Multi-power hybrid energy storage system capacity configuration method

Abstract

The invention relates to the technical field of energy storage systems, and provides a capacity configuration method for a multi-power hybrid energy storage system, which comprises the following steps: S1, analyzing load requirements in different application scenes in detail, and accurately determining energy storage requirements and formulating differentiated capacity configuration strategies by analyzing the load requirements in the application scenes and characteristics of a power grid and new energy in detail; the hybrid system is constructed by combining various energy storage technologies, effective complementation of energy and power is realized, meanwhile, based on a multi-target optimization model and an advanced optimization algorithm, an optimal solution can be found under complex constraints, the energy storage economy is improved, the power state of an energy storage unit is estimated in real time by adopting a recursive least square method, and the energy storage efficiency is improved. The self-adaptive energy management strategy is designed in combination with the rolling optimization concept, the accuracy of power estimation is improved, efficient energy management of the energy storage system is achieved, the service life of equipment is prolonged, energy loss is reduced, and the problem of insufficient energy storage economy in the prior art is solved through the technical scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage systems, and in particular to a capacity configuration method for a multi-power hybrid energy storage system. Background Art

[0002] At present, multi-power hybrid energy storage system refers to a new type of energy storage technology that combines two or more energy storage devices with different power characteristics to utilize their respective advantages in energy density, power density, response speed, etc. to achieve complementary advantages.

[0003] There are several significant deficiencies in the design and operation of energy storage systems in existing technologies. First, the analysis of load demand, grid characteristics and new energy power generation characteristics in application scenarios is not in-depth enough, which leads to a lack of precision in determining energy storage needs and makes it difficult to formulate differentiated capacity configuration strategies. In contrast, in-depth analysis can more accurately grasp energy storage needs, thereby optimizing capacity configuration and improving the adaptability and efficiency of energy storage systems. Secondly, existing technologies are often limited to a single energy storage technology and fail to fully utilize the advantages of different energy storage technologies to achieve effective complementarity between energy and power. This single energy storage system configuration method limits the performance and potential of the energy storage system. Furthermore, when optimizing energy storage systems, existing technologies usually use relatively simple optimization models, which makes it difficult to find the optimal solution under complex constraints. This affects the economic efficiency of the energy storage system and prevents it from fully realizing its economic benefits. Summary of the invention

[0004] The present invention proposes a capacity configuration method for a multi-power hybrid energy storage system, which solves the problem of insufficient energy storage economy in related technologies.

[0005] The technical solution of the present invention is as follows: A method for configuring the capacity of a multi-power hybrid energy storage system, comprising:

[0006] Step S1: Analyze the load demand in different application scenarios in detail, including the load size, fluctuation, and peak-to-valley difference, and determine the amount of electric energy that the energy storage system needs to store and release in combination with the characteristics of the power grid and new energy generation;

[0007] Step S2: clarify the application scenarios of the energy storage system and formulate differentiated capacity configuration strategies for different scenarios;

[0008] Step S3: Combining different types of energy storage technologies to build a hybrid energy storage system, taking advantage of their respective strengths to achieve energy and power complementarity;

[0009] Step S4: Based on the type of energy storage system, select a suitable energy storage unit and optimize its parameter configuration;

[0010] Step S5: Establish a multi-objective optimization model, consider the full life cycle cost of the energy storage system, power supply reliability index, energy storage efficiency, system response speed factors, and set a variety of electrical and non-electrical constraints;

[0011] Step S6: Using advanced optimization algorithms, it is possible to find the optimal solution or Pareto solution set under complex constraints, and optimize the solution through economic indicators such as cost recovery period;

[0012] Step S7: Based on the comprehensive electrothermal model of the energy storage system, a recursive least squares algorithm is used to estimate the power state of the energy storage unit in real time, taking into account factors such as temperature constraints to ensure the accuracy of power estimation;

[0013] Step S8: Combine power state estimation with the rolling optimization concept to design an adaptive model predictive control energy management strategy. By constructing a prediction model, designing a rolling optimization function and a feedback correction method, efficient energy management of the energy storage system can be achieved, the service life of the energy storage equipment can be extended, and the energy loss of the system can be reduced.

[0014] As a preferred solution of the present invention, the detailed analysis of the load requirements under different application scenarios in step S1, including the load size, fluctuation and peak-to-valley difference, combined with the characteristics of the power grid and the characteristics of new energy generation, to determine the amount of electric energy that the energy storage system needs to store and release is as follows:

[0015] Step S11: First, clarify the application scenarios and specific goals of the energy storage system for subsequent analysis;

[0016] Step S12: for the selected application scenario, collect historical load data, including load size, fluctuation, peak-to-valley difference, and collect historical data of renewable energy power generation;

[0017] Step S13: Analyze historical load data using statistical methods (the statistical method here is: ARIMA model, ARIMA model (Autoregressive Integrated Moving Average) is a classic time series prediction algorithm, which combines autoregression (AR), difference (I) and moving average (MA) to capture trends, seasonality and random fluctuations in time series. In load data analysis, ARIMA model can be used to predict future load trends, especially when load data shows obvious time dependence and seasonality) to identify load size, fluctuation cycle, peak and valley time characteristics. For new energy power generation, it is also necessary to analyze its output characteristics, including output fluctuation range and predictability;

[0018] Step S14: Based on historical load data and new energy power generation characteristics, use load forecasting algorithms to predict future load demand;

[0019] The load forecasting algorithm here adopts the ARIMA (p, d, q) model formula algorithm: (1-B)d(Yt-μ)=φ(B)θ(B)∈t

[0020] Where: Yt is the time series data, μ is the mean of the time series. B is the lag operator, BkYt = Yt-k, d is the difference order, φ(B) and θ(B) are the polynomials of the autoregressive part and the moving average part, φ(B) = 1-φ1B-φ2B2-…-φpBp, θ(B) = 1-θ1B-θ2B2-…-θqBq, ∈t is the white noise sequence;

[0021] Step S15: Based on the load forecast results, grid characteristics and new energy power generation characteristics, an optimization algorithm is used to determine the storage capacity and power requirements of the energy storage system. At the same time, an operation strategy for the energy storage system is formulated, including charging timing, discharging timing, and charging and discharging power, to ensure that the energy storage system can efficiently meet the supply and demand balance requirements of the grid;

[0022] The optimization algorithm here adopts mixed integer linear programming, and its formula algorithm is:

[0023] Objective function (taking minimizing total cost as an example):

[0024] textminimizeZ=Ccap·Ecap+Cpow·Pmax+Cop·T

[0025] Among them, Ccap is the cost per unit energy storage capacity, Ecap is the total capacity of the energy storage system, Cpow is the cost per unit power, Pmax is the maximum power of the energy storage system, Cop is the operating cost per unit time, and T is the total operating time.

[0026] Constraints (taking the energy balance equation as an example):

[0027] E(t+1)=E(t)+Pch(t)·ηch-ηdchPdch(t)

[0028] 0≤Pch (t)≤Pmax ·uch (t)

[0029] 0≤Pdch (t)≤Pmax·udch (t)

[0030] uch (t)+udch (t)≤1

[0031] Where E(t) is the energy storage capacity at time t, Pch(t) and Pdch(t) are the charging power and discharging power at time t, ηch and ηdch are the charging efficiency and discharging efficiency, uch(t) and udch(t) are binary decision variables for charging and discharging (1 means proceeding, 0 means not proceeding).

[0032] As a preferred solution of the present invention, the specific steps of clarifying the application scenarios of the energy storage system in step S2 and formulating differentiated capacity configuration strategies for different scenarios are as follows:

[0033] Step S21: For each application scenario, collect and analyze relevant key parameters:

[0034] 1. Power generation side: Based on the volatility and intermittency of renewable energy power generation, configure a storage system with sufficient capacity to smooth the output power and reduce the wind and solar power abandonment rate;

[0035] 2. Grid side: Based on the peak-to-valley difference of the grid load, the demand for peak and frequency regulation, and the ability to alleviate grid congestion, configure the energy storage system to achieve peak load shaving and valley filling, and improve power supply reliability and efficiency;

[0036] 3. User side: Based on the electricity consumption characteristics of industrial and commercial users and household users, energy storage systems are configured to take advantage of the peak-valley electricity price difference to achieve arbitrage and improve power supply reliability;

[0037] 4. Special environments: In special environments such as islands and remote areas, energy storage systems are configured to meet the needs of energy storage, supply and regulation.

[0038] 5. Transportation: Based on the energy needs of transportation systems such as new energy vehicle charging stations and urban rail transit, energy storage systems are configured to optimize the energy structure and improve energy utilization efficiency.

[0039] Step S22: When formulating differentiated capacity configuration strategies, use optimization algorithms to further refine the configuration scheme;

[0040] Step S23: Evaluate the initially formulated differentiated capacity configuration strategy, and make necessary adjustments and optimizations to the configuration strategy based on the evaluation results to ensure the scientificity, rationality and operability of the final solution.

[0041] As a preferred solution of the present invention, the specific steps of combining different types of energy storage technologies to build a hybrid energy storage system in step S3, utilizing the advantages of each, and realizing the complementarity of energy and power are as follows:

[0042] Step S31: Conduct an in-depth analysis of the characteristics of various energy storage technologies, including their energy density, power density, charge and discharge efficiency, cycle life, cost, and safety;

[0043] Step S32: clarifying the objectives and performance requirements of the hybrid energy storage system according to the specific application scenarios and requirements of the energy storage system;

[0044] Step S33: Based on the characteristics of the energy storage technology and the system objectives, a combination of energy storage technologies that can complement each other is selected;

[0045] Step S34: designing the overall architecture and specific configuration of the hybrid energy storage system according to the selected energy storage technology combination;

[0046] Step S35: formulating a suitable control strategy and optimization algorithm for the hybrid energy storage system to achieve efficient management of energy and power;

[0047] The optimization algorithm here adopts the particle swarm optimization algorithm, and its formula is:

[0048] Suppose the position of the i-th particle at the t-th iteration is Xi(t) = (xi1(t), xi2(t), ..., xiD(t)), and the velocity is Vi(t) = (vi1(t), vi2(t), ..., viD(t)), where D is the dimension of the solution. The individual optimal solution is Pi = (pi1, pi2, ..., piD), and the global optimal solution is Pg = (pg1, pg2, ..., pgD).

[0049] The particle speed and position update formula is as follows:

[0050] vid(t+1)=w·vid(t)+c1 ·r1 ·(pid -xid(t))+c2·r2 ·(pgd -xid(t))

[0051] xid(t+1)=xid(t)+vid(t+1)

[0052] Among them, w is the inertia weight, which is used to adjust the degree to which the particle maintains the current speed; c1 and c2 are learning factors, which represent the intensity of the particle learning from the individual optimal solution and the global optimal solution respectively; r1 and r2 are random numbers used to increase the randomness of the search; pid and pgd are the individual optimal solution of the ith particle in the dth dimension and the global optimal solution of the dth dimension respectively.

[0053] As a preferred solution of the present invention, the specific steps of selecting a suitable energy storage unit and optimizing its parameter configuration based on the type of energy storage system in step S4 are as follows:

[0054] Step S41: Preliminarily screen out energy storage units that meet the requirements based on the type and requirements of the energy storage system, taking into account factors such as whether the characteristics of the energy storage unit meet the requirements of the application scenario, as well as a comprehensive evaluation of cost, technical maturity, and reliability;

[0055] Step S42: establishing an optimization model for the selected energy storage unit to maximize the performance and economic benefits of the energy storage system;

[0056] Step S43: Verify the optimization results, including simulation and field testing. The energy storage system is simulated by simulation software to verify the validity of the optimization results and the overall performance of the system. The field test is to test and debug the system in the actual application scenario to ensure that it can meet the expected needs and performance requirements. According to the verification results, necessary adjustments and optimizations are made to the optimization results.

[0057] As a preferred solution of the present invention, the step S5 of establishing a multi-objective optimization model takes into account the full life cycle cost of the energy storage system, power supply reliability index, energy storage efficiency, and system response speed factors, and sets a variety of electrical and non-electrical constraints. The specific steps are as follows:

[0058] Step S51: according to the actual operation requirements of the energy storage system, a variety of electrical and non-electrical constraints are set. Electrical constraints include power balance, voltage stability, and current limitation; non-electrical constraints involve physical limitations, safety regulations, and environmental protection requirements of the energy storage unit. These constraints will be incorporated into the optimization model in the form of inequalities or equations.

[0059] Step S52: Since the dimensions and magnitudes of the optimization objectives may be different, the objective function needs to be standardized or normalized for the convenience of comparison and solution;

[0060] Step S53: Compare the performance of different solutions on various objectives and the degree to which they satisfy the constraints, and select the most appropriate solution as the optimal configuration scheme for the energy storage system based on the needs and preferences of the actual application scenario.

[0061] As a preferred solution of the present invention, the advanced optimization algorithm in step S6 can find the optimal solution or Pareto solution set under complex constraints, and the specific steps of optimizing the solution by economic indicators such as cost recovery period are as follows:

[0062] Step S61: determine the main optimization goal, introduce the cost recovery period as an economic indicator to evaluate the feasibility and economy of the solution;

[0063] Step S62: Considering electrical constraints and non-electrical constraints, these constraints are incorporated into the optimization model in the form of mathematical expressions;

[0064] Step S63: Algorithm implementation: 1. Initialization: randomly generate a set of initial solutions (population), each solution represents a configuration scheme of the energy storage system;

[0065] 2. Fitness evaluation: adapt and constrain each solution, introduce the cost recovery period indicator, and calculate the economic score of each solution;

[0066] 3. Selection operation: According to the fitness value and economic score, select excellent solutions to enter the next generation. Roulette selection, tournament selection and other strategies can be used.

[0067] 4. Crossover and mutation operations: Perform crossover operations on the selected solutions to generate new solutions, and perform mutation operations on the new solutions to increase the diversity of solutions;

[0068] 5. Update the population: replace the old solution with the newly generated solution and update the population;

[0069] 6. Iteration process: Repeat the operations of fitness evaluation, selection, crossover, mutation and population update until the predetermined number of iterations or convergence condition is reached;

[0070] The specific expression formula is:

[0071] initialization:

[0072] P0 = {x1, x2, ..., xN}

[0073] Among them, P0 represents the initial population, xi represents the i-th solution (individual), and N represents the population size.

[0074] Fitness evaluation:

[0075] f(xi) = j = 1∑mwjgj(xi) + economic efficiency score(xi)

[0076] Among them, f(xi ) represents the fitness value of the i-th solution, gj (xi ) represents the j-th optimization objective function value, wj represents the corresponding weight, and m represents the number of optimization objectives.

[0077] Selection operation (roulette wheel selection):

[0078] pi =∑k=1N f(xk )f(xi )

[0079] Among them, pi represents the probability of the i-th solution being selected.

[0080] Crossover operation (single point crossover):

[0081] textChild's solution = Parent's solution 1 [first part] + Parent's solution 2 [second part]

[0082] Mutation operation:

[0083] xi′=xi+Δx

[0084] Among them, xi′ represents the solution after mutation, and Δx represents the mutation amount.

[0085] Update population:

[0086] Pt+1 ={x1′,x2′,…,xN′}

[0087] Among them, Pt+1 represents the updated population.

[0088] As a preferred solution of the present invention, the comprehensive electrothermal model based on the energy storage system in step S7 adopts a recursive least squares algorithm to estimate the power state of the energy storage unit in real time, taking into account factors such as temperature constraints, and ensuring the accuracy of power estimation. The specific steps are as follows:

[0089] Step S71: Model establishment: for energy storage systems (such as lithium batteries and supercapacitors), based on Kirchhoff's voltage-current theorem and the principle of energy conservation, an equivalent circuit model and an equivalent thermal model are established; a bidirectional DC / DC efficiency model: combined with the efficiency model of the bidirectional DC / DC converter, a comprehensive electrical state and thermal state space expression of the energy storage system is established;

[0090] Step S72: parameter identification, 1. Based on the recursive least squares method: the recursive least squares method is used to identify the parameters of the battery open circuit voltage equation, the thermal model parameters at standard temperature, and the system electrical behavior model parameters at different temperature points. The recursive least squares method continuously introduces new observation data to update the estimated value, thereby improving the accuracy and real-time performance of the model; 2. Experimental data support: the relationship between the open circuit voltage and the SOC is calibrated through experimental data (such as constant current discharge experimental data), and the model is further verified and optimized;

[0091] Step S73: Real-time power state estimation, using a multi-constraint power state estimation method: 1. Temperature constraint: Based on a comprehensive electrothermal model, a direct relationship from temperature constraint to power constraint is constructed; 2. Other constraints: Combine current constraint, voltage constraint and state of charge constraint to construct a multi-constraint power state estimation method for a hybrid energy storage system;

[0092] As a preferred solution of the present invention, the power state estimation is combined with the rolling optimization concept in step S8 to design an adaptive model predictive control energy management strategy. By constructing a prediction model, designing a rolling optimization function and a feedback correction method, efficient energy management of the energy storage system is achieved, the service life of the energy storage equipment is extended, and the energy loss of the system is reduced. The specific steps are as follows:

[0093] Step S81: using experimental data or actual operation data, determine model parameters through parameter identification method to ensure the accuracy of the model;

[0094] Step S82: Clarify the optimization goal and establish constraints. According to the actual operating conditions of the energy storage system, establish corresponding constraints. Use a rolling optimization algorithm. At each sampling moment, use the prediction model to predict the system state in the future. Based on the optimization goal and constraints, solve the optimal control sequence in a limited time domain. Then, only the first control instruction is applied to the system, and the process is repeated at the next sampling moment to achieve rolling optimization.

[0095] Step S83: At each sampling moment, the state variables of the prediction model are updated using the latest information of the system, and the prediction output at the current moment is corrected according to the deviation between the prediction output at the previous moment and the actual output to make it closer to the actual output;

[0096] Step S84: Verify the effectiveness of the designed energy management strategy through experiments and optimize it according to the experimental results.

[0097] The working principle and beneficial effects of the present invention are:

[0098] 1. The present invention can accurately determine the energy storage demand, formulate differentiated capacity configuration strategies, and combine a variety of energy storage technologies to build a hybrid system through detailed analysis of the load demand and power grid and new energy characteristics in the application scenario, thereby achieving effective complementarity between energy and power. At the same time, based on the multi-objective optimization model and advanced optimization algorithm, it can find the optimal solution under complex constraints and improve the economic efficiency of energy storage. In addition, the recursive least squares method is used to estimate the power state of the energy storage unit in real time, and the adaptive energy management strategy is designed in combination with the rolling optimization concept, which not only improves the accuracy of power estimation, but also realizes efficient energy management of the energy storage system, extends the service life of the equipment, and reduces energy loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0100] Figure 1 It is a schematic diagram of the principle structure of the present invention. DETAILED DESCRIPTION

[0101] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0102] In this embodiment, a method for configuring capacity of a multi-power hybrid energy storage system is provided, characterized in that it includes:

[0103] Step S1: Analyze the load demand in different application scenarios in detail, including the load size, fluctuation, and peak-to-valley difference, and determine the amount of electric energy that the energy storage system needs to store and release in combination with the characteristics of the power grid and new energy generation;

[0104] Step S2: clarify the application scenarios of the energy storage system and formulate differentiated capacity configuration strategies for different scenarios;

[0105] Step S3: Combining different types of energy storage technologies to build a hybrid energy storage system, taking advantage of their respective strengths to achieve energy and power complementarity;

[0106] Step S4: Based on the type of energy storage system, select a suitable energy storage unit and optimize its parameter configuration;

[0107] Step S5: Establish a multi-objective optimization model, consider the full life cycle cost of the energy storage system, power supply reliability index, energy storage efficiency, system response speed factors, and set a variety of electrical and non-electrical constraints;

[0108] Step S6: Using advanced optimization algorithms, it is possible to find the optimal solution or Pareto solution set under complex constraints, and optimize the solution through economic indicators such as cost recovery period;

[0109] Step S7: Based on the comprehensive electrothermal model of the energy storage system, a recursive least squares algorithm is used to estimate the power state of the energy storage unit in real time, taking into account factors such as temperature constraints to ensure the accuracy of power estimation;

[0110] Step S8: Combine power state estimation with the rolling optimization concept to design an adaptive model predictive control energy management strategy. By constructing a prediction model, designing a rolling optimization function and a feedback correction method, efficient energy management of the energy storage system can be achieved, the service life of the energy storage equipment can be extended, and the energy loss of the system can be reduced.

[0111] Specifically, the detailed analysis of the load requirements in different application scenarios in step S1, including the load size, fluctuation, and peak-to-valley difference, combined with the characteristics of the power grid and the characteristics of new energy generation, to determine the amount of electric energy that the energy storage system needs to store and release is as follows:

[0112] Step S11: First, clarify the application scenarios and specific goals of the energy storage system for subsequent analysis;

[0113] Step S12: for the selected application scenario, collect historical load data, including load size, fluctuation, peak-to-valley difference, and collect historical data of renewable energy power generation;

[0114] Step S13: Analyze historical load data using statistical methods (the statistical method here is: ARIMA model, ARIMA model (Autoregressive Integrated Moving Average) is a classic time series prediction algorithm, which combines autoregression (AR), difference (I) and moving average (MA) to capture trends, seasonality and random fluctuations in time series. In load data analysis, ARIMA model can be used to predict future load trends, especially when load data shows obvious time dependence and seasonality) to identify load size, fluctuation cycle, peak and valley time characteristics. For new energy power generation, it is also necessary to analyze its output characteristics, including output fluctuation range and predictability;

[0115] Step S14: Based on historical load data and new energy power generation characteristics, use load forecasting algorithms to predict future load demand;

[0116] The load forecasting algorithm here adopts the ARIMA (p, d, q) model formula algorithm: (1-B)d(Yt-μ)=φ(B)θ(B)∈t

[0117] Where: Yt is the time series data, μ is the mean of the time series. B is the lag operator, BkYt = Yt-k, d is the difference order, φ(B) and θ(B) are the polynomials of the autoregressive part and the moving average part, φ(B) = 1-φ1B-φ2B2-…-φpBp, θ(B) = 1-θ1B-θ2B2-…-θqBq, ∈t is the white noise sequence;

[0118] Step S15: Based on the load forecast results, grid characteristics and new energy power generation characteristics, an optimization algorithm is used to determine the storage capacity and power requirements of the energy storage system. At the same time, an operation strategy for the energy storage system is formulated, including charging timing, discharging timing, and charging and discharging power, to ensure that the energy storage system can efficiently meet the supply and demand balance requirements of the grid;

[0119] The optimization algorithm here adopts mixed integer linear programming, and its formula algorithm is:

[0120] Objective function (taking minimizing total cost as an example):

[0121] textminimizeZ=Ccap·Ecap+Cpow·Pmax+Cop·T

[0122] Among them, Ccap is the cost per unit energy storage capacity, Ecap is the total capacity of the energy storage system, Cpow is the cost per unit power, Pmax is the maximum power of the energy storage system, Cop is the operating cost per unit time, and T is the total operating time.

[0123] Constraints (taking the energy balance equation as an example):

[0124] E(t+1)=E(t)+Pch(t)·ηch-ηdchPdch(t)

[0125] 0≤Pch (t)≤Pmax ·uch (t)

[0126] 0≤Pdch (t)≤Pmax·udch (t)

[0127] uch (t)+udch(t)≤1

[0128] Where E(t) is the energy storage capacity at time t, Pch(t) and Pdch(t) are the charging power and discharging power at time t, respectively, ηch and ηdch are the charging efficiency and discharging efficiency, respectively, uch(t) and udch(t) are the binary decision variables for charging and discharging (1 means proceeding, 0 means not proceeding).

[0129] In this embodiment, the specific steps of clarifying the application scenarios of the energy storage system in step S2 and formulating differentiated capacity configuration strategies for different scenarios are as follows:

[0130] Step S21: For each application scenario, collect and analyze relevant key parameters:

[0131] 1. Power generation side: Based on the volatility and intermittency of renewable energy power generation, configure a storage system with sufficient capacity to smooth the output power and reduce the wind and solar power abandonment rate;

[0132] 2. Grid side: Based on the peak-to-valley difference of the grid load, the demand for peak and frequency regulation, and the ability to alleviate grid congestion, configure the energy storage system to achieve peak load shaving and valley filling, and improve power supply reliability and efficiency;

[0133] 3. User side: Based on the electricity consumption characteristics of industrial and commercial users and household users, energy storage systems are configured to take advantage of the peak-valley electricity price difference to achieve arbitrage and improve power supply reliability;

[0134] 4. Special environments: In special environments such as islands and remote areas, energy storage systems are configured to meet the needs of energy storage, supply and regulation.

[0135] 5. Transportation: Based on the energy needs of transportation systems such as new energy vehicle charging stations and urban rail transit, energy storage systems are configured to optimize the energy structure and improve energy utilization efficiency.

[0136] Step S22: When formulating differentiated capacity configuration strategies, use optimization algorithms to further refine the configuration scheme;

[0137] Step S23: Evaluate the initially formulated differentiated capacity configuration strategy, and make necessary adjustments and optimizations to the configuration strategy based on the evaluation results to ensure the scientificity, rationality and operability of the final solution.

[0138] Specifically, the steps of combining different types of energy storage technologies to build a hybrid energy storage system in step S3, utilizing the advantages of each, and realizing the complementarity of energy and power are as follows:

[0139] Step S31: Conduct an in-depth analysis of the characteristics of various energy storage technologies, including their energy density, power density, charge and discharge efficiency, cycle life, cost, and safety;

[0140] Step S32: clarifying the objectives and performance requirements of the hybrid energy storage system according to the specific application scenarios and requirements of the energy storage system;

[0141] Step S33: Based on the characteristics of the energy storage technology and the system objectives, a combination of energy storage technologies that can complement each other is selected;

[0142] Step S34: designing the overall architecture and specific configuration of the hybrid energy storage system according to the selected energy storage technology combination;

[0143] Step S35: formulating a suitable control strategy and optimization algorithm for the hybrid energy storage system to achieve efficient management of energy and power;

[0144] The optimization algorithm here adopts the particle swarm optimization algorithm, and its formula is:

[0145] Suppose the position of the i-th particle at the t-th iteration is Xi(t) = (xi1(t), xi2(t), ..., xiD(t)), and the velocity is Vi(t) = (vi1(t), vi2(t), ..., viD(t)), where D is the dimension of the solution. The individual optimal solution is Pi = (pi1, pi2, ..., piD), and the global optimal solution is Pg = (pg1, pg2, ..., pgD).

[0146] The particle speed and position update formula is as follows:

[0147] vid (t+1)=w·vid(t)+c1 ·r1 ·(pid -xid(t))+c2 ·r2 ·(pgd -xid(t))

[0148] xid(t+1)=xid(t)+vid(t+1)

[0149] Among them, w is the inertia weight, which is used to adjust the degree to which the particle maintains the current speed; c1 and c2 are learning factors, which represent the intensity of the particle learning from the individual optimal solution and the global optimal solution respectively; r1 and r2 are random numbers used to increase the randomness of the search; pid and pgd are the individual optimal solution of the ith particle in the dth dimension and the global optimal solution of the dth dimension respectively.

[0150] In this embodiment, the specific steps of selecting a suitable energy storage unit and optimizing its parameter configuration based on the energy storage system type in step S4 are as follows:

[0151] Step S41: Preliminarily screen out energy storage units that meet the requirements based on the type and requirements of the energy storage system, taking into account factors such as whether the characteristics of the energy storage unit meet the requirements of the application scenario, as well as a comprehensive evaluation of cost, technical maturity, and reliability;

[0152] Step S42: establishing an optimization model for the selected energy storage unit to maximize the performance and economic benefits of the energy storage system;

[0153] Step S43: Verify the optimization results, including simulation and field testing. The energy storage system is simulated by simulation software to verify the validity of the optimization results and the overall performance of the system. The field test is to test and debug the system in the actual application scenario to ensure that it can meet the expected needs and performance requirements. According to the verification results, necessary adjustments and optimizations are made to the optimization results.

[0154] Specifically, the steps of establishing a multi-objective optimization model in step S5, taking into account the full life cycle cost of the energy storage system, power supply reliability index, energy storage efficiency, and system response speed factors, and setting a variety of electrical and non-electrical constraints are as follows:

[0155] Step S51: according to the actual operation requirements of the energy storage system, a variety of electrical and non-electrical constraints are set. Electrical constraints include power balance, voltage stability, and current limitation; non-electrical constraints involve physical limitations, safety regulations, and environmental protection requirements of the energy storage unit. These constraints will be incorporated into the optimization model in the form of inequalities or equations.

[0156] Step S52: Since the dimensions and magnitudes of the optimization objectives may be different, the objective function needs to be standardized or normalized for the convenience of comparison and solution;

[0157] Step S53: Compare the performance of different solutions on various objectives and the degree to which they satisfy the constraints, and select the most appropriate solution as the optimal configuration scheme for the energy storage system based on the needs and preferences of the actual application scenario.

[0158] In this embodiment, the advanced optimization algorithm in step S6 can find the optimal solution or Pareto solution set under complex constraints, and the specific steps of optimizing the solution by economic indicators such as cost recovery period are as follows:

[0159] Step S61: determine the main optimization goal, introduce the cost recovery period as an economic indicator to evaluate the feasibility and economy of the solution;

[0160] Step S62: Considering electrical constraints and non-electrical constraints, these constraints are incorporated into the optimization model in the form of mathematical expressions;

[0161] Step S63: Algorithm implementation: 1. Initialization: randomly generate a set of initial solutions (population), each solution represents a configuration scheme of the energy storage system;

[0162] 2. Fitness evaluation: adapt and constrain each solution, introduce the cost recovery period indicator, and calculate the economic score of each solution;

[0163] 3. Selection operation: According to the fitness value and economic score, select excellent solutions to enter the next generation. Roulette selection, tournament selection and other strategies can be used.

[0164] 4. Crossover and mutation operations: Perform crossover operations on the selected solutions to generate new solutions, and perform mutation operations on the new solutions to increase the diversity of solutions;

[0165] 5. Update the population: replace the old solution with the newly generated solution and update the population;

[0166] 6. Iteration process: Repeat the operations of fitness evaluation, selection, crossover, mutation and population update until the predetermined number of iterations or convergence condition is reached;

[0167] The specific expression formula is:

[0168] initialization:

[0169] P0 = {x1, x2, ..., xN}

[0170] Among them, P0 represents the initial population, xi represents the i-th solution (individual), and N represents the population size.

[0171] Fitness evaluation:

[0172] f(xi) = j = 1∑mwjgj(xi) + economic efficiency score(xi)

[0173] Among them, f(xi ) represents the fitness value of the i-th solution, gj (xi ) represents the j-th optimization objective function value, wj represents the corresponding weight, and m represents the number of optimization objectives.

[0174] Selection operation (roulette wheel selection):

[0175] pi =∑k=1Nf(xk)f(xi)

[0176] Among them, pi represents the probability of the i-th solution being selected.

[0177] Crossover operation (single point crossover):

[0178] textChild's solution = Parent's solution 1 [first part] + Parent's solution 2 [second part]

[0179] Mutation operation:

[0180] xi′=xi+Δx

[0181] Among them, xi′ represents the solution after mutation, and Δx represents the mutation amount.

[0182] Update population:

[0183] Pt+1 ={x1′,x2′,…,xN′}

[0184] Among them, Pt+1 represents the updated population.

[0185] Specifically, the comprehensive electrothermal model based on the energy storage system in step S7 uses a recursive least squares algorithm to estimate the power state of the energy storage unit in real time, taking into account factors such as temperature constraints to ensure the accuracy of power estimation. The specific steps are as follows:

[0186] Step S71: Model establishment: for energy storage systems (such as lithium batteries and supercapacitors), based on Kirchhoff's voltage-current theorem and the principle of energy conservation, an equivalent circuit model and an equivalent thermal model are established; a bidirectional DC / DC efficiency model: combined with the efficiency model of the bidirectional DC / DC converter, a comprehensive electrical state and thermal state space expression of the energy storage system is established;

[0187] Step S72: parameter identification, 1. Based on the recursive least squares method: the recursive least squares method is used to identify the parameters of the battery open circuit voltage equation, the thermal model parameters at standard temperature, and the system electrical behavior model parameters at different temperature points. The recursive least squares method continuously introduces new observation data to update the estimated value, thereby improving the accuracy and real-time performance of the model; 2. Experimental data support: the relationship between the open circuit voltage and the SOC is calibrated through experimental data (such as constant current discharge experimental data), and the model is further verified and optimized;

[0188] Step S73: Real-time power state estimation, using a multi-constraint power state estimation method: 1. Temperature constraint: Based on a comprehensive electrothermal model, a direct relationship from temperature constraint to power constraint is constructed; 2. Other constraints: Combine current constraint, voltage constraint and state of charge constraint to construct a multi-constraint power state estimation method for a hybrid energy storage system;

[0189] 9. A method for capacity configuration of a multi-power hybrid energy storage system according to claim 8, characterized in that the power state estimation in step S8 is combined with the rolling optimization concept to design an adaptive model predictive control energy management strategy, and by constructing a prediction model, designing a rolling optimization function and a feedback correction method, efficient energy management of the energy storage system is achieved, the service life of the energy storage equipment is extended, and the energy loss of the system is reduced. The specific steps are as follows:

[0190] Step S81: using experimental data or actual operation data, determine model parameters through parameter identification method to ensure the accuracy of the model;

[0191] Step S82: Clarify the optimization goal and establish constraints. According to the actual operating conditions of the energy storage system, establish corresponding constraints. Use a rolling optimization algorithm. At each sampling moment, use the prediction model to predict the system state in the future. Based on the optimization goal and constraints, solve the optimal control sequence in a limited time domain. Then, only the first control instruction is applied to the system, and the process is repeated at the next sampling moment to achieve rolling optimization.

[0192] Step S83: At each sampling moment, the state variables of the prediction model are updated using the latest information of the system, and the prediction output at the current moment is corrected according to the deviation between the prediction output at the previous moment and the actual output to make it closer to the actual output;

[0193] Step S84: Verify the effectiveness of the designed energy management strategy through experiments, and optimize it according to the experimental results. The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for configuring the capacity of a multi-power hybrid energy storage system, characterized in that: include: Step S1: Analyze the load demand in different application scenarios in detail, including the load size, fluctuation, and peak-to-valley difference, and determine the amount of electric energy that the energy storage system needs to store and release in combination with the characteristics of the power grid and new energy generation; Step S2: clarify the application scenarios of the energy storage system and formulate differentiated capacity configuration strategies for different scenarios; Step S3: Combining different types of energy storage technologies to build a hybrid energy storage system, taking advantage of their respective strengths to achieve energy and power complementarity; Step S4: Based on the type of energy storage system, select a suitable energy storage unit and optimize its parameter configuration; Step S5: Establish a multi-objective optimization model, consider the full life cycle cost of the energy storage system, power supply reliability index, energy storage efficiency, system response speed factors, and set a variety of electrical and non-electrical constraints; Step S6: Using advanced optimization algorithms, it is possible to find the optimal solution or Pareto solution set under complex constraints, and optimize the solution through economic indicators such as cost recovery period; Step S7: Based on the comprehensive electrothermal model of the energy storage system, a recursive least squares algorithm is used to estimate the power state of the energy storage unit in real time, taking into account factors such as temperature constraints to ensure the accuracy of power estimation; Step S8: Combine power state estimation with the rolling optimization concept to design an adaptive model predictive control energy management strategy. By constructing a prediction model, designing a rolling optimization function and a feedback correction method, efficient energy management of the energy storage system can be achieved, the service life of the energy storage equipment can be extended, and the energy loss of the system can be reduced.

2. A method for configuring capacity of a multi-power hybrid energy storage system according to claim 1, characterized in that: The detailed analysis of the load requirements in different application scenarios in step S1, including the load size, fluctuation, and peak-to-valley difference, combined with the characteristics of the power grid and the characteristics of new energy generation, determines the amount of electric energy that the energy storage system needs to store and release. The specific steps are as follows: Step S11: First, clarify the application scenarios and specific goals of the energy storage system for subsequent analysis; Step S12: for the selected application scenario, collect historical load data, including load size, fluctuation, peak-to-valley difference, and collect historical data of renewable energy power generation; Step S13: Analyze historical load data using statistical methods (the statistical method here is: ARIMA model, ARIMA model (Autoregressive Integrated Moving Average) is a classic time series prediction algorithm, which combines autoregression (AR), difference (I) and moving average (MA) to capture trends, seasonality and random fluctuations in time series. In load data analysis, ARIMA model can be used to predict future load trends, especially when load data shows obvious time dependence and seasonality) to identify load size, fluctuation cycle, peak and valley time characteristics. For new energy power generation, it is also necessary to analyze its output characteristics, including output fluctuation range and predictability; Step S14: Based on historical load data and new energy power generation characteristics, use load forecasting algorithms to predict future load demand; The load forecasting algorithm here adopts the ARIMA (p, d, q) model formula algorithm: (1-B)d(Yt-μ)=φ(B)θ(B)∈t Where: Yt is the time series data, μ is the mean of the time series. B is the lag operator, BkYt = Yt-k, d is the difference order, φ(B) and θ(B) are the polynomials of the autoregressive part and the moving average part, φ(B) = 1-φ1B-φ2B2-…-φpBp, θ(B) = 1-θ1B-θ2B2-…-θqBq, ∈t is a white noise sequence; Step S15: Based on the load forecast results, grid characteristics and new energy power generation characteristics, an optimization algorithm is used to determine the storage capacity and power requirements of the energy storage system. At the same time, an operation strategy for the energy storage system is formulated, including charging timing, discharging timing, and charging and discharging power, to ensure that the energy storage system can efficiently meet the supply and demand balance requirements of the grid; The optimization algorithm here adopts mixed integer linear programming, and its formula algorithm is: Objective function (taking minimizing total cost as an example): textminimizeZ=Ccap·Ecap+Cpow·Pmax+Cop·T Among them, Ccap is the cost per unit energy storage capacity, Ecap is the total capacity of the energy storage system, Cpow is the cost per unit power, Pmax is the maximum power of the energy storage system, Cop is the operating cost per unit time, and T is the total operating time. Constraints (taking the energy balance equation as an example): E(t+1)=E(t)+Pch(t)·ηch-ηdchPdch(t) 0≤Pch(t)≤Pmax·uch(t) 0≤Pdch(t)≤Pmax·udch(t) uch(t)+udch(t)≤1 Where E(t) is the energy storage capacity at time t, Pch(t) and Pdch(t) are the charging power and discharging power at time t, respectively, ηch and ηdch are the charging efficiency and discharging efficiency, respectively, uch(t) and udch(t) are the binary decision variables for charging and discharging (1 means proceeding, 0 means not proceeding).

3. A method for configuring capacity of a multi-power hybrid energy storage system according to claim 2, characterized in that: The specific steps of clarifying the application scenarios of the energy storage system in step S2 and formulating differentiated capacity configuration strategies for different scenarios are as follows: Step S21: For each application scenario, collect and analyze relevant key parameters:

1. Power generation side: Based on the volatility and intermittency of renewable energy power generation, configure energy storage systems with sufficient capacity to smooth output power and reduce the wind and solar power abandonment rate; 2. Grid side: Based on the peak-to-valley difference of the grid load, the demand for peak and frequency regulation, and the ability to alleviate grid congestion, configure the energy storage system to achieve peak load shaving and valley filling, and improve power supply reliability and efficiency; 3. User side: Based on the electricity consumption characteristics of industrial and commercial users and household users, energy storage systems are configured to take advantage of the peak-valley electricity price difference to achieve arbitrage and improve power supply reliability; 4. Special environments: In special environments such as islands and remote areas, energy storage systems are configured to meet the needs of energy storage, supply and regulation.

5. Transportation: Based on the energy needs of transportation systems such as new energy vehicle charging stations and urban rail transit, energy storage systems are configured to optimize the energy structure and improve energy utilization efficiency. Step S22: When formulating differentiated capacity configuration strategies, use optimization algorithms to further refine the configuration scheme; Step S23: Evaluate the initially formulated differentiated capacity configuration strategy, and make necessary adjustments and optimizations to the configuration strategy based on the evaluation results to ensure the scientificity, rationality and operability of the final solution.

4. A method for configuring capacity of a multi-power hybrid energy storage system according to claim 3, characterized in that: The specific steps of combining different types of energy storage technologies to build a hybrid energy storage system in step S3, utilizing the advantages of each, and realizing the complementarity of energy and power are as follows: Step S31: Conduct an in-depth analysis of the characteristics of various energy storage technologies, including their energy density, power density, charge and discharge efficiency, cycle life, cost, and safety; Step S32: clarifying the objectives and performance requirements of the hybrid energy storage system according to the specific application scenarios and requirements of the energy storage system; Step S33: Based on the characteristics of the energy storage technology and the system objectives, a combination of energy storage technologies that can complement each other is selected; Step S34: designing the overall architecture and specific configuration of the hybrid energy storage system according to the selected energy storage technology combination; Step S35: formulating a suitable control strategy and optimization algorithm for the hybrid energy storage system to achieve efficient management of energy and power; The optimization algorithm here adopts the particle swarm optimization algorithm, and its formula is: Suppose the position of the i-th particle at the t-th iteration is Xi(t) = (xi1(t), xi2(t), ..., xiD(t)), and the velocity is Vi(t) = (vi1(t), vi2(t), ..., viD(t)), where D is the dimension of the solution. The individual optimal solution is Pi = (pi1, pi2, ..., piD), and the global optimal solution is Pg = (pg1, pg2, ..., pgD). The particle speed and position update formula is as follows: vid(t+1)=w·vid(t)+c1·r1·(pid-xid(t))+c2·r2·(pgd-xid(t)) xid(t+1)=xid(t)+vid(t+1) Among them, w is the inertia weight, which is used to adjust the degree to which the particle maintains the current speed; c1 and c2 are learning factors, which represent the intensity of the particle learning from the individual optimal solution and the global optimal solution, respectively; r1 and r2 are random numbers used to increase the randomness of the search; pid and pgd are the individual optimal solution of the ith particle in the dth dimension and the global optimal solution in the dth dimension, respectively.

5. A method for configuring capacity of a multi-power hybrid energy storage system according to claim 4, characterized in that: The specific steps of selecting a suitable energy storage unit and optimizing its parameter configuration based on the energy storage system type in step S4 are as follows: Step S41: Preliminarily screen out energy storage units that meet the requirements based on the type and requirements of the energy storage system, taking into account factors such as whether the characteristics of the energy storage unit meet the requirements of the application scenario, as well as a comprehensive evaluation of cost, technical maturity, and reliability; Step S42: establishing an optimization model for the selected energy storage unit to maximize the performance and economic benefits of the energy storage system; Step S43: Verify the optimization results, including simulation and field testing. The energy storage system is simulated by simulation software to verify the validity of the optimization results and the overall performance of the system. The field test is to test and debug the system in the actual application scenario to ensure that it can meet the expected needs and performance requirements. According to the verification results, necessary adjustments and optimizations are made to the optimization results.

6. A method for configuring capacity of a multi-power hybrid energy storage system according to claim 5, characterized in that: The specific steps of establishing a multi-objective optimization model in step S5, taking into account the full life cycle cost of the energy storage system, power supply reliability index, energy storage efficiency, and system response speed factors, and setting a variety of electrical and non-electrical constraints are as follows: Step S51: according to the actual operation requirements of the energy storage system, a variety of electrical and non-electrical constraints are set. Electrical constraints include power balance, voltage stability, and current limitation; non-electrical constraints involve physical limitations, safety regulations, and environmental protection requirements of the energy storage unit. These constraints will be incorporated into the optimization model in the form of inequalities or equations. Step S52: Since the dimensions and magnitudes of the optimization objectives may be different, the objective function needs to be standardized or normalized for the convenience of comparison and solution; Step S53: Compare the performance of different solutions on various objectives and the degree to which they satisfy the constraints, and select the most appropriate solution as the optimal configuration scheme for the energy storage system based on the needs and preferences of the actual application scenario.

7. A method for configuring capacity of a multi-power hybrid energy storage system according to claim 6, characterized in that: The specific steps of using advanced optimization algorithms in step S6 to find the optimal solution or Pareto solution set under complex constraints and optimizing the solution through economic indicators such as cost recovery period are as follows: Step S61: determine the main optimization goal, introduce the cost recovery period as an economic indicator to evaluate the feasibility and economy of the solution; Step S62: Considering electrical constraints and non-electrical constraints, these constraints are incorporated into the optimization model in the form of mathematical expressions; Step S63: Algorithm implementation:

1. Initialization: randomly generate a set of initial solutions (population), each solution represents a configuration scheme of the energy storage system; 2. Fitness evaluation: adapt and constrain each solution, introduce the cost recovery period indicator, and calculate the economic score of each solution; 3. Selection operation: According to the fitness value and economic score, select excellent solutions to enter the next generation. Roulette selection, tournament selection and other strategies can be used.

4. Crossover and mutation operations: Perform crossover operations on the selected solutions to generate new solutions, and perform mutation operations on the new solutions to increase the diversity of solutions; 5. Update the population: replace the old solution with the newly generated solution and update the population; 6. Iteration process: Repeat the operations of fitness evaluation, selection, crossover, mutation and population update until the predetermined number of iterations or convergence condition is reached; The specific expression formula is: initialization: P0={x1,x2,…,xN} Among them, P0 represents the initial population, xi represents the i-th solution (individual), and N represents the population size. Fitness evaluation: f(xi)=j=1∑mwj·gj(xi)+economic score(xi) Among them, f(xi) represents the fitness value of the i-th solution, gj(xi) represents the j-th optimization objective function value, wj represents the corresponding weight, and m represents the number of optimization objectives. Selection operation (roulette wheel selection): pi=∑k=1N f(xk)f(xi) Among them, pi represents the probability of the i-th solution being selected. Crossover operation (single point crossover): textChild's solution = Parent's solution 1 [first part] + Parent's solution 2 [second part] Mutation operation: xi′=xi+Δx Among them, xi′ represents the solution after mutation, and Δx represents the mutation amount. Update population: Pt+1={x1′,x2′,…,xN′} Among them, Pt+1 represents the updated population.

8. A method for configuring capacity of a multi-power hybrid energy storage system according to claim 7, characterized in that: In step S7, based on the comprehensive electrothermal model of the energy storage system, the recursive least squares algorithm is used to estimate the power state of the energy storage unit in real time, taking into account factors such as temperature constraints to ensure the accuracy of power estimation. The specific steps are as follows: Step S71: Model establishment: for energy storage systems (such as lithium batteries and supercapacitors), based on Kirchhoff's voltage-current theorem and the principle of energy conservation, an equivalent circuit model and an equivalent thermal model are established; a bidirectional DC / DC efficiency model: combined with the efficiency model of the bidirectional DC / DC converter, a comprehensive electrical state and thermal state space expression of the energy storage system is established; Step S72: parameter identification, 1. Based on the recursive least squares method: the recursive least squares method is used to identify the parameters of the battery open circuit voltage equation, the thermal model parameters at standard temperature, and the system electrical behavior model parameters at different temperature points. The recursive least squares method continuously introduces new observation data to update the estimated value, thereby improving the accuracy and real-time performance of the model; 2. Experimental data support: the relationship between the open circuit voltage and the SOC is calibrated through experimental data (such as constant current discharge experimental data), and the model is further verified and optimized; Step S73: Real-time power state estimation, using a multi-constraint power state estimation method:

1. Temperature constraint: Based on a comprehensive electrothermal model, a direct relationship from temperature constraint to power constraint is constructed; 2. Other constraints: Combine current constraint, voltage constraint and state of charge constraint to construct a multi-constraint power state estimation method for a hybrid energy storage system.

9. A method for configuring capacity of a multi-power hybrid energy storage system according to claim 8, characterized in that: The specific steps of combining power state estimation with the rolling optimization concept in step S8 to design an adaptive model predictive control energy management strategy, constructing a prediction model, designing a rolling optimization function and a feedback correction method, realizing efficient energy management of the energy storage system, extending the service life of the energy storage equipment, and reducing system energy loss are as follows: Step S81: using experimental data or actual operation data, determine model parameters through parameter identification method to ensure the accuracy of the model; Step S82: Clarify the optimization goal and establish constraints. According to the actual operating conditions of the energy storage system, establish corresponding constraints. Use a rolling optimization algorithm. At each sampling moment, use the prediction model to predict the system state in the future. Based on the optimization goal and constraints, solve the optimal control sequence in a limited time domain. Then, only the first control instruction is applied to the system, and the process is repeated at the next sampling moment to achieve rolling optimization. Step S83: At each sampling moment, the state variables of the prediction model are updated using the latest information of the system, and the prediction output at the current moment is corrected according to the deviation between the prediction output at the previous moment and the actual output to make it closer to the actual output; Step S84: Verify the effectiveness of the designed energy management strategy through experiments and optimize it according to the experimental results.

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