An interconnected power grid backup capacity cooperative planning method and device, electronic equipment and storage medium

Abstract

The application discloses an interconnected power grid backup capacity cooperative planning method and device, electronic equipment and a storage medium, and belongs to the technical field of power system planning. The method comprises the following steps: acquiring new energy output sequence, load data, disaster scenario set and to-be-configured capacity of each generator unit; determining a shared backup upper limit according to the to-be-configured capacity, and determining a backup lower limit meeting a safety threshold requirement through capacity iteration; generating a random state set based on the new energy output sequence and the load data, performing power balance calculation under backup constraints, and obtaining a balance result set; evaluating a backup configuration total cost and a power supply adequacy improvement index according to the balance result set, constructing an objective function, adjusting the to-be-configured capacity of each generator unit by using an optimization algorithm, and generating an optimal configuration scheme. Through implementation of the application, the problem that the prior art cannot effectively consider overall economy and regional safety operation bottom line can be solved.

Description

Technical Field

[0001] This invention relates to the field of power system planning technology, specifically to a method, apparatus, electronic device, and storage medium for collaborative planning of backup capacity in interconnected power grids. Background Technology

[0002] With the large-scale integration of high-proportion renewable energy sources, the randomness and uncertainty of system output are increasing, making collaborative planning of reserve capacity in interconnected power grids particularly important. By coordinating the scheduling of generator reserve resources across multiple regional power grids, collaborative planning can break down the barriers of traditional independent configuration in single regions, achieving optimized mutual support of reserve resources across the entire network. This is a crucial foundation for enhancing the comprehensive risk resistance and economic benefits of modern interconnected power grids.

[0003] However, current collaborative planning methods for interconnected power grid reserve capacity have shortcomings in effectively balancing overall economic efficiency and regional safety baselines when dealing with extreme operating environments. This problem arises because existing planning models often assume unrestricted mutual support among regional reserve resources, lacking quantitative constraints on the physical boundaries of cross-regional sharing, which can easily lead to excessive compression of local reserves. Furthermore, traditional methods are largely oriented towards minimizing expected costs under normal conditions, failing to perform capacity iteration calculations to determine the required baseline capacity for load shedding risks in extreme disaster scenarios, and lacking mechanisms to incorporate risk preferences into the trade-off between economics and reliability. This results in the final configuration scheme being prone to large-scale power outages in actual disaster situations due to limited cross-regional support or insufficient local reserve capacity. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for collaborative planning of backup capacity in interconnected power grids. It can solve the problem in the prior art that, when dealing with extreme operating environments, the lack of quantitative constraints on cross-regional sharing boundaries and the lack of baseline capacity verification for disaster scenarios make it difficult to effectively balance overall economic efficiency and regional safety operating baselines.

[0005] An embodiment of the present invention provides a method for coordinated planning of reserve capacity in interconnected power grids, comprising: Acquire the renewable energy output sequence, load data, disaster scenario set, and the capacity to be configured for each generator unit in the interconnected power grid; the interconnected power grid includes multiple regional power grids, and each regional power grid contains generator units; Calculate the shared reserve limit of each regional power grid based on the preset planning parameters and the capacity to be configured for each generator unit; repeat the capacity iteration operation until the current maximum load shedding rate is less than or equal to the preset safety threshold; The current initial reserve value at the time of stopping iteration is determined as the lower reserve limit; a random state set is generated based on the new energy output sequence and load data; with the lower reserve limit and the shared reserve upper limit as constraints, power balance calculations are performed for each random state in the random state set to generate the corresponding balance results, and a balance result set is obtained. The total cost parameter of standby configuration and the power supply adequacy improvement index are evaluated based on the preset risk threshold and the balance result set. An objective function is constructed based on the principle of maximizing the difference between the power supply adequacy improvement index and the total cost parameter of standby configuration. An optimization algorithm is used to solve the objective function to adjust the configuration capacity of each generator set and generate the optimal configuration scheme. The standby capacity of each generator set is configured according to the optimal configuration scheme. The capacity iteration operation includes: The current reserve initial value is allocated to each generator set to generate the current simulated capacity of each generator set; where the initial reserve initial value is the preset initial value; Under the constraint of the shared reserve limit, and combined with the current simulated capacity of each generator unit, the corresponding load shedding rate is generated by redistribution calculation for each disaster scenario in the disaster scenario set, and the maximum value of each load shedding rate is determined as the current maximum load shedding rate. If the current maximum load shedding rate is greater than the preset safety threshold, the current reserve initial value will be updated to the current reserve initial value by increasing the preset step size.

[0006] Furthermore, the preset parameters include the local self-reserve ratio; based on the preset planning parameters and the capacity to be configured for each generator unit, the shared reserve limit for each regional power grid is calculated, including: The total reserve capacity of each generator set in each regional power grid is generated by summing up the capacity to be configured for each regional power grid. Calculate the product of the local self-reserve ratio and the total regional reserve capacity of each regional power grid to generate the local self-reserve capacity of each regional power grid to cope with local risks; Calculate the difference between the total regional reserve capacity and the local self-reserved reserve capacity of each regional power grid, and generate the shared reserve limit for each regional power grid.

[0007] Furthermore, the current initial reserve value is allocated to each generator set to generate the current simulated capacity of each generator set, including: Obtain the original rated capacity of each generator set; Calculate the sum of the original rated capacities of all generator sets; The ratio of the original rated capacity of each generator set to the total value is determined as the rated capacity weight of each generator set. The product of the current initial reserve value and the rated capacity weight corresponding to each generator set is determined as the current simulated capacity corresponding to each generator set.

[0008] Furthermore, each disaster scenario in the disaster scenario set includes the component failure status and the available output sequence of new energy sources corresponding to the disaster scenario. Under the constraint of the shared reserve limit, and in conjunction with the current simulated capacity of each generator unit, a redistribution calculation is performed for each disaster scenario within the disaster scenario set to generate the corresponding load shedding rate. The maximum value of each load shedding rate is determined as the current maximum load shedding rate, including: Calculate the total load of the entire network based on the load data, and establish the power balance constraints of each regional power grid based on the load data; For each disaster scenario in the disaster scenario set, the power balance constraints of each regional power grid, the upper limit of shared reserve, and the current simulated capacity of each generator unit are used as constraints. The optimization objective is to minimize the total load shedding of the entire network. The redistribution solution is performed by combining the component fault status and the available output sequence of new energy sources corresponding to the disaster scenario, and the total load shedding of the entire network corresponding to the disaster scenario is generated. The ratio of the total load shedding amount to the total load of the entire network corresponding to a disaster scenario is determined as the load shedding rate corresponding to the disaster scenario. Compare the load shedding rates of all disaster scenarios in the disaster scenario set, and determine the maximum value among the load shedding rates of all disaster scenarios as the current maximum load shedding rate.

[0009] Furthermore, if the current maximum load shedding rate is greater than the preset safety threshold, the current reserve initial value will be updated to the current reserve initial value by increasing it by a preset step size, including: If the current maximum load shedding rate is greater than the preset safety threshold, calculate the sum of the current initial reserve value and the preset step size, and generate an increased initial reserve value. The increased initial value is set as the current initial value for the backup.

[0010] Furthermore, the current initial reserve value at the time of stopping iteration is determined as the lower reserve limit; a random state set is generated based on the new energy output sequence and load data; using the lower reserve limit and the shared reserve upper limit as constraints, power balance calculations are performed for each random state in the random state set to generate corresponding balance results, resulting in a balance result set, including: The current standby initial value at the point where iteration stops is determined as the standby lower limit; Using a pre-defined random fluctuation probability model of new energy power output, non-sequential Monte Carlo sampling is performed on the new energy power output sequence to generate multiple sets of random available new energy power output sequences. Using a pre-defined system component forced outage probability model, non-sequential Monte Carlo sampling is performed on the system components of the interconnected power grid to generate multiple sets of random component availability states. Based on load data, multiple sets of random renewable energy available output sequences are combined with multiple sets of random component available states to generate multiple random states, and multiple random states are combined to generate a random state set. For each random state in the random state set, the power balance constraints, reserve lower limit and shared reserve upper limit of each regional power grid are used as constraints, and the optimization objective is to minimize the total load shedding of the entire network. The power balance optimization is performed by combining the available state of random components and the available output sequence of random new energy sources corresponding to the random state, and the actual output of each generator unit and the total load shedding of the entire network corresponding to the random state are generated. The actual output of each generator unit corresponding to the random state and the total load shedding of the entire network are determined as the balance result corresponding to the random state. The equilibrium result set is obtained by combining the equilibrium results corresponding to all random states in the random state set.

[0011] Furthermore, based on the preset risk threshold and the balance result set, the total cost parameter of the standby configuration and the power supply adequacy improvement index are evaluated. An objective function is constructed based on maximizing the difference between the power supply adequacy improvement index and the total cost parameter of the standby configuration. An optimization algorithm is used to solve the objective function to adjust the configuration capacity of each generator unit, generating the optimal configuration scheme, including: Obtain the baseline condition power shortage penalty parameters corresponding to the zero configuration capacity of each generator set; Extract the power shortage corresponding to each random state in the balance result set; Based on the power shortage amount corresponding to each random state and the preset unit load shedding penalty factor, generate the single power shortage penalty parameter corresponding to each random state. The power shortage amount corresponding to the single power shortage penalty parameter that exceeds the preset risk threshold is determined as the target power shortage amount; the conditional power shortage penalty parameter is determined based on all target power shortage amounts and the preset unit load shedding penalty factor; the difference between the baseline conditional power shortage penalty parameter and the conditional power shortage penalty parameter is determined as the power supply adequacy improvement index. Based on the capacity to be configured for each generator set, determine the capacity configuration cost parameter and operation adjustment cost parameter corresponding to each generator set; integrate the capacity configuration cost parameter and operation adjustment cost parameter corresponding to each generator set into the total standby configuration cost parameter; Based on the power supply adequacy improvement index and the total cost parameter of backup configuration, an objective function is constructed to characterize the balance between disaster resistance adequacy and cost of interconnected power grids; the optimal configuration scheme is generated by using an optimization algorithm to find the best solution for the objective function.

[0012] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

[0013] One embodiment of the present invention provides an interconnected power grid reserve capacity collaborative planning device, comprising: a data acquisition module, a capacity iteration verification module, a random state evaluation module, and a collaborative optimization configuration module; The data acquisition module is used to acquire the new energy output sequence, load data, disaster scenario set, and the capacity to be configured for each generator unit of the interconnected power grid; wherein, the interconnected power grid includes multiple regional power grids, and each regional power grid contains generator units; The capacity iteration verification module is used to calculate the shared reserve limit of each regional power grid based on preset planning parameters and the capacity to be configured for each generator set; repeatedly execute the capacity iteration operation until the current maximum load shedding rate is less than or equal to a preset safety threshold; wherein, the capacity iteration operation includes: allocating the current reserve initial value to each generator set to generate the current simulated capacity of each generator set; wherein, the initial reserve initial value is a preset initial value; under the constraint of the shared reserve limit, combined with the current simulated capacity of each generator set, perform redistribution calculation for each disaster scenario in the disaster scenario set to generate the corresponding load shedding rate, and determine the maximum value of each load shedding rate as the current maximum load shedding rate; if the current maximum load shedding rate is greater than the preset safety threshold, update the current reserve initial value by increasing it by a preset step size; The random state evaluation module is used to determine the current initial reserve value at the time of stopping iteration as the reserve lower limit; generate a random state set based on the new energy output sequence and load data; and perform power balance calculations for each random state in the random state set, using the reserve lower limit and the shared reserve upper limit as constraints, to generate corresponding balance results and obtain a balance result set. The collaborative optimization configuration module is used to evaluate the total cost parameter of standby configuration and the power supply adequacy improvement index based on the preset risk threshold and the balance result set. It constructs an objective function based on maximizing the difference between the power supply adequacy improvement index and the total cost parameter of standby configuration, and uses an optimization algorithm to solve the objective function to adjust the configuration capacity of each generator set and generate the optimal configuration scheme. The module then configures the standby capacity of each generator set according to the optimal configuration scheme.

[0014] Based on the above method embodiments, the present invention provides corresponding electronic device embodiments.

[0015] One embodiment of the present invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the interconnected power grid reserve capacity collaborative planning method according to any one of the above-described method embodiments.

[0016] Based on the above method embodiments, the present invention provides corresponding storage medium embodiments.

[0017] One embodiment of the present invention provides a storage medium storing a computer program thereon, wherein, when the computer program is running, it controls the device where the storage medium is located to execute any of the interconnected power grid backup capacity collaborative planning methods described in the above-described method embodiments.

[0018] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a method, apparatus, electronic device, and storage medium for collaborative planning of backup capacity in interconnected power grids. The method obtains the renewable energy output sequence, load data, disaster scenario set, and the capacity to be configured for each generator unit in the interconnected power grid; calculates the shared reserve upper limit for each regional power grid based on preset planning parameters and the capacity to be configured for each generator unit, and determines the reserve lower limit that meets the safety threshold requirements through capacity iteration operation; generates a random state set based on the renewable energy output sequence and load data, and performs power balance calculations on each random state under the constraints of the reserve lower limit and the shared reserve upper limit to obtain a balance result set; evaluates the total cost parameter of reserve configuration and the power supply adequacy improvement index based on the balance result set, constructs an optimization function with the objective of maximizing the difference between the power supply adequacy improvement index and the total cost parameter of reserve configuration, and uses an optimization algorithm to adjust the capacity to be configured for each generator unit to generate the optimal configuration scheme; wherein, the capacity iteration operation includes: allocating the current initial reserve value to each generator unit to generate the current simulated capacity, performing redistribution calculations in combination with disaster scenarios under the constraints of the shared reserve upper limit to obtain the load shedding rate corresponding to each disaster scenario, and comparing the maximum load shedding rate with the preset safety threshold; if the current maximum load shedding rate is greater than the preset safety threshold, the current initial reserve value is increased by a preset step size and the iteration continues.

[0019] This invention improves upon the assumption of unrestricted mutual support of reserve resources in existing technologies by calculating the upper limit of shared reserve for each regional power grid based on the local self-reserved reserve ratio and the capacity to be configured for each generator unit. It quantifies the boundary of cross-regional sharing, thereby mitigating the operational risk of excessive compression of local reserves under extreme conditions. Simultaneously, under the constraint of the shared reserve upper limit, this scheme repeatedly performs capacity iteration operations for disaster scenario sets, combined with the current simulated capacity of each generator unit, until the current maximum load shedding rate meets a preset safety threshold to determine the lower limit of reserve. Using the lower limit of reserve and the upper limit of shared reserve as constraints, an objective function is constructed to maximize the difference between the power supply adequacy improvement index and the total cost parameter of reserve configuration for optimization. These technical steps compensate for the shortcomings of traditional methods, such as the lack of disaster scenario baseline capacity verification and the failure to incorporate system disaster resilience adequacy into the comprehensive cost trade-off. This leads to an optimal configuration scheme that balances regional operational safety baselines with global configuration cost. Attached Figure Description

[0020] Figure 1This is a flowchart illustrating a collaborative planning method for backup capacity of an interconnected power grid provided in an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of the structure of an interconnected power grid backup capacity collaborative planning device provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] like Figure 1 As shown, to address the problem in existing technologies that, when dealing with extreme operating environments, lack quantitative constraints on cross-regional shared boundaries and lack baseline capacity verification for disaster scenarios, making it difficult to effectively balance overall economic efficiency and regional safety operating baselines, an embodiment of the present invention provides a collaborative planning method for interconnected power grid reserve capacity, comprising at least the following steps: Step S1: Obtain the new energy output sequence, load data, disaster scenario set, and the capacity to be configured for each generator unit in the interconnected power grid; wherein, the interconnected power grid includes multiple regional power grids, and each regional power grid contains generator units; Specifically, obtaining basic operational data and boundary conditions of the interconnected power grid is the primary prerequisite for conducting collaborative planning of reserve capacity. The interconnected power grid itself encompasses multiple interconnected regional power grids, and each regional power grid has corresponding generating units physically configured within it. To accurately construct the underlying physical planning framework, it is necessary to comprehensively collect network topology information of the interconnected power grid and the actual operating parameters of the generating units.

[0024] For network topology information, the backbone transmission network of the interconnected power grid is equivalently abstracted as multiple regional power grid nodes interconnected by inter-regional tie lines. Ignoring transmission congestion within regional power grids, the specific number of regional power grid nodes, the physical connection topology between inter-regional tie lines, and the absolute upper limit of the active power capacity allowed to be transmitted by each inter-regional tie line are extracted. For generator units, the obtained basic operating parameters include the inherent rated generating capacity of all generator units in the interconnected power grid, the maximum allowable technical output level, the minimum technical output level limited by the physical limitations of the units, the ramp rate limits for upward and downward adjustment of generator units, the unit economic cost of power generation, and the time requirements for generator units to perform start-up and shutdown operations.

[0025] Meanwhile, considering the impact of renewable energy integration, the collected renewable energy output sequences clearly define the historical actual output time series data and spatial geographical distribution characteristics of wind farms and photovoltaic power stations within the interconnected grid. The load data comprehensively reflects the total actual power demand of various regional power grids within the interconnected grid at different time points.

[0026] To quantify the high-risk impact of extreme weather, a disaster scenario set was specifically extracted. The disaster scenario set aims to simulate the spatiotemporal distributional damage caused by extreme natural disasters such as typhoons to the interconnected power grid. The disaster scenario set includes the theoretical available output time series of wind farms and photovoltaic power stations recorded at hourly time steps, and simultaneously includes the actual physical failure and outage status of generator units and transmission lines in the interconnected power grid under the influence of extreme weather such as typhoons.

[0027] In addition, the configuration capacity of each generator set is further clarified. The configuration capacity represents the initial available space range in which each generator set can participate in the reserve capacity optimization. The configuration capacity of each generator set is used as the boundary input benchmark for capacity iterative optimization, which strictly defines the physical allocation upper limit of the reserve capacity sharing mechanism.

[0028] By accurately collecting a complete set of basic operational characteristic parameters, including multi-dimensional network topology, unit physical boundaries, and extreme disaster conditions, an absolutely reliable data foundation is provided for subsequent quantification of cross-regional backup sharing support capabilities and precise execution of capacity optimization. This effectively avoids the risk that planning schemes may deviate from the actual physical operation constraints of the power grid due to missing underlying parameters.

[0029] Step S2: Calculate the shared reserve limit of each regional power grid based on the preset planning parameters and the capacity to be configured for each generator set; repeat the capacity iteration operation until the current maximum load shedding rate is less than or equal to the preset safety threshold. In a preferred embodiment, the capacity iteration operation includes: The current reserve initial value is allocated to each generator set to generate the current simulated capacity of each generator set; where the initial reserve initial value is the preset initial value; Under the constraint of the shared reserve limit, and combined with the current simulated capacity of each generator unit, the corresponding load shedding rate is generated by redistribution calculation for each disaster scenario in the disaster scenario set, and the maximum value of each load shedding rate is determined as the current maximum load shedding rate. If the current maximum load shedding rate is greater than the preset safety threshold, the current reserve initial value will be updated to the current reserve initial value by increasing the preset step size.

[0030] In a preferred embodiment, the preset parameters include the local self-reservation ratio; the shared reserve limit of each regional power grid is calculated based on the preset planning parameters and the capacity to be configured for each generator set, including: The total reserve capacity of each generator set in each regional power grid is generated by summing up the capacity to be configured for each regional power grid. Calculate the product of the local self-reserve ratio and the total regional reserve capacity of each regional power grid to generate the local self-reserve capacity of each regional power grid to cope with local risks; Calculate the difference between the total regional reserve capacity and the local self-reserved reserve capacity of each regional power grid, and generate the shared reserve limit for each regional power grid.

[0031] In a preferred embodiment, allocating the current reserve initial value to each generator set to generate the current simulated capacity of each generator set includes: Obtain the original rated capacity of each generator set; Calculate the sum of the original rated capacities of all generator sets; The ratio of the original rated capacity of each generator set to the total value is determined as the rated capacity weight of each generator set. The product of the current initial reserve value and the rated capacity weight corresponding to each generator set is determined as the current simulated capacity corresponding to each generator set.

[0032] In a preferred embodiment, each disaster scenario in the disaster scenario set includes the component failure status and the available power output sequence of new energy sources corresponding to the disaster scenario. Under the constraint of the shared reserve limit, and in conjunction with the current simulated capacity of each generator unit, a redistribution calculation is performed for each disaster scenario within the disaster scenario set to generate the corresponding load shedding rate. The maximum value of each load shedding rate is determined as the current maximum load shedding rate, including: Calculate the total load of the entire network based on the load data, and establish the power balance constraints of each regional power grid based on the load data; For each disaster scenario in the disaster scenario set, the power balance constraints of each regional power grid, the upper limit of shared reserve, and the current simulated capacity of each generator unit are used as constraints. The optimization objective is to minimize the total load shedding of the entire network. The redistribution solution is performed by combining the component fault status and the available output sequence of new energy sources corresponding to the disaster scenario, and the total load shedding of the entire network corresponding to the disaster scenario is generated. The ratio of the total load shedding amount to the total load of the entire network corresponding to a disaster scenario is determined as the load shedding rate corresponding to the disaster scenario. Compare the load shedding rates of all disaster scenarios in the disaster scenario set, and determine the maximum value among the load shedding rates of all disaster scenarios as the current maximum load shedding rate.

[0033] In a preferred embodiment, if the current maximum load shedding rate is greater than a preset safety threshold, the current reserve initial value will be updated to the current reserve initial value by increasing it by a preset step size, including: If the current maximum load shedding rate is greater than the preset safety threshold, calculate the sum of the current initial reserve value and the preset step size, and generate an increased initial reserve value. The increased initial value is set as the current initial value for the backup.

[0034] Specifically, the planning and control process of the interconnected power grid enters the stage of boundary parameter establishment and adequacy verification. This involves calculating the shared reserve ceiling for each regional power grid based on preset planning parameters and the capacity to be configured for each generator unit. Subsequently, based on the determined shared reserve ceiling boundary, the capacity iteration operation is repeated until the current maximum load shedding rate is less than or equal to a preset safety threshold.

[0035] The preset planning parameters explicitly include the local self-reserve ratio. In calculating the shared reserve ceiling for each regional power grid based on the preset planning parameters and the configurable capacity of each generator unit, for each regional power grid covered by the interconnected grid, the configurable capacity of each generator unit within each regional power grid is mathematically summed to generate the total regional reserve capacity for each regional power grid to cope with emergencies. The calculation logic of the total regional reserve capacity satisfies the following mathematical expression: The specific physical interpretation of the formula parameters is as follows: Represents the regional power grid index number, The representative belongs to the regional power grid A collection of generator sets, Represents the generator set index number. Representing the regional power grid Total regional reserve capacity, Representative generator set The capacity to be configured.

[0036] Next, the product of the local reserve ratio and the total regional reserve capacity of each regional power grid is calculated to generate the local reserve capacity of each regional power grid to cope with local risks. Further, the difference between the total regional reserve capacity and the local reserve capacity of each regional power grid is calculated to generate the shared reserve ceiling for each regional power grid. The mathematical derivation of the resource allocation process is as follows: Among the aforementioned parameters, Representing the regional power grid Locally reserved backup capacity, This represents the proportion of local reserves. Representing the regional power grid The maximum shared backup limit.

[0037] The capacity iteration operation begins with the issuance of a baseline reserve capacity. Its core function is to allocate the current initial reserve value to each generator set to generate the current simulated capacity of each generator set. The initial reserve value in its initial state is set as a preset initial value. In the specific allocation process, the inherent original rated capacity of each generator set is obtained, and the sum of the original rated capacities of all generator sets is calculated. Subsequently, the mathematical ratio of the original rated capacity of each generator set to the sum is calculated, and the calculated mathematical ratio is determined as the rated capacity weight corresponding to each generator set. Finally, the product of the current initial reserve value and the rated capacity weight corresponding to each generator set is calculated, and the final product is determined as the current simulated capacity corresponding to each generator set. The calculation model is as follows: Physical parameters are defined as follows: Representative generator set The corresponding rated capacity weight, Representative generator set The original rated capacity, This represents the sum of the original rated capacities of all generator sets. Representative generator set The corresponding current simulation capacity, This represents the current standby initial value.

[0038] Each disaster scenario within the disaster scenario set includes the corresponding component failure status and the available output sequence of new energy sources. Under the absolute physical boundary constraint of the shared reserve upper limit, and combined with the current simulated capacity of each generator unit, a redistribution calculation is performed for each disaster scenario within the disaster scenario set to generate the corresponding load shedding rate, and the maximum value among all load shedding rates is determined as the current maximum load shedding rate. Specifically, the total load of the entire network is calculated based on the load data of the load nodes, and the power balance constraints of each regional power grid are established based on the load data. The statistical logic of the total load of the entire network is as follows: In the formula, Represents the total network load. This represents the total time span of the simulation. Represents time period number, Represents the set of load nodes. Represents the load node number. Represents load node During the period The actual load data.

[0039] For each disaster scenario within the disaster scenario set, the power balance constraints of each regional power grid, the shared reserve limit, and the current simulated capacity of each generator unit are used as insurmountable boundary constraints. Simultaneously, minimizing the total load shedding of the entire network is used as the global optimization objective. A redistribution solution is performed by combining the component fault states corresponding to the disaster scenario and the available output sequence of new energy sources, generating the total load shedding of the entire network corresponding to the disaster scenario. The objective function model for minimizing the total load shedding of the entire network is constructed as follows: In the formula, Represents the calculated value of the global optimization objective. Represents load node During the period The actual load shear value, This represents the preset penalty coefficient for deviation of unit output. Represents the entire collection of all generator sets covered by the interconnected power grid. Representative generator set During the period The absolute amount of output adjustment deviating from the baseline operating state. Introducing the absolute amount of output adjustment as a penalty correlation term can effectively eliminate unnecessary power transfer behavior between units under the same load shedding level.

[0040] After solving the problem, the ratio of the total load shedding to the total load of the entire network corresponding to the disaster scenario is calculated, and the derived ratio is directly determined as the load shedding rate corresponding to the disaster scenario. The corresponding mathematical relationship is as follows: In the formula, Representative disaster scenarios The corresponding load shedding rate, Represents the disaster scenario label. Representative disaster scenarios The corresponding total load shedding for the entire network.

[0041] After obtaining independent assessment results for all disaster scenarios, the load shedding rates corresponding to all disaster scenarios in the disaster scenario set are compared. The extreme value with the largest value is selected from the load shedding rates corresponding to all disaster scenarios, and the selected extreme value is determined as the current maximum load shedding rate.

[0042] Subsequently, a check is performed on the iteration stopping condition. If the current maximum load shedding rate is greater than a preset safety threshold, it indicates that the current power supply adequacy level of the interconnected grid has not yet met the survival baseline under severe disasters, and it is necessary to update the current reserve initial value with the current reserve initial value after increasing it by a preset step size. The specific process is as follows: if the current maximum load shedding rate is greater than the preset safety threshold, the mathematical sum of the current reserve initial value and the preset step size is calculated to generate the increased reserve initial value used to perform the next round of redistribution checks. Finally, the increased reserve initial value is determined as the latest current reserve initial value to trigger a new round of capacity iteration operations.

[0043] By accurately quantifying the cross-regional backup and mutual support potential of each regional power grid and deeply integrating dynamic fault optimization calculation based on extreme weather conditions, the aforementioned capacity iteration logic effectively avoids the problem of redundant physical resource investment caused by blindly maintaining excessive reserve margins in each region. On the basis of firmly safeguarding the bottom line of the interconnected power grid to withstand multiple component outages, it significantly reduces the overall reserve configuration requirements of the entire network.

[0044] Step S3: Determine the current initial reserve value at the time of stopping iteration as the lower reserve limit; generate a random state set based on the new energy output sequence and load data; with the lower reserve limit and the shared reserve upper limit as constraints, perform power balance calculations for each random state in the random state set to generate the corresponding balance results, and obtain the balance result set. In a preferred embodiment, the current initial reserve value at the time of stopping iteration is determined as the lower reserve limit; a random state set is generated based on the new energy output sequence and load data; constrained by the lower reserve limit and the shared reserve upper limit, power balance calculations are performed for each random state in the random state set to generate corresponding balance results, resulting in a balance result set, including: The current standby initial value at the point where iteration stops is determined as the standby lower limit; Using a pre-defined random fluctuation probability model of new energy power output, non-sequential Monte Carlo sampling is performed on the new energy power output sequence to generate multiple sets of random available new energy power output sequences. Using a pre-defined system component forced outage probability model, non-sequential Monte Carlo sampling is performed on the system components of the interconnected power grid to generate multiple sets of random component availability states. Based on load data, multiple sets of random renewable energy available output sequences are combined with multiple sets of random component available states to generate multiple random states, and multiple random states are combined to generate a random state set. For each random state in the random state set, the power balance constraints, reserve lower limit and shared reserve upper limit of each regional power grid are used as constraints, and the optimization objective is to minimize the total load shedding of the entire network. The power balance optimization is performed by combining the available state of random components and the available output sequence of random new energy sources corresponding to the random state, and the actual output of each generator unit and the total load shedding of the entire network corresponding to the random state are generated. The actual output of each generator unit corresponding to the random state and the total load shedding of the entire network are determined as the balance result corresponding to the random state. The equilibrium results are obtained by combining the equilibrium outcomes corresponding to all random states in the set of random states.

[0045] Specifically, after completing the capacity iteration verification phase, the current initial reserve value at the point where iteration stops is directly determined as the reserve lower limit. The reserve lower limit represents the baseline power capacity that the interconnected power grid must retain to cope with basic operational risks. Subsequently, a random state set needs to be generated based on the renewable energy output sequence and load data to construct a discrete scenario sample library for evaluating the comprehensive operational benefits of the interconnected power grid.

[0046] The specific execution logic is as follows: Non-sequential Monte Carlo sampling is performed on the renewable energy output sequence using a pre-defined random fluctuation probability model, generating multiple sets of random renewable energy available output sequences. The pre-defined random fluctuation probability model for renewable energy output includes a Weibull distribution probability density function for wind speed in wind farms and a Beta distribution probability density function for solar irradiance in photovoltaic power plants. The mathematical expression of the Weibull distribution probability density function for wind speed is as follows: In the formula, This represents the calculated probability density value of the wind speed distribution. Represents wind speed variable, The shape parameter that determines the shape of the wind speed distribution curve. This represents a scale parameter reflecting the average wind speed level. Based on the correlation curve of wind turbine power characteristics, wind speed variables can be directly converted into corresponding random values ​​of wind power output.

[0047] The probability density function of the beta distribution of solar irradiance is constructed as follows: In the formula, This represents the calculated probability density value of the solar irradiance distribution. Represents the variable of solar irradiance. Represents the maximum solar irradiance limit. and The shape adjustment parameter represents the unique characteristics of the beta distribution. This represents the gamma function operator. The output power of a photovoltaic array varies proportionally with the solar irradiance.

[0048] Furthermore, non-sequential Monte Carlo sampling is performed on the system components of the interconnected power grid using a pre-defined system component forced outage probability model to generate multiple sets of random component availability states. The system component forced outage probability model employs a two-state Markov model to describe the stochastic transition process between generator units and transmission lines in two discrete states: normal operation and fault outage. The derivation logic of the forced outage rate parameters for system components within the interconnected power grid is as follows: In the formula, Representative system components Forced shutdown rate Representative system components The probability and frequency of physical failures. Representative system components The probability and frequency of completing the repair. Global index labels representing system components.

[0049] Based on the acquired load data, multiple sets of random renewable energy available output sequences are strictly matched one-to-one with multiple sets of random component available states to generate multiple random states containing multiple random disturbance scenarios, and the multiple random states are summarized and combined to generate a random state set.

[0050] For each random state within the random state set, the power balance constraints, reserve lower limit, and shared reserve upper limit of each regional power grid are insurmountable hard constraints. The global optimization objective is to minimize the total load shedding of the entire network. Power balance optimization is performed by combining the available states of random components and the available output sequence of random new energy sources corresponding to the random state, generating the actual output of each generator unit and the total load shedding of the entire network for each random state. The core optimization objective function for minimizing the total load shedding of the entire network is constructed as follows: In the formula, Represents a random state The calculated value of the state evaluation optimization objective is as follows. Represents the independent index label of a random state within a set of random states. Represents load node In a random state The actual load shear value is below. This represents the minimum penalty factor used to eliminate redundant power transfer between units at the same load shedding level. Representative generator set In a random state Compared to the upward adjustment of the power output in the fault-free ground state, Representative generator set In a random state The downward adjustment amount compared to the fault-free ground state output.

[0051] The node power balance optimization solution must satisfy the node power balance constraints. The specific node power balance correlation expressions are as follows: In the formula, Representative generator set In a random state The actual output of the force, Represents wind farm In a random state The actual output of the force, Represents photovoltaic power plants In a random state The actual output of the force, Represents power grid nodes The baseline load, Represents power grid nodes In a random state The shear load below, Represents power transmission tie line to In a random state The transmitted active power. At the same time, the total configured capacity boundary of all generator units must be strictly not less than the determined reserve lower limit.

[0052] To ensure the orderly implementation of cross-regional mutual assistance, the mathematical logic constraint of the cross-regional reserve sharing balance is as follows: In the formula, Representing the regional power grid In a random state The net power imbalance below, This represents the physical change in the output of a conventional generator set. Represents the physical change in wind power output. Represents the physical change in photovoltaic power generation output. Represents the physical change in load shearing in the region. Representing the regional power grid Cross-regional backup support power transmitted to external areas. Representing the regional power grid Cross-regional backup support power received from external areas. This represents the global set of all regional power grids encompassed by the interconnected power grid. Furthermore, it is necessary to enforce restrictions on regional power grids. Cross-regional backup power The absolute value must not exceed the calculated upper limit of shared backup.

[0053] Throughout the power balance optimization process, constraints on generator unit operating extreme values, renewable energy physical output, and transmission tie line power flow safety must be simultaneously satisfied. Specifically, generator unit operating extreme value constraints limit the actual and downward adjustment ranges of each generator unit, ensuring they do not exceed reserved reserve limits and physical ramping extreme values. Renewable energy physical output constraints guarantee that the actual output extracted by wind farms and photovoltaic power stations will never exceed the available physical limits corresponding to the current stochastic state. The mathematical logic constraints of transmission tie line power flow safety are as follows: In the formula, The equivalent DC susceptance parameter represents the nodes at both ends of the transmission tie line under the current random state. and These represent the voltage phase angle parameters of the independent nodes at both ends of the transmission tie line. This represents the absolute physical maximum active power that the transmission tie line is allowed to pass through. By incorporating multi-dimensional and rigorous underlying physical boundary constraints, it ensures that the output values ​​generated by the optimized redistribution strictly conform to the actual physical operating laws of the interconnected power grid, completely preventing risk assessment distortions caused by idealized out-of-sync data.

[0054] After completing the single-state assessment, the actual output of each generator unit and the total load shedding of the entire network corresponding to the random state are clearly defined as the single balance result corresponding to the current random state. After sampling all scenarios sequentially, the balance results corresponding to all random states within the random state set are combined to finally obtain a balance result set containing massive operational boundary feedback. By introducing non-sequential Monte Carlo sampling and combining it with cross-regional mutual assistance boundary constraints for fault redistribution solutions, the actual defense capability of the interconnected power grid to resist concurrent disturbances from both the source and load sides under various high-risk, extreme, and discrete damage scenarios can be accurately quantified. This lays a solid data foundation for subsequent high-precision economic benefit and safety reliability trade-off analysis, completely eliminating subjective assumptions based on human experience.

[0055] Step S4: Evaluate the total cost parameter of standby configuration and the power supply adequacy improvement index based on the preset risk threshold and balance result set. Construct an objective function based on maximizing the difference between the power supply adequacy improvement index and the total cost parameter of standby configuration. Solve the objective function using an optimization algorithm to adjust the configuration capacity of each generator set and generate the optimal configuration scheme. Configure the standby capacity of each generator set according to the optimal configuration scheme. In a preferred embodiment, the total cost parameter of the standby configuration and the power supply adequacy improvement index are evaluated based on a preset risk threshold and a balance result set. An objective function is constructed based on maximizing the difference between the power supply adequacy improvement index and the total cost parameter of the standby configuration. An optimization algorithm is used to solve the objective function to adjust the configuration capacity of each generator unit, generating the optimal configuration scheme, including: Obtain the baseline condition power shortage penalty parameters corresponding to the zero configuration capacity of each generator set; Extract the power shortage corresponding to each random state in the balance result set; Based on the power shortage amount corresponding to each random state and the preset unit load shedding penalty factor, generate the single power shortage penalty parameter corresponding to each random state. The power shortage amount corresponding to the single power shortage penalty parameter that exceeds the preset risk threshold is determined as the target power shortage amount; the conditional power shortage penalty parameter is determined based on all target power shortage amounts and the preset unit load shedding penalty factor; the difference between the baseline conditional power shortage penalty parameter and the conditional power shortage penalty parameter is determined as the power supply adequacy improvement index. Based on the capacity to be configured for each generator set, determine the capacity configuration cost parameter and operation adjustment cost parameter corresponding to each generator set; integrate the capacity configuration cost parameter and operation adjustment cost parameter corresponding to each generator set into the total standby configuration cost parameter; Based on the power supply adequacy improvement index and the total cost parameter of backup configuration, an objective function is constructed to characterize the balance between disaster resistance adequacy and cost of interconnected power grids; the optimal configuration scheme is generated by using an optimization algorithm to find the best solution for the objective function.

[0056] Specifically, after obtaining the balance result set, the standby capacity collaborative planning process of the interconnected power grid enters the comprehensive benefit assessment and optimal allocation stage based on risk preference. The core logic is to evaluate the total cost parameter of standby configuration and the power supply adequacy improvement index based on the preset risk threshold and the balance result set, and construct an objective function based on maximizing the difference between the power supply adequacy improvement index and the total cost parameter of standby configuration. Subsequently, an optimization algorithm is used to solve the objective function to adjust the configuration capacity of each generator set, generate the optimal configuration scheme, and finally perform physical configuration of standby capacity for each generator set according to the optimal configuration scheme.

[0057] A detailed evaluation and simulation process is then conducted. First, the baseline power shortage penalty parameters are obtained when the configured capacity of each generator unit is all zero. These baseline power shortage penalty parameters characterize the initial state risk cost of the interconnected power grid without any additional reserve capacity. Next, the power shortage quantity corresponding to each random state is extracted from the balance result set generated in the previous steps. Based on the power shortage quantity corresponding to each random state and the preset unit load shedding penalty factor, the single power shortage penalty parameter corresponding to each random state is generated through multiplication. The preset unit load shedding penalty factor reflects the objective economic loss caused by a unit load loss.

[0058] Subsequently, a preset risk threshold is introduced for judgment and screening. This preset risk threshold is used to define the defense boundary of the interconnected power grid in response to low-probability, high-loss extreme disaster events. The single-outage penalty parameter is compared with the preset risk threshold, and the outage amount corresponding to the single-outage penalty parameter exceeding the preset risk threshold is screened out and determined as the target outage amount. For normal random fluctuations where the single-outage penalty parameter is not greater than the preset risk threshold, the outage amount in the corresponding state is not included in the target statistics. The expected mean of all target outage amounts is calculated, and combined with a preset unit load shedding penalty factor, the conditional outage penalty parameter is determined. The conditional outage penalty parameter accurately reflects the expected outage loss penalty cost that the interconnected power grid will bear when facing extreme severe disasters under the current reserve capacity configuration scheme. Furthermore, the mathematical difference between the baseline conditional outage penalty parameter and the conditional outage penalty parameter is determined as the power supply adequacy improvement indicator.

[0059] Simultaneously, quantitative accounting from an economic perspective is conducted. Based on the planned capacity of each generator unit, the capacity configuration cost parameter and operation and adjustment cost parameter for each generator unit are determined. The capacity configuration cost parameter measures the direct hardware investment brought about by the addition of physical reserve capacity. The accounting logic includes amortizing the total reserve investment cost over the entire planning period using an equal-annual value method. The specific investment calculation formula is constructed as follows: In the formula, This represents the calculated total investment cost of the newly added standby capacity. This represents the total number of generator units contained within the interconnected power grid. The global numerical designation representing the generator set. Representative generator set The corresponding unit investment price per unit capacity, Representative generator set The capacity to be configured This represents the capacity configuration cost parameter after equal-annualization. This represents the preset discount rate variable. This represents the physical life cycle variable of the generator set.

[0060] The operational adjustment cost parameter characterizes the overall change in operating, maintenance, and fuel consumption costs for all generator units in the interconnected power grid before and after applying the current standby plan. The total operating cost of generator units under the standby plan includes both fuel costs and operating costs, and its corresponding mathematical model is expressed as: In the formula, This represents the total cost of fuel consumed by all generator units. Representative generator set The total amount of electricity generated output during the simulation. Representative generator set The corresponding fuel cost per unit of electricity, This represents the total unit operation and maintenance cost of all generator sets. Representative generator set The corresponding variable operation and maintenance unit price, Representative generator set The original rated physical capacity, Representative generator set The corresponding fixed operation and maintenance unit price, The parameter representing the calculated operational adjustment cost. This represents the initial baseline total operating cost of the interconnected power grid with all available capacity at zero. Finally, the capacity configuration cost parameters and operation and regulation cost parameters corresponding to each generator unit are mathematically summed and integrated into the total standby configuration cost parameter.

[0061] After completing the evaluation of both sides of the indicators, an objective function representing the balance between the disaster resilience adequacy and the cost of the interconnected power grid is constructed based on the power supply adequacy improvement indicator and the total cost parameter of the reserve configuration. Optimization algorithms with global search capabilities, such as particle swarm optimization, are used to iteratively optimize the constructed objective function through multiple rounds. Under the premise of satisfying operational constraints such as the reserve lower limit, the optimization algorithm continuously adjusts the capacity decision variables, and when the solution converges, it generates the final optimal configuration scheme from a global perspective.

[0062] In the process of using optimization algorithms to iteratively solve the objective function, the configurable capacity of each generator unit is integrated into the position vector of the algorithm particles, and velocity vectors are introduced to drive the optimization. In each iteration, the mathematical equations for updating the velocity and position of each particle are constructed as follows: In the formula, Representative generator set In the Optimization speed in the next iteration Representative generator set In the Optimization speed in the next iteration Represents the inertia weight in the optimization process. Represents the cognitive acceleration coefficient. Represents the social acceleration coefficient. and All represent uniformly random real numbers distributed in the interval between zero and one. Representative generator set The optimal configuration capacity experienced in the process of seeking the best in an individual's history. This represents the optimal configuration capacity throughout the entire history of global optimization of the interconnected power grid. Representative generator set In the The current capacity to be configured in the next iteration This represents the updated capacity to be configured. To enhance global search capabilities in the early stages of optimization and improve local development accuracy in the later stages, dynamic adaptive adjustment is implemented for the cognitive acceleration coefficient and the social acceleration coefficient. The adjustment formula is as follows: In the formula, and These represent the preset maximum and minimum boundaries of the cognitive acceleration coefficient, respectively. and These represent the preset maximum and minimum boundaries of the social acceleration coefficient, respectively. This represents the preset maximum number of iterations. This represents the current iteration cycle.

[0063] To reduce solution complexity and match the actual engineering parameter specifications of power equipment, the subsequent capacity to be configured is discretized after the particle position is updated: In the formula, This represents the capacity to be configured after discretization. This represents the preset discretization capacity granularity step size. This represents the floor operation. By introducing an adaptive acceleration mechanism and forced discretization control, not only is the computational risk of getting trapped in local minima during the multi-dimensional optimization process effectively avoided, but the optimal configuration scheme output can also be seamlessly converted into direct control commands for physical units, significantly improving the engineering implementation efficiency of the collaborative planning scheme.

[0064] By deeply integrating the conditional power outage loss penalty mechanism with the overall investment cost of generator sets into the evaluation objective function, the aforementioned planning and configuration method can accurately identify and eliminate invalid reserve capacity redundancy within the interconnected power grid, thereby minimizing the overall construction and operation investment cost while strictly preventing the risk of power outages due to multiple component disconnections.

[0065] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

[0066] like Figure 2 As shown, an embodiment of the present invention provides an interconnected power grid reserve capacity collaborative planning device, including: a data acquisition module, a capacity iteration verification module, a random state evaluation module, and a collaborative optimization configuration module; The data acquisition module is used to acquire the new energy output sequence, load data, disaster scenario set, and the capacity to be configured for each generator unit of the interconnected power grid; wherein, the interconnected power grid includes multiple regional power grids, and each regional power grid contains generator units; The capacity iteration verification module is used to calculate the shared reserve limit of each regional power grid based on preset planning parameters and the capacity to be configured for each generator set; repeatedly execute the capacity iteration operation until the current maximum load shedding rate is less than or equal to a preset safety threshold; wherein, the capacity iteration operation includes: allocating the current reserve initial value to each generator set to generate the current simulated capacity of each generator set; wherein, the initial reserve initial value is a preset initial value; under the constraint of the shared reserve limit, combined with the current simulated capacity of each generator set, perform redistribution calculation for each disaster scenario in the disaster scenario set to generate the corresponding load shedding rate, and determine the maximum value of each load shedding rate as the current maximum load shedding rate; if the current maximum load shedding rate is greater than the preset safety threshold, update the current reserve initial value by increasing it by a preset step size; The random state evaluation module is used to determine the current initial reserve value at the time of stopping iteration as the reserve lower limit; generate a random state set based on the new energy output sequence and load data; and perform power balance calculations for each random state in the random state set, using the reserve lower limit and the shared reserve upper limit as constraints, to generate corresponding balance results and obtain a balance result set. The collaborative optimization configuration module is used to evaluate the total cost parameter of standby configuration and the power supply adequacy improvement index based on the preset risk threshold and the balance result set. It constructs an objective function based on maximizing the difference between the power supply adequacy improvement index and the total cost parameter of standby configuration, and uses an optimization algorithm to solve the objective function to adjust the configuration capacity of each generator set and generate the optimal configuration scheme. The module then configures the standby capacity of each generator set according to the optimal configuration scheme.

[0067] It should be noted that the embodiments of the device described above correspond to the embodiments of the present invention described above, and can realize the interconnected power grid reserve capacity collaborative planning method described in any one of the above embodiments of the present invention. Furthermore, the embodiments of the device described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without creative effort.

[0068] Based on the above-described method embodiments of the present invention, a corresponding embodiment of an electronic device is provided.

[0069] An embodiment of the present invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the interconnected power grid reserve capacity collaborative planning method according to any one of the present invention, or the processor executes the computer program to implement the functions of each module in the above-described device embodiments.

[0070] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the terminal device.

[0071] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0072] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0073] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD card), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0074] Based on the above method embodiments, the present invention provides corresponding storage medium embodiments; Another embodiment of the present invention provides a storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute any of the above-described interconnected power grid reserve capacity collaborative planning methods of the present invention.

[0075] The aforementioned storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0076] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0077] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for collaborative planning of reserve capacity in interconnected power grids, characterized in that, include: Acquire the renewable energy output sequence, load data, disaster scenario set, and the capacity to be configured for each generator unit in the interconnected power grid; the interconnected power grid includes multiple regional power grids, and each regional power grid contains generator units; Calculate the shared reserve limit of each regional power grid based on the preset planning parameters and the capacity to be configured for each generator unit; repeat the capacity iteration operation until the current maximum load shedding rate is less than or equal to the preset safety threshold; The current initial reserve value at the time of stopping iteration is determined as the lower reserve limit; a random state set is generated based on the new energy output sequence and load data; with the lower reserve limit and the shared reserve upper limit as constraints, power balance calculations are performed for each random state in the random state set to generate the corresponding balance results, and a balance result set is obtained. The total cost parameter of standby configuration and the power supply adequacy improvement index are evaluated based on the preset risk threshold and the balance result set. An objective function is constructed based on the principle of maximizing the difference between the power supply adequacy improvement index and the total cost parameter of standby configuration. An optimization algorithm is used to solve the objective function to adjust the configuration capacity of each generator set and generate the optimal configuration scheme. The standby capacity of each generator set is configured according to the optimal configuration scheme. The capacity iteration operation includes: The current reserve initial value is allocated to each generator set to generate the current simulated capacity of each generator set; where the initial reserve initial value is the preset initial value; Under the constraint of the shared reserve limit, and combined with the current simulated capacity of each generator unit, the corresponding load shedding rate is generated by redistribution calculation for each disaster scenario in the disaster scenario set, and the maximum value of each load shedding rate is determined as the current maximum load shedding rate. If the current maximum load shedding rate is greater than the preset safety threshold, the current reserve initial value will be updated to the current reserve initial value by increasing the preset step size.

2. The interconnected power grid reserve capacity collaborative planning method as described in claim 1, characterized in that, Preset parameters include the local reserve ratio; Calculate the shared reserve limit of each regional power grid based on the preset planning parameters and the capacity to be configured for each generator unit, including: The total reserve capacity of each generator set in each regional power grid is generated by summing up the capacity to be configured for each regional power grid. Calculate the product of the local self-reserve ratio and the total regional reserve capacity of each regional power grid to generate the local self-reserve capacity of each regional power grid to cope with local risks; Calculate the difference between the total regional reserve capacity and the local self-reserved reserve capacity of each regional power grid, and generate the shared reserve limit for each regional power grid.

3. The interconnected power grid reserve capacity collaborative planning method as described in claim 2, characterized in that, The current reserve initial value is allocated to each generator set to generate the current simulated capacity of each generator set, including: Obtain the original rated capacity of each generator set; Calculate the sum of the original rated capacities of all generator sets; The ratio of the original rated capacity of each generator set to the total value is determined as the rated capacity weight of each generator set. The product of the current initial reserve value and the rated capacity weight corresponding to each generator set is determined as the current simulated capacity corresponding to each generator set.

4. The interconnected power grid reserve capacity collaborative planning method as described in claim 3, characterized in that, Each disaster scenario in the disaster scenario set includes the component failure status and the available power output sequence of new energy sources corresponding to the disaster scenario. Under the constraint of the shared reserve limit, and in conjunction with the current simulated capacity of each generator unit, a redistribution calculation is performed for each disaster scenario within the disaster scenario set to generate the corresponding load shedding rate. The maximum value of each load shedding rate is determined as the current maximum load shedding rate, including: Calculate the total load of the entire network based on the load data, and establish the power balance constraints of each regional power grid based on the load data; For each disaster scenario in the disaster scenario set, the power balance constraints of each regional power grid, the upper limit of shared reserve, and the current simulated capacity of each generator unit are used as constraints. The optimization objective is to minimize the total load shedding of the entire network. The redistribution solution is performed by combining the component fault status and the available output sequence of new energy sources corresponding to the disaster scenario, and the total load shedding of the entire network corresponding to the disaster scenario is generated. The ratio of the total load shedding amount to the total load of the entire network corresponding to a disaster scenario is determined as the load shedding rate corresponding to the disaster scenario. Compare the load shedding rates of all disaster scenarios in the disaster scenario set, and determine the maximum value among the load shedding rates of all disaster scenarios as the current maximum load shedding rate.

5. The interconnected power grid reserve capacity collaborative planning method as described in claim 4, characterized in that, If the current maximum load shedding rate is greater than the preset safety threshold, the current reserve initial value will be updated to the current reserve initial value by increasing it by a preset step size, including: If the current maximum load shedding rate is greater than the preset safety threshold, calculate the sum of the current initial reserve value and the preset step size, and generate an increased initial reserve value. The increased initial value is set as the current initial value for the backup.

6. The interconnected power grid reserve capacity collaborative planning method as described in claim 5, characterized in that, The current initial reserve value at the time of stopping iteration is determined as the lower reserve limit; a random state set is generated based on the new energy output sequence and load data; constrained by the lower reserve limit and the shared reserve upper limit, power balance calculations are performed for each random state in the random state set to generate the corresponding balance results, resulting in a balance result set, including: The current standby initial value at the point where iteration stops is determined as the standby lower limit; Using a pre-defined random fluctuation probability model of new energy power output, non-sequential Monte Carlo sampling is performed on the new energy power output sequence to generate multiple sets of random available new energy power output sequences. Using a pre-defined system component forced outage probability model, non-sequential Monte Carlo sampling is performed on the system components of the interconnected power grid to generate multiple sets of random component availability states. Based on load data, multiple sets of random renewable energy available output sequences are combined with multiple sets of random component available states to generate multiple random states, and multiple random states are combined to generate a random state set. For each random state in the random state set, the power balance constraints, reserve lower limit and shared reserve upper limit of each regional power grid are used as constraints, and the optimization objective is to minimize the total load shedding of the entire network. The power balance optimization is performed by combining the available state of random components and the available output sequence of random new energy sources corresponding to the random state, and the actual output of each generator unit and the total load shedding of the entire network corresponding to the random state are generated. The actual output of each generator unit corresponding to the random state and the total load shedding of the entire network are determined as the balance result corresponding to the random state. The equilibrium result set is obtained by combining the equilibrium results corresponding to all random states in the random state set.

7. The interconnected power grid reserve capacity collaborative planning method as described in claim 6, characterized in that, Based on the preset risk threshold and the balance result set, the total cost parameter of the standby configuration and the power supply adequacy improvement index are evaluated. An objective function is constructed based on maximizing the difference between the power supply adequacy improvement index and the total cost parameter of the standby configuration. An optimization algorithm is used to solve the objective function to adjust the capacity to be configured for each generator unit, generating the optimal configuration scheme, including: Obtain the baseline condition power shortage penalty parameters corresponding to the zero configuration capacity of each generator set; Extract the power shortage corresponding to each random state in the balance result set; Based on the power shortage amount corresponding to each random state and the preset unit load shedding penalty factor, generate the single power shortage penalty parameter corresponding to each random state. The power shortage amount corresponding to the single power shortage penalty parameter that exceeds the preset risk threshold is determined as the target power shortage amount; the conditional power shortage penalty parameter is determined based on all target power shortage amounts and the preset unit load shedding penalty factor; the difference between the baseline conditional power shortage penalty parameter and the conditional power shortage penalty parameter is determined as the power supply adequacy improvement index. Based on the capacity to be configured for each generator set, determine the capacity configuration cost parameter and operation adjustment cost parameter corresponding to each generator set; integrate the capacity configuration cost parameter and operation adjustment cost parameter corresponding to each generator set into the total standby configuration cost parameter; Based on the power supply adequacy improvement index and the total cost parameter of backup configuration, an objective function is constructed to characterize the balance between disaster resistance adequacy and cost of interconnected power grids; the optimal configuration scheme is generated by using an optimization algorithm to find the best solution for the objective function.

8. A device for collaborative planning of backup capacity in an interconnected power grid, characterized in that, include: The module includes a data acquisition module, a capacity iteration verification module, a random state evaluation module, and a collaborative optimization configuration module. The data acquisition module is used to acquire the new energy output sequence, load data, disaster scenario set, and the capacity to be configured for each generator unit of the interconnected power grid; wherein, the interconnected power grid includes multiple regional power grids, and each regional power grid contains generator units; The capacity iteration verification module is used to calculate the shared reserve limit of each regional power grid based on preset planning parameters and the capacity to be configured for each generator set; repeatedly execute the capacity iteration operation until the current maximum load shedding rate is less than or equal to a preset safety threshold; wherein, the capacity iteration operation includes: allocating the current reserve initial value to each generator set to generate the current simulated capacity of each generator set; wherein, the initial reserve initial value is a preset initial value; under the constraint of the shared reserve limit, combined with the current simulated capacity of each generator set, perform redistribution calculation for each disaster scenario in the disaster scenario set to generate the corresponding load shedding rate, and determine the maximum value of each load shedding rate as the current maximum load shedding rate; if the current maximum load shedding rate is greater than the preset safety threshold, update the current reserve initial value by increasing it by a preset step size; The random state evaluation module is used to determine the current initial reserve value at the time of stopping iteration as the reserve lower limit; generate a random state set based on the new energy output sequence and load data; and perform power balance calculations for each random state in the random state set, using the reserve lower limit and the shared reserve upper limit as constraints, to generate corresponding balance results and obtain a balance result set. The collaborative optimization configuration module is used to evaluate the total cost parameter of standby configuration and the power supply adequacy improvement index based on the preset risk threshold and the balance result set. It constructs an objective function based on maximizing the difference between the power supply adequacy improvement index and the total cost parameter of standby configuration, and uses an optimization algorithm to solve the objective function to adjust the configuration capacity of each generator set and generate the optimal configuration scheme. The module then configures the standby capacity of each generator set according to the optimal configuration scheme.

9. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the interconnected power grid reserve capacity collaborative planning method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to perform the interconnected power grid backup capacity collaborative planning method as described in any one of claims 1 to 7.

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