A hybrid energy storage capacity optimization configuration method based on a cooperative game method

Abstract

The application discloses a hybrid energy storage capacity optimization configuration method based on a cooperative game method, which innovatively constructs target functions under different application scenarios, and optimizes the energy storage capacity of energy storage devices under corresponding scenarios according to the target functions under different application scenarios, forms an energy storage capacity combination, and then calculates the energy storage output of the energy storage devices based on the energy storage charging and discharging characteristics of the different energy storage devices, the energy storage capacity combination and calculation parameters, to obtain energy storage capacity configuration schemes under different application scenarios by using the cooperative game method. Therefore, the application can construct a hybrid energy storage capacity optimization configuration model, and the energy storage capacity optimization configuration of different scenarios can be decomposed in the solving process of the model, so that the solving complexity of the model is reduced. In addition, while solving the problem of how to optimize the capacity of the energy storage devices, the application can further implement effectiveness evaluation to obtain an optimal capacity combination scheme of different energy storage units.

Description

Technical Field

[0001] This invention belongs to the field of energy storage technology, and in particular to a method for optimizing the configuration of hybrid energy storage capacity based on cooperative game theory. Background Technology

[0002] The randomness and volatility of renewable energy sources such as wind power and photovoltaics pose a significant challenge to their high-proportion grid connection. However, the emergence and technological development of microgrids and integrated generation, grid, load and storage technologies have provided the conditions for achieving high-proportion coverage of renewable energy.

[0003] Energy storage has significant advantages in peak shaving and valley filling and ancillary services, and can increase the penetration rate of renewable energy. The rational allocation of energy storage plays an important role in improving the stability and economy of the system.

[0004] Single-type energy storage systems, such as energy-type systems, have high energy density but low charge / discharge power, and their lifespan is easily affected by the number of charge / discharge cycles and depth of discharge. Configuring only a single type of energy storage can reduce the system's economics. In contrast, power-type systems have high power density, fast response, and long lifespan. Combining them with energy-type energy storage to form a hybrid energy storage system can significantly improve the system's economics. Furthermore, considering feasibility and economics, people expect energy storage systems to be able to meet multiple operating scenarios, including smoothing power fluctuations, serving as thermal backups, and fulfilling peak shaving and valley filling functions.

[0005] As a result, various advanced energy storage technologies (including electrochemical energy storage, flywheel energy storage, hydrogen energy storage, supercapacitor energy storage, compressed air energy storage, etc.) are constantly being developed and optimized.

[0006] Currently, hybrid energy storage systems are an effective means to address the contradiction between current energy storage technology demands and overall performance. By combining the advantages of multiple energy storage technologies, they can achieve excellent performance characteristics such as large energy storage capacity, low self-discharge rate, long lifespan, and low cost. However, there is limited research on the multi-element energy storage structures involved in hybrid energy storage systems for different application scenarios.

[0007] In view of this, how to rationally design the multi-element energy storage structure and ratio of new energy power generation systems so as to effectively ensure the reliability of new power systems under operating conditions such as insufficient power generation from clean energy systems and large fluctuations in power load has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides a method for optimizing the configuration of hybrid energy storage capacity based on a cooperative game theory approach, characterized in that the method includes the following steps:

[0009] S100, Construct objective functions for different application scenarios;

[0010] S200. Based on the objective function under different application scenarios, optimize the energy storage capacity of the energy storage devices in the corresponding scenarios to form an energy storage capacity combination, wherein the energy storage devices are multiple energy storage devices that are not completely the same type.

[0011] S300. Based on the energy storage charging and discharging characteristics of different energy storage devices and the energy storage capacity combination and calculation parameter conditions, the energy storage output is calculated using a cooperative game theory method.

[0012] Preferably, in the hybrid energy storage capacity optimization configuration method based on cooperative game theory, wherein:

[0013] In step S100, the different application scenarios include the following three scenarios: power smoothing scenario, load-side peak shaving and valley filling scenario, and load-side economic operation scenario.

[0014] Preferably, in the hybrid energy storage capacity optimization configuration method based on cooperative game theory, wherein:

[0015] In step S100, the energy storage capacity under the power smoothing scenario is optimized with the objective function of maximizing the reduction of power fluctuations per unit cost. The corresponding model consists of the reduction of power fluctuations after smoothing and the initial investment cost of the energy storage equipment.

[0016] Preferably, in the hybrid energy storage capacity optimization configuration method based on cooperative game theory, wherein:

[0017] In the power stabilization scenario, the objective function is expressed as:

[0018] τ=max(Δψ / C 初始 )

[0019] In the formula:

[0020] τ is the performance evaluation indicator: the reduction in power fluctuation per unit cost;

[0021] Δψ represents the reduction in power fluctuation after smoothing.

[0022] C 初始 This refers to the initial investment cost of energy storage equipment.

[0023] Preferably, in the hybrid energy storage capacity optimization configuration method based on cooperative game theory, wherein:

[0024] In step S100, the energy storage capacity under the load-side peak shaving and valley filling scenario is optimized with the objective function of maximizing the peak shaving amount per unit cost. The corresponding model consists of the maximum power before peak shaving and valley filling, the maximum power after peak shaving and valley filling, and the initial investment cost of the energy storage equipment.

[0025] Preferably, in the hybrid energy storage capacity optimization configuration method based on cooperative game theory, wherein:

[0026] In the scenario of load-side peak shaving and valley filling, the objective function is expressed as:

[0027] τ=max(( P 前 - P 后 ) / C 初始 )

[0028] In the formula:

[0029] τ is the performance evaluation indicator: the maximum power reduction achievable per unit cost;

[0030] P 前 This represents the maximum power output before peak shaving and valley filling.

[0031] P 后 This represents the maximum power output after peak shaving and valley filling.

[0032] C 初始 This refers to the initial investment cost of energy storage equipment.

[0033] Preferably, in the hybrid energy storage capacity optimization configuration method based on cooperative game theory, wherein:

[0034] In step S100, the energy storage capacity under the economic operation scenario on the load side is optimized with the charging and discharging revenue as the objective function. The corresponding model consists of peak electricity price, valley electricity price, peak discharge amount, valley charging amount, and initial investment cost.

[0035] Preferably, in the hybrid energy storage capacity optimization configuration method based on cooperative game theory, wherein:

[0036] Under the scenario of economic operation on the load side, the objective function is expressed as:

[0037] τ=max((∑Q 峰 *Pr 峰 -∑Q 谷 *Pr 谷 ) / C 初始 )

[0038] In the formula:

[0039] τ is an economic performance evaluation indicator: the return obtained per unit of initial investment;

[0040] Q 峰 Q 谷 These represent discharge volume during peak electricity price periods and charging volume during off-peak electricity price periods, respectively.

[0041] Pr 峰 Pr 谷 These are peak-hour electricity prices and off-peak-hour electricity prices, respectively.

[0042] C 初始 This refers to the initial investment cost of energy storage equipment.

[0043] Preferably, the hybrid energy storage capacity optimization configuration based on a cooperative game theory method is described above.

[0044] Method, wherein:

[0045] Energy storage charging and discharging characteristics include: regulation response rate and maximum charging and discharging power.

[0046] Preferably, in the hybrid energy storage capacity optimization configuration method based on cooperative game theory, wherein:

[0047] The calculation parameters include any one or any combination of the following: power grid regulation commands, deviation rate constraints, and peak and off-peak electricity prices.

[0048] Compared with the prior art, the present invention has the following advantages:

[0049] This invention innovatively constructs objective functions for different application scenarios and optimizes the energy storage capacity of energy storage devices in corresponding scenarios based on these objective functions, forming energy storage capacity combinations. Then, based on the energy storage charging and discharging characteristics of different energy storage devices, the energy storage capacity combinations, and calculation parameters, a cooperative game theory method is used to calculate energy storage output, obtaining energy storage capacity configuration schemes for different application scenarios. Therefore, this invention provides a hybrid energy storage capacity optimization configuration method based on cooperative game theory, capable of constructing a hybrid energy storage capacity optimization configuration model. Furthermore, during the model solution process, the energy storage capacity optimization configuration for different scenarios is decomposed, reducing the complexity of the model solution.

[0050] Furthermore, while solving the problem of how to optimize the capacity configuration of energy storage devices, this invention can also further implement effectiveness evaluation to obtain the optimal solution.

[0051] In summary, this invention provides a complete solution for how to rationally design the multi-element energy storage structure and ratio of new energy power generation systems so as to effectively ensure the reliability of new power systems under operating conditions such as insufficient power generation from clean energy systems and large fluctuations in power load. This solution not only considers technical aspects but also comprehensive benefits. Attached Figure Description

[0052] The accompanying drawings illustrate exemplary embodiments of the invention and, together with the description thereof, serve to explain the principles of the invention. These drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification.

[0053] Figure 1 This is a flowchart of a hybrid energy storage capacity optimization configuration method based on a cooperative game theory approach in one embodiment of the present invention.

[0054] Figure 2 This is a flowchart illustrating the capacity configuration calculation of a hybrid energy storage system in one embodiment of the present invention.

[0055] Figure 3 This is a flowchart illustrating the capacity configuration of a hybrid energy storage system in one embodiment of the present invention.

[0056] Figure 4 This is a flowchart illustrating the charging and discharging process of a hybrid energy storage system in one embodiment of the present invention. Detailed Implementation

[0057] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present invention are shown in the accompanying drawings.

[0058] It should be noted that, unless otherwise specified, the embodiments and features described in this invention can be combined with each other. The technical solution of this invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0059] Unless otherwise stated, the exemplary embodiments / exemplifications shown are to be understood as providing exemplary features of various details that provide ways in which the technical concept of the invention can be implemented in practice. Therefore, unless otherwise stated, the features of the various embodiments / exemplifications may be additionally combined, separated, interchanged and / or rearranged without departing from the technical concept of the invention.

[0060] Crosshairs and / or shading may be used in the accompanying drawings to clarify the boundaries between adjacent components. Thus, unless otherwise stated, the presence or absence of crosshairs or shading does not convey or indicate any preference or requirement for the specific material, material properties, dimensions, proportions, commonalities between the illustrated components, or any other characteristics, properties, etc., of the components. Furthermore, the dimensions and relative dimensions of components may be exaggerated in the accompanying drawings for clarity and / or descriptive purposes. When exemplary embodiments can be implemented differently, a specific process sequence may be performed in a manner different from the described order of steps. For example, two consecutively described processes may be performed substantially simultaneously or in the reverse order of the described process. Moreover, the same reference numerals denote the same components.

[0061] When a component is referred to as being "on" or "above" another component, "connected to," or "joined to" another component, the component may be directly on, directly connected to, or directly joined to the other component, or there may be intermediate components. However, when a component is referred to as being "directly on" another component, "directly connected to," or "directly joined to" another component, there are no intermediate components. Therefore, the term "connection" can refer to a physical connection, an electrical connection, etc., and may or may not have intermediate components.

[0062] For descriptive purposes, the present invention may use spatial relative terms such as “below,” “under,” “below,” “down,” “above,” “above,” “higher,” and “side (e.g., in a “sidewall”)” to describe the relationship between one component and another component as shown in the accompanying drawings. In addition to the orientations depicted in the drawings, the spatial relative terms are also intended to encompass different orientations of the device during use, operation, and / or manufacture. For example, if the device in the drawings is flipped, a component described as “below” or “under” another component or feature would subsequently be positioned “above” said other component or feature. Thus, the exemplary term “below” can encompass both “above” and “below” orientations. Furthermore, the device may be otherwise positioned (e.g., rotated 90 degrees or in other orientations), thus interpreting the spatial relative descriptive terms used herein accordingly.

[0063] The terminology used herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used herein, unless the context clearly indicates otherwise, the singular forms “a” and “the” are intended to include the plural forms as well. Furthermore, when the terms “comprising” and / or “including” and variations thereof are used in this specification, it indicates the presence of the stated features, integrals, steps, operations, parts, components, and / or groups thereof, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, parts, components, and / or groups thereof. It should also be noted that, as used herein, the terms “substantially,” “about,” and other similar terms are used as approximate terms rather than as terms of degree, thus explaining the inherent biases in measurements, calculated values, and / or provided values ​​that would be recognized by one of ordinary skill in the art.

[0064] See Figure 1 In one embodiment, the present invention discloses a method for optimizing the configuration of hybrid energy storage capacity based on a cooperative game theory approach, wherein the method includes the following steps:

[0065] S100, Construct objective functions for different application scenarios;

[0066] S200. Based on the objective function under different application scenarios, optimize the energy storage capacity of the energy storage devices in the corresponding scenarios to form an energy storage capacity combination, wherein the energy storage devices are multiple energy storage devices that are not completely the same type.

[0067] S300. Based on the energy storage charging and discharging characteristics of different energy storage devices and the energy storage capacity combination and calculation parameter conditions, the energy storage output is calculated using a cooperative game theory method.

[0068] Preferably, in the hybrid energy storage capacity optimization configuration method based on cooperative game theory, wherein:

[0069] In step S100, the different application scenarios include the following three scenarios: power smoothing scenario, load-side peak shaving and valley filling scenario, and load-side economic operation scenario.

[0070] Preferably, in the hybrid energy storage capacity optimization configuration method based on cooperative game theory, wherein:

[0071] In step S100, the energy storage capacity under the power smoothing scenario is optimized with the objective function of maximizing the reduction of power fluctuations per unit cost. The corresponding model consists of the reduction of power fluctuations after smoothing and the initial investment cost of the energy storage equipment.

[0072] Preferably, in the hybrid energy storage capacity optimization configuration method based on cooperative game theory, wherein:

[0073] In the power stabilization scenario, the objective function is expressed as:

[0074] τ=max(Δψ / C 初始 )

[0075] In the formula:

[0076] τ is the performance evaluation indicator: the reduction in power fluctuation per unit cost;

[0077] Δψ represents the reduction in power fluctuation after smoothing.

[0078] C 初始 This refers to the initial investment cost of energy storage equipment.

[0079] For example, C 初始 =E1*P1+E2*P2+E3*P3

[0080] E1, E2, and E3 represent the configuration capacities of lithium iron phosphate energy storage, electrolytic hydrogen energy storage, and flywheel energy storage, respectively.

[0081] P1, P2, and P3 represent the unit capacity prices of lithium iron phosphate energy storage, electrolytic hydrogen energy storage, and flywheel energy storage, respectively.

[0082] Preferably, in the hybrid energy storage capacity optimization configuration method based on cooperative game theory, wherein:

[0083] In step S100, the energy storage capacity under the load-side peak shaving and valley filling scenario is optimized with the objective function of maximizing the peak shaving amount per unit cost. The corresponding model consists of the maximum power before peak shaving and valley filling, the maximum power after peak shaving and valley filling, and the initial investment cost of the energy storage equipment.

[0084] Preferably, in the hybrid energy storage capacity optimization configuration method based on cooperative game theory, wherein:

[0085] In the scenario of load-side peak shaving and valley filling, the objective function is expressed as:

[0086] τ=max((P 前 -P 后 ) / C 初始 )

[0087] In the formula:

[0088] τ is the performance evaluation indicator: the maximum power reduction achievable per unit cost;

[0089] P 前 This represents the maximum power output before peak shaving and valley filling.

[0090] P 后 This represents the maximum power output after peak shaving and valley filling.

[0091] C初始 This refers to the initial investment cost of energy storage equipment.

[0092] Preferably, in the hybrid energy storage capacity optimization configuration method based on cooperative game theory, wherein:

[0093] In step S100, the energy storage capacity under the economic operation scenario on the load side is optimized with the charging and discharging revenue as the objective function. The corresponding model consists of peak electricity price, valley electricity price, peak discharge amount, valley charging amount, and initial investment cost.

[0094] Preferably, in the hybrid energy storage capacity optimization configuration method based on cooperative game theory, wherein:

[0095] Under the scenario of economic operation on the load side, the objective function is expressed as:

[0096] τ=max((∑Q 峰 *Pr 峰 -∑Q 谷 *Pr 谷 ) / C 初始 )

[0097] In the formula:

[0098] τ is an economic performance evaluation indicator: the return obtained per unit of initial investment;

[0099] Q 峰 Q 谷 These represent discharge volume during peak electricity price periods and charging volume during off-peak electricity price periods, respectively.

[0100] Pr 峰 Pr 谷 These are peak-hour electricity prices and off-peak-hour electricity prices, respectively.

[0101] C 初始 This refers to the initial investment cost of energy storage equipment.

[0102] Preferably, in the hybrid energy storage capacity optimization configuration method based on cooperative game theory, wherein:

[0103] Energy storage charging and discharging characteristics include: regulation response rate and maximum charging and discharging power.

[0104] Preferably, in the hybrid energy storage capacity optimization configuration method based on cooperative game theory, wherein:

[0105] The calculation parameters include any one or any combination of the following: power grid regulation commands, deviation rate constraints, and peak and off-peak electricity prices.

[0106] The following sections will describe in detail, through more specific examples, the methods for optimizing energy storage capacity configuration in various application scenarios.

[0107] 1. Hybrid energy storage system based on power smoothing scenarios

[0108] The model optimizes the energy storage capacity configuration for power smoothing scenarios by maximizing the reduction in power fluctuation per unit cost. This model consists of the reduction in power fluctuation and the initial investment cost, and its objective function can be expressed as follows:

[0109] τ=max(Δψ / C 初始 )

[0110] In the formula:

[0111] τ is the performance evaluation indicator: the reduction in power fluctuation per unit cost;

[0112] Δψ represents the reduction in power fluctuation after smoothing.

[0113] C 初始 For the initial investment cost, C 初始 =E1*P1+E2*P2+E3*P3

[0114] E1, E2, and E3 represent the configuration capacities of lithium iron phosphate energy storage, electrolytic hydrogen energy storage, and flywheel energy storage, respectively.

[0115] P1, P2, and P3 represent the unit capacity prices of lithium iron phosphate energy storage, electrolytic hydrogen energy storage, and flywheel energy storage, respectively.

[0116] (1) Capacity Configuration

[0117] The three energy storage capacities should be assigned values, and their capacities should meet the following requirements:

[0118] E sum =E1+E2+E3

[0119] in:

[0120] E sum The total capacity of the required energy storage system;

[0121] E1, E2, and E3 represent the configuration capacities of lithium iron phosphate energy storage, electrolytic hydrogen energy storage, and flywheel energy storage, respectively.

[0122] (2) Simulation and superposition of source and load output characteristics and calculation of deviation rate

[0123] The source and load data at the same point in time are added together, and the results are compared with the grid regulation instructions to calculate the deviation rate at each point.

[0124] The formula for calculating the deviation rate at each point is as follows:

[0125]

[0126] In the formula:

[0127] P (t)指令 Let t be the adjustment value required by the power grid command at time t;

[0128] P (t)(源+荷) The sum of the source and load power at time t.

[0129] (3) Determine the start and end times of charging and discharging.

[0130] If the deviation rate calculated in the previous step exceeds the allowable deviation range, then adjustment is made using energy storage charging and discharging, and the start and end times of energy storage charging and discharging can be determined at the same time; if it does not exceed the allowable deviation range, then no power adjustment is required.

[0131] When ε (t) >ε max At that time, the energy storage system begins charging until the next ε (t) <ε max Charging ends at the designated time;

[0132] When ε (t) <ε min At that time, the energy storage system begins to discharge until the next ε (t) >ε min The discharge ends at that time.

[0133] in:

[0134] ε (t) The power deviation at time t;

[0135] ε min , ε max These are the lower and upper limits of the allowable deviation rate for power grid regulation commands, respectively.

[0136] (4) Energy storage output calculation

[0137] Based on the energy storage charging and discharging characteristics (regulation response rate, maximum charging and discharging power, etc.) and the combination of energy storage capacity and calculation parameter conditions (grid regulation commands, deviation rate constraints, etc.), the energy storage output is calculated.

[0138] When ε (t) >ε max At time t, the charging power of the energy storage system is:

[0139] P (t)储 =P (t)(源+菏) -P (t)指令 *(1+ε max )

[0140] When ε (t) <ε min At time t, the discharge power of the energy storage system is:

[0141] P(t)储 =P (t)指令 *(1-ε min )-P (t)(源+菏)

[0142] During the charging and discharging process, when multiple types of energy storage meet the power regulation requirements, the energy storage battery with the lowest cost per charge and discharge cycle is used first for charge and discharge regulation.

[0143] Performance Evaluation

[0144] T=Δψ / C 初始

[0145] 2. Hybrid energy storage system based on load-side peak shaving and valley filling scenarios

[0146] The model optimizes energy storage capacity configuration for peak shaving and valley filling scenarios with the objective function of maximizing peak shaving per unit cost. This model consists of the maximum power before peak shaving and valley filling, the maximum power after peak shaving and valley filling, and the initial investment cost. Its objective function can be expressed as:

[0147] τ=max((P 前 -P 后 ) / C 初始 )

[0148] In the formula:

[0149] τ is the performance evaluation indicator: the maximum power reduction achievable per unit cost;

[0150] P 前 This represents the maximum power output before peak shaving and valley filling.

[0151] P 后 This represents the maximum power output after peak shaving and valley filling.

[0152] C 初始 This refers to the initial investment cost.

[0153] (1) Capacity Configuration

[0154] The three energy storage capacities should be assigned values, and their capacities should meet the following requirements:

[0155] E sum =E1+E2+E3

[0156] in:

[0157] E sum The total capacity of the required energy storage system;

[0158] E1, E2, and E3 represent the configuration capacities of lithium iron phosphate energy storage, electrolytic hydrogen energy storage, and flywheel energy storage, respectively.

[0159] (2) Calculate the electricity demand during peak hours

[0160]

[0161] In the formula:

[0162] Q 需 This refers to the electricity shortage caused when the source load is adjusted according to the grid command requirements during peak electricity consumption periods;

[0163] t 0，峰 t represents the start time of the peak period. n，峰 This refers to the end time of the peak period.

[0164] P (t)指令 Let t be the adjustment value required by the power grid command at time t;

[0165] P (t)(源+菏) The sum of the source and load power at time t.

[0166] (3) Calculate the start and end times of peak discharge.

[0167] Let Q 需 =E sum t was calculated 0，峰 , t n，峰 This refers to the start and end times of the discharge.

[0168] (4) Calculate the remaining electricity during off-peak hours

[0169]

[0170] In the formula:

[0171] Q 剩 The remaining electricity after the source load has been adjusted according to the grid command requirements during off-peak hours;

[0172] t 0，谷 t is the start time of the valley period. n，谷 The end time of the valley period;

[0173] P (t)指令 Let t be the adjustment value required by the power grid command at time t;

[0174] P (t)(源+荷) The sum of the source and load power at time t.

[0175] (5) Calculate the start and end times of charging during off-peak hours.

[0176] Let Q 剩 =E sum t was calculated 0，谷 , t n，谷 This refers to the start and end times of charging.

[0177] (6) Energy storage output calculation

[0178] Based on the energy storage charging and discharging characteristics (regulation response rate, maximum charging and discharging power, etc.) and the combination of energy storage capacity and calculation parameter conditions (grid regulation commands, etc.), the energy storage output is calculated.

[0179] During off-peak electricity consumption periods, the charging power of the energy storage system at time t is: P (t)储 =P (t)(源+荷) -P (t)指令 ;

[0180] During peak electricity consumption, the discharge power of the energy storage system at time t is: P (t)储 =P (t)指令 -P (t)(源+菏) .

[0181] During the charging and discharging process, when multiple types of energy storage meet the power regulation requirements, the energy storage battery with the lowest cost per charge and discharge cycle is used first for charge and discharge regulation.

[0182] Performance Evaluation

[0183] T = (P 前 -P 后 ) / C 初始

[0184] 3. Hybrid energy storage system based on load-side economic operation scenarios

[0185] The model optimizes the energy storage capacity under economic operation scenarios by taking the charging and discharging revenue as the objective function. This model consists of peak and valley electricity prices, discharge and charging amounts during peak and valley periods, and initial investment costs. Its objective function can be expressed as:

[0186] τ=max((∑Q 峰 *Pr 峰 -∑Q 谷 *Pr 谷 ) / C 初始 )

[0187] In the formula:

[0188] τ is an economic performance evaluation indicator: the return obtained per unit of initial investment;

[0189] Q 峰 Q 谷 These represent discharge volume during peak electricity price periods and charging volume during off-peak electricity price periods, respectively.

[0190] Pr 峰 Pr 谷 These are peak-hour electricity prices and off-peak-hour electricity prices, respectively.

[0191] C 初始 This refers to the initial investment cost.

[0192] (1) Capacity Configuration

[0193] The three energy storage capacities should be assigned values, and their capacities should meet the following requirements:

[0194] E sum =E1+E2+E3

[0195] in:

[0196] E sum The total capacity of the required energy storage system;

[0197] E1, E2, and E3 represent the configuration capacities of lithium iron phosphate energy storage, electrolytic hydrogen energy storage, and flywheel energy storage, respectively.

[0198] (2) Calculate the electricity demand during peak hours.

[0199]

[0200] In the formula:

[0201] Q 需 This refers to the electricity shortfall when only renewable energy output is used during peak electricity price periods;

[0202] t 0，峰 t represents the start time of the peak electricity price period. n，峰 This is the end time of the peak electricity price period;

[0203] P (t)源 To contribute to new energy sources at time t;

[0204] P (t)荷 Let t be the total load at time t.

[0205] (3) Calculate the remaining electricity during off-peak hours.

[0206]

[0207] In the formula:

[0208] Q 剩 This refers to the remaining electricity generated when only renewable energy is used during off-peak electricity pricing periods.

[0209] t 0，谷 t represents the start time of the off-peak electricity price period. n，谷 This is the end time of the off-peak electricity price period;

[0210] P (t)源 To contribute to new energy sources at time t;

[0211] P (t)菏 Let t be the total load at time t.

[0212] (4) Calculate the start and end times of charging and discharging.

[0213] During peak electricity price periods, when P (t)荷 >P (t)源 The time is the discharge start time, when P (t)菏 <P (t)源 The time is the discharge end time.

[0214] During the off-peak electricity price period, when P (t)源 >P (t)荷 The time is the start time of charging. (t)源 <P (t)荷 The time is the end of the charging process.

[0215] (5) Energy storage output calculation

[0216] Based on the energy storage charging and discharging characteristics (adjustment response rate, maximum charging and discharging power, etc.) and the combination of energy storage capacity and calculation parameters (electricity price peak and valley periods, etc.), the energy storage output is calculated.

[0217] During off-peak electricity prices, the charging power of the energy storage system at time t is: P (t)储 =P (t)源 -P (t)荷 ;

[0218] During peak electricity price periods, the discharge power of the energy storage system at time t is: P (t)储 =P (t)荷 -P (t)源 .

[0219] During the charging and discharging process, when multiple types of energy storage meet the power regulation requirements, the energy storage battery with the lowest cost per charge and discharge cycle is used first for charge and discharge regulation.

[0220] Performance Evaluation

[0221] The effectiveness of the charging and discharging process of each combination is evaluated based on the magnitude of the charging and discharging benefits.

[0222] τ=(∑Q 峰 *Pr 峰 -∑Q 谷 *Pr 谷 ) / C 初始

[0223] It should be noted that the constraints in the energy storage capacity optimization configuration method of the present invention can be as follows:

[0224] (1) The cost of a single charge and discharge cycle for each type of energy storage is related to its configured capacity, lifespan, depth of charge and discharge, and unit price of the energy storage system. The specific calculation formula is as follows:

[0225] C = E * Pr * D / L

[0226] in:

[0227] C represents the cost of a single charge / discharge cycle for various types of energy storage.

[0228] E represents the configured capacity of the energy storage;

[0229] Pr is the unit price of energy storage system capacity;

[0230] D represents the depth of charge / discharge (%);

[0231] L represents the lifespan of the energy storage system.

[0232] (2) Energy storage system output P 储 With grid connection point power P 网 New energy output P 源 Load-side demand P 荷 The following relationship should be satisfied between them:

[0233] P 源 +P 网 -P 荷 +P 储 =0

[0234] In the formula P 储 When P > 0, the energy storage system discharges. 储 The energy storage system is charged when the temperature is below 0.

[0235] (3) Energy storage system charge limit:

[0236] E min ≤E (t) ≤E max

[0237] In the formula:

[0238] E (t) Let t be the energy charge of the energy storage system.

[0239] E min Minimum capacity limit (MW·h) for energy storage systems;

[0240] E max This represents the maximum capacity limit (MW·h) for energy storage systems.

[0241] (4) The limitations on the maximum output of energy storage during charging and discharging processes are as follows:

[0242] P ch，max ≤P 储 ≤0

[0243] 0≤P 储 ≤P dis，max

[0244] In the formula:

[0245] Pch，max This refers to the maximum allowable charging power of the energy storage system.

[0246] P dis，max It is the maximum allowable discharge power of the energy storage system.

[0247] (5) The discharge response rate of the energy storage system should meet the requirements of the load-side power regulation rate, i.e., S dis ≥S adj

[0248] In the formula:

[0249] S dis The discharge response rate of the energy storage system;

[0250] S adj This refers to the load-side power regulation rate.

[0251] (6) Electricity price peak-valley constraints:

[0252] Peak period: 09:00~13:00, priority is given to triggering discharge. If there is any remaining dischargeable capacity, it will be discharged during the next normal electricity price period.

[0253] During off-peak hours (00:00~06:00), charging is prioritized (first determine if charging conditions are met; if conditions are met, charging will proceed; otherwise, the next normal period [06:00~10:00] will be used to find a charging opportunity).

[0254] (7) Performance indicators for grid connection points:

[0255] Adjusting the power of sources, loads, and storage according to grid dispatch instructions, spot prices, power quality, and rated power curves, with deviations not exceeding the allowable deviation range.

[0256] ε min ≤ε (t) ≤ε max

[0257] In the formula:

[0258] ε (t) The power deviation at time t;

[0259] ε min , ε max These represent the lower and upper limits of the allowable deviation for the performance indicators, respectively.

[0260] (8) Wind curtailment rate constraint:

[0261] The wind curtailment rate of renewable energy power plants must not exceed the upper limit of the permissible wind curtailment rate.

[0262]

[0263] η风 ≤η 风max

[0264] In the formula:

[0265] η 风 Wind curtailment rate;

[0266] Q 弃风 This refers to the amount of electricity that can be generated from the wind curtailment portion of a wind farm;

[0267] Q 风发 This represents the actual power generation of the wind farm.

[0268] (9) Waste rate constraint:

[0269] The curtailment rate of solar power plants must not exceed the permitted upper limit.

[0270]

[0271] η 光 ≤η 光max

[0272] In the formula:

[0273] η 光 This refers to the light rejection rate;

[0274] Q 传 This represents the maximum amount of electricity that can be transmitted by the power system.

[0275] Q 荷 The amount of electricity consumed by the load;

[0276] Q 储 Electricity consumed by energy storage;

[0277] Q 光发 This refers to the power generation of a photovoltaic power station.

[0278] In other words, in addition to the energy storage capacity optimization configuration method described in this invention under different scenarios such as power smoothing, peak shaving and valley filling and economic operation, this invention also introduces performance evaluation to evaluate the optimal energy storage capacity configuration scheme.

[0279] In addition, see Figure 2 In one embodiment, for a hybrid energy storage system, the hybrid energy storage capacity optimization configuration method based on cooperative game theory further includes:

[0280] After importing the data related to multi-element energy storage: obtain the generation-side fitting data and load-side operation data respectively so that the source-load curves can be superimposed in the next step, i.e., superposition calculation; at the same time, obtain the grid regulation command and optional electricity price data to determine the peak and valley periods of the energy storage system; usually, the grid regulation command dominates the peak and valley periods, and the peak and valley periods can be determined according to the grid regulation command. Of course, if the electricity price data dominates the peak and valley periods, then the peak and valley periods are determined by combining the electricity price data and the grid regulation command.

[0281] For the superposition process, it is preferable to perform the superposition operation through multiple threads; thereby obtaining the superimposed source load curve.

[0282] Furthermore, by performing joint control calculations on the superimposed source-load curve and the determined peak-valley time periods, data processing can be performed using three threads:

[0283] The first thread calculates the smoothness of the curve to measure the filtering rate per unit time, and then calculates the interval deviation rate based on the determined peak and valley periods.

[0284] The second thread calculates the charging and discharging amount during the peak and valley periods based on the determined peak and valley periods, prioritizing the calculation of the start and stop times for peak discharge, and calculating the start and stop times for valley charging based on the remaining power.

[0285] The third thread is to calculate the power shortage during peak hours, perform discharge operations, and determine whether there is any remaining power: if there is no remaining power, the discharge ends and the process enters the stage of determining whether charging is possible during off-peak hours; if there is any remaining power, the discharge operation is performed when the power generation is insufficient during normal hours, and then the process enters the stage of determining whether charging is possible during off-peak hours.

[0286] If the determination of whether charging is possible during off-peak hours is that charging is possible, then the energy storage system charging operation will be executed; if charging is not possible, then a normal-peak charging strategy will be designed to carry out charging during normal-peak hours.

[0287] Then, the data processing results of the above three threads provide charging and discharging strategies for hybrid energy storage systems and provide data support for obtaining the computational results of capacity optimization in multiple scenarios.

[0288] See Figure 3 In one embodiment, the hybrid energy storage capacity optimization configuration method based on cooperative game theory further includes:

[0289] Determine the total capacity of hybrid energy storage based on the system's installed capacity requirements;

[0290] Assign initial values ​​to each unit of the hybrid energy storage;

[0291] Capacity combination of each unit: Combining the capacity configurations of different units;

[0292] Capacity optimization calculations for multiple scenarios, wherein the capacity optimization is a calculation process based on economic constraints;

[0293] Determine whether the capacity combination traversal operation is complete: if yes, continue to the next step; otherwise, return to the previous steps of performing capacity combination on each unit, and perform capacity combination on each unit again until the result of determining whether the capacity combination traversal operation is complete is yes.

[0294] Evaluate all outcomes: Ensure the economic efficiency of all cases is evaluated by determining whether the capacity combination has been traversed.

[0295] The optimal evaluation result is determined as the capacity configuration design scheme for the hybrid energy storage system. Specifically, the capacity combination corresponding to the optimal evaluation result is determined and used as the optimal capacity configuration design scheme for this hybrid energy storage system.

[0296] See Figure 4 In one embodiment, since the hybrid energy storage system in this disclosure is typically composed of a combination of flywheel energy storage, hydrogen energy storage, and electrochemical energy storage, the hybrid energy storage capacity optimization configuration method based on cooperative game theory further includes:

[0297] Hybrid energy storage systems, also known as multi-element energy storage systems, are in the discharge operation state. For example, they are given priority to enter the discharge operation state to ensure power supply priority.

[0298] After entering the discharge operation state, the flywheel energy storage responds quickly and releases electrical energy to the required power. Then it monitors to determine whether the flywheel energy storage has reached the minimum discharge state. If it has not reached the minimum discharge state, the flywheel energy storage continues to release electrical energy. If it has reached the minimum discharge state, the flywheel energy storage stops discharging and maintains the discharge state of other energy storage (units).

[0299] In addition to flywheel energy storage, which can prioritize and respond quickly, hydrogen energy storage and electrochemical energy storage can simultaneously initiate discharge response.

[0300] Furthermore, it determines whether the discharge power of hydrogen energy storage cannot meet the required power. That is, while hydrogen energy storage is discharging, it determines whether its own discharge power meets the demand: if it does, it maintains the state of "hydrogen energy storage starting discharge response" (for example, by maintaining power supply through fuel cells related to hydrogen energy storage); if the discharge power of hydrogen energy storage cannot meet the required power, it adjusts the output power of electrochemical energy storage and ensures that the total output power of electrochemical energy storage and hydrogen energy storage meets the required power.

[0301] Then, the multi-energy storage system starts charging operation;

[0302] Because flywheel energy storage prioritizes rapid response and releases electrical energy to the required power, hydrogen energy storage prioritizes charging response during charging operation, while electrochemical energy storage also initiates charging response.

[0303] Determine if the charging power of hydrogen energy storage cannot meet the power demand:

[0304] If no, it means that the hydrogen energy storage charging can meet the system requirements, and the "hydrogen energy storage start-up charging response" will always be maintained to keep the hydrogen energy storage charging state.

[0305] If yes, it means that hydrogen energy storage charging cannot meet the system's needs, then:

[0306] On the one hand, the flywheel energy storage system can quickly start to store electrical energy at the required power level; on the other hand, it can determine whether the flywheel energy storage system has reached the maximum charging state. If it has, the flywheel energy storage system stops charging, that is, it stops charging its own modules; and it maintains the charging state of other energy storage systems, such as keeping hydrogen energy storage and electrochemical energy storage in a charging state.

[0307] On the other hand, the energy storage capacity of electrochemical energy storage is adjusted, and the synergy between electrochemical energy storage and hydrogen energy storage is ensured so that their total power meets the charging power demand. Even better, when a single energy storage, especially flywheel energy storage, cannot meet the system's charging power demand, the system ensures that electrochemical energy storage, hydrogen energy storage, and flywheel energy storage work together to meet the total power response of the hybrid energy storage system to the charging power demand.

[0308] It should be noted that, in the description of this specification, the references to terms such as "one embodiment / mode," "some embodiments / modes," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment / mode or example is included in at least one embodiment / mode or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment / mode or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments / modes or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments / modes or examples described in this specification, as well as the features of different embodiments / modes or examples.

[0309] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0310] Those skilled in the art should understand that the above embodiments are merely for illustrating the present invention and are not intended to limit the scope of the invention. Those skilled in the art can make other changes or modifications based on the above disclosure, and these changes or modifications still fall within the scope of the present invention.

Claims

1. A method for optimizing the allocation of hybrid energy storage capacity based on cooperative game theory, characterized in that, The method includes the following steps: S100, objective function under three application scenarios: power smoothing scenario, load-side peak shaving and valley filling scenario, and load-side economic operation scenario; In the power smoothing scenario, the objective function is to maximize the reduction in power fluctuation per unit cost. The corresponding model consists of the reduced power fluctuation after smoothing and the initial investment cost of energy storage equipment. The objective function is expressed as follows: ； In the formula: The performance evaluation indicator is the reduction in power fluctuation per unit cost. This represents the reduction in power fluctuations after smoothing. The initial investment cost of energy storage equipment; In load-side peak shaving and valley filling scenarios, the objective function is to maximize the peak shaving amount per unit cost. The corresponding model consists of the maximum power before peak shaving and valley filling, the maximum power after peak shaving and valley filling, and the initial investment cost of energy storage equipment. The objective function is expressed as follows: ； In the formula: The performance evaluation indicator is the maximum power reduction achievable per unit cost. This represents the maximum power output before peak shaving and valley filling. This represents the maximum power output after peak shaving and valley filling. The initial investment cost of energy storage equipment; Under the load-side economic operation scenario, with the charging and discharging revenue as the objective function, the corresponding model consists of peak-hour electricity price, off-peak-hour electricity price, peak-hour discharging volume at the electricity price, off-peak-hour charging volume at the electricity price, and initial investment cost. The objective function is expressed as: ； In the formula: As an indicator for evaluating economic performance: the return obtained per unit of initial investment; These represent discharge volume during peak electricity price periods and charging volume during off-peak electricity price periods, respectively. These are peak-hour electricity prices and off-peak-hour electricity prices, respectively. The initial investment cost of energy storage equipment; S200. Based on the objective function in the application scenario, optimize the energy storage capacity of the energy storage device in the corresponding scenario to form an energy storage capacity combination, wherein the energy storage device is a combination of multiple energy storage devices that are not completely the same type. S300. Based on the energy storage charging and discharging characteristics of different energy storage devices and the energy storage capacity combination and calculation parameter conditions, the energy storage output is calculated using a cooperative game theory method.

2. The method for optimizing the configuration of hybrid energy storage capacity based on cooperative game theory as described in claim 1, characterized in that: Energy storage charging and discharging characteristics include: regulation response rate and maximum charging and discharging power.

3. The method for optimizing the configuration of hybrid energy storage capacity based on cooperative game theory as described in claim 1, characterized in that: The calculation parameters include any one or any combination of the following: power grid regulation commands, deviation rate constraints, and peak and off-peak electricity prices.

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