Long-term and short-term energy storage planning methods, systems, media and equipment

Abstract

The present invention provides a method, system, medium, and device for long-term and short-term energy storage planning. The method comprises the following steps: Step S1: Processing the unbalanced power time series for the entire year using a seasonal and trend decomposition method; Step S2: Based on cluster analysis of intraday fluctuation components using a k-means algorithm, linearly transforming the power fluctuation curve under a typical scenario to obtain a transformed power fluctuation curve; Step S3: Establishing a long-term and short-term energy storage optimization configuration model; Step S4: Solving the problem based on the concept of scenario transformation. The seasonal and trend decomposition method employed by the present invention fully preserves long-term fluctuation characteristics, thereby ensuring the reliability of seasonal energy storage planning; and decoupling power fluctuations at different time scales simplifies solving the optimization model.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and specifically to a method, system, medium and equipment for long-term and short-term energy storage planning, and more particularly to a method for long-term and short-term energy storage system planning based on the concept of "scenario change". Background Art

[0002] In recent years, with the continuous expansion of installed renewable energy capacity, seasonal power imbalances between sources and loads in power systems have become increasingly prominent. The challenge is to rationally deploy various energy storage technologies to enable multi-timescale energy transfer, achieve peak load shifting, and increase the grid's capacity to absorb renewable energy.

[0003] Current technologies often analyze the capacity allocation of short-term energy storage, particularly electrochemical energy storage, for intraday regulation, while ignoring seasonal power imbalances and the capacity allocation of seasonal energy storage. Some studies address the multi-timescale power imbalance problem by proposing long- and short-term energy storage capacity allocation schemes on an annual basis. However, long timescales such as the entire year introduce a large number of decision variables into the optimization problem, increasing the scale of the solution and reducing solution efficiency.

[0004] Jiang Haiyang, Du Ershun, Jin Chen, Xiao Jinyu, Hou Jinming, Zhang Ning. Multi-timescale energy storage capacity optimization planning for cross-border interconnected power systems with high proportion of clean energy grid integration[J]. Proceedings of the CSEE, 2021, 41(06): 2101-2115.

[0005] Abstract: The integration of high clean energy penetration into the grid places higher demands on the flexible regulation capabilities of power systems. Energy storage is a key element in this system's flexible regulation capabilities, and the system's demand for energy storage is closely related to the scarcity of its flexibility. Relying solely on energy storage to provide the required flexibility will significantly increase power system investment costs and reduce equipment utilization. Coordinating energy storage with interconnected power grids and clean energy deployment, and leveraging their complementary benefits, is a key approach to improving the security and economic efficiency of future clean power systems. This paper considers the optimal planning and operation of a transnational interconnected power system with the coordinated participation of multiple flexibility resources from a technical and economic perspective. First, based on existing research, various flexibility resources are modeled and an annual 8760-hour panoramic time-series operation simulation is incorporated into the optimization model. A planning model for power systems with high clean energy penetration, considering the participation of multiple flexibility resources, is proposed. An empirical analysis is conducted based on the Northeast Asia transnational interconnected power grid. The impact of grid interconnection and clean energy deployment on the optimal energy storage configuration is quantitatively evaluated, and planning results for the Northeast Asia region from 2035 to 2050 based on different flexibility resource planning models are presented. The example demonstrates that grid interconnection and coordinated planning of wind power and photovoltaic clean energy across multiple regions can effectively reduce energy storage capacity within each region and lower the system's cost per kilowatt-hour. The example also includes a sensitivity analysis of the relationships between energy storage capacity, system cost per kilowatt-hour, and interconnection capacity.

[0006] This paper proposes a multi-timescale energy storage capacity planning method for cross-border interconnected power systems based on an annual 8760-hour panoramic time-series operation simulation. This method optimizes decisions based on a full-year time-series scenario. While ensuring the reliability of the planning results, it inevitably introduces a large number of decision variables, resulting in low solution efficiency. However, the authors do not consider this issue in the paper.

[0007] Therefore, it is necessary to propose a new technical solution to improve the above technical problems. Summary of the Invention

[0008] In view of the deficiencies in the prior art, the present invention aims to provide a method, system, medium and device for long-term and short-term energy storage planning.

[0009] According to the present invention, a long-term and short-term energy storage planning method is provided, the method comprising the following steps:

[0010] Step S1: Use the seasonal and trend decomposition method to process the unbalanced power time series throughout the year;

[0011] Step S2: Based on the cluster analysis of the intraday fluctuation components using the k-means algorithm, a power fluctuation curve under a typical scenario is linearly transformed to obtain a transformed power fluctuation curve;

[0012] Step S3: Establishing a long-term and short-term energy storage optimization configuration model;

[0013] Step S4: Solve based on the concept of scene change and perform energy storage planning.

[0014] Preferably, the step S1 uses a seasonal and trend decomposition method to process the unbalanced power time series throughout the year, as shown in the following formula:

[0015]

[0016]

[0017] Among them, formula (1) reveals the source of power imbalance, denote the annual unbalanced power, annual photovoltaic output and annual load demand respectively; (2) In the formula, the unbalanced power is decomposed into long-term trend component, intraday fluctuation component and random component, respectively. Indicates; d, h indicate natural day and hour respectively.

[0018] Preferably, step S2 proposes a linearized re-characterization method for clustering results based on clustering analysis of intraday fluctuation components using the k-means algorithm. This method linearly transforms the power fluctuation curve in a typical scenario so that the transformation result approximates the power fluctuation curve in other natural scenarios. The linearized re-characterization method is introduced to reduce the distortion of high-dimensional information by the traditional clustering method by adding degrees of freedom. The core expression of the linearized re-characterization is as follows:

[0019]

[0020] Where, denote the unbalanced power vectors on a natural day and a typical day, respectively, and I denotes a vector whose elements are all 1. The purpose of formula (3) is to express the unbalanced vector on any natural day through linear combination using the unbalanced power vector on a typical day and the vector I as bases. However, in an N-dimensional vector space, at least N linearly independent vectors are required as bases to represent any vector. Here, N>2, but there are only two bases, so it is impossible to obtain a strict representation of any N-dimensional vector. Therefore, the remainder ε is introduced. d Used to indicate error.

[0021] Preferably, step S3 includes the following steps:

[0022] Step S3.1: Obtain the objective function. Through various energy storage configuration plans, the economic objective function consists of three parts: the capacity / installation cost of various energy storage systems, and the operating cost of the system, including hydrogen sales revenue, curtailment losses, and load shedding costs:

[0023]

[0024] Among them, C Inv with C Ope They represent the investment cost and operating cost of the entire system respectively; CRF represents the capacity decay factor, which is used to allocate the total investment to a certain year; γ is the discount rate; n is the planned life of the system; C Inv,B with C Inv,H represent the investment costs of battery energy storage and hydrogen storage systems respectively; They represent the unit power / energy capacity price of battery energy storage, the unit capacity price of electrolyzer, fuel cell, and hydrogen storage tank respectively; E Bat / P Bat 、P Elec 、P FC 、m H Respectively represent the configured energy storage energy / power capacity, electrolyzer, fuel cell, and hydrogen storage tank capacity; C Cur,PV / C Cur,Load , R Sale Respectively represent the penalty for curtailing solar power, the cost of load shedding, and the revenue from selling hydrogen; r H , They represent the price of hydrogen per unit mass and the unit power cost of curtailed solar power / load shedding respectively; They represent the hourly curtailed solar / load power and daily curtailed solar / load power respectively; Indicates the daily hydrogen sales quality; Respectively represent the total number of days and hours in the operating cycle;

[0025] Step S3.2: Specify constraints, system power balance constraints:

[0026]

[0027]

[0028] in, Respectively represent the charging / discharging power of battery energy storage; They represent the power consumption of the electrolyzer and the power generated by the fuel cell respectively;

[0029] Equations (8) and (9) express the power balance relationship with hourly and daily time resolutions, respectively. Hourly power balance is achieved by battery energy storage by smoothing intraday power fluctuations; daily power balance is achieved by seasonal power transfer from the hydrogen storage system.

[0030] Battery energy storage related constraints:

[0031]

[0032] in, Indicates the state of charge of the battery energy storage; Respectively represent the charging / discharging efficiency of battery energy storage; Indicates the minimum value of the battery's allowed state of charge;

[0033] Formula (10) constrains the change of battery energy storage level within a day, the change of battery energy storage level between two consecutive days, the upper and lower limits of charging power, the upper and lower limits of discharging power, the upper and lower limits of battery energy storage level, and the balance of battery energy storage level at the beginning and end of the cycle from top to bottom.

[0034] Constraints related to hydrogen storage systems:

[0035]

[0036] in, Indicates the mass of hydrogen in the hydrogen storage tank; LHV indicates the calorific value of hydrogen, which is used to reflect the relationship between unit electricity and unit mass of hydrogen; represent the electricity-to-gas energy conversion efficiency of the electrolyzer / fuel cell, respectively; Indicates the minimum storage mass allowed by the hydrogen storage tank;

[0037] Formula (11) constrains the change of hydrogen quality in the hydrogen storage tank in a year, the lower limit of hydrogen quality for sale, the upper and lower limits of electrolytic cell power, the upper and lower limits of fuel cell power, the upper and lower limits of hydrogen quality in the hydrogen storage tank, and the balance of hydrogen instructions at the beginning and end of a cycle.

[0038] Constraints related to curtailed solar power and load:

[0039]

[0040] Among them, from top to bottom, they represent the upper limit constraint of the sum of load shedding power / curtailed power at different time scales and the lower limit constraint of load shedding power / curtailed power at different time scales.

[0041] The present invention also provides a long-term and short-term energy storage planning system, which includes the following modules:

[0042] Module M1: Use the seasonal and trend decomposition system to process the unbalanced power time series throughout the year;

[0043] Module M2: Based on the cluster analysis of intraday fluctuation components using the k-means algorithm, the power fluctuation curve under typical scenarios is linearly transformed to obtain the transformed power fluctuation curve;

[0044] Module M3: Establishing a long-term and short-term energy storage optimization configuration model;

[0045] Module M4: Solve problems based on the concept of scenario transformation and perform energy storage planning.

[0046] Preferably, the module M1 processes the unbalanced power time series throughout the year using a seasonal and trend decomposition system, as shown in the following formula:

[0047]

[0048]

[0049] Among them, formula (1) reveals the source of power imbalance, denote the annual unbalanced power, annual photovoltaic output and annual load demand respectively; (2) In the formula, the unbalanced power is decomposed into long-term trend component, intraday fluctuation component and random component, respectively. Indicates; d, h indicate natural day and hour respectively.

[0050] Preferably, the module M2 proposes a linearized re-characterization system for clustering results based on the clustering analysis of intraday fluctuation components using the k-means algorithm. The system linearly transforms the power fluctuation curve in a typical scenario so that the transformation result approximates the power fluctuation curve in other natural scenarios. The linearized re-characterization system is introduced to reduce the distortion of high-dimensional information by the traditional clustering system by adding degrees of freedom. The core expression of the linearized re-characterization is as follows:

[0051]

[0052] Where, denote the unbalanced power vectors on a natural day and a typical day, respectively, and I denotes a vector whose elements are all 1. The purpose of formula (3) is to express the unbalanced vector on any natural day through linear combination using the unbalanced power vector on a typical day and the vector I as bases. However, in an N-dimensional vector space, at least N linearly independent vectors are required as bases to represent any vector. Here, N>2, but there are only two bases, so it is impossible to obtain a strict representation of any N-dimensional vector. Therefore, the remainder ε is introduced. d Used to indicate error.

[0053] Preferably, the module M3 includes the following modules:

[0054] Module M3.1: Obtaining the objective function. Through various energy storage configuration plans, the economic objective function consists of three parts: the capacity / installation cost of various energy storage systems, and the operating costs of the system, including hydrogen sales revenue, curtailment losses, and load shedding costs:

[0055]

[0056] Among them, C Inv with C OpeThey represent the investment cost and operating cost of the entire system respectively; CRF represents the capacity decay factor, which is used to allocate the total investment to a certain year; γ is the discount rate; n is the planned life of the system; C Inv,B with C Inv,H represent the investment costs of battery energy storage and hydrogen storage systems respectively; They represent the unit power / energy capacity price of battery energy storage, the unit capacity price of electrolyzer, fuel cell, and hydrogen storage tank respectively; E Bat / P Bat 、P Elec 、P FC 、m H Respectively represent the configured energy storage energy / power capacity, electrolyzer, fuel cell, and hydrogen storage tank capacity; C Cur,PV / C Cur,Load , R Sale Respectively represent the penalty for curtailing solar power, the cost of load shedding, and the revenue from selling hydrogen; r H , They represent the price of hydrogen per unit mass and the unit power cost of curtailed solar power / load shedding respectively; They represent the hourly curtailed solar / load power and daily curtailed solar / load power respectively; Indicates the daily hydrogen sales quality; Respectively represent the total number of days and hours in the operating cycle;

[0057] Module M3.2: Specify constraints, system power balance constraints:

[0058]

[0059]

[0060] in, Respectively represent the charging / discharging power of battery energy storage; They represent the power consumption of the electrolyzer and the power generated by the fuel cell respectively;

[0061] Equations (8) and (9) express the power balance relationship with hourly and daily time resolutions, respectively. Hourly power balance is achieved by battery energy storage by smoothing intraday power fluctuations; daily power balance is achieved by seasonal power transfer from the hydrogen storage system.

[0062] Battery energy storage related constraints:

[0063]

[0064] in, Indicates the state of charge of the battery energy storage; Respectively represent the charging / discharging efficiency of battery energy storage; Indicates the minimum value of the battery's allowed state of charge;

[0065] Formula (10) constrains the change of battery energy storage level within a day, the change of battery energy storage level between two consecutive days, the upper and lower limits of charging power, the upper and lower limits of discharging power, the upper and lower limits of battery energy storage level, and the balance of battery energy storage level at the beginning and end of the cycle from top to bottom.

[0066] Constraints related to hydrogen storage systems:

[0067]

[0068] in, Indicates the mass of hydrogen in the hydrogen storage tank; LHV indicates the calorific value of hydrogen, which is used to reflect the relationship between unit electricity and unit mass of hydrogen; represent the electricity-to-gas energy conversion efficiency of the electrolyzer / fuel cell, respectively; Indicates the minimum storage mass allowed by the hydrogen storage tank;

[0069] Formula (11) constrains the change of hydrogen quality in the hydrogen storage tank in a year, the lower limit of hydrogen quality for sale, the upper and lower limits of electrolytic cell power, the upper and lower limits of fuel cell power, the upper and lower limits of hydrogen quality in the hydrogen storage tank, and the balance of hydrogen instructions at the beginning and end of a cycle.

[0070] Constraints related to curtailed solar power and load:

[0071]

[0072] Among them, from top to bottom, they represent the upper limit constraint of the sum of load shedding power / curtailed power at different time scales and the lower limit constraint of load shedding power / curtailed power at different time scales.

[0073] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-mentioned long-term and short-term energy storage planning method.

[0074] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the above-mentioned long-term and short-term energy storage planning method when executed by the processor.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] 1. This invention adopts a seasonal and trend decomposition method. On the one hand, it can fully preserve the long-term fluctuation characteristics, thereby ensuring the reliability of seasonal energy storage planning. On the other hand, by decoupling power fluctuations at different time scales, it makes it easier to solve the optimization model.

[0077] 2. This invention further quantifies the differences between scenes of the same type by linearizing the clustering results, thereby improving the accuracy of the clustering results at a lower dimensionality cost.

[0078] 3. The present invention reduces decision variables and improves optimization efficiency by using a solution method based on scene transformation while ensuring the accuracy of the solution results;

[0079] 4. The solution method proposed in this invention is not limited to energy storage systems, but can be used for planning and designing all aspects of power system source-grid-storage.

[0080] 5. The present invention enriches and refines the operating cost objective function, taking into account the system's hydrogen sales revenue, solar curtailment cost, and load shedding cost, making it more suitable for actual operating scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0082] Figure 1 This is a diagram of the energy storage regulation situation on a certain day of the present invention;

[0083] Figure 2 This is a diagram showing the regulation status of the hydrogen storage system in a certain year of the present invention;

[0084] Figure 3 A comparison chart of the results of the method proposed in the present invention and the traditional optimization method;

[0085] Figure 4 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0086] The present invention will be described in detail below with reference to specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those skilled in the art, several changes and improvements can be made without departing from the scope of the present invention. These all fall within the scope of protection of the present invention.

[0087] Example 1:

[0088] According to the present invention, a long-term and short-term energy storage planning method is provided, the method comprising the following steps:

[0089] Step S1: Use the seasonal and trend decomposition method to process the unbalanced power time series throughout the year;

[0090] Step S2: Based on the cluster analysis of the intraday fluctuation components using the k-means algorithm, a power fluctuation curve under a typical scenario is linearly transformed to obtain a transformed power fluctuation curve;

[0091] Step S3: Establishing a long-term and short-term energy storage optimization configuration model;

[0092] Step S4: Solve based on the concept of scene change and perform energy storage planning.

[0093] The step S1 processes the unbalanced power time series for the whole year using the seasonal and trend decomposition method, as shown in the following formula:

[0094]

[0095]

[0096] Among them, formula (1) reveals the source of power imbalance, denote the annual unbalanced power, annual photovoltaic output and annual load demand respectively; (2) In the formula, the unbalanced power is decomposed into long-term trend component, intraday fluctuation component and random component, respectively. Indicates; d, h indicate natural day and hour respectively.

[0097] In step S2, based on the clustering analysis of intraday fluctuation components using the k-means algorithm, a linearized re-characterization method for clustering results is proposed. This method linearly transforms the power fluctuation curve in a typical scenario so that the transformation result approximates the power fluctuation curve in other natural scenarios. The linearized re-characterization method is introduced to reduce the distortion of high-dimensional information by the traditional clustering method by adding degrees of freedom. The core expression of the linearized re-characterization is as follows:

[0098]

[0099] Where, denote the unbalanced power vectors on a natural day and a typical day, respectively, and I denotes a vector whose elements are all 1. The purpose of formula (3) is to express the unbalanced vector on any natural day through linear combination using the unbalanced power vector on a typical day and the vector I as bases. However, in an N-dimensional vector space, at least N linearly independent vectors are required as bases to represent any vector. Here, N>2, but there are only two bases, so it is impossible to obtain a strict representation of any N-dimensional vector. Therefore, the remainder ε is introduced. d Used to indicate error.

[0100] The step S3 comprises the following steps:

[0101] Step S3.1: Obtain the objective function. Through various energy storage configuration plans, the economic objective function consists of three parts: the capacity / installation cost of various energy storage systems, and the operating cost of the system, including hydrogen sales revenue, curtailment losses, and load shedding costs:

[0102]

[0103] Among them, C Inv with C Ope They represent the investment cost and operating cost of the entire system respectively; CRF represents the capacity decay factor, which is used to allocate the total investment to a certain year; γ is the discount rate; n is the planned life of the system; C Inv,B with C Inv,H represent the investment costs of battery energy storage and hydrogen storage systems respectively; They represent the unit power / energy capacity price of battery energy storage, the unit capacity price of electrolyzer, fuel cell, and hydrogen storage tank respectively; E Bat / P Bat 、P Elec 、P FC 、m H Respectively represent the configured energy storage energy / power capacity, electrolyzer, fuel cell, and hydrogen storage tank capacity; C Cur,PV / C Cur,Load , R Sale Respectively represent the penalty for curtailing solar power, the cost of load shedding, and the revenue from selling hydrogen; r H , They represent the price of hydrogen per unit mass and the unit power cost of curtailed solar power / load shedding respectively; They represent the hourly curtailed solar / load power and daily curtailed solar / load power respectively; Indicates the daily hydrogen sales quality; Respectively represent the total number of days and hours in the operating cycle;

[0104] Step S3.2: Specify constraints, system power balance constraints:

[0105]

[0106]

[0107] in, Respectively represent the charging / discharging power of battery energy storage; They represent the power consumption of the electrolyzer and the power generated by the fuel cell respectively;

[0108] Equations (8) and (9) express the power balance relationship with hourly and daily time resolutions, respectively. Hourly power balance is achieved by battery energy storage by smoothing intraday power fluctuations; daily power balance is achieved by seasonal power transfer from the hydrogen storage system.

[0109] Battery energy storage related constraints:

[0110]

[0111] in, Indicates the state of charge of the battery energy storage; Respectively represent the charging / discharging efficiency of battery energy storage; Indicates the minimum value of the battery's allowed state of charge;

[0112] Formula (10) constrains the change of battery energy storage level within a day, the change of battery energy storage level between two consecutive days, the upper and lower limits of charging power, the upper and lower limits of discharging power, the upper and lower limits of battery energy storage level, and the balance of battery energy storage level at the beginning and end of the cycle from top to bottom.

[0113] Constraints related to hydrogen storage systems:

[0114]

[0115] in, Indicates the mass of hydrogen in the hydrogen storage tank; LHV indicates the calorific value of hydrogen, which is used to reflect the relationship between unit electricity and unit mass of hydrogen; represent the electricity-to-gas energy conversion efficiency of the electrolyzer / fuel cell, respectively; Indicates the minimum storage mass allowed by the hydrogen storage tank;

[0116] Formula (11) constrains the change of hydrogen quality in the hydrogen storage tank in a year, the lower limit of hydrogen quality for sale, the upper and lower limits of electrolytic cell power, the upper and lower limits of fuel cell power, the upper and lower limits of hydrogen quality in the hydrogen storage tank, and the balance of hydrogen instructions at the beginning and end of a cycle.

[0117] Constraints related to curtailed solar power and load:

[0118]

[0119] Among them, from top to bottom, they represent the upper limit constraint of the sum of load shedding power / curtailed power at different time scales and the lower limit constraint of load shedding power / curtailed power at different time scales.

[0120] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-mentioned long-term and short-term energy storage planning method.

[0121] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the above-mentioned long-term and short-term energy storage planning method when executed by the processor.

[0122] The present invention also provides a long-term and short-term energy storage planning system, which can be implemented by executing the process steps of the long-term and short-term energy storage planning method. That is, those skilled in the art can understand the long-term and short-term energy storage planning method as a preferred implementation of the long-term and short-term energy storage planning system.

[0123] Example 2:

[0124] The present invention also provides a long-term and short-term energy storage planning system, which includes the following modules:

[0125] Module M1: Use the seasonal and trend decomposition system to process the unbalanced power time series throughout the year;

[0126] Module M2: Based on the cluster analysis of intraday fluctuation components using the k-means algorithm, the power fluctuation curve under typical scenarios is linearly transformed to obtain the transformed power fluctuation curve;

[0127] Module M3: Establishing a long-term and short-term energy storage optimization configuration model;

[0128] Module M4: Solve problems based on the concept of scenario transformation and perform energy storage planning.

[0129] The module M1 uses the seasonal and trend decomposition system to process the unbalanced power time series throughout the year, as shown in the following formula:

[0130]

[0131]

[0132] Among them, formula (1) reveals the source of power imbalance, denote the annual unbalanced power, annual photovoltaic output and annual load demand respectively; (2) In the formula, the unbalanced power is decomposed into long-term trend component, intraday fluctuation component and random component, respectively. Indicates; d, h indicate natural day and hour respectively.

[0133] The module M2 uses the k-means algorithm to perform cluster analysis on the intraday fluctuation components and proposes a linearized re-characterization system for clustering results. This system linearly transforms the power fluctuation curve in a typical scenario so that the transformation result approximates the power fluctuation curve in other natural scenarios. The linearized re-characterization system is introduced to reduce the distortion of high-dimensional information by the traditional clustering system by adding degrees of freedom. The core expression of the linearized re-characterization is as follows:

[0134]

[0135] Where, denote the unbalanced power vectors on a natural day and a typical day, respectively, and I denotes a vector whose elements are all 1. The purpose of formula (3) is to express the unbalanced vector on any natural day through linear combination using the unbalanced power vector on a typical day and the vector I as bases. However, in an N-dimensional vector space, at least N linearly independent vectors are required as bases to represent any vector. Here, N>2, but there are only two bases, so it is impossible to obtain a strict representation of any N-dimensional vector. Therefore, the remainder ε is introduced. d Used to indicate error.

[0136] The module M3 includes the following modules:

[0137] Module M3.1: Obtaining the objective function. Through various energy storage configuration plans, the economic objective function consists of three parts: the capacity / installation cost of various energy storage systems, and the operating costs of the system, including hydrogen sales revenue, curtailment losses, and load shedding costs:

[0138]

[0139] Among them, C Inv with C Ope They represent the investment cost and operating cost of the entire system respectively; CRF represents the capacity decay factor, which is used to allocate the total investment to a certain year; γ is the discount rate; n is the planned life of the system; C Inv,B with C Inv,H represent the investment costs of battery energy storage and hydrogen storage systems respectively; They represent the unit power / energy capacity price of battery energy storage, the unit capacity price of electrolyzer, fuel cell, and hydrogen storage tank respectively; E Bat / P Bat 、P Elec 、P FC 、m H Respectively represent the configured energy storage energy / power capacity, electrolyzer, fuel cell, and hydrogen storage tank capacity; C Cur,PV / C Cur,Load , R Sale Respectively represent the penalty for curtailing solar power, the cost of load shedding, and the revenue from selling hydrogen; rH , They represent the price of hydrogen per unit mass and the unit power cost of curtailed solar power / load shedding respectively; They represent the hourly curtailed solar / load power and daily curtailed solar / load power respectively; Indicates the daily hydrogen sales quality; Respectively represent the total number of days and hours in the operating cycle;

[0140] Module M3.2: Specify constraints, system power balance constraints:

[0141]

[0142]

[0143] in, Respectively represent the charging / discharging power of battery energy storage; They represent the power consumption of the electrolyzer and the power generated by the fuel cell respectively;

[0144] Equations (8) and (9) express the power balance relationship with hourly and daily time resolutions, respectively. Hourly power balance is achieved by battery energy storage by smoothing intraday power fluctuations; daily power balance is achieved by seasonal power transfer from the hydrogen storage system.

[0145] Battery energy storage related constraints:

[0146]

[0147] in, Indicates the state of charge of the battery energy storage; Respectively represent the charging / discharging efficiency of battery energy storage; Indicates the minimum value of the battery's allowed state of charge;

[0148] Formula (10) constrains the change of battery energy storage level within a day, the change of battery energy storage level between two consecutive days, the upper and lower limits of charging power, the upper and lower limits of discharging power, the upper and lower limits of battery energy storage level, and the balance of battery energy storage level at the beginning and end of the cycle from top to bottom.

[0149] Constraints related to hydrogen storage systems:

[0150]

[0151] in, Indicates the mass of hydrogen in the hydrogen storage tank; LHV indicates the calorific value of hydrogen, which is used to reflect the relationship between unit electricity and unit mass of hydrogen; represent the electricity-to-gas energy conversion efficiency of the electrolyzer / fuel cell, respectively; Indicates the minimum storage mass allowed by the hydrogen storage tank;

[0152] Formula (11) constrains the change of hydrogen quality in the hydrogen storage tank in a year, the lower limit of hydrogen quality for sale, the upper and lower limits of electrolytic cell power, the upper and lower limits of fuel cell power, the upper and lower limits of hydrogen quality in the hydrogen storage tank, and the balance of hydrogen instructions at the beginning and end of a cycle.

[0153] Constraints related to curtailed solar power and load:

[0154]

[0155] Among them, from top to bottom, they represent the upper limit constraint of the sum of load shedding power / curtailed power at different time scales and the lower limit constraint of load shedding power / curtailed power at different time scales.

[0156] Example 3:

[0157] To compensate for multi-timescale power imbalances in the power system, increase renewable energy consumption, and reduce carbon emissions, this paper utilizes a combined battery and hydrogen energy storage system for both intraday and seasonal peak shaving. To address the challenges of energy storage configuration within a year-round operating cycle, a solution framework centered on the concept of scenario transformation is proposed. This framework significantly reduces the number of decision variables in the optimization problem, while ensuring accurate solutions, thus simplifying the solution.

[0158] The present invention aims to address the shortcomings of existing long-term and short-term energy storage planning methods. To this end, the present invention aims to provide a long-term and short-term energy storage planning method based on the concept of scenario transformation, in order to achieve high-precision planning results with a smaller problem scale and solution cost.

[0159] The solution has the following features:

[0160] The seasonal and trend decomposition method is used to extract seasonal and trend components from the unbalanced power. On the one hand, the long-term power fluctuation trend is fully preserved, thereby ensuring the planning reliability of long-term energy storage; on the other hand, the fluctuations at different time scales are decoupled, reducing the complexity of modeling.

[0161] This invention breaks through the application paradigm of traditional clustering methods. After clustering analysis of intraday power fluctuation curves, all similar natural daily curves are linearly re-characterized based on typical daily curves, and the differences between similar curves are further refined and modeled, thereby improving the accuracy of clustering results at a lower dimensionality cost.

[0162] This paper provides a novel modeling approach for energy storage capacity planning. By approximately reconstructing and transforming the traditional power balance equation and proposing the concept of scenario transformation, a linear coupling relationship is established between the decision variables within a natural day throughout the year and the decision variables within its corresponding typical day. This significantly reduces the number of decision variables required for long-term planning problems and significantly reduces computational costs.

[0163] This paper uses a solar-storage-hydrogen microgrid as its framework and jointly plans the battery and hydrogen storage systems. To fully consider the specific characteristics of the planning target, hydrogen sales revenue, solar curtailment penalties, and load shedding costs are factored into the objective function, enabling a precise assessment of system costs and improving the reliability of the planning structure.

[0164] The technical solution applied for in this patent is a method for planning short- and long-term energy storage based on the concept of scenario transformation. This solution proposes a new planning framework to achieve efficient planning of such energy storage with embedded long operating cycles, and realize the economic construction of solar-storage-hydrogen microgrids.

[0165] The specific implementation methods are as follows:

[0166] Season-trend breakdown:

[0167] Influenced by the natural environment and human activities, the power imbalance between wind and solar power output and load demand includes both daily intraday power fluctuations and seasonal power imbalances on an annual time scale. Over the entire year, recurring intraday power fluctuations exhibit a certain degree of self-similarity. Seasonal and trend decomposition is a time series analysis method that extracts recurring seasonal components (including daily, weekly, monthly, and seasonal components) with fixed periods in a time series, thereby facilitating the analysis of long-term trends implicit in the series.

[0168] The present invention uses the seasonal and trend decomposition method to process the unbalanced power time series throughout the year.

[0169]

[0170]

[0171] Among them, formula (1) reveals the source of power imbalance, denote the annual unbalanced power, annual photovoltaic output and annual load demand respectively; (2) In the formula, the unbalanced power is decomposed into long-term trend component, intraday fluctuation component and random component, respectively. d and h represent natural day and hour respectively.

[0172] In this paper, this decomposition operation forms the foundation of the entire planning framework. On the one hand, separating the intraday fluctuation component from the long-term trend component allows for direct consideration of intraday fluctuations in subsequent cluster analysis, avoiding the neglect of long-term trends due to the discontinuity of typical cluster days in previous studies. On the other hand, the decoupling of fluctuations at different time scales also facilitates the development of optimization models.

[0173] Linearized re-representation of clustering results:

[0174] In previous power system planning research, the operation of selecting each type of typical curve as the input scenario has been widely accepted and adopted. This idea can effectively reduce the number of scenarios and reduce the scale of the problem to be solved. However, a typical scenario cannot accurately represent all the scenarios of its class, especially when the number of cluster centers is small, and there is a large difference between the typical scenario and other extreme scenarios. Therefore, based on the clustering analysis of intraday fluctuation components using the k-means algorithm, the present invention proposes a linearized re-characterization method for clustering results. This method linearly transforms the power fluctuation curve under the typical scenario so that the transformation result is close to the power fluctuation curve under other natural scenarios. In essence, the traditional clustering method is a dimensionality reduction mapping of elements in high-dimensional space; and the present invention introduces a linearized re-characterization method to reduce the distortion of high-dimensional information by the traditional clustering method by adding degrees of freedom. In the present invention, the core expression of linearized re-characterization is as follows:

[0175]

[0176] Where, Represent the unbalanced power vectors on natural days and typical days respectively, and I represents a vector whose elements are all 1. The purpose of formula (3) is to use the unbalanced power vector on a typical day and the vector I as a basis to express the unbalanced vector on any natural day through linear combination. However, in an N-dimensional vector space, at least N linearly independent vectors are required as a basis to represent any vector. Here, N>2, but there are only two bases, and it is impossible to obtain a strict representation of any N-dimensional vector. Therefore, the remainder ε is introduced. d Used to indicate error.

[0177] To ensure the accuracy of the approximation, it is necessary to find the best coefficient to minimize the remainder. With the goal of minimizing the bi-norm of the remainder, it can be written as the following optimization problem:

[0178]

[0179]

[0180] The optimization problem shown in (4) can be transformed into the problem of optimal approximate solution of a linear equation system:

[0181]

[0182] Let the coefficient matrix in (5) be A and the constant term on the right be b, then it is easy to know that the best approximate solution of the above formula in the sense of least squares is:

[0183]

[0184] Therefore, based on typical daily scenes and the corresponding conversion coefficients for each scene, all natural daytime scenes can be characterized. By introducing conversion coefficients, clustering results can be refined at a low dimensional cost, breaking through the traditional clustering application paradigm.

[0185] Long-term and short-term energy storage optimization configuration model:

[0186] (1) Objective function

[0187] This patent aims to ensure the long-term economic benefits of a solar-storage-hydrogen microgrid by planning various energy storage configurations. Therefore, the economic objective function consists of three parts: the capacity / installation cost of various energy storage systems; and the operating costs of the system, including hydrogen sales revenue, solar curtailment losses, and load shedding costs.

[0188]

[0189] Among them, C Inv with C Ope They represent the investment cost and operating cost of the entire system respectively; CRF represents the capacity decay factor, which is used to allocate the total investment to a certain year; γ is the discount rate; n is the planned life of the system; C Inv,B with C Inv,H represent the investment costs of battery energy storage and hydrogen storage systems respectively; They represent the unit power / energy capacity price of battery energy storage, the unit capacity price of electrolyzer, fuel cell, and hydrogen storage tank respectively; E Bat / P Bat 、P Elec 、P FC 、m H Respectively represent the configured energy storage energy / power capacity, electrolyzer, fuel cell, and hydrogen storage tank capacity; C Cur,PV / C Cur,Load , R Sale Respectively represent the penalty for curtailing solar power, the cost of load shedding, and the revenue from selling hydrogen; r H , They represent the price of hydrogen per unit mass and the unit power cost of curtailed solar power / load shedding respectively; They represent the hourly curtailed solar / load power and daily curtailed solar / load power respectively; Indicates the daily hydrogen sales quality; Respectively represent the total number of days and hours in the operating cycle.

[0190] (2) Constraints

[0191] System power balance constraints:

[0192]

[0193]

[0194] in, Respectively represent the charging / discharging power of battery energy storage; They represent the power consumption of the electrolyzer and the power generated by the fuel cell respectively.

[0195] Equations (8) and (9) express the power balance relationship with hourly and daily time resolutions, respectively. Hourly power balance is achieved by battery energy storage by smoothing intraday power fluctuations; daily power balance is achieved by seasonal power transfer from the hydrogen storage system.

[0196] Battery energy storage related constraints:

[0197]

[0198] in, Indicates the state of charge of the battery energy storage; Respectively represent the charging / discharging efficiency of battery energy storage; Indicates the minimum state of charge allowed for the battery.

[0199] Formula (10) constrains the change of battery energy storage level within a day, the change of battery energy storage level between two consecutive days, the upper and lower limits of charging power, the upper and lower limits of discharging power, the upper and lower limits of battery energy storage level, and the balance of battery energy storage level at the beginning and end of the cycle, from top to bottom.

[0200] Constraints related to hydrogen storage systems:

[0201]

[0202] in, Indicates the mass of hydrogen in the hydrogen storage tank; LHV indicates the calorific value of hydrogen, which is used to reflect the relationship between unit electricity and unit mass of hydrogen; represent the electricity-to-gas energy conversion efficiency of the electrolyzer / fuel cell, respectively; Indicates the minimum storage mass allowed by the hydrogen storage tank.

[0203] Formula (11) constrains the change of hydrogen quality in the hydrogen storage tank in a year, the lower limit of hydrogen quality for sale, the upper and lower limits of electrolytic cell power, the upper and lower limits of fuel cell power, the upper and lower limits of hydrogen quality in the hydrogen storage tank, and the balance of hydrogen instructions at the beginning and end of a cycle.

[0204] Constraints related to curtailed solar power and load:

[0205]

[0206] Among them, from top to bottom, they represent the upper limit constraint of the sum of load shedding power / curtailed power at different time scales and the lower limit constraint of load shedding power / curtailed power at different time scales.

[0207] Solution method based on the concept of scene change:

[0208] First, we rewrite Equation (8) to reduce the power balance equation originally established on each natural day to the power balance equation for a typical day:

[0209]

[0210] in, Respectively represent the charging / discharging power of battery energy storage in a typical day; They represent the curtailed solar power and curtailed load power in a typical day.

[0211] Substituting equation (3) into equation (13) and ignoring the least squares norm remainder, we have:

[0212]

[0213] It is worth noting that the decision variables in Equation (14) are all modeled under typical scenarios. However, the unbalanced power to be offset in the equation belongs to any natural scenario and does not conform to the traditional concept of power balance. However, we transform Equation (14) and establish the relationship between the decision variables in natural scenarios and typical scenarios through mathematical construction:

[0214]

[0215] In the above formula, a series of additional variables X are introduced o, The purpose of h is to assign constant terms to each typical day's decision variable, ensuring that Equation (15) is formally consistent with Equation (8), which represents the natural day power balance. Comparing the two equations, we use the constructed variables in brackets within Equation (15) as the natural day's decision variables, replacing the natural day's decision variables in the energy storage optimization configuration model. This significantly reduces the number of variables in the optimization model and reduces computational costs.

[0216] This paper takes the photovoltaic-load historical data of a school's microgrid system throughout 2019 as an example, and applies a collaborative planning method based on the concept of scenario transformation to configure the capacity of the electrochemical energy storage and hydrogen storage systems.

[0217] The optimization results of the method proposed in this invention are compared with those of the traditional global timing method, as shown in Table 1.

[0218] Table 1 Comparison of optimization results based on traditional method and the method proposed in this invention

[0219]

[0220]

[0221] This example demonstrates that the proposed optimization solution method reduces the number of decision variables in the optimization model by 52% and improves the efficiency of the optimization calculation by 42%, while achieving similar planning results to those of the traditional global sequential method. This demonstrates that the proposed method significantly reduces the planning complexity of traditional planning methods and offers significant efficiency advantages.

[0222] Further, Figure 1 The energy storage charging and discharging trajectory of the microgrid system on January 1, 2019 is shown. From 9 a.m. to 18 p.m. when photovoltaic output is sufficient, the battery energy storage is charged to promote the consumption of excess electricity; while in other periods when power generation is insufficient, the battery energy storage is discharged to meet the load demand; the hydrogen storage system basically does not participate in flexibility adjustment throughout the day. Figure 2 The figure shows the charging and discharging status of the hydrogen storage system of the microgrid throughout 2019. Comparing the two figures above and below, it can be seen that the working seasons of the electrolyzer and fuel cell are basically staggered throughout the year, which plays a role in compensating for seasonal power imbalance. Figure 3 The state of charge trajectories of energy storage under two optimization solution methods are demonstrated, proving the accuracy of the optimization method proposed in this invention.

[0223] Those skilled in the art may understand this embodiment as a more specific description of Embodiment 1 and Embodiment 2.

[0224] Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, and units provided by the present invention in purely computer-readable program code, it is entirely possible to implement the same functions of the system and its various devices, modules, and units provided by the present invention in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; the devices, modules, and units for implementing various functions can also be considered as both software modules implementing the method and structures within the hardware component.

[0225] The above describes specific embodiments of the present invention. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art may make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. The embodiments of this application and the features in the embodiments may be combined with each other in any manner unless there is a conflict.

Claims

1. A long-term and short-term energy storage planning method, characterized in that: The method comprises the following steps: Step S1: Use the seasonal and trend decomposition method to process the unbalanced power time series throughout the year; Step S2: Based on the cluster analysis of the intraday fluctuation components using the k-means algorithm, a power fluctuation curve under a typical scenario is linearly transformed to obtain a transformed power fluctuation curve; Step S3: Establishing a long-term and short-term energy storage optimization configuration model; Step S4: Solve based on the concept of scenario change and perform energy storage planning; The step S1 processes the unbalanced power time series for the whole year using the seasonal and trend decomposition method, as shown in the following formula: Among them, formula (1) reveals the source of power imbalance, denote the annual unbalanced power, annual photovoltaic output and annual load demand respectively; (2) In the formula, the unbalanced power is decomposed into long-term trend component, intraday fluctuation component and random component, respectively. Indicates; d, h indicate natural day and hour respectively; In step S2, based on the clustering analysis of intraday fluctuation components using the k-means algorithm, a linearized re-characterization method for clustering results is proposed. This method linearly transforms the power fluctuation curve in a typical scenario so that the transformation result approximates the power fluctuation curve in other natural scenarios. The linearized re-characterization method is introduced to reduce the distortion of high-dimensional information by the traditional clustering method by adding degrees of freedom. The core expression of the linearized re-characterization is as follows: Where, denote the unbalanced power vectors on a natural day and a typical day, respectively, and I denotes a vector whose elements are all 1. The purpose of formula (3) is to express the unbalanced vector on any natural day through linear combination using the unbalanced power vector on a typical day and the vector I as bases. However, in an N-dimensional vector space, at least N linearly independent vectors are required as bases to represent any vector. Here, N>2, but there are only two bases, so it is impossible to obtain a strict representation of any N-dimensional vector. Therefore, the remainder ε is introduced. d Used to indicate error.

2. The long-term and short-term energy storage planning method according to claim 1, characterized in that: The step S3 comprises the following steps: Step S3.1: Obtain the objective function. Through various energy storage configuration plans, the economic objective function consists of three parts: the capacity / installation cost of various energy storage systems, and the operating cost of the system, including hydrogen sales revenue, curtailment losses, and load shedding costs: Among them, C Inv with C Ope They represent the investment cost and operating cost of the entire system respectively; CRF represents the capacity decay factor, which is used to allocate the total investment to a certain year; γ is the discount rate; n is the planned life of the system; C Inv,B with C Inv,H represent the investment costs of battery energy storage and hydrogen storage systems respectively; They represent the unit power / energy capacity price of battery energy storage, the unit capacity price of electrolyzer, fuel cell, and hydrogen storage tank respectively; E Bat / P Bat 、P Elec 、P FC 、m H Respectively represent the configured energy storage energy / power capacity, electrolyzer, fuel cell, and hydrogen storage tank capacity; C Cur,PV / C Cur,Load , R Sale Respectively represent the penalty for curtailing solar power, the cost of load shedding, and the revenue from selling hydrogen; r H , They represent the price of hydrogen per unit mass and the unit power cost of curtailed solar power / load shedding respectively; They represent the hourly curtailed solar / load power and daily curtailed solar / load power respectively; Indicates the daily hydrogen sales quality; Respectively represent the total number of days and hours in the operating cycle; Step S3.2: Specify constraints, system power balance constraints: in, Respectively represent the charging / discharging power of battery energy storage; They represent the power consumption of the electrolyzer and the power generated by the fuel cell respectively; Equations (8) and (9) express the power balance relationship with hourly and daily time resolutions, respectively. Hourly power balance is achieved by battery energy storage by smoothing intraday power fluctuations; daily power balance is achieved by seasonal power transfer from the hydrogen storage system. Battery energy storage related constraints: in, Indicates the state of charge of the battery energy storage; Respectively represent the charging / discharging efficiency of battery energy storage; Indicates the minimum value of the battery's allowed state of charge; Formula (10) constrains the change of battery energy storage level within a day, the change of battery energy storage level between two consecutive days, the upper and lower limits of charging power, the upper and lower limits of discharging power, the upper and lower limits of battery energy storage level, and the balance of battery energy storage level at the beginning and end of the cycle from top to bottom. Constraints related to hydrogen storage systems: in, Indicates the mass of hydrogen in the hydrogen storage tank; LHV indicates the calorific value of hydrogen, which is used to reflect the relationship between unit electricity and unit mass of hydrogen; represent the electricity-to-gas energy conversion efficiency of the electrolyzer / fuel cell, respectively; Indicates the minimum storage mass allowed by the hydrogen storage tank; Formula (11) constrains the change of hydrogen quality in the hydrogen storage tank in a year, the lower limit of hydrogen quality for sale, the upper and lower limits of electrolytic cell power, the upper and lower limits of fuel cell power, the upper and lower limits of hydrogen quality in the hydrogen storage tank, and the balance of hydrogen instructions at the beginning and end of a cycle. Constraints related to curtailed solar power and load: Among them, from top to bottom, they represent the upper limit constraint of the sum of load shedding power / curtailed power at different time scales and the lower limit constraint of load shedding power / curtailed power at different time scales.

3. A long-term and short-term energy storage planning system, characterized in that: The system includes the following modules: Module M1: Use the seasonal and trend decomposition system to process the unbalanced power time series throughout the year; Module M2: Based on the cluster analysis of intraday fluctuation components using the k-means algorithm, the power fluctuation curve under typical scenarios is linearly transformed to obtain the transformed power fluctuation curve; Module M3: Establishing a long-term and short-term energy storage optimization configuration model; Module M4: Solve the problem based on the concept of scenario transformation and conduct energy storage planning; The module M1 uses the seasonal and trend decomposition system to process the unbalanced power time series throughout the year, as shown in the following formula: Among them, formula (1) reveals the source of power imbalance, denote the annual unbalanced power, annual photovoltaic output and annual load demand respectively; (2) In the formula, the unbalanced power is decomposed into long-term trend component, intraday fluctuation component and random component, respectively. Indicates; d, h indicate natural day and hour respectively; The module M2 uses the k-means algorithm to perform cluster analysis on the intraday fluctuation components and proposes a linearized re-characterization system for clustering results. This system linearly transforms the power fluctuation curve in a typical scenario so that the transformation result approximates the power fluctuation curve in other natural scenarios. The linearized re-characterization system is introduced to reduce the distortion of high-dimensional information by the traditional clustering system by adding degrees of freedom. The core expression of the linearized re-characterization is as follows: Where, denote the unbalanced power vectors on a natural day and a typical day, respectively, and I denotes a vector whose elements are all 1. The purpose of formula (3) is to express the unbalanced vector on any natural day through linear combination using the unbalanced power vector on a typical day and the vector I as bases. However, in an N-dimensional vector space, at least N linearly independent vectors are required as bases to represent any vector. Here, N>2, but there are only two bases, so it is impossible to obtain a strict representation of any N-dimensional vector. Therefore, the remainder ε is introduced. d Used to indicate error.

4. The long-term and short-term energy storage planning system according to claim 3, characterized in that: The module M3 includes the following modules: Module M3.1: Obtaining the objective function. Through various energy storage configuration plans, the economic objective function consists of three parts: the capacity / installation cost of various energy storage systems, and the operating costs of the system, including hydrogen sales revenue, curtailment losses, and load shedding costs: Among them, C Inv with C Ope They represent the investment cost and operating cost of the entire system respectively; CRF represents the capacity decay factor, which is used to allocate the total investment to a certain year; γ is the discount rate; n is the planned life of the system; C Inv,B with C Inv,H represent the investment costs of battery energy storage and hydrogen storage systems respectively; They represent the unit power / energy capacity price of battery energy storage, the unit capacity price of electrolyzer, fuel cell, and hydrogen storage tank respectively; E Bat / P Bat 、P Elec 、P FC 、m H Respectively represent the configured energy storage energy / power capacity, electrolyzer, fuel cell, and hydrogen storage tank capacity; C Cur,PV / C Cur,Load , R Sale Respectively represent the penalty for curtailing solar power, the cost of load shedding, and the revenue from selling hydrogen; r H , They represent the price of hydrogen per unit mass and the unit power cost of curtailed solar power / load shedding respectively; They represent the hourly curtailed solar / load power and daily curtailed solar / load power respectively; Indicates the daily hydrogen sales quality; Respectively represent the total number of days and hours in the operating cycle; Module M3.2: Specify constraints, system power balance constraints: in, Respectively represent the charging / discharging power of battery energy storage; They represent the power consumption of the electrolyzer and the power generated by the fuel cell respectively; Equations (8) and (9) express the power balance relationship with hourly and daily time resolutions, respectively. Hourly power balance is achieved by battery energy storage by smoothing intraday power fluctuations; daily power balance is achieved by seasonal power transfer from the hydrogen storage system. Battery energy storage related constraints: in, Indicates the state of charge of the battery energy storage; Respectively represent the charging / discharging efficiency of battery energy storage; Indicates the minimum value of the battery's allowed state of charge; Formula (10) constrains the change of battery energy storage level within a day, the change of battery energy storage level between two consecutive days, the upper and lower limits of charging power, the upper and lower limits of discharging power, the upper and lower limits of battery energy storage level, and the balance of battery energy storage level at the beginning and end of the cycle from top to bottom. Constraints related to hydrogen storage systems: in, Indicates the mass of hydrogen in the hydrogen storage tank; LHV indicates the calorific value of hydrogen, which is used to reflect the relationship between unit electricity and unit mass of hydrogen; represent the electricity-to-gas energy conversion efficiency of the electrolyzer / fuel cell, respectively; Indicates the minimum storage mass allowed by the hydrogen storage tank; Formula (11) constrains the change of hydrogen quality in the hydrogen storage tank in a year, the lower limit of hydrogen quality for sale, the upper and lower limits of electrolytic cell power, the upper and lower limits of fuel cell power, the upper and lower limits of hydrogen quality in the hydrogen storage tank, and the balance of hydrogen instructions at the beginning and end of a cycle. Constraints related to curtailed solar power and load: Among them, from top to bottom, they represent the upper limit constraint of the sum of load shedding power / curtailed power at different time scales and the lower limit constraint of load shedding power / curtailed power at different time scales.

5. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the long-term and short-term energy storage planning method according to any one of claims 1 to 2 are implemented.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer program is executed by a processor, the steps of the long-term and short-term energy storage planning method according to any one of claims 1 to 2 are implemented.

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