Energy storage optimal configuration method and equipment applied to new energy station and medium

Abstract

The invention provides an energy storage optimal configuration method and device applied to a new energy station, and a medium. The method comprises the following steps: establishing an energy storage system model and a photovoltaic array power generation model; a photovoltaic side control and load side control model is established based on the energy storage system model and the photovoltaic array power generation model, the power grid demand state responded by the light storage combined control system model is verified by combining operation characteristics, and frequency modulation and peak regulation processes are executed; based on an energy storage system model optimization configuration mode of minimizing the annual average total cost of the system, the optimal capacity configuration of the energy storage system model is determined by setting constraint conditions and combining with an optimization grey wolf algorithm. According to the invention, a light storage combined control model is combined with multi-constraint multi-objective optimization, so that an energy storage system optimal configuration method in the process of gradually increasing the photovoltaic ratio in a high-proportion new energy system is formed; the whole system can still operate safely and stably in the face of load switching, and meanwhile the whole life cycle cost and the total cost of the hybrid energy storage system under the optimal configuration are obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage of new energy stations, and in particular relates to an energy storage optimization configuration method, equipment and medium applied to new energy stations. Background Art

[0002] In the prior art, a hybrid energy storage system is a system that combines wind energy, photovoltaics, and multiple energy storage device technologies to improve the efficiency and reliability of energy storage. Among them, a hybrid energy storage system is mainly composed of two or more energy storage technologies, such as battery energy storage and supercapacitor energy storage, as well as renewable energy sources such as solar energy and wind energy. A hybrid energy storage system can combine the advantages of multiple energy storage technologies. It can optimize the energy storage and release process by coordinating the characteristics of different energy storage devices, thereby improving the overall performance of the system.

[0003] However, in the actual application of new energy stations, due to the instability and intermittency of photovoltaic power generation, how to accurately adjust the capacity configuration of the energy storage system, improve the operating efficiency of the energy storage system, and reduce the economic cost of the system are still unsolvable technical problems. Therefore, how to achieve a reasonable combination of lithium-ion batteries and supercapacitors in new energy stations, the optimal configuration of the photovoltaic storage system, and the system optimization based on economic and operational constraints have become technical problems that need to be solved in the current field of energy storage technology. Summary of the invention

[0004] The present invention provides an energy storage optimization configuration method applied to new energy stations. The method can achieve efficient charging when photovoltaic power generation is greater than load demand, and ensure that the energy storage system can release the stored energy for load use in a timely manner when power generation is insufficient, thereby improving the energy efficiency and response speed of the system.

[0005] Methods include: S101: Establish energy storage system model based on lithium-ion batteries and supercapacitors; S102: Establishing a photovoltaic array power generation model; S103: Establish photovoltaic side control and load side control models based on the energy storage system model and photovoltaic array power generation model, and verify that the photovoltaic and storage joint control system model responds to the grid demand state and executes frequency regulation and peak regulation processes in combination with the operating characteristics under load fluctuations; S104: Based on the energy storage system model optimization configuration method that minimizes the system's average annual total cost, the optimal capacity configuration of the energy storage system model is determined by setting constraints and combining the optimized grey wolf algorithm.

[0006] It should be further explained that the energy storage system model in the method includes multiple lithium-ion batteries and supercapacitors; The multiple lithium-ion batteries are composed of multiple lithium-ion batteries connected in series.

[0007] It should be further explained that in step S102, the photovoltaic array power generation model adopts a multi-level control method, and the photovoltaic array power generation model includes photovoltaic side control and load side control, wherein the photovoltaic side control adopts MPPT control, and the load side control adopts inner and outer loop control; In the multi-level control method, the first level is MPPT control, which is based on maximizing the efficiency of the photovoltaic array in capturing solar energy; The second stage is the DC bus voltage control, which realizes MPPT control based on the dynamic adjustment between the output current and voltage change rate of the photovoltaic array.

[0008] It should be further explained that in MPPT control, control is performed based on the incremental conductance method, and the specific control steps are: Calculate the power change rate: ,in I k , V k is the current and voltage at the current sampling moment, I k-1 , V k-1 is the current and voltage at the last sampling moment; Determine the position of the current working point of the photovoltaic array relative to the maximum power point based on the value of the incremental conductance; exist When , the voltage at the sampling moment is the voltage at the maximum power point; When , the current operating point is on the left side of the maximum power point; When , the current operating point is on the right side of the maximum power point; when When the maximum power point is reached, the working voltage of the photovoltaic array remains unchanged; when When it is not equal to 0, the voltage parameters are adjusted to achieve the preset maximum power point.

[0009] It should be further explained that, in the inner and outer loop control, the outer loop control adopts power PQ control, which controls the active power and reactive power output on the load side by adjusting the current and voltage parameters output by the inverter; The inner loop control adopts voltage outer loop and current inner loop dual closed loop control and is based on Buck / Boost converter control.

[0010] It should be further explained that the control method based on Buck / Boost converter includes: on the load side, using droop control to adjust the active power and reactive power; When the photovoltaic output power is less than the power required by the load, the DC / DC converter controls the energy storage to release energy; When the electric energy output by photovoltaic power generation is greater than the energy required by the load, the DC / DC converter operates in Buck mode, controls the energy storage to absorb energy, and the power flow direction is the DC bus to the energy storage system model.

[0011] It should be further explained that step S104 also includes: establishing a total cost minimum function model of the energy storage system model to seek the optimization target of the ESS system; The life cycle cost of the ESS system includes the average annual initial investment cost, fuel cost, operation and maintenance cost, and replacement cost; the optimization objectives are:

[0012] In the optimized gray wolf algorithm, the position of each wolf is defined to represent a solution in the solution space, and the fitness value represents the quality of the solution. The goal of setting the optimized gray wolf algorithm is to match the optimal solution that meets the preset fitness value.

[0013] It should be further explained that, in the method, the constraints set include: system power constraints, lithium battery constraints, and supercapacitor constraints; At any time t, the system power constraints are as follows:

[0014] In the formula, , , , and They are photovoltaic power, lithium battery power, supercapacitor power, gas turbine power and load power; At any time t, the constraints on the lithium battery capacity are as follows:

[0015] In the formula, is the capacity of the lithium battery at a certain moment, The minimum / maximum capacity of lithium battery; At any time t, the constraints for lithium battery power balance are as follows:

[0016] In the formula, is the power of the lithium battery at a certain moment, The minimum / maximum charge and discharge power of lithium batteries; The state of charge constraints for lithium-ion batteries are as follows:

[0017] In the formula, is the charge state of the lithium battery at a certain moment, The upper and lower limits of the lithium battery SOC; At any time t, the constraints on the supercapacitor capacity are as follows:

[0018] In the formula, is the capacity of the supercapacitor at a certain moment, is the minimum / maximum capacity of the supercapacitor; At any time t, the constraints on the supercapacitor power balance are as follows:

[0019] In the formula, is the power of the supercapacitor at a certain moment, is the minimum / maximum charge and discharge power of the supercapacitor; The supercapacitor charge state constraints are as follows: .

[0020] According to another embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the steps of the energy storage optimization configuration method as applied to a new energy station are implemented.

[0021] According to another embodiment of the present application, a storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the energy storage optimization configuration method applied to new energy stations are implemented.

[0022] It can be seen from the above technical solutions that the present invention has the following advantages: The energy storage optimization configuration method for new energy stations provided in this application is based on a joint energy storage system model of photovoltaic arrays and energy storage configuration, and combines an optimization algorithm to optimize the energy storage capacity, thereby effectively reducing the average annual total cost of the system. This application optimizes the selection of lithium battery and supercapacitor capacity, so that the energy storage system can meet the load demand while reducing investment and operating costs.

[0023] This application adopts a multi-level control strategy to ensure that the photovoltaic power generation and energy storage system can adjust the output power according to the real-time light changes and load requirements. In addition, in the case of large load fluctuations or unstable photovoltaic power generation, the control strategy can smoothly switch the charging and discharging mode, maintain the stability of the power grid, and dynamically adjust the charging and discharging power of the energy storage system. The present invention can also achieve efficient charging when photovoltaic power generation is greater than the load demand, and ensure that the energy storage system can release the stored energy for the load in time when power generation is insufficient, thereby improving the energy efficiency and response speed of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0025] Figure 1 A flow chart of the energy storage optimization configuration method applied to new energy stations; Figure 2 This is the control structure diagram of the photovoltaic system; Figure 3 This is the structure diagram of the photovoltaic power station power generation system; Figure 4 It is the control structure diagram of the energy storage system; Figure 5 To optimize the gray wolf algorithm flow chart; Figure 6 Schematic diagram of an electronic device. DETAILED DESCRIPTION

[0026] The energy storage optimization configuration method for new energy stations provided in this application establishes an energy storage system model based on a mixture of lithium-ion batteries and supercapacitors. Among them, lithium batteries are used to balance the long-term load requirements of the system, and supercapacitors are used to cope with instantaneous load fluctuations and short-term power requirements. The energy storage system model can dynamically adjust the charging and discharging state of the energy storage equipment according to the actual load demand and the volatility of photovoltaic power generation, ensuring that the system maintains efficient operation in a changing working environment.

[0027] Based on the energy storage system model, this application further establishes a model of the photovoltaic array power generation system and designs a photovoltaic-storage joint power generation control strategy. MPPT control and internal and external loop control are used on the photovoltaic side and the energy storage side to achieve optimal power generation efficiency and ensure the stability of the grid voltage and frequency. The hybrid energy storage system and the photovoltaic array power generation system are combined to form a photovoltaic-storage joint control model. The control strategy effectively adjusts the charging and discharging state of the energy storage equipment. When the photovoltaic power generation power is greater than the load demand, the energy storage system absorbs excess electricity for charging; when the photovoltaic power generation is insufficient, the energy storage system releases the stored electricity to make up for the gap, thereby ensuring stable power supply to the load demand.

[0028] The specific steps of the energy storage optimization configuration method for new energy stations provided by the present application will be described in detail below. For the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to facilitate a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details.

[0029] The phrases such as "one embodiment" or "some embodiments" described in the present application mean that the specific features, structures or characteristics described in the embodiment are included in one or more embodiments of the present application. Therefore, the phrases such as "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments" etc. that appear in different places in the present application do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways.

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] See also Figure 1 The figure is a flow chart of a method for optimizing energy storage configuration applied to a new energy station in a specific embodiment, the method comprising: S101: Establish an energy storage system model based on lithium-ion batteries and supercapacitors.

[0032] In some embodiments, the energy storage system model is composed of lithium-ion batteries and supercapacitors. This embodiment can obtain and store key parameters of lithium-ion batteries and supercapacitors, such as battery capacity, charge and discharge efficiency, cycle life, internal resistance, voltage range, etc.

[0033] For lithium-ion batteries, data support can be provided for the established energy storage system model based on the electrochemical characteristics of the battery, such as the relationship between open circuit voltage and state of charge (SOC), as well as the changes in voltage and current during charging and discharging.

[0034] For supercapacitors, due to their fast charging and discharging speed and long cycle life, it is necessary to focus on their power density and energy efficiency, and the established energy storage system model has the characteristics of supercapacitors. Specifically, the mathematical models of lithium-ion batteries and supercapacitors can be integrated to consider the collaborative work between the two, such as energy distribution and charging and discharging control strategies.

[0035] In this way, the actual performance of the energy storage system is accurately reflected, and the overall performance of the energy storage system, such as energy density, power density, and cycle life, is improved by integrating the advantages of lithium-ion batteries and supercapacitors.

[0036] Optionally, in the energy storage system model, the mathematical model with lithium-ion batteries and supercapacitors has corresponding control strategies including charge and discharge control, energy distribution, etc.

[0037] In order to facilitate the explanation of the execution process of this method, the system and energy storage system involved in the following description may refer to the energy storage system model.

[0038] S102: Establish a photovoltaic array power generation model.

[0039] In some embodiments, basic parameters of the photovoltaic array are determined, such as the type, quantity, and arrangement of photovoltaic cells. The mathematical model of the photovoltaic cell is established based on the volt-ampere characteristic curve of the photovoltaic cell, taking into account the influence of factors such as temperature and light intensity on the cell performance, and establishing the mathematical model of the photovoltaic cell.

[0040] The photovoltaic array power generation model is established by integrating the mathematical models of multiple photovoltaic cells, considering the series-parallel relationship between the cells and the overall performance of the photovoltaic array. This embodiment also verifies and corrects the established photovoltaic array power generation model.

[0041] Optionally, the model can be verified and corrected using actual photovoltaic power generation data to ensure the accuracy of the model. In this way, the power generation capacity of the photovoltaic array can be accurately predicted, providing data support for the optimal configuration of the energy storage system, improving the efficiency and reliability of the photovoltaic power generation system and optimizing energy utilization.

[0042] S103: Based on the energy storage system model and the photovoltaic array power generation model, the photovoltaic side control and load side control models are established. Combined with the operating characteristics under load fluctuations, the photovoltaic and storage joint control system model is verified to respond to the grid demand state and execute the frequency and peak regulation process.

[0043] In some embodiments, based on the photovoltaic array power generation model, the control strategy of the photovoltaic side is designed, such as maximum power point tracking (MPPT) control, voltage and current double closed-loop feedback control, etc. Considering the impact of environmental factors such as light intensity and temperature on photovoltaic power generation, the control strategy is adjusted in real time to ensure the stable operation and efficient power generation of the photovoltaic array.

[0044] For the load-side control model of this embodiment, the fluctuation characteristics of the load and the response capability of the demand side can be analyzed, and the control strategy of the load side can be designed, such as demand-side response control, intelligent load scheduling, etc. Through the coordinated control with the energy storage system, the smooth regulation of the load and the optimal matching of the grid demand can be achieved.

[0045] The model verification of the photovoltaic and energy storage combined control system in this embodiment is to verify the responsiveness of the photovoltaic and energy storage combined control system model in combination with the operating characteristics under load fluctuations. Taking into account the execution of frequency modulation and peak regulation processes, the overall performance and stability of the system can be evaluated. In this way, the stability and efficiency of the photovoltaic power generation system can be improved, and the optimal matching of power demand and the stable operation of the power grid can be achieved through load-side control.

[0046] It should be noted that the operating characteristics under load fluctuations in this embodiment are based on the fact that load fluctuations are caused by the operating state of the power system, and the characteristics include the amplitude, frequency, and duration of the fluctuations. In the optimization configuration and control strategy design of the energy storage system, the impact of load fluctuations on the system is considered, such as the charging and discharging requirements of the energy storage system and the stability of the power grid. In this way, it is ensured that the energy storage system can operate stably under load fluctuations and meet the needs of the power grid, thereby improving the overall performance and stability of the power system.

[0047] S104: Based on the energy storage system model optimization configuration method that minimizes the system's average annual total cost, the optimal capacity configuration of the energy storage system model is determined by setting constraints and combining the optimized grey wolf algorithm.

[0048] The objective function determined in this embodiment is to minimize the average annual total cost of the system, including the investment cost, operation and maintenance cost, and power loss cost of the energy storage system.

[0049] The set constraints include, but are not limited to, capacity and power constraints of the energy storage system, power generation capacity constraints of the photovoltaic array, grid demand and stability constraints, and other related constraints.

[0050] Optionally, this embodiment uses an optimized grey wolf algorithm to optimize the configuration of the energy storage system model. The optimization algorithm is used to solve the minimum value of the objective function to obtain the optimal capacity configuration of the energy storage system.

[0051] This embodiment reduces the average annual total cost of the energy storage system and improves economic benefits. Moreover, by optimizing the configuration, the overall performance and stability of the energy storage system are improved.

[0052] Furthermore, as a refinement and expansion of the specific implementation methods of the above-mentioned embodiment, in order to fully illustrate the specific implementation process in this embodiment, a specific implementation method of an energy storage optimization configuration method applied to a new energy station is given below, and the method includes the following steps.

[0053] refer to Figure 2 , a photovoltaic system control structure diagram is provided in the first embodiment of the present disclosure. The photovoltaic array power generation model established in this embodiment adopts a multi-level control method, and the photovoltaic array power generation model includes photovoltaic side control and load side control.

[0054] For the multi-level control method, it is divided into photovoltaic side control and load side control. The photovoltaic side adopts the incremental conductance method in the MPPT control technology for control. The specific control steps are: Calculate the power change rate: .

[0055] in I k , V k is the current and voltage at the current sampling moment, I k-1 , V k-1 is the current and voltage at the previous sampling moment.

[0056] Determine the position of the working point: The value of the incremental conductance determines the position of the current working point of the photovoltaic array relative to the maximum power point. When , the voltage at this time is the voltage of the maximum power point; When , the current operating point is on the left side of the maximum power point; , the current operating point is to the right of the maximum power point.

[0057] Real-time voltage adjustment: when When the photovoltaic array power generation model reaches the maximum power point, the working voltage of the photovoltaic array should remain unchanged.

[0058] when When the voltage is adjusted and increased, the photovoltaic array power generation model is close to the maximum power point.

[0059] when When the voltage is reduced, the photovoltaic array power generation model is adjusted towards the maximum power point.

[0060] For the load side of this embodiment, inner and outer loop control can be adopted.

[0061] Among them, the outer loop control mainly adopts power PQ control. In this control mode, the inverter adjusts the output of active power and reactive power according to load demand.

[0062] The power outer loop control can effectively ensure that the power demand of the power grid or load end is met and ensure the stability of the power grid.

[0063] The inner loop control of this embodiment adopts a dual closed-loop control of a voltage outer loop and a current inner loop. The voltage outer loop is responsible for controlling the stability of the output voltage, while the current inner loop is responsible for accurately regulating the current. The fast responsiveness of the inner loop control of this embodiment enables the system to quickly adjust system parameters when the load fluctuates or the voltage changes, thereby ensuring the dynamic performance and stability of the system.

[0064] As a further implementation, refer to Figure 3 This is a structural diagram of a photovoltaic power station power generation system provided by the present invention. The photovoltaic panels here are respectively passed through DC / DC converters, which can adjust the higher voltage generated by photovoltaic power generation to a lower voltage for energy storage in Buck mode or increase the lower voltage of the hybrid energy storage system to a higher voltage for output in Boost mode. The DC / DC converter is connected to the filter inductor and the filter capacitor to control the power quality of the input or output electric energy. A DC / AC converter is connected between the hybrid energy storage system and the power grid to output the energy stored in the hybrid energy storage system to the power grid in the form of alternating current. Finally, the unit transformers are connected in parallel to the AC bus of the power grid.

[0065] like Figure 4 Shown is a control structure diagram of the energy storage system provided in this embodiment.

[0066] Specifically, when the power generation of the photovoltaic array is greater than the load demand, the energy storage system model operates in charging mode, and the DC / DC converter operates in Buck mode, converting the higher-voltage excess electrical energy of the photovoltaic array into low-voltage electrical energy that can be stored by the energy storage system through a filtering circuit. When charging is completed or the charging current reaches the set value, the energy storage system model stops charging or enters standby mode, waiting for the next charging cycle.

[0067] When the power generation of the photovoltaic array is insufficient to meet the load demand, the energy storage system model enters the discharge mode, and the DC / DC converter usually works in the Boost mode to increase the voltage to a suitable level and convert DC power into AC power in combination with the inverter, and ensure that the output voltage and frequency match the requirements of the grid or load.

[0068] like Figure 5As shown, it is a flow chart of the optimized grey wolf algorithm provided by the present disclosure. Here, the full life cycle cost of the ESS system is used for calculation, which specifically includes four parts: the average annual initial investment cost of the ESS, fuel cost, operation and maintenance cost, and replacement cost.

[0069] The optimization objective is defined as:

[0070] The constraints set in this embodiment include system power constraints, lithium battery constraints, and supercapacitor constraints. At any time t, the power constraints of the energy storage system model are as follows:

[0071] In the formula, , , , and They are photovoltaic power, lithium battery power, supercapacitor power, gas turbine power and load power respectively.

[0072] At any time t, the constraints on the lithium battery capacity are as follows:

[0073] In the formula, is the capacity of the lithium battery at a certain moment, The minimum / maximum capacity of lithium battery.

[0074] At any time t, the constraints for lithium battery power balance are as follows:

[0075] In the formula, is the power of the lithium battery at a certain moment, It is the minimum / maximum charge and discharge power of lithium battery.

[0076] The state of charge constraints for lithium-ion batteries are as follows:

[0077] In the formula, is the charge state of the lithium battery at a certain moment, The upper and lower limits of the lithium battery SOC.

[0078] At any time t, the constraints on the supercapacitor capacity are as follows:

[0079] In the formula, is the capacity of the supercapacitor at a certain moment, It is the minimum / maximum capacity of supercapacitor.

[0080] At any time t, the constraints on the supercapacitor power balance are as follows:

[0081] In the formula, is the power of the supercapacitor at a certain moment, It is the minimum / maximum charging and discharging power of the supercapacitor.

[0082] The supercapacitor charge state constraints are as follows:

[0083] It should be noted that in the optimized gray wolf algorithm, the position of each wolf represents a solution in the solution space, and the fitness value represents the quality of the solution. The goal of the algorithm is to find the solution with the best fitness value. The hierarchical structure of the gray wolf group helps the gray wolf to organize hunting and resource allocation in an efficient way. The above model is now calculated using the gray wolf algorithm, with the capacity of lithium batteries and supercapacitors as decision variables. Under the above constraints, the optimized gray wolf algorithm is used to calculate the best configuration combination with the minimum objective function.

[0084] It can be seen that the overall cost set in this application is composed of hybrid energy storage cost (ESS), photovoltaic cost (PV) and gas turbine cost (MT). The total cost optimization lies in establishing a minimum function model of the total cost of the hybrid energy storage system and seeking the optimal configuration of the ESS. The life cycle cost of the ESS system includes the average annual initial investment cost, fuel cost, operation and maintenance cost and replacement cost. The constraints mainly include system power constraints, lithium battery constraints and supercapacitor constraints.

[0085] In other words, the method of the present application combines the photovoltaic and storage joint control model with multi-constraint and multi-objective optimization, and forms an optimal configuration method for the energy storage system in the process of gradually increasing photovoltaic proportion in a high-proportion new energy system from the perspective of the economy and reliability of the energy storage system model. The energy storage system model can still operate safely and stably in the face of load switching, while obtaining the full life cycle cost and total system cost of the hybrid energy storage system under the optimal configuration, realizing the rational use of energy and ensuring the stability, safety and economy of the base power grid operation.

[0086] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0087] like Figure 6As shown, the present application also provides an electronic device, including a display module 103, a memory 102, a processor 101, and a computer program stored in the memory and executable on the processor 101, wherein the processor 101 implements the steps of an energy storage optimization configuration method applied to a new energy station when executing the program.

[0088] In embodiments of the present invention, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described and / or required herein.

[0089] In the embodiment of the present application, the processor 101 can be implemented by using at least one of an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), a processor, a controller, a microcontroller, a microprocessor, and an electronic unit designed to perform the functions described herein. In some cases, such an implementation can be implemented in a controller. For software implementation, implementations such as processes or functions can be implemented with separate software modules that allow execution of at least one function or operation. The software code can be implemented by a software application (or program) written in any appropriate programming language, and the software code can be stored in a memory and executed by a controller.

[0090] The display module 103 is used to display information input by the user or information provided to the user. The display module 103 may include a display panel, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc.

[0091] The memory 102 may be used to store software programs and various data. The memory 102 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0092] The present application also provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the energy storage optimization configuration method applied to a new energy station are implemented.

[0093] The storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0094] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing energy storage configuration applied to a new energy station, characterized in that: Methods include: S101: Establish energy storage system model based on lithium-ion batteries and supercapacitors; S102: Establishing a photovoltaic array power generation model; S103: Establish photovoltaic side control and load side control models based on the energy storage system model and photovoltaic array power generation model, and verify that the photovoltaic and storage joint control system model responds to the grid demand state and executes frequency regulation and peak regulation processes in combination with the operating characteristics under load fluctuations; S104: Based on the energy storage system model optimization configuration method that minimizes the system's average annual total cost, the optimal capacity configuration of the energy storage system model is determined by setting constraints and combining the optimized grey wolf algorithm.

2. The energy storage optimization configuration method applied to a new energy station according to claim 1 is characterized in that: The energy storage system model in the method includes multiple lithium-ion batteries and supercapacitors; The multiple lithium-ion batteries are composed of multiple lithium-ion batteries connected in series.

3. The energy storage optimization configuration method applied to a new energy station according to claim 1 is characterized in that: In step S102, the photovoltaic array power generation model adopts a multi-level control method, and the photovoltaic array power generation model includes photovoltaic side control and load side control, wherein the photovoltaic side control adopts MPPT control, and the load side control adopts inner and outer loop control; In the multi-level control method, the first level is MPPT control, which is based on maximizing the efficiency of the photovoltaic array in capturing solar energy; The second stage is the DC bus voltage control, which realizes MPPT control based on the dynamic adjustment between the output current and voltage change rate of the photovoltaic array.

4. The energy storage optimization configuration method applied to a new energy station according to claim 3 is characterized in that: In MPPT control, control is performed based on the incremental conductance method. The specific control steps are: Calculate the power change rate: ,in I k , V k is the current and voltage at the current sampling moment, I k-1 , V k-1 is the current and voltage at the last sampling moment; Determine the position of the current working point of the photovoltaic array relative to the maximum power point based on the value of the incremental conductance; exist When , the voltage at the sampling moment is the voltage at the maximum power point; When , the current operating point is on the left side of the maximum power point; When , the current operating point is on the right side of the maximum power point; when When the maximum power point is reached, the working voltage of the photovoltaic array remains unchanged; when When it is not equal to 0, the voltage parameters are adjusted to achieve the preset maximum power point.

5. The energy storage optimization configuration method applied to a new energy station according to claim 3 is characterized in that: In the inner and outer loop control, the outer loop control adopts power PQ control, which controls the active power and reactive power output on the load side by adjusting the current and voltage parameters output by the inverter; The inner loop control adopts voltage outer loop and current inner loop dual closed loop control and is based on Buck / Boost converter control.

6. The energy storage optimization configuration method applied to a new energy station according to claim 5 is characterized in that: The control methods based on Buck / Boost converter include: on the load side, using droop control to adjust the active power and reactive power; When the photovoltaic output power is less than the power required by the load, the DC / DC converter controls the energy storage to release energy; When the electric energy output by photovoltaic power generation is greater than the energy required by the load, the DC / DC converter operates in Buck mode, controls the energy storage to absorb energy, and the power flow direction is the DC bus to the energy storage system model.

7. The energy storage optimization configuration method applied to a new energy station according to claim 1 is characterized in that: Step S104 also includes: establishing a total cost minimum function model of the energy storage system model to seek an optimization target for the ESS system; The life cycle cost of the ESS system includes the average annual initial investment cost, fuel cost, operation and maintenance cost, and replacement cost; the optimization objectives are: In the optimized gray wolf algorithm, the position of each wolf is defined to represent a solution in the solution space, and the fitness value represents the quality of the solution. The goal of setting the optimized gray wolf algorithm is to match the optimal solution that meets the preset fitness value.

8. The energy storage optimization configuration method applied to a new energy station according to claim 7 is characterized in that: In the method, the set constraints include: system power constraints, lithium battery constraints, and supercapacitor constraints; At any time t, the system power constraints are as follows: In the formula, , , , and They are photovoltaic power, lithium battery power, supercapacitor power, gas turbine power and load power; At any time t, the constraints on the lithium battery capacity are as follows: In the formula, is the capacity of the lithium battery at a certain moment, The minimum / maximum capacity of lithium battery; At any time t, the constraints for lithium battery power balance are as follows: In the formula, is the power of the lithium battery at a certain moment, The minimum / maximum charge and discharge power of lithium batteries; The state of charge constraints for lithium-ion batteries are as follows: In the formula, is the charge state of the lithium battery at a certain moment, The upper and lower limits of the lithium battery SOC; At any time t, the constraints on the supercapacitor capacity are as follows: In the formula, is the capacity of the supercapacitor at a certain moment, is the minimum / maximum capacity of the supercapacitor; At any time t, the constraints on the supercapacitor power balance are as follows: In the formula, is the power of the supercapacitor at a certain moment, is the minimum / maximum charge and discharge power of the supercapacitor; The supercapacitor charge state constraints are as follows: 。 9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the energy storage optimization configuration method applied to a new energy station as described in any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the energy storage optimization configuration method applied to a new energy station as described in any one of claims 1 to 7 are implemented.

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