Energy storage configuration optimization method and system giving consideration to ground state peak regulation and extreme guaranteed power supply

Abstract

The invention belongs to the technical field of energy storage planning and configuration, and discloses an energy storage configuration optimization method and system giving consideration to ground state peak regulation and extreme guaranteed power supply, and the method comprises the steps: constructing an energy storage equipment configuration model giving consideration to ground state peak regulation and extreme guaranteed power supply; the conventional scene operation model is solved, iteration is repeated until the configuration investment model and the conventional scene operation model converge, and a first optimal cut set is output; the extreme scene operation model is solved, iteration is repeated until the configuration investment model and the extreme scene operation model converge, and a second optimal cut set is output; and taking the first optimal cut set and the second optimal cut set as constraint conditions, alternately transmitting the constraint conditions to a conventional scene operation model and an extreme scene operation model for iterative solution until the models converge, and obtaining an energy storage equipment configuration scheme considering both ground state peak regulation and extreme guaranteed power supply. According to the invention, the limitation of single scene application in traditional planning is broken through, and dual-benefit fusion is realized under the same investment level.

Description

Technical Field

[0001] This invention relates to the field of energy storage planning and configuration technology, and in particular to an energy storage configuration optimization method and system that takes into account both ground-state peak shaving and extreme power supply guarantee. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] my country's power system is evolving in an orderly manner towards a new type of power system dominated by new energy sources, balancing safe and stable operation with a high proportion of new energy consumption. To enhance the system's safe and stable operation under extreme events, research and planning on energy storage devices for this new power system, with its focus on resilience enhancement, is urgently needed. Considering the high proportion of new energy consumption required by the new power system, and taking into account the ability of energy storage devices to participate in peak shaving and valley filling and mitigate new energy fluctuations in normal scenarios, the planning and construction of the new power system should comprehensively consider both the economic benefits of energy storage devices in normal scenarios and their resilience benefits in extreme scenarios. This will fully explore the multi-scenario application potential of resource allocation and improve the comprehensive benefits of energy storage device deployment in the new power system across multiple scenarios.

[0004] Current research only focuses on distributed resource allocation for routine or extreme scenarios, failing to effectively explore the multi-scenario application potential of flexible resources, and neglecting to consider the benefits of improved system elasticity and daily economic efficiency under resource allocation from an operational perspective.

[0005] Therefore, it is necessary to conduct research on various distributed power supply configuration methods that take into account both the economic efficiency of conventional scenarios and the resilience of extreme scenarios. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes an energy storage configuration optimization method and system that balances ground-state peak shaving with extreme power supply assurance. This breaks through the limitations of single-scenario applications in traditional planning and achieves a dual benefit fusion under the same investment level.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides an energy storage configuration optimization method that balances ground-state peak shaving and extreme power supply assurance, comprising the following steps: A configuration model for energy storage devices is constructed that takes into account both ground-state peak shaving and extreme power supply. The configuration model includes a configuration investment model, a normal scenario operation model, and an extreme scenario operation model. Based on the configuration investment model, an energy storage configuration scheme is provided. The conventional scenario operation model is solved and iterated repeatedly until the configuration investment model and the conventional scenario operation model converge, and the first optimal cut set is output. Based on the configuration investment model, an energy storage configuration scheme is provided, and the extreme scenario operation model is solved. The process is repeated iteratively until the configuration investment model and the extreme scenario operation model converge, and the second optimal cut set is output. The first and second optimal cut sets are used as constraints, which are alternately passed to the normal scenario operation model and the extreme scenario operation model for iterative solution until the model converges, thus obtaining an energy storage device configuration scheme that takes into account both ground state peak regulation and extreme power supply guarantee.

[0008] As an alternative implementation method, a model based on Nash negotiation is adopted as the convergence criterion. Taking into account the order of magnitude difference between different objectives, multiple objectives to be optimized are treated as game participants, balancing the needs of multiple objectives, and obtaining the optimal comprehensive benefits in the game.

[0009] As an alternative implementation method, the solution process for the conventional scenario running model is as follows: The normal scenario operation model provides an energy storage configuration scheme based on the configuration investment model, performs peak shaving and valley filling operation simulation under normal scenarios, obtains the optimal energy storage operation scheduling scheme under this configuration, and returns the operation status parameters to the configuration investment model to guide the energy storage configuration. Iterative solution is performed until the configuration investment model and the normal scenario operation model converge, and the optimal energy storage configuration and the first optimal cut set are output. The solution process for the extreme scenario running model is as follows: The extreme scenario operation model provides energy storage configuration schemes based on the configuration investment model, finds the worst component failure scenario, and coordinates the scheduling of multiple types of resources to restore system load based on the worst component failure scenario, so as to minimize system load loss. This process is repeated iteratively until the configuration investment model and the extreme scenario operation model converge, and outputs the optimal energy storage configuration scheme and the second optimal cut set.

[0010] As an alternative implementation method, the configuration investment model takes minimizing investment costs as the objective function and fixed energy storage configuration cost constraints and mobile energy storage configuration cost constraints as constraints.

[0011] As an alternative implementation method, the conventional scenario operation model uses minimizing the equivalent load variance of the power grid as the objective function: ; In the formula, Typical daily hours for t Time Node f Typical daily load value at the location, for t Time Node f The sum of the power of different types of energy storage, This represents the average daily load of the power grid.

[0012] As an alternative implementation method, the extreme scenario operation model uses minimizing power loss as the objective function: ; In the formula, The power cost factor per unit load loss. Recovery period under extreme scenarios For nodes f Load weight, for t Time Node f The load loss value.

[0013] Secondly, the present invention provides an energy storage configuration optimization system that balances ground-state peak shaving and extreme power supply assurance, comprising: The model building module is configured to: build an energy storage device configuration model that takes into account both ground-state peak shaving and extreme power supply guarantee. The energy storage device configuration model includes a configuration investment model, a normal scenario operation model, and an extreme scenario operation model. The module for solving the conventional scenario operation model is configured to: provide an energy storage configuration scheme based on the configuration investment model, solve the conventional scenario operation model, repeat the iteration until the configuration investment model and the conventional scenario operation model converge, and output the first optimal cut set; The extreme scenario operation model solving module is configured to: provide energy storage configuration schemes based on the configuration investment model, solve the extreme scenario operation model, repeat the iteration until the configuration investment model and the extreme scenario operation model converge, and output the second optimal cut set; The energy storage configuration optimization module is configured to use the first and second optimal cut sets as constraints, and alternately pass them to the normal scenario operation model and the extreme scenario operation model for iterative solution until the model converges, thereby obtaining an energy storage device configuration scheme that takes into account both ground state peak regulation and extreme power supply.

[0014] Thirdly, the present invention provides an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0015] Fourthly, the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in the first aspect.

[0016] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention proposes an energy storage configuration optimization method that balances ground-state peak shaving and extreme power supply assurance. By coordinating the configuration of fixed and mobile energy storage devices, it achieves for the first time the synergistic optimization of power system peak shaving benefits in ground-state scenarios and power supply assurance capabilities in extreme scenarios. The large-scale deployment of fixed energy storage effectively supports daily peak shaving and valley filling, significantly reducing the load peak-valley difference; mobile energy storage, with its spatiotemporal flexibility, rapidly responds to the needs of power-loss areas during disasters, providing emergency power support. An innovative "multi-scenario coupled planning-operation" framework is constructed, enabling energy storage resources to release their economic potential during normal operation, while simultaneously activating resilient assurance functions under extreme events, breaking through the limitations of single-scenario applications in traditional planning.

[0018] By introducing the Nash negotiation game mechanism as a convergence criterion, this invention overcomes the critical challenge of reconciling economic benefits and resilience benefits due to their order-of-magnitude differences. This method avoids the subjectivity of manually setting weights and ensures the robustness of the configuration scheme even under extremely low-probability events by dynamically balancing the negotiation breakdown threshold of the two types of objectives. Compared to traditional independent scenario planning, this invention achieves a dual benefit fusion at the same investment level: it strengthens the high-proportion renewable energy absorption capacity of the new power system and significantly improves its resilience against severe faults such as typhoons and thunderstorms, providing core technical support for building an energy storage configuration system with both economic and resilience advantages.

[0019] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0020] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0021] Figure 1 A framework diagram for configuring sub-models for energy storage devices aimed at economic benefits in conventional scenarios; Figure 2 A framework diagram of a sub-model for configuring energy storage devices to enhance resilience in extreme scenarios; Figure 3 This is a flowchart of the energy storage configuration optimization method of the present invention, which takes into account both ground-state peak regulation and extreme power supply guarantee. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] It should be noted that the following detailed description is exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0024] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. Furthermore, it should be understood that 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 includes other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0026] Example 1 like Figure 1 As shown, this embodiment provides an energy storage configuration optimization method that balances ground-state peak shaving and extreme power supply assurance, including the following steps: A configuration model for energy storage devices is constructed that takes into account both ground-state peak shaving and extreme power supply. The configuration model includes a configuration investment model, a normal scenario operation model, and an extreme scenario operation model. Based on the configuration investment model, an energy storage configuration scheme is provided. The conventional scenario operation model is solved and iterated repeatedly until the configuration investment model and the conventional scenario operation model converge, and the first optimal cut set is output. Based on the configuration investment model, an energy storage configuration scheme is provided, and the extreme scenario operation model is solved. The process is repeated iteratively until the configuration investment model and the extreme scenario operation model converge, and the second optimal cut set is output. The first and second optimal cut sets are used as constraints, which are alternately passed to the normal scenario operation model and the extreme scenario operation model for iterative solution until the model converges, thus obtaining an energy storage device configuration scheme that takes into account both ground state peak regulation and extreme power supply guarantee.

[0027] The technical problem to be solved by this invention: (a) Configuration of stationary and mobile energy storage equipment.

[0028] This invention primarily addresses how to collaboratively optimize the differentiated configuration of stationary and mobile energy storage to overcome the inherent contradictions between the two in terms of functional attributes, spatial constraints, and cost structure. Traditional methods fail to comprehensively consider the characteristics of stationary energy storage (low unit cost but fixed location) and mobile energy storage (flexible in time and space but high investment cost). This results in the system being unable to fully realize the economic benefits of peak shaving and valley filling in conventional scenarios, and also struggling to quickly respond to emergency support needs of power outages in extreme scenarios. This invention aims to overcome the limitations of single equipment type or single application scenario by quantifying the synergistic effect of the two types of energy storage in peak shaving economy and power supply flexibility, and establishing a configuration mechanism that balances optimal investment cost with the integration of benefits across multiple scenarios.

[0029] (ii) A method for modeling routine scenarios with consideration of economic benefits and constructing extreme scenarios with elastic enhancement.

[0030] The key technical problem this invention aims to solve lies in how to uniformly quantify the differentiated benefits of energy storage devices under conventional peak-shaving and extreme power supply scenarios, and overcome the obstacles to collaborative optimization caused by the conflicting operational objectives of the two scenarios. Traditional methods cannot coordinate economic benefits and elastic benefits, making it difficult for configuration schemes to simultaneously address the sensitivity of daily operating costs and the need for rapid response under extreme events. A coupled "dual-scenario, dual-objective" operational framework needs to be established: it must design operational constraints that conform to the physical characteristics of energy storage, and it must also overcome the difficulty of integrating economic and elastic objectives due to their order-of-magnitude differences, ultimately maximizing the collaborative scheduling potential of energy storage resources under both scenarios.

[0031] (III) Solution method for convergence criterion of model based on Nash negotiation.

[0032] This invention addresses how to dynamically coordinate the magnitude difference between two types of benefits and ensure global convergence during the alternating iteration of the main investment problem and the operational subproblem. Traditional weighted summation methods, due to the magnitude gap between economic and extreme elasticity benefits, cannot reasonably set weights; while alternating cut-set transfers do not consider the mutual constraints between the two, easily leading to oscillations in the solution space or local optima. A Nash negotiation game framework needs to be constructed, using the negotiation breakdown threshold between economic and elasticity objectives as a dynamic equilibrium fulcrum. Through a nonlinear utility function, the magnitude difference between the two objectives is autonomously adapted, ultimately achieving a convergence criterion for Pareto optimal comprehensive benefit.

[0033] The specific solution of the present invention is as follows: (a) Configuration of stationary and mobile energy storage equipment.

[0034] Stationary and mobile energy storage can participate in peak shaving and valley filling in conventional power system scenarios and provide power support for power outages in extreme scenarios. However, due to the different characteristics and spatiotemporal features of various types of energy storage, their regulatory roles and functional positioning in the power system differ. Stationary energy storage has a fixed installation location and cannot be spatially moved, but it has a large single-unit power-capacity ratio. Its investment cost mainly consists of site cost + energy storage unit cost, resulting in a low cost per unit power / capacity. Mobile energy storage, on the other hand, is spatially and temporally mobile, capable of meeting the power supply needs of different regions. It has a relatively large power-capacity ratio, and its investment cost mainly consists of the energy storage unit + vehicle unit, resulting in the highest cost per unit power / capacity.

[0035] This invention uses minimizing investment cost as the objective function of a multi-type energy storage device configuration model.

[0036] (1) In the formula, For total cost, , , respectively, are the configuration costs for stationary energy storage and mobile energy storage.

[0037] The investment constraint set includes fixed energy storage configuration cost constraints and mobile energy storage configuration cost constraints.

[0038] (1) Fixed energy storage configuration constraints.

[0039] 1) Fixed energy storage investment costs.

[0040] (2) (3) (4) (5) (6) In the formula, To fix the cost of the energy storage unit, For maintenance costs, B represents the fixed cost of energy storage sites; B represents the set of grid nodes. and These are the unit power and capacity cost coefficients for stationary energy storage, respectively. To fix the annual operation and maintenance cost coefficient of energy storage, For nodes f Fixed energy storage site cost coefficient; and These are the fixed energy storage power and capacity configured for node f, respectively. The investment recovery factor for fixed energy storage is calculated by converting annual operation and maintenance costs into initial operation and maintenance costs. The discount rate; The lifespan of the energy storage; Representation Nodes f Whether to configure fixed energy storage: set to 1 if configured, otherwise set to 0.

[0041] 2) Constraints on fixed energy storage configuration.

[0042] (7) (8) In the formula, , , They are nodes f The maximum power, capacity, and number of fixed energy storage devices allowed to be installed.

[0043] (1) Constraints on the configuration of mobile energy storage.

[0044] 1) Investment cost of mobile energy storage.

[0045] (9) (10) (11) (12) In the formula, the total cost of mobile energy storage configuration is... Cost of mobile energy storage unit and maintenance costs It consists of components with no floor space cost. and These are the unit power and capacity cost coefficients for mobile energy storage, respectively. For the operating costs of mobile energy storage; and They are nodes f Pre-deployed mobile energy storage power and capacity; Investment recovery factor configured for mobile energy storage r For mobile energy storage discount rate, This refers to the service life of mobile energy storage.

[0046] 2) Constraints on the installation of mobile energy storage.

[0047] (13) (14) In the formula, , , They are nodes f Maximum permissible power, capacity, and quantity of pre-deployed mobile energy storage.

[0048] (ii) A method for modeling routine scenarios with consideration of economic benefits and constructing extreme scenarios with elastic enhancement.

[0049] (1) Modeling of conventional scenarios considering economic benefits.

[0050] In conventional scenarios, energy storage participates in daily safety and stability regulation, mainly by reducing the peak-valley difference of load under normal conditions through energy storage charging and discharging, thereby achieving the economic benefits of daily peak shaving and valley filling.

[0051] The optimization objective of the subproblem in the conventional scenario is to maximize the peak shaving and valley filling benefits of different types of energy storage participating in the system, and the objective function is to minimize the equivalent load variance of the power grid. The objective function is as follows: (15) In the formula, Typical daily hours for t Time Node f Typical daily load value at the location, for t Time Node f The sum of different types of energy storage power, when When the value is greater than 0, it indicates the charging behavior of different types of energy storage. <0 indicates energy storage and discharge behavior, where This represents the average daily load of the power grid. The revenue in a typical scenario is expressed as: , The power subsidy coefficient for conventional scenarios has been adjusted to apply subsidies to the three types of energy storage devices.

[0052] 1) Constraints on fixed energy storage operation in conventional scenarios.

[0053] (16) (17) (18) (19) In the formula, / for t Time Node f Fixed energy storage charging / discharging power, / for t Time Node f Currently in energy storage charging / discharging state; / For charging / discharging efficiency; / This represents the upper and lower limits of capacity as a percentage. fort Time Node f The amount of electricity stored in a fixed location.

[0054] (2) Consider the extreme scenario operation modeling of elastic enhancement.

[0055] Following extreme events such as typhoons and thunderstorms, power system components experience widespread line outages and tower collapses, resulting in severe load losses. Providing emergency power support to critical power-deficient loads through energy storage charging and discharging can effectively enhance the power system's ability to cope with extreme events. This invention studies an extreme scenario where distribution network lines are broken after a disaster, and energy storage supports power-deficient loads through charging, discharging, and spatiotemporal transfer. The charging and discharging behaviors of different types of energy storage under extreme events are as follows: stationary energy storage provides power support through discharging; mobile energy storage achieves power time-space scheduling through spatiotemporal transfer and charging / discharging to support power-deficient loads.

[0056] The model uses minimizing power loss as its objective function and quantifies elastic benefits based on the power support provided by energy storage devices. Furthermore, it formulates operational constraints for various types of energy storage under extreme scenarios, considering their functional utility. The objective function is as follows: (20) In the formula, The unit load loss power cost coefficient; Recovery period under extreme scenarios; For nodes f Load weight; for t Time Node f The load loss value. The elastic gain is... , The total cost of power leasing for participation in extreme events.

[0057] 1) Constraints on mobile energy storage operation in extreme scenarios.

[0058] In extreme scenarios, the power constraints for stationary and mobile energy storage are the same as in conventional scenarios. However, mobile energy storage achieves cross-regional power dispatch through spatiotemporal transfer. Therefore, this section characterizes the spatiotemporal transfer behavior of mobile energy storage by adding the following model constraints.

[0059] (twenty one) (twenty two) (twenty three) (twenty four) In the formula, This represents the set of nodes that mobile energy storage can reach. Represents a set of mobile energy storage configurations; Indicates mobile energy storage m At any moment t docked at the node u ; express m During the period t Currently in motion express m exist[ u , v Time taken to move between two nodes. For nodes u Allowable energy storage capacity Power is transferred to the mobile energy storage system at any time, and power is restored. Equation (21) represents the mobile energy storage system at any given time. m It can only dock at one node at most; Equation (22) represents the mobile energy storage at any given time. m The state can only be docked at a node or in motion; Equation (23) indicates that when the time interval exceeds the time consumed by the movement between two nodes, the mobile energy storage... m To achieve the transfer; Equation (24) is the constraint on the number of energy storage nodes that can be accommodated by the mobile energy storage access node.

[0060] (III) Solution method for convergence criterion of model based on Nash negotiation.

[0061] The energy storage equipment configuration model that balances the economic efficiency of conventional scenarios with the flexibility of extreme scenarios consists of a configuration investment model (configuration investment main problem model), a conventional scenario operation model (sub-model one), and an extreme scenario operation model (sub-model two).

[0062] In the configuration master model, the objective function is to minimize the total investment cost. The decision variables are mainly the configuration power and capacity of fixed energy storage, the configuration power and capacity of mobile energy storage, and the constraints are equations (2)-(8). The first optimal cut set is obtained by solving the conventional scenario operation sub-problem considering economic benefits, and the second optimal cut set is obtained by solving the extreme scenario operation sub-problem considering elastic benefits.

[0063] (1) Solving the configuration sub-model for economic benefits in conventional scenarios.

[0064] Based on the main configuration problem and the sub-problem of normal scenario operation, a sub-model for configuring energy storage devices oriented towards the economic benefits of normal scenarios is built. For example... Figure 1 As shown. In the solution process, the configuration master problem provides energy storage device configuration schemes to the sub-problems of normal scenario operation. The sub-problems simulate peak shaving and valley filling operation under normal scenario based on the energy storage configuration scheme, obtain the optimal energy storage operation scheduling scheme under this configuration, and return the operation status parameters to the configuration master problem to guide resource configuration. The iteration finally converges and outputs the optimal configuration and the optimal cut set Γ.

[0065] (2) Energy storage equipment configuration sub-model for elastic enhancement in extreme scenarios, with attack-defense-attack model as the basic framework, to ensure the robustness of relevant energy storage equipment in extreme scenarios.

[0066] Sub-model framework such as Figure 2 As shown, the first layer is the energy storage device configuration layer, which formulates a resource configuration scheme based on the worst-case scenario set. The second layer is the extreme event attack layer, which, based on the configuration scheme and recovery strategy, finds the worst component failure scenario to maximize system load loss. The third layer is the operation simulation layer, which, based on the failure scenarios given by the second layer, coordinates and schedules multiple types of resources to restore system load and minimize system load loss. In the solution process, the second and third layers find the worst-case failure scenario and return it to the first layer to guide the configuration. This process is repeated iteratively until the main problem and subproblems converge, outputting the optimal resource configuration scheme and the optimal cut set N. Through this model framework, decision-makers formulate configuration schemes to reduce losses when the system suffers the most severe attack.

[0067] (3) When solving a single sub-model, the energy storage configuration scheme cannot meet the requirements of both economic efficiency in normal scenarios and resilience in extreme scenarios. Based on the solution process and optimal solution of the energy storage configuration sub-model considering economic benefits, this paper forms multiple types of energy storage configuration constraints related to economic benefits as the first optimal cut constraint; based on the solution process and optimal solution of the energy storage configuration sub-model for resilience improvement in extreme scenarios, multiple types of energy storage configuration constraints related to resilience benefits are formed as the second optimal cut constraint. During the overall model solution, the constraints are alternately passed to another sub-model for iterative solution. The optimal cut sets corresponding to the two sub-models in a single iteration are as follows: (25) (26) In the formula, and Configure the energy storage power and the maximum allowable power in sub-model 1; and The energy storage configuration power and the maximum allowable configuration power are defined for the l-th iteration in sub-model 2. and As auxiliary variables. Equation (25) is the first optimal cut set; Equation (26) represents the set of the most severe fault scenarios returned in sub-model two. The corresponding energy storage configuration is the second optimal cut set.

[0068] (4) Solve the convergence criterion of the model based on Nash negotiation.

[0069] Considering the low probability and high loss characteristics of extreme scenarios, the elastic benefits of resource scheduling in extreme scenarios are extremely unstable, resulting in an order-of-magnitude difference between its economic benefits and those of resource scheduling in normal scenarios. The traditional entropy weight method simply transforms a multi-objective problem into a single-objective problem through a simple weighting method, but it is difficult to accurately set the weights. This invention considers that Nash negotiation can take into account the order-of-magnitude differences between different objectives, and introduces a game model as shown in Equation (27), taking the objective to be optimized as a game participant, balancing the needs of multiple objectives, and obtaining the optimal comprehensive benefit in the game.

[0070] (27) In the formula, F represents the comprehensive benefit. and To balance the economic benefits and resilience benefits of energy storage equipment configuration models that take into account both the economic efficiency in conventional scenarios and the flexibility in extreme scenarios; The threshold for a breakdown in negotiations regarding economic benefits. This is the threshold for a breakdown in negotiations regarding elastic gains.

[0071] Example 2 This embodiment provides an energy storage configuration optimization system that balances ground-state peak shaving and extreme power supply assurance, including: The model building module is configured to: build an energy storage device configuration model that takes into account both ground-state peak shaving and extreme power supply guarantee. The energy storage device configuration model includes a configuration investment model, a normal scenario operation model, and an extreme scenario operation model. The module for solving the conventional scenario operation model is configured to: provide an energy storage configuration scheme based on the configuration investment model, solve the conventional scenario operation model, repeat the iteration until the configuration investment model and the conventional scenario operation model converge, and output the first optimal cut set; The extreme scenario operation model solving module is configured to: provide energy storage configuration schemes based on the configuration investment model, solve the extreme scenario operation model, repeat the iteration until the configuration investment model and the extreme scenario operation model converge, and output the second optimal cut set; The energy storage configuration optimization module is configured to use the first and second optimal cut sets as constraints, and alternately pass them to the normal scenario operation model and the extreme scenario operation model for iterative solution until the model converges, thereby obtaining an energy storage device configuration scheme that takes into account both ground state peak regulation and extreme power supply.

[0072] It should be noted that the above modules correspond to the steps in Embodiment 1, and the examples and application scenarios implemented by the above modules and their corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should also be noted that the above modules can be executed in a computer system as part of the system.

[0073] In further embodiments, the following is also provided: An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0074] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be 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.

[0075] A computer-readable storage medium for storing computer instructions that, when executed by a processor, perform the method of Embodiment 1.

[0076] The method in Example 1 can be directly executed by a hardware processor, or it can be executed by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0077] A computer program product includes a computer program that, when executed by a processor, implements the method in Embodiment 1.

[0078] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0079] The computer program code used to implement the methods of the present invention may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the computer or other programmable data processing device, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0080] In the context of this invention, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0081] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0082] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. An energy storage configuration optimization method that balances ground-state peak shaving and extreme power supply assurance, characterized in that, Includes the following steps: A configuration model for energy storage devices is constructed that takes into account both ground-state peak shaving and extreme power supply. The configuration model includes a configuration investment model, a normal scenario operation model, and an extreme scenario operation model. Based on the configuration investment model, an energy storage configuration scheme is provided. The conventional scenario operation model is solved and iterated repeatedly until the configuration investment model and the conventional scenario operation model converge, and the first optimal cut set is output. Based on the configuration investment model, an energy storage configuration scheme is provided, and the extreme scenario operation model is solved. The process is repeated iteratively until the configuration investment model and the extreme scenario operation model converge, and the second optimal cut set is output. The first and second optimal cut sets are used as constraints, which are alternately passed to the normal scenario operation model and the extreme scenario operation model for iterative solution until the model converges, thus obtaining an energy storage device configuration scheme that takes into account both ground state peak regulation and extreme power supply guarantee.

2. The energy storage configuration optimization method that balances ground-state peak shaving and extreme power supply as described in claim 1, characterized in that, The convergence criterion of the model based on Nash negotiation is adopted, taking into account the order-of-magnitude differences between different objectives. Multiple objectives to be optimized are treated as game participants, balancing the needs of multiple objectives and obtaining the optimal comprehensive benefits in the game.

3. The energy storage configuration optimization method that balances ground-state peak shaving and extreme power supply as described in claim 1, characterized in that, The solution process for the standard scenario running model is as follows: The normal scenario operation model provides an energy storage configuration scheme based on the configuration investment model, performs peak shaving and valley filling operation simulation under normal scenarios, obtains the optimal energy storage operation scheduling scheme under this configuration, and returns the operation status parameters to the configuration investment model to guide the energy storage configuration. Iterative solution is performed until the configuration investment model and the normal scenario operation model converge, and the optimal energy storage configuration and the first optimal cut set are output. The solution process for the extreme scenario running model is as follows: The extreme scenario operation model provides energy storage configuration schemes based on the configuration investment model, finds the worst component failure scenario, and coordinates the scheduling of multiple types of resources to restore system load based on the worst component failure scenario, so as to minimize system load loss. This process is repeated iteratively until the configuration investment model and the extreme scenario operation model converge, and outputs the optimal energy storage configuration scheme and the second optimal cut set.

4. The energy storage configuration optimization method that balances ground-state peak shaving and extreme power supply as described in claim 1, characterized in that, The configuration investment model takes minimizing investment cost as the objective function and fixed energy storage configuration cost constraints and mobile energy storage configuration cost constraints as constraints.

5. The energy storage configuration optimization method that balances ground-state peak shaving and extreme power supply as described in claim 1, characterized in that, The standard operating model uses minimizing the equivalent load variance of the power grid as its objective function. ； In the formula, Typical daily hours for t Time Node f Typical daily load value at the location, for t Time Node f The sum of the power of different types of energy storage, This represents the average daily load of the power grid.

6. The energy storage configuration optimization method that balances ground-state peak shaving and extreme power supply as described in claim 1, characterized in that, The extreme scenario operation model uses minimizing power loss as its objective function. ； In the formula, The power cost factor per unit load loss. Recovery period under extreme scenarios For nodes f Load weight, for t Time Node f The load loss value.

7. An energy storage configuration optimization system that balances ground-state peak shaving and extreme power supply assurance, characterized in that, include: The model building module is configured to: build an energy storage device configuration model that takes into account both ground-state peak shaving and extreme power supply guarantee. The energy storage device configuration model includes a configuration investment model, a normal scenario operation model, and an extreme scenario operation model. The module for solving the conventional scenario operation model is configured to: provide an energy storage configuration scheme based on the configuration investment model, solve the conventional scenario operation model, repeat the iteration until the configuration investment model and the conventional scenario operation model converge, and output the first optimal cut set; The extreme scenario operation model solving module is configured to: provide energy storage configuration schemes based on the configuration investment model, solve the extreme scenario operation model, repeat the iteration until the configuration investment model and the extreme scenario operation model converge, and output the second optimal cut set; The energy storage configuration optimization module is configured to use the first and second optimal cut sets as constraints, and alternately pass them to the normal scenario operation model and the extreme scenario operation model for iterative solution until the model converges, thereby obtaining an energy storage device configuration scheme that takes into account both ground state peak regulation and extreme power supply.

8. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the method described in any one of claims 1-6.

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