Electro-hydrogen-water hybrid energy storage site selection and capacity planning method and system

Abstract

The embodiment of the application discloses a kind of electricity-hydrogen-water hybrid energy storage site selection constant volume planning method and system, the method includes: S1, constructs electricity-hydrogen-water hybrid energy storage upper layer planning model: with the minimum comprehensive cost of hybrid energy storage full life cycle as optimization target and output candidate planning scheme;S2, constructs electricity-hydrogen-water hybrid energy storage lower layer operation model, i.e. under the time and space coupling flexibility resource scheduling framework, establish multi-objective optimization model;S3, establish nested algorithm solution framework to solve the aforementioned two models, electricity-hydrogen-water hybrid energy storage upper layer planning model is completed global optimization by improving fish eagle optimization algorithm, electricity-hydrogen-water hybrid energy storage lower layer operation model is realized multi-objective optimization by improving the second generation non-dominated sorting genetic algorithm, and the optimal site selection constant volume scheme is output after convergence by iteration interaction between upper and lower layer algorithms.The planning scheme and system actual operation demand are matched by the upper and lower layer iteration feedback mechanism.

Description

Technical Field

[0001] This invention relates to the field of new power system planning and operation optimization technology, and in particular to a method and system for site selection and capacity planning of electric-hydrogen-water hybrid energy storage that integrates investment decision-making at the planning level and scheduling optimization at the operation level for high-proportion renewable energy grid connection scenarios. Background Technology

[0002] With the large-scale grid connection of new energy sources, the power system exhibits characteristics such as strong power fluctuations, spatiotemporal mismatches between supply and demand, and insufficient flexibility resources, placing higher demands on the rational configuration of energy storage systems. Electricity-hydrogen-water hybrid energy storage, due to its complementary power / energy characteristics, large regulation span, and significant differences in response speed, has become an important technical means to improve the flexibility of new power systems, mitigate the fluctuations of new energy sources, and ensure the safe and stable operation of the power grid.

[0003] The site selection and capacity determination of hybrid energy storage systems is a complex coupled problem integrating investment decisions at the planning level and scheduling optimization at the operation level. The node site selection and capacity configuration at the planning level directly determine the flexibility adjustment effect and economic operating cost at the operation level, while the actual scheduling results at the operation level provide a scientific and feasible basis for investment decisions at the planning level. These two aspects are mutually restrictive and coupled, and cannot be solved independently. If optimization is carried out from only a single dimension, it is easy to encounter problems where the planned configuration is disconnected from actual operational needs. For example, excessive planned capacity may lead to wasted investment, or unreasonable site selection may result in an inability to effectively compensate for local system flexibility deficiencies. Therefore, it is urgent to propose a two-layer planning method for the site selection and capacity determination of electric-hydrogen-water hybrid energy storage that integrates collaborative optimization at the planning and operation levels, considers both economic efficiency and spatiotemporal flexibility, adapts to the characteristics of high-proportion renewable energy grid connection, and can achieve a globally optimal solution. This method aims to solve the problems of disconnect between planning and operation, single optimization dimension, insufficient flexibility characterization, and low solution accuracy in existing technologies, and to provide theoretical support and technical means for the scientific configuration of hybrid energy storage in new power systems. Summary of the Invention

[0004] Based on this, in order to address the shortcomings of existing technologies, a site selection and capacity planning method and system for hybrid energy storage based on electricity, hydrogen, and water is proposed.

[0005] To achieve the above design objectives, the technical solution of the present invention is as follows: A method for site selection and capacity planning of an electric-hydrogen-water hybrid energy storage system includes the following steps: S1. Construct an upper-level planning model for hybrid energy storage (electricity-hydrogen-water): The optimization objective is to minimize the comprehensive cost of the hybrid energy storage over its entire life cycle. The decision variables are the installation nodes of the hybrid energy storage, the rated power and capacity configuration of battery energy storage, pumped hydro storage and hydrogen energy storage. The model is constrained by at least the installed capacity constraint and the node layout number constraint. The model outputs candidate planning schemes, where the comprehensive cost includes the comprehensive investment cost and the comprehensive operating cost. S2. Constructing a lower-level operation model for the electric-hydrogen-water hybrid energy storage system: A multi-objective optimization model is established within a spatiotemporally coupled flexible resource scheduling framework. This model uses the candidate planning scheme as operational constraints, conventional generating units, electric-hydrogen-water hybrid energy storage, and controllable loads as scheduling resources. Simultaneously, it considers system operational economy, spatial scale flexibility, and temporal scale flexibility as multiple objectives, and at least a predefined multi-dimensional constraint system as constraints. The model simulates the operational results under the candidate planning scheme and provides feedback on key indicators. The multi-dimensional constraint system includes conventional generating unit operation constraints, hybrid energy storage operation constraints, controllable load operation constraints, power balance constraints, and line power flow safety constraints. S3. Establish a nested algorithm solution framework to solve the two-layer planning model consisting of the upper-layer planning model of the electric-hydrogen-water hybrid energy storage and the lower-layer operation model of the electric-hydrogen-water hybrid energy storage. The upper-layer planning model of the electric-hydrogen-water hybrid energy storage completes global optimization through an improved Osprey optimization algorithm, and the lower-layer operation model of the electric-hydrogen-water hybrid energy storage achieves multi-objective optimization through an improved second-generation non-dominated sorting genetic algorithm. After convergence through iterative interaction between the upper and lower layer algorithms, the optimal addressing and capacity sizing scheme is output.

[0006] Based on the same inventive concept, the present invention also provides a planning system established according to the described method for site selection and capacity determination of hybrid electric-hydrogen-water energy storage, comprising: The upper-level planning unit for hybrid energy storage (electric-hydrogen-water) is used to minimize the comprehensive cost of the hybrid energy storage over its entire life cycle. The unit takes the rated power and capacity configuration of the hybrid energy storage installation nodes, battery energy storage, pumped hydro storage, and hydrogen energy storage as decision variables, and at least the installed capacity constraint and the node layout number constraint as constraints, and outputs candidate planning schemes. The comprehensive cost includes comprehensive investment cost and comprehensive operating cost. The lower-level operation unit of the electric-hydrogen-water hybrid energy storage is used to establish a multi-objective optimization model under a spatiotemporally coupled flexible resource scheduling framework. This multi-objective optimization model takes the candidate planning scheme as the operation constraint, conventional units, electric-hydrogen-water hybrid energy storage, and controllable loads as scheduling resources, and takes system operation economy, spatial scale flexibility, and temporal scale flexibility as multiple objectives. It also uses at least a predefined multi-dimensional constraint system as constraints to simulate the operation results under the candidate planning scheme and feed back key indicators. The multi-dimensional constraint system includes conventional unit operation constraints, hybrid energy storage operation constraints, controllable load operation constraints, power balance constraints, and line power flow safety constraints. A nested solution unit is used to solve a two-layer planning model consisting of the upper-layer planning unit of the electric-hydrogen-water hybrid energy storage and the lower-layer operation unit of the electric-hydrogen-water hybrid energy storage. The nested solution unit includes: The upper-level optimization subunit adopts the improved Osprey optimization algorithm, which introduces the optimal point set population initialization and Levy flight perturbation strategy. The lower-level optimization subunit adopts an improved second-generation non-dominated sorting genetic algorithm. The improved second-generation non-dominated sorting genetic algorithm adopts adaptive crossover probability and dynamic mutation probability, outputs the Pareto optimal solution set, and determines the comprehensive optimal solution through the entropy weight method.

[0007] Implementing the embodiments of the present invention will have the following beneficial effects: This invention achieves the matching of planning schemes with actual system operation requirements through an iterative feedback mechanism between upper and lower layers (a two-layer planning model consisting of an upper-layer planning model of electric-hydrogen-water hybrid energy storage and a lower-layer operation model of electric-hydrogen-water hybrid energy storage, along with a supporting nested algorithm solution framework). This effectively solves the problem of operational disconnect in traditional planning and avoids defects such as energy storage capacity redundancy, unreasonable site selection, investment waste, or insufficient flexibility. Consequently, the spatiotemporal flexibility of the configuration scheme for site selection and capacity planning of electric-hydrogen-water hybrid energy storage is significantly improved. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] in: Figure 1 This is a flowchart of the basic steps corresponding to the solution described in this invention; Figure 2 Principle architecture diagram of a two-layer planning model for site selection and capacity determination of hybrid energy storage systems; Figure 3 A resource scheduling framework diagram for system flexibility considering spatiotemporal coupling; Figure 4 A detailed diagram illustrating the solution process for the bilevel programming model. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0011] Unless otherwise defined, 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. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. It is understood that the terms “first,” “second,” etc., as used herein may be used to describe various elements, but these elements are not limited by these terms. These terms are used only to distinguish one element from another. For example, a first element may be referred to as a second element without departing from the scope of this application, and similarly, a second element may be referred to as a first element. Both the first element and the second element are elements, but they are not the same element.

[0012] To overcome the shortcomings of existing hybrid energy storage planning technologies, such as decoupling of the planning and operation layers, single optimization objective, insufficient flexibility characterization, lack of spatiotemporal coupling mechanism, poor synergy among multiple energy storage types, low solution accuracy, and susceptibility to local optima, this invention provides a two-layer planning method and system for the site selection and capacity determination of electric-hydrogen-water hybrid energy storage. This method is applicable to the optimization of hybrid energy storage configuration in regional power grids with high proportions of wind and solar power integration and diverse complementary flexible resources. Specifically, this invention targets scenarios with high proportions of new energy grid connection, constructs a two-layer optimization model that integrates investment planning and operation scheduling, and achieves coordinated optimization of the economic efficiency of hybrid energy storage configuration, system spatiotemporal flexibility, and grid security constraints. This solves the engineering pain points of traditional single-layer optimization methods, such as unreasonable configuration, investment waste, insufficient flexibility, and high wind and solar curtailment rates.

[0013] Based on the aforementioned design requirements, the overall architectural approach of this application is as follows: This invention constructs a two-layer planning model for the site selection and capacity determination of an electric-hydrogen-water hybrid energy storage system. The upper layer is the hybrid energy storage investment planning layer, which aims to minimize the comprehensive cost over the entire life cycle and determines the installation nodes, rated power, and rated capacity of the hybrid energy storage. The lower layer is the system operation optimization layer, which aims to achieve operational economy and spatiotemporal flexibility, taking into account conventional units, hybrid energy storage, coordinated scheduling of controllable loads, and grid security constraints. It simulates the operation results under the upper-layer planning scheme and provides feedback on key indicators. Simultaneously, a corresponding nested algorithm solution framework is established to solve the two-layer planning model. The upper layer introduces an improved Osprey optimization algorithm with optimal point set and Levy flight disturbance for global optimization, while the lower layer uses an improved second-generation non-dominated sorting genetic algorithm with adaptive crossover and dynamic mutation to achieve multi-objective optimization. The optimal planning scheme for the site selection and capacity determination of the electric-hydrogen-water hybrid energy storage system is output through iterative convergence of the upper and lower layers.

[0014] Based on the above design framework, this embodiment proposes a site selection and capacity planning method for an electric-hydrogen-water hybrid energy storage system, such as... Figure 1 As shown, the method includes the following steps: S1. Construct an upper-level planning model for hybrid energy storage (electricity-hydrogen-water): The optimization objective is to minimize the comprehensive cost of the hybrid energy storage over its entire life cycle. The decision variables are the installation nodes of the hybrid energy storage, the rated power and capacity configuration of battery energy storage, pumped hydro storage and hydrogen energy storage. The model is constrained by at least the installed capacity constraint and the node layout number constraint. The model outputs candidate planning schemes, where the comprehensive cost includes the comprehensive investment cost and the comprehensive operating cost. S2. Constructing a lower-level operation model for the electric-hydrogen-water hybrid energy storage system: A multi-objective optimization model is established within a spatiotemporally coupled flexible resource scheduling framework. This model uses the candidate planning scheme as operational constraints, conventional generating units, electric-hydrogen-water hybrid energy storage, and controllable loads as scheduling resources. Simultaneously, it considers system operational economy, spatial scale flexibility, and temporal scale flexibility as multiple objectives, and at least a predefined multi-dimensional constraint system as constraints. The model simulates the operational results under the candidate planning scheme and provides feedback on key indicators. The multi-dimensional constraint system includes conventional generating unit operation constraints, hybrid energy storage operation constraints, controllable load operation constraints, power balance constraints, and line power flow safety constraints. S3. Establish a nested algorithm solution framework to solve the two-layer planning model consisting of the upper-layer planning model of the electric-hydrogen-water hybrid energy storage and the lower-layer operation model of the electric-hydrogen-water hybrid energy storage. The upper-layer planning model of the electric-hydrogen-water hybrid energy storage completes global optimization through an improved Osprey optimization algorithm, and the lower-layer operation model of the electric-hydrogen-water hybrid energy storage achieves multi-objective optimization through an improved second-generation non-dominated sorting genetic algorithm. After convergence through iterative interaction between the upper and lower layer algorithms, the optimal addressing and capacity sizing scheme is output.

[0015] Based on the above scheme, it can be seen that the present invention constructs a two-layer optimization model for hybrid energy storage site selection and capacity determination that takes into account the coupling characteristics of planning and operation, such as... Figure 2 As shown, the site selection and capacity determination of hybrid energy storage are taken as the core decision-making content of the upper-level planning model, realizing the scientific planning of the spatial layout and capacity scale of hybrid energy storage. At the same time, the multi-dimensional operation optimization of the system is taken as the core content of the lower-level operation model, simulating the actual operation status of the system under different planning schemes, and providing quantitative operation feedback results for the upper-level model. Through the interactive iteration and information feedback between the upper and lower-level models, the synergistic coupling of hybrid energy storage planning and configuration and system operation optimization is realized, ultimately obtaining the optimal site selection and capacity determination scheme for hybrid energy storage that balances investment economy and system flexibility improvement.

[0016] In some specific embodiments, the specific steps of S1 include: The objective function of the upper-level planning model for the electric-hydrogen-water hybrid energy storage is to minimize the comprehensive investment and overall operating cost of the hybrid energy storage throughout its entire life cycle, in order to achieve optimal overall system economics. Its specific expression is as follows: (49) In the formula: The comprehensive investment cost over the entire lifecycle of a hybrid energy storage system includes the initial investment cost. Equipment replacement costs Total lifecycle operation and maintenance costs Residual value income from equipment recycling after decommissioning ; This refers to the overall operating cost of the system.

[0017] The initial investment cost This refers to the one-time investment cost during the initial construction of various energy storage devices, covering the investment in power-type and capacity-type equipment for battery energy storage, pumped hydro storage, and hydrogen energy storage. Specifically: (50) In the formula: , These represent the investment cost per unit power and per unit capacity of battery energy storage, respectively. , These are the rated power and rated capacity of the battery energy storage, respectively. , 、 These are the investment costs per unit pumping power, per unit generating power, and per unit capacity of pumped storage, respectively. and These are the rated power for pumped storage hydroelectric pumping and power generation, respectively. This is the maximum capacity of the upper reservoir; , These are the unit power investment costs for hydrogen energy storage electrolyzers and fuel cells, respectively. , These are the rated power of the water electrolysis hydrogen production system and the fuel cell power generation system, respectively. , These are the unit capacity investment cost of hydrogen storage equipment and the maximum capacity of hydrogen storage equipment, respectively. Equipment replacement cost Its design takes into account the differences in equipment lifespan among different energy storage technologies. Pumped hydro storage and hydrogen storage do not require replacement of their media and main equipment throughout their entire life cycle. Only lithium battery storage requires equipment replacement due to its limited cycle life. The corresponding replacement cost is calculated as follows: (51) In the formula: N This refers to the number of times a lithium battery can be replaced throughout its entire lifespan. j ∈ ; r The preferred discount rate is 5%. Y The number of years used throughout the entire life cycle of the battery energy storage system; Total lifecycle operation and maintenance costs This refers to the ongoing costs of daily operation, maintenance, and management of various energy storage devices throughout their entire lifecycle. These costs can be categorized into power-type and capacity-type devices and calculated separately. Specifically: (52) In the formula: T The year of operation; , These are the unit power and unit capacity operation and maintenance costs of battery energy storage, respectively. , These are the unit power and unit capacity operation and maintenance costs of pumped storage, respectively. , These are the unit power and unit capacity operation and maintenance costs of hydrogen energy storage, respectively. Residual value income from equipment recycling after decommissioning This refers to the revenue generated from the decommissioning and recycling of various energy storage devices at the end of the system's entire life cycle, calculated based on a fixed residual value rate of the initial investment cost of the equipment. Specifically: (53) In the formula: , , The equipment recycling residual value rates are for battery energy storage, pumped hydro storage, and hydrogen storage, respectively. To ensure that the solution of the upper-level planning model conforms to the actual engineering and power grid operation requirements, multiple hard constraints need to be set, such as installed capacity, node layout, and equipment quantity. These constraints include installed capacity constraints and node layout constraints. The installed capacity constraints include the rated power constraint and the rated capacity constraint of the hybrid energy storage system. The node layout constraints include the installation node constraint and the installation quantity constraint of the hybrid energy storage system. The specific constraints are as follows: The installation node constraints of a hybrid energy storage system are expressed as follows: (54) In the formula: For nodes j The binary decision variables for energy storage deployment; The rated power constraint of a hybrid energy storage system is expressed as follows: (55) In the formula: For nodes j Rated power of the configured hybrid energy storage system; , They are nodes j The lower and upper limits of the rated power of energy storage are determined by the node grid structure, load level, and power output characteristics; The rated capacity constraint for hybrid energy storage is expressed as: (56) In the formula: For nodes j Rated capacity of the configured hybrid energy storage system; , They are nodes j The lower and upper limits of the rated capacity of energy storage should be matched with the level of node flexibility deficit and the demand for energy storage regulation. The constraint on the number of hybrid energy storage installations is expressed as follows: (57) In the formula: J This represents the total number of energy storage nodes that can be deployed in the system. The maximum number of hybrid energy storage systems that can be deployed in the system is determined by the power grid planning and construction scale and investment budget.

[0018] Based on the above, it can be seen that this application, through the synergistic effect of the installed capacity constraint and the node layout quantity constraint, forces the battery energy storage, pumped hydro storage, and hydrogen energy storage to be distributed in different grid nodes, so as to avoid the risk of grid-connected capacity exceeding the limit, voltage exceeding the limit, and single point of failure caused by excessive concentration of energy storage in a single node.

[0019] In summary, the hybrid energy storage upper-level planning model focuses on the long-term investment planning problem of hybrid energy storage. It aims to minimize the comprehensive investment cost and comprehensive operating cost of hybrid energy storage throughout its entire life cycle. It takes the selection of installation nodes for hybrid energy storage and the rated power and rated capacity configuration of battery energy storage / pumped hydro storage / hydrogen energy storage as decision variables. At the same time, it takes into account the constraints such as the installed capacity and the number of nodes in actual engineering projects. Through the feedback of the optimization results of the lower-level operation model, it realizes the adaptability verification between the planning scheme and the actual operation requirements of the system, and finally outputs the economically optimal hybrid energy storage planning and configuration scheme.

[0020] In some specific embodiments, based on the design of the aforementioned upper-level planning model, it is known that the lower-level operation model, namely the lower-level planning model for the electric-hydrogen-water hybrid energy storage, needs to serve as a feedback and verification layer for the upper-level planning model. Using the hybrid energy storage site selection and capacity determination scheme output by the upper-level model as known constraints, a multi-objective optimization model is established focusing on the multi-objective optimization problem in the actual operation phase of the new power system. This model takes conventional units, hybrid energy storage, and controllable loads as core scheduling resources, comprehensively considering three major optimization objectives: system operation economy, spatial scale flexibility, and temporal scale flexibility. It also incorporates practical engineering constraints such as conventional unit operation, energy storage device charging and discharging, controllable load regulation, and grid power flow security. Under the time-space coupling flexibility resource scheduling framework, by simulating the full-scenario operation state of the system under different planning schemes, it quantifies and outputs key indicators such as system operating costs and flexibility margins, providing operational-level feedback for the investment decisions of the upper-level model. Specifically, the construction process of the lower-level operation model for the electric-hydrogen-water hybrid energy storage includes: A multi-objective optimization model is established, which takes into account three dimensions: operational economy, spatial scale flexibility, and temporal scale flexibility. The objectives are coupled and optimized synergistically to ultimately achieve the optimal balance between economy and flexibility under the premise of safe and stable system operation. The specific objective function is constructed as follows: (58) In the formula: f 1 represents the operational economic objective, namely the overall operating cost of the system. f 2 represents the expectation of insufficient time-scale flexibility, i.e., the system's flexibility and adaptability. f 3 represents the spatial scale flexibility target, namely the overall system flexibility margin of the key cross-section.

[0021] The operational economic objective aims to minimize the overall system operating cost. It comprehensively considers the costs of conventional unit power generation and start-up / shutdown, hybrid energy storage dispatch costs, controllable load dispatch costs, and the penalty costs associated with wind and solar curtailment and load shedding. The specific expression is as follows: (59) in: Costs for power generation and start-up / shutdown of conventional generating units; To controllable load scheduling costs; The penalty cost for curtailing wind and solar power loads; among which: Conventional unit power generation and start-up / shutdown costs It can be represented as: (60) In the formula: This is the number of conventional generating units; For the region mconventional units g exist t The operating status at any given time: 1 if the unit is running, 0 otherwise; , , This is the fuel cost coefficient for the generator unit; For the region m conventional units g exist t Power at any given moment; , These are the unit start-up and shutdown cost coefficients, respectively. , They are respectively regions m conventional units g The state variable during startup and shutdown is 1 when the unit starts up and 0 otherwise. The energy storage dispatch cost can be expressed as: (61) In the formula: For the region m One-time purchase cost of hybrid energy storage system; For the region m Hybrid energy storage systems in t Actual power at any given moment; For the region m The lifespan of hybrid energy storage systems; For the region m Rated capacity of hybrid energy storage system; Controllable load dispatching cost It can be represented as: (62) Where: Where: To utilize the cost factor for controllable load, For the region m exist t Controllable load allocation at all times; Meanwhile, given that insufficient upward flexibility leads to the risk of load shedding and insufficient downward flexibility leads to the risk of wind and solar curtailment, this scheme needs to introduce wind and solar curtailment penalty coefficients and load shedding penalty coefficients, incorporating them into the aforementioned objective function in the form of wind and solar curtailment penalty costs and load shedding penalty costs. Therefore, the wind and solar curtailment load shedding penalty cost... It can be represented as: (63) In the formula: , These are the unit wind and solar curtailment penalty coefficient and the load shedding penalty coefficient, respectively. , , These are respectively the system's wind curtailment, solar curtailment, and load shedding. The time-scale flexibility objective aims to minimize the expected inadequacy of the system's flexibility adjustment capability, thereby quantifying the risk of system flexibility imbalance at different time scales: (64) In the formula: The system's flexibility and adaptability are insufficient. and for t The system's upward and downward flexibility adjustment capabilities are insufficient to meet expectations. T For the evaluation period, N This represents the total number of loading units; The spatial scale flexibility objective aims to maximize the overall system flexibility margin considering key cross-sections, and to quantify the bottlenecks in cross-regional flexibility coordination. Specifically, it can be expressed as: (65) In the formula: To take into account the overall system flexibility margin at key sections, , Cross-sections k Overall upward / downward flexibility margin of the connected sending and receiving ends; K The total number of critical sections; To ensure that the optimization results of the lower-level model conform to the actual operating rules and physical characteristics of the new power system, a multi-dimensional constraint system is constructed. This constraint system covers constraints such as conventional unit operation constraints, hybrid energy storage operation constraints, controllable load operation constraints, power balance constraints, and line power flow safety constraints.

[0022] The operating constraints for conventional generating units are as follows: (66) (67) (68) (69) In the formula: , The units g Minimum and maximum technical output; , The units g The maximum upward and downward climbing rates; For the region m unit g exist t Start and stop state variables at specific times; For the region m unit g State variables that have been running or stopped for a period of time, where δ This indicates the time the service has been running or offline. , For the region m unit g Minimum start-up and shutdown times; Hybrid energy storage operation constraints include power operation constraints and energy operation constraints for battery energy storage, pumped hydro storage, and hydrogen energy storage: The operating constraints for battery energy storage power are as follows: (70) (71) (72) (73) In the formula: and They are respectively m Battery energy storage in the region t Constant charging and discharging power, for m Rated power of battery energy storage within the region; and They are respectively m Maximum ramp rate for battery charging and discharging within the region; The energy operation constraints of battery storage are as follows: (74) (75) (76) (77) In the formula: , for m Battery energy storage in the region t The decision variables for the charging and discharging states at time 0-1 ensure that the charging and discharging states are mutually exclusive. for m Battery energy storage in the region t At any given moment, the state of charge and They are m Upper and lower limits of battery energy storage state of charge within the region. and They are m The initial state of charge (SOC) of the battery energy storage within the region and the SOC at the end of the operating cycle; The power operation constraints of pumped storage are as follows: (78) (79) (80) (81) In the formula: 、 respectively m Pumped storage in the region t Constant water pumping and power generation capacity; and They are respectively m Rated power capacity for pumped storage hydroelectric power generation within the region; and They are respectively m Maximum ramp rate for pumped storage hydroelectric power generation within the region; The energy operation constraints of pumped storage are as follows: (82) (83) (84) In the formula: , for t time m 0-1 start-stop state decision variables for pumped storage pumping devices and power generation devices within the region; , They are respectively m The upper and lower limits of the water storage capacity of the upper reservoirs in the region. and They are m The initial water storage capacity and the water storage capacity at the end of the operating cycle of the pumped storage upper reservoir within the region; The power operation constraints for hydrogen energy storage are as follows: (85) (86) (87) (88) In the formula: , respectively m Regional hydrogen energy storage t The power output of hydrogen production and power generation devices at all times; and They are respectively m Rated power of hydrogen storage and hydrogen production devices and power generation devices within the region; and They are respectively m Maximum ramp rate of hydrogen storage and hydrogen production devices and power generation devices within the region; The energy operation constraints for hydrogen energy storage are as follows: (89) (90) (91) In the formula: , for t Decision variables for the 0-1 start-stop state of hydrogen storage and hydrogen production devices and power generation devices at any time; , They are respectively m The upper and lower limits of the capacity of hydrogen storage devices in the region. and They are m The initial hydrogen storage capacity of the hydrogen energy storage device within the region and the hydrogen storage capacity at the end of the operating cycle; The controllable load operation constraints are as follows: (92) In the formula: for m within the area t The load size can be controlled at any time. and They are respectively regions m Controllable load t Always operate with upper and lower bounds; To ensure the safety of system operation, the following power balance constraints must be met: (93) In the formula: , and They are respectively regions m exist t Net load, load shedding, and abandoned renewable energy at any given time. , , , They are respectively regions m The number of conventional generating units, hybrid energy storage devices, access lines, and controllable load users included.

[0023] Among them, line power flow safety constraints include branch power flow constraints and line safety constraints. Branch flow constraints are as follows: (94) In the formula: This represents the number of system nodes. , Each region and time point represents a node. iInjected active and reactive power; , For nodes i and j voltage amplitude, , For nodes i , j The conductivity and susceptance between them For nodes i , j The phase angle difference between them; The line safety constraints are as follows: (95) In the formula: For the region m line b The power transmission limit; , , , , , , They are respectively regions m China Wind Power w Photovoltaics s Conventional units g ,load r , connecting lines l Energy storage j Controllable load d The current transfer factor; , , They are respectively regions m The number of wind turbines, photovoltaic units, and load users included; , , , , , and They are respectively regions m exist t The amount of wind power output, photovoltaic power load, line transmission power, hybrid energy storage power, and controllable load power at any given time.

[0024] The spatiotemporally coupled flexibility resource scheduling framework includes: dividing the power grid into multiple independent regional scheduling units; introducing regional flexibility shadow prices to quantify the degree of flexibility scarcity as decision variables; and combining section criticality and transmission priority coefficients to determine cross-regional support paths. Specifically, based on the power grid partitioning results, the system is divided into Z={1,2,…,z,…,Z} mutually independent regional scheduling units, with the flexibility supply and demand balance of each regional scheduling unit as the scheduling basis, and cross-regional flexibility mutual assistance using the available transmission capacity of critical sections as a hard constraint; simultaneously, assuming that a certain regional scheduling unit z at time... t The flexibility margin for upward and downward adjustments is and The flexibility to adjust the upward and downward supply of resources are respectively , Then, it represents the time when the regional scheduling unit z is at time ; t The degree of flexibility in adjusting upwards and downwards is scarce. Regional flexibility shadow price , The corresponding calculation formula is: (96) (97) In the formula: This is a flexibility sensitivity coefficient used to adjust the quantitative scale of shadow prices; its value is determined by the flexibility resource characteristics of the system. It is a non-zero minimum value.

[0025] As can be seen from the above formula, the higher the shadow price, the more severe the regional flexibility deficit. In this case, cross-regional flexibility support should be prioritized. The corresponding priority can be determined by combining the shadow price. The shadow price is not an economic market price, but a priority signal for spatial scheduling, which is only used for the allocation and ranking of cross-regional flexibility resources.

[0026] The calculation process for the cross-sectional criticality index and the transmission priority coefficient includes: Let the system be the first k The tidal current at each cross section is The cross-sectional tidal current is normalized using the following formula: : (98) Let the first k Limiting transmission power of each section This value characterizes the flexible transmission capacity of the cross section, and further, the transmission margin of the cross section is proposed by combining it with the line load rate. The corresponding calculation formula is: (99) Based on the transmission margin of the cross section The criticality indicators of all transmission sections are obtained, and after sorting the criticality indicators of all transmission sections in descending order, the critical sections that meet the criticality indicator thresholds are selected. The calculation formula for the criticality indicators of the sections is as follows: (100) In the formula: Indicates the first k The criticality index of a cross section; the above formula shows that the heavier the power flow and the smaller the transmission margin of a cross section, the larger its criticality index, and the more it needs to be monitored, that is, it belongs to a critical cross section.

[0027] Based on the above cross-sectional keyness indicators Define the cross-sectional transmission priority coefficient. The transmission priority coefficient of this section This coefficient is used to characterize the flexibility transmission priority of critical and non-critical sections. The larger the coefficient, the higher the flexibility transmission priority of the section. The calculation formula is: (101) In the formula: K The number of system sections, non-critical sections The transmission priority coefficient is significantly higher than that of critical sections, and cross-regional flexibility resources are preferentially transmitted through non-critical sections to ensure the flexibility of the system's power transmission channels and the balance between supply and demand.

[0028] Since spatial hierarchical scheduling and temporal scale matching are not independent, the temporal scale determines the order of resource allocation and power distribution within a given range, while the spatial hierarchy determines the scheduling scope and transmission path of flexible resources at a given moment. Ultimately, the system's flexibility is improved under spatiotemporal coupling through the synergistic complementarity of flexible scheduling resources. For scenarios involving insufficient upward and downward flexibility adjustments, a regional flexibility shadow price is introduced to quantify the degree of flexibility scarcity as a decision variable. This price is then combined with cross-sectional criticality and transmission priority coefficients to determine the specific triggering process and resource call order for spatiotemporal coupling scheduling corresponding to cross-regional support paths. Figure 3 As shown, it includes: When a region of the system experiences insufficient flexibility for increased power supply, a spatiotemporal coupling scheduling process for increased flexibility is triggered. Various flexibility resources are sequentially invoked until a balance between supply and demand for increased flexibility is achieved. The specific process is as follows: First, within the deficit region, conventional generating units and hybrid energy storage are invoked for self-balancing regulation within the region. If self-balancing within the region cannot compensate for the deficit, the shadow price of flexibility in surplus regions (surplus regions refer to regions where flexibility supply exceeds flexibility demand) and the cross-section transmission priority coefficient are calculated. Flexibility resources from surplus regions are then transmitted to deficit regions via non-critical cross-sections. If non-critical cross-section reconciliation still cannot compensate for the deficit, available transmission capacity from critical cross-sections is released for cross-regional flexibility reconciliation. If a deficit in increased flexibility still exists after cross-regional reconciliation, user-side incentive-based controllable loads are invoked to guide load reduction and lower the demand for increased flexibility. If none of the above measures can compensate for the deficit, load shedding measures are implemented to ensure the safe and stable operation of the system. If there are several critical cross-sections, their transmission priority coefficients are used to prioritize those with higher priority coefficients.

[0029] When a region of the system experiences insufficient flexibility for downward adjustments, a spatiotemporal coupling scheduling process for downward flexibility is triggered. The overall logic is consistent with the upward adjustment scenario, only the resource adjustment method differs. Specifically, it manifests as follows: First, within the deficit region, conventional generating units and hybrid energy storage are used for self-balancing adjustment within the region. If self-balancing within the region cannot compensate for the deficit, the shadow price of flexibility in surplus regions within the system and the cross-section transmission priority coefficient are calculated, and the flexibility resources of surplus regions are transmitted to the deficit region through non-critical cross-sections. If mutual assistance between non-critical cross-sections still cannot compensate for the deficit, the available transmission capacity of critical cross-sections is released, and cross-regional flexibility mutual assistance is carried out through critical cross-sections. If a deficit in downward flexibility still exists after cross-regional mutual assistance, user-side incentive-based controllable loads are called to guide load increases, absorb excess power, and reduce the demand for downward flexibility. If none of the above measures can compensate for the deficit, certain measures such as wind and solar curtailment are taken.

[0030] Based on the above, this step first defines a topology of "independent partitions and mutual support across regions" and scheduling rules of "determining scheduling priority by flexibility shadow price and determining cross-regional support path by section criticality and transmission priority coefficient" through a spatiotemporally coupled flexible resource scheduling framework as the logical architecture layer (i.e., the spatiotemporally coupled flexible resource scheduling framework provides topology and scheduling logic for the lower-level operation model). Based on these scheduling rules, a triggering process of "adjusting up / down by scenario and hierarchical within / across regions" is designed. Secondly, through a multi-objective optimization model as the algorithm implementation layer, the rules in the flexible resource scheduling framework are transformed into corresponding calculation formulas or models, so as to specifically quantify the multi-objective optimization problem (the solution objective of the aforementioned framework) into multi-objective functions f1, f2, and f3, i.e., f1 (economy) corresponds to operating cost, f2 (time flexibility) corresponds to insufficient expectation, and f3 (spatial flexibility) corresponds to critical section margin.

[0031] In some specific embodiments, given that the hybrid energy storage site selection and capacity determination two-layer optimization model has the characteristics of coupled decision variables between the planning layer and the operation layer, heterogeneous objective function types, and complex constraint system, the upper layer is a single-objective investment planning problem combining discrete and continuous variables, and the lower layer is a multi-objective operation optimization problem with multi-dimensional constraints, traditional analytical methods and single optimization algorithms are difficult to solve efficiently. Therefore, the design of S3 includes establishing a nested algorithm solution framework to solve a two-layer planning model consisting of the upper-layer planning model and the lower-layer operation model of the electric-hydrogen-water hybrid energy storage. The upper-layer planning model uses an improved Osprey Optimization (OOA) algorithm to achieve global optimization of hybrid energy storage site selection and capacity determination, obtaining key feedback indicators such as system operating cost and flexibility margin under the planning scheme. The lower-layer operation model uses an improved second-generation non-dominated sorting genetic algorithm (NSGA-II) to achieve multi-objective optimization of system operation, updating the optimized planning scheme through population iteration, repeating the above process until convergence conditions are met, and outputting the optimal hybrid energy storage site selection and capacity configuration scheme. Specifically, as shown... Figure 4As shown, an improved Osprey Optimization (OOA) algorithm is used to perform global optimization for hybrid energy storage site selection and capacity determination. A lower-level improved Non-Dominated Sorting Genetic Algorithm (NSGA-II) is used to achieve multi-objective optimization of system operation. Through information transfer and feedback correction between the two algorithms, the collaborative solution of hybrid energy storage planning and configuration and system operation optimization is achieved, ultimately outputting an optimal solution that balances economy and flexibility. The overall solution logic is as follows: The upper-level OOA algorithm generates a set of hybrid energy storage site selection and capacity determination schemes, which are then passed as known constraints to the lower-level improved NSGA-II algorithm to obtain key feedback indicators such as system operating cost and flexibility margin under the given scheme. The upper-level algorithm calculates the objective function value based on the feedback results, iteratively updates the optimized planning scheme through population, and repeats the above process until the convergence condition is met, outputting the optimal hybrid energy storage site selection and capacity determination configuration scheme.

[0032] Preferably, given that the aforementioned upper-level planning model for hybrid energy storage (electric-hydrogen-water hybrid energy storage) uses hybrid energy storage installation nodes, rated power and capacity of various energy storage types as decision variables, and aims to minimize the comprehensive investment cost over the entire life cycle, it belongs to a high-dimensional, non-convex combinatorial optimization problem. Therefore, although the traditional OEA optimization algorithm has the advantages of strong global search capability and fast convergence speed, its uneven initial population distribution easily leads to local optima, and insufficient local development capability in the later stage easily leads to convergence stagnation. To this end, this application introduces the optimal point set population initialization and Levy flight perturbation strategy to improve the traditional OOA algorithm, enhance the population diversity and global optimization capability of the algorithm, and ensure that the globally optimal solution for hybrid energy storage site selection and capacity determination is found efficiently, thereby improving the global search capability and avoiding getting trapped in local optima. Specifically, the core solution process of the improved OOA algorithm is divided into a global search stage (positioning and fishing) and a local development stage (transporting fish to feed), and its specific implementation steps are as follows: (1) Initialization of the best point set population Instead of traditional random initialization, a best-point-set method is used to generate the initial population, ensuring a uniform distribution of the population within the solution space and improving the quality and diversity of the initial solutions. The specific steps are as follows: 1) Get random numbers r , r =( r 1, r 2,…, r n ) (102) Where: 1≤ j ≤ n , m i Indicates the first in the population i Individual.

[0033] 2) Number of constructions n Best spots (103) 3) P n Mapping to the feasible region where the population resides, the corresponding mapping formula is: (104) In the formula: This indicates the lower bound of the current dimension. This indicates the upper limit of the current dimension.

[0034] The above-mentioned optimal point set mapping process generates an initial population that is uniformly distributed and highly diverse in the solution space, thereby ensuring that the initial "Ospreys" can uniformly cover the entire search range. (2) The first optimization iteration based on the initial position provided by the initial population, i.e., the global search stage (positional fishing): Identifying the location of the target fish school: Given that ospreys locate fish schools through inter-species information exchange, and each osprey considers the location with the better objective function value in the search space as the fish school, that is, within the current osprey population, the target fish school is defined as the one with the better objective function value than the current [number]th [bird / bird]. i The positions of the other individual ospreys, together with the current globally optimal position, constitute the first... i The "gathering of fish" by a single osprey ( FP i ), The corresponding formula is expressed as: (105) In the formula: FP i It is the first i A school of fish brooked by an osprey; X k For the first k The position of the osprey; F k and F i The first k The and the first i The objective function value of an osprey; X best This is the best position for the osprey; N This refers to the number of ospreys.

[0035] Randomly select attack targets: i The osprey randomly selects the location (SF) of one fish from the aforementioned group of fish as its attack target; Introducing Levy flight perturbation to update position: Since the osprey will randomly select the position of a fish in the school to attack, and the position of the osprey can be updated by simulating the movement of the osprey, in order to improve the optimization ability of OOA and escape the local optimum, the osprey needs to have both short-range exploration and long-range search capabilities. Therefore, in the process of simulating the movement of the osprey towards the target fish, it is necessary to introduce the Levy flight mechanism to generate a random step size, that is, to add Levy perturbation when updating the position of the osprey, as shown in equation (105). The perturbation strategy of generating a random step size using Levy distribution can escape the local optimum to perturb and update the position of the osprey. The improved position update formula is shown in equation (106). (106) (107) In the formula: μ , v Given two independent normally distributed random numbers; For Osprey i The new location; x i The location of the osprey; SF The fish chosen for the cormorant; I The value is any number in {1, 2}. Levy Let be the random step size of the Levy perturbation; β Γ is the distribution characteristic index, taken as 1.5; Γ is the gamma function.

[0036] (3) Partial development stage (transporting fish for feeding) Ospreys transport their captured prey to a safe location for feeding, achieving precise local searches through small positional adjustments. By introducing Levy flight perturbations and generating random step sizes to perturb individual positions, the improved position update formula is: (108) In the formula: For Osprey i The feeding position; r A random number between [0,1] t For the number of iterations, T it represents the maximum number of iterations.

[0037] (4) Fitness calculation and convergence judgment: The hybrid energy storage site selection and capacity determination scheme corresponding to each individual in the population is input into the lower-level model to obtain the operational feasibility results fed back from the lower level. The upper-level objective function (comprehensive investment cost throughout the entire life cycle) is calculated as the individual fitness value. If the current iteration number reaches the maximum value or the change in fitness value is less than the preset threshold, the algorithm converges and outputs the optimal individual; otherwise, it returns to the global search stage to continue iterating.

[0038] Preferably, given the multi-objective optimization problem of the lower-level operation model of the electric-hydrogen-water hybrid energy storage, it is necessary to simultaneously achieve coordinated optimization of operational economy, spatial scale flexibility, and temporal scale flexibility. The traditional NSGA-II algorithm can effectively solve the multi-objective Pareto optimal solution through fast non-dominated sorting, crowding degree calculation, and elite retention strategy. However, it suffers from slow convergence speed and insufficient local search ability due to fixed crossover and mutation parameters. To address this, this study introduces adaptive crossover probability and dynamic mutation probability to improve the traditional NSGA-II algorithm. This allows the algorithm to maintain population diversity in the early stages of iteration and improve convergence accuracy in the later stages, efficiently solving the optimal solution for system operation under different planning schemes. The core solution steps of the improved NSGA-II algorithm are as follows: (1) Population initialization and encoding Decision variables are encoded using real-number encoding, including conventional unit output, the charging and discharging power of the electric-hydrogen-water hybrid energy storage system, and controllable load regulation. Within the feasible region that satisfies the operational constraints of each device, an initial population of size N is randomly generated. The specific population size can be set by technical personnel based on the problem complexity. (2) Fast non-dominated sorting and crowding calculation Individuals in the population are subjected to non-dominated stratification, divided into different non-dominated layers based on Pareto dominance relationships. The first layer represents the current optimal Pareto front solution, with higher layer numbers indicating poorer fitness. Simultaneously, the crowding distance of each individual is calculated to quantify its distribution density in the target space; a larger crowding distance indicates fewer solutions surrounding the individual, suggesting that retaining that individual helps maintain population diversity.

[0039] (3) Adaptive genetic operations By dynamically adjusting the crossover and mutation probabilities in conjunction with the algorithm's iterative process, an adaptive balance between global exploration and local exploitation is achieved. 1) Adaptive crossover operation: A real-valued crossover operator is used to generate offspring individuals, and the crossover probability is set to a relatively small value (0.6) in the early stages of iteration. To maintain population diversity, the crossover probability is initially set to 0.7; as iterations progress, the crossover probability is gradually increased (e.g., to 0.8-0.9) to accelerate algorithm convergence. The crossover process follows the formula below: (109) (110) (111) In the formula: R 1,j , R 2,j For the first and second generations of offspring 1 jCapacity configuration results for each device; γ j For crossover operators; The random number between 0 and 1 during the crossover operation; η 1 represents the cross parameter.

[0040] 2) Dynamic Mutation Operation: A polynomial mutation operator is used to perturb individuals to adapt to the characteristics of different problems and the needs of different search stages. During algorithm execution, the population may concentrate in certain regions and get trapped in local optima. The mutation operation improves population diversity by expanding the search range. Therefore, the mutation rate is increased in the later stages of the algorithm to enhance global exploration capabilities and effectively prevent premature convergence. The mutation process follows the formula below: (112) (112) In the formula: δ j For the first j The variation in the capacity configuration of each device; The random number between 0 and 1 during the mutation operation; η 2 represents the variation parameter.

[0041] (4) Elite preservation and new population generation The parent population is merged with the offspring population generated through genetic manipulation. Non-dominated sorting and crowding calculation are performed again. Individuals are selected according to the principle of hierarchical priority and priority of greater crowding to form a new generation population. To ensure the continuity of superior genes, the merged population size is 2N (N is the size of the parent population). After re-sorting the non-dominated hierarchy, individuals at the top of the non-dominated hierarchy are retained first. When a certain hierarchy cannot be completely retained, individuals are selected in descending order of crowding distance until the size of the new generation population reaches N.

[0042] (5) Results output and feedback When the algorithm reaches the preset convergence conditions (such as reaching the maximum number of iterations or no significant change in the Pareto front), it outputs the final Pareto optimal solution set. The entropy weight method is used to comprehensively weight the multi-objective solutions in the multi-objective solution set, selecting a single comprehensive optimal solution. Key indicators corresponding to this optimal solution, such as system operating cost, time scale and spatial scale flexibility margin, are extracted and fed back to the upper-level OOA algorithm as the basis for calculating the fitness value of the site selection and sizing scheme, thereby driving the iterative optimization of the upper-level planning model.

[0043] Based on the same inventive concept, this invention also proposes an electric-hydrogen-water hybrid energy storage site selection and capacity planning system, comprising: The upper-level planning unit for hybrid energy storage (electric-hydrogen-water) is used to minimize the comprehensive cost of the hybrid energy storage over its entire life cycle. The unit takes the rated power and capacity configuration of the hybrid energy storage installation nodes, battery energy storage, pumped hydro storage, and hydrogen energy storage as decision variables, and at least the installed capacity constraint and the node layout number constraint as constraints, and outputs candidate planning schemes. The comprehensive cost includes comprehensive investment cost and comprehensive operating cost. The lower-level operation unit of the electric-hydrogen-water hybrid energy storage is used to establish a multi-objective optimization model under a spatiotemporally coupled flexible resource scheduling framework. This multi-objective optimization model takes the candidate planning scheme as the operation constraint, conventional units, electric-hydrogen-water hybrid energy storage, and controllable loads as scheduling resources, and takes system operation economy, spatial scale flexibility, and temporal scale flexibility as multiple objectives. It also uses at least a predefined multi-dimensional constraint system as constraints to simulate the operation results under the candidate planning scheme and feed back key indicators. The multi-dimensional constraint system includes conventional unit operation constraints, hybrid energy storage operation constraints, controllable load operation constraints, power balance constraints, and line power flow safety constraints. A nested solution unit is used to solve a two-layer planning model consisting of the upper-layer planning unit of the electric-hydrogen-water hybrid energy storage and the lower-layer operation unit of the electric-hydrogen-water hybrid energy storage. The nested solution module includes: The upper-level optimization unit adopts an improved Osprey optimization algorithm, which introduces a good point set population initialization and Levy flight perturbation strategy. The lower-level optimization unit adopts an improved second-generation non-dominated sorting genetic algorithm. The improved second-generation non-dominated sorting genetic algorithm adopts adaptive crossover probability and dynamic mutation probability, outputs the Pareto optimal solution set, and determines the comprehensive optimal solution through the entropy weight method.

[0044] Based on the same inventive concept, the present invention also proposes a computer-readable storage medium including computer instructions that, when executed on a computer, cause the computer to perform the method described thereon.

[0045] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. An electric-hydrogen-water hybrid energy storage site selection and sizing planning method, characterized in that, The method includes the following steps: S1. Construct an upper-level planning model for hybrid energy storage (electricity-hydrogen-water): The optimization objective is to minimize the comprehensive cost of the hybrid energy storage over its entire life cycle. The decision variables are the installation nodes of the hybrid energy storage, the rated power and capacity configuration of battery energy storage, pumped hydro storage and hydrogen energy storage. The model is constrained by at least the installed capacity constraint and the node layout number constraint. The model outputs candidate planning schemes, where the comprehensive cost includes the comprehensive investment cost and the comprehensive operating cost. S2. Constructing a lower-level operation model for the electric-hydrogen-water hybrid energy storage system: A multi-objective optimization model is established within a spatiotemporally coupled flexible resource scheduling framework. This model uses the candidate planning scheme as operational constraints, conventional generating units, electric-hydrogen-water hybrid energy storage, and controllable loads as scheduling resources. Simultaneously, it considers system operational economy, spatial scale flexibility, and temporal scale flexibility as multiple objectives, and at least a predefined multi-dimensional constraint system as constraints. The model simulates the operational results under the candidate planning scheme and provides feedback on key indicators. The multi-dimensional constraint system includes conventional generating unit operation constraints, hybrid energy storage operation constraints, controllable load operation constraints, power balance constraints, and line power flow safety constraints. S3. Establish a nested algorithm solution framework to solve the two-layer planning model consisting of the upper-layer planning model of the electric-hydrogen-water hybrid energy storage and the lower-layer operation model of the electric-hydrogen-water hybrid energy storage. The upper-layer planning model of the electric-hydrogen-water hybrid energy storage completes global optimization through an improved Osprey optimization algorithm, and the lower-layer operation model of the electric-hydrogen-water hybrid energy storage achieves multi-objective optimization through an improved second-generation non-dominated sorting genetic algorithm. After convergence through iterative interaction between the upper and lower layer algorithms, the optimal addressing and capacity sizing scheme is output.

2. The electro-hydro-hydrogen-water hybrid energy storage site selection and sizing planning method of claim 1, wherein, The objective function of the upper-level planning model for the electric-hydrogen-water hybrid energy storage is to minimize the comprehensive investment and overall operating cost of the hybrid energy storage throughout its entire life cycle, in order to achieve optimal overall system economics. Its specific expression is as follows: (2) In the formula: is the comprehensive investment cost of the hybrid energy storage system in the whole life cycle, which includes the initial investment cost , equipment replacement cost , whole life cycle operation and maintenance cost , and residual value income after equipment recycling after retirement ; is the comprehensive operation cost of the system.

3. The method for site selection and capacity planning of hybrid energy storage based on an electric-hydrogen-water hybrid system according to claim 2, characterized in that, The initial investment cost Refers to the one-time investment cost in the initial stage of construction of various energy storage devices, which covers the power and capacity investment of battery energy storage, pumped storage, and hydrogen energy storage. (3) In the formula: , These represent the investment cost per unit power and per unit capacity of battery energy storage, respectively. , These are the rated power and rated capacity of the battery energy storage, respectively. , 、 These are the investment costs per unit pumping power, per unit generating power, and per unit capacity of pumped storage, respectively. and These are the rated power for pumped storage hydroelectric pumping and power generation, respectively. This is the maximum capacity of the upper reservoir; , These are the unit power investment costs for hydrogen energy storage electrolyzers and fuel cells, respectively. , These are the rated power of the water electrolysis hydrogen production system and the fuel cell power generation system, respectively. , These are the unit capacity investment cost of hydrogen storage equipment and the maximum capacity of hydrogen storage equipment, respectively. Equipment replacement cost Its design takes into account the differences in equipment lifespan among different energy storage technologies. Pumped hydro storage and hydrogen storage do not require replacement of their media and main equipment throughout their entire life cycle. Only lithium battery storage requires equipment replacement due to its limited cycle life. The corresponding replacement cost is calculated as follows: (4) In the formula: N This refers to the number of times a lithium battery can be replaced throughout its entire lifespan. j ∈ ; r The benchmark discount rate; Y The number of years used throughout the entire life cycle of the battery energy storage system; Total lifecycle operation and maintenance costs This refers to the ongoing investment costs associated with the daily operation, maintenance, and management of various energy storage devices throughout their entire lifecycle. These costs can be categorized into power-type and capacity-type devices and calculated separately. Specifically: (5) In the formula: T The year of operation; , These are the unit power and unit capacity operation and maintenance costs of battery energy storage, respectively. , These are the unit power and unit capacity operation and maintenance costs of pumped storage, respectively. , These are the unit power and unit capacity operation and maintenance costs of hydrogen energy storage, respectively. Residual value income from equipment recycling after decommissioning This refers to the revenue generated from the decommissioning and recycling of various energy storage devices at the end of the system's entire life cycle, calculated based on a fixed residual value rate of the initial investment cost of the equipment. Specifically: (6) In the formula: , , The equipment recycling residual value rates are for battery energy storage, pumped hydro storage, and hydrogen energy storage, respectively. The constraints include installed capacity constraints and node layout constraints. The installed capacity constraints include the rated power constraint of the hybrid energy storage system and the rated capacity constraint of the hybrid energy storage system. The node layout constraints include the installation node constraints of the hybrid energy storage system and the constraint on the number of hybrid energy storage installations. The specific constraints are as follows: The installation node constraints of a hybrid energy storage system are expressed as follows: (7) In the formula: For nodes j The binary decision variables for energy storage deployment; The rated power constraint of a hybrid energy storage system is expressed as: (8) In the formula: For nodes j Rated power of the configured hybrid energy storage system; , They are nodes j The lower and upper limits of the rated power of energy storage are determined by the node grid structure, load level, and power output characteristics; The rated capacity constraint for hybrid energy storage is expressed as: (9) In the formula: For nodes j Rated capacity of the configured hybrid energy storage system; , They are nodes j The lower and upper limits of the rated capacity of energy storage should be matched with the level of node flexibility deficit and the demand for energy storage regulation. The constraint on the number of hybrid energy storage installations is expressed as follows: (10) In the formula: J This represents the total number of energy storage nodes that can be deployed in the system. The maximum number of hybrid energy storage systems that can be deployed in the system is determined by the power grid planning and construction scale and investment budget.

4. The method for site selection and capacity planning of hybrid energy storage based on electricity, hydrogen, and water according to claim 1, characterized in that, The construction process of the lower-level operation model of the electric-hydrogen-water hybrid energy storage includes: A multi-objective optimization model is established, which takes into account three dimensions: operational economy, spatial scale flexibility, and temporal scale flexibility. The objectives are coupled and optimized synergistically to ultimately achieve the optimal balance between economy and flexibility under the premise of safe and stable system operation. The specific objective function is constructed as follows: (11) In the formula: f 1 represents the operational economic objective, namely the overall operating cost of the system. f 2 represents the expectation of insufficient time-scale flexibility, i.e., the system's flexibility and adaptability. f 3 represents the spatial scale flexibility target, namely the overall system flexibility margin of the key cross-section.

5. The method for site selection and capacity planning of hybrid energy storage based on an electric-hydrogen-water hybrid system according to claim 4, characterized in that, in, The operational economic objective aims to minimize the overall system operating cost. This involves comprehensively considering the costs of conventional unit power generation and start-up / shutdown, hybrid energy storage dispatching, controllable load dispatching, and the penalty costs associated with wind and solar curtailment and load shedding. The specific expression is as follows: (12) in: Costs for power generation and start-up / shutdown of conventional generating units; To controllable load scheduling costs; The cost of curtailing wind and solar power loads; in: Conventional unit power generation and start-up / shutdown costs It can be represented as: (13) In the formula: This is the number of conventional generating units; For the region m conventional units g exist t The operating status at any given time: 1 if the unit is running, 0 otherwise; , , This is the fuel cost coefficient for the generator unit; For the region m conventional units g exist t Power at any given moment; , These are the unit start-up and shutdown cost coefficients, respectively. , They are respectively regions m conventional units g The state variable during startup and shutdown is 1 when the unit starts up and 0 otherwise. The energy storage dispatch cost can be expressed as: (14) In the formula: For the region m One-time purchase cost of hybrid energy storage system; For the region m Hybrid energy storage systems in t Actual power at any given moment; For the region m The lifespan of hybrid energy storage systems; For the region m Rated capacity of hybrid energy storage system; Controllable load dispatching cost It can be represented as: (15) In the formula: To utilize the cost factor for controllable load, For the region m exist t Controllable load allocation at all times; Curtailment of wind and solar power load shedding penalties It can be represented as: (16) Where: Where: , These are the unit wind and solar curtailment penalty coefficient and the load shedding penalty coefficient, respectively. , , These are respectively the system's wind curtailment, solar curtailment, and load shedding. The time-scale flexibility target aims to minimize the expected insufficiency of the system's flexibility adjustment capability, quantifying the risk of system flexibility imbalance at different time scales. The corresponding formula is: (17) In the formula: The system's flexibility and adaptability are insufficient. and for t The system's upward and downward flexibility adjustment capabilities are insufficient to meet expectations. T For the evaluation period, N This represents the total number of loading units; The spatial scale flexibility objective aims to maximize the overall system flexibility margin considering key cross-sections, and to quantify the bottlenecks in cross-regional flexibility coordination. Specifically, it can be expressed as: (18) In the formula: To take into account the overall system flexibility margin at key sections, , Cross-sections k Overall upward / downward flexibility margin of the connected sending and receiving ends; K The total number of critical sections; The multi-dimensional constraint system covers constraints such as conventional unit operation constraints, hybrid energy storage operation constraints, controllable load operation constraints, power balance constraints, and line power flow safety constraints. The operating constraints for conventional generating units are as follows: (19) (20) (21) (22) In the formula: , The units g Minimum and maximum technical output; , The units g The maximum upward and downward climbing rates; For the region m unit g exist t Start and stop state variables at specific times; For the region m unit g State variables that have been running or stopped for a period of time, where δ This indicates the time the service has been running or offline. , For the region m unit g Minimum start-up and shutdown times; t For time intervals; Hybrid energy storage operation constraints include power operation constraints and energy operation constraints for battery energy storage, pumped hydro storage, and hydrogen energy storage: The operating constraints for battery energy storage power are as follows: (23) (24) (25) (26) In the formula: and They are respectively m Battery energy storage in the region t Constant charging and discharging power, for m Rated power of battery energy storage within the region; and They are respectively m Maximum ramp rate for battery charging and discharging within the region; The energy operation constraints of battery storage are as follows: (27) (28) (29) (30) In the formula: , for m Battery energy storage in the region t The decision variables for the charging and discharging states at time 0-1 ensure that the charging and discharging states are mutually exclusive. for m Battery energy storage in the region t At any given moment, the state of charge and They are m Upper and lower limits of battery energy storage state of charge within the region. 0 and They are m The initial state of charge (SOC) of the battery energy storage within the region and the SOC at the end of the operating cycle; The power operation constraints of pumped storage are as follows: (31) (32) (33) (34) In the formula: 、 respectively m Pumped storage in the region t Constant water pumping and power generation capacity; and They are respectively m Rated power capacity for pumped storage hydroelectric power generation within the region; and They are respectively m Maximum ramp rate for pumped storage hydroelectric power generation within the region; The energy operation constraints of pumped storage are as follows: (35) (36) (37) In the formula: , for t time m 0-1 start-stop state decision variables for pumped storage pumping devices and power generation devices within the region; , They are respectively m The upper and lower limits of the water storage capacity of the upper reservoirs in the region. and They are m The initial water storage capacity and the water storage capacity at the end of the operating cycle of the pumped storage upper reservoir within the region; The power operation constraints for hydrogen energy storage are as follows: (38) (39) (40) (41) In the formula: , respectively m Regional hydrogen energy storage t The power output of hydrogen production and power generation devices at all times; and They are respectively m Rated power of hydrogen storage and hydrogen production devices and power generation devices within the region; and They are respectively m Maximum ramp rate of hydrogen storage and hydrogen production devices and power generation devices within the region; The energy operation constraints for hydrogen energy storage are as follows: (42) (43) (44) In the formula: , for t Decision variables for the 0-1 start-stop state of hydrogen storage and hydrogen production devices and power generation devices at any time; , They are respectively m The upper and lower limits of the capacity of hydrogen storage devices in the region. and They are m The initial hydrogen storage capacity of the hydrogen energy storage device within the region and the hydrogen storage capacity at the end of the operating cycle; The controllable load operation constraints are as follows: (45) In the formula: for m within the area t The load size can be controlled at any time. and They are respectively regions m Controllable load t Always operate with upper and lower bounds; To ensure the safety of system operation, the following power balance constraints must be met: (46) In the formula: , and They are respectively regions m exist t Net load, load shedding, and abandoned renewable energy at any given time. , , , They are respectively regions m Includes conventional generating units, hybrid energy storage devices, access lines, and the number of controllable load users; Among them, line power flow safety constraints include branch power flow constraints and line safety constraints. Branch flow constraints are as follows: (47) In the formula: This represents the number of system nodes. , Each region and time point represents a specific node. i Injected active and reactive power; , For nodes i and j voltage amplitude, , For nodes i , j The conductivity and susceptance between them For nodes i , j The phase angle difference between them; The line safety constraints are as follows: (48) In the formula: For the region m line b The power transmission limit; , , , , , , They are respectively regions m China Wind Power w Photovoltaics s Conventional units g ,load r , connecting lines l Energy storage j Controllable load d The current transfer factor; , , They are respectively regions m The number of wind turbines, photovoltaic units, and load users included; , , , , , and They are respectively regions m exist t The amount of wind power output, photovoltaic power load, line transmission power, hybrid energy storage power, and controllable load power at any given time.

6. The method for site selection and capacity planning of hybrid energy storage based on electricity, hydrogen, and water according to claim 1, characterized in that, The spatiotemporally coupled flexibility resource scheduling framework includes: dividing the power grid into multiple independent regional scheduling units, introducing regional flexibility shadow prices to quantify the degree of flexibility scarcity as decision variables, and combining cross-section criticality and transmission priority coefficients to determine cross-regional support paths; specifically, based on the power grid partitioning results, dividing the system into... Z = {1, 2, ..., z, ..., Z} Each region is a separate regional dispatching unit. The flexibility of each regional dispatching unit is based on the balance of supply and demand. The cross-regional flexibility is mutually supported by the available transmission capacity of key sections as a hard constraint.

7. A planning system established by the site selection and capacity planning method for hybrid energy storage based on any one of claims 1-6, characterized in that, include: The upper-level planning unit for hybrid energy storage (electric-hydrogen-water) is used to minimize the comprehensive cost of the hybrid energy storage over its entire life cycle. The unit takes the rated power and capacity configuration of the hybrid energy storage installation nodes, battery energy storage, pumped hydro storage, and hydrogen energy storage as decision variables, and at least the installed capacity constraint and the node layout number constraint as constraints, and outputs candidate planning schemes. The comprehensive cost includes comprehensive investment cost and comprehensive operating cost. The lower-level operation unit of the electric-hydrogen-water hybrid energy storage is used to establish a multi-objective optimization model under a spatiotemporally coupled flexible resource scheduling framework. This multi-objective optimization model takes the candidate planning scheme as the operation constraint, conventional units, electric-hydrogen-water hybrid energy storage, and controllable loads as scheduling resources, and takes system operation economy, spatial scale flexibility, and temporal scale flexibility as multiple objectives. It also uses at least a predefined multi-dimensional constraint system as constraints to simulate the operation results under the candidate planning scheme and feed back key indicators. The multi-dimensional constraint system includes conventional unit operation constraints, hybrid energy storage operation constraints, controllable load operation constraints, power balance constraints, and line power flow safety constraints. A nested solution unit is used to solve a two-layer planning model consisting of the upper-layer planning unit of the electric-hydrogen-water hybrid energy storage and the lower-layer operation unit of the electric-hydrogen-water hybrid energy storage. The nested solution unit includes: The upper-level optimization subunit adopts the improved Osprey optimization algorithm, which introduces the optimal point set population initialization and Levy flight perturbation strategy. The lower-level optimization subunit adopts an improved second-generation non-dominated sorting genetic algorithm. The improved second-generation non-dominated sorting genetic algorithm adopts adaptive crossover probability and dynamic mutation probability, outputs the Pareto optimal solution set, and determines the comprehensive optimal solution through the entropy weight method.

Images