A power system multi-type backup resource collaborative optimization method, system, device and storage medium

Abstract

The application discloses a kind of power system multi-type backup resource collaborative optimization method, system, equipment and storage medium, it is related to power system and automation control technical field, including constructing regional grade disaster chain vulnerability model, output failure probability;Utilize failure probability to combine meteorology-load mapping theory, generate including new energy output and load demand uncertainty extreme weather scenario set;Based on the extreme weather scenario set constructed, execute bidirectional black start recovery strategy, after system identifies fault, in distribution network, independently form network cell, and execute bidirectional recovery strategy to recover surrounding load power supply in combination with transmission network;Consider the coupling characteristics between multi-time scales, and the demand side, power generation side and energy storage side resources are coordinated and rolled optimization using two-stage robust optimization model.The method described in the application greatly shortens the average outage time during disaster, reduces annual average outage loss, and ensures the reliability of power supply for key infrastructure is improved.

Description

Technical Field

[0001] This invention relates to the field of power system and automation control technology, specifically to a method, system, device and storage medium for collaborative optimization of multiple types of backup resources in a power system. Background Technology

[0002] With the increasingly severe global climate change, the frequency and intensity of natural disasters such as typhoons, earthquakes, and extreme snowstorms have increased significantly, posing a huge threat to the safe and stable operation of modern power systems. Traditional power system security analysis focuses primarily on deterministic criteria, but when dealing with extreme disasters with extremely low probability but high destructive power, the system often exhibits vulnerability and is highly susceptible to large-scale power outages. Currently, improving the resilience of power systems has become a research hotspot in the international power industry. However, existing resilience enhancement methods still have many limitations: First, in the disaster risk assessment stage, existing vulnerability models mostly focus on single physical damage to power equipment, lacking quantitative consideration of the deep interdependence between the power system and infrastructure such as communication networks, transportation networks, and medical and social services. This makes it impossible to accurately assess the cascading failure effects caused by extreme disasters and their impact on comprehensive social functions. Second, with the increasing penetration rate of renewable energy, the power system exhibits a high degree of randomness and uncertainty. Traditional dispatching methods based on deterministic scenarios are difficult to cover extreme situations such as load surges and sudden reductions in renewable energy output under extreme weather conditions, leading to blind allocation of system reserve resources.

[0003] During the recovery phase after system damage, traditional black-start solutions heavily rely on large synchronous generator sets, which have fixed geographical distributions and long start-up preparation times, making it difficult to meet the rapid recovery needs of localized areas of the distribution network. Although the development of distributed resources, energy storage, and virtual power plant technologies has provided new resources for grid recovery, the intermittent output of distributed resources makes voltage and frequency control during the black-start process extremely difficult. Especially in the early stages of forming local "network cells" or islanded operation, without effective coordinated control strategies, the state of charge of distributed energy storage can easily be depleted, leading to black-start failure. In addition, existing power conversion equipment often generates severe surge currents and bias currents at the moment of black-start due to parameter matching issues with buffer capacitors and auxiliary inductors, causing electrical stress damage to critical power switching devices such as IGCTs and HDCTs, and even triggering secondary faults.

[0004] Finally, in terms of resource scheduling decisions, the recovery process of transmission and distribution networks often lacks an effective two-way coordination mechanism. Existing optimization models are mostly static, making it difficult to adapt to the dynamic evolution of the post-disaster environment. Furthermore, when dealing with coupled optimization involving multiple stakeholders and time scales (source-grid-load-storage), it is difficult to balance computational complexity and convergence. In extreme scenarios with multiple uncertainties, how to maximize the sufficiency and speed of system power restoration while ensuring economic efficiency and low carbon emissions remains a key technical bottleneck that urgently needs to be addressed in improving the resilience of the entire power system chain. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is that existing power system reliability assessment and fault recovery methods have the following problems: low accuracy in identifying multi-dimensional coupled infrastructure disaster cascading faults, inaccurate characterization of the uncertainty of new energy output and load in extreme scenarios leading to imbalance in backup resource allocation, poor stability of distributed resource black start process and easy to cause electrical stress damage to power conversion devices, and how to achieve efficient coordination among multiple entities in the bidirectional recovery process of transmission and distribution networks and improve the resilience of the entire power system chain.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a collaborative optimization method for multiple types of backup resources in a power system, comprising: integrating the damage probabilities of various equipment through a decision tree model to construct a regional-level disaster chain vulnerability model and outputting failure probabilities; utilizing failure probabilities in conjunction with weather-load mapping theory, and employing a stochastic optimization method to generate a set of extreme weather scenarios that include uncertainties in renewable energy output and load demand; executing a bidirectional black-start recovery strategy based on the constructed extreme weather scenario set; after the system identifies a fault, utilizing the distributed resource black-start capacity aggregated by virtual power plants to autonomously form network cells within the distribution network, and combining with the transmission network to execute a bidirectional recovery strategy to restore power supply to surrounding loads; based on the bidirectional black-start recovery strategy, considering the coupling characteristics between multiple time scales, employing a two-stage robust optimization model to coordinate and continuously optimize resources on the demand side, generation side, and energy storage side.

[0008] As a preferred embodiment of the collaborative optimization method for multiple types of backup resources in the power system described in this invention, the regional-level disaster chain vulnerability model includes: using a vulnerability curve with a two-parameter cumulative log-normal distribution to quantitatively analyze the damage to various types of equipment; using a decision tree model to integrate the damage probability curves of various types of equipment to quantitatively assess the fault risk; and constructing the model by integrating the functional dependencies between multi-dimensional coupled subsystems of power, transportation, communication, and social services.

[0009] As a preferred embodiment of the collaborative optimization method for multiple types of backup resources in the power system described in this invention, the generation of an extreme weather scenario set containing uncertainties in renewable energy output and load demand includes: extracting the nonlinear relationship between meteorological elements and power load based on the meteorological-load mapping theory; using stochastic optimization methods to model the uncertainties in renewable energy output and load demand through expected value programming, multi-scenario programming, and chance constraint models; and combining failure probabilities to construct an operational optimization simulation scenario set containing multiple deterministic boundary conditions through sampling of uncertainty factors.

[0010] As a preferred embodiment of the power system multi-type backup resource collaborative optimization method described in this invention, the execution of the bidirectional black-start recovery strategy includes: after the system identifies the fault, using the distributed resource black-start capacity aggregated by the virtual power plant to form network cells in the distribution network, further expanding the network cells, and combining with the transmission network to execute the bidirectional recovery strategy, and executing the black-start strategy based on soft converter on the equipment side.

[0011] As a preferred embodiment of the power system multi-type backup resource collaborative optimization method described in this invention, the black start strategy based on soft switching is implemented on the equipment side, which includes the following: the startup process is divided into an extended phase-shifting startup stage and a single phase-shifting startup stage; by analyzing the influence of the parameters of the buffer capacitor and auxiliary inductor on the minimum soft switching current of the device during the startup process, all devices are in ZVS state during the startup process.

[0012] As a preferred embodiment of the collaborative optimization method for multiple types of backup resources in the power system described in this invention, the two-stage robust optimization model includes: the first part of the decision variables must be decided before the uncertainty factor is realized; the second part of the decision variables are decided only after the uncertainty factor is realized; the parameter fluctuations are described by constructing a box-shaped uncertainty set or an ellipsoidal uncertainty set, and the optimal solution that is feasible under all possible realizations of the uncertainty factor is determined.

[0013] As a preferred embodiment of the power system multi-type reserve resource collaborative optimization method described in this invention, the coordinated rolling optimization of demand-side, generation-side, and energy storage-side resources includes: comprehensively considering the economic, low-carbon, and sufficiency requirements of power system reserve operation, taking into account the coupling characteristics between multiple time scales, and establishing reserve resource operation models for the demand-side, generation-side, and energy storage-side; and using a robust optimization dual transformation method to convert the reserve resource operation model into a linear model for solution.

[0014] Another objective of this invention is to provide a multi-type backup resource collaborative optimization system for power systems. This system can execute a bidirectional black-start recovery strategy based on a constructed set of extreme weather scenarios. After the system identifies a fault, it can autonomously form network cells within the distribution network by utilizing the distributed resource black-start capacity aggregated by virtual power plants. It can then combine this with the transmission network to execute a bidirectional recovery strategy to restore power supply to surrounding loads. This solves the problems of poor response flexibility and low utilization rate of local distributed resources in the distribution network in current power system fault recovery technologies.

[0015] As a preferred embodiment of the power system multi-type backup resource collaborative optimization system described in this invention, it includes: a vulnerability modeling module, a scenario generation module, a bidirectional recovery module, and a rolling optimization module; the vulnerability modeling module is used to integrate the damage probabilities of various equipment through a decision tree model, construct a regional-level disaster chain vulnerability model, and output the failure probability; the scenario generation module is used to generate a set of extreme weather scenarios that take into account the uncertainty of new energy output and load demand by using the failure probability combined with the weather-load mapping theory and a stochastic optimization method; the bidirectional recovery module is used to form network cells in the distribution network based on the extreme weather scenario set by using the distributed resource black-start capacity aggregated by virtual power plants, and to execute a bidirectional black-start recovery strategy in conjunction with the transmission network; the rolling optimization module is used to coordinate and perform rolling optimization of resources on the demand side, generation side, and energy storage side by considering the multi-time-scale coupling characteristics based on the bidirectional black-start recovery strategy and using a two-stage robust optimization model.

[0016] Another object of the present invention is to provide a power system multi-type backup resource collaborative optimization device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program as a step to implement a power system multi-type backup resource collaborative optimization method.

[0017] Another object of the present invention is to provide a storage medium for collaborative optimization of multiple types of backup resources in a power system, wherein a computer program is stored thereon, and when the computer program is executed by a processor, the steps of a method for collaborative optimization of multiple types of backup resources in a power system are implemented.

[0018] The beneficial effects of this invention are: The present invention provides a collaborative optimization method for multiple types of backup resources in power systems. This method integrates the damage probabilities of various equipment using a decision tree model, constructs a regional-level disaster chain vulnerability model, and outputs failure probabilities. This addresses the difficulty of traditional assessment methods in quantifying cross-system cascading failure effects, significantly improving the accuracy of perception of the depth of damage to power and related social infrastructure under extreme disasters. Utilizing failure probabilities combined with weather-load mapping theory, a stochastic optimization method is employed to generate a set of extreme weather scenarios that include uncertainties in renewable energy output and load demand. This comprehensively considers the intermittency of renewable energy output and the uncertainty of load demand fluctuations, effectively compensating for the blind spots in backup resource allocation caused by insufficient scenario coverage in traditional dispatch models under extreme environments. Based on the constructed extreme weather scenario set, a bidirectional black-start recovery strategy is executed. After the system identifies a fault, virtual power plant aggregation is utilized. The distributed resource black-start capacity autonomously forms network cells within the distribution network and, in conjunction with the transmission network, executes a bidirectional recovery strategy to restore power supply to surrounding loads. This eliminates the excessive reliance on large centralized units for black-start operations. Furthermore, the coordinated response of the transmission and distribution networks shortens the recovery path and expands the load supply range, significantly reducing outage time and economic losses. Based on the bidirectional black-start recovery strategy, considering the coupling characteristics between multiple time scales, a two-stage robust optimization model is adopted to coordinate and continuously optimize resources on the demand side, generation side, and energy storage side. While ensuring economic efficiency and low carbon emissions, this invention achieves an optimal balance between resource allocation conservatism and operational safety. This invention achieves better results in terms of the accuracy of post-disaster risk quantification and perception in power systems, the adequacy of backup resource allocation in extreme scenarios, and rapid self-healing and response resilience after damage. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 The above is an overall flowchart of a collaborative optimization method for multiple types of backup resources in a power system provided in Embodiment 1 of the present invention.

[0021] Figure 2 This is a timing diagram of a collaborative optimization method for multiple types of backup resources in a power system provided in Embodiment 1 of the present invention.

[0022] Figure 3 This is a system diagram of a collaborative optimization method for multiple types of backup resources in a power system provided in Embodiment 2 of the present invention. Detailed Implementation

[0023] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] Example 1, referring to Figures 1-2 As an embodiment of the present invention, a method for coordinated optimization of multiple types of reserve resources in a power system is provided, comprising: S1: By integrating the damage probabilities of various equipment through the decision tree model M1, a regional disaster chain vulnerability model 100 is constructed, and the failure probability P is output.

[0025] Specifically, a regional-level disaster chain vulnerability model 100 is constructed. This model integrates the damage probabilities of various power equipment using a decision tree model M1, outputting an accurate failure probability P. For complex systems integrating multiple devices, the decision tree model M1 is used to integrate the damage probability curves of various devices, enabling a quantitative assessment of system-level failure risk. Extending to multi-dimensional coupled systems such as power, transportation, communication, and social services, the regional-level disaster chain vulnerability model 100 is constructed by integrating the functional dependencies between subsystems. In geological disaster scenarios, this model primarily covers equipment types including overhead power lines, substations, distributed resources, transportation roads, residential buildings, and medical buildings.

[0026] The failure probability of overhead power lines is determined by the combined damage condition of the conductors and towers. The failure probability of conductors under the influence of an earthquake is: , in, For the line In the grid The failure probability of the conductor segment in the middle. For the first Lines in the grid The length inside, The total length of all lines in the distribution network. For the first The proportion of damage in the vulnerability curve under various damaged states. For the line In the grid In the middle of The probability of a damaged state. For the number of power distribution lines, For the line The number of grids involved For grid Peak ground acceleration within, For different damage conditions of power distribution lines, For the first The standard deviation of the vulnerability curve under various damaged states The intensity of the current line damage state. For the first The logarithmic mean of the vulnerability curves under various damaged states.

[0027] Earthquakes can cause power poles to collapse, thus affecting the normal operation of power lines. The failure probability model for power poles under earthquake influence is as follows: , in, For the tower at The maximum horizontal displacement of the vertex at that time For the line In the grid The tower in the middle is in the first The probability of different earthquake disaster levels. For relational constants, The expected value of the normal distribution. The standard deviation of the normal distribution. For the line In the grid The number of poles and towers inside.

[0028] Taking into account the impact of earthquakes on conductors and towers, the distribution line was obtained. The overall failure probability is: , in, For power distribution lines The overall failure probability.

[0029] Furthermore, substations have complex structures and numerous components. During an earthquake, the collapse of buildings such as the main control building and distribution room, the failure of supporting structural equipment such as circuit breakers and disconnect switches, and the breakage or short circuit of vulnerable components such as bushings and transformers can all cause substation failure, thereby affecting power supply. Substations are mainly affected by PGA parameters.

[0030] The farther away from the epicenter, the less energy the seismic waves have as the distance increases. In seismology, considering the damping effect of seismic waves, a seismic attenuation model is established, characterized by the PGA (Programme for Gaussian Wave Attenuation), and expressed as: , in, For variables Take the natural logarithm. For regression coefficients, Surface wave magnitude, For distance.

[0031] Based on the vulnerability curves of the components, a substation failure probability model is established regarding the PGA parameters, expressed as: , in, This represents the probability of substation failure. The cumulative distribution function of the standard normal distribution. The standard deviation is the logarithm. The mean is logarithmic.

[0032] Based on the vulnerability curve of each component, the final probability of substation damage is calculated using fault tree analysis.

[0033] It should be noted that the installed capacity of distributed resources has been continuously increasing in recent years, playing an increasingly important role in energy supply. However, precisely because of their widespread distribution, distributed resources not only face the risk of damage themselves during geological disasters, but are also entrusted with the important mission of providing emergency power supply during disasters, requiring reliable power support for critical facilities and emergency responses.

[0034] The impact of earthquakes on wind turbines is mainly manifested in the strong ground vibrations that trigger a dynamic response in the tower structure, leading to a significant increase in tower top displacement and ultimately tower instability and collapse. Strong winds accompanying strong earthquakes create a wind-seismic coupling effect, further amplifying the load. Slippage at bolted connections reduces tower stiffness, increasing displacement response and significantly raising the risk of failure. The probability of wind turbine collapse is expressed as: , in, This represents the probability of structural failure due to wind-vibration coupling within the wind turbine's design life. This represents the maximum displacement at the top of the tower. For wind speed, The ultimate structural bearing capacity represents the load-bearing capacity of the wind turbine tower. Let V be the structural vulnerability function, representing the conditional probability that the maximum displacement at the top of the tower exceeds the structural bearing capacity under specific PGA and V. This represents the combined wind-earthquake exceedance probability distribution.

[0035] The seismic attenuation model of photovoltaic (PV) systems consists of a structural attenuation model and an electrical attenuation model. Because distributed PV systems are widely distributed, they are managed and optimized in the form of PV clusters. Therefore, a common seismic vulnerability model for PV systems is expressed as follows: , in, This represents the actual power output of the photovoltaic system under seismic loading. This represents the photovoltaic output under normal conditions. For structural attenuation model, For electrical attenuation model, The damping ratio of the structural material. For the displacement response of the structure, For the effective acceleration of photovoltaic systems during earthquakes, For the conversion efficiency of photovoltaic systems, The frequency of ground motion, This is the inherent frequency of the photovoltaic system.

[0036] It should also be noted that the impact mechanism of earthquakes on transportation infrastructure mainly manifests in the direct damage to road structures caused by ground motion and secondary geological disasters, as well as the functional interruption of the transportation system. Strong ground vibrations triggered by earthquakes can lead to varying degrees of damage to key components such as roadbeds, pavements, bridges, and tunnels, resulting in cracks, slippage, settlement, and even collapse. These structural damages typically stem from shear forces and tensile stresses generated during seismic wave propagation exceeding the material's strength limit, leading to concrete cracking or steel reinforcement yielding. As a highly interconnected system, the transportation network comprises various types of transportation routes, including bridges, highways, and tunnels. In a coupled system, it is difficult to fully model each type of route using finite element simulation. Therefore, the vulnerability curve method based on the log-normal distribution assumption is often employed.

[0037] Bridge components are typically categorized into large bridges, medium bridges, and small bridges based on the total length of the multi-span structure and the length of each individual span. Under the same PGA (Programme for Gauge and Weight) value, large bridges suffer the most severe damage, far exceeding that of medium and small bridges. In earthquake disasters, cases of damage to small and medium bridges are rare, with a very low probability. Therefore, during earthquakes, especially major earthquakes, special attention should be paid to the damage status of large bridges. This is because large bridges not only serve as important nodes in urban transportation systems, handling a greater volume of traffic, but they are also more susceptible to complete destruction and loss of road accessibility during major earthquakes.

[0038] The impact of earthquakes on residential buildings primarily stems from the structural dynamic response triggered by ground vibrations. This leads to excessive stress on components such as walls, floors, beams, and columns, resulting in cracks, displacement, and even collapse. Older residences or those with inadequate seismic design are more susceptible to damage, exhibiting brittle failure. Furthermore, earthquakes can cause site effects such as soil liquefaction and foundation settlement, exacerbating uneven settlement and tilting of buildings. The degree of structural damage depends not only on the intensity of the earthquake but also on the building's inherent characteristics, such as its natural period, stiffness, and material properties. Building structures can be categorized by their primary load-bearing system, such as reinforced concrete structures and masonry structures. The probability of damage for each type of building is typically described using a log-normal distribution model.

[0039] Damage conditions of reinforced concrete structures include yield threshold and collapse threshold. Yield threshold corresponds to the first curve of the equipment vulnerability curve, which is slight damage, while collapse threshold corresponds to the last curve of the equipment vulnerability curve, which is severe damage.

[0040] Masonry buildings often have a long history, consisting of different types of masonry and structural systems spanning historical periods and geographical regions. Ancient and rural masonry buildings, constructed according to conventional experience, are highly susceptible to localized earthquake mechanisms. In earthquake-prone areas, however, masonry buildings often employ specialized earthquake-resistant structures. Therefore, masonry buildings are diverse and can be represented using a macroscopic vulnerability assessment: , in, To represent the average degree of damage, It is a fragility index. It is the ductility index. This refers to the macroscopic earthquake intensity.

[0041] Therefore, the vulnerability model is expressed as: , , in, For being in the first The probability of a certain disruptive state. Macroscopic earthquake intensity The average degree of damage.

[0042] It should also be noted that hospitals belong to the so-called "complex-social" system because they rely on several components of different natures to function properly and provide social services to citizens. The basic components of a hospital are: personnel, organization, and facilities. Medical buildings constructed in the context of geological hazards need to consider the medical needs of the area and their capacity to provide medical services to patients.

[0043] Medical needs in the area where the medical building is located Represented as: , in, The proportion of seriously injured patients requiring surgical treatment. The proportion of seriously injured to the total number of injured is used to measure the medical severity of the incident. This represents the relative proportion of mild, moderate, and severe injuries, used to measure the distribution of damage caused by the event. The percentage of casualties out of the total population. This is the model error term, used to introduce significant uncertainties that affect the model. This refers to the number of people affected in the region.

[0044] Medical service capacity in the region Represented as: , in, For organizational efficiency, Human factors such as operator competence, training, and preparedness. The number of operating rooms that remain operational after a dangerous incident. This indicates whether the medical building "survived" the geological disaster; if it "survived," the value is 1, otherwise it is empty. This refers to the average duration of a surgical procedure.

[0045] S2: Using the failure probability P combined with the weather-load mapping theory, a set of extreme weather scenarios 200 containing uncertainties in new energy output and load demand is generated by using a stochastic optimization method.

[0046] Specifically, the meteorological-load mapping theory provides a framework for understanding the complex nonlinear relationship between meteorological elements such as temperature and power load, especially under extreme high and low temperature conditions where this relationship exhibits significant nonlinear characteristics. The SPT theory, originating from the field of natural language processing, can effectively extract latent patterns and feature dependencies from time-series data through symbolic representation and pre-training, providing technical support for the fusion of meteorological text information and numerical prediction. The physical constraint guidance mechanism theory emphasizes the need to consider the physical impact of extreme temperatures on the performance of power generation equipment during scenario construction, such as the decrease in cooling efficiency and photovoltaic efficiency caused by high temperatures, and equipment icing caused by low temperatures. These physical constraints ensure the reliability of the scenario model.

[0047] The theory of cascading disasters posits that extreme temperature events often trigger a series of secondary disasters, forming complex disaster chains whose spatiotemporal dynamics require systematic modeling. Probabilistic risk assessment theory considers the uncertainty of disaster occurrence and the variability of its impact, providing a methodological foundation for quantifying the risks of disasters of varying intensities. Social tolerance theory indicates that public and user acceptance of power outages is influenced by multiple factors, including outage duration, frequency, and user adaptation costs; these factors form the theoretical basis for a multidimensional indicator system. Multi-objective decision-making theory supports assessing disaster risk under multiple objective functions, forming a decision-making framework that balances socioeconomic impacts and system reliability.

[0048] Furthermore, the multi-objective optimization problem can be explicitly represented mathematically as: , in, For multi-objective optimization functions, For which target space is the target, the mapping from the decision space to the target space is determined by... It consists of several objective functions. For decision-making space Variables in This provides decision-making space.

[0049] In the context of multi-hazard coupling, based on composite state coding, external comprehensive meteorological elements can be divided into main state components and auxiliary state components according to the mechanisms by which disasters such as lightning, precipitation, freezing, wildfires, strong winds (or typhoons), and pollution (or sandstorms) cause transmission line faults. Each state variable is then encoded using a binary table of (feature α, intensity β). The intensity level classification employs a symbolic time series analysis method, dividing the data space into a small number of discrete elements and using a coarse-grained approach to describe the complex system without losing the essential characteristics of the system. The key to symbolization lies in interval division. Meteorological disaster intensity levels are generally classified into four warning levels (red, orange, yellow, and blue) plus a level 1 normal state, with five states (0-4) coded. Meteorological disaster characteristics can also be divided into up to five types using 0-4. Based on the binary table (feature α, intensity β) coding of the comprehensive meteorological element states, the intensity of any given moment can be calculated. Geographic grid or line segment The disaster feature vector and intensity feature vector.

[0050] It should be noted that the uncertainty of new energy output and load demand is modeled using stochastic optimization methods, mainly through expected value programming model, multi-scenario programming model, and chance constraint model.

[0051] Expected value programming refers to an optimization model in which various state variables achieve the optimal expected value of the objective function under expected constraints, expressed as: , in, For the expectation value operator, As decision variables, For random variables, For random constraint functions, also representing an uncertain environment, The number of constraints.

[0052] Multi-scenario planning model: Multi-scenario construction is essentially sampling of uncertain factors, using each sampled scenario as a deterministic boundary condition for running optimization simulation, and finally selecting a planning scheme that can satisfy all scenarios, expressed as: , in, These are the weighting coefficients for the scene. For the objective function in different scenarios In the scene Random constraint function.

[0053] The chance-constraint model allows a planning solution to violate the constraints under certain conditions, but the probability of violation must be less than a given confidence level, expressed as: , in, For the set confidence level, This represents the probability of the event occurring.

[0054] Stochastic optimization methods can effectively address the uncertainty of new energy output and load in the optimization modeling process, providing a theoretical basis for the analysis of uncertainty in new energy output and load demand and the construction of energy storage operation planning models in this invention.

[0055] S3: Based on the constructed extreme weather scenario set 200, execute the bidirectional black start recovery strategy 300. After the system identifies the fault, it uses the distributed resource black start capacity aggregated by the virtual power plant to autonomously form network cells C in the distribution network, and combines the transmission network to execute the bidirectional recovery strategy to restore power supply to the surrounding loads.

[0056] Specifically, for the identified system faults, a bidirectional black-start recovery strategy 300 is implemented, which utilizes the distributed resource black-start capacity aggregated by the virtual power plant (VPP) to achieve coordinated recovery of the transmission and distribution network. On the distribution network side, for the initial recovery after a large-scale power outage, the system uses integrated photovoltaic, energy storage and charging resources with black-start capability as the core guiding power source.

[0057] The boundary of network cell C is constrained by the dynamic balance coefficient of supply and demand. The sum of the total output of distributed resources aggregated by the virtual power plant and the instantaneous power of energy storage must reach more than 1.2 times the demand of the first-level critical load in the region. Nodes with black start capability at the communication level act as cell nuclei to continuously broadcast reference frequency and voltage signals. Before adjacent load nodes are connected, they need to be quasi-synchronous verified through a handshake protocol. If the estimated frequency offset after connection is within a safe range, the system will send a closing command. At the same time, if the Dijkstra algorithm identifies changes in path weights caused by secondary disasters, the system will dynamically update the topology matrix and reconstruct the cell boundary.

[0058] Through self-synchronization control technology, distributed resources can autonomously establish stable voltage and frequency references when they are not supported by the main network, thereby forming multiple network cells C in a local area. These network cells C are not static, but rather search for the optimal recovery path based on Dijkstra's algorithm and dynamically absorb surrounding critical loads (such as hospitals, data centers, and communication base stations) through the orderly closing of logic switches.

[0059] Furthermore, the complex distribution network structure is abstracted into a mathematical topology diagram, and an improved Dijkstra algorithm is introduced for path weight modeling. This model not only considers physical distance but also deeply integrates multiple parameters such as load level, line loss, and path security. After network cell C successfully black-starts from distributed resources (BESS, gas turbines, etc.) and establishes a reference frequency and voltage, the algorithm uses the node where the black-start source is located as the root node and performs shortest path iterations across the entire network topology. Each time the algorithm searches for an adjacent node, it automatically retrieves the logical switch status on that branch. When the search path reaches a critical load node, the system does not blindly close the switch but first performs a quasi-synchronous check or power flow security assessment to ensure that the remaining capacity and ramp rate of the current network cell C can support the instantaneous impact of the load absorption. After the Dijkstra algorithm outputs the optimal recovery path, the distribution automation terminal receives the dispatch command and executes the switch closing operation according to the logical sequence. In the process of absorbing critical loads in the surrounding area (such as communication base stations), if a path experiences abnormal branch impedance or physical disconnection due to secondary disasters, the algorithm immediately updates the topology matrix, sets the weight of the damaged edge to infinity, and triggers a re-search logic, thereby achieving path self-healing and dynamic reconstruction.

[0060] It should be noted that, at the equipment execution level, to eliminate the impact of inrush current during the initial black start on the vulnerable islanded system, a soft-commutation black start strategy is used. By dividing the start-up process into an extended phase-shift start-up stage and a single phase-shift start-up stage, ZVS of all devices during the IGCT-HDCT black start-up process is achieved, and no inrush current is generated in the buffer capacitors. In the extended phase-shift start-up stage, the controller adjusts the phase difference between the leading and lagging bridge arms, using the energy stored in the auxiliary inductor to charge and discharge the parallel buffer capacitors of the power devices, ensuring that the device voltage has dropped to zero before turn-on, thereby achieving zero-voltage switching. As the DC side voltage of the system is established, it automatically and seamlessly switches to the single phase-shift start-up stage. By dynamically adjusting the minimum soft-commutation current threshold, soft-switching characteristics are maintained across the entire power range, effectively reducing switching losses and eliminating electromagnetic interference. This bidirectional logic, combining bottom-up recovery from the distribution network's cellular structure with top-down guidance from the transmission network's large power source, breaks the waiting chain of several hours in the traditional recovery mode, achieving deep collaboration between the transmission and distribution networks in both time and space dimensions.

[0061] S4: Based on the bidirectional black start recovery strategy 300, considering the coupling characteristics between multiple time scales, a two-stage robust optimization model M2 is adopted to coordinate and perform rolling optimization of resources on the demand side, power generation side and energy storage side.

[0062] Specifically, by integrating the coordinated scheduling of resources on the demand side, generation side, and energy storage side, a more flexible and economical system operation strategy can be formed. Stochastic optimization theory guides the design of objective functions based on scenario probability weighting under uncertain conditions, enabling a better balance between deterministic decision-making and risk adaptability. Hierarchical control theory divides system operation strategies into three levels: preventative control, emergency control, and risk control, forming a tiered defense system and enhancing the system's ability to cope with extreme events. Multi-timescale decision theory addresses the matching problem of decisions at different timescales in short- and medium-term resource coordination, including the connection between day-ahead, intraday, and real-time scheduling, providing theoretical guidance for constructing a complete decision-making system. These theories collectively constitute the theoretical framework for power system adaptability methods under temperature-sensitive extreme weather scenarios, providing a solid theoretical foundation for the safe and economical operation of the system.

[0063] Furthermore, this study investigates the techno-economic characteristics of different flexible resources on the demand side, generation side, and energy storage side, and analyzes the adaptability of these reserve resources to various reserve market mechanisms such as contingency reserve, load reserve, and frequency regulation reserve. Based on theories such as cooperative game theory, it studies the commercial operation models of different types of energy storage on the power generation, grid, and load sides, and constructs operating cost and revenue models for multiple types of flexible resources in a multi-market environment of the electricity market. Considering the coupling characteristics between multiple time scales, it establishes operation models for reserve resources on the demand side, generation side, and energy storage side, studies the operating boundaries and constraints of multiple types of reserve resources, and transforms the operation models of multiple types of resources into linear models based on robust optimization dual transformation and other methods, which is beneficial for optimizing the coordination strategies of multiple types of resources. This study investigates the coupling mechanism of multiple time scales, multiple objective scales, multiple subject sides, and multiple uncertainties. It comprehensively considers the economic, low-carbon, and sufficiency requirements of power system reserve operation, and proposes a rolling optimization model for the coordinated operation of demand-side reserve resources with generation-side and energy storage-side reserves, taking into account multiple uncertainties. Multi-objective optimization and robust optimization methods are employed to address multi-objective problems and model uncertainties. A solution method for the coordinated operation optimization of multiple types of power system reserve is proposed, and the allocation method of power system reserve capacity among source-load-storage resources is studied to achieve coordinated operation of multiple types of power system reserve.

[0064] A two-stage robust optimization model M2 is adopted to coordinate and continuously optimize resources on the demand side, generation side, and energy storage side. The first stage focuses on "contingency planning," determining the start-up and shutdown status of various generating units, reserve capacity, and the initial state of charge (SOC) of the energy storage system based on the range of meteorological forecasts at the day-ahead or hour-ahead scale. The objective function of this stage aims to minimize the sum of the system's operating cost and reserve cost under the baseline scenario. In the second stage, "adjustment and correction," the model implements compensatory scheduling decisions for the worst-case scenario in the uncertainty set. By calling upon the flexible reserve resources reserved in the first stage, it ensures that the system can still operate stably under power balance constraints, line power flow limits, and distributed resource ramping constraints.

[0065] It should be noted that the two-stage robust model includes a first part of decision variables and a second part of decision variables. The first part of decision variables requires a decision before the uncertainty occurs; these variables are called "here-and-now" variables. The second part of decision variables can be decided after the uncertainty occurs; these variables are called "wait-and-see" variables, represented as follows: , , , in, For the decision variables of the first-stage min problem, These are the constraints for the first stage problem. For the compensation variable of the second stage max-min subproblem, These are the constraints for the second-stage max-min subproblem. For uncertain variables, For an uncertain set, for and After the decision The feasible domain range.

[0066] To accurately describe uncertainty, a box-shaped uncertainty set model considering spatial correlation is adopted. By introducing uncertainty adjustment parameters, dispatchers can seek a balance between "conservatism" and "economy." For the "Max-Min-Max" three-level nested structure in the model, a column and constraint generation algorithm is used for iterative solution. First, strong duality theory is used to transform the minimization problem of the inner layer into an equivalent maximization form, which is then merged into a single-level subproblem. Through iterative iteration of the main problem and subproblems, secant constraints containing tangent planes for extreme scenarios are continuously added to the main problem. Through this rolling optimization mechanism, the system can perform a closed-loop correction of the reserve capacity every 15 minutes, ensuring that the system always has sufficient rotational inertia and adjustment margin when there are sudden changes in renewable energy output or equipment failures caused by disasters, fundamentally guaranteeing the integrated resilience of the entire power system chain.

[0067] Example 2, refer to Figure 3 As an embodiment of the present invention, a collaborative optimization system for multiple types of backup resources in a power system is provided, including a vulnerability modeling module, a scenario generation module, a bidirectional recovery module, and a rolling optimization module.

[0068] Among them, the vulnerability modeling module is used to integrate the damage probabilities of various equipment through the decision tree model M1, construct a regional disaster chain vulnerability model 100 and output the failure probability P.

[0069] The scenario generation module is used to generate a set of 200 extreme weather scenarios that take into account the uncertainty of new energy output and load demand by combining the failure probability P with the weather-load mapping theory and using a stochastic optimization method.

[0070] The bidirectional recovery module is used to form network cells C in the distribution network based on the black start capacity of distributed resources aggregated by the virtual power plant, based on the extreme weather scenario set 200, and to execute the bidirectional black start recovery strategy 300 in conjunction with the transmission network.

[0071] The rolling optimization module is used to coordinate and perform rolling optimization of resources on the demand side, generation side and energy storage side based on the bidirectional black start recovery strategy 300, taking into account the multi-time scale coupling characteristics. It adopts a two-stage robust optimization model M2 to optimize the resources on the demand side, generation side and energy storage side.

[0072] This embodiment also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the power system multi-type backup resource collaborative optimization method proposed in the above embodiment.

[0073] This embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the power system multi-type backup resource collaborative optimization method proposed in the above embodiments.

[0074] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion 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, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. 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.

[0075] 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-including 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.

[0076] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), 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). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0077] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple 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.

[0078] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for collaborative optimization of multiple types of reserve resources in a power system, characterized in that, include: By integrating the damage probabilities of various equipment through a decision tree model (M1), a regional disaster chain vulnerability model (100) is constructed, and the failure probability (P) is output. Using failure probability (P) and meteorological-load mapping theory, a set of extreme meteorological scenarios (200) containing uncertainties in new energy output and load demand is generated by using a stochastic optimization method. Based on the constructed extreme weather scenario set (200), a bidirectional black start recovery strategy (300) is executed. After the system identifies the fault, the distributed resource black start capacity aggregated by the virtual power plant is used to autonomously form network cells (C) in the distribution network, and the bidirectional recovery strategy is executed in conjunction with the transmission network to restore the power supply to the surrounding loads. Based on the bidirectional black-start recovery strategy (300), considering the coupling characteristics between multiple time scales, a two-stage robust optimization model (M2) is adopted to coordinate and perform rolling optimization of resources on the demand side, generation side and energy storage side.

2. The method for coordinated optimization of multiple types of reserve resources in a power system as described in claim 1, characterized in that: The regional-level disaster chain vulnerability model (100) includes, We use the vulnerability curves of the two-parameter cumulative log-normal distribution to quantitatively analyze the damage to various types of equipment; A decision tree model (M1) is used to integrate the damage probability curves of various types of equipment to quantitatively assess the failure risk; It is constructed by integrating the functional dependencies between multidimensional coupled subsystems of power, transportation, communication and social services.

3. The method for coordinated optimization of multiple types of reserve resources in a power system as described in claim 1 or 2, characterized in that: The generation of the extreme weather scenario set (200) that includes uncertainties in new energy output and load demand includes, The nonlinear relationship between meteorological elements and power load is extracted based on the meteorological-load mapping theory; Stochastic optimization methods are employed to model the uncertainties of renewable energy output and load demand through expected value programming, multi-scenario programming, and chance-constrained models. By combining the failure probability (P), a set of operational optimization simulation scenarios containing multiple deterministic boundary conditions is constructed through sampling of uncertainty factors.

4. The method for coordinated optimization of multiple types of reserve resources in a power system as described in claim 3, characterized in that: The execution of the bidirectional black boot recovery strategy (300) includes, After the system identifies the fault, it utilizes the distributed resource black-start capacity aggregated by the virtual power plant to form network cells (C) in the distribution network, further expands the network cells (C), and combines the transmission network to execute a bidirectional recovery strategy, and executes a black-start strategy based on soft converter on the equipment side.

5. The method for coordinated optimization of multiple types of reserve resources in a power system as described in claim 4, characterized in that: The implementation of the black boot strategy based on soft switching on the device side includes, The startup process is divided into an extended phase-shift startup stage and a single phase-shift startup stage; By analyzing the influence of the parameters of the buffer capacitor and auxiliary inductor on the minimum soft commutation current of the device during the startup process, all devices are kept in ZVS state during startup.

6. The method for coordinated optimization of multiple types of reserve resources in a power system as described in any one of claims 1, 2, 4-5, characterized in that: The two-stage robust optimization model (M2) includes, The first part involves decision variables that must be assigned values ​​before the uncertainties materialize; The second part of the decision variables involves making decisions only after the uncertainties have materialized; By constructing box-shaped or ellipsoidal uncertainty sets to describe parameter fluctuations, the optimal solution that is feasible under all possible realizations of the uncertainty factors can be determined.

7. The method for coordinated optimization of multiple types of reserve resources in a power system as described in claim 6, characterized in that: The coordinated and rolling optimization of resources on the demand side, generation side, and energy storage side includes... Considering the economic, low-carbon, and sufficiency requirements of the power system's reserve operation, and taking into account the coupling characteristics across multiple time scales, an operation model for reserve resources on the demand side, generation side, and energy storage side is established. The robust optimization dual transformation method is used to transform the standby resource operation model into a linear model for solution.

8. A power system multi-type reserve resource collaborative optimization system, employing the power system multi-type reserve resource collaborative optimization method as described in any one of claims 1 to 7, characterized in that: It includes a vulnerable modeling module, a scene generation module, a two-way recovery module, and a rolling optimization module; The vulnerability modeling module is used to integrate the damage probabilities of various equipment through the decision tree model (M1), construct a regional disaster chain vulnerability model (100), and output the failure probability (P). The scenario generation module is used to generate a set of extreme weather scenarios (200) that take into account the uncertainty of new energy output and load demand by using the failure probability (P) combined with the weather-load mapping theory and a stochastic optimization method. The bidirectional recovery module is used to form network cells (C) in the distribution network based on the black start capacity of distributed resources aggregated by the virtual power plant, based on the extreme weather scenario set (200), and to execute the bidirectional black start recovery strategy (300) in conjunction with the transmission network. The rolling optimization module is used to coordinate and perform rolling optimization of resources on the demand side, generation side and energy storage side based on the bidirectional black start recovery strategy (300), taking into account the multi-time scale coupling characteristics, and using a two-stage robust optimization model (M2).

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the power system multi-type backup resource collaborative optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the power system multi-type backup resource collaborative optimization method as described in any one of claims 1 to 7.

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