Efficiency optimization method and system of distributed energy storage system, equipment and medium

Abstract

The invention discloses an efficiency optimization method, system, equipment and medium for a distributed energy storage system, and the method comprises the steps: selecting any energy storage converter in the distributed energy storage system as an initial target, and collecting the power loss data of each bridge arm switch tube under different bus voltage conditions; then, combining the data with a power distribution coefficient of a target energy storage converter to construct an efficiency optimization model for the converter, and updating the target energy storage converter through iteration until all the energy storage converters are traversed, thereby obtaining a series of efficiency optimization models for single energy storage converters; the models are integrated to form a comprehensive distributed energy storage system efficiency optimization model, the model is solved by applying an optimization algorithm to determine the optimal power distribution coefficient of each energy storage converter under different bus voltages, and the operation of each energy storage converter is controlled according to the optimal power distribution coefficient. The purposes of improving the overall efficiency of the system and reducing the loss are achieved.

Description

Technical Field

[0001] The present invention relates to the field of distributed energy storage technology, and in particular to an efficiency optimization method, system, equipment and medium for a distributed energy storage system. Background Art

[0002] With the development of new energy and the popularization of distributed energy storage systems, how to improve the efficiency of energy storage converters has become a research focus. In the prior art, the efficiency of energy storage converters is usually improved by adding hardware devices or complex circuit structures, but this method increases the complexity and cost of distributed energy storage systems.

[0003] It can be seen that how to improve the efficiency of the energy storage converter without adding additional hardware has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the invention

[0004] The present invention provides a method, system, device and medium for optimizing the efficiency of a distributed energy storage system, which solves the problem of how to improve the efficiency of an energy storage converter without adding additional hardware.

[0005] In order to solve the above technical problems, the first aspect of the present invention provides a method for optimizing the efficiency of a distributed energy storage system, comprising: Selecting an energy storage converter in the distributed energy storage system as a target energy storage converter, and obtaining power loss data of multiple bridge arm switch tubes in the target energy storage converter under multiple bus voltages; In the model building step, a target energy storage converter efficiency optimization model is built according to the power loss data and the power allocation coefficient corresponding to the target energy storage converter; Iteratively updating the target energy storage converter, and repeatedly executing the model building step based on the updated target energy storage converter, until all energy storage converters in the distributed energy storage system are traversed, and a plurality of target energy storage converter efficiency optimization models are obtained to build a distributed energy storage system efficiency optimization model; The distributed energy storage system efficiency optimization model is solved by an optimization algorithm to obtain the optimal solution of the power distribution coefficient of each energy storage converter under a variety of bus voltages and control the operation of the corresponding energy storage converter to achieve efficiency optimization of the distributed energy storage system.

[0006] As one of the preferred solutions, when the circuit topology of the target energy storage converter is a three-phase two-level topology, the power loss data of multiple bridge arm switch tubes in the target energy storage converter is expressed by the following formula: Where P loss,nis the power loss and data of the bridge arm switch tube in the target energy storage converter; P con , P sw are the conduction and switching losses of the bridge arm switch tube in the target energy storage converter respectively; i a 、i b and i c are the instantaneous values ​​of the three-phase current of the target energy storage converter; V bat,n and V rated are the current value and rated value of the bus voltage respectively; r con and v con are the slope and intercept of the current curve corresponding to the on-state voltage drop of the bridge arm switch tube in the target energy storage converter; ω is the angular frequency of the power grid voltage; j is the number of switching actions of the switch tube in one power frequency voltage cycle; r sw and e sw are the slope and intercept of the current curve corresponding to the switching loss of the switch tube; f s and f g are switching frequency and grid frequency respectively; I g is the effective value of the grid-side current of the target energy storage converter; n is the number of energy storage converters in the distributed energy storage system.

[0007] As one of the preferred solutions, when the circuit topology of the target energy storage converter is a three-phase T-type three-level topology, the power loss data of multiple bridge arm switch tubes in the target energy storage converter is expressed by the following formula: In the formula, r con1 and v con1 are the slope and intercept of the current curve corresponding to the conduction voltage drop of the first type of switch tube in the target energy storage converter; r con2 and v con2 are the slope and intercept of the current curve corresponding to the conduction voltage drop of the second type of switch tube in the target energy storage converter; r sw1 and e sw1 are the slope and intercept of the current curve corresponding to the switching loss of the first type of switch tube in the target energy storage converter; r sw2 and e sw2 They are respectively the slope and intercept of the current curve corresponding to the switching loss of the second type of switch tube in the target energy storage converter.

[0008] As one of the preferred solutions, in the model building step, building a target energy storage converter efficiency optimization model according to the power loss data and the power allocation coefficient corresponding to the target energy storage converter includes: Acquire a power allocation coefficient corresponding to the target energy storage converter, and quantify a power loss component of the target energy storage converter according to the power loss data and the power allocation coefficient; A target energy storage converter efficiency optimization model is constructed based on the power loss component, taking into account power distribution coefficient and loss under various bus voltages.

[0009] As one of the preferred solutions, the distributed energy storage system efficiency optimization model is expressed by the following formula: Where P loss is the total loss of the distributed energy storage system; k n A is the power allocation coefficient of the nth target energy storage converter; n , B n , C n are the power loss of the nth target energy storage converter and the quadratic term coefficient, linear term coefficient and constant term coefficient in the data respectively.

[0010] As one of the preferred solutions, the distributed energy storage system efficiency optimization model is solved by an optimization algorithm to obtain the optimal solution of the power distribution coefficient of each energy storage converter under a variety of bus voltages, including: Taking the minimization of the distributed energy storage system efficiency optimization model as the objective function and the power capacity limit of each energy storage converter in the distributed energy storage system as the constraint condition, a group of particles are randomly generated, the position and speed of the particles are initialized, and the parameters of the particle group are set; each particle represents a group of power allocation coefficient solutions; In the particle updating step, the initial fitness value of each particle is quantified according to the objective function, so as to update the speed and position of each particle through the particle swarm algorithm based on each initial fitness value, and quantify the fitness value of the new position to obtain the global optimal position; In the gradient projection step, a target gradient of the objective function with respect to the power allocation coefficient is calculated according to the global optimal position, and a projection direction is determined according to the target gradient, so as to calculate a new projection position along the projection direction and the selected step length under the constraint condition to obtain a target global optimal position; The particle updating step and the gradient projection step are repeatedly executed until a preset number of iterations is reached, and the target global optimal position finally outputted is used as the optimal solution of the power distribution coefficient of each energy storage converter under the multiple bus voltages.

[0011] As one of the preferred solutions, obtaining the optimal solution of the power distribution coefficient of each energy storage converter under the multiple bus voltages and controlling the operation of the corresponding energy storage converter to achieve efficiency optimization of the distributed energy storage system includes: Obtaining grid-side current sampling values ​​of each energy storage converter, and generating grid-side current reference values ​​of each energy storage converter under a plurality of bus voltages according to the optimal solution of the power allocation coefficient; The difference between each grid-side current reference value and each grid-side current sampling value is used as the input of the current controller corresponding to each energy storage converter in the distributed energy storage system, and is processed through the grid-side active current control loop and the grid-side reactive current control loop in the distributed energy storage system to obtain the voltage reference signal of the multiple bridge arm switch tubes in each energy storage converter; Based on the circuit topology of each energy storage converter, the voltage reference signal is converted into a switching signal through a pulse width modulation method and synchronously sent to the corresponding energy storage converter for operation, so as to optimize the efficiency of the distributed energy storage system.

[0012] A second aspect of the present invention provides an efficiency optimization system for a distributed energy storage system, comprising: A data acquisition module, used to select any energy storage converter in the distributed energy storage system as a target energy storage converter, and obtain power loss data of multiple bridge arm switch tubes in the target energy storage converter under multiple bus voltages; A model building module, used for building a target energy storage converter efficiency optimization model according to the power loss data and the power allocation coefficient corresponding to the target energy storage converter in the model building step; An iterative update module, used for iteratively updating the target energy storage converter, and repeatedly executing the model building step based on the updated target energy storage converter, until all energy storage converters in the distributed energy storage system are traversed, and a plurality of target energy storage converter efficiency optimization models are obtained to construct a distributed energy storage system efficiency optimization model; The efficiency optimization module is used to solve the efficiency optimization model of the distributed energy storage system through an optimization algorithm, obtain the optimal solution of the power distribution coefficient of each energy storage converter under a variety of bus voltages and control the operation of the corresponding energy storage converter to achieve efficiency optimization of the distributed energy storage system.

[0013] A third aspect of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the efficiency optimization method of the distributed energy storage system as described above is implemented.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the efficiency optimization method of the distributed energy storage system as described above is implemented.

[0015] Compared with the prior art, the embodiments of the present invention have the following advantages: (1) Without adding additional hardware and without affecting the total power output, the power allocation coefficient of each energy storage converter in the distributed energy storage system is optimized, and the power output is optimally allocated to the energy storage converters with different bus voltages according to the optimization results, so as to significantly reduce the overall energy loss of the system, thereby improving the operating efficiency of the distributed energy storage system; (2) By building and optimizing the model, intelligent management of the distributed energy storage system is achieved, which not only improves the automation level of the system but also reduces the operation and maintenance costs. The solution can adapt to a variety of bus voltage conditions to ensure that the system can maintain efficient operation under different power grid environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the implementation mode will be briefly introduced below. Obviously, the drawings described below are only some implementation modes of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 is a flow chart of a method for optimizing the efficiency of a distributed energy storage system provided by a certain embodiment of the present invention; Figure 2 is a topological diagram of a distributed energy storage system provided by a certain embodiment of the present invention; Figure 3 is a circuit topology diagram of a three-phase two-level energy storage converter provided by a certain embodiment of the present invention; Figure 4 It is a circuit topology diagram of a three-phase T-type three-level energy storage converter provided by a certain embodiment of the present invention; Figure 5 is a schematic diagram of a current controller in a distributed energy storage system provided by a certain embodiment of the present invention; Figure 6 It is a control block diagram of an energy storage converter in a distributed energy storage system provided by a certain embodiment of the present invention; Figure 7 is a structural diagram of an efficiency optimization system of a distributed energy storage system provided by a certain embodiment of the present invention; Figure 8 It is a structural diagram of an electronic device provided by a certain embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. 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.

[0019] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0020] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two components. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are for illustrative purposes only, and do not indicate or imply that the system or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0021] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0022] In one embodiment, if Figure 1 As shown, the first aspect of the present invention provides a method for optimizing the efficiency of a distributed energy storage system, comprising: S1. Select any energy storage converter in the distributed energy storage system as the target energy storage converter, and obtain power loss data of multiple bridge arm switch tubes in the target energy storage converter under multiple bus voltages; Specifically, the topology of the distributed energy storage system is as follows: Figure 2 As shown, it is composed of multiple battery clusters connected in parallel; wherein the number of battery clusters is n (n is a positive integer), including the first battery cluster Bat1, the second battery cluster Bat2, ..., the nth battery cluster Bat n ; Each battery cluster has a power converter (energy storage converter), including the first energy storage converter, the second energy storage converter, ..., the nth energy storage converter; the output of the battery cluster is connected to the input of the corresponding energy storage converter, and the output of each energy storage converter is connected in parallel to form the output of the distributed energy storage system; please also refer to Figure 5 The entire distributed energy storage system is controlled by a main controller, and each energy storage converter is connected to a current controller. The main controller and the current controller can be communicated to exchange data and convey instructions.

[0023] Select any energy storage converter from the distributed energy storage system as the initial target energy storage converter. This selection can be random or based on a priority strategy (such as capacity, current load, etc.). The power loss data of multiple bridge arm switches in the target energy storage converter under various bus voltages can be expressed by the following formula: Where P loss,n is the power loss and data of the bridge arm switch tube in the nth target energy storage converter; I g is the effective value of the grid-side current of the target energy storage converter; n is the number of energy storage converters in the distributed energy storage system; A n , B n , C n are the quadratic term coefficient, linear term coefficient, and constant term coefficient in the power loss and data of the nth target energy storage converter. Regardless of the circuit topology adopted by the energy storage converter, its power loss and can be converted into the above expression.

[0024] In one embodiment, when the circuit topology of the target energy storage converter is a three-phase two-level topology, its structure is as follows: Figure 3 As shown, it is composed of three independent bridge arms, each bridge arm includes two switch tubes, wherein the switch tube is responsible for connecting the positive or negative pole of the DC bus, located at the upper and lower ends of the bridge arm, and is called the first type of switch tube; the power loss data of multiple bridge arm switch tubes in the target energy storage converter is expressed by the following formula: Where P con , P sw are the conduction and switching losses of the bridge arm switch tube in the target energy storage converter respectively; i a 、i b and i care the instantaneous values ​​of the three-phase current of the target energy storage converter; V bat,n and V rated are the current value and rated value of the bus voltage respectively; r con and v con are the slope and intercept of the current curve corresponding to the on-state voltage drop of the bridge arm switch tube in the target energy storage converter; ω is the angular frequency of the power frequency grid voltage; j is the number of switching actions of the switch tube in one power frequency voltage cycle, and the maximum value is f s / f g ; r sw and e sw are the slope and intercept of the current curve corresponding to the switching loss of the switch tube; f s and f g The present invention adopts a three-phase two-level topology as the circuit topology of the target energy storage converter, which has a simpler structure and requires fewer components, thus helping to reduce the complexity and cost of the system.

[0025] In one embodiment, when the circuit topology of the target energy storage converter is a three-phase T-type three-level topology, its structure is as follows: Figure 4 As shown, it is composed of three independent bridge arms, each bridge arm contains four switch tubes. The switch tubes located at the upper and lower ends of the bridge arm and responsible for connecting the positive or negative pole of the DC bus are called the first type of switch tubes, and the switch tubes located in the middle of the bridge arm and responsible for connecting the midpoint of the bridge arm and the AC output end are called the second type of switch tubes; among them, Figure 3 , 4 C up , C down are the upper capacitance of the bridge arm and the lower capacitance of the bridge arm respectively; the power loss data of the multiple bridge arm switch tubes in the target energy storage converter is expressed by the following formula: In the formula, r con1 and v con1 are the slope and intercept of the current curve corresponding to the conduction voltage drop of the first type of switch tube in the target energy storage converter; r con2 and v con2 are the slope and intercept of the current curve corresponding to the conduction voltage drop of the second type of switch tube in the target energy storage converter; r sw1 and e sw1 are the slope and intercept of the current curve corresponding to the switching loss of the first type of switch tube in the target energy storage converter; r sw2 and e sw2 They are respectively the slope and intercept of the current curve corresponding to the switching loss of the second type of switch tube in the target energy storage converter.

[0026] The present invention adopts a three-phase T-type three-level topology as the circuit topology of the target energy storage converter, which can effectively reduce the voltage stress of semiconductor devices, which helps to extend the service life of the devices and improve the reliability of the system. By optimizing power distribution and reducing losses, it helps to improve the overall efficiency of the system. Especially in high-power application scenarios, it has significant advantages in improving output characteristics and operating efficiency. At the same time, it is applicable to a variety of bus voltage conditions and can enhance the flexibility and adaptability of the system.

[0027] S2. In the model building step, a target energy storage converter efficiency optimization model is built according to the power loss data and the power allocation coefficient corresponding to the target energy storage converter; In one embodiment, step S2 includes: Acquire a power allocation coefficient corresponding to the target energy storage converter, and quantify a power loss component of the target energy storage converter according to the power loss data and the power allocation coefficient; A target energy storage converter efficiency optimization model is constructed based on the power loss component, taking into account power distribution coefficient and loss under various bus voltages.

[0028] Specifically, in the present invention, the initial value of the power distribution coefficient corresponding to each energy storage converter is set to 1 / n, and the power loss component is obtained by multiplying the loss of the target energy storage converter by the corresponding power distribution coefficient. Then, the power distribution coefficient of the target energy storage converter under different bus voltages is used as the independent variable, and the power loss component of the target energy storage converter is used as the dependent variable to construct the target energy storage converter efficiency optimization model. By optimizing the power distribution coefficient, each energy storage converter can allocate power more reasonably under a variety of bus voltages, thereby reducing the total power loss of the system and improving the system efficiency; this solution can adapt to a variety of bus voltage conditions, ensuring that the system can maintain efficient operation under different power grid environments.

[0029] S3, iteratively updating the target energy storage converter, repeating the model building step based on the updated target energy storage converter, until all energy storage converters in the distributed energy storage system are traversed, and obtaining several target energy storage converter efficiency optimization models to construct a distributed energy storage system efficiency optimization model; wherein the distributed energy storage system efficiency optimization model is represented by the following formula: Where P loss is the total loss of the distributed energy storage system; k n is the power allocation coefficient of the nth target energy storage converter.

[0030] Similarly, when updating the target energy storage converter, the selection of the new target energy storage converter is not specifically limited, but for the convenience of subsequent processing, the target energy storage converters need to be sorted, such as the first target energy storage converter selected is the first target energy storage converter, and the last target energy storage converter is the nth target energy storage converter. Through iterative updating and model construction, the present invention can consider the efficiency characteristics of all energy storage converters in the distributed energy storage system to achieve global optimization, help avoid local optimal solutions, and improve the overall efficiency of the system; the solution can adapt to a variety of bus voltage conditions to ensure that the system can maintain efficient operation under different power grid environments. At the same time, since the model construction is based on actual data, it can reflect the true efficiency characteristics of the energy storage converter and improve the accuracy of the optimization results.

[0031] S4. Solving the distributed energy storage system efficiency optimization model through an optimization algorithm to obtain the optimal solution of the power distribution coefficient of each energy storage converter under the multiple bus voltages and controlling the operation of the corresponding energy storage converter to achieve efficiency optimization of the distributed energy storage system; In one embodiment, the distributed energy storage system efficiency optimization model is solved by an optimization algorithm to obtain the optimal solution of the power distribution coefficient of each energy storage converter under a plurality of bus voltages, including: Taking the minimization of the distributed energy storage system efficiency optimization model as the objective function and the power capacity limit of each energy storage converter in the distributed energy storage system as the constraint condition, a group of particles are randomly generated, the position and speed of the particles are initialized, and the parameters of the particle group are set; each particle represents a group of power allocation coefficient solutions; In the particle updating step, the initial fitness value of each particle is quantified according to the objective function, so as to update the speed and position of each particle through the particle swarm algorithm based on each initial fitness value, and quantify the fitness value of the new position to obtain the global optimal position; In the gradient projection step, a target gradient of the objective function with respect to the power allocation coefficient is calculated according to the global optimal position, and a projection direction is determined according to the target gradient, so as to calculate a new projection position along the projection direction and the selected step length under the constraint condition to obtain a target global optimal position; The particle updating step and the gradient projection step are repeatedly executed until a preset number of iterations is reached, and the target global optimal position finally outputted is used as the optimal solution of the power distribution coefficient of each energy storage converter under the multiple bus voltages.

[0032] The present invention combines the gradient projection method (GP) with the particle swarm optimization algorithm (PSO) to solve the distributed energy storage converter system efficiency optimization model, aiming to integrate the advantages of both: the fast local convergence of gradient projection and the global search capability of particle swarm, specifically: The efficiency optimization model of the distributed energy storage converter system is minimized as an objective function, which contains the power allocation coefficient and bus voltage of each energy storage converter as variables. Then, the constraints are determined, such as power balance, physical limitations of the energy storage converter (that is, the power capacity limitation of the energy storage converter), etc.; then, the particle swarm is initialized, that is, a group of particles are randomly generated, each particle represents a group of possible power allocation coefficient solutions, and the position and velocity of the particles are initialized, and the PSO parameters such as the size of the particle swarm, the number of iterations, the inertia weight, the cognitive coefficient and the social coefficient are set; and for each particle, its initial fitness value is calculated according to the objective function to reflect the efficiency performance of the current power allocation coefficient solution.

[0033] The next step is the particle update step: based on the initial fitness value of each particle, the particle swarm algorithm is used to update the speed and position of each particle, and the fitness value of the new position is calculated (to evaluate the quality of the updated solution) and the global optimal position is updated (that is, the best power allocation coefficient solution currently found).

[0034] The following is a gradient projection step: the target gradient of the objective function with respect to the power allocation coefficient is calculated according to the global optimal position, and the projection direction is determined according to the target gradient, and this direction points to the direction in which the objective function value is reduced; along the projection direction and the selected step length (the step length can be determined by a line search method, such as an Armijo condition or a Wolfe condition), a new projection position is calculated under constraints; if the new projection position satisfies the constraints and the objective function value is smaller, the global optimal position is updated to the new projection position; if the new projection position does not satisfy the constraints, a linear programming or quadratic programming solver is used to find the nearest point that satisfies the constraints to ensure that it is within the feasible domain; the present invention uses a gradient projection method to project the position of the particle into the feasible domain that satisfies the constraints to improve the feasibility of the solution and accelerate convergence.

[0035] Repeat the particle update step and the gradient projection step, and use the global search capability of PSO and the local convergence of GP. In each iteration, first perform the PSO update step, and then apply the gradient projection step to adjust the solution until the preset number of iterations is reached. In each iteration, the particle speed and position are updated according to the new global optimal position; finally, the final output target global optimal position is used as the optimal solution for the power allocation coefficient of each energy storage converter under various bus voltages. In addition, other heuristic optimization methods (such as genetic algorithms, simulated annealing algorithms, ant colony algorithms, etc.) can also be used to solve the optimal solution.

[0036] The present invention combines the particle swarm algorithm and the gradient projection method, and can quickly converge to the global optimal solution in the solution space, thereby improving the solution efficiency; through the gradient projection step, it is ensured that the constraints such as the power capacity limit of each energy storage converter are always met during the solution process, thereby enhancing the feasibility of the solution; and the particle swarm algorithm has a global search capability, which can avoid falling into a local optimal solution, thereby finding a power distribution coefficient solution that is closer to the global optimal solution; the scheme can adapt to a variety of bus voltage conditions, ensuring that the system can maintain efficient operation under different power grid environments; by solving the optimal solution of the power distribution coefficient through an automated algorithm, intelligent management of the distributed energy storage system is realized, operation and maintenance costs are reduced, and system reliability and stability are improved.

[0037] In one embodiment, obtaining the optimal solution of the power distribution coefficient of each energy storage converter under the multiple bus voltages and controlling the corresponding energy storage converter to operate, so as to optimize the efficiency of the distributed energy storage system, includes: Obtaining grid-side current sampling values ​​of each energy storage converter, and generating grid-side current reference values ​​of each energy storage converter under a plurality of bus voltages according to the optimal solution of the power allocation coefficient; The difference between each grid-side current reference value and each grid-side current sampling value is used as the input of the current controller corresponding to each energy storage converter in the distributed energy storage system, and is processed through the grid-side active current control loop and the grid-side reactive current control loop in the distributed energy storage system to obtain the voltage reference signal of the multiple bridge arm switch tubes in each energy storage converter; Based on the circuit topology of each energy storage converter, the voltage reference signal is converted into a switching signal through a pulse width modulation method and synchronously sent to the corresponding energy storage converter for operation, so as to optimize the efficiency of the distributed energy storage system.

[0038] The schematic diagram of the current controller in the distributed energy storage system is as follows: Figure 5As shown in FIG. 1 , the main controller and each current controller transmit data and instructions through low-bandwidth communication. The current controller collects the instantaneous value of the three-phase current of the energy storage converter, the instantaneous value of the three-phase voltage, the current value of the bus voltage and the grid frequency, and uploads these sampled information to the system main controller through low-bandwidth communication. The main controller completes the establishment and solution of the optimization function, and then transmits the I of each energy storage converter to the main controller through low-bandwidth communication. refk (That is, the main controller uses the optimal solution of power allocation coefficients k1, k2, ..., k n The active current reference values ​​i of different energy storage converters calculated respectively d * and reactive current reference value i q * ) is transmitted to the corresponding current controller to reduce the total loss of the distributed energy storage system without adding additional hardware and without affecting the total power output.

[0039] The control block diagram of the energy storage converter in the distributed energy storage system is as follows: Figure 6 As shown, the main controller obtains the grid-side current sampling value of each energy storage converter in the distributed energy storage system in real time, which reflects the actual current exchange situation between the current energy storage converter and the power grid, and calculates the active current reference value and reactive current reference value of different energy storage converters according to the optimal solution of the power distribution coefficient, so as to pass them to the corresponding current controller to generate the grid-side current reference value of the energy storage converter under different bus voltages. These reference values ​​are key parameters for achieving efficiency optimization; then the difference between the grid-side current reference value and the actual sampling value of each energy storage converter is used as the input signal of the corresponding current controller; wherein, the function of the current controller is to reduce this difference, so that the actual current is as close to the reference value as possible, and through Figure 6 The grid-side active current control loop in the control circuit regulates the active current to ensure accurate power transmission, and Figure 6 The grid-side reactive current control loop in the system is used to adjust the reactive current to achieve reactive compensation. The two control loops are used to process the current difference, and the voltage reference signals of multiple bridge arm switches in each energy storage converter in the dq coordinate system are calculated. The three-phase AC voltage reference signal is obtained through the park inverse transformation, and the corresponding three-phase modulation wave is obtained. Finally, the voltage reference signal is converted into a switch signal through the pulse width modulation (PWM) technology. These switch signals control the on and off of the bridge arm switch tubes, thereby adjusting the output voltage and current of the energy storage converter; finally, the generated switch signal is synchronously sent to the corresponding energy storage converter, decoded and executed by the control system of the energy storage converter, and the on and off of the switch tube is controlled to generate the required current and voltage waveforms, realizing power exchange with the power grid. By continuously optimizing the power allocation coefficient and accurately controlling the current, the overall efficiency of the distributed energy storage system can be significantly improved.

[0040] The present invention adopts a control strategy of parallel operation of the main controller and the current controller, realizes real-time data upload and accurate issuance of control instructions through low-bandwidth communication, and effectively coordinates the operating status of multiple energy storage converters in the distributed energy storage system; wherein, the main controller calculates the power distribution coefficient under each bus voltage based on the optimization algorithm, and determines the optimal power distribution scheme by minimizing the power loss of multiple bridge arm switch tubes in the energy storage converter, thereby reducing the overall power loss of the system and improving the operating efficiency. In actual operation, the main controller generates a grid-side current reference value according to the optimization result, and sends it to each current controller. Each current controller performs current closed-loop control based on the reference value, and adjusts the conduction state of the bridge arm switch in real time to achieve optimal control. Through the optimization method described in the present invention, the total power loss of the distributed energy storage system can be significantly reduced without adding additional hardware, and efficient energy conversion can be achieved. While meeting the total power output requirements, the system has strong flexibility and adaptability, and each current controller can operate independently to ensure the efficiency and stability of distributed control.

[0041] In the embodiment of the present application, based on the problem of how to improve the efficiency of the energy storage converter without adding additional hardware, a method for optimizing the efficiency of a distributed energy storage system is designed, which realizes selecting any energy storage converter in the distributed energy storage system as a target energy storage converter, and obtaining power loss data of multiple bridge arm switch tubes in the target energy storage converter under multiple bus voltages; in the model construction step, a target energy storage converter efficiency optimization model is constructed according to the power loss data and the power allocation coefficient corresponding to the target energy storage converter; the target energy storage converter is iteratively updated to repeatedly execute the model construction based on the updated target energy storage converter. The steps of building are performed until all the energy storage converters in the distributed energy storage system are traversed, and several target energy storage converter efficiency optimization models are obtained to construct a distributed energy storage system efficiency optimization model; the distributed energy storage system efficiency optimization model is solved by an optimization algorithm to obtain the optimal solution of the power distribution coefficient of each energy storage converter under a plurality of bus voltages and control the operation of the corresponding energy storage converter to realize a technical solution for optimizing the efficiency of the distributed energy storage system; by optimizing the power output and distributing it to the energy storage converters with different bus voltages, the total loss of the distributed energy storage system is reduced without adding additional hardware and without affecting the total power output.

[0042] It should be noted that although the steps in the above flowchart are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders.

[0043] In another embodiment, if Figure 7As shown, the second aspect of the present invention provides an efficiency optimization system for a distributed energy storage system, comprising: The data acquisition module 10 is used to select any energy storage converter in the distributed energy storage system as a target energy storage converter, and obtain power loss data of multiple bridge arm switch tubes in the target energy storage converter under multiple bus voltages; A model building module 20, used for building a target energy storage converter efficiency optimization model according to the power loss data and the power allocation coefficient corresponding to the target energy storage converter in the model building step; An iterative update module 30 is used to iteratively update the target energy storage converter, so as to repeatedly execute the model building step based on the updated target energy storage converter, until all energy storage converters in the distributed energy storage system are traversed, and a plurality of target energy storage converter efficiency optimization models are obtained to construct a distributed energy storage system efficiency optimization model; The efficiency optimization module 40 is used to solve the efficiency optimization model of the distributed energy storage system through an optimization algorithm, obtain the optimal solution of the power distribution coefficient of each energy storage converter under the multiple bus voltages and control the operation of the corresponding energy storage converter to achieve efficiency optimization of the distributed energy storage system.

[0044] It should be noted that each module in the above-mentioned efficiency optimization system of a distributed energy storage system can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. For the specific definition of the efficiency optimization system of a distributed energy storage system, please refer to the definition of the efficiency optimization method of a distributed energy storage system above. The two have the same functions and effects, which will not be repeated here.

[0045] A third aspect of the present invention provides an electronic device, the electronic device comprising: processor, memory, and bus; The bus is used to connect the processor and the memory; The memory is used to store operation instructions; The processor is used to call the operation instruction, and the executable instruction enables the processor to perform operations corresponding to the efficiency optimization method of a distributed energy storage system as shown in the first aspect of the present application.

[0046] In an alternative embodiment, an electronic device is provided, such as Figure 8 As shown, Figure 8The electronic device 5000 shown includes: a processor 5001 and a memory 5003. The processor 5001 and the memory 5003 are connected, such as through a bus 5002. Optionally, the electronic device 5000 may also include a transceiver 5004. It should be noted that in actual applications, the transceiver 5004 is not limited to one, and the structure of the electronic device 5000 does not constitute a limitation on the embodiments of the present application.

[0047] Processor 5001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 5001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0048] The bus 5002 may include a path to transmit information between the above components. The bus 5002 may be a PCI bus or an EISA bus, etc. The bus 5002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0049] The memory 5003 may be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an EEPROM, a CD-ROM or other optical disk storage, an optical disk storage (including a compressed optical disk, a laser disk, an optical disk, a digital versatile disk, a Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0050] The memory 5003 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 5001. The processor 5001 is used to execute the application code stored in the memory 5003 to implement the content shown in any of the above method embodiments.

[0051] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc.

[0052] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for optimizing the efficiency of a distributed energy storage system as shown in the first aspect of the present application.

[0053] Another embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0054] In addition, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented.

[0055] In summary, the present invention relates to the field of distributed energy storage technology, and discloses an efficiency optimization method, system, device and medium for a distributed energy storage system. By selecting any energy storage converter in the distributed energy storage system as the initial target, the power loss data of each bridge arm switch tube under different bus voltage conditions are collected, and then these data are combined with the power distribution coefficient of the target energy storage converter to construct an efficiency optimization model for the converter, and the target energy storage converter is iteratively updated until all energy storage converters are traversed, thereby obtaining a series of efficiency optimization models for a single energy storage converter; these models are integrated to form a comprehensive distributed energy storage system efficiency optimization model, and the optimization algorithm is used to solve the model to determine the optimal power distribution coefficient of each energy storage converter under different bus voltages, and the operation of each energy storage converter is controlled accordingly to achieve the purpose of improving the overall efficiency of the system and reducing losses.

[0056] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0057] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A method for optimizing the efficiency of a distributed energy storage system, characterized in that: include: Selecting an energy storage converter in the distributed energy storage system as a target energy storage converter, and obtaining power loss data of multiple bridge arm switch tubes in the target energy storage converter under multiple bus voltages; In the model building step, a target energy storage converter efficiency optimization model is built according to the power loss data and the power allocation coefficient corresponding to the target energy storage converter; Iteratively updating the target energy storage converter, and repeatedly executing the model building step based on the updated target energy storage converter, until all energy storage converters in the distributed energy storage system are traversed, and a plurality of target energy storage converter efficiency optimization models are obtained to build a distributed energy storage system efficiency optimization model; The distributed energy storage system efficiency optimization model is solved by an optimization algorithm to obtain the optimal solution of the power distribution coefficient of each energy storage converter under a variety of bus voltages and control the operation of the corresponding energy storage converter to achieve efficiency optimization of the distributed energy storage system.

2. The method for optimizing the efficiency of a distributed energy storage system according to claim 1, characterized in that: When the circuit topology of the target energy storage converter is a three-phase two-level topology, the power loss data of multiple bridge arm switch tubes in the target energy storage converter is expressed by the following formula: Where P loss,n is the power loss and data of the bridge arm switch tube in the target energy storage converter; P con , P sw are the conduction and switching losses of the bridge arm switch tube in the target energy storage converter respectively; i a 、i b and i c are the instantaneous values ​​of the three-phase current of the target energy storage converter; V bat,n and V rated are the current value and rated value of the bus voltage respectively; r con and v con are the slope and intercept of the current curve corresponding to the on-state voltage drop of the bridge arm switch tube in the target energy storage converter; ω is the angular frequency of the power grid voltage; j is the number of switching actions of the switch tube in one power frequency voltage cycle; r sw and e sw are the slope and intercept of the current curve corresponding to the switching loss of the switch tube; f s and f g are switching frequency and grid frequency respectively; I g is the effective value of the grid-side current of the target energy storage converter; n is the number of energy storage converters in the distributed energy storage system.

3. The method for optimizing the efficiency of a distributed energy storage system according to claim 2, characterized in that: When the circuit topology of the target energy storage converter is a three-phase T-type three-level topology, the power loss data of multiple bridge arm switch tubes in the target energy storage converter is expressed by the following formula: In the formula, r con1 and v con1 are the slope and intercept of the current curve corresponding to the conduction voltage drop of the first type of switch tube in the target energy storage converter; r con2 and v con2 are the slope and intercept of the current curve corresponding to the conduction voltage drop of the second type of switch tube in the target energy storage converter; r sw1 and e sw1 are the slope and intercept of the current curve corresponding to the switching loss of the first type of switch tube in the target energy storage converter; r sw2 and e sw2 They are respectively the slope and intercept of the current curve corresponding to the switching loss of the second type of switch tube in the target energy storage converter.

4. The method for optimizing the efficiency of a distributed energy storage system according to claim 1, characterized in that: In the model building step, a target energy storage converter efficiency optimization model is built according to the power loss data and the power allocation coefficient corresponding to the target energy storage converter, including: Acquire a power allocation coefficient corresponding to the target energy storage converter, and quantify a power loss component of the target energy storage converter according to the power loss data and the power allocation coefficient; A target energy storage converter efficiency optimization model is constructed based on the power loss component, taking into account power distribution coefficient and loss under various bus voltages.

5. The method for optimizing the efficiency of a distributed energy storage system according to claim 2, characterized in that: The distributed energy storage system efficiency optimization model is expressed by the following formula: Where P loss is the total loss of the distributed energy storage system; k n A is the power allocation coefficient of the nth target energy storage converter; n , B n , C n are the power loss of the nth target energy storage converter and the quadratic term coefficient, linear term coefficient and constant term coefficient in the data respectively.

6. The method for optimizing the efficiency of a distributed energy storage system according to claim 1, characterized in that: The distributed energy storage system efficiency optimization model is solved by the optimization algorithm to obtain the optimal solution of the power distribution coefficient of each energy storage converter under the multiple bus voltages, including: Taking the minimization of the distributed energy storage system efficiency optimization model as the objective function and the power capacity limit of each energy storage converter in the distributed energy storage system as the constraint condition, a group of particles are randomly generated, the position and speed of the particles are initialized, and the parameters of the particle group are set; each particle represents a group of power allocation coefficient solutions; In the particle updating step, the initial fitness value of each particle is quantified according to the objective function, so as to update the speed and position of each particle through the particle swarm algorithm based on each initial fitness value, and quantify the fitness value of the new position to obtain the global optimal position; In the gradient projection step, a target gradient of the objective function with respect to the power allocation coefficient is calculated according to the global optimal position, and a projection direction is determined according to the target gradient, so as to calculate a new projection position along the projection direction and the selected step length under the constraint condition to obtain a target global optimal position; The particle updating step and the gradient projection step are repeatedly executed until a preset number of iterations is reached, and the target global optimal position finally outputted is used as the optimal solution of the power distribution coefficient of each energy storage converter under the multiple bus voltages.

7. The method for optimizing the efficiency of a distributed energy storage system according to claim 1, characterized in that: The obtaining of the optimal solution of the power distribution coefficient of each energy storage converter under the multiple bus voltages and controlling the operation of the corresponding energy storage converter to achieve efficiency optimization of the distributed energy storage system includes: Obtaining grid-side current sampling values ​​of each energy storage converter, and generating grid-side current reference values ​​of each energy storage converter under a plurality of bus voltages according to the optimal solution of the power allocation coefficient; The difference between each grid-side current reference value and each grid-side current sampling value is used as the input of the current controller corresponding to each energy storage converter in the distributed energy storage system, and is processed through the grid-side active current control loop and the grid-side reactive current control loop in the distributed energy storage system to obtain the voltage reference signal of the multiple bridge arm switch tubes in each energy storage converter; Based on the circuit topology of each energy storage converter, the voltage reference signal is converted into a switching signal through a pulse width modulation method and synchronously sent to the corresponding energy storage converter for operation, so as to optimize the efficiency of the distributed energy storage system.

8. An efficiency optimization system for a distributed energy storage system, characterized in that: include: A data acquisition module, used to select any energy storage converter in the distributed energy storage system as a target energy storage converter, and obtain power loss data of multiple bridge arm switch tubes in the target energy storage converter under multiple bus voltages; A model building module, used for building a target energy storage converter efficiency optimization model according to the power loss data and the power allocation coefficient corresponding to the target energy storage converter in the model building step; An iterative update module, used for iteratively updating the target energy storage converter, and repeatedly executing the model building step based on the updated target energy storage converter, until all energy storage converters in the distributed energy storage system are traversed, and a plurality of target energy storage converter efficiency optimization models are obtained to construct a distributed energy storage system efficiency optimization model; The efficiency optimization module is used to solve the efficiency optimization model of the distributed energy storage system through an optimization algorithm, obtain the optimal solution of the power distribution coefficient of each energy storage converter under a variety of bus voltages and control the operation of the corresponding energy storage converter to achieve efficiency optimization of the distributed energy storage system.

9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for optimizing the efficiency of the distributed energy storage system as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the efficiency optimization method of the distributed energy storage system according to any one of claims 1 to 7 is implemented.

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