Hybrid energy storage system configuration method and device

Abstract

This specification relates to the field of hybrid energy storage technology, and specifically discloses a hybrid energy storage system configuration method and device, wherein the method includes: establishing an energy storage device database and an auxiliary device database based on the type of energy storage device required by the hybrid energy storage system; obtaining the power load forecast curve of the hybrid energy storage system, and establishing an expected load model to determine the output data of the hybrid energy storage system; allocating the power required by various energy storage devices among the various energy storage devices based on the energy storage device database and the total power required by the hybrid energy storage system, and generating multiple power configuration schemes for the hybrid energy storage system; using a multi-objective evolutionary algorithm to determine the target energy storage system configuration scheme of the hybrid energy storage system from the multiple power configuration schemes; multiple constraints include energy storage device constraints and auxiliary device constraints. The above scheme can quickly obtain the required energy storage equipment and auxiliary equipment selection and configuration schemes for the hybrid energy storage system.

Description

Technical Field

[0001] The present invention relates to the field of hybrid energy storage technology, and in particular to a hybrid energy storage system configuration method and device. Background Art

[0002] At present, there are energy storage devices with various technical routes. Different energy storage technologies have different technical parameters, advantages and characteristics, and applicable scenarios. With the increasing diversification of power system needs, a single type of energy storage technology can no longer meet the requirements of construction cycle, configuration flexibility, safety, response speed, energy storage duration, service life, and economic benefits.

[0003] Therefore, in the design of hybrid energy storage systems, the selection and configuration methods of energy storage units are particularly important, which is directly related to the performance, cost and efficiency of the system. Through advanced algorithms and models, it is possible to achieve effective scheduling of energy storage system power, reasonable allocation of energy, and optimize system performance. At present, most hybrid energy storage system selection and configuration methods are mainly aimed at the combination of two energy storage technology routes, and the optimization targets are mostly concentrated on initial investment costs, operation and maintenance costs, and life cycle costs, aiming to select the most cost-effective technology combination.

[0004] However, for new hybrid energy storage systems composed of multiple (two or more) energy storage technology routes, there is still a lack of systematic and targeted selection and configuration methods when comprehensively considering multiple factors such as cost, installation conditions, auxiliary equipment and actual needs.

[0005] To address the above problems, no effective solution has been proposed yet. Summary of the invention

[0006] The embodiments of this specification provide a hybrid energy storage system configuration method and device to solve the problem that it is difficult to comprehensively consider multiple factors in the hybrid energy storage system configuration scheme in the prior art.

[0007] The embodiment of this specification provides a hybrid energy storage system configuration method, including:

[0008] Based on the types of energy storage devices required by the hybrid energy storage system, an energy storage device database and an auxiliary device database are established; the energy storage device database includes device parameters of various energy storage devices required by the hybrid energy storage system, and the auxiliary device database includes device parameters of auxiliary devices required by the hybrid energy storage system;

[0009] Obtaining the power load forecast curve of the hybrid energy storage system and establishing an expected load model to determine the output data of the hybrid energy storage system; the power load forecast curve includes the real-time load power of each time node in a preset time period and the maximum load power, load duration and load change data in the preset time period; the output data includes at least the total power required by the hybrid energy storage system;

[0010] Based on the energy storage device database and the total power required by the hybrid energy storage system, allocating the power required by various energy storage devices among the multiple energy storage devices, and generating multiple power configuration schemes for the hybrid energy storage system;

[0011] A target energy storage system configuration scheme for the hybrid energy storage system is determined from the multiple power configuration schemes by using a multi-objective evolutionary algorithm; the multi-objective evolutionary algorithm optimizes an objective function based on multiple constraints, wherein the objective function includes a weighted sum of multiple objective items, and the corresponding multiple weights in the multiple objective items are automatically generated in combination with the demand characteristics of each stage in multiple stages during the entire life cycle of the hybrid energy storage system; the multiple constraints include energy storage device constraints and auxiliary equipment constraints; the target energy storage system configuration scheme includes the quantity and device parameters of various energy storage devices among the multiple energy storage devices required for the hybrid energy storage system and the quantity and configuration parameters of auxiliary equipment required for the hybrid energy storage system.

[0012] In one embodiment, the equipment parameters of the energy storage device include common parameters and characteristic parameters; the common parameters include at least: power, energy, cycle efficiency, weight, and floor space; the characteristic parameters include at least: cycle life and geographical conditions; and / or,

[0013] The equipment parameters of the auxiliary equipment include at least: the rated power of the power conversion system, the DC side voltage of the power conversion system and the conversion efficiency of the power conversion system.

[0014] In one embodiment, the energy storage device constraint condition includes at least one of the following: a minimum power constraint of the energy storage device, a minimum energy constraint of the energy storage device, a comprehensive charge and discharge efficiency constraint of the energy storage device; and / or,

[0015] The auxiliary equipment constraint condition includes at least one of the following: a DC side voltage constraint of a power conversion system and a minimum power constraint of a power conversion system.

[0016] In one embodiment, obtaining a power load forecast curve of the hybrid energy storage system and establishing an expected load model to determine output data of the hybrid energy storage system includes:

[0017] According to the application scenario and load demand of the hybrid energy storage system, a load forecast curve of the hybrid energy storage system in the power system is obtained, and a load curve model of the expected energy storage power station is established;

[0018] Based on the load curve model, the power demand of the nuclear reduction power station and the power demand of auxiliary equipment, the output data of the hybrid energy storage system is determined.

[0019] In one embodiment, the multiple energy storage devices include a first technical route energy storage device, a second technical route energy storage device, and a third technical route energy storage device;

[0020] Accordingly, based on the energy storage device database and the total power required by the hybrid energy storage system, the power required by each of the multiple energy storage devices is allocated to generate multiple power configuration schemes of the hybrid energy storage system, including:

[0021] Based on the total power required by the energy storage system equipment, the power demand of the energy storage equipment of the first technical route is allocated; the power demand of the energy storage equipment of the first technical route is gradually increased from zero to the total power required by the hybrid energy storage system, and the power increase step is determined according to the power of the minimum unit energy storage device;

[0022] Allocate the power requirements of the second technical route energy storage device and the third technical route energy storage device according to the power requirements of the first technical route energy storage device;

[0023] The power requirements of the energy storage devices of the first technical route, the power requirements of the energy storage devices of the second technical route, and the power requirements of the energy storage devices of the third technical route are combined to form a plurality of power configuration schemes for the hybrid energy storage system.

[0024] In one embodiment, a target energy storage system configuration scheme of the hybrid energy storage system is determined from the multiple power configuration schemes using a multi-objective evolutionary algorithm, including:

[0025] Obtaining an objective function and establishing multiple constraints; the objective function includes a weighted sum of multiple objective items; the multiple constraints include energy storage device constraints and auxiliary device constraints;

[0026] Initializing a population; each individual in the population represents one of the multiple power configuration schemes;

[0027] Automatically generate corresponding weights of the multiple target items in combination with the demand characteristics of each stage in the multiple stages of the full life cycle of the hybrid energy storage system;

[0028] Performing multi-objective optimization on the objective function based on the multiple constraints to obtain a power configuration solution set for the hybrid energy storage system;

[0029] According to the actual application scenario of the hybrid energy storage system, a target energy storage system configuration scheme is selected from a set of power configuration schemes of the hybrid energy storage system.

[0030] In one embodiment, the objective function is optimized by multiple objectives based on the multiple constraints to obtain a power configuration scheme set for the hybrid energy storage system, including:

[0031] sorting the multiple power configuration schemes according to multiple target items in the target function, and dividing the multiple power configuration schemes into different non-dominated levels;

[0032] Based on the current life cycle stage, dynamically adjust the multiple weights corresponding to the multiple target items; update the objective function value of the objective function according to the adjusted multiple weights;

[0033] Selecting the next set of power configuration schemes according to the objective function value;

[0034] Repeat the above steps until the preset conditions are met to obtain a power configuration solution set for the hybrid energy storage system.

[0035] In one embodiment, the multiple target items include at least a cost target item and a performance target item, the cost weight corresponding to the cost target item gradually decreases over time, and the performance weight corresponding to the performance target item increases accordingly.

[0036] The embodiment of this specification also provides a hybrid energy storage system configuration device, including:

[0037] An establishment module is used to establish an energy storage device database and an auxiliary device database based on the energy storage device types required by the hybrid energy storage system; the energy storage device database includes device parameters of multiple energy storage devices required by the hybrid energy storage system, and the auxiliary device database includes device parameters of auxiliary devices required by the hybrid energy storage system;

[0038] A determination module is used to obtain the power load prediction curve of the hybrid energy storage system and establish an expected load model to determine the output data of the hybrid energy storage system; the power load prediction curve includes the real-time load power of each time node in a preset time period and the maximum load power, load duration and load change data in the preset time period; the output data includes at least the total power required by the hybrid energy storage system;

[0039] A generating module, configured to allocate the power required by various energy storage devices among the multiple energy storage devices based on the energy storage device database and the total power required by the hybrid energy storage system, and generate multiple power configuration schemes for the hybrid energy storage system;

[0040] An optimization module is used to determine a target energy storage system configuration scheme of the hybrid energy storage system from the multiple power configuration schemes using a multi-objective evolutionary algorithm; the multi-objective evolutionary algorithm optimizes an objective function based on multiple constraints, the objective function includes a weighted sum of multiple objective items, and the corresponding multiple weights in the multiple objective items are automatically generated in combination with the demand characteristics of each stage in the multiple stages of the full life cycle of the hybrid energy storage system; the multiple constraints include energy storage device constraints and auxiliary equipment constraints; the target energy storage system configuration scheme includes the number and device parameters of various energy storage devices among the multiple energy storage devices required for the hybrid energy storage system and the number and configuration parameters of auxiliary equipment required for the hybrid energy storage system.

[0041] An embodiment of the present specification also provides a computer device, including a processor and a memory for storing processor executable instructions, wherein when the processor executes the instructions, the steps of the hybrid energy storage system configuration method described in any of the above embodiments are implemented.

[0042] The embodiments of this specification also provide a computer-readable storage medium on which computer instructions are stored. When the instructions are executed, the steps of the hybrid energy storage system configuration method described in any of the above embodiments are implemented.

[0043] In the embodiments of this specification, a hybrid energy storage system configuration method is provided, which can establish an energy storage device database and an auxiliary device database based on the type of energy storage device required by the hybrid energy storage system. The energy storage device database contains the equipment parameters of various energy storage devices required by the hybrid energy storage system, and the auxiliary device database includes the equipment parameters of the auxiliary devices required by the hybrid energy storage system. By storing the energy storage device information of various technical routes, it is convenient to select according to different engineering requirements. The parameters of different energy storage devices distinguish the commonality and characteristics, and provide a data basis for the establishment of subsequent calculations and constraints. The auxiliary device database contains the parameters of the auxiliary devices, taking into account the collaborative relationship between the auxiliary devices and the energy storage devices, which are reflected in the constraints to ensure the overall coordination of the system. The power load prediction curve of the hybrid energy storage system can be obtained, and the expected load model can be established to determine the output data of the hybrid energy storage system. The power load prediction curve includes the real-time load power of each time node in the preset time period and the maximum load power, load duration and load change data in the preset time period. The output data includes at least the total power required by the hybrid energy storage system. By establishing an expected load model based on the power load forecast curve and determining the output of the energy storage system based on it, the energy storage system configuration is closely combined with the actual power demand, making the energy storage system configuration more in line with the actual operation situation, and enhancing the practicality and innovation of the configuration method. Based on the energy storage device database and the total power required by the hybrid energy storage system, the power required by various energy storage devices in various energy storage devices is allocated to generate various power configuration schemes for the hybrid energy storage system. Using the multi-objective evolutionary algorithm, the target energy storage system configuration scheme of the hybrid energy storage system is determined from the various power configuration schemes. The multi-objective evolutionary algorithm optimizes the objective function based on multiple constraints, and the objective function includes the weighted sum of multiple objective items. The corresponding multiple weights in the multiple objective items are automatically generated in combination with the demand characteristics of each stage in multiple stages of the full life cycle of the hybrid energy storage system. The multiple constraints include energy storage device constraints and auxiliary equipment constraints. The target energy storage system configuration scheme includes the number and device parameters of various energy storage devices in the multiple energy storage devices required by the hybrid energy storage system and the number and configuration parameters of the auxiliary equipment required by the hybrid energy storage system. An optimization model of a multi-objective evolutionary algorithm was established, which contains multiple objective items and multiple constraints, involving multiple constraints on energy storage equipment and auxiliary equipment to meet the system optimization needs at different stages of the entire life cycle. Multiple objective items and corresponding weights can be flexibly adjusted according to different scenarios, can adapt well to different performance and cost requirements, has high flexibility, and is suitable for different optimization focuses and energy storage equipment selections.In addition, the introduction of a full life cycle multi-stage optimization strategy, combined with a weight self-generation mechanism, comprehensively considers the key factors of the entire life cycle, and generates weights based on the demand characteristics of different stages, rather than relying on artificial settings or simple subjective weight allocation, effectively overcoming the limitations of traditional weight allocation methods (such as empirical weight methods or subjective assignment methods) and improving the scientificity and applicability of the optimization results. Moreover, the hybrid energy storage system configuration method in this embodiment also considers the optimization of energy storage auxiliary equipment, which can significantly improve the actual applicability of the optimization results and the overall performance of the system, accurately evaluate the system cost, and ensure that the hybrid energy storage system reaches the best state at the technical and economic levels.

[0044] With reference to the following description and drawings, specific embodiments of the present invention are disclosed in detail, indicating the manner in which the principles of the present invention can be adopted. It should be understood that the embodiments of the present invention are not limited in scope. Features described and / or shown for one embodiment can be used in one or more other embodiments in the same or similar manner, combined with features in other embodiments, or replace features in other embodiments.

[0045] It should be emphasized that the term “include / comprises” when used herein refers to the presence of features, parts, steps or components, but does not exclude the presence or addition of one or more other features, parts, steps or components. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings described herein are for illustrative purposes only and are not intended to limit the scope of the present disclosure in any way. In the drawings:

[0047] Figure 1 A flow chart of a hybrid energy storage system configuration method in an embodiment of this specification is shown;

[0048] Figure 2 An example diagram of a daytime power load curve in an embodiment of this specification is shown;

[0049] Figure 3 A flow chart of a hybrid energy storage system configuration method in an embodiment of this specification is shown;

[0050] Figure 4 A schematic diagram of the structure of a hybrid energy storage system configuration device in an embodiment of this specification is shown;

[0051] Figure 5 A schematic diagram of the structure of a computer device in one embodiment of the present specification is shown. DETAILED DESCRIPTION

[0052] The principles and spirit of this specification will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement this specification, and are not intended to limit the scope of this specification in any way. On the contrary, these embodiments are provided to make this specification more thorough and complete, and to fully convey the scope of this disclosure to those skilled in the art.

[0053] Those skilled in the art know that the embodiments of this specification can be implemented as a system, device, method or computer program product. Therefore, this specification can be implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this specification belongs. The terms used herein in this specification are only for the purpose of describing specific embodiments and are not intended to limit this specification. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0055] The embodiments of this specification provide a hybrid energy storage system configuration method. Figure 1 A flow chart of a hybrid energy storage system configuration method in an embodiment of this specification is shown. Although this specification provides method operation steps or device structures as shown in the following embodiments or drawings, more or fewer operation steps or module units may be included in the method or device based on routine or no creative labor. In the steps or structures where there is no necessary causal relationship logically, the execution order of these steps or the module structure of the device is not limited to the execution order or module structure described in the embodiments of this specification and shown in the drawings. When the method or module structure described is applied to an actual device or terminal product, it can be connected according to the method or module structure shown in the embodiments or drawings for sequential execution or parallel execution (for example, a parallel processor or a multi-threaded processing environment, or even a distributed processing environment).

[0056] Specifically, Figure 1 As shown, a hybrid energy storage system configuration method provided in one embodiment of this specification may include the following steps.

[0057] Step S101, based on the energy storage device types required by the hybrid energy storage system, establish an energy storage device database and an auxiliary device database; the energy storage device database includes device parameters of various energy storage devices required by the hybrid energy storage system, and the auxiliary device database includes device parameters of auxiliary devices required by the hybrid energy storage system.

[0058] The method in this embodiment can be applied to computer equipment. The type of energy storage equipment required in the hybrid energy storage system can be determined according to engineering needs. Based on the type of energy storage equipment required for the hybrid energy storage system, an energy storage equipment database and an auxiliary equipment database are established. The energy storage equipment database can include multiple models of energy storage equipment with multiple common technical routes, such as different models of lithium-ion batteries, sodium-ion batteries, flow batteries, supercapacitors, compressed air energy storage, flywheel energy storage, gravity energy storage, pumped storage, etc. Energy storage equipment of different technical routes can be described by different equipment parameters. The auxiliary equipment database includes equipment parameters of auxiliary equipment required for the hybrid energy storage system.

[0059] By storing the information of energy storage equipment of various technical routes, it is convenient to select according to different engineering requirements. The parameters of different energy storage equipment distinguish the commonalities and characteristics, providing a data basis for subsequent calculations and the establishment of constraints. The auxiliary equipment database contains the parameters of auxiliary equipment, taking into account the synergistic relationship between auxiliary equipment and energy storage equipment, which is reflected in the constraints to ensure the overall coordination of the system.

[0060] In some embodiments of this specification, the equipment parameters of the energy storage device include common parameters and characteristic parameters; the common parameters at least include: power, energy, cycle efficiency, weight, and floor space. The characteristic parameters at least include: cycle life and geographical conditions.

[0061] Specifically, the common parameters of energy storage equipment include the power, energy, cycle efficiency, weight, floor space, etc. of a single energy storage equipment unit. The characteristic parameters of energy storage equipment include cycle life (compressed air energy storage, pumped storage and other energy storage technology routes can be converted according to the engineering design life), geographical conditions, etc.

[0062] In some embodiments of the present specification, the equipment parameters of the auxiliary equipment include at least: the rated power of the power conversion system, the DC side voltage of the power conversion system, and the conversion efficiency of the power conversion system. The auxiliary equipment database includes PCs, transformers, etc., and the equipment parameters in the auxiliary equipment database need to include the rated power, DC side voltage, conversion efficiency, etc. of the PCs.

[0063] Step S102, obtaining the power load forecast curve of the hybrid energy storage system, establishing an expected load model, so as to determine the output data of the hybrid energy storage system; the power load forecast curve includes the real-time load power of each time node in a preset time period and the maximum load power, load duration and load change data in the preset time period; the output data includes at least the total power required by the hybrid energy storage system.

[0064] By establishing an expected load model based on the electricity load forecast curve and using it to determine the output of the energy storage system, closely combining it with the actual electricity demand, the energy storage system configuration is more in line with the actual operating conditions, enhancing the practicality and innovation of the configuration method.

[0065] In some embodiments of the present specification, an electricity load forecast curve of the hybrid energy storage system is obtained, and an expected load model is established to determine the output data of the hybrid energy storage system, including: according to the application scenario and load demand of the hybrid energy storage system, a load forecast curve of the hybrid energy storage system in the power system is obtained, and an expected load curve model of the energy storage power station is established; based on the load curve model, the power demand of the reduced power station and the power demand of auxiliary equipment, the output data of the hybrid energy storage system is determined.

[0066] Specifically, according to the characteristics of the hybrid energy storage system (application scenario) and load demand, the load forecast curve of the hybrid energy storage system in the power system is obtained, including the charging and discharging power of the nodes of the hybrid energy storage system in the power system, and the load curve model of the expected energy storage power station is established. By reducing the additional power demand such as power station power consumption and auxiliary equipment power consumption, the output of the energy storage system is finally determined.

[0067] The power load forecast curve must include the real-time load power at each time node in the preset time period. , Maximum load power during a preset time period , the duration of the load in the preset time period , load changes during the day, month, and season in the preset time period, etc. Use relevant data to establish a cross-year electricity load curve model to determine the final output of the energy storage system as the basis for the selection and configuration of energy storage equipment in energy storage power stations. The load model can be used to determine the total power, total energy, system duration, etc. required by the hybrid energy storage system to describe the parameter requirements of the energy storage equipment.

[0068] Step S103, based on the energy storage device database and the total power required by the hybrid energy storage system, allocating the power required by each energy storage device among the multiple energy storage devices, and generating multiple power configuration schemes for the hybrid energy storage system.

[0069] Based on the equipment parameters of various energy storage devices in the energy storage device database and the total power required by the hybrid energy storage system, the power required by various energy storage devices in the various energy storage devices can be allocated to obtain various power configuration schemes. The various energy storage devices include energy storage devices of various technical routes.

[0070] Taking energy storage devices of three technical routes as an example, in some embodiments of the present specification, the multiple energy storage devices include energy storage devices of the first technical route, energy storage devices of the second technical route, and energy storage devices of the third technical route; accordingly, based on the energy storage device database and the total power required by the hybrid energy storage system, the power required by various energy storage devices in the multiple energy storage devices is allocated, and multiple power configuration schemes for the hybrid energy storage system are generated, including: based on the total power required by the energy storage system equipment, the power demand of the energy storage devices of the first technical route is allocated; the power demand of the energy storage devices of the first technical route gradually increases from zero to the total power required by the hybrid energy storage system, and the power increase step is determined according to the power of the minimum unit energy storage device; according to the power demand of the energy storage devices of the first technical route, the power demand of the energy storage devices of the second technical route and the energy storage devices of the third technical route are allocated; the power demand of the energy storage devices of the first technical route, the power demand of the energy storage devices of the second technical route and the power demand of the energy storage devices of the third technical route are combined to form multiple power configuration schemes for the hybrid energy storage system.

[0071] Similarly, taking energy storage devices of four technical routes as an example, in some embodiments of the present specification, the multiple energy storage devices include energy storage devices of the first technical route, energy storage devices of the second technical route, energy storage devices of the third technical route, and energy storage devices of the fourth technical route; accordingly, based on the energy storage device database and the total power required by the hybrid energy storage system, the power required by various energy storage devices in the multiple energy storage devices is allocated to generate multiple power configuration schemes for the hybrid energy storage system, including: based on the total power required by the energy storage system equipment, the power demand of the energy storage device of the first technical route is allocated; the power demand of the energy storage device of the first technical route is allocated; The power demand gradually increases from zero to the total power required by the hybrid energy storage system, and the power increase step is determined according to the power of the minimum unit energy storage device; according to the power demand of the energy storage device of the first technical route, the power demand of the energy storage device of the second technical route, the power demand of the energy storage device of the third technical route and the power demand of the energy storage device of the fourth technical route are allocated; the power demand of the energy storage device of the first technical route, the power demand of the energy storage device of the second technical route, the power demand of the energy storage device of the third technical route and the power demand of the energy storage device of the fourth technical route are combined to form a plurality of power configuration schemes of the hybrid energy storage system.

[0072] It is understandable that this can be expanded to five or more types of energy storage devices with different technical routes.

[0073] Step S104, using a multi-objective evolutionary algorithm to determine a target energy storage system configuration scheme for the hybrid energy storage system from the multiple power configuration schemes; the multi-objective evolutionary algorithm optimizes the objective function based on multiple constraints, the objective function includes a weighted sum of multiple objective items, and the corresponding multiple weights in the multiple objective items are automatically generated in combination with the demand characteristics of each stage in the multiple stages of the full life cycle of the hybrid energy storage system; the multiple constraints include energy storage device constraints and auxiliary equipment constraints; the target energy storage system configuration scheme includes the number and device parameters of various energy storage devices among the multiple energy storage devices required for the hybrid energy storage system and the number and configuration parameters of auxiliary equipment required for the hybrid energy storage system.

[0074] After obtaining a plurality of power configuration schemes of the hybrid energy storage system, a multi-objective evolutionary algorithm can be used to determine the target energy storage system configuration scheme of the hybrid energy storage system from the plurality of power configuration schemes. The multi-objective evolutionary algorithm optimizes the objective function according to a plurality of constraints. The objective function includes a weighted sum of a plurality of objective items. In one embodiment, the plurality of objective items may include a plurality of the following: a cost function, a performance function, a safety function, an area function, etc. The plurality of weights corresponding to the plurality of objective items are automatically generated in combination with the demand characteristics of each of the plurality of stages in the full life cycle of the hybrid energy storage system. The full life cycle may include a plurality of stages, for example, it may include: an early stage and a late stage. For another example, the plurality of stages may include: an early stage, a mid-term, and a late stage. Weights may be generated according to the demand characteristics of different stages. The demand characteristics here may be determined according to actual application scenarios and actual needs. Exemplary and non-restrictive, the demand characteristics of different stages may be that the initial investment in the early stage of the full life cycle may be optimized, the system efficiency in the mid-term may be optimized, and the cycle life in the late stage may be optimized, and the evaluation weights may be automatically matched. For another example, the demand characteristics of different stages may be that the initial investment in the early stage of the full life cycle may be optimized, the system efficiency in the late stage may be optimized, and the evaluation weights may be automatically matched.

[0075] The multiple constraints include energy storage device constraints and auxiliary device constraints. The energy storage device constraints are used to characterize the preset conditions that the energy storage devices required by the hybrid energy storage system need to meet. The auxiliary device constraints are used to characterize the preset conditions that the auxiliary devices required by the hybrid energy storage system need to meet.

[0076] The target energy storage system configuration scheme includes the quantity and device parameters of various energy storage devices among the multiple energy storage devices required by the hybrid energy storage system and the quantity and configuration parameters of auxiliary devices required by the hybrid energy storage system.

[0077] An optimization model of a multi-objective evolutionary algorithm is established, which includes multiple target items and multiple constraints, involving various constraints of energy storage equipment and auxiliary equipment to meet the system optimization requirements at different stages of the life cycle. Multiple target items and corresponding weights can be flexibly adjusted according to different scenarios, which can adapt well to different performance and cost requirements, have high flexibility, and are suitable for different optimization focuses and energy storage equipment selection. In addition, a multi-stage optimization strategy for the entire life cycle is introduced, combined with a weight self-generation mechanism, and by comprehensively considering the key factors of the entire life cycle, weights are generated according to the demand characteristics of different stages, rather than relying on artificial settings or simple subjective weight allocation, which effectively overcomes the limitations of traditional weight allocation methods (such as empirical weight methods or subjective assignment methods) and improves the scientificity and applicability of the optimization results. Moreover, the hybrid energy storage system configuration method in this embodiment also considers the optimization of energy storage auxiliary equipment, which can significantly improve the actual applicability of the optimization results and the overall performance of the system, accurately evaluate the system cost, and ensure that the hybrid energy storage system reaches the best state at the technical and economic levels.

[0078] In some embodiments of the present specification, the energy storage device constraint condition includes at least one of the following: energy storage device minimum power constraint, energy storage device minimum energy constraint, energy storage device comprehensive charge and discharge efficiency constraint; and / or, the auxiliary device constraint condition includes at least one of the following: power conversion system DC side voltage constraint, power conversion system minimum power constraint. The energy storage device minimum power constraint is used to ensure that the energy storage device power meets the load power demand of the hybrid energy storage system, ensuring that the system has sufficient capacity to cope with load changes. The energy storage device minimum energy constraint requires that the energy storage device energy meets the load energy demand, ensuring that the energy storage capacity of the energy storage system meets the requirements. The energy storage device comprehensive charge and discharge efficiency constraint is used to ensure that the comprehensive charge and discharge efficiency meets certain standards and ensures the energy efficiency of the system. The power conversion system DC side voltage constraint ensures that the auxiliary device DC side voltage and the energy storage device DC side voltage are within a certain range to ensure the voltage adaptability of the system. The power conversion system minimum power constraint is used to ensure that the auxiliary device power meets the maximum power demand of the energy storage device and ensures the power transmission of the system. The model constructed in this embodiment contains multiple constraints, covering the minimum power, minimum energy, comprehensive charging and discharging efficiency of the energy storage equipment, and taking into account the DC side voltage constraint and minimum power constraint of the auxiliary equipment, making the model's description and optimization of the energy storage system more comprehensive and detailed, and comprehensively considering the collaborative work and mutual constraint relationship between the energy storage equipment and the auxiliary equipment from the perspective of the whole system.

[0079] In some embodiments of the present specification, a multi-objective evolutionary algorithm is used to determine a target energy storage system configuration scheme of the hybrid energy storage system from the multiple power configuration schemes, including: obtaining an objective function and establishing multiple constraints; the objective function includes a weighted sum of multiple objective items; the multiple constraints include energy storage device constraints and auxiliary equipment constraints; initializing a population; each population individual represents a power configuration scheme among the multiple power configuration schemes; automatically generating multiple weights corresponding to the multiple objective items in combination with the demand characteristics of each stage in the multiple stages of the full life cycle of the hybrid energy storage system; performing multi-objective optimization on the objective function based on the multiple constraints to obtain a power configuration scheme set for the hybrid energy storage system; and selecting a target energy storage system configuration scheme from the power configuration scheme set of the hybrid energy storage system according to the actual application scenario of the hybrid energy storage system.

[0080] Specifically, the objective function may include a weighted sum of multiple objective items. In one embodiment, the multiple objective items may include multiple of the following: cost function, performance function, safety function, floor space function, etc. In one embodiment, the multiple constraints include energy storage device constraints and auxiliary device constraints. In one embodiment, the energy storage device constraints include at least one of the following: energy storage device minimum power constraint, energy storage device minimum energy constraint, energy storage device comprehensive charge and discharge efficiency constraint; and / or, the auxiliary device constraints include at least one of the following: power conversion system DC side voltage constraint, power conversion system minimum power constraint.

[0081] Initialize the population. Each individual in the population represents one of the multiple power configuration schemes. Combined with the demand characteristics of each stage in the multiple stages of the full life cycle of the hybrid energy storage system, the corresponding multiple weights in the multiple target items are automatically generated. Based on the multiple constraints, the objective function is multi-objective optimized to obtain a power configuration scheme set for the hybrid energy storage system. According to the actual application scenario of the hybrid energy storage system, the target energy storage system configuration scheme is selected from the power configuration scheme set of the hybrid energy storage system. In one embodiment, the target energy storage system configuration scheme can be selected from the power configuration scheme set of the hybrid energy storage system according to the needs in different scenarios, including large-scale energy storage power stations giving priority to cost, industrial and commercial energy storage giving priority to cost and volume, and mobile energy storage giving priority to energy density. As described above, weights can be generated according to the demand characteristics at different stages of the full life cycle, and the optimal solution can be selected according to the flexibility of actual needs, taking into account different optimization focuses in different scenarios, and enhancing the adaptability and practicality of the configuration method in this embodiment in different application scenarios and different stages of the full life cycle.

[0082] In some embodiments of the present specification, the objective function is subjected to multi-objective optimization based on the multiple constraints to obtain a set of power configuration schemes for the hybrid energy storage system, including: sorting the multiple power configuration schemes according to the multiple target items in the objective function, and dividing the multiple power configuration schemes into different non-dominated levels; dynamically adjusting the multiple weights corresponding to the multiple target items based on the current life cycle stage; updating the objective function value of the objective function according to the adjusted multiple weights; selecting the next set of power configuration schemes according to the objective function value; and repeating the above steps until the preset conditions are met to obtain the set of power configuration schemes for the hybrid energy storage system.

[0083] Specifically, non-dominated sorting divides solutions into different non-dominated levels. Solutions of the same level are relatively optimal under the current circumstances, avoiding the one-sidedness of single-objective optimization. Dynamic weight adjustment dynamically adjusts the weights of cost and performance according to different life cycle stages, reflecting the idea of ​​multi-stage optimization of the entire life cycle and making the optimization more in line with the actual situation. Considering the pre-, mid-, and late stages of design, the generation of weights is based on the time proportion and attenuation coefficient, and the importance of cost, performance safety, and / or floor space at different stages is automatically adjusted. Weights are automatically generated based on the life cycle stage, non-dominated sorting, dynamic weight adjustment, and update of objective function values ​​are performed, and the configuration scheme is continuously updated through crossover and mutation operations until the stopping condition is met.

[0084] In some embodiments of the present specification, the multiple target items include at least a cost target item and a performance target item, the cost weight corresponding to the cost target item gradually decreases over time, and the performance weight corresponding to the performance target item increases accordingly. In this way, the optimization focus of different life cycle stages can be balanced.

[0085] Specifically, if the optimization goals for a hybrid energy storage system are cost minimization and performance maximization, the two cannot be met at the same time, and the optimization focuses on different aspects throughout the life cycle, a multi-objective evolutionary algorithm is used to balance the cost and performance of the energy storage system to find the Pareto optimal solution. The optimization goals include: Goal 1 (cost minimization): Minimize the total cost of the energy storage system, including initial investment, operating costs, and maintenance costs. Goal 2 (performance maximization): Optimize system performance indicators such as energy efficiency, power response speed, power, etc. The optimization goal can be determined according to specific needs and can be a variety of goals. In this specific embodiment, only cost minimization and performance maximization are taken as examples. Weights can be defined based on the life cycle stage:

[0086]

[0087]

[0088] is the weight of the cost function, is the weight of the performance function, : The proportion of time in the early, middle and late stages of the life cycle, normalized to [0, 1].

[0089] : Attenuation coefficient, cost target priority reduction coefficient. Through the above method, it is possible to give priority to cost in the early stage and performance in the later stage.

[0090] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. For details, please refer to the description of the above-mentioned related processing related embodiments, and no further description is given here.

[0091] The above specific embodiments of this specification are described. In some cases, the actions or steps recorded in this specification can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0092] The above method is described below in conjunction with a specific embodiment. However, it should be noted that the specific embodiment is only for better illustrating the present specification and does not constitute an improper limitation on the present specification.

[0093] This specific embodiment provides a hybrid energy storage system configuration method, which uses a multi-objective evolutionary algorithm to perform multi-objective optimization on the hybrid energy storage system, introduces a multi-stage optimization and weight self-generation strategy for the entire life cycle of energy storage equipment, and automatically generates evaluation weights for each stage from the perspective of the pre-, mid- and late stages of design. The optimization algorithm generates a selection and configuration plan for hybrid energy storage equipment based on the weights. Comprehensively consider the key technical parameters of energy storage equipment (power, energy, charge and discharge efficiency, voltage, response time, etc.), and optimize the energy storage system selection plan while meeting external load requirements.

[0094] This specific embodiment relies on the database of each energy storage device unit, the expected load curve and the parameters of key auxiliary equipment (PCs (power conversion system), etc.), and obtains the optimal hybrid energy storage system solution through the proposed selection and configuration method, including the required quantity, energy, weight, volume, cost of each energy storage device and the quantity, cost, voltage, power, etc. of related auxiliary equipment. The configuration method is based on a multi-objective evolutionary algorithm, according to the demand weight (cost, volume, safety, etc.), under multiple optimization objectives and multiple constraints to obtain the optimal solution and meet the load demand. Please refer to Figure 2, which shows a flow chart of the hybrid energy storage system configuration method in this specific embodiment. Figure 2 As shown, the method in this specific embodiment may include the following steps.

[0095] Step S1: Determine the type of energy storage equipment required in the energy storage system according to project requirements, and establish a database of energy storage equipment and auxiliary equipment.

[0096] Specifically, the equipment database is constructed according to the actual engineering needs. The energy storage equipment database can contain multiple models of energy storage equipment with common technical routes, such as different models of lithium-ion batteries, sodium-ion batteries, flow batteries, supercapacitors, compressed air energy storage, flywheel energy storage, gravity energy storage, pumped storage, etc. Energy storage equipment with different technical routes can be described by different equipment parameters. Equipment parameters are divided into common parameters of energy storage equipment and characteristic parameters of energy storage equipment. For common parameters of energy storage equipment, the database needs to specifically include the power, energy, cycle efficiency, weight, and floor space of a single energy storage equipment unit. The characteristic parameters of energy storage equipment include cycle life (compressed air energy storage, pumped storage and other energy storage technology routes can be converted according to the engineering design life), geographical conditions, etc. The auxiliary equipment database includes PCs, transformers, etc. The equipment parameters in the database need to include the rated power, DC side voltage, conversion efficiency, etc. of PCs.

[0097] Step S2: Obtain the power load forecast curve, establish the expected load model, and determine the output of the energy storage system.

[0098] Specifically, according to the characteristics of energy storage equipment (application scenarios) and load demand, obtain the load forecast curve of energy storage equipment in the power system, including the charging and discharging power of energy storage equipment at nodes in the power system, and establish the expected load curve model of energy storage power station. By reducing the additional power demand such as power station power consumption and auxiliary equipment power consumption, the output of the energy storage system is finally determined. Please refer to Figure 3 , shows an example diagram of a daytime power load curve in this specific embodiment.

[0099] Specifically, the power load forecast curve needs to include the real-time load power at each time node in the preset time period. , Maximum load power during a preset time period , the duration of the load in the preset time period , load changes during the day, month, and season in the preset time period, etc. Use relevant data to establish a cross-year electricity load curve model to determine the final output of the energy storage system as the basis for the selection and configuration of energy storage equipment in energy storage power stations. The load model can be used to determine the power, energy, and discharge time of the energy storage equipment, which is used to describe the parameter requirements of the energy storage equipment by expressing the total power, total energy, system duration, etc. required by the energy storage system equipment.

[0100] Step S3: Based on the total power and total energy required by the energy storage system equipment, the power and energy requirements of each energy storage technology route equipment are allocated.

[0101] Specifically, based on the total power required by the energy storage system equipment, the power demand of the energy storage equipment of the first technical route is first allocated, and the power demand gradually increases from zero to the total power required by the energy storage equipment. The power increase step is determined according to the power of the smallest unit energy storage equipment. According to the power demand of the first energy storage equipment, the power of the second, third, and Nth energy storage equipment is allocated. The power requirements of each energy storage device are combined to form a power configuration plan for each energy storage device in the hybrid energy storage system.

[0102] Specifically, the maximum load power of the energy storage system equipment is determined according to the energy storage system load model. , allocate the output power required by each energy storage device. For a hybrid energy storage system that considers multiple energy storage routes, taking three types of energy storage devices as an example, it can include energy storage device A power ,Energy storage device B power , Energy storage device C power .

[0103] Specifically, according to the total power demand of the energy storage system, the power demand of energy storage equipment of each technical route is allocated to form a hybrid energy storage power configuration plan. The plan must meet the constraints:

[0104]

[0105] This constraint describes that the total power of each energy storage device must be greater than the total power required by the energy storage system to meet the load demand.

[0106] Step S4: Based on the multi-objective evolutionary algorithm, the whole life cycle scenario is considered, the evaluation weights are automatically generated, the Pareto frontier solution set of energy storage equipment that meets the multi-objective requirements is obtained, and a hybrid energy storage system configuration solution set is formed.

[0107] Specifically, according to the power configuration scheme of energy storage equipment of each technical route generated in step S3, a multi-objective evolutionary algorithm is used to optimize multiple objectives at the same time based on boundary conditions such as energy storage equipment power and energy, DC side voltage, PCs power, etc., taking into account the early, middle and late scenes of the entire life cycle, and automatically generating evaluation weights and finding a set of balanced solutions. The optimal minimum number of energy storage devices that meet the demand is obtained. According to the number of energy storage devices of each technical route obtained, the power, energy and other parameters of each energy storage device are calculated to form a set of hybrid energy storage system configuration schemes.

[0108] Specifically, according to the power and energy of the smallest unit of each energy storage device, the power of the smallest unit energy storage device A ,Power of energy storage device B , energy storage device C power , considering a variety of constraints, including the minimum power constraint of energy storage equipment, the minimum energy constraint of energy storage equipment, the comprehensive charging and discharging efficiency constraint of energy storage equipment, the DC side voltage constraint, the minimum power constraint of PCs, etc. Through the multi-objective evolutionary algorithm, considering the optimization of initial investment in the early stage of the whole life cycle, the optimization of mid-term system efficiency, and the optimization of late cycle life, the evaluation weights are automatically matched, and the minimum number of equipment units required is calculated, including the number of energy storage equipment A. ,Number of energy storage devices B ,Number of energy storage devices C .

[0109] Specifically, if the optimization objectives for a hybrid energy storage system are cost minimization, performance maximization, and safety maximization, the three cannot be met at the same time, and the optimization focuses on different aspects throughout the life cycle, a multi-objective evolutionary algorithm is used to balance the cost, performance, and safety of the energy storage system to find the Pareto optimal solution.

[0110] Optimization goals include:

[0111] Objective 1 (cost minimization): Minimize the total cost of the energy storage system, including initial investment, operating costs, and maintenance costs.

[0112] Goal 2 (maximum performance): Optimize system performance indicators such as energy efficiency, power response speed, power, etc.

[0113] Goal 3 (maximum safety): Optimize system safety indicators, such as accident risk, accident frequency, accident consequences, etc.

[0114] The optimization target can be determined according to specific needs and can be a variety of targets. In this specific embodiment, only cost minimization, performance maximization and security maximization are taken as examples.

[0115] The variables that can be optimized include:

[0116] Battery capacity: capacity (kWh).

[0117] Charging and discharging power: the maximum charging and discharging power of the energy storage system (kW).

[0118] Conversion efficiency: The energy conversion efficiency of the system inverter or other subsystems.

[0119] Voltage level: DC side voltage of energy storage equipment.

[0120] Auxiliary equipment: PCs power level.

[0121] The constraints include:

[0122] Constraint 1. The power of the energy storage device is not lower than the load power demand, reflecting the ability of the energy storage system to cope with the load power.

[0123] Constraint 2. The energy storage equipment meets the energy demand of the load, reflecting the ability of the energy storage system to meet the energy requirements of the load.

[0124] Constraint 3. The comprehensive charging and discharging efficiency of the energy storage equipment meets the efficiency requirements and reflects the energy efficiency of the energy storage system.

[0125] Constraint 4. The DC side voltage of the auxiliary equipment meets the DC side voltage requirement of the energy storage equipment, reflecting the voltage requirement of the energy storage equipment for the auxiliary equipment.

[0126] Constraint 5. The power of the PCs meets the maximum power requirement of the energy storage device, reflecting the ability of the auxiliary equipment to output power in the application of the energy storage device.

[0127] The optimization process includes the following steps:

[0128] Step 4.1. Define the optimization objective function.

[0129] Objective 1: Cost Function

[0130]

[0131] Objective 2: Performance Function

[0132]

[0133] Target 3: Safe Functions

[0134]

[0135] Step 4.2. Establish constraints.

[0136] Minimum power constraints for energy storage equipment:

[0137]

[0138] Minimum energy constraints for energy storage equipment:

[0139]

[0140] Constraints on comprehensive charging and discharging efficiency of energy storage equipment:

[0141]

[0142] Auxiliary equipment DC side voltage constraints:

[0143]

[0144]

[0145]

[0146] Minimum power constraints for PCs:

[0147]

[0148]

[0149]

[0150] in, is the energy of the smallest unit energy storage device A, is the energy of the smallest unit energy storage device B, is the energy of the smallest unit energy storage device C, Energy demand for energy storage equipment. is the total energy of energy storage device A, is the total energy of energy storage device B, is the total energy of energy storage device C. is the energy conversion efficiency of energy storage device A, is the energy conversion efficiency of energy storage device B, is the energy conversion efficiency of energy storage device C. Minimum unit energy storage device A voltage, Voltage of the smallest unit energy storage device B, Minimum unit energy storage device C voltage. For single PCs power.

[0151] Step 4.3. Initialize the population.

[0152] Population individuals represent: Each energy storage system power configuration is a set of design schemes, including energy storage device configuration, power allocation strategy, etc.

[0153] Step 4.4. Automatically generate weights for all life cycle stages.

[0154] Define weights based on life cycle phase:

[0155]

[0156]

[0157]

[0158] : The proportion of time in the early, middle and late stages of the life cycle, normalized to [0, 1]

[0159] : initial cost weight;

[0160] : initial performance weight;

[0161] : Initial security weight;

[0162] With this setup, you can prioritize cost in the early stages, performance in the mid-term, and security in the late stages.

[0163] : Attenuation coefficient, cost target priority reduction coefficient

[0164] : Gain coefficient, safety target priority increase coefficient

[0165] To ensure that the sum of the weights is always equal to 1, the weight calculation results are normalized and adjusted:

[0166]

[0167]

[0168]

[0169] Step 4.5. Multi-objective optimization process.

[0170] Objective function calculation:

[0171] +

[0172]

[0173] in, is the objective function, Transform() is a min-max normalization function.

[0174] In order to eliminate the dimensionality impact between indicators and ensure the comparability of the scores of hardware performance, security and network status indicators, it is necessary to standardize the data of different indicators so that each indicator is at the same order of magnitude. Therefore, the min-max standardization method can be used for score calculation. The min-max standardization method is also called deviation standardization, which is a linear transformation of the original data so that the result value is mapped between 0 and 1. The conversion function is shown in the following formula, where max is the maximum value of the sample data and min is the minimum value of the sample data.

[0175]

[0176] in, is the converted indicator, is the indicator before conversion.

[0177] , its value is between 0-1, the higher the better.

[0178] , its value is between 0-1, the higher the better.

[0179] , its value is between 0-1, the higher the better.

[0180] Among them, Transform() is a min-max normalization function, which is a linear transformation of the original data so that the result value is mapped to between 0 and 1.

[0181] Calculate the initial objective function based on the initial normalized weights.

[0182] Non-dominated sorting: Sort the energy storage system configuration schemes according to the cost function, performance function and safety function, and divide the solutions into different non-dominated levels.

[0183] Specifically, if a configuration scheme A is better than another configuration scheme B in at least one of the three optimization objectives of cost, performance, and safety, then configuration scheme A dominates configuration scheme B. If configuration scheme C is not dominated by any other configuration scheme in the population, then configuration scheme C is a non-dominated solution.

[0184] Find all non-dominated solutions in the population and assign them to the first non-dominated level F1.

[0185] After removing these solutions from the population, the remaining solutions are searched for non-dominated solutions again and classified into the second non-dominated level F2.

[0186] This process is repeated until all solutions in the population are divided into different non-dominated levels.

[0187] Through non-dominated sorting, we can systematically screen out high-quality configuration solutions that minimize costs while improving performance and security. At the same time, dominated sorting ensures the diversity of the solution set, allowing decision makers to choose from multiple trade-off solutions based on specific needs.

[0188] Dynamic weight adjustment: based on the current life cycle stage , dynamically adjust weights , and . Update the objective function value:

[0189] +

[0190]

[0191] Crossover and mutation operations: Select the next set of energy storage system configuration schemes based on the objective function value. Increase the capacity of one energy storage device in the hybrid energy storage system, and increase the step size to the minimum unit capacity of the energy storage device. Merge the parent and child generations, and select the next generation of energy storage system configuration schemes according to non-dominated sorting and crowding distance. Repeat the non-dominated sorting, crossover, mutation, and selection operations until the stop condition is met (the maximum number of scheme configurations is reached). Generate N hybrid energy storage power configuration schemes.

[0192] Step 4.6. Output the results.

[0193] After the optimization is completed, a set of Pareto configuration solutions is output. Each solution corresponds to a set of optimal weight allocation solutions, which include weights at the beginning, middle and end of the life cycle. Each set of Pareto solutions contains a weight vector and the corresponding objective function value. These solutions represent the best trade-offs between different objectives.

[0194] Step S5: Select an energy storage system configuration scheme based on the Pareto configuration scheme set and actual needs.

[0195] Specifically, according to the actual needs and priorities of the hybrid energy storage system (for example, preference for priority, preference for performance, preference for safety, preference for response time, etc.), a solution that meets the requirements is selected from the obtained configuration solution set. If the user has a preference, the optimal solution that meets the preference can be selected from the Pareto solution set.

[0196] Step S6: Output the configuration plan.

[0197] Specifically, according to the selected optimal configuration scheme, various parameters of the energy storage equipment and auxiliary equipment of the scheme are output as optimization results.

[0198] In this specific embodiment, for a hybrid energy storage system composed of two or more energy storage devices, the selection and configuration scheme of the hybrid energy storage system can be quickly obtained. According to the needs in different scenarios (large energy storage power stations-cost priority, industrial and commercial energy storage-cost and volume priority, mobile energy storage-energy density priority, etc.), multiple demand indicators of the use scenarios of the hybrid energy storage system can be weighed to obtain the optimal solution. A multi-stage optimization strategy for the entire life cycle of energy storage equipment is introduced, and a weight self-generation mechanism is combined. By comprehensively considering the key factors of the entire life cycle, the weight setting is automatically generated by the proposed global optimization algorithm, rather than relying on manual setting or simple subjective weight allocation. This method effectively overcomes the limitations of traditional weight allocation methods (such as empirical weight method or subjective assignment method) and improves the scientificity and applicability of the optimization results. The optimization algorithm takes into account the optimization of energy storage auxiliary equipment, which can significantly improve the practical applicability of the optimization results and the overall system performance, accurately evaluate the system cost, and ensure that the energy storage system reaches the optimal state at the technical and economic levels.

[0199] Based on the same inventive concept, a hybrid energy storage system configuration device is also provided in the embodiments of this specification, as described in the following embodiments. Since the principle of solving the problem by the hybrid energy storage system configuration device is similar to that of the hybrid energy storage system configuration method, the implementation of the hybrid energy storage system configuration device can refer to the implementation of the hybrid energy storage system configuration method, and the repeated parts will not be repeated. As used below, the terms "unit" or "module" can be a combination of software and / or hardware that implements predetermined functions. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived. Figure 4 is a structural block diagram of a hybrid energy storage system configuration device according to an embodiment of this specification, such as Figure 4 As shown, it includes: an establishment module 401, a determination module 402, a generation module 403 and an optimization module 404. The structure is described below.

[0200] Establishing module 401 is used to establish an energy storage device database and an auxiliary device database based on the energy storage device type required by the hybrid energy storage system; the energy storage device database includes device parameters of various energy storage devices required by the hybrid energy storage system, and the auxiliary device database includes device parameters of auxiliary devices required by the hybrid energy storage system.

[0201] The determination module 402 is used to obtain the power load prediction curve of the hybrid energy storage system and establish an expected load model to determine the output data of the hybrid energy storage system; the power load prediction curve includes the real-time load power of each time node in a preset time period and the maximum load power, load duration and load change data in the preset time period; the output data includes at least the total power required by the hybrid energy storage system.

[0202] The generation module 403 is used to allocate the power required by various energy storage devices among the multiple energy storage devices based on the energy storage device database and the total power required by the hybrid energy storage system, and generate multiple power configuration schemes for the hybrid energy storage system.

[0203] The optimization module 404 is used to use a multi-objective evolutionary algorithm to determine the target energy storage system configuration scheme of the hybrid energy storage system from the multiple power configuration schemes; the multi-objective evolutionary algorithm optimizes the objective function based on multiple constraints, and the objective function includes a weighted sum of multiple objective items, and the corresponding multiple weights in the multiple objective items are automatically generated in combination with the demand characteristics of each stage in the multiple stages of the full life cycle of the hybrid energy storage system; the multiple constraints include energy storage device constraints and auxiliary equipment constraints; the target energy storage system configuration scheme includes the number and device parameters of various energy storage devices among the multiple energy storage devices required for the hybrid energy storage system and the number and configuration parameters of auxiliary equipment required for the hybrid energy storage system.

[0204] In some embodiments of the present specification, the equipment parameters of the energy storage device include common parameters and characteristic parameters; the common parameters include at least: power, energy, cycle efficiency, weight, and floor space; the characteristic parameters include at least: cycle life, geographical conditions; and / or, the equipment parameters of the auxiliary equipment include at least: the rated power of the power conversion system, the DC side voltage of the power conversion system, and the conversion efficiency of the power conversion system.

[0205] In some embodiments of the present specification, the energy storage device constraint conditions include at least one of the following: a minimum power constraint of the energy storage device, a minimum energy constraint of the energy storage device, and a comprehensive charging and discharging efficiency constraint of the energy storage device; and / or, the auxiliary equipment constraint conditions include at least one of the following: a DC side voltage constraint of the power conversion system, and a minimum power constraint of the power conversion system.

[0206] In some embodiments of the present specification, the determination module is specifically used to: obtain the load forecast curve of the hybrid energy storage system in the power system according to the application scenario and load demand of the hybrid energy storage system, and establish an expected load curve model of the energy storage power station; determine the output data of the hybrid energy storage system based on the load curve model, the power demand of the reduced power station and the power demand of auxiliary equipment.

[0207] In some embodiments of the present specification, the multiple energy storage devices include energy storage devices of the first technical route, energy storage devices of the second technical route, and energy storage devices of the third technical route; accordingly, the generation module is specifically used to: allocate the power requirements of the energy storage devices of the first technical route based on the total power required by the energy storage system equipment; gradually increase the power requirements of the energy storage devices of the first technical route from zero to the total power required by the hybrid energy storage system, and the power increase step is determined according to the power of the minimum unit energy storage device; allocate the power requirements of the energy storage devices of the second technical route and the energy storage devices of the third technical route according to the power requirements of the energy storage devices of the first technical route; combine the power requirements of the energy storage devices of the first technical route, the power requirements of the energy storage devices of the second technical route, and the power requirements of the energy storage devices of the third technical route to form multiple power configuration schemes for the hybrid energy storage system.

[0208] In some embodiments of the present specification, the optimization module is specifically used to: obtain an objective function and establish multiple constraints; the objective function includes a weighted sum of multiple objective items; the multiple constraints include energy storage device constraints and auxiliary equipment constraints; initialize a population; each population individual represents one of the multiple power configuration schemes; automatically generate multiple weights corresponding to the multiple objective items in combination with the demand characteristics of each stage in the multiple stages of the full life cycle of the hybrid energy storage system; perform multi-objective optimization on the objective function based on the multiple constraints to obtain a power configuration scheme set for the hybrid energy storage system; and select a target energy storage system configuration scheme from the power configuration scheme set of the hybrid energy storage system according to the actual application scenario of the hybrid energy storage system.

[0209] In some embodiments of the present specification, the objective function is subjected to multi-objective optimization based on the multiple constraints to obtain a set of power configuration schemes for the hybrid energy storage system, including: sorting the multiple power configuration schemes according to the multiple target items in the objective function, and dividing the multiple power configuration schemes into different non-dominated levels; dynamically adjusting the multiple weights corresponding to the multiple target items based on the current life cycle stage; updating the objective function value of the objective function according to the adjusted multiple weights; selecting the next set of power configuration schemes according to the objective function value; and repeating the above steps until the preset conditions are met to obtain the set of power configuration schemes for the hybrid energy storage system.

[0210] In some embodiments of the present specification, the multiple target items include at least a cost target item and a performance target item, the cost weight corresponding to the cost target item gradually decreases over time, and the performance weight corresponding to the performance target item increases accordingly.

[0211] This specification also provides a computer device, which can be found in Figure 5 The computer device structure diagram of the hybrid energy storage system configuration method provided in the embodiment of this specification is shown, and the computer device may specifically include an input device 51, a processor 52, and a memory 53. The memory 53 is used to store processor executable instructions.

[0212] In this embodiment, the input device may specifically be one of the main devices for information exchange between the user and the computer system. The input device may include a keyboard, a mouse, a camera, a scanner, a light pen, a handwriting input board, a voice input device, etc.; the input device is used to input the original data and the program for processing these numbers into the computer. The input device can also obtain and receive data transmitted from other modules, units, and devices. The processor can be implemented in any appropriate manner. For example, the processor can take the form of a computer-readable medium, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, etc., such as a microprocessor or a processor and a computer-readable program code (such as software or firmware) that can be executed by the (micro) processor. The memory may specifically be a memory device used to store information in modern information technology. The memory may include multiple levels. In a digital system, anything that can store binary data can be a memory; in an integrated circuit, a circuit with a storage function without a physical form is also called a memory, such as a RAM, a FIFO, etc.; in a system, a storage device with a physical form is also called a memory, such as a memory stick, a TF card, etc.

[0213] In this embodiment, the functions and effects specifically realized by the computer device can be explained in comparison with other embodiments and will not be described in detail here.

[0214] A computer storage medium based on a hybrid energy storage system configuration method is also provided in an implementation manner of this specification, wherein the computer storage medium stores computer program instructions, and when the computer program instructions are executed, the steps of the hybrid energy storage system configuration method described in any of the above embodiments are implemented.

[0215] In this embodiment, the storage medium includes but is not limited to a random access memory (RAM), a read-only memory (ROM), a cache, a hard disk (HDD) or a memory card. The memory can be used to store computer program instructions. The network communication unit can be an interface for network connection communication set in accordance with the standard specified by the communication protocol.

[0216] In this embodiment, the functions and effects specifically implemented by the program instructions stored in the computer storage medium can be explained in comparison with other embodiments and will not be repeated here.

[0217] Obviously, those skilled in the art should understand that the modules or steps of the above-mentioned embodiments of this specification can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the embodiments of this specification are not limited to any specific combination of hardware and software.

[0218] It should be understood that the above description is for illustration and not for limitation. Many embodiments and many applications beyond the examples provided will be apparent to those skilled in the art by reading the above description.

[0219] The above description is only the preferred embodiment of this specification and is not intended to limit this specification. For those skilled in the art, the embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this specification shall be included in the protection scope of this specification.

Claims

1. A hybrid energy storage system configuration method, characterized in that: include: Based on the types of energy storage devices required by the hybrid energy storage system, an energy storage device database and an auxiliary device database are established; the energy storage device database includes device parameters of various energy storage devices required by the hybrid energy storage system, and the auxiliary device database includes device parameters of auxiliary devices required by the hybrid energy storage system; Obtaining the power load forecast curve of the hybrid energy storage system and establishing an expected load model to determine the output data of the hybrid energy storage system; the power load forecast curve includes the real-time load power of each time node in a preset time period and the maximum load power, load duration and load change data in the preset time period; the output data includes at least the total power required by the hybrid energy storage system; Based on the energy storage device database and the total power required by the hybrid energy storage system, allocating the power required by various energy storage devices among the multiple energy storage devices, and generating multiple power configuration schemes for the hybrid energy storage system; A target energy storage system configuration scheme for the hybrid energy storage system is determined from the multiple power configuration schemes by using a multi-objective evolutionary algorithm; the multi-objective evolutionary algorithm optimizes an objective function based on multiple constraints, wherein the objective function includes a weighted sum of multiple objective items, and the corresponding multiple weights in the multiple objective items are automatically generated in combination with the demand characteristics of each stage in multiple stages during the entire life cycle of the hybrid energy storage system; the multiple constraints include energy storage device constraints and auxiliary equipment constraints; the target energy storage system configuration scheme includes the quantity and device parameters of various energy storage devices among the multiple energy storage devices required for the hybrid energy storage system and the quantity and configuration parameters of auxiliary equipment required for the hybrid energy storage system.

2. The hybrid energy storage system configuration method according to claim 1, characterized in that: The equipment parameters of the energy storage device include common parameters and characteristic parameters; the common parameters include at least: power, energy, cycle efficiency, weight, and floor space; the characteristic parameters include at least: cycle life and geographical conditions; and / or, The equipment parameters of the auxiliary equipment include at least: the rated power of the power conversion system, the DC side voltage of the power conversion system and the conversion efficiency of the power conversion system.

3. The hybrid energy storage system configuration method according to claim 1, characterized in that: The energy storage device constraint condition includes at least one of the following: a minimum power constraint of the energy storage device, a minimum energy constraint of the energy storage device, a comprehensive charge and discharge efficiency constraint of the energy storage device; and / or, The auxiliary equipment constraint condition includes at least one of the following: a DC side voltage constraint of a power conversion system and a minimum power constraint of a power conversion system.

4. The hybrid energy storage system configuration method according to claim 1, characterized in that: Obtaining the power load forecast curve of the hybrid energy storage system and establishing an expected load model to determine the output data of the hybrid energy storage system, including: According to the application scenario and load demand of the hybrid energy storage system, a load forecast curve of the hybrid energy storage system in the power system is obtained, and a load curve model of the expected energy storage power station is established; Based on the load curve model, the power demand of the nuclear reduction power station and the power demand of auxiliary equipment, the output data of the hybrid energy storage system is determined.

5. The hybrid energy storage system configuration method according to claim 1, characterized in that: The multiple energy storage devices include energy storage devices of the first technical route, energy storage devices of the second technical route, and energy storage devices of the third technical route; Accordingly, based on the energy storage device database and the total power required by the hybrid energy storage system, the power required by each of the multiple energy storage devices is allocated to generate multiple power configuration schemes of the hybrid energy storage system, including: Based on the total power required by the energy storage system equipment, the power demand of the energy storage equipment of the first technical route is allocated; the power demand of the energy storage equipment of the first technical route is gradually increased from zero to the total power required by the hybrid energy storage system, and the power increase step is determined according to the power of the minimum unit energy storage device; Allocate the power requirements of the second technical route energy storage device and the third technical route energy storage device according to the power requirements of the first technical route energy storage device; The power requirements of the energy storage devices of the first technical route, the power requirements of the energy storage devices of the second technical route, and the power requirements of the energy storage devices of the third technical route are combined to form a plurality of power configuration schemes for the hybrid energy storage system.

6. The hybrid energy storage system configuration method according to claim 1, characterized in that: Using a multi-objective evolutionary algorithm, a target energy storage system configuration scheme of the hybrid energy storage system is determined from the multiple power configuration schemes, including: Obtaining an objective function and establishing a plurality of constraints; the objective function includes a weighted sum of a plurality of objective items; the plurality of constraints include energy storage device constraints and auxiliary device constraints; Initializing a population; each individual in the population represents one of the multiple power configuration schemes; Automatically generate corresponding weights of the multiple target items in combination with the demand characteristics of each stage in the multiple stages of the full life cycle of the hybrid energy storage system; Performing multi-objective optimization on the objective function based on the multiple constraints to obtain a power configuration solution set for the hybrid energy storage system; According to the actual application scenario of the hybrid energy storage system, a target energy storage system configuration scheme is selected from a set of power configuration schemes of the hybrid energy storage system.

7. The hybrid energy storage system configuration method according to claim 6, characterized in that: The objective function is optimized multi-objectively based on the multiple constraints to obtain a power configuration scheme set for the hybrid energy storage system, including: sorting the multiple power configuration schemes according to multiple target items in the target function, and dividing the multiple power configuration schemes into different non-dominated levels; Based on the current life cycle stage, dynamically adjust the multiple weights corresponding to the multiple target items; update the objective function value of the objective function according to the adjusted multiple weights; Selecting the next set of power configuration schemes according to the objective function value; Repeat the above steps until the preset conditions are met to obtain a power configuration scheme set for the hybrid energy storage system.

8. The hybrid energy storage system configuration method according to claim 1, characterized in that: The multiple target items include at least a cost target item and a performance target item. The cost weight corresponding to the cost target item gradually decreases over time, and the performance weight corresponding to the performance target item increases accordingly.

9. A hybrid energy storage system configuration device, characterized in that: include: An establishment module is used to establish an energy storage device database and an auxiliary device database based on the energy storage device types required by the hybrid energy storage system; the energy storage device database includes device parameters of multiple energy storage devices required by the hybrid energy storage system, and the auxiliary device database includes device parameters of auxiliary devices required by the hybrid energy storage system; A determination module is used to obtain the power load prediction curve of the hybrid energy storage system and establish an expected load model to determine the output data of the hybrid energy storage system; the power load prediction curve includes the real-time load power of each time node in a preset time period and the maximum load power, load duration and load change data in the preset time period; the output data includes at least the total power required by the hybrid energy storage system; A generating module, configured to allocate the power required by various energy storage devices among the multiple energy storage devices based on the energy storage device database and the total power required by the hybrid energy storage system, and generate multiple power configuration schemes for the hybrid energy storage system; An optimization module is used to determine a target energy storage system configuration scheme of the hybrid energy storage system from the multiple power configuration schemes using a multi-objective evolutionary algorithm; the multi-objective evolutionary algorithm optimizes an objective function based on multiple constraints, the objective function includes a weighted sum of multiple objective items, and the corresponding multiple weights in the multiple objective items are automatically generated in combination with the demand characteristics of each stage in the multiple stages of the full life cycle of the hybrid energy storage system; the multiple constraints include energy storage device constraints and auxiliary equipment constraints; the target energy storage system configuration scheme includes the number and device parameters of various energy storage devices among the multiple energy storage devices required for the hybrid energy storage system and the number and configuration parameters of auxiliary equipment required for the hybrid energy storage system.

10. A computer device, characterized in that: The method comprises a processor and a memory for storing processor-executable instructions, wherein the processor implements the steps of the method according to any one of claims 1 to 8 when executing the instructions.

11. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the instructions are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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