A method and system for optimizing the capacity configuration of vanadium-lithium hybrid energy storage considering efficiency.

Abstract

This invention relates to the field of energy storage technology for power systems, and discloses a method and system for optimizing the capacity configuration of vanadium-lithium hybrid energy storage considering efficiency. The invention constructs dynamic efficiency models for both vanadium redox flow batteries and lithium-ion batteries, which change dynamically with charge / discharge power and state of charge (SOC), and establishes an SOC update equation considering dynamic efficiency. Based on the dynamic efficiency model, it completes high- and low-frequency decomposition of net load power and power boundary verification and correction allocation. A capacity optimization configuration model is constructed with the objective of minimizing the overall cost over the system's entire lifecycle, and a heuristic optimization algorithm is used to solve for the optimal configuration scheme. This invention achieves precise energy storage capacity configuration, ensures stable operation of microgrids, and significantly reduces the overall cost over the system's entire lifecycle.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage technology, and more specifically, to a method and system for optimizing the configuration of vanadium-lithium hybrid energy storage capacity taking efficiency into account. Background Technology

[0002] With the high proportion of renewable energy sources such as wind and solar power integrated into microgrids, the randomness and volatility of their output pose a severe challenge to the stable operation of power systems. Configuring energy storage systems has become a key means to mitigate power fluctuations and achieve source-load balance. However, a single type of energy storage technology cannot simultaneously meet the system's multiple demands for high power and large capacity: lithium-ion batteries have high power density and fast response speed, but are prone to accelerated aging under frequent alternating operating conditions; vanadium redox flow batteries have long cycle life and easily expandable capacity, but have higher initial investment and relatively slow response. Therefore, combining vanadium redox flow batteries with lithium-ion batteries to form a vanadium-lithium hybrid energy storage system can fully leverage the technological advantages of both to achieve complementary performance. How to scientifically and rationally optimize the capacity configuration of this system to balance system reliability and economic costs has become a crucial issue that urgently needs to be addressed.

[0003] Existing hybrid energy storage system capacity configuration schemes are typically based on historical source-load output data, with the objective function of minimizing the overall cost over the system's entire lifecycle. They combine power balance and State of Charge (SOC) constraints, using optimization algorithms to determine the optimal rated power and capacity of the two energy storage devices. However, when establishing energy storage operation models and allocating power, existing schemes generally employ constant efficiency models. This means that the charge-discharge efficiency of both vanadium redox flow batteries and lithium batteries is assumed to be fixed constant values ​​throughout the entire optimization cycle. The calculation of charge-discharge power and SOC updates at any given time are derived based on these fixed efficiency constants, which serve as the basis for the final capacity configuration.

[0004] However, under actual operating conditions, the charge-discharge efficiency of vanadium redox flow batteries and lithium-ion batteries is not a constant value, but rather varies significantly dynamically with real-time charge-discharge power and State of Charge (SOC). Ignoring this nonlinear dynamic characteristic leads to large cumulative errors in the calculation of the actual power exchange between the energy storage system and the microgrid, as well as the system's SOC state, in the optimization model. This error directly weakens the accuracy of capacity configuration results, easily leading to over-configuration or under-configuration of the energy storage system, increasing initial investment and life-cycle redundancy costs, failing to meet actual power smoothing requirements, and increasing wind / solar curtailment rates or power shortage penalty costs. This makes it difficult for the system to achieve true economic optimization and reliable operation. Therefore, there is an urgent need to provide an efficiency-based vanadium-lithium hybrid energy storage capacity optimization configuration method and system to solve the above problems. Summary of the Invention

[0005] To address the aforementioned technical issues, this invention provides a method and system for optimizing the capacity configuration of vanadium-lithium hybrid energy storage, taking efficiency into account. It establishes an operational model for both vanadium redox flow batteries and lithium batteries that accurately reflects the dynamic changes in charge / discharge efficiency with power and state of charge (SOC), and deeply integrates this model into the real-time power allocation strategy and overall lifecycle cost optimization objectives of the hybrid energy storage system. This method effectively corrects the calculation biases caused by the traditional constant efficiency assumption, achieving precise and on-demand configuration of energy storage capacity. This ensures the safe and stable operation of the microgrid while minimizing the overall lifecycle cost of the system.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] This invention provides a method for optimizing the capacity configuration of vanadium-lithium hybrid energy storage considering efficiency, applicable to a vanadium-lithium hybrid energy storage system in a microgrid connected to renewable energy sources. The vanadium-lithium hybrid energy storage system includes a vanadium redox flow battery and a lithium-ion battery, and includes the following steps:

[0008] S1. Establish dynamic efficiency models for vanadium redox flow batteries and lithium-ion batteries respectively. Based on the dynamic efficiency models for vanadium redox flow batteries and lithium-ion batteries, establish respective state-of-charge update equations that take into account dynamic efficiency.

[0009] S2. Obtain the load demand and renewable energy output data of the microgrid, and calculate the net load power; use a filtering algorithm to decompose the net load power into high and low frequencies to obtain the initial reference power of the vanadium redox flow battery and the initial reference power of the lithium-ion battery; based on the dynamic efficiency model of the vanadium redox flow battery, the dynamic efficiency model of the lithium-ion battery, and the current state of charge of the two energy storage devices, perform physical boundary constraint verification and power deficit compensation correction on the initial reference power to determine the actual charging and discharging power of the two energy storage devices and the unbalanced power of the system;

[0010] S3. Using the rated power and rated capacity of vanadium redox flow batteries and lithium-ion batteries as decision variables, and minimizing the overall cost of the system throughout its entire life cycle as the objective function, a capacity optimization configuration model is constructed by combining power balance, charge and discharge power, state of charge and consistency of initial and final states.

[0011] S4. The capacity optimization configuration model is iteratively solved using a heuristic optimization algorithm to output the optimal rated power and optimal rated capacity configuration scheme that minimizes the overall cost over the entire life cycle.

[0012] As a preferred embodiment of the present invention, the dynamic efficiency model of the vanadium redox flow battery is established based on its equivalent internal resistance and circulation pump loss characteristics, and the dynamic efficiency model of the lithium-ion battery is established based on the nonlinear characteristics of its equivalent internal resistance changing with the state of charge.

[0013] As a preferred embodiment of the present invention, in S2, the low-frequency power component after the net load power decomposition is used as the initial reference power of the vanadium redox flow battery, and the high-frequency power component after the net load power decomposition is used as the initial reference power of the lithium-ion battery.

[0014] The physical boundary constraint verification and power deficit compensation correction performed in S2 are specifically as follows:

[0015] The maximum available charge and discharge power boundary of the vanadium redox flow battery is determined based on its rated power and current state of charge. The initial reference power of the vanadium redox flow battery is compared with the maximum available charge and discharge power boundary to determine the actual charge and discharge power of the vanadium redox flow battery and to calculate the power deficit that cannot be met.

[0016] The power deficit is superimposed on the initial reference power of the lithium-ion battery to obtain the corrected reference power. The maximum available charge and discharge power boundary is determined based on the rated power and current state of charge of the lithium-ion battery. The corrected reference power is compared with the maximum available charge and discharge power boundary to determine the actual charge and discharge power of the lithium-ion battery.

[0017] The system power shortage or power wastage is determined based on the difference between the actual charge / discharge power of the vanadium redox flow battery, the actual charge / discharge power of the lithium-ion battery, and the net load power.

[0018] As a preferred embodiment of the present invention, step S3 specifically includes:

[0019] S31. Construct a comprehensive lifecycle cost objective function, wherein the comprehensive lifecycle cost includes initial investment cost, equipment replacement cost, operation and maintenance cost, and supply-demand mismatch penalty cost; wherein the operation and maintenance cost is calculated based on the actual charging and discharging power determined in step S2;

[0020] S32. Establish a power balance constraint that requires the load demand minus the renewable energy output to equal the algebraic sum of the actual charging and discharging power of the two energy storage devices and the power shortage or curtailment power of the system.

[0021] S33. Establish energy storage charging and discharging power constraints, requiring that the absolute value of the real-time charging and discharging power of the two energy storage devices does not exceed the rated power of their optimized configuration;

[0022] S34. Establish state of charge constraints, requiring that the real-time state of charge of the two energy storage devices be within the safe operating range. The real-time state of charge is calculated by the state of charge update equation considering dynamic efficiency established in S1.

[0023] S35. Establish a consistency constraint between the initial and final states, requiring that the state of charge of the energy storage system at the end of a complete scheduling cycle is equal to the initial state of charge.

[0024] As a preferred embodiment of the present invention, step S4 specifically includes:

[0025] S41. Set the basic operating parameters of the heuristic optimization algorithm, map the decision variables to be optimized to the position vector of the optimization individuals of the algorithm, and complete the initialization of the optimization population within the physical allowable range of the energy storage device.

[0026] S42. Set the comprehensive cost of the entire life cycle of the hybrid energy storage system as the fitness function of the algorithm, introduce a penalty function mechanism to handle the constraints, and add a penalty term to the optimization solution that violates the operating constraints to limit the algorithm to optimize within the feasible solution interval.

[0027] S43. In each iteration, for each optimization individual in the population, complete the operation simulation of the microgrid's full scheduling cycle and calculate the fitness value corresponding to the configuration scheme.

[0028] S44. Compare the current fitness value of each optimization individual with the historical best value, update the local best position of the individual, and at the same time compare the fitness values ​​of all optimization individuals in the population, update the global best position, and complete the iterative update of optimization individuals.

[0029] S45. Determine whether the algorithm meets the preset termination condition. If it does, stop the optimization and output the optimal rated power and optimal rated capacity configuration scheme of the vanadium redox flow battery and lithium-ion battery corresponding to the global optimal position.

[0030] As a preferred embodiment of the present invention, the dynamic efficiency model of the all-vanadium redox flow battery expresses the equivalent power loss inside the stack as a function of the operating current and the equivalent internal resistance, and introduces the parasitic power loss of the circulating pump to establish a unified dynamic efficiency model; a charge and discharge state identifier variable is introduced to distinguish between the charging state and the discharging state, and the dynamic efficiency under different states is calculated respectively.

[0031] As a preferred embodiment of the present invention, the lithium-ion battery dynamic efficiency model is constructed by using a polynomial fitting method to construct a dynamic function of the equivalent internal resistance of the lithium-ion battery as a function of the real-time state of charge. The dynamic function is then substituted into the internal ohmic loss power calculation, and a charge / discharge state identifier variable is introduced to establish a unified dynamic efficiency model.

[0032] As a preferred embodiment of the present invention, the comprehensive life cycle cost mentioned in S31 includes four sub-items: initial investment cost of energy storage equipment, equipment replacement cost, operation and maintenance cost, and penalty cost caused by microgrid supply and demand mismatch; wherein the initial investment cost includes the power investment cost and capacity investment cost of vanadium redox flow battery and lithium-ion battery; the equipment replacement cost is set only for lithium-ion battery; the operation and maintenance cost is calculated based on the real-time charging and discharging power of the two types of energy storage equipment during the whole dispatch cycle; and the penalty cost is calculated based on the system power shortage power and power curtailment power during the whole dispatch cycle.

[0033] The present invention also provides an efficiency-based vanadium-lithium hybrid energy storage capacity optimization configuration system, comprising:

[0034] The dynamic efficiency modeling module is used to establish dynamic efficiency models for vanadium redox flow batteries and lithium-ion batteries respectively, and to establish respective state-of-charge update equations that take into account dynamic efficiency based on the dynamic efficiency models for vanadium redox flow batteries and lithium-ion batteries.

[0035] The power allocation optimization module is used to acquire the load demand and renewable energy output data of the microgrid and calculate the net load power. A filtering algorithm is used to decompose the net load power into high and low frequencies to obtain the initial reference power of the vanadium redox flow battery and the initial reference power of the lithium-ion battery. Based on the dynamic efficiency model of the vanadium redox flow battery, the dynamic efficiency model of the lithium-ion battery, and the current state of charge of the two energy storage devices, the initial reference power is checked for physical boundary constraints and corrected for power deficit compensation to determine the actual charging and discharging power of the two energy storage devices and the unbalanced power of the system.

[0036] The capacity optimization modeling module is used to construct a capacity optimization configuration model with the rated power and rated capacity of vanadium redox flow batteries and lithium-ion batteries as decision variables, the goal function being to minimize the overall cost of the system throughout its entire life cycle, and in combination with constraints such as power balance, charge and discharge power, state of charge, and consistency between the initial and final states.

[0037] The optimization solution module is used to iteratively solve the capacity optimization configuration model using a heuristic optimization algorithm, and output the optimal rated power and optimal rated capacity configuration scheme that minimizes the overall cost over the entire life cycle.

[0038] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the above-described method for optimizing the capacity configuration of vanadium-lithium hybrid energy storage taking efficiency into account.

[0039] The beneficial technical effects of this invention are:

[0040] 1. Significantly improves the accuracy and scientific rigor of hybrid energy storage capacity configuration, fundamentally solving the configuration distortion problem of traditional methods. This invention abandons the simplistic assumption in existing technologies that treat energy storage charge and discharge efficiency as a fixed constant. It establishes dedicated dynamic efficiency models for the operating loss characteristics of vanadium redox flow batteries and lithium-ion batteries, respectively, and constructs corresponding state-of-charge update equations based on the dynamic efficiency models. It accurately quantifies the nonlinear impact of real-time changes in charge and discharge power and state of charge on energy storage charge and discharge efficiency, effectively eliminating the power accumulation error generated by traditional constant efficiency models in long-term scheduling calculations. It avoids investment redundancy caused by over-configuration of energy storage systems or the lack of power smoothing capability caused by under-configuration, ensuring that the final capacity configuration result closely matches the actual physical operating conditions of energy storage devices, guaranteeing the scientific rigor and feasibility of the configuration scheme.

[0041] 2. Extends the overall service life of the hybrid energy storage system and improves operational stability. This invention uses a filtering algorithm to decompose the net load power of the microgrid into high and low frequencies, matching the large capacity and long cycle life of vanadium redox flow batteries with the high response and fast adjustment of lithium-ion batteries. This achieves a complementary advantage where low-frequency fluctuations are handled by vanadium redox flow batteries and high-frequency fluctuations are smoothed by lithium-ion batteries. Simultaneously, during power allocation, a dynamic efficiency model and the real-time state of charge of the energy storage devices are combined to perform physical boundary constraint verification and power deficit compensation correction on the initial reference power. This avoids equipment damage caused by the energy storage devices operating beyond their power limits and significantly reduces deep charge-discharge cycles of lithium-ion batteries under extreme conditions, effectively delaying battery aging and ensuring the long-term stability and reliability of the hybrid energy storage system.

[0042] 3. This invention achieves true optimization of the comprehensive economic benefits of the hybrid energy storage system throughout its entire lifecycle, demonstrating significant engineering and economic value. Using the rated power and capacity of vanadium redox flow batteries and lithium-ion batteries as decision variables, and minimizing the comprehensive cost throughout the system's lifecycle as the optimization objective, this invention constructs a comprehensive cost optimization model covering initial investment, equipment replacement, operation and maintenance, and supply-demand mismatch penalties. It integrates multiple constraints of system operation to build a capacity optimization configuration model and uses a heuristic optimization algorithm to achieve global optimization. Simultaneously, relying on the underlying dynamic efficiency model to accurately correct operating data, the cost accounting and constraint verification during capacity optimization closely match the actual operating state of the energy storage equipment. The final output capacity configuration scheme is no longer a theoretical pseudo-optimal obtained based on simplified assumptions in traditional methods, but a true optimal solution that simultaneously meets power smoothing requirements and lifecycle cost control in practical microgrid engineering applications. This minimizes the investment and operating costs throughout the system's lifecycle, demonstrating outstanding economic advantages and engineering promotion value. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the overall process of the present invention.

[0044] Figure 2 This is a schematic diagram of the power allocation strategy process in this invention.

[0045] Figure 3 This is a topology diagram of the microgrid system in this invention. Detailed Implementation

[0046] In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0047] Combination Figures 1-3 The present invention provides the following embodiments:

[0048] Example 1:

[0049] A method for optimizing the capacity configuration of vanadium-lithium hybrid energy storage considering efficiency, applied to a vanadium-lithium hybrid energy storage system in a microgrid connected to renewable energy sources, wherein the vanadium-lithium hybrid energy storage system includes a vanadium redox flow battery and a lithium-ion battery, comprising the following steps:

[0050] S1. Establish dynamic efficiency models for vanadium redox flow batteries and lithium-ion batteries respectively. Based on the dynamic efficiency models for vanadium redox flow batteries and lithium-ion batteries, establish respective state-of-charge update equations that take into account dynamic efficiency.

[0051] S2. Obtain the load demand and renewable energy output data of the microgrid, and calculate the net load power; use a filtering algorithm to decompose the net load power into high and low frequencies to obtain the initial reference power of the vanadium redox flow battery and the initial reference power of the lithium-ion battery; based on the dynamic efficiency model of the vanadium redox flow battery, the dynamic efficiency model of the lithium-ion battery, and the current state of charge of the two energy storage devices, perform physical boundary constraint verification and power deficit compensation correction on the initial reference power to determine the actual charging and discharging power of the two energy storage devices and the unbalanced power of the system;

[0052] S3. Using the rated power and rated capacity of vanadium redox flow batteries and lithium-ion batteries as decision variables, and minimizing the overall cost of the system throughout its entire life cycle as the objective function, a capacity optimization configuration model is constructed by combining power balance, charge and discharge power, state of charge and consistency of initial and final states.

[0053] S4. The capacity optimization configuration model is iteratively solved using a heuristic optimization algorithm to output the optimal rated power and optimal rated capacity configuration scheme that minimizes the overall cost over the entire life cycle.

[0054] Furthermore, the dynamic efficiency model of the vanadium redox flow battery is established based on its equivalent internal resistance and circulation pump loss characteristics, while the dynamic efficiency model of the lithium-ion battery is established based on the nonlinear characteristics of its equivalent internal resistance changing with the state of charge.

[0055] A dedicated dynamic efficiency model is constructed to address the differences in the core loss mechanisms between vanadium redox flow batteries and lithium-ion batteries. This model closely reflects the actual operating physical characteristics of the two types of energy storage devices, avoiding the distortion caused by using a uniform constant efficiency to characterize the efficiency of energy storage devices with different loss characteristics. This provides a realistic basis of efficiency data for subsequent accurate updates of state of charge and power allocation.

[0056] Furthermore, the dynamic efficiency model of the vanadium redox flow battery expresses the equivalent power loss inside the stack as a function of the operating current and the equivalent internal resistance, and introduces the parasitic power loss of the circulating pump to establish a unified dynamic efficiency model; it introduces a charge and discharge state identifier variable to distinguish between the charging state and the discharging state, and calculates the dynamic efficiency under different states respectively.

[0057] It comprehensively covers the core loss sources of vanadium redox flow battery stack internal resistance and circulation pump, and realizes unified modeling and differentiated calculation of efficiency under charging and discharging conditions by identifying variables of charging and discharging status. It accurately quantifies the actual efficiency changes under different operating conditions and eliminates the efficiency calculation deviation caused by ignoring loss sources or differences in operating conditions.

[0058] Furthermore, the lithium-ion battery dynamic efficiency model uses a polynomial fitting method to construct a dynamic function of the equivalent internal resistance of the lithium-ion battery as a function of the real-time state of charge. The dynamic function is then substituted into the internal ohmic loss power calculation, and a charge / discharge state identifier variable is introduced to establish a unified dynamic efficiency model.

[0059] By using polynomial fitting, the nonlinear correlation between the internal resistance and state of charge of lithium-ion batteries is accurately characterized. Dynamic internal resistance is incorporated into the calculation of ohmic loss, and combined with charge and discharge state indicators, full-condition efficiency modeling is achieved. This accurately reflects the real-time impact of changes in state of charge on the charge and discharge efficiency of lithium-ion batteries, avoiding efficiency calculation errors caused by the assumption of fixed internal resistance.

[0060] Furthermore, in S2, the low-frequency power component after the net load power decomposition is used as the initial reference power of the vanadium redox flow battery, and the high-frequency power component after the net load power decomposition is used as the initial reference power of the lithium-ion battery.

[0061] The physical boundary constraint verification and power deficit compensation correction performed in S2 are specifically as follows:

[0062] The maximum available charge and discharge power boundary of the vanadium redox flow battery is determined based on its rated power and current state of charge. The initial reference power of the vanadium redox flow battery is compared with the maximum available charge and discharge power boundary to determine the actual charge and discharge power of the vanadium redox flow battery and to calculate the power deficit that cannot be met.

[0063] The power deficit is superimposed on the initial reference power of the lithium-ion battery to obtain the corrected reference power. The maximum available charge and discharge power boundary is determined based on the rated power and current state of charge of the lithium-ion battery. The corrected reference power is compared with the maximum available charge and discharge power boundary to determine the actual charge and discharge power of the lithium-ion battery.

[0064] The system power shortage or power wastage is determined based on the difference between the actual charge / discharge power of the vanadium redox flow battery, the actual charge / discharge power of the lithium-ion battery, and the net load power.

[0065] Based on the technical characteristics of the two types of energy storage devices, high and low frequency power components are matched to achieve the basic allocation of complementary advantages of vanadium-lithium hybrid energy storage; physical boundary constraint verification is used to avoid the operation of energy storage devices in over-power and over-charge states, and power deficit compensation correction is combined to achieve synergistic compensation between the two types of energy storage, accurately calculate the actual charging and discharging power and the unbalanced power of the system, and provide real operating data support for subsequent cost accounting.

[0066] Furthermore, the specific steps of S3 include:

[0067] S31. Construct a comprehensive lifecycle cost objective function, wherein the comprehensive lifecycle cost includes initial investment cost, equipment replacement cost, operation and maintenance cost, and supply-demand mismatch penalty cost; wherein the operation and maintenance cost is calculated based on the actual charging and discharging power determined in step S2;

[0068] S32. Establish a power balance constraint that requires the load demand minus the renewable energy output to equal the algebraic sum of the actual charging and discharging power of the two energy storage devices and the power shortage or curtailment power of the system.

[0069] S33. Establish energy storage charging and discharging power constraints, requiring that the absolute value of the real-time charging and discharging power of the two energy storage devices does not exceed the rated power of their optimized configuration;

[0070] S34. Establish state of charge constraints, requiring that the real-time state of charge of the two energy storage devices be within the safe operating range. The real-time state of charge is calculated by the state of charge update equation considering dynamic efficiency established in S1.

[0071] S35. Establish a consistency constraint between the initial and final states, requiring that the state of charge of the energy storage system at the end of a complete scheduling cycle is equal to the initial state of charge.

[0072] A comprehensive cost objective function covering the entire life cycle is constructed, and the cost is calculated in combination with actual operation data to make the objective more in line with engineering practice. The optimization boundary is limited layer by layer by multi-dimensional constraints. Power balance constraints ensure the power matching of microgrid source and load, power and state of charge constraints ensure the safe operation of energy storage equipment, and the consistency of the initial and final states ensures the feasibility of cyclic scheduling of energy storage system, so as to ensure that the solution of the optimization model is both economical and practical for engineering.

[0073] Furthermore, the comprehensive life-cycle cost described in S31 includes four sub-items: initial investment cost of energy storage equipment, equipment replacement cost, operation and maintenance cost, and penalty cost caused by microgrid supply and demand mismatch. The initial investment cost includes the power investment cost and capacity investment cost of vanadium redox flow batteries and lithium-ion batteries. The equipment replacement cost is only set for lithium-ion batteries. The operation and maintenance cost is calculated based on the real-time charging and discharging power of the two types of energy storage equipment during the entire dispatch cycle. The penalty cost is calculated based on the system power shortage power and power curtailment power during the entire dispatch cycle.

[0074] Cost items are set according to the differences in lifespan characteristics of the two types of energy storage devices, which is in line with the actual engineering characteristics of lithium batteries being prone to aging and vanadium redox flow batteries having a long lifespan. The operation and maintenance and penalty costs are calculated based on real-time operating data, replacing the traditional static calculation method based on rated parameters, making cost calculation more accurate and ensuring that the objective function can truly reflect the actual economic expenditure of the system throughout its entire life cycle.

[0075] Furthermore, the specific steps of S4 include:

[0076] S41. Set the basic operating parameters of the heuristic optimization algorithm, map the decision variables to be optimized to the position vector of the optimization individuals of the algorithm, and complete the initialization of the optimization population within the physical allowable range of the energy storage device.

[0077] S42. Set the comprehensive cost of the entire life cycle of the hybrid energy storage system as the fitness function of the algorithm, introduce a penalty function mechanism to handle the constraints, and add a penalty term to the optimization solution that violates the operating constraints to limit the algorithm to optimize within the feasible solution interval.

[0078] S43. In each iteration, for each optimization individual in the population, complete the operation simulation of the microgrid's full scheduling cycle and calculate the fitness value corresponding to the configuration scheme.

[0079] S44. Compare the current fitness value of each optimization individual with the historical best value, update the local best position of the individual, and at the same time compare the fitness values ​​of all optimization individuals in the population, update the global best position, and complete the iterative update of optimization individuals.

[0080] S45. Determine whether the algorithm meets the preset termination condition. If it does, stop the optimization and output the optimal rated power and optimal rated capacity configuration scheme of the vanadium redox flow battery and lithium-ion battery corresponding to the global optimal position.

[0081] By mapping capacity configuration decision variables to individual optimization algorithms, algorithm adaptation for nonlinear multivariate optimization problems is achieved. Infeasible solutions are filtered out through a penalty function mechanism to ensure that the optimization process always operates within the range of equipment safety and system operational feasibility. Fitness values ​​are calculated by combining full scheduling cycle operation simulation to make the algorithm evaluation criteria fit the actual operating effect. Global optimization is achieved through iterative updates of individual and global optimal solutions, and finally, the optimal capacity configuration scheme that combines economy and feasibility is output.

[0082] Example 2:

[0083] A vanadium-lithium hybrid energy storage capacity optimization configuration system considering efficiency, comprising:

[0084] The dynamic efficiency modeling module is used to establish dynamic efficiency models for vanadium redox flow batteries and lithium-ion batteries respectively, and to establish respective state-of-charge update equations that take into account dynamic efficiency based on the dynamic efficiency models for vanadium redox flow batteries and lithium-ion batteries.

[0085] The power allocation optimization module is used to acquire the load demand and renewable energy output data of the microgrid and calculate the net load power. A filtering algorithm is used to decompose the net load power into high and low frequencies to obtain the initial reference power of the vanadium redox flow battery and the initial reference power of the lithium-ion battery. Based on the dynamic efficiency model of the vanadium redox flow battery, the dynamic efficiency model of the lithium-ion battery, and the current state of charge of the two energy storage devices, the initial reference power is checked for physical boundary constraints and corrected for power deficit compensation to determine the actual charging and discharging power of the two energy storage devices and the unbalanced power of the system.

[0086] The capacity optimization modeling module is used to construct a capacity optimization configuration model with the rated power and rated capacity of vanadium redox flow batteries and lithium-ion batteries as decision variables, the goal function being to minimize the overall cost of the system throughout its entire life cycle, and in combination with constraints such as power balance, charge and discharge power, state of charge, and consistency between the initial and final states.

[0087] The optimization solution module is used to iteratively solve the capacity optimization configuration model using a heuristic optimization algorithm, and output the optimal rated power and optimal rated capacity configuration scheme that minimizes the overall cost over the entire life cycle.

[0088] Application example:

[0089] A method for optimizing the capacity configuration of vanadium-lithium hybrid energy storage considering efficiency is proposed. This method is implemented in a microgrid scenario with a high proportion of wind power and photovoltaic power, and the microgrid topology is as follows: Figure 3 As shown. Specifically, it includes the following steps:

[0090] Collect and preprocess microgrid operation data, energy storage device characteristic data, and economic parameters; the microgrid operation data includes at least historical or predicted microgrid load demand data and renewable energy output data; the energy storage device characteristic data includes at least internal loss characteristic parameters, operating boundary parameters, and initial state of charge of vanadium redox flow batteries and lithium-ion batteries; the economic parameters include at least the unit cost of equipment, operation and maintenance cost coefficient, and supply-demand mismatch penalty coefficient; perform time scale unification, missing value processing, and outlier correction on the data to form a standardized input dataset for subsequent dynamic efficiency modeling, power allocation calculation, and capacity optimization solution.

[0091] S1. Construct a dynamic efficiency model and state-of-charge update equation for a vanadium-lithium hybrid energy storage system considering operating conditions.

[0092] This step breaks with the traditional constant efficiency assumption, models the internal loss mechanism of vanadium redox flow batteries and lithium-ion batteries respectively, and derives the state of charge (SOC) update formula taking into account dynamic efficiency.

[0093] S11. Establish a dynamic efficiency model for vanadium redox flow batteries (VRBs).

[0094] The energy loss of a vanadium redox flow battery includes the equivalent internal resistance loss within the stack and the parasitic power loss of the external circulation pump. A charge / discharge state indicator variable is introduced. During charging During discharge ,but Uniform dynamic efficiency of all-vanadium redox flow batteries for:

[0095]

[0096] In the formula: for The actual power of the vanadium redox flow battery at any given moment; for The operating current of the vanadium redox flow battery at all times; The equivalent internal resistance of the vanadium redox flow battery stack; for Parasitic power loss of the circulating pump.

[0097] S12. Establish a dynamic efficiency model for lithium-ion batteries (LIBs).

[0098] The internal resistance of a lithium-ion battery changes nonlinearly with state of charge (SOC). First, a dynamic function of the internal resistance is constructed by fitting experimental data:

[0099]

[0100] In the formula: for The dynamic equivalent internal resistance of a lithium-ion battery at any given time; for The state of charge of the lithium-ion battery at all times; , , These are the coefficients of the internal resistance polynomial obtained by fitting experimental data.

[0101] Introducing charge / discharge status indicator variables During charging Discharge ,but Uniform dynamic efficiency of lithium-ion batteries at all times for:

[0102]

[0103] In the formula: for The actual power of the lithium-ion battery at any given time; for The real-time operating current of the lithium-ion battery.

[0104] S13. Establish the SOC update equation considering dynamic efficiency.

[0105] Substituting the above dynamic efficiency into the SOC update formula, we get:

[0106]

[0107] In the formula: , For example, vanadium redox flow batteries are in , The state of charge at any given moment; , Lithium-ion batteries , The state of charge at any given moment; , These are the rated capacities of the vanadium redox flow battery and the lithium-ion battery, respectively. This is the scheduling time step.

[0108] By changing with operating conditions and Substituting this into the equation effectively eliminates the cumulative power calculation error generated by the traditional constant efficiency model in long-term capacity planning.

[0109] S2. Hybrid energy storage power allocation optimization based on dynamic efficiency

[0110] To fully leverage the energy advantages of vanadium redox flow batteries and the power advantages of lithium batteries, a power allocation strategy based on dynamic efficiency is adopted, and real-time corrections are made by combining the dynamic efficiency and SOC update model in S1. The specific process is shown in the attached figure. Figure 2 As shown.

[0111] S21. Calculate the net load power of the microgrid.

[0112] Get microgrid raw load demand and renewable energy output Calculate net load power :

[0113]

[0114] when When this occurs, it indicates a power deficit in the system, requiring the hybrid energy storage system to discharge; when When this occurs, it indicates that the system has excess power and needs to be charged by the hybrid energy storage system.

[0115] S22, Preliminary power breakdown of the first-order low-pass filter

[0116] Will The low-frequency power is extracted by inputting a first-order low-pass filter and used as the initial reference power for the vanadium redox flow battery. The discretization calculation formula is as follows:

[0117]

[0118] In the formula: This is the filtering time constant factor. It is related to the filter's cutoff frequency and sampling step size; This is the reference power of the all-vanadium redox flow battery at the previous moment.

[0119] The initial reference power of a lithium-ion battery is the net load power minus the low-frequency portion.

[0120]

[0121] S23. Hybrid energy storage power allocation optimization strategy based on dynamic efficiency

[0122] The above is obtained through low-pass filtering. and This is only an ideal reference power. In actual operation, it must be corrected a second time by combining the dynamic efficiency model established by S1 and the physical boundaries of the battery. The specific correction logic and formula are as follows, and it is stipulated that the energy storage discharge power is positive and the charging power is negative:

[0123] (1) Calculate the actual usable power boundary of the all-vanadium redox flow cell:

[0124] exist At any given time, the vanadium redox flow battery is affected by its rated power. and current state of charge The dual constraints. Its maximum usable discharge power. and maximum available charging power They are respectively represented as

[0125]

[0126] In the formula: This is the rated power of the all-vanadium redox flow battery; , These are the upper and lower limits of the safe state of charge (SOC) for vanadium redox flow batteries.

[0127] (2) Calculation of actual charge and discharge power and power deficit of vanadium redox flow battery

[0128] According to the reference power The charge and discharge states of the vanadium redox flow battery were determined to be within the range of [specific parameters]. Actual charge / discharge power at any given time for:

[0129]

[0130] Calculate the power deficit that the all-vanadium redox flow battery fails to meet. :

[0131]

[0132] (3) Calculation of actual charge and discharge power of lithium-ion battery and system deficit

[0133] When a vanadium redox flow battery experiences a power deficit, this deficit is used as a compensation command and superimposed onto the faster-responding lithium-ion battery to obtain a corrected reference power for the lithium battery. :

[0134]

[0135] Similarly, the calculation of lithium battery rated power and current state of charge Maximum available discharge power under constraint and maximum available charging power :

[0136]

[0137] In the formula: This refers to the rated power of the lithium-ion battery. , These are the upper and lower limits of the safe state of charge (SOC) for lithium-ion batteries.

[0138] Determine the lithium battery in Actual charge / discharge power at any given time for:

[0139]

[0140] (4) Calculate the power shortage and power abandonment of the microgrid system.

[0141] After all components of the hybrid energy storage system reach their output limits, calculate the final unbalanced power of the system. :

[0142]

[0143] when At this time, power shortage occurs. abandoned power ;when At that time, abandoned power is generated. Power shortage .

[0144] S3. Construct a capacity optimization configuration model

[0145] Based on the established dynamic efficiency model and power allocation strategy, this invention utilizes the rated power of vanadium redox flow batteries and lithium-ion batteries. , With rated capacity , As the decision variables to be optimized, a comprehensive cost objective function and operational constraints are constructed.

[0146] S31, Objective Function

[0147] Taking into account the initial investment, equipment replacement, operation and maintenance, and the supply and demand mismatch penalty of the microgrid, the comprehensive life cycle cost of the system is calculated using the equivalent annual value. Minimize as the optimization objective:

[0148]

[0149] (1) Initial investment cost

[0150]

[0151] In the formula: , The unit power cost of vanadium redox flow batteries and lithium batteries are respectively. , The unit capacity costs are respectively for vanadium redox flow batteries and lithium batteries; For the discount rate, this embodiment takes... ; The project's planned operating life is defined as 10 years; the fractional part is the capital recovery coefficient, used to convert the total investment into an equivalent value over 10 years.

[0152] (2) Equipment replacement cost

[0153] Because lithium batteries are extremely prone to lifespan degradation under high-frequency fluctuations, and their cycle life is usually less than the project's planned lifespan. Therefore, replacement costs need to be considered. The lifespan of a full vanadium redox flow battery is usually equivalent to the project cycle, so replacement is not necessary.

[0154]

[0155] In the formula: The number of lithium battery replacements during the project cycle; This represents the actual service life of a lithium battery, as assessed based on the actual depth of charge and discharge.

[0156] (3) Operation and maintenance costs

[0157] Unlike traditional methods that calculate operation and maintenance costs based on fixed capacity, this method uses real-time dynamic charging and discharging power obtained in S2 for precise calculations.

[0158]

[0159] In the formula: , These are the maintenance coefficients for the unit throughput of vanadium redox flow batteries and lithium batteries, respectively. This represents the total number of typical intraday scheduling cycles.

[0160] (4) Penalty costs

[0161]

[0162] In the formula: , These are the unit penalty coefficients for power shortage and power abandonment, respectively.

[0163] S32, Constraints

[0164] To ensure the safe and stable operation of the energy storage system and the microgrid, the optimization model must meet the following constraints:

[0165] (1) Power balance constraint

[0166]

[0167] The load demand minus the output of new energy sources must be strictly matched with the actual output of energy storage and the imbalance between supply and demand in the system.

[0168] (2) Charging and discharging power constraints

[0169]

[0170] The real-time charging and discharging power of the equipment must not exceed the rated power of its optimized configuration.

[0171] Combining dynamic efficiency in S1 Update the formula to ensure that the system does not overcharge or over-discharge at any time.

[0172] (3) SOC constraint

[0173]

[0174] Combining dynamic efficiency in S1 Update the formula to ensure that the system does not overcharge or over-discharge at any time.

[0175] (4) Consistency constraint between initial and final states

[0176]

[0177] Ensure that the energy storage system can be restored to its initial power state after a complete typical daily dispatch cycle to meet the needs of the next day's cycle operation.

[0178] S4. Solving the model based on the Particle Swarm Optimization (PSO) algorithm

[0179] The constructed lifecycle integrated cost optimization model, which considers dynamic efficiency, is a typical nonlinear, multivariable optimization problem. This invention employs the Particle Swarm Optimization (PSO) algorithm to solve this model, with the specific steps as follows:

[0180] S41. Algorithm Parameters and Population Initialization

[0181] Setting PSO parameters: Population size Maximum number of iterations Inertial weight Learning factor and .

[0182] A particle swarm is constructed, and the rated power and rated capacity of the vanadium redox flow battery and lithium-ion battery to be optimized are set as the position vectors of the particles. ,in Number the particles. The position and initial velocity of each particle are randomly initialized within the physical limits of the device.

[0183] S42. Fitness Function and Constraint Handling

[0184] Objective function As the fitness function, the objective is to find the position vector with the minimum fitness value. Since the charging / discharging boundary and SOC constraints must be strictly satisfied during the configuration process, this invention introduces an external penalty function method to handle the constraints. If a particle exceeds the power limit or SOC limit during the simulation, a very large penalty term is superimposed on its fitness value, forcing the algorithm to eliminate the infeasible solution and search for a safe operating region.

[0185] S43. Microgrid Operation Simulation Combining Dynamic Efficiency and Allocation Strategies

[0186] In each iteration, for each capacity configuration scheme represented by a particle, source-load prediction data for a typical day (or year) of the microgrid are input:

[0187] (1) Call the power allocation strategy of S2 to calculate the ideal reference power of the two energy storage systems at each time under this configuration;

[0188] (2) Call the dynamic efficiency model and SOC update model of S1, perform boundary verification and power secondary correction, and calculate the real charging and discharging power and real-time SOC.

[0189] (3) Based on actual operating data, calculate the initial investment cost, dynamic operation and maintenance cost, replacement cost and penalty cost of the particle, and obtain the final fitness value. .

[0190] S44, Iterative Update of Particle Velocity and Position

[0191] Compare each particle's current fitness value with its historical best fitness value to update the individual's local optimum position. Compare the fitness values ​​of all particles in the population to find the global optimum. .

[0192] Subsequently, the velocity and position of the next generation of particles are updated according to the PSO core formula, driving the population to evolve towards a better capacity ratio.

[0193] S45. Algorithm Termination and Output of Optimal Result

[0194] Determine if the algorithm has reached the maximum number of iterations. If the optimal fitness value changes less than a set threshold after multiple consecutive iterations, the optimization process stops and the global optimal position is output if the termination condition is met. The location corresponding to this position This is the optimal capacity configuration scheme.

[0195] It should be noted that the above embodiments are merely preferred embodiments of the present invention, and the present invention is not limited to the above embodiments. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention. For example, heuristic optimization algorithms can also employ genetic algorithms, simulated annealing algorithms, etc., and filtering algorithms can also employ other high- and low-frequency decomposition methods such as wavelet transform.

[0196] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing the capacity configuration of vanadium-lithium hybrid energy storage considering efficiency, applied to a vanadium-lithium hybrid energy storage system in a microgrid connected to renewable energy sources, wherein the vanadium-lithium hybrid energy storage system comprises a vanadium redox flow battery and a lithium-ion battery, characterized in that, Includes the following steps: S1. Establish dynamic efficiency models for vanadium redox flow batteries and lithium-ion batteries respectively. Based on the dynamic efficiency models for vanadium redox flow batteries and lithium-ion batteries, establish respective state-of-charge update equations that take into account dynamic efficiency. S2. Obtain the load demand and renewable energy output data of the microgrid, and calculate the net load power; use a filtering algorithm to decompose the net load power into high and low frequencies to obtain the initial reference power of the vanadium redox flow battery and the initial reference power of the lithium-ion battery. Based on the dynamic efficiency model of the vanadium redox flow battery, the dynamic efficiency model of the lithium-ion battery, and the current state of charge of the two energy storage devices, the initial reference power is verified by physical boundary constraints and corrected by power deficit compensation to determine the actual charging and discharging power of the two energy storage devices and the unbalanced power of the system. S3. Using the rated power and rated capacity of vanadium redox flow batteries and lithium-ion batteries as decision variables, and minimizing the overall cost of the system throughout its entire life cycle as the objective function, a capacity optimization configuration model is constructed by combining power balance, charge and discharge power, state of charge and consistency of initial and final states. S4. The capacity optimization configuration model is iteratively solved using a heuristic optimization algorithm to output the optimal rated power and optimal rated capacity configuration scheme that minimizes the overall cost over the entire life cycle.

2. The method for optimizing the capacity configuration of vanadium-lithium hybrid energy storage considering efficiency according to claim 1, characterized in that, The dynamic efficiency model of the vanadium redox flow battery is established based on its equivalent internal resistance and circulation pump loss characteristics, while the dynamic efficiency model of the lithium-ion battery is established based on the nonlinear characteristics of its equivalent internal resistance changing with the state of charge.

3. The method for optimizing the capacity configuration of vanadium-lithium hybrid energy storage considering efficiency according to claim 1, characterized in that, In S2, the low-frequency power component after the net load power is decomposed is used as the initial reference power of the vanadium redox flow battery, and the high-frequency power component after the net load power is decomposed is used as the initial reference power of the lithium-ion battery. The physical boundary constraint verification and power deficit compensation correction performed in S2 are specifically as follows: The maximum available charge and discharge power boundary of the vanadium redox flow battery is determined based on its rated power and current state of charge. The initial reference power of the vanadium redox flow battery is compared with the maximum available charge and discharge power boundary to determine the actual charge and discharge power of the vanadium redox flow battery and to calculate the power deficit that cannot be met. The power deficit is superimposed on the initial reference power of the lithium-ion battery to obtain the corrected reference power. The maximum available charge and discharge power boundary is determined based on the rated power and current state of charge of the lithium-ion battery. The corrected reference power is compared with the maximum available charge and discharge power boundary to determine the actual charge and discharge power of the lithium-ion battery. The system power shortage or power wastage is determined based on the difference between the actual charge / discharge power of the vanadium redox flow battery, the actual charge / discharge power of the lithium-ion battery, and the net load power.

4. The method for optimizing the capacity configuration of vanadium-lithium hybrid energy storage considering efficiency according to claim 1, characterized in that, The specific steps in S3 include: S31. Construct a comprehensive lifecycle cost objective function, wherein the comprehensive lifecycle cost includes initial investment cost, equipment replacement cost, operation and maintenance cost, and supply-demand mismatch penalty cost; wherein the operation and maintenance cost is calculated based on the actual charging and discharging power determined in step S2; S32. Establish a power balance constraint that requires the load demand minus the renewable energy output to equal the algebraic sum of the actual charging and discharging power of the two energy storage devices and the power shortage or curtailment power of the system. S33. Establish energy storage charging and discharging power constraints, requiring that the absolute value of the real-time charging and discharging power of the two energy storage devices does not exceed the rated power of their optimized configuration; S34. Establish state of charge constraints, requiring that the real-time state of charge of the two energy storage devices be within the safe operating range. The real-time state of charge is calculated by the state of charge update equation considering dynamic efficiency established in S1. S35. Establish a consistency constraint between the initial and final states, requiring that the state of charge of the energy storage system at the end of a complete scheduling cycle is equal to the initial state of charge.

5. The method for optimizing the capacity configuration of vanadium-lithium hybrid energy storage considering efficiency according to claim 1, characterized in that, The specific steps in S4 include: S41. Set the basic operating parameters of the heuristic optimization algorithm, map the decision variables to be optimized to the position vector of the optimization individuals of the algorithm, and complete the initialization of the optimization population within the physical allowable range of the energy storage device. S42. Set the comprehensive cost of the entire life cycle of the hybrid energy storage system as the fitness function of the algorithm, introduce a penalty function mechanism to handle the constraints, and add a penalty term to the optimization solution that violates the operating constraints to limit the algorithm to optimize within the feasible solution interval. S43. In each iteration, for each optimization individual in the population, complete the operation simulation of the microgrid's full scheduling cycle and calculate the fitness value corresponding to the configuration scheme. S44. Compare the current fitness value of each optimization individual with the historical best value, update the local best position of the individual, and at the same time compare the fitness values ​​of all optimization individuals in the population, update the global best position, and complete the iterative update of optimization individuals. S45. Determine whether the algorithm meets the preset termination condition. If it does, stop the optimization and output the optimal rated power and optimal rated capacity configuration scheme of the vanadium redox flow battery and lithium-ion battery corresponding to the global optimal position.

6. The method for optimizing the capacity configuration of vanadium-lithium hybrid energy storage considering efficiency according to claim 2, characterized in that, The dynamic efficiency model of the vanadium redox flow battery expresses the equivalent power loss inside the stack as a function of the operating current and the equivalent internal resistance, and introduces the parasitic power loss of the circulating pump to establish a unified dynamic efficiency model; it introduces a charge and discharge state identifier variable to distinguish between the charging state and the discharging state, and calculates the dynamic efficiency under different states respectively.

7. The method for optimizing the capacity configuration of vanadium-lithium hybrid energy storage considering efficiency according to claim 2, characterized in that, The dynamic efficiency model of the lithium-ion battery uses a polynomial fitting method to construct a dynamic function of the equivalent internal resistance of the lithium-ion battery as a function of the real-time state of charge. The dynamic function is then substituted into the internal ohmic loss power calculation, and a charge / discharge state identifier variable is introduced to establish a unified dynamic efficiency model.

8. The method for optimizing the capacity configuration of vanadium-lithium hybrid energy storage considering efficiency according to claim 4, characterized in that, The comprehensive lifecycle cost described in S31 includes four sub-items: initial investment cost of energy storage equipment, equipment replacement cost, operation and maintenance cost, and penalty cost caused by microgrid supply and demand mismatch. The initial investment cost includes the power investment cost and capacity investment cost of vanadium redox flow batteries and lithium-ion batteries. The equipment replacement cost is only set for lithium-ion batteries. The operation and maintenance cost is calculated based on the real-time charging and discharging power of the two types of energy storage equipment during the entire dispatch cycle. The penalty cost is calculated based on the system power shortage power and power curtailment power during the entire dispatch cycle.

9. A vanadium-lithium hybrid energy storage capacity optimization configuration system considering efficiency, characterized in that, include: The dynamic efficiency modeling module is used to establish dynamic efficiency models for vanadium redox flow batteries and lithium-ion batteries respectively, and to establish respective state-of-charge update equations that take into account dynamic efficiency based on the dynamic efficiency models for vanadium redox flow batteries and lithium-ion batteries. The power allocation optimization module is used to acquire the load demand and renewable energy output data of the microgrid, calculate the net load power, and use a filtering algorithm to decompose the net load power into high and low frequencies to obtain the initial reference power of the vanadium redox flow battery and the initial reference power of the lithium-ion battery. Based on the dynamic efficiency model of the vanadium redox flow battery, the dynamic efficiency model of the lithium-ion battery, and the current state of charge of the two energy storage devices, the initial reference power is verified by physical boundary constraints and corrected by power deficit compensation to determine the actual charging and discharging power of the two energy storage devices and the unbalanced power of the system. The capacity optimization modeling module is used to construct a capacity optimization configuration model with the rated power and rated capacity of vanadium redox flow batteries and lithium-ion batteries as decision variables, the goal function being to minimize the overall cost of the system throughout its entire life cycle, and in combination with constraints such as power balance, charge and discharge power, state of charge, and consistency between the initial and final states. The optimization solution module is used to iteratively solve the capacity optimization configuration model using a heuristic optimization algorithm, and output the optimal rated power and optimal rated capacity configuration scheme that minimizes the overall cost over the entire life cycle.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed, implements the vanadium-lithium hybrid energy storage capacity optimization configuration method taking into account efficiency as described in any one of claims 1-8.

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