Power system multi-element energy storage planning method and device considering extreme disasters

Abstract

The invention discloses an electric power system multi-element energy storage planning method and equipment considering extreme disasters, and belongs to the technical field of electric power system planning. Through uniformly describing the operation characteristics of battery energy storage and hydrogen energy storage, the short-time rapid adjustment capability and the long-time large-scale energy transfer capability are integrated into the same planning framework; compared with an existing method which only depends on a single energy storage mode or independently models different energy storage devices, the method can construct effective energy reserve before a disaster, provide continuous power supply support in the disaster and promote rapid recovery of a power grid after the disaster, so that the bearing capacity and the self-healing capacity of the system under the extreme disaster condition are remarkably enhanced; according to the invention, the energy storage construction cost, the maintenance cost and the operation cost in a disaster scene are integrated into a double-layer stochastic programming framework, so that the energy storage configuration can give consideration to both economy and system reliability, and the maximization of energy storage investment income and the synchronous improvement of power supply reliability in a disaster period are realized.

Description

Technical Field

[0001] This invention belongs to the field of power system planning technology, specifically relating to a power system multi-element energy storage planning method and equipment that takes into account extreme disasters. Background Technology

[0002] With the large-scale integration of new energy sources into the power system, the randomness, intermittency, and seasonality of renewable energy sources such as wind and solar power are becoming increasingly prominent. The power system faces significant source-load mismatches on both intraday and seasonal scales. Simultaneously, under the influence of global climate change, the frequency and destructive power of extreme natural disasters such as hurricanes, blizzards, severe convection, extreme heat, and prolonged periods of calm are continuously increasing, posing severe challenges to the disturbance resistance, robustness, and post-disaster recovery capabilities of traditional power systems. Current research typically relies on configuring conventional regulating power sources or strengthening the grid structure to improve system stability. However, such methods are insufficient to cope with the strong uncertainties under conditions of high proportions of new energy sources, and cannot simultaneously meet the demands of frequent power fluctuations on short timescales and energy imbalances on long timescales.

[0003] With the rapid development of energy storage technology, various types of energy storage, such as battery energy storage and hydrogen energy storage, are gradually becoming important resources for improving the flexibility of the power system. Short-term battery energy storage can effectively smooth out the rapid fluctuations of new energy sources, providing peak shaving, frequency regulation, and rapid ramp-up services; long-term hydrogen energy storage can achieve large-scale cross-day, cross-week, and even seasonal energy storage, playing a crucial capacity support role in deep renewable energy scenarios. However, most existing energy storage configuration methods are geared towards normal operation, lacking modeling and assessment of extreme disaster scenarios, and failing to systematically consider the different operational needs before, during, and after a disaster, making it difficult to support continuous power supply and rapid recovery of the power system in disaster situations. In addition, there are significant time scale differences and coupling relationships among various types of energy storage, but existing technologies generally configure energy storage as a single device independently, ignoring the complementarity of different energy storage resources in collaborative operation, resulting in the inability to fully realize the flexibility and efficiency of the overall energy storage system.

[0004] Meanwhile, under complex conditions such as grid topology damage caused by extreme disasters, large-scale disconnection of new energy sources from the grid, and increased load uncertainty, the operation mode of the power system exhibits high dynamism and uncertainty. Traditional energy storage planning models rely on static scenarios or fixed parameters for optimization calculations, making it difficult to adapt to the rapid changes in operating states under disaster conditions. Their planning results lack robustness in real disaster scenarios, failing to provide effective support for improving the system's disaster resistance capabilities. In summary, existing technologies have significant shortcomings in multi-type energy storage collaborative planning, extreme disaster modeling, full life-cycle emergency control, and robustness optimization under dynamic operating conditions. There is an urgent need to propose a multi-energy storage planning method for power systems that can comprehensively consider the impact of extreme disasters and fully utilize the characteristics of diverse energy storage systems to improve the safety, stability, and resilience of new power systems in complex environments. Summary of the Invention

[0005] The purpose of this invention is to provide a method and device for planning multi-energy storage in power systems that takes into account extreme disasters, so as to solve the technical problem that existing technologies lack multi-energy storage planning methods for extreme scenarios.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A method for planning multi-element energy storage in power systems considering extreme disasters includes the following steps: Construct energy storage operation constraints, which include battery energy storage constraints, hydrogen energy storage constraints, and long-term and short-term energy storage constraints applied to battery energy storage and hydrogen energy storage respectively. Construct a line fault probability model and a line recovery time model under extreme disasters; Based on energy storage operation constraints, as well as line fault probability models and line recovery time models under extreme disasters, a multi-element energy storage bi-layer stochastic programming model is established to output energy storage configuration schemes. The upper-layer energy storage site selection and capacity determination model takes "the annualized investment cost of diversified energy storage + the expected value of the annual operating cost of the system under extreme disaster scenarios" as the optimization objective, and uses the rated power and rated capacity of battery energy storage and the rated power and rated capacity of hydrogen energy storage as constraints to output energy storage configuration schemes. The lower-level power grid dispatch model, based on the energy storage configuration scheme output by the upper-level energy storage location and capacity model and under extreme disaster scenarios, takes "minimizing the sum of power generation cost, wind and solar curtailment penalty cost, and full load penalty cost" as its optimization objective. It considers power system operation constraints and energy storage operation constraints, outputs the optimal operating cost for each scenario, and feeds it back to the upper-level energy storage location and capacity model.

[0007] Furthermore, the battery energy storage constraints include charging and discharging process constraints, state of charge constraints, and charging and discharging power constraints; The hydrogen energy storage constraints include hydrogen production constraints and power range constraints of electrolyzers, hydrogen storage capacity constraints of hydrogen storage tanks, state of hydrogen (SOH) constraints and hydrogen transport capacity range constraints, and power generation constraints and power range constraints of fuel cells. The long-term and short-term energy storage constraints include battery energy storage ramp-up constraints, hydrogen energy storage ramp-up constraints, battery energy storage duration constraints, and hydrogen energy storage duration constraints.

[0008] Furthermore, the process of constructing the line fault probability model under extreme disasters includes: Establish the relationship between line fault probability and actual wind speed; Each transmission line is divided into several sections, and the failure rate of the entire line is obtained based on the probability series system.

[0009] Furthermore, the line recovery time model is constructed using a log-normal distribution. By fitting historical emergency repair records, the repair time of faulty lines is randomly sampled to generate a line availability time series.

[0010] Furthermore, the objective function of the upper-layer energy storage location and capacity determination model is:

[0011] In the formula, For the upper-level objective function, To cover the investment costs of planned diversified energy storage, This represents the system's expected annual operating cost. For a set of random faults under extreme weather conditions, For the scene The probability of occurrence for Power system operating costs in this scenario;

[0012] In the formula, For energy storage purchase costs, For energy storage replacement costs, For energy storage maintenance costs, The residual value of energy storage; The constraints are as follows:

[0013] In the formula, The maximum rated capacity that is allowed to be installed for battery energy storage. The maximum rated power that is allowed to be installed for battery energy storage. This refers to the maximum rated capacity that the hydrogen storage tank is allowed to install. This is the maximum rated power of the electrolytic cell. This represents the maximum rated power of the fuel cell.

[0014] Furthermore, the objective function of the lower-level power grid operation and scheduling model is:

[0015] In the formula, For generator operating costs, The cost of penalties for curtailing wind and solar power, To reduce load costs.

[0016] Furthermore, the power system operation constraints include node power balance constraints, line DC power flow constraints, line transmission capacity constraints, generator output constraints, generator ramping constraints, load shedding constraints, and new energy-related constraints.

[0017] Secondly, the present invention provides a multi-element energy storage planning system for power systems considering extreme disasters, comprising a first building module, a second building module, and an output module, wherein: The first construction module is used to construct energy storage operation constraints, which include battery energy storage constraints, hydrogen energy storage constraints, and long-term and short-term energy storage constraints applied to battery energy storage and hydrogen energy storage respectively. The second building module is used to construct line fault probability models and line recovery time models under extreme disasters. Output module: Used to establish a multi-element energy storage bi-layer stochastic programming model based on energy storage operation constraints, line fault probability model under extreme disasters, and line recovery time model, and output energy storage configuration scheme; The upper-layer energy storage site selection and capacity determination model takes "the annualized investment cost of diversified energy storage + the expected value of the annual operating cost of the system under extreme disaster scenarios" as the optimization objective, and uses the rated power and rated capacity of battery energy storage and the rated power and rated capacity of hydrogen energy storage as constraints to output energy storage configuration schemes. The lower-level power grid dispatch model, based on the energy storage configuration scheme output by the upper-level energy storage location and capacity model and under extreme disaster scenarios, takes "minimizing the sum of power generation cost, wind and solar curtailment penalty cost, and full load penalty cost" as its optimization objective. It considers power system operation constraints and energy storage operation constraints, outputs the optimal operating cost for each scenario, and feeds it back to the upper-level energy storage location and capacity model.

[0018] Thirdly, a terminal device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0019] Fourthly, a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0020] Compared with the prior art, the present invention has the following beneficial technical effects: This invention integrates short-term rapid adjustment capabilities and long-term large-scale energy transfer capabilities into a single planning framework by uniformly describing the operational characteristics of battery energy storage and hydrogen energy storage. Compared to existing methods that rely on only a single energy storage form or model different energy storage devices independently, this invention can build effective energy reserves before a disaster, provide continuous power supply support during a disaster, and promote rapid grid recovery after a disaster, thereby significantly enhancing the system's resilience and self-healing capabilities under extreme disaster conditions.

[0021] This invention correlates spatial wind fields during extreme weather with the structural characteristics of transmission lines, constructing a fault probability model and a repair time model based on statistical laws. This allows for the quantitative expression of topological changes in the power grid throughout the entire cycle of disaster occurrence, evolution, and dissipation. Using this model, energy storage site selection and capacity configuration no longer depend on ideal operating conditions but can be optimized based on the grid weakening patterns caused by disasters, thereby improving the effectiveness and robustness of planning schemes in real-world disaster scenarios.

[0022] This invention integrates the construction cost, maintenance cost, and operation cost under disaster scenarios of energy storage into a two-layer stochastic programming framework. By feeding back the scheduling results from multiple scenarios to the upper-level planning layer, it enables energy storage configuration to balance economic efficiency and system reliability. Compared to traditional planning methods that rely on single scenarios or static operating conditions, this invention can significantly reduce operational risks such as wind and solar power curtailment and load losses caused by extreme disasters, maximizing the return on energy storage investment and simultaneously improving power supply reliability during disasters. Attached Figure Description

[0023] Figure 1 This is a flowchart of a power system multi-element energy storage planning method considering extreme disasters, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of the test system. Figure 3 This is a schematic diagram of wind speed curves at some nodes; Figure 4 This is a graph showing the performance of the power system under extreme disasters. Figure 5 This diagram illustrates the amount of wind and solar power curtailment under different configuration scenarios. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0025] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] The present invention will now be described in further detail with reference to the accompanying drawings: Based on the system scheduling cycle, this invention discretizes the research period into several equal time steps (such as hours). It assumes that power and energy are in a quasi-steady state within each time step, and no longer considers the internal electrochemical processes of the battery, the transient dynamics of the electrolyzer and fuel cell. It only retains the energy balance relationship and operating boundary conditions that are closely related to the engineering plan.

[0028] like Figure 1 As shown, a multi-element energy storage planning method for power systems considering extreme disasters includes the following steps: Step 1: Construct energy storage operation constraints, which include battery energy storage constraints, hydrogen energy storage constraints, and long-term and short-term energy storage constraints applied to battery energy storage and hydrogen energy storage respectively. Battery energy storage constraints include charging and discharging process constraints, state of charge constraints, and charging and discharging power constraints, specifically; Battery energy storage is considered a node-level controllable power source / load, with its core state variables being the stored energy and state of charge. In this invention, the energy evolution of battery energy storage between adjacent time periods follows the constraints of the charge-discharge process: (1) In the formula, for t Within a certain time i The battery energy storage capacity of the node, for t Within a certain time i The battery storage charging power of the node, for t Within a certain timei The battery energy storage discharge power of the node, Charging efficiency for battery energy storage This refers to the discharge efficiency of the battery for energy storage. Furthermore, T During the total scheduling period, the initial and final energy storage capacity should be equal to ensure the repeatability of energy storage system scheduling.

[0029] To facilitate unified planning and operation, this invention introduces a state of charge (SOC) constraint, normalizing the stored energy into a dimensionless variable within the range of 0 to 1: (2) In the formula, Indicates the state of charge of the battery energy storage. This indicates the rated capacity of the battery's energy storage. SOC max , SOC min These represent the maximum and minimum values ​​of the state of charge (SOC). SOC is an important indicator of a battery's remaining capacity, and its value ranges from [0,1]. Overcharging and over-discharging of energy storage batteries can affect their lifespan; therefore, upper and lower limits must be set for the SOC.

[0030] On the power side, the battery charging and discharging power are limited by the inverter's rated capacity, corresponding to the following charging and discharging power constraints: (3) In the formula, P rate,i The rated power for battery energy storage.

[0031] Hydrogen storage constraints include hydrogen production constraints and power range constraints for electrolyzers, hydrogen storage capacity constraints, state of hydrogen (SOH) constraints, and hydrogen transport capacity range constraints for hydrogen storage tanks, as well as power generation constraints and power range constraints for fuel cells. Specifically: Hydrogen energy storage consists of three parts: hydrogen production through water electrolysis, hydrogen storage, and fuel cell power generation, corresponding to an energy chain of "electricity-hydrogen-electricity". This invention breaks down this chain into three sub-modules, models them separately, and then achieves energy conservation and operational boundary control throughout the entire chain through hydrogen flow coupling.

[0032] The electrolyzer converts electrical energy into hydrogen energy, and its hydrogen production capacity satisfies the hydrogen production constraint with respect to the input electrical power. (4) In the formula, for i The electrolyzer at the node is t Hydrogen production capacity at any given time for i The electrolyzer at the node is t Hydrogen production rate at any given time. This indicates the hydrogen production efficiency of the electrolyzer.

[0033] The output of the electrolytic cell cannot be varied indefinitely; it must meet the upper and lower limits of the rated power and the technical output range, corresponding to the following power range constraints: (5) In the formula, This refers to the rated power of the electrolytic cell. and These represent the maximum and minimum technical outputs of the electrolytic cell, respectively.

[0034] Hydrogen is stored in hydrogen storage tanks after electrolysis. This invention describes the changes in hydrogen inventory between adjacent time periods using capacity balance constraints: (6) In the formula, H i,t for t Within a certain time i The amount of hydrogen stored in the node's hydrogen storage tank. T During the overall scheduling period, the initial hydrogen storage capacity and the final hydrogen storage capacity should remain equal to ensure that the energy storage system has the same control performance in the new scheduling cycle.

[0035] To facilitate unified management of hydrogen storage status, a concept similar to State of Storage (SOH) is introduced, linking the amount of hydrogen stored to the rated hydrogen storage capacity, and setting upper and lower limits for SOH: (7) Meanwhile, the maximum continuous hydrogen release capacity of the hydrogen storage tank is limited by its structure and valve capabilities, and must meet the constraints of the hydrogen transport capacity range. (8) In the formula, h rate,i This refers to the rated hydrogen delivery capacity of the hydrogen storage tank.

[0036] Fuel cells convert the chemical energy of hydrogen back into electrical energy. This invention couples hydrogen consumption with output power through power generation constraints: (9) In the formula, for t Within a certain time i The fuel cell power generation of the node, for t Within a certain time i The hydrogen consumption of the fuel cell at the node. This refers to the power generation efficiency of fuel cells.

[0037] Meanwhile, the output power of fuel cells is limited by rated capacity and minimum technical output, corresponding to power range constraints: (10) In the formula, This refers to the rated power of the fuel cell. and These represent the maximum and minimum technical outputs of the electrolytic cell, respectively.

[0038] To differentiate the functional positioning of battery energy storage and hydrogen energy storage in short-term and long-term application scenarios, this invention systematically analyzes them from three aspects: energy conversion efficiency, power ramp-up characteristics, and sustainable discharge duration. The preceding model section has already covered the efficiency differences between the two energy storage methods; therefore, this paper focuses on discussing the constraints that they must meet in terms of power ramp-up capability and continuous energy storage capability.

[0039] Battery energy storage ramp-up constraints: (11) In the formula, The maximum ramp rate for battery energy storage at node i.

[0040] Hydrogen storage ramp-up constraints: (12) In the formula, Let be the maximum ramp rate of the electrolytic cell at node i. for i The maximum ramp rate of the fuel cell at the node.

[0041] Energy storage duration constraints of battery energy storage: (13) In the formula, T Bmax , T Bmin These represent the maximum and minimum energy storage durations of the battery, respectively.

[0042] Energy storage duration constraints for hydrogen energy storage: (14) In the formula, T Hmax 、T Hmin These represent the maximum and minimum energy storage durations for hydrogen energy storage, respectively.

[0043] Step 2: Construct a line fault probability model and a line recovery time model under extreme disasters; In extreme disaster scenarios, the mechanical stress and environmental conditions of transmission lines differ significantly from normal operation. This invention addresses strong wind disasters such as typhoons by establishing a mapping relationship between external wind speed and line fault probability, and further characterizes the repair time distribution of faulty lines.

[0044] From an engineering perspective, the wind resistance of transmission lines is determined by indicators such as the maximum design wind speed. When the actual wind speed is lower than this design value, the line is unlikely to fail due to wind-induced damage; however, when the wind speed approaches or exceeds the wind resistance limit, the structural safety margin decreases rapidly, and the probability of failure increases non-linearly. Based on this, this invention describes the relationship between the probability of line failure and the actual wind speed: (15) In the formula, This represents the probability of line failure. This represents the maximum wind speed that the line can withstand. This refers to the actual wind speed that the line can withstand.

[0045] In practical implementation, each overhead transmission line is divided into several equal-length segments along the spatial axis. It is assumed that typhoon intensity and geographical conditions are approximately the same within each segment, and that fault events between segments are statistically independent. If a fault occurs in any sub-segment, the entire line is considered to be out of service. Based on the probabilistic series system, the fault rate expression for the entire line can be obtained: (16) In the formula, For the line ij Failure rate, For the line ij The number of grids divided, For the line ij The Middle k Line fault rate within the segment grid, For the first ij The number of conductors in each line.

[0046] After an extreme disaster, the time it takes for a power line to become usable again after a failure is significantly random, due to factors such as the on-site environment, traffic conditions, and the allocation of repair resources. Conventional reliability assessments often use a fixed average repair time, while this invention employs a more realistic log-normal distribution model. Specifically, it is assumed that the line repair time T follows a log-normal distribution, with its probability density function as shown in the following equation: (17) In the formula, and Let represent the mean and standard deviation of the recovery time distribution, respectively. The mean and standard deviation parameters of the distribution can be determined by statistically fitting historical repair records. Using this distribution to randomly sample the repair time of faulty lines, time series of line availability under different disaster scenarios can be generated in simulations, which can be used to assess the support capacity of energy storage at different stages during and after a disaster.

[0047] Step 3: Based on the constraints of energy storage operation, the line fault probability model under extreme disasters, and the line recovery time model, establish a multi-element energy storage two-layer stochastic programming model and output the energy storage configuration scheme. Based on the multi-element energy storage steady-state model and the disaster-failure-repair model, this invention constructs a two-layer stochastic programming framework of "planning-operation". The upper layer is responsible for determining the spatial layout and capacity level of the energy storage station, while the lower layer performs operation scheduling under given energy storage configuration and disaster scenarios, thereby achieving the optimal overall cost of investment and operation.

[0048] The two-level stochastic programming model includes an upper-level energy storage location and capacity determination model and a lower-level power grid dispatch model, specifically: 1) Upper-layer energy storage location and capacity determination model The upper-level problem has the optimization objective of "the expected value of the annualized investment cost of diversified energy storage + the annual operating cost of the system under extreme disaster scenarios", and the objective function is as follows: (18) In the formula, For the upper-level objective function, To cover the investment costs of planned diversified energy storage, This represents the system's expected annual operating cost. For a set of random faults under extreme weather conditions, For the scene The probability of occurrence for Operating costs of the power system in this scenario.

[0049] The investment cost is further broken down into four parts: energy storage acquisition cost, energy storage replacement cost, energy storage maintenance cost, and energy storage residual value. (19) Energy storage purchase cost: The initial purchase cost of energy storage is mainly related to the purchase costs of battery energy storage and hydrogen energy storage. The purchase cost of hydrogen energy storage includes the costs of the electrolyzer, hydrogen storage tank, and fuel cell. The specific expressions for each component are as follows: (20) In the formula, These are the initial purchase costs of battery energy storage, electrolyzer, hydrogen storage tank, and fuel cell, respectively. These represent the purchase cost per unit capacity and per unit power of battery energy storage, respectively. , , These are the unit power purchase cost of an electrolyzer, the unit capacity purchase cost of a hydrogen storage tank, and the unit power purchase cost of a fuel cell in hydrogen energy storage. This is the capital recovery coefficient, where For the discount rate, For the planning cycle.

[0050] Energy storage replacement cost: The energy storage replacement cost is mainly related to the replacement costs of battery energy storage and hydrogen energy storage, and the specific expression is as follows: (twenty one) In the formula, , , , These are the replacement costs for battery energy storage, electrolyzers, hydrogen storage tanks, and fuel cells, respectively. , , , These refer to the number of times battery energy storage, electrolyzer, hydrogen storage tank, and fuel cell will be replaced within the planned cycle.

[0051] Energy storage maintenance costs: To ensure the normal operation of energy storage throughout its lifecycle, maintenance and repair work are required. Energy storage maintenance costs are mainly related to the maintenance costs of battery energy storage and hydrogen energy storage, as shown in the following expression: (twenty two) In the formula, , , , The annual maintenance costs are for battery energy storage, electrolyzers, hydrogen energy storage, and fuel cells, respectively. Maintenance cost coefficient per unit capacity of battery energy storage , , These are the maintenance cost coefficient per unit power of the electrolyzer in hydrogen energy storage, the maintenance cost coefficient per unit capacity of the hydrogen storage tank, and the maintenance cost coefficient per unit power of the fuel cell.

[0052] Annual residual value of energy storage: After the energy storage lifecycle ends, the energy storage performance is not yet fully depleted; this portion will be deducted from the cost. The annual residual value of energy storage is mainly related to the energy storage purchase cost and the residual value rate, and its specific expression is as follows: (twenty three) In the formula, , , , These are the annual residual values ​​of battery energy storage, electrolyzers, hydrogen energy storage, and fuel cells, respectively. This represents the residual value rate of energy storage.

[0053] By using the capital recovery factor and discount rate, the one-time investment is converted into an annualized cost, and combined with the equipment life and replacement frequency during the planning period, the full life cycle cost of different types of energy storage (batteries, electrolyzers, hydrogen storage tanks, fuel cells) is characterized.

[0054] The upper-level model uses the rated power and rated capacity of battery energy storage and the rated power and rated capacity of hydrogen energy storage as constraints, namely:

[0055] In the formula, The maximum rated capacity that is allowed to be installed for battery energy storage. The maximum rated power that is allowed to be installed for battery energy storage. This refers to the maximum rated capacity that the hydrogen storage tank is allowed to install. This is the maximum rated power of the electrolytic cell. This represents the maximum rated power of the fuel cell.

[0056] 2) Lower-level power grid operation and scheduling model The lower-level model performs multi-period operation scheduling optimization under the given energy storage configuration scheme and sampled disaster-failure-repair scenarios from the upper-level model. Its objective is to minimize the sum of generation cost, wind / solar curtailment penalty cost, and load shedding penalty cost. The objective function is shown below:

[0057] In the formula, This refers to the generator operating cost. The cost of penalties for curtailing wind and solar power, To reduce load costs, the specific components are as follows: (26) In the formula, For generator t Power generation output during a given period , , This is the cost coefficient for generator sets. for s Scenario 1 i node t The amount of wind and solar energy removed during a given period. The unit penalty cost for curtailing wind and solar power. for s Scenario 1 i node t Loss load during the period The penalty cost is levied on the unit of load reduction.

[0058] To ensure the reliability of the power system, the lower-level model must satisfy power system operation constraints, which include node power balance constraints, line DC power flow constraints, line transmission capacity constraints, generator output constraints, generator ramping constraints, load shedding constraints, and new energy-related constraints. Node power balance constraints: (27) In the formula, , They are respectively s Scenario 1 i Each node t Solar and wind power generation during the period for s Scenario 1 i Each node t Load power at any given time for s Scenario 1 t During the period from i Node flow j Active power of a node.

[0059] DC power flow constraints on the line: (28) In the formula, They are respectively s Nodes in the scene i With nodes j exist t Voltage phase angle during the time period For nodes i With nodes j The line impedance between them.

[0060] Line transmission capacity constraints: (29) In the formula, For nodes i With nodes j The transmission capacity of the lines between them has been increased.

[0061] Generator output constraints: (30) In the formula, The first g The upper and lower limits of the generator output.

[0062] Generator ramping constraints: (31) In the formula, , The first g The maximum ascending and descending ramp rates of the generator.

[0063] Shear load constraint: (32) New energy-related constraints: (33) In the formula, , The first i The maximum output of wind and solar power in the node.

[0064] The constraints of the upper and lower level models also include energy storage operation constraints and long-term and short-term energy storage constraints, as well as energy storage planning constraints.

[0065] The constraints for battery energy storage operation are Equations (1) to (3), the constraints for hydrogen energy storage operation are Equations (4) to (10), and the constraints for long-term and short-term energy storage are Equations (11) to (14). After the model analysis and construction of the planning and operation phases described above, and considering the relationship between various decision variables and parameters, the multi-element energy storage two-layer stochastic programming model proposed in this invention can be described as follows: (34) In the formula, x These are the planning and decision variables for the upper-level model. for s The decision variables for the lower-level model in the scenario. S For a set of random scenes under extreme scenarios, For planning decision variables x Once the value is determined, in all scenarios S The expected annual operating cost of the power system under the following conditions Decision variables x With Scene s Once determined, the annual operating cost of the lower-level function is calculated.

[0066] In one embodiment of the present invention, in order to construct a research environment under high-penetration new energy conditions, the present invention selects a comprehensive testing system that includes traditional units and multiple types of renewable energy, the overall topology of which is as follows: Figure 2 As shown, the system consists of 24 electrical nodes interconnected by 38 transmission lines, and is equipped with 14 conventional synchronous generators, 5 transformers, and 17 load connection points. The basic system has a conventional installed capacity of approximately 3455 MW and a peak load level of approximately 2850 MW, and can be used to simulate different load changes and operating conditions.

[0067] Building upon this infrastructure, to enhance the penetration rate of new energy sources and create more challenging operating scenarios, this invention further introduces multiple wind and photovoltaic power sources: wind farms with rated capacities of 400 MW, 350 MW, and 200 MW are deployed at nodes 8, 19, and 21, respectively; and photovoltaic power plants with rated capacities of 100 MW, 150 MW, and 270 MW are deployed at nodes 4, 5, and 17. With these expansions, the system presents a pattern where traditional power sources and multiple types of new energy sources coexist, which can be fully used to verify energy storage planning methods under complex power structure.

[0068] To simulate the impact of extreme weather on power grid operation, this embodiment selects a typhoon process as the external disturbance. During the movement of the typhoon, the wind speed in different regions varies non-uniformly over time, and its impact on the mechanical load of the power lines has a significant spatiotemporal correlation.

[0069] Figure 3 Example curves showing wind speed variations over time at some key nodes are presented. As can be seen from the figures, the peak wind speed, duration, and variation pattern differ at different nodes, indicating significant differences in the stress experienced by each power line throughout the typhoon. Combined with the aforementioned wind speed-failure probability model, the instantaneous failure rate of transmission lines exhibits a single-peak or double-peak variation, and is not only affected by typhoon intensity but also closely related to factors such as the spatial location of the line and the typhoon's path. Therefore, it is necessary to fully consider the spatial disturbances of the system caused by the typhoon when constructing disaster scenarios.

[0070] Based on the obtained time-varying line fault probability, this embodiment uses Monte Carlo sampling to generate 1,000 possible line breakage combination scenarios. To reduce the computational burden of subsequent planning, the probability distance reduction algorithm is used to select four typical fault scenarios that can represent different risk distributions. On this basis, the multi-element energy storage collaborative planning method proposed in this invention is executed.

[0071] Running the two-level stochastic programming model of this invention under the above typical fault scenarios yields the optimal layout scheme for the multi-element energy storage system. The planning results are shown in Table 1: Table 1. Results of Multi-Energy Storage Planning

[0072] Battery energy storage is mainly concentrated at nodes 1, 12, 13, and 17. Deploying battery energy storage at these nodes enhances the local response capability to rapid power shortages and meets the demand for frequent short-term fluctuations. The resulting battery energy storage has a storage duration of approximately 2 hours, consistent with its application positioning of "short-term adjustment and rapid response." Hydrogen energy storage is mainly deployed at nodes 3, 9, and 21. This configuration can provide more than ten hours of continuous discharge capability when disaster shocks cause sustained high loads or insufficient renewable energy output, with a storage duration of nearly 20 hours, suitable for addressing cross-day energy gaps and effectively compensating for the limited capacity of battery energy storage. In terms of investment structure, hydrogen energy storage accounts for approximately 86% of the total investment, with fuel cell equipment accounting for the largest share. This is consistent with the characteristics of hydrogen energy storage systems: long chain, complex equipment structure, and large rated capacity.

[0073] To verify the effect of the proposed multi-energy storage planning method on improving the resilience of the power system, the following four different energy storage configuration schemes are compared and analyzed: Scheme 1: No energy storage system; Scheme 2: Only short-term energy storage (battery energy storage); Scheme 3: Only long-term energy storage (hydrogen energy storage); Scheme 4: Using the multi-energy storage configuration scheme obtained by this invention. Figure 4 , Figure 5 The system performance changes and the amount of wind and solar power curtailment under different schemes are presented respectively.

[0074] The results show that: The energy storage-free solution experienced significant load inability during typhoon impacts, with severe wind and solar curtailment at certain times, making it the least resilient overall.

[0075] With only battery energy storage, the battery can quickly adapt to changes in wind and solar power output, effectively mitigating short-term power shortages before and after peak loads. However, in the case of prolonged energy shortages on the fourth day, the battery energy storage, due to its limited capacity, still cannot fully support system operation. For example, the energy shortage on the night of the fourth day could not be fully compensated.

[0076] When only hydrogen storage is configured, the system has a longer support capacity in the event of a daytime energy gap, and the load shedding level is significantly reduced. However, due to the slow response rate of the electrolyzer and fuel cell, they cannot closely follow the fluctuations in wind and solar power, resulting in a relatively high level of wind and solar curtailment at certain times.

[0077] The proposed multi-energy storage scheme achieves complementarity between short-term and long-term energy storage: battery energy storage is responsible for quickly smoothing out short-term fluctuations, while hydrogen energy storage provides continuous energy support over long periods. This combination exhibits almost no load shedding throughout the entire simulation period and significantly reduces wind and solar curtailment while maintaining high utilization of renewable energy, making it the best-performing configuration strategy among the four schemes.

[0078] In summary, the multi-energy storage planning method proposed in this invention can not only rationally deploy battery energy storage and hydrogen energy storage, but also significantly improve the power supply continuity, renewable energy utilization rate and overall resilience of the power system under extreme disaster conditions.

[0079] In another embodiment of the present invention, a multi-element energy storage planning system for power systems considering extreme disasters is provided, comprising a first building module, a second building module, and an output module, wherein: The first construction module is used to construct energy storage operation constraints, which include battery energy storage constraints, hydrogen energy storage constraints, and long-term and short-term energy storage constraints applied to battery energy storage and hydrogen energy storage respectively. The second building module is used to construct line fault probability models and line recovery time models under extreme disasters. Output module: Used to establish a multi-element energy storage bi-layer stochastic programming model based on energy storage operation constraints, line fault probability model under extreme disasters, and line recovery time model, and output energy storage configuration scheme; The upper-layer energy storage site selection and capacity determination model takes "the annualized investment cost of diversified energy storage + the expected value of the annual operating cost of the system under extreme disaster scenarios" as the optimization objective, and uses the rated power and rated capacity of battery energy storage and the rated power and rated capacity of hydrogen energy storage as constraints to output energy storage configuration schemes. The lower-level power grid dispatch model takes the energy storage configuration scheme output by the upper-level energy storage location and capacity model and extreme disaster scenarios as the optimization objective of "minimizing the sum of power generation cost, wind and solar curtailment penalty cost and full load penalty cost". It considers power system operation constraints and energy storage operation constraints, outputs the optimal operating cost in each scenario and feeds it back to the upper-level energy storage location and capacity model.

[0080] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can be implemented in one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs) containing computer-usable program code. The form of a computer program product implemented on ROM, optical memory, etc.

[0081] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0082] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0083] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

Claims

1. A method for multi-energy storage planning of power systems considering extreme disasters, characterized in that, The method comprises the following steps: a storage energy operation constraint is constructed, the storage energy operation constraint comprising a battery storage energy constraint, a hydrogen storage energy constraint, and long and short time storage energy constraints respectively imposed on the battery storage energy and the hydrogen storage energy; a line fault probability model and a line recovery time model under extreme disasters are constructed; a multi-element storage energy double-layer stochastic programming model is established based on the storage energy operation constraint, the line fault probability model under extreme disasters, and the line recovery time model, and a storage energy configuration scheme is output; the upper-layer storage energy site selection and capacity determination model takes the "multi-element storage energy annual investment cost + expected value of system annual operation cost under extreme disaster scenarios" as an optimization target, takes the rated power and rated capacity of the battery storage energy and the rated power and rated capacity of the hydrogen storage energy as constraint conditions, and outputs the storage energy configuration scheme; the lower-layer power grid dispatching model takes the "sum of generation cost, wind and light curtailment penalty cost, and load full load penalty cost" as an optimization target, considers power system operation constraints and storage energy operation constraints under the storage energy configuration scheme output by the upper-layer storage energy site selection and capacity determination model and under extreme disaster scenarios, and outputs optimal operation costs under each scenario and feeds back to the upper-layer storage energy site selection and capacity determination model. 2.The method of claim 1, wherein, The battery storage energy constraint comprises a charging and discharging process constraint, a state of charge constraint, and a charging and discharging power constraint; The hydrogen storage energy constraint comprises an electrolytic cell hydrogen production constraint and a power range constraint, a hydrogen storage tank hydrogen storage amount constraint, a hydrogen storage state SOH constraint, and a hydrogen delivery capacity range constraint, a fuel cell power generation constraint, and a power range constraint; The long and short time storage energy constraint comprises a battery storage energy ramping constraint, a hydrogen storage energy ramping constraint, a battery storage energy storage time constraint, and a hydrogen storage energy storage time constraint. 3.The method of claim 1, wherein, The construction process of the line fault probability model under extreme disasters comprises: a relationship between line fault probability and actual wind speed is established; each power transmission line is divided into a plurality of sections, and a failure rate of the entire line is obtained according to a probability series connection system. 4.The method of claim 1, wherein, The line recovery time model is constructed using a logarithmic normal distribution, random sampling is performed on the repair time of a fault line by fitting historical repair records, and a line availability time sequence is generated.

5. The method of claim 1, wherein, The objective function of the upper-layer storage energy site selection and capacity determination model is: wherein, is the upper-level objective function, is the planned multi-energy storage investment cost, denotes the system annual expected operation cost, is the random failure set under extreme weather, is the scenario occurred probability, is the power system operation cost under the scenario; wherein is the energy storage acquisition cost, is the energy storage replacement cost, is the energy storage maintenance cost, is the energy storage annual residual value; The constraint conditions are: wherein is the maximum rated capacity allowed for installation of battery energy storage, is the maximum rated power allowed for installation of battery energy storage, is the maximum rated capacity allowed for installation of hydrogen storage tanks, is the maximum rated power for electrolysis, is the maximum rated power for fuel cells.

6. The method of claim 1, wherein, The objective function of the lower-layer power grid operation dispatching model is: wherein is the operating cost of the generator, is the penalty cost of curtailment of wind and solar power, is the load shedding cost.

7. The method of claim 1, wherein, The power system operation constraints comprise a node power balance constraint, a line direct current flow constraint, a line transmission capacity constraint, a generator output constraint, a generator ramping constraint, a load shedding constraint, and a new energy related constraint.

8. A multi-energy storage planning system for power systems considering extreme disasters, characterized by, The method comprises a first construction module, a second construction module, and an output module, wherein: The first construction module is configured to construct a storage energy operation constraint, the storage energy operation constraint comprising a battery storage energy constraint, a hydrogen storage energy constraint, and long and short time storage energy constraints respectively imposed on the battery storage energy and the hydrogen storage energy; The second construction module is configured to construct a line fault probability model and a line recovery time model under extreme disasters; The output module is configured to establish a multi-element storage energy double-layer stochastic programming model based on the storage energy operation constraint, the line fault probability model under extreme disasters, and the line recovery time model, and output a storage energy configuration scheme. The upper-layer energy storage site selection and capacity determination model takes "multi-element energy storage annual investment cost + expected value of system annual operation cost under extreme disaster scenarios" as an optimization target, takes the rated power and rated capacity of battery energy storage and the rated power and rated capacity of hydrogen energy storage as constraint conditions, and outputs an energy storage configuration scheme; The lower-layer power grid dispatching model takes the energy storage configuration scheme output by the upper-layer energy storage site selection and capacity determination model and extreme disaster scenarios as input, takes "sum of generation cost, wind and light curtailment penalty cost and load full load penalty cost minimum" as an optimization target, considers power system operation constraints and energy storage operation constraints, and outputs optimal operation cost under each scenario and feeds back to the upper-layer energy storage site selection and capacity determination model.

9. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor implements the computer program when the computer program is executed by the processor to realize the method of claim 1 7. The step of the method of any one.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1-9. The computer program, which is executed by a processor, implements the method as claimed in claim 1 7. The step of the method of any one.

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