Distributed optical storage system collaborative optimization method, system, equipment and medium

Abstract

The invention belongs to the technical field of optical storage collaborative optimization, and discloses a distributed optical storage system collaborative optimization method, system and device and a medium, and the method comprises the steps: building a distributed optical storage system structure based on battery energy storage and flywheel energy storage, establishing a hybrid energy storage degradation cost function according to the battery energy storage actual cycle life and flywheel energy storage rated power information; and constructing a distributed optical storage system collaborative optimization model by using the hybrid energy storage degradation cost function, solving the distributed optical storage system collaborative optimization model based on a solving rule in which hybrid energy storage participates, and generating a power distribution strategy according to a solving result. According to the method, a power distribution strategy of battery energy storage and flywheel energy storage in hybrid energy storage is obtained in three scenes of peak regulation, frequency modulation and multifunctional multiplexing, and an economic operation target in the scenes of peak regulation, frequency modulation and multiplexing optimization of the power distribution network is realized through source-load-storage collaborative optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic-storage collaborative optimization, and in particular to a distributed photovoltaic-storage system collaborative optimization method, system, device and medium. Background Art

[0002] In the context of large-scale distributed photovoltaic access, the distribution network faces many challenges such as peak regulation, frequency regulation, and voltage regulation. Due to the randomness and intermittency of photovoltaic power generation, it is difficult to connect to the grid and has low resource utilization. These problems pose a threat to the stable operation of the power grid. The addition of energy storage technology provides a new idea for solving these problems. It has the ability of fast power throughput and flexible operation, and is one of the important means to improve the regulation capacity of the distribution network. In order to give full play to the regulation capacity of distributed photovoltaic storage systems under the premise of economic operation, it is particularly important to conduct a quantitative evaluation of their regulation capacity. This not only helps to optimize the configuration of distributed photovoltaic and energy storage systems, but also provides strong support for the stable operation of the power system.

[0003] With the continuous development of distribution network energy storage technology, the multifunctional reuse of energy storage has gradually been paid attention to. The multifunctional reuse of energy storage means that the energy storage system is not only used for a single function, but is used for multiple functions such as peak load regulation and frequency regulation to improve the economic benefits and utilization efficiency of the energy storage system. The multifunctional reuse of hybrid energy storage in the distribution network is an important means of coordinated optimization of distributed photovoltaic storage systems. Hybrid energy storage systems usually include the comprehensive application of two or more energy storage technologies, such as battery energy storage (BES), flywheel energy storage (FES), supercapacitors, etc. Different types of energy storage have different performance characteristics such as energy density, power density, response speed, and service life. Through comprehensive utilization, the technical and economic advantages of different types of energy storage can be fully utilized, the shortcomings of a single energy storage technology can be avoided or reduced, and resource allocation optimization can be achieved.

[0004] The main applications and advantages of hybrid energy storage multifunctional reuse in the coordinated optimization of distributed photovoltaic storage systems include: smoothing the output of new energy. Since the output of new energy such as wind energy and photovoltaics has significant randomness and intermittency, the hybrid energy storage system can effectively smooth the output and reduce the impact on the power grid; providing auxiliary services. The hybrid energy storage system can provide a variety of auxiliary services such as frequency regulation, voltage regulation, peak shaving and valley filling to improve the stability and reliability of the power system; participating in power market transactions. The hybrid energy storage system can participate in power market transactions such as energy market, backup market, and auxiliary service market to achieve economic benefits; optimizing the operation of the power system. The hybrid energy storage system can achieve coordinated optimization of source-load-storage based on information such as the operating status of the power system and price signals from the power market, thereby improving the operating efficiency and economic benefits of the power system.

[0005] However, the existing mathematical model of battery energy storage degradation cost does not consider the health state (SOH) of energy storage, but calculates the battery degradation cost through the linear approximation of the rainflow cycle counting method. It does not establish an energy storage degradation cost coefficient model that considers key factors such as battery discharge depth, health state, and initial investment. As a result, the degradation cost coefficient is inaccurate and inconsistent with the real-time operating conditions of energy storage, resulting in inaccurate calculation of degradation cost.

[0006] At the same time, the existing flywheel energy storage degradation cost is mainly calculated by forming a flywheel life attenuation curve through simulation tests based on the properties of the flywheel material itself, and based on this, it is used as the basis for calculating its degradation cost. It does not involve factors such as flywheel energy storage life, charging / discharging response time, rated power and investment cost, resulting in inaccurate degradation cost coefficients, which are inconsistent with the real-time operating conditions of energy storage and inaccurate degradation cost calculations.

[0007] In addition, the existing research on hybrid energy storage has established an operation optimization model for hybrid energy storage to participate in one of the auxiliary services of peak load regulation and frequency regulation, but has not established an operation optimization model for the multifunctional reuse of battery energy storage-flywheel energy storage hybrid energy storage system to participate in peak load regulation and frequency regulation at the same time. At the same time, no power allocation strategy has been proposed when hybrid energy storage participates in peak load regulation and frequency regulation at the same time, and no collaborative optimization model for maximizing operating benefits has been established. It is impossible to achieve efficient collaborative optimization among the system operating economy, energy storage operating life, and system real-time operating conditions.

[0008] Therefore, how to provide a distributed photovoltaic storage system collaborative optimization method, system, equipment and medium is a problem that needs to be solved urgently. Summary of the invention

[0009] The embodiments of the present invention provide a distributed photovoltaic energy storage system collaborative optimization method, system, device and medium to solve the problem that the health status of energy storage is not considered in the prior art.

[0010] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0011] According to a first aspect of an embodiment of the present invention, a distributed photovoltaic storage system collaborative optimization method is provided.

[0012] In one embodiment, a distributed photovoltaic storage system collaborative optimization method includes:

[0013] A distributed photovoltaic energy storage system structure is established based on battery energy storage and flywheel energy storage, and a hybrid energy storage degradation cost function is established based on the actual cycle life of battery energy storage and the rated power information of flywheel energy storage;

[0014] The distributed photovoltaic and storage system collaborative optimization model is constructed using the hybrid energy storage degradation cost function, and the distributed photovoltaic and storage system collaborative optimization model is solved based on the rules for obtaining hybrid energy storage participation, and the power allocation strategy is generated according to the solution results.

[0015] In one embodiment, a distributed photovoltaic storage system structure is established based on battery energy storage and flywheel energy storage, and a hybrid energy storage degradation cost function is established based on the actual cycle life of the battery energy storage and the rated power information of the flywheel energy storage, including:

[0016] Establish a distributed photovoltaic storage system structure based on the distribution network, distributed photovoltaic power generation, flywheel energy storage, battery energy storage and user load, and determine the operation mode of the distributed photovoltaic storage system structure;

[0017] Based on the operating data of the distributed photovoltaic storage system structure, the discharge depth and discharge law of the battery energy storage are obtained, and the actual cycle life of the battery energy storage is analyzed in combination with the maximum discharge power of the battery energy storage;

[0018] Determine the operational health status of battery energy storage based on actual cycle life, analyze the relationship between discharge depth and operational health status, and establish a battery energy storage degradation cost function based on the relationship results;

[0019] The flywheel energy storage degradation cost function is established by obtaining the rated power and unit power investment cost of the flywheel energy storage. The flywheel energy storage degradation cost function is combined with the battery energy storage degradation cost function to obtain the hybrid energy storage degradation cost function.

[0020] In one embodiment, judging the operational health status of the battery energy storage according to the actual cycle service life, analyzing the relationship between the discharge depth and the operational health status, and establishing the battery energy storage degradation cost function based on the relationship result includes:

[0021] Analyze the battery degradation cost based on the unit capacity investment cost and actual cycle service life of battery energy storage, obtain the battery degradation coefficient based on the battery degradation cost, and combine the battery degradation coefficient with the battery capacity to determine the maximum storage capacity of the battery energy storage;

[0022] Determine the maximum discharge depth of battery energy storage based on the maximum storage capacity and battery capacity, and use the maximum storage capacity and maximum discharge depth to analyze the battery health status and obtain the relationship between the battery health status and the maximum discharge depth;

[0023] The relationship results are combined with the unit capacity investment cost of battery energy storage to establish a battery energy storage degradation cost function, and the degradation speed and maintenance cost of battery energy storage during operation are analyzed based on the battery energy storage degradation cost model.

[0024] In one embodiment, the expression of the battery energy storage degradation cost function is:

[0025]

[0026] In the formula, Indicates the extreme value of the degradation cost coefficient at each stage of the battery life cycle, L R Indicates the cycle life under standard cycle conditions, represents the unit capacity investment cost of battery energy storage, E bat represents the battery capacity, k1, u1 and u2 represent the fitting coefficients, Indicates the maximum discharge power of the battery. represents the battery energy storage discharge power under standard cycle conditions, n represents the total number of cycle life stages in the whole life cycle, i represents the i-th cycle life stage in the whole life cycle, e represents the base of the natural logarithm, D R Indicates the discharge depth of battery energy storage under standard cycle conditions. Indicates the maximum discharge depth, SOH i Indicates the battery health status.

[0027] In one embodiment, a distributed photovoltaic storage system collaborative optimization model is constructed using a hybrid energy storage degradation cost function, and the distributed photovoltaic storage system collaborative optimization model is solved based on the rules for obtaining hybrid energy storage participation, and a power allocation strategy is generated according to the solution results, including:

[0028] Based on the hybrid energy storage degradation cost function, the unit power maintenance cost of flywheel energy storage and battery energy storage, a multifunctional distributed photovoltaic storage system collaborative optimization model is established to calculate the total operating cost of the distributed photovoltaic storage system.

[0029] Using the operating characteristics of flywheel energy storage and battery energy storage, the energy storage charge state constraints of flywheel energy storage and battery energy storage are set respectively, and the operating power ranges of distribution network, photovoltaic power generation, battery energy storage and flywheel energy storage are defined according to the operating requirements of distributed photovoltaic storage system;

[0030] The optimal peak-shaving threshold is determined according to the average value of the power purchased by the distribution network from the upper power grid, and the distributed photovoltaic storage system collaborative optimization model is solved by combining the optimal peak-shaving threshold with the peak-shaving demand of the distribution network, and the power allocation result is output based on the solution result.

[0031] In one embodiment, the optimal peak-shaving threshold is determined according to the average value of the power purchased by the distribution network from the upper power grid, and the optimal peak-shaving threshold is combined with the peak-shaving demand of the distribution network to solve the distributed photovoltaic storage system collaborative optimization model. The output power allocation result based on the solution result includes:

[0032] Obtain the average power input from the distribution network, analyze the average value of the power purchased by the distribution network from the upper power grid based on the average power, and set the optimal peak-shaving threshold of hybrid energy storage based on the average value;

[0033] The distributed photovoltaic storage system is used to determine the peak-shaving demand of the distribution network, and the power load, optimal peak-shaving threshold and photovoltaic power generation output power are set as input parameters of the distributed photovoltaic storage system collaborative optimization model based on the determination results;

[0034] Initialize the energy storage charge state constraints of flywheel energy storage and battery energy storage, receive electricity price signals and frequency regulation signals, calculate the demand peak at the corresponding peak, and compare the difference between the demand peak and the optimal peak-shaving threshold;

[0035] According to the difference result, the charging and discharging power of the battery energy storage and the flywheel energy storage is output, the corresponding energy storage charge state is updated, and the power allocation result is determined.

[0036] According to a second aspect of an embodiment of the present invention, a distributed photovoltaic storage system collaborative optimization system is provided.

[0037] In one embodiment, a distributed photovoltaic storage system collaborative optimization system includes:

[0038] A hybrid energy storage analysis and judgment unit is used to establish a distributed photovoltaic storage system structure based on battery energy storage and flywheel energy storage, and to establish a hybrid energy storage degradation cost function based on the actual cycle life of the battery energy storage and the rated power information of the flywheel energy storage;

[0039] The collaborative optimization calculation and analysis unit is used to construct a distributed photovoltaic storage system collaborative optimization model using the hybrid energy storage degradation cost function, and solve the distributed photovoltaic storage system collaborative optimization model based on the rules for obtaining hybrid energy storage participation, and generate a power allocation strategy based on the solution results.

[0040] In one embodiment, the hybrid energy storage analysis and judgment unit includes:

[0041] Distributed photovoltaic storage system structure building module, used to establish a distributed photovoltaic storage system structure based on the distribution network, distributed photovoltaic power generation, flywheel energy storage, battery energy storage and user load, and determine the operation mode of the distributed photovoltaic storage system structure;

[0042] The actual cycle life calculation module is used to obtain the discharge depth and discharge law of the battery energy storage based on the operating data of the distributed photovoltaic storage system structure, and analyze the actual cycle life of the battery energy storage in combination with the maximum discharge power of the battery energy storage;

[0043] The degradation cost function definition module is used to determine the operational health status of the battery energy storage according to the actual cycle service life, analyze the relationship between the discharge depth and the operational health status, and establish the battery energy storage degradation cost function based on the relationship results;

[0044] The hybrid degradation cost acquisition module is used to acquire the rated power and unit power investment cost of the flywheel energy storage to establish the flywheel energy storage degradation cost function, and combine the flywheel energy storage degradation cost function with the battery energy storage degradation cost function to obtain the hybrid energy storage degradation cost function.

[0045] In one embodiment, when the degradation cost function definition module determines the operational health status of the battery energy storage according to the actual cycle service life, analyzes the relationship between the discharge depth and the operational health status, and establishes the battery energy storage degradation cost function based on the relationship result, the battery degradation cost can be analyzed based on the unit capacity investment cost of the battery energy storage and the actual cycle service life, the battery degradation coefficient can be obtained according to the battery degradation cost, and the maximum storage capacity of the battery energy storage can be determined by combining the battery degradation coefficient with the battery capacity; the maximum discharge depth of the battery energy storage can be determined according to the maximum storage capacity and the battery capacity, and the battery health status can be analyzed using the maximum storage capacity and the maximum discharge depth to obtain the relationship between the battery health status and the maximum discharge depth; the relationship result can be combined with the unit capacity investment cost of the battery energy storage to establish the battery energy storage degradation cost function, and the degradation speed and maintenance cost of the battery energy storage during operation can be analyzed according to the battery energy storage degradation cost model.

[0046] In one embodiment, the collaborative optimization computing and analysis unit includes:

[0047] The collaborative optimization model building module is used to establish a collaborative optimization model for a multifunctional and reused distributed photovoltaic storage system based on the hybrid energy storage degradation cost function, the unit power maintenance cost of flywheel energy storage and battery energy storage, and calculate the total operating cost of the distributed photovoltaic storage system;

[0048] The operating power range definition module is used to use the operating characteristics of flywheel energy storage and battery energy storage to set the energy storage charge state constraints of flywheel energy storage and battery energy storage respectively, and define the operating power range of distribution network, photovoltaic power generation, battery energy storage and flywheel energy storage according to the operating requirements of distributed photovoltaic storage system;

[0049] The collaborative optimization calculation module is used to determine the optimal peak-shaving threshold based on the average value of the power purchased by the distribution network from the upper power grid, and to solve the distributed photovoltaic storage system collaborative optimization model by combining the optimal peak-shaving threshold with the peak-shaving demand of the distribution network, and output the power allocation result based on the solution result.

[0050] In one embodiment, the collaborative optimization calculation module determines the optimal peak-shaving threshold value according to the average value of the power purchased by the distribution network from the upper power grid, and solves the distributed photovoltaic storage system collaborative optimization model by combining the optimal peak-shaving threshold value with the peak-shaving demand of the distribution network. When outputting the power allocation result based on the solution result, the average power input from the distribution network can be obtained, and the average value of the power purchased by the distribution network from the upper power grid is analyzed based on the average power, and the optimal peak-shaving threshold value of the hybrid energy storage is set based on the average value; the distributed photovoltaic storage system is used to judge the peak-shaving demand of the distribution network, and the power load, the optimal peak-shaving threshold value and the output power of photovoltaic power generation are set as input parameters of the distributed photovoltaic storage system collaborative optimization model according to the judgment result; the energy storage charge state constraints of the flywheel energy storage and the battery energy storage are initialized, and the demand peak value is calculated at the corresponding peak value by receiving the electricity price signal and the frequency regulation signal, and the demand peak value is compared with the optimal peak-shaving threshold value by difference; the charging and discharging power of the battery energy storage and the flywheel energy storage is output according to the difference result, the corresponding energy storage charge state is updated, and the power allocation result is determined.

[0051] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0052] In one embodiment, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0053] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0054] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0055] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0056] 1. The present invention establishes a battery energy storage degradation cost model, takes into account the impact of key factors and parameters such as battery discharge depth, health status, initial investment, etc. on the battery energy storage degradation cost, and changes the problem that the traditional battery energy storage degradation cost calculation model does not consider similar factors and real-time operating conditions of energy storage. At the same time, a flywheel energy storage degradation cost model is established, taking into account the impact of factors such as flywheel energy storage life, charge / discharge response time, rated power and investment cost on the flywheel energy storage degradation cost, and changes the problem that the existing flywheel energy storage degradation cost calculation model does not consider similar factors and real-time operating conditions of flywheel energy storage.

[0057] 2. The present invention establishes a collaborative optimization model of a distributed photovoltaic storage system based on hybrid energy storage peak regulation, taking into account the degradation costs of battery energy storage and flywheel energy storage, as well as the comprehensive benefits of hybrid energy storage when participating in distribution network peak regulation. At the same time, a collaborative optimization model of a distributed photovoltaic storage system based on hybrid energy storage frequency regulation is established, combining the degradation costs of battery energy storage and flywheel energy storage, as well as the comprehensive benefits of hybrid energy storage when participating in distribution network frequency regulation. A collaborative optimization model of a distributed photovoltaic storage system based on multifunctional reuse of hybrid energy storage is established, combining the degradation costs of battery energy storage and flywheel energy storage, as well as the comprehensive benefits of hybrid energy storage when participating in distribution network peak regulation and frequency regulation multifunctional reuse. A solution strategy for the collaborative optimization model of a distributed photovoltaic storage system with the participation of hybrid energy storage is proposed, and a power allocation strategy for battery energy storage and flywheel energy storage in hybrid energy storage is proposed under three scenarios of peak regulation, frequency regulation, and multifunctional reuse. Then, the economic operation goals under the distribution network peak regulation, frequency regulation, and reuse optimization scenarios are achieved through source-load-storage collaborative optimization.

[0058] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0060] Figure 1 is a flow chart of a distributed photovoltaic storage system collaborative optimization method according to an exemplary embodiment;

[0061] Figure 2 is a principle block diagram of a distributed photovoltaic storage system collaborative optimization system according to an exemplary embodiment;

[0062] Figure 3 is a structural schematic diagram of a computer device according to an exemplary embodiment;

[0063] Figure 4 It is a schematic structural diagram of a distributed photovoltaic storage system including hybrid energy storage according to an exemplary embodiment. DETAILED DESCRIPTION

[0064] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0065] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0066] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0067] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0068] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0069] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0070] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0071] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0072] Figure 1 An embodiment of the distributed photovoltaic storage system collaborative optimization method of the present invention is shown.

[0073] In this optional embodiment, the distributed photovoltaic storage system collaborative optimization method includes:

[0074] Step S101, establishing a distributed photovoltaic storage system structure based on battery energy storage and flywheel energy storage, and establishing a hybrid energy storage degradation cost function according to the actual cycle life of the battery energy storage and the rated power information of the flywheel energy storage;

[0075] Step S103, using the hybrid energy storage degradation cost function to build a distributed photovoltaic storage system collaborative optimization model, and solving the distributed photovoltaic storage system collaborative optimization model based on the rules for obtaining hybrid energy storage participation, and generating a power allocation strategy according to the solution results.

[0076] In this optional embodiment, when a distributed photovoltaic storage system structure is established based on battery energy storage and flywheel energy storage, and a hybrid energy storage degradation cost function is established based on the actual cycle life of the battery energy storage and the rated power information of the flywheel energy storage, a distributed photovoltaic storage system structure can be established based on the distribution network, distributed photovoltaic power generation, flywheel energy storage, battery energy storage and user load, and the operation mode of the distributed photovoltaic storage system structure is determined; the discharge depth and discharge law of the battery energy storage are obtained based on the operation data of the distributed photovoltaic storage system structure, and the actual cycle life of the battery energy storage is analyzed in combination with the maximum discharge power of the battery energy storage; the operating health status of the battery energy storage is judged according to the actual cycle service life, the relationship between the discharge depth and the operating health status is analyzed, and the battery energy storage degradation cost function is established based on the relationship results; the rated power of the flywheel energy storage and the unit power investment cost are obtained to establish the flywheel energy storage degradation cost function, and the flywheel energy storage degradation cost function is combined with the battery energy storage degradation cost function to obtain the hybrid energy storage degradation cost function.

[0077] In this optional embodiment, when judging the operational health status of the battery energy storage according to the actual cycle service life, analyzing the relationship between the discharge depth and the operational health status, and establishing the battery energy storage degradation cost function based on the relationship results, the battery degradation cost can be analyzed based on the unit capacity investment cost of the battery energy storage and the actual cycle service life, the battery degradation coefficient can be obtained according to the battery degradation cost, and the maximum storage capacity of the battery energy storage can be judged by combining the battery degradation coefficient with the battery capacity; the maximum discharge depth of the battery energy storage is determined according to the maximum storage capacity and the battery capacity, and the battery health status is analyzed using the maximum storage capacity and the maximum discharge depth to obtain the relationship between the battery health status and the maximum discharge depth; the relationship results are combined with the unit capacity investment cost of the battery energy storage to establish the battery energy storage degradation cost function, and the degradation speed and maintenance cost of the battery energy storage during operation are analyzed according to the battery energy storage degradation cost model.

[0078] In this optional embodiment, when constructing a distributed photovoltaic storage system collaborative optimization model using a hybrid energy storage degradation cost function, and solving the distributed photovoltaic storage system collaborative optimization model based on the rules for obtaining hybrid energy storage participation, and generating a power allocation strategy based on the solution results, a multifunctional and reused distributed photovoltaic storage system collaborative optimization model can be established based on the hybrid energy storage degradation cost function, the unit power maintenance cost of flywheel energy storage and battery energy storage, and the total operating cost of the distributed photovoltaic storage system is calculated; using the operating characteristics of flywheel energy storage and battery energy storage, the energy storage charge state constraints of flywheel energy storage and battery energy storage are set respectively, and the operating power ranges of the distribution network, photovoltaic power generation, battery energy storage and flywheel energy storage are defined according to the operating requirements of the distributed photovoltaic storage system; the optimal peak-shaving threshold is determined based on the average value of the power purchased by the distribution network from the upper power grid, and the distributed photovoltaic storage system collaborative optimization model is solved by combining the optimal peak-shaving threshold with the peak-shaving demand of the distribution network, and the power allocation result is output based on the solution result.

[0079] In this optional embodiment, when determining the optimal peak-shaving threshold value based on the average value of the power purchased by the distribution network from the upper power grid, and solving the distributed photovoltaic storage system collaborative optimization model by combining the optimal peak-shaving threshold value with the peak-shaving demand of the distribution network, and outputting the power allocation result based on the solution result, the average power input from the distribution network can be obtained, and the average value of the power purchased by the distribution network from the upper power grid is analyzed based on the average power, and the optimal peak-shaving threshold value of the hybrid energy storage is set based on the average value; the distributed photovoltaic storage system is used to judge the peak-shaving demand of the distribution network, and the power load, the optimal peak-shaving threshold value and the output power of photovoltaic power generation are set as input parameters of the distributed photovoltaic storage system collaborative optimization model based on the judgment result; the energy storage charge state constraints of the flywheel energy storage and the battery energy storage are initialized, and the electricity price signal and the frequency regulation signal are received to calculate the demand peak value at the corresponding peak value, and the demand peak value is compared with the optimal peak-shaving threshold value by difference; the charging and discharging power of the battery energy storage and the flywheel energy storage is output according to the difference result, the corresponding energy storage charge state is updated, and the power allocation result is determined.

[0080] Figure 2 An embodiment of a distributed photovoltaic storage system collaborative optimization system of the present invention is shown.

[0081] In this optional embodiment, the distributed photovoltaic storage system collaborative optimization system includes:

[0082] A hybrid energy storage analysis and judgment unit 201 is used to establish a distributed photovoltaic storage system structure based on battery energy storage and flywheel energy storage, and to establish a hybrid energy storage degradation cost function according to the actual cycle life of the battery energy storage and the rated power information of the flywheel energy storage;

[0083] The collaborative optimization calculation and analysis unit 203 is used to construct a distributed photovoltaic storage system collaborative optimization model using the hybrid energy storage degradation cost function, and solve the distributed photovoltaic storage system collaborative optimization model based on the rules for obtaining hybrid energy storage participation, and generate a power allocation strategy according to the solution results.

[0084] In this optional embodiment, the hybrid energy storage analysis and judgment unit 201 includes a distributed photovoltaic storage system structure building module (not shown in the figure), an actual cycle life calculation module (not shown in the figure), a degradation cost function definition module (not shown in the figure) and a hybrid degradation cost acquisition module (not shown in the figure), wherein:

[0085] A distributed photovoltaic storage system structure construction module is used to establish a distributed photovoltaic storage system structure based on the distribution network, distributed photovoltaic power generation, flywheel energy storage, battery energy storage and user load, and determine the operation mode of the distributed photovoltaic storage system structure; an actual cycle life calculation module is used to obtain the discharge depth and discharge law of the battery energy storage based on the operation data of the distributed photovoltaic storage system structure, and analyze the actual cycle life of the battery energy storage in combination with the maximum discharge power of the battery energy storage; a degradation cost function definition module is used to judge the operating health status of the battery energy storage according to the actual cycle service life, analyze the relationship between the discharge depth and the operating health status, and establish the battery energy storage degradation cost function based on the relationship results; a hybrid degradation cost acquisition module is used to obtain the rated power and unit power investment cost of the flywheel energy storage to establish the flywheel energy storage degradation cost function, and combine the flywheel energy storage degradation cost function with the battery energy storage degradation cost function to obtain the hybrid energy storage degradation cost function.

[0086] In this optional embodiment, when the degradation cost function definition module determines the operational health status of the battery energy storage according to the actual cycle service life, analyzes the relationship between the discharge depth and the operational health status, and establishes the battery energy storage degradation cost function based on the relationship result, the battery degradation cost can be analyzed based on the unit capacity investment cost of the battery energy storage and the actual cycle service life, the battery degradation coefficient can be obtained based on the battery degradation cost, and the maximum storage capacity of the battery energy storage can be determined by combining the battery degradation coefficient with the battery capacity; the maximum discharge depth of the battery energy storage can be determined based on the maximum storage capacity and the battery capacity, and the battery health status can be analyzed using the maximum storage capacity and the maximum discharge depth to obtain the relationship between the battery health status and the maximum discharge depth; the relationship result can be combined with the unit capacity investment cost of the battery energy storage to establish the battery energy storage degradation cost function, and the degradation speed and maintenance cost of the battery energy storage during operation can be analyzed based on the battery energy storage degradation cost model.

[0087] In this optional embodiment, the collaborative optimization calculation and analysis unit 203 includes a collaborative optimization model building module (not shown in the figure), an operating power range definition module (not shown in the figure) and a collaborative optimization calculation module (not shown in the figure), wherein:

[0088] The collaborative optimization model construction module is used to establish a multifunctional and reused distributed photovoltaic storage system collaborative optimization model based on the hybrid energy storage degradation cost function, the unit power maintenance cost of flywheel energy storage and battery energy storage, and calculate the total operating cost of the distributed photovoltaic storage system; the operating power range definition module is used to use the operating characteristics of flywheel energy storage and battery energy storage to set the energy storage charge state constraints of flywheel energy storage and battery energy storage respectively, and define the operating power range of distribution network, photovoltaic power generation, battery energy storage and flywheel energy storage according to the operating requirements of distributed photovoltaic storage system; the collaborative optimization calculation module is used to determine the optimal peak-shaving threshold according to the average value of the power purchased by the distribution network from the upper power grid, and solve the distributed photovoltaic storage system collaborative optimization model by combining the optimal peak-shaving threshold with the peak-shaving demand of the distribution network, and output the power allocation result based on the solution result.

[0089] In this optional embodiment, the collaborative optimization calculation module determines the optimal peak-shaving threshold value according to the average value of the power purchased by the distribution network from the upper power grid, and solves the distributed photovoltaic storage system collaborative optimization model by combining the optimal peak-shaving threshold value with the peak-shaving demand of the distribution network. When outputting the power allocation result based on the solution result, the average power input from the distribution network can be obtained, and the average value of the power purchased by the distribution network from the upper power grid is analyzed based on the average power, and the optimal peak-shaving threshold value of the hybrid energy storage is set based on the average value; the distributed photovoltaic storage system is used to judge the peak-shaving demand of the distribution network, and the power load, the optimal peak-shaving threshold value and the output power of photovoltaic power generation are set as input parameters of the distributed photovoltaic storage system collaborative optimization model according to the judgment result; the energy storage charge state constraints of the flywheel energy storage and the battery energy storage are initialized, and the demand peak value is calculated at the corresponding peak value by receiving the electricity price signal and the frequency regulation signal, and the demand peak value is compared with the optimal peak-shaving threshold value by difference; the charging and discharging power of the battery energy storage and the flywheel energy storage is output according to the difference result, the corresponding energy storage charge state is updated, and the power allocation result is determined.

[0090] This embodiment proposes a distributed photovoltaic storage system collaborative optimization method considering hybrid energy storage multifunctional reuse, and the specific steps are as follows:

[0091] Step 1: Establish a typical structure of a distributed photovoltaic storage system;

[0092] like Figure 4 As shown in the figure, distributed photovoltaic storage consists of distribution network, distributed photovoltaic power generation, hybrid energy storage (flywheel energy storage + battery energy storage), and user load (load aggregator). Figure 4The distribution network shown in the figure can operate in grid-connected mode or independently as an island grid. The choice of its operating mode depends on system requirements, photovoltaic conditions and electricity market, and can be flexibly adjusted according to the economic and safety conditions of operation. During peak electricity prices, the distribution network gives priority to the use of photovoltaics and hybrid energy storage, and sells the remaining electricity to the upper-level grid. During low electricity price periods, the distribution network gives priority to purchasing electricity from the upper-level grid company and makes full use of the performance of hybrid energy storage to achieve optimal operating benefits.

[0093] Step 2: Establish a hybrid degradation cost model;

[0094] (1) Battery energy storage degradation cost model: The health status of the battery is a key indicator for measuring battery performance. It is quantitatively described by the ratio of the remaining cycle life of the battery to the cycle life of a new battery of the same type. This ratio reflects the current health status of the battery and its expected service life. When the ratio is high, it indicates that the battery is in good health. When the ratio is low, there is a risk of reduced battery health, and it is necessary to increase the maintenance frequency or replace it in advance. Therefore, the battery health status indicator can not only be used to evaluate the current state of the battery, but also help users and maintenance personnel predict the future performance of the battery, so as to make more reasonable call and maintenance decisions. According to the correlation between the actual cycle life of the battery and the depth of discharge, the mathematical expression of the actual cycle life of the battery can be derived, as follows:

[0095]

[0096] In the formula, Indicates the actual cycle life of the battery; D R and They represent the discharge depth and discharge power of energy storage under standard cycle conditions, L R Indicates the cycle life under standard cycle conditions; d b Indicates the actual discharge depth; k1, u1 and u2 are fitting coefficients. Indicates the maximum discharge power of the battery.

[0097] Under normal use conditions, the remaining capacity of the battery will gradually decay, and the degradation cost per power of the battery at different health stages can be expressed as:

[0098]

[0099] In the formula, represents the degradation cost of each stage of the battery life cycle, N represents the number of healthy stages, E bat Indicates the battery capacity. Represents the unit capacity investment cost of the battery.

[0100] The maximum storage capacity and maximum discharge depth of the battery can be expressed as:

[0101]

[0102] In the formula, Indicates the maximum storage capacity, represents the battery degradation coefficient, Indicates the maximum discharge depth.

[0103] In addition, according to the definition of battery state of health (SOH), the battery state of health is the ratio of the maximum capacity and rated capacity of the battery at a certain operating stage, specifically:

[0104]

[0105] The relationship between the maximum discharge depth and SOH of a battery can be expressed as:

[0106]

[0107] Furthermore, monitoring and understanding the maximum discharge depth of the battery can help determine the life cycle stage of the battery, thereby effectively managing the battery degradation process and reducing the battery degradation cost. In the early stage of the battery life, since the battery is in the best condition, its capacity is large and can support deeper discharge cycles, but this also means that the battery degrades faster at this stage, so the maintenance and management costs will be relatively high. As the battery is continuously used, its capacity gradually decreases, and the maximum discharge depth also decreases, resulting in a relatively slow degradation rate of the battery in the later stage of the life cycle, and the corresponding maintenance cost will also be reduced. By distinguishing the battery degradation characteristics at different stages, corresponding maintenance strategies can be formulated to extend the battery life and reduce the total cost. Based on the above analysis, this embodiment defines the degradation cost coefficient of battery energy storage, specifically:

[0108] In this optional embodiment, the expression of the battery energy storage degradation cost function is:

[0109]

[0110] In the formula, Indicates the extreme value of the degradation cost coefficient at each stage of the battery life cycle, L R Indicates the cycle life under standard cycle conditions, represents the unit capacity investment cost of battery energy storage, E bat represents the battery capacity, k1, u1 and u2 represent the fitting coefficients, Indicates the maximum discharge power of the battery. represents the battery energy storage discharge power under standard cycle conditions, n represents the total number of cycle life stages in the whole life cycle, i represents the i-th cycle life stage in the whole life cycle, e represents the base of the natural logarithm, D R Indicates the discharge depth of battery energy storage under standard cycle conditions. Indicates the maximum discharge depth, SOH i Indicates the battery health status. and Indicates the minimum and maximum values ​​of SOH at each stage of the battery life cycle.

[0111] (2) Flywheel energy storage degradation cost model: Flywheel energy storage technology is a mechanical energy storage method. Its basic principle is to use the rotational motion of the flywheel to store energy. The flywheel energy storage system is mainly composed of a flywheel, a motor / generator, power electronic equipment and a control system. Its working principle is: when the system needs energy, the flywheel slows down and converts its kinetic energy into electrical energy. When the system has excess energy, the motor drives the flywheel to accelerate, converting the electrical energy into the kinetic energy of the flywheel and storing it. Flywheel energy storage technology has many advantages such as fast response speed, high charging and discharging efficiency, long life, high reliability and environmental protection. Therefore, it is widely used in various aspects of power system operation optimization, such as frequency regulation, grid stability, load balancing, etc.

[0112] The life of flywheel energy storage technology is mainly affected by factors such as flywheel material, design, operating speed and operating temperature. During the manufacturing process, an appropriate operating speed and maximum operating temperature are set according to the material and design characteristics of the flywheel. According to the maximum operating temperature at an appropriate speed given by the equipment manufacturing specification, it can be reasonably assumed that the flywheel energy storage has a continuous life under normal operating conditions. Therefore, regardless of the depth of discharge of each charge / discharge cycle, the degradation cost of the flywheel energy storage can be regarded as a linear function of time, which means that as the system operating time increases, the system performance will gradually decline, and corresponding maintenance and management are required. By evaluating and analyzing factors such as the flywheel energy storage life, charge / discharge response time, rated power and investment cost, a suitable operation and maintenance strategy can be formulated to ensure the long-term reliability and economic benefits of the system. Based on the above analysis, this embodiment defines the degradation cost coefficient of the flywheel energy storage, which is specifically defined as:

[0113]

[0114] In the formula, represents the unit power degradation cost of flywheel energy storage, c fes represents the unit power investment cost of flywheel energy storage, L fes represents the designed charge / discharge times of flywheel energy storage, t fes represents the charge / discharge response time of the flywheel energy storage, Δt represents any scheduling time interval, and Pfes Indicates the rated power of the flywheel energy storage.

[0115] The degradation cost coefficient based on flywheel energy storage shows that the degradation cost of flywheel energy storage is constant regardless of the cycle conditions, and its degradation cost is a linear function of time. Therefore, compared with battery energy storage, flywheel energy storage is more suitable for filling instantaneous power imbalance through frequent charging / discharging.

[0116] Step 3: Establish a distributed photovoltaic storage system collaborative optimization model based on hybrid energy storage multifunctional reuse;

[0117] It should be noted that during peak electricity demand periods, distributed photovoltaic storage systems containing hybrid energy storage can release stored electricity to meet additional demand, thereby reducing grid load and avoiding or reducing the purchase of expensive peak electricity. During low demand periods, hybrid energy storage systems can store excess electricity for later use. Users' peak load demands account for a large proportion of the distribution network operating costs, so smoothing peak load demands is an important method to reduce the distribution network operating costs. This embodiment focuses on the quantitative evaluation and economic modeling of the regulation capabilities of distributed photovoltaic storage systems with the participation of hybrid energy storage and load aggregators. Hybrid energy storage can be discharged when demand is high and charged when demand is low to smooth the consumption curve. The economic operation model when participating in the peak regulation of the distribution network is:

[0118]

[0119] P grid (t) = P load (t)-P pv (t)-P hes (t);

[0120] P hes (t) = P bes (t)+P fes (t);

[0121] In the formula, C cope,peak represents the total operating cost of the distributed photovoltaic storage system when participating in the peak load regulation of the distribution network, C ele represents the electricity purchase fee paid by the distributed photovoltaic storage system to the distribution network, (P grid (t)) av Represents the average power input from the distribution network; (P load (t)-P pv (t)) av represents the average net load of the distributed photovoltaic storage system; P pv (t) represents the output power of photovoltaic; λ ele represents the electricity price; peak (t) represents the peak load subsidy price of the power grid; P hes(t) represents the output power of the hybrid energy storage system, P bes (t) represents the output power of the battery energy storage, P fes (t) represents the output power of the flywheel energy storage, θ represents the load peak judgment factor, if it is the load peak, θ=1, otherwise θ=0.

[0122] At the same time, the frequency of the power grid needs to be maintained at a stable value (such as 50Hz or 60Hz), and the constant changes in the load and power supply of the power grid will cause frequency fluctuations. The distributed photovoltaic storage system with hybrid energy storage can quickly respond to such fluctuations, adjust the grid frequency by releasing or storing electric energy, and maintain the stability of the grid. In addition to the peak load scenario, the distributed photovoltaic storage system can also mobilize hybrid energy storage and load aggregators to perform source-load-storage collaborative optimization, and provide frequency regulation services for the distribution network to obtain revenue. The economic operation model of the distributed photovoltaic storage system and the distribution network frequency regulation is:

[0123]

[0124] In the formula, C ope,frequency represents the total operating cost of the distributed photovoltaic storage system when participating in the frequency regulation of the distribution network, λ fre represents the frequency regulation subsidy price; represents the frequency regulation mismatch penalty for the absolute error between the command dispatch and the actual response; C represents the hourly reserve power capacity of the distributed photovoltaic storage system to provide frequency support for the distribution network; S fre (t) represents the frequency adjustment signal, T fre Indicates the duration of the frequency support service.

[0125] At the same time, the distributed photovoltaic storage system with hybrid energy storage can be applied to multiple regulation scenarios at the same time, such as peak regulation, frequency regulation, voltage support, grid stability control, etc. Through intelligent control and scheduling, the hybrid energy storage system can flexibly respond to the real-time needs and status of the power grid, efficiently allocate its energy storage capacity and power, and realize multi-functional reuse. In addition to participating in peak regulation and frequency regulation alone, the distributed photovoltaic storage system can also mobilize hybrid energy storage and load aggregators to optimize source-load-storage collaboration, and participate in the peak regulation and frequency regulation services of the distribution network to improve the economic benefits of the distribution network, and then establish an economic model for quantitative evaluation of regulation capacity under the peak regulation and frequency regulation multi-functional reuse scenario. The model aims to minimize the total operating cost under the peak regulation and frequency regulation scenario, where the total operating cost includes the cost of purchasing and selling electricity, equipment maintenance cost, degradation cost, and peak regulation frequency benefit, specifically:

[0126]

[0127] Where minC ope represents the total operating cost of the distributed photovoltaic storage system when it participates in the peak load and frequency regulation of the distribution network, λ elerepresents the electricity price, P grid (t) represents the power input from the grid, represents the frequency adjustment mismatch penalty, C represents the frequency adjustment mismatch penalty for the error, S fre (t) represents the frequency adjustment signal, α fes represents the unit power maintenance cost of flywheel energy storage, represents the unit power degradation cost of flywheel energy storage, P fes (t) represents the charge and discharge power of the flywheel energy storage, α bes represents the unit power maintenance cost of battery energy storage, represents the extreme value of the degradation cost coefficient at each stage in the battery life cycle, P bes (t) represents the charging and discharging power of the battery energy storage, α pv Represents the unit power maintenance cost of photovoltaic, P pv (t) represents the output power of photovoltaic power generation, λ fre represents the frequency regulation subsidy price, θ represents the load peak judgment factor, and λ peak represents the peak load subsidy price of the power grid, P load (t) represents the power load, (P grid (t)) av represents the average power input from the distribution network, (P load (t)-P pv (t)) av represents the average net load of the distributed photovoltaic storage system, T represents the total optimization time period, t represents the tth moment in the optimization time period, and P hes (t) represents the output power of the hybrid energy storage system.

[0128] The distributed photovoltaic storage system optimizes source-load-storage synergy by mobilizing hybrid energy storage and load aggregators, and provides frequency regulation and peak regulation services. At this time, the power balance constraint must always be met, and the formula is:

[0129]

[0130] Where P load (t) represents the power load, P grid (t) represents the power input from the grid, P pv (t) represents the output power of photovoltaic power generation, Indicates the discharge power of battery energy storage; Indicates the charging power of the battery energy storage, represents the discharge power of the flywheel energy storage, Indicates the charging power of the flywheel energy storage.

[0131] In addition, the operating characteristics of hybrid energy storage itself require that the capacity and operating power of battery energy storage and flywheel energy storage meet certain requirements. Taking into account the charging and discharging efficiency of different energy storage forms, the discrete time capacity difference equations of battery energy storage and flywheel energy storage are shown below respectively.

[0132]

[0133] In the formula, E bes (t) and E fes (t) represents the capacity of battery energy storage and flywheel energy storage, η bes，c and η bes，d Respectively represent the charging and discharging efficiency of battery energy storage; η fes，c and η fes，d They represent the charging and discharging efficiency of flywheel energy storage respectively;

[0134] For battery energy storage systems, overcharging may cause battery overheating or even fire, while overdischarging may cause battery performance degradation or even damage the battery in severe cases. The state of charge (SOC) is a parameter that describes the remaining energy of a battery, usually expressed as a percentage. For battery energy storage, in order to prevent overcharging and overdischarging, it is necessary to set appropriate constraints on the SOC of the battery energy storage, as shown below:

[0135]

[0136] In the formula, SOC bes (t) represents the SOC of the battery energy storage.

[0137] For flywheel energy storage, its SOC constraint is more complicated. The SOC of the flywheel energy storage system is usually expressed by the speed and position of the flywheel. Its maximum speed and minimum speed represent the maximum and minimum SOC of the flywheel. Exceeding the SOC setting range may cause the flywheel to become unstable or even damaged. Therefore, the SOC of the flywheel energy storage system also needs to set appropriate constraints to ensure the safe operation of the flywheel, specifically:

[0138]

[0139] In the formula, SOC fes (t) represents the SOC of flywheel energy storage; E fes Indicates the capacity of the flywheel energy storage.

[0140] In addition, in order to ensure the normal operation and stable performance of the equipment, the operating power of the grid, photovoltaic, battery energy storage and flywheel energy storage must be maintained within a certain range, as shown below:

[0141]

[0142] Step 4: Solving the collaborative optimization model and power allocation strategy of the distributed photovoltaic storage system with hybrid energy storage;

[0143] Peak shaving can not only help the distribution network reduce peak load, but also save the electricity purchase cost of the distributed photovoltaic storage system, bringing peak shaving benefits to the distribution network. Hybrid energy storage, as a flexible dispatching resource of the distribution network, is mainly used to respond to the real-time power regulation needs of the distribution network. When the distribution network participates in the grid frequency regulation service, its optimization dispatching mode has undergone fundamental changes. While meeting its own economic operation, the distribution network needs to reserve a part of its capacity to meet the needs of rapid grid frequency regulation. In addition, if the distribution network deviates from the requirements of grid frequency regulation when participating in grid frequency regulation, it will be punished accordingly, resulting in efficiency loss and economic loss. Therefore, it is necessary to set the optimal peak shaving threshold of hybrid energy storage, and on this basis, a collaborative optimization model solution method and hybrid energy storage power allocation strategy are proposed under three scenarios of hybrid energy storage participating in peak shaving, frequency regulation, and peak shaving and frequency regulation. The optimal peak shaving threshold is defined as follows:

[0144]

[0145] In the formula, Indicates the optimal peak-shaving threshold of hybrid energy storage; P grid (t) represents the average value of the power purchased by the distribution network from the upper power grid in each optimization time interval;

[0146] In combination with different scenarios in which the distribution network participates in grid ancillary services through source-load-storage collaborative optimization, this embodiment proposes a model solution method for peak-shaving scenarios, frequency regulation scenarios, and peak-shaving and frequency regulation scenarios.

[0147] (1) Peak load model solution method and hybrid energy storage power allocation strategy: In the peak load scenario, the distributed photovoltaic storage system is optimized with the goal of minimizing the total operating cost, and the hybrid energy storage is called upon to participate in the power regulation of the distribution network. The distributed photovoltaic storage system allocates power between battery energy storage and flywheel energy storage through source-load-storage collaborative optimization, thereby reducing the peak load of the distributed photovoltaic storage system. Specifically:

[0148] The parameter input is P load (t), P pv (t) And implement the initialization specific: t = 0, The iterative process is:

[0149] for t=1→T executes:

[0150] Receive electricity price signal λ ele , locate the current time t in the corresponding peak calculation window, [τ0, τ e ];

[0151] Calculate the current peak demand:

[0152] Optimize the distribution network system level:

[0153]

[0154] P bes (t)+P fes (t) = P load (t)-P pv (t)-P grid (t);

[0155] Else

[0156]

[0157] End

[0158] Update the SOC status of the battery energy storage and flywheel energy storage, and then proceed to the next step: t←t+1;

[0159] End

[0160] The output results include photovoltaic operating power, load aggregator load power, hybrid energy storage power, and transaction power between the distribution network and the upper-level power grid.

[0161] (2) Frequency regulation model solution method and hybrid energy storage power allocation strategy: In the frequency regulation scenario, the hybrid energy storage in the distributed photovoltaic storage system, as a flexible regulation resource, participates in the power balance optimization of the distribution network and responds to the frequency regulation signal of the distribution network. The energy storage BME first calls the flywheel energy storage for fast charging / discharging to respond to the grid frequency regulation, and decides whether the flywheel energy storage participates in the coordinated optimization of the distribution network together with the battery energy storage according to its remaining capacity status. The specific implementation process is as follows:

[0162] The parameter input is P load (t), P pv (t) And implement the initialization specific: t = 0, The iterative process is:

[0163] for t=1→T executes:

[0164] Receive electricity price signal λ ele , FM signal S ele (t);

[0165] P fes (t) = C·S fre (t), P bes (t) = P load (t)-Ppv (t)-P grid (t);

[0166] End

[0167] Update the SOC status of the battery energy storage and flywheel energy storage, and then proceed to the next step: t←t+1;

[0168] End

[0169] The output results include photovoltaic operating power, load aggregator load power, hybrid energy storage power, and transaction power between the distribution network and the upper-level power grid.

[0170] (3) Multifunctional reuse model solution method and hybrid energy storage power allocation strategy: In the scenario of peak load regulation and frequency regulation, hybrid energy storage, as a flexible regulation resource, participates in the power balance optimization and frequency regulation of the distribution network at the same time. The hybrid energy storage system first needs to determine whether there is a peak load demand in the distribution network, and then dispatch the hybrid energy storage and allocate power between the battery energy storage and the flywheel energy storage. In the peak load regulation and frequency regulation multifunctional reuse scenario, the energy storage BMS first calls the flywheel energy storage for fast charging / discharging to support frequency regulation, and then decides whether the flywheel energy storage participates in the coordinated optimization of the distribution network together with the battery energy storage based on its remaining capacity status. The specific process is as follows:

[0171] The parameter input is P load (t), P pv (t) And implement the initialization specific: t = 0, τ0 represents the time when the peak value of peak regulation begins, τ e Indicates the moment when the peak value of peak regulation ends. The iterative process is:

[0172] for t=1→T executes:

[0173] Receive electricity price signal λ ele , FM signal S ele (t);

[0174] Locate the current time t in the corresponding peak calculation window, [τ0, τ e ], calculate the current peak demand:

[0175]

[0176] P bes (t)+P fes (t)-C·S fre (t) = P load (t)-P pv (t)-P grid (t);

[0177] else

[0178]

[0179] else

[0180]

[0181] else

[0182]

[0183] P fes (t) = C·S fre (t);

[0184] End

[0185] Update the SOC status of the battery energy storage and flywheel energy storage, and then proceed to the next step: t←t+1;

[0186] End

[0187] The output results include photovoltaic operating power, load aggregator load power, hybrid energy storage power, and transaction power between the distribution network and the upper-level power grid.

[0188] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0189] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0190] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0191] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0192] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0193] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A distributed photovoltaic storage system collaborative optimization method, characterized in that: The collaborative optimization method includes: A distributed photovoltaic energy storage system structure is established based on battery energy storage and flywheel energy storage, and a hybrid energy storage degradation cost function is established based on the actual cycle life of battery energy storage and the rated power information of flywheel energy storage; The distributed photovoltaic and storage system collaborative optimization model is constructed using the hybrid energy storage degradation cost function, and the distributed photovoltaic and storage system collaborative optimization model is solved based on the rules for obtaining hybrid energy storage participation, and the power allocation strategy is generated according to the solution results.

2. The distributed photovoltaic storage system collaborative optimization method according to claim 1, characterized in that: The distributed photovoltaic storage system structure is established based on battery energy storage and flywheel energy storage, and the hybrid energy storage degradation cost function is established according to the actual cycle life of the battery energy storage and the rated power information of the flywheel energy storage, including: Establish a distributed photovoltaic storage system structure based on the distribution network, distributed photovoltaic power generation, flywheel energy storage, battery energy storage and user load, and determine the operation mode of the distributed photovoltaic storage system structure; Based on the operating data of the distributed photovoltaic storage system structure, the discharge depth and discharge law of the battery energy storage are obtained, and the actual cycle life of the battery energy storage is analyzed in combination with the maximum discharge power of the battery energy storage; Determine the operational health status of battery energy storage based on actual cycle life, analyze the relationship between discharge depth and operational health status, and establish a battery energy storage degradation cost function based on the relationship results; The flywheel energy storage degradation cost function is established by obtaining the rated power and unit power investment cost of the flywheel energy storage. The flywheel energy storage degradation cost function is combined with the battery energy storage degradation cost function to obtain the hybrid energy storage degradation cost function.

3. The distributed photovoltaic storage system collaborative optimization method according to claim 2, characterized in that: The method of judging the operational health status of the battery energy storage according to the actual cycle service life, analyzing the relationship between the discharge depth and the operational health status, and establishing the battery energy storage degradation cost function based on the relationship results includes: Analyze the battery degradation cost based on the unit capacity investment cost and actual cycle service life of battery energy storage, obtain the battery degradation coefficient based on the battery degradation cost, and combine the battery degradation coefficient with the battery capacity to determine the maximum storage capacity of the battery energy storage; Determine the maximum discharge depth of battery energy storage based on the maximum storage capacity and battery capacity, and use the maximum storage capacity and maximum discharge depth to analyze the battery health status and obtain the relationship between the battery health status and the maximum discharge depth; The relationship results are combined with the unit capacity investment cost of battery energy storage to establish a battery energy storage degradation cost function, and the degradation speed and maintenance cost of battery energy storage during operation are analyzed based on the battery energy storage degradation cost model.

4. The distributed photovoltaic storage system collaborative optimization method according to claim 3, characterized in that: The expression of the battery energy storage degradation cost function is: In the formula, Indicates the extreme value of the degradation cost coefficient at each stage of the battery life cycle, L R Indicates the cycle life under standard cycle conditions, represents the unit capacity investment cost of battery energy storage, E bat represents the battery capacity, k1, u1 and u2 represent the fitting coefficients, Indicates the maximum discharge power of the battery. represents the battery energy storage discharge power under standard cycle conditions, n represents the total number of cycle life stages in the whole life cycle, i represents the i-th cycle life stage in the whole life cycle, e represents the base of the natural logarithm, D R Indicates the discharge depth of battery energy storage under standard cycle conditions. Indicates the maximum depth of discharge, SOH i Indicates the battery health status.

5. The distributed photovoltaic storage system collaborative optimization method according to claim 4, characterized in that: The distributed photovoltaic storage system collaborative optimization model is constructed by using the hybrid energy storage degradation cost function, and the distributed photovoltaic storage system collaborative optimization model is solved based on the rules for obtaining the participation of hybrid energy storage, and the power allocation strategy is generated according to the solution results, including: Based on the hybrid energy storage degradation cost function, the unit power maintenance cost of flywheel energy storage and battery energy storage, a multifunctional distributed photovoltaic storage system collaborative optimization model is established to calculate the total operating cost of the distributed photovoltaic storage system. Using the operating characteristics of flywheel energy storage and battery energy storage, the energy storage charge state constraints of flywheel energy storage and battery energy storage are set respectively, and the operating power ranges of distribution network, photovoltaic power generation, battery energy storage and flywheel energy storage are defined according to the operating requirements of distributed photovoltaic storage system; The optimal peak-shaving threshold is determined according to the average value of the power purchased by the distribution network from the upper power grid, and the distributed photovoltaic storage system collaborative optimization model is solved by combining the optimal peak-shaving threshold with the peak-shaving demand of the distribution network, and the power allocation result is output based on the solution result.

6. The distributed photovoltaic storage system collaborative optimization method according to claim 5, characterized in that: The expression of the distributed photovoltaic storage system collaborative optimization model is: Where minC ope represents the total operating cost of the distributed photovoltaic storage system when it participates in the peak load and frequency regulation of the distribution network, λ ele represents the electricity price, P grid (t) represents the power input from the grid, represents the frequency adjustment mismatch penalty, C represents the frequency adjustment mismatch penalty for the error, S fre (t) represents the frequency adjustment signal, α fes represents the unit power maintenance cost of flywheel energy storage, represents the unit power degradation cost of flywheel energy storage, P fes (t) represents the charge and discharge power of the flywheel energy storage, α bes represents the unit power maintenance cost of battery energy storage, represents the extreme value of the degradation cost coefficient at each stage in the battery life cycle, P bes (t) represents the charging and discharging power of the battery energy storage, α pv Represents the unit power maintenance cost of photovoltaic, P pv (t) represents the output power of photovoltaic power generation, λ fre represents the frequency regulation subsidy price, θ represents the load peak judgment factor, and λ peak represents the peak load subsidy price of the power grid, P load (t) represents the power load, (P grid (t)) av represents the average power input from the distribution network, (P load (t)-P pv (t)) av represents the average net load of the distributed photovoltaic storage system, T represents the total optimization time period, t represents the tth moment in the optimization time period, and P hes (t) represents the output power of the hybrid energy storage system.

7. The distributed photovoltaic storage system collaborative optimization method according to claim 6, characterized in that: The optimal peak-shaving threshold is determined according to the average value of the power purchased by the distribution network from the upper power grid, and the distributed photovoltaic storage system collaborative optimization model is solved by combining the optimal peak-shaving threshold with the peak-shaving demand of the distribution network. The output power allocation result based on the solution result includes: Obtain the average power input from the distribution network, analyze the average value of the power purchased by the distribution network from the upper power grid based on the average power, and set the optimal peak-shaving threshold of hybrid energy storage based on the average value; The distributed photovoltaic storage system is used to determine the peak-shaving demand of the distribution network, and the power load, optimal peak-shaving threshold and photovoltaic power generation output power are set as input parameters of the distributed photovoltaic storage system collaborative optimization model based on the determination results; Initialize the energy storage charge state constraints of flywheel energy storage and battery energy storage, receive electricity price signals and frequency regulation signals, calculate the demand peak at the corresponding peak, and compare the difference between the demand peak and the optimal peak-shaving threshold; According to the difference result, the charging and discharging power of the battery energy storage and the flywheel energy storage is output, the corresponding energy storage charge state is updated, and the power allocation result is determined.

8. A distributed photovoltaic storage system collaborative optimization system, characterized in that: The collaborative optimization system includes: A hybrid energy storage analysis and judgment unit is used to establish a distributed photovoltaic storage system structure based on battery energy storage and flywheel energy storage, and to establish a hybrid energy storage degradation cost function based on the actual cycle life of the battery energy storage and the rated power information of the flywheel energy storage; The collaborative optimization calculation and analysis unit is used to construct a distributed photovoltaic storage system collaborative optimization model using the hybrid energy storage degradation cost function, and solve the distributed photovoltaic storage system collaborative optimization model based on the rules for obtaining hybrid energy storage participation, and generate a power allocation strategy based on the solution results.

9. The distributed photovoltaic storage system collaborative optimization system according to claim 8, characterized in that: The hybrid energy storage analysis and judgment unit comprises: Distributed photovoltaic storage system structure building module, used to establish a distributed photovoltaic storage system structure based on the distribution network, distributed photovoltaic power generation, flywheel energy storage, battery energy storage and user load, and determine the operation mode of the distributed photovoltaic storage system structure; The actual cycle life calculation module is used to obtain the discharge depth and discharge law of the battery energy storage based on the operating data of the distributed photovoltaic storage system structure, and analyze the actual cycle life of the battery energy storage in combination with the maximum discharge power of the battery energy storage; The degradation cost function definition module is used to determine the operational health status of the battery energy storage according to the actual cycle service life, analyze the relationship between the discharge depth and the operational health status, and establish the battery energy storage degradation cost function based on the relationship results; The hybrid degradation cost acquisition module is used to acquire the rated power and unit power investment cost of the flywheel energy storage to establish the flywheel energy storage degradation cost function, and combine the flywheel energy storage degradation cost function with the battery energy storage degradation cost function to obtain the hybrid energy storage degradation cost function.

10. The distributed photovoltaic storage system collaborative optimization system according to claim 9, characterized in that: The degradation cost function definition module determines the operational health status of the battery energy storage according to the actual cycle service life, analyzes the relationship between the discharge depth and the operational health status, and establishes the battery energy storage degradation cost function based on the relationship result. The battery degradation cost can be analyzed based on the unit capacity investment cost of the battery energy storage and the actual cycle service life, and the battery degradation coefficient is obtained according to the battery degradation cost. The battery degradation coefficient is combined with the battery capacity to determine the maximum storage capacity of the battery energy storage; Determine the maximum discharge depth of battery energy storage based on the maximum storage capacity and battery capacity, and use the maximum storage capacity and maximum discharge depth to analyze the battery health status and obtain the relationship between the battery health status and the maximum discharge depth; The relationship results are combined with the unit capacity investment cost of battery energy storage to establish a battery energy storage degradation cost function, and the degradation speed and maintenance cost of battery energy storage during operation are analyzed based on the battery energy storage degradation cost model.

11. The distributed photovoltaic storage system collaborative optimization system according to claim 10, characterized in that: The expression of the battery energy storage degradation cost function is: In the formula, Indicates the extreme value of the degradation cost coefficient at each stage of the battery life cycle, L R Indicates the cycle life under standard cycle conditions, represents the unit capacity investment cost of battery energy storage, E bat represents the battery capacity, k1, u1 and u2 represent the fitting coefficients, Indicates the maximum discharge power of the battery. represents the battery energy storage discharge power under standard cycle conditions, n represents the total number of cycle life stages in the whole life cycle, i represents the i-th cycle life stage in the whole life cycle, e represents the base of the natural logarithm, D R Indicates the discharge depth of battery energy storage under standard cycle conditions. Indicates the maximum depth of discharge, SOH i Indicates the battery health status.

12. The distributed photovoltaic storage system collaborative optimization system according to claim 8, characterized in that: The collaborative optimization calculation and analysis unit comprises: The collaborative optimization model building module is used to establish a collaborative optimization model for a multifunctional and reused distributed photovoltaic storage system based on the hybrid energy storage degradation cost function, the unit power maintenance cost of flywheel energy storage and battery energy storage, and calculate the total operating cost of the distributed photovoltaic storage system; The operating power range definition module is used to use the operating characteristics of flywheel energy storage and battery energy storage to set the energy storage charge state constraints of flywheel energy storage and battery energy storage respectively, and define the operating power range of distribution network, photovoltaic power generation, battery energy storage and flywheel energy storage according to the operating requirements of distributed photovoltaic storage system; The collaborative optimization calculation module is used to determine the optimal peak-shaving threshold based on the average value of the power purchased by the distribution network from the upper power grid, and to solve the distributed photovoltaic storage system collaborative optimization model by combining the optimal peak-shaving threshold with the peak-shaving demand of the distribution network, and output the power allocation result based on the solution result.

13. The distributed photovoltaic storage system collaborative optimization system according to claim 12, characterized in that: The expression of the distributed photovoltaic storage system collaborative optimization model is: Where minC ope represents the total operating cost of the distributed photovoltaic storage system when it participates in the peak load and frequency regulation of the distribution network, λ ele represents the electricity price, P grid (t) represents the power input from the grid, represents the frequency adjustment mismatch penalty, C represents the frequency adjustment mismatch penalty for the error, S fre (t) represents the frequency adjustment signal, α fes represents the unit power maintenance cost of flywheel energy storage, represents the unit power degradation cost of flywheel energy storage, P fes (t) represents the charge and discharge power of the flywheel energy storage, α bes represents the unit power maintenance cost of battery energy storage, represents the extreme value of the degradation cost coefficient at each stage in the battery life cycle, P bes (t) represents the charging and discharging power of the battery energy storage, α pv Represents the unit power maintenance cost of photovoltaic, P pv (t) represents the output power of photovoltaic power generation, λ fre represents the frequency regulation subsidy price, θ represents the load peak judgment factor, and λ peak represents the peak load subsidy price of the power grid, P load (t) represents the power load, (P grid (t)) av represents the average power input from the distribution network, (P load (t)-P pv (t)) av represents the average net load of the distributed photovoltaic storage system, T represents the total optimization time period, t represents the tth moment in the optimization time period, and P hes (t) represents the output power of the hybrid energy storage system.

14. The distributed photovoltaic storage system collaborative optimization system according to claim 13, characterized in that: The collaborative optimization calculation module determines the optimal peak-shaving threshold value according to the average value of the power purchased by the distribution network from the upper power grid, and solves the distributed photovoltaic storage system collaborative optimization model by combining the optimal peak-shaving threshold value with the peak-shaving demand of the distribution network. When outputting the power allocation result based on the solution result, the average power input from the distribution network can be obtained, and the average value of the power purchased by the distribution network from the upper power grid is analyzed according to the average power, and the optimal peak-shaving threshold value of the hybrid energy storage is set based on the average value; the distributed photovoltaic storage system is used to judge the peak-shaving demand of the distribution network, and the power load, the optimal peak-shaving threshold value and the output power of photovoltaic power generation are set as input parameters of the distributed photovoltaic storage system collaborative optimization model according to the judgment result; the energy storage charge state constraints of the flywheel energy storage and the battery energy storage are initialized, and the demand peak value is calculated at the corresponding peak value by receiving the electricity price signal and the frequency regulation signal, and the demand peak value is compared with the optimal peak-shaving threshold value by difference; the charging and discharging power of the battery energy storage and the flywheel energy storage is output according to the difference result, the corresponding energy storage charge state is updated, and the power allocation result is determined.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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