the increase of the prediction time range, while the time range requiring optimization gradually decreases as the system gradually recovers. Therefore, to characterize the impact of the supply and demand level and flexibility level of the recovered system on the importance of the recovery subject, and to reduce the adverse impact of wind power fluctuations on the optimization results, the SHIMPC method can be used. At each decision step, based on the current system state and predicted wind power parameters, the optimization time domain and the importance of the recovery subject are updated, and model optimization is performed on this basis. Figure 2 As shown, the solution process may specifically include:

[0119] The expected power output of the wind turbine is obtained using predicted wind speed data. Assuming that wind speed variation follows a normal distribution, the probability density function and mean are obtained by fitting historical data. The upper and lower bounds of wind power output are determined by the confidence level α, and wind power fluctuation is defined as the difference between the expected power output of the wind turbine and the lower limit of the output.

[0120] Prioritize the startup of black start units in the power transmission network. Based on the SHIMPC model rolling optimization framework, the importance of the recovery subject is dynamically adjusted and solved by utilizing the system recovery status of the previous time step decision. Rolling optimization continues until the power transmission network is fully restored.

[0121] In one specific embodiment of this application, the power transmission network structure is as follows: Figure 3 As shown, the predicted wind power output and fluctuations of the transmission network are as follows: Figure 4 As shown, the trends in unit recovery sequence and unit importance are as follows: Figure 5As shown, the overall trend of thermal power units is decreasing, while that of wind turbines is increasing. Starting from the third time step, thermal power units and wind power gradually recover. In the third time step, bus 2 recovers and thermal power unit 2 has the highest importance. In the fourth time step, thermal power unit 27 has the highest importance, but at this time, the flexibility resources provided by the restored system are sufficient to smooth the fluctuations of wind turbine 13. Since the wind turbine has no starting power, on the one hand, restoring the wind turbine can immediately provide output, while restoring thermal power requires a longer grid connection time and start-up time, and cannot quickly provide more power to restore the load; on the other hand, restoring the wind turbine at this time can bring a net benefit of 1291.54 MWh more than restoring thermal power. Therefore, under the combined influence of importance and recovery benefits, wind turbine 13 recovers before thermal power unit 27. In the fifth time step, there is an imbalance between the flexibility support and flexibility demand of the restored system. Both energy storage and thermal power units can provide flexibility support for wind power fluctuations, but energy storage is far from wind turbine 23 and is not advantageous. Therefore, thermal power units are restored to provide flexibility resources. Unit 27 was restored first because there were many busbar connected loads around Unit 22 and the line impedance was relatively large. At this time, the importance of the loads had exceeded that of the unit. Restoring the loads would generate a benefit of 199.14MW, while restoring Unit 22 would generate a benefit of 147.17MW. Obviously, the loads contributed more to the optimization goal, and since Unit 27 had a larger capacity, restoring Unit 27 would yield a higher benefit than restoring Unit 22.

[0122] This application provides an optimization method for the entire process of power system restoration based on dynamic assessment of the importance of the restoration entity. It considers the comprehensive importance of the restoration entity in the context of network topology and actual conditions, especially with the participation of large-scale wind power. It dynamically assesses the importance of the new restoration entity by utilizing the flexibility resources of the already restored system and the source-load balance level, coordinating its own power generation resources and efficiently coordinating the restoration process. It uniformly measures the restoration benefits of the target entity, objectively guiding the entire process of grid restoration and avoiding the problem of delayed restoration of certain important loads caused by traditional stage-based division.

[0123] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0124] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0126] It should be noted that if the function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0128] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An optimization method for the full-process recovery of a power system based on dynamic assessment of the importance of key entities, characterized in that, Power restoration plan includes The step size and the global recovery status of each recovery subject are determined by the following method: The global recovery status of each recovery subject at each step size: Based on the The source-load power, flexibility resource balance, and energy storage charge status of the restored power system at each step are assessed, and the dynamic adjustment index of each restoration entity is updated to determine the importance value of each restoration entity at the current step. Determine the wind power output and its fluctuating power in the optimized time domain at the current step size; Solving the objective function for the recovery return, we obtain... Each step length and the local recovery status of each recovery subject corresponding to each step length; Will The local recovery state of each recovery subject at the first step length and the local recovery state of each recovery subject at each step length is taken as the first step length. The global recovery status of each recovery subject at each step size; Where k is the step size number; The restoration entities include thermal power units and new energy units, independent energy storage devices, and loads, which are determined through the following methods. Restore the main body at any time Importance value : ； ； ； ； in, The importance of nodes is determined through network topology evaluation; To restore the main body The dynamic adjustment index; and The units Maximum power and maximum spinning reserve capacity The proportion of total unit power and total spinning reserve; For load The active power; This refers to the system's supply and demand coefficients. The system flexibility coefficient, for The unit in the system has been restored. Actual output For load level coefficient, For energy storage nodes Total capacity For energy storage nodes exist t The capacity of a moment.

2. The method according to claim 1, characterized in that, The system supply and demand coefficients are calculated using the following method: ； in, for The unit in the system has been restored. Actual output; This refers to the set of units in the restored system; for Ready to restore load at any time The load capacity; The system flexibility coefficient is calculated using the following methods: ； in, for The thermal power units in the system have been restored. The amount of flexible resources provided; for The system has now restored the amount of flexibility resources provided by independent energy storage devices. for New energy generating units already connected to the grid and those awaiting connection The fluctuation power value.

3. The method according to claim 1, characterized in that, The objective function for the recovery benefit is: ； in, For power transmission network loads or unit collections; A collection of synchronous generator units or independent energy storage devices; For the recovery benefits of load or unit; For the unit's recovery costs or risks; The indirect benefits derived from providing backup power to synchronous generator units or independent energy storage devices to mitigate power fluctuations.

4. The method according to claim 3, characterized in that, The objective function for recovery revenue also includes constraints on component commissioning, continuous power supply, unit output, unit start-up time, energy storage, node power balance, line capacity, unit reserve, flexibility resources, voltage safety, and frequency.

5. An optimized system for full-process power system recovery based on dynamic assessment of principal importance, characterized in that, Power restoration plan includes The optimization system determines the step size and the global recovery status of each recovery subject through the following methods: The global recovery status of each recovery subject at each step size: Based on the The source-load power, flexibility resource balance, and energy storage charge status of the restored power system at each step are assessed, and the dynamic adjustment index of each restoration entity is updated to determine the importance value of each restoration entity at the current step. Determine the wind power output and its fluctuating power in the optimized time domain at the current step size; Solving the objective function for the recovery return, we obtain... Each step length and the local recovery status of each recovery subject corresponding to each step length; Will The local recovery state of each recovery subject at the first step length and the local recovery state of each recovery subject at each step length is taken as the first step length. The global recovery status of each recovery entity under each step size, wherein the recovery entities include thermal power units and new energy units, independent energy storage devices and loads, is determined by the optimization system in the following ways. Restore the main body at any time Importance value : ； ； ； ； in, The importance of nodes is determined through network topology evaluation; To restore the main body The dynamic adjustment index; and The units Maximum power and maximum spinning reserve capacity The proportion of total unit power and total spinning reserve; For load The active power; This refers to the system's supply and demand coefficients. The system flexibility coefficient, for The unit in the system has been restored. Actual output For load level coefficient, For energy storage nodes Total capacity For energy storage nodes exist t The capacity of a moment.

6. The system according to claim 5, characterized in that, The optimization system calculates the system supply and demand coefficients in the following manner: ； in, for The unit in the system has been restored. Actual output; This refers to the set of units in the restored system; for Ready to restore load at any time The load capacity; The optimized system calculates the system flexibility coefficient in the following manner: ； in, for The thermal power units in the system have been restored. The amount of flexible resources provided; for The system has now restored the amount of flexibility resources provided by independent energy storage devices. for New energy generating units that are already connected to the grid and awaiting connection. The fluctuation power value.

7. The system according to claim 5, characterized in that, The objective function for the recovery benefit is: ； in, For power transmission network loads or unit collections; A collection of synchronous generator units or independent energy storage devices; For the recovery benefits of load or unit; For the unit's recovery costs or risks; The indirect benefits derived from providing backup power to synchronous generator units or independent energy storage devices to mitigate power fluctuations.

8. The system according to claim 7, characterized in that, The objective function for recovery revenue also includes constraints on component commissioning, continuous power supply, unit output, unit start-up time, energy storage, node power balance, line capacity, unit reserve, flexibility resources, voltage safety, and frequency.

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