Energy storage power station charging and discharging management method based on particle swarm optimization

Abstract

The invention discloses an energy storage power station charging and discharging management method based on a particle swarm algorithm. The method comprises the steps of obtaining operation data of an energy storage power station and constructing an internal data set of the energy storage power station; constructing a multi-dimensional decision model based on a particle swarm algorithm, and setting a constraint condition and a target function of the multi-dimensional decision model according to the internal data set of the energy storage power station; a particle swarm algorithm is executed, each particle in a particle swarm represents a set of energy storage power station charging and discharging strategies, and when the particle swarm algorithm is terminated, an optimal charging and discharging strategy is found; and managing charging and discharging of the energy storage power station based on the optimal charging and discharging management strategy. According to the invention, the multi-dimensional decision model is obtained based on the improved particle swarm, the operation targets of the energy storage power station are integrated, and the charging and discharging decision process of the energy storage power station is optimized, so that the operation of the energy storage power station can be carried out under the comprehensive consideration of multiple factors. The energy utilization efficiency is improved, the economic benefit of the energy storage power station is maximized, and the operation and maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage and conversion in smart grids, and in particular to a charge and discharge management method for an energy storage power station based on a particle swarm algorithm. Background Art

[0002] In the current energy system, energy storage power stations play a vital role, especially in promoting the widespread application of renewable energy and improving the stability and economy of the power system. However, the traditional energy storage power station charging and discharging management methods face many technical challenges, especially in optimizing energy utilization efficiency, improving economy and extending the service life of energy storage systems.

[0003] These challenges mainly stem from the following aspects: First, traditional charging and discharging strategies are often based on simple rules or manual decisions, which is not only inefficient, but also difficult to accurately predict and respond to dynamic changes in the electricity market and real-time fluctuations in electricity demand. In addition, this method makes it difficult to fully consider the internal state of the energy storage system, such as battery health and external environmental factors, such as renewable energy output and electricity price fluctuations, resulting in failure to optimize energy scheduling, thereby affecting the operational efficiency and economy of energy storage power stations. Finally, traditional methods also have shortcomings in battery health management. Due to the lack of an effective battery status monitoring and evaluation mechanism, charging and discharging operations may accelerate battery aging, reduce its service life, and increase operating and maintenance costs.

[0004] Therefore, how to optimize the charging and discharging of energy storage power stations is an urgent problem that technicians in this field need to solve. Summary of the invention

[0005] In view of the above problems in the prior art, the present application provides a charging and discharging management method for an energy storage power station based on a particle swarm algorithm, which can optimize the charging and discharging decision-making process of the energy storage power station, improve energy utilization efficiency, and maximize the economic benefits of the energy storage power station.

[0006] The present application provides a method for managing charging and discharging of an energy storage power station based on a particle swarm algorithm, comprising:

[0007] A. Obtain the operating data of the energy storage power station and build an internal data set of the energy storage power station;

[0008] B. constructing a multidimensional decision model based on a particle swarm algorithm, and setting constraints and objective functions of the multidimensional decision model according to the internal data set of the energy storage power station;

[0009] C. Execute the particle swarm algorithm, wherein each particle in the particle swarm represents a set of energy storage power station charging and discharging strategies, and when the particle swarm algorithm terminates, the optimal charging and discharging strategy is found;

[0010] D. Manage the charging and discharging of the energy storage power station based on the optimal charging and discharging management strategy.

[0011] In some embodiments, the step A of obtaining the operating data of the energy storage power station and constructing an internal data set of the energy storage power station includes:

[0012] Get the total energy storage capacity C of all energy storage devices in the energy storage power station total 、Current battery level E current , Battery health status S health , historical charge and discharge mode data D charge_discharge , Ambient temperature and battery performance relationship data T env →P battery And the power station internal energy demand and supply data N demand_supply ;

[0013] Construct the internal data set DS of the energy storage power station internal , which satisfies:

[0014] DS internal ={C total ,E current ,S health ,D charge_discharge ,T env →P battery ,N demand_supply}.

[0015] In some embodiments, setting the constraints and objective function of the multi-dimensional decision model in step B includes:

[0016] Setting constraints includes: setting battery capacity constraints, charge and discharge rate constraints, and ambient temperature constraints, as follows:

[0017] The battery capacity constraints are:

[0018] The constraints on the charge and discharge rates are:

[0019] The constraint condition of ambient temperature is: T min ≤T env ≤T max ;

[0020] Among them, C min and C max are the minimum and maximum values ​​of the battery capacity limit, respectively, charge and η discharge are the charging and discharging efficiencies, and are the charge and discharge amounts in time period t, Rate charge ,Rate discharge They are charging rate and discharging rate, Rate max is the maximum charge and discharge rate, T min and T max are the minimum and maximum temperature ranges of battery operation, T env is the ambient temperature;

[0021] Set the objective function F objective for:

[0022]

[0023] Among them, P t is the electricity market price in time period t, and are the electricity sold and purchased in time period t, is the electricity purchase price in time period t, C fixed is a fixed operating cost, is the variable operating cost in time period t, D i is the number of charge and discharge cycles of the ith battery cell, D max is the maximum number of charge and discharge cycles of the battery cell, n is the total number of battery cells, T is the total number of time periods t, and α, β, and γ are coefficients.

[0024] In some embodiments, executing the particle swarm algorithm in step C includes:

[0025] Each particle represents a set of charging and discharging strategies, including the charging and discharging time, charging amount and discharging amount parameters; and the position vector of particle i is expressed as: X i =(x i1 ,x i2 ,...,x in ) ; The velocity vector of particle i is expressed as: V i =(v i1 ,v i2 ,...,v in ), where n is the number of decision variables;

[0026] Let the fitness of the particle F i By the objective function F objective It is calculated that, where the higher the fitness, the better the solution;

[0027] Each particle updates its individual optimal position P using the following formula best,i and the global optimal position G best :

[0028]

[0029] The speed update formula and position update formula using the nonlinear dynamic weight factor w(t) and random factor are as follows, where the information exchange mechanism between particles is introduced into the speed update formula:

[0030] Speed ​​update formula:

[0031] Position update formula:

[0032] Among them, c1c2c3 are learning factors that control the tendency of particles to move toward individual optimal and global optimal, r1, r2 and r3 are random numbers in the interval [0,1]. is the average position of the group, w(t) is the nonlinear dynamic weight, which changes with the number of iterations;

[0033] Where w(t) is calculated by the following formula:

[0034] Among them, w max and w min are the maximum and minimum values ​​of the weight, T max is the maximum number of iterations, and t represents the current number of iterations;

[0035] The constraint handling mechanism is introduced to allow particles to explore the solution space while satisfying all operational constraints. If any constraint is violated, the adjustment function will be used to correct it, pulling the particle position back to the feasible solution space or adjusting its position according to the degree of violation. The adjustment function Φ(X) used is:

[0036]

[0037] Among them, λ is the control factor, δ k is the weight that is dynamically adjusted according to the degree of satisfaction of constraint k, x best,k It is the local optimal solution under the constraint k;

[0038] When the particle swarm algorithm is iterated to the preset maximum number of iterations T max , or the change of the global optimal solution is less than the predetermined threshold, select G best As a charging and discharging management strategy for energy storage power stations:

[0039]

[0040] Among them, G best represents the optimal charge and discharge management strategy, G is the set of all possible charge and discharge strategies, is the total operating cost calculated according to strategy g, and are the electricity sold and purchased at time t according to strategy g, C maintain (g) is the maintenance cost according to strategy g, L extension is the battery life extension function, which depends on the battery health status S under strategy g health (g), α, β, and γ are coefficients.

[0041] In some embodiments, the step D of managing the charging and discharging of the energy storage power station includes:

[0042] The operating parameters for charge and discharge are calculated using the following formula:

[0043]

[0044] Among them, t charge,start is the charging start time, t charge,end is the charging end time, t discharge,start is the discharge start time, t discharge,end is the discharge end time, E charge and E discharge is the corresponding charge and discharge amount;

[0045] Based on the calculated charging and discharging operating parameters Perform charging and discharging of energy storage power stations.

[0046] In some embodiments, managing the charging and discharging of the energy storage power station in step D further includes:

[0047] According to the real-time energy demand and supply, the charge and discharge amount obtained based on the electricity market price and price changes, the charge and discharge amount adjustment parameter E is calculated using the following formula adjust (t):

[0048] E adjust (t) = E demand (t)-E supply (t)+ΔE market (P t ,ΔP t ),

[0049] Among them, E demand (t) is the real-time energy demand, E supply (t) is the real-time energy supply, P t is the electricity price in the electricity market, ΔP t is the change in electricity market price, ΔE market is the charge and discharge capacity;

[0050] Adjust parameter E based on the calculated charge and discharge amount adjust(t), adjust the charging and discharging of the energy storage power station.

[0051] In some embodiments, the battery health status S health , calculated using the following formula based on the number of charge and discharge cycles, temperature history, and voltage fluctuations of each battery cell:

[0052]

[0053] Among them, D i is the number of charge and discharge cycles of the ith battery cell, D max is the maximum number of cycles designed for the battery, T i is the battery operating temperature, T ref is the reference temperature, Δv i is the voltage fluctuation when the battery is discharged, and α and β are the attenuation coefficients.

[0054] In some embodiments, the power station internal energy demand and supply data N demand_supply , calculated according to the following formula:

[0055] N demand_supply =Φ demand (t,Θ demand )-Ψ supply (t,Θ supply ,S renewable );

[0056] Among them, Φ demand (t,Θ demand ) is at time t and the demand parameter Θ demand The energy demand function under supply (t,Θ supply ,S renewable ) is at the same time, supply parameter Θ supply and renewable energy status S renewable The energy supply function under .

[0057] In some embodiments, it also includes: applying a battery health management strategy, including adjusting the charge and discharge depth and the charge and discharge rate; wherein the charge and discharge depth and the charge and discharge rate are adjusted according to the battery health status and historical operation data.

[0058] These and other aspects of the invention will become apparent from the following description of the embodiment(s). BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The following further illustrates the various features of the present invention and the relationship between the various features with reference to the accompanying drawings. The accompanying drawings are all exemplary, some features are not shown in actual proportion, and some drawings may omit the conventional features in the field involved in the present application and are not necessary for the present application, or additionally show the features that are not necessary for the present application. The combination of the various features shown in the accompanying drawings is not intended to limit the present application. In addition, throughout this specification, the same figure numerals refer to the same content. The specific description of the drawings is as follows:

[0060] Figure 1 The present application provides a flow chart of a method for managing charging and discharging of an energy storage power station based on a particle swarm algorithm. DETAILED DESCRIPTION

[0061] The technical solution provided by the present application is further described below with reference to the accompanying drawings and examples. It should be understood that the system structure and business scenarios provided in the examples of the present application are mainly to illustrate the possible implementation methods of the technical solution of the present application and should not be interpreted as the only limitation on the technical solution of the present application. It is known to those of ordinary skill in the art that with the evolution of the system structure and the emergence of new business scenarios, the technical solution provided by the present application is also applicable to similar technical problems.

[0062] It should be understood that the energy storage power station charging and discharging solution based on the improved particle swarm provided in the embodiments of the present application includes an energy storage power station charging and discharging management method, device, computing device and computer-readable storage medium based on the improved particle swarm. Since the principles of solving the problems in these technical solutions are the same or similar, some repetitions may not be repeated in the introduction of the following specific embodiments, but it should be regarded as that these specific embodiments have been referenced to each other and can be combined with each other.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of this application. In the event of any inconsistency, the meaning described in this specification or the meaning derived from the contents recorded in this specification shall prevail. In addition, the terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application. In order to accurately describe the technical content in this application and to accurately understand the present invention, the following explanations or definitions are given to the terms used in this specification before describing the specific embodiments:

[0064] 1) Particle Swarm Optimization (PSO): In the field of energy storage power station charging and discharging, PSO optimizes the charging and discharging strategy by simulating the foraging behavior of bird flocks. It transforms the charging and discharging problem of energy storage power stations into a multi-dimensional optimization problem and searches for the optimal solution by adjusting the position and speed of particles.

[0065] In order to solve the problem of lack of global consideration of the charging and discharging of energy storage systems in the prior art, the embodiment of the present application proposes an improved charging and discharging scheme of an energy storage power station based on a particle swarm algorithm, obtains a multi-dimensional decision model based on a particle swarm, integrates the operating objectives of the energy storage power station, and optimizes the charging and discharging decision-making process of the energy storage power station, so that the operation of the energy storage power station can be carried out under the comprehensive consideration of multiple factors such as energy demand, market electricity price and energy storage battery status, thereby improving energy utilization efficiency, maximizing the economic benefits of the energy storage power station and reducing operation and maintenance costs. Through the automated energy scheduling and optimization decision support system, energy demand fluctuations and market changes can be effectively responded to, and the high operational stability of the energy storage power station can be maintained. The energy storage power station mentioned in the present application can be various energy storage systems, including but not limited to electromagnetic energy storage, mechanical energy storage or electrochemical energy storage. In this embodiment, a battery energy storage system is used as an example for illustration.

[0066] In the following, in conjunction with a specific implementation, an embodiment of the improved particle swarm algorithm-based energy storage power station charging and discharging management method provided by the present application is introduced. Figure 1 This is a schematic diagram of the process provided by the implementation method of this application. Figure 1 As shown, the particle swarm-based energy storage power station charge and discharge management method provided in this embodiment includes the following steps:

[0067] S1: Obtain the operating data of the energy storage power station and build an internal data set of the energy storage power station.

[0068] The operating data of the energy storage power station may include: the energy storage capacity of the power station, the current battery power, the battery health status, the historical charging and discharging mode data, the relationship between ambient temperature and battery performance, and the power station's internal energy demand and supply forecast data.

[0069] Specifically, step S1 of constructing the internal data set of the energy storage power station includes the following sub-steps S11-S17:

[0070] S11: Obtain the power station energy storage capacity C total .

[0071] Define the power station energy storage capacity C total The total capacity of all energy storage devices in the power station is obtained directly through the energy storage device management system and the rated capacity of each energy storage device is accumulated: Among them, C i is the rated capacity of the i-th energy storage device, η i,storage is the storage efficiency combined with the annual degradation rate, and N is the total number of energy storage devices.

[0072] S12: Get the current battery power E current .

[0073] The remaining power of each battery cell is monitored in real time through the energy storage device management system: Among them, SOC i is the current state of charge ratio of the ith battery cell, C i is the total capacity of the unit, η i,discharge is the discharge efficiency, and n is the total number of battery cells.

[0074] S13: Determine battery health status S health .

[0075] Evaluate the battery health status based on the relevant data of the battery health , by analyzing the number of battery charge and discharge cycles, temperature history and voltage fluctuations, a health assessment model is used to score the health status of each battery cell, and the scores are summarized to represent the overall health status of the power station battery: Among them, D i is the number of charge and discharge cycles of the ith battery cell, D max is the maximum number of cycles designed for the battery, T i is the battery operating temperature, T ref is the reference temperature, ΔV i is the voltage fluctuation when the battery is discharged, and α and β are the attenuation coefficients.

[0076] S14: Collect historical charge and discharge mode data D charge_discharge .

[0077] Including the start and end time of each charge and discharge, and the charge and discharge amount, which are obtained through the history recording function of the energy storage device management system, and the charge and discharge strategy is adjusted according to the change of battery performance. The specific formula is as follows:

[0078] Among them, E charge,i and E discharge,i are the energies of the i-th charge and discharge, Δt i is the time difference of the charge and discharge process, m is the number of historical charge and discharge events, γ i It is a coefficient adjusted according to environmental conditions.

[0079] S15: Determine the relationship between ambient temperature and battery performance.

[0080] According to the acquired data on the relationship between ambient temperature and battery performance T env →P battery , by analyzing the change rules of battery charging and discharging efficiency and capacity under different ambient temperatures, recording the ambient temperature through a temperature sensor and combining it with battery performance test data for analysis: P battery =P0×(1-κ(T env -T0);

[0081] Among them, P battery is the battery at ambient temperature T env P0 is the performance of the battery at the reference temperature T0, P0 is the standard performance of the battery at the reference temperature T0, κ is the temperature sensitivity coefficient;

[0082] S16: Predict the internal energy demand and supply data of the power station N demand_supply .

[0083] The demand forecasting model is established by using historical energy demand data, seasonal changes, and special event factors, and combined with the solar and wind energy output forecasting models to calculate the energy supply and demand in a certain period of time in the future:

[0084] N demand_supply =Φ demand (t,Θ demand )-Ψ supply (t,Θ supply ,S renewable );

[0085] Among them, Φ demand (t,Θ demand ) is at time t and the demand parameter Θ demand The energy demand function under supply (t,Θ supply ,S renewable ) is at the same time, supply parameter Θ supply and renewable energy status S renewable The energy supply function under .

[0086] S17: Based on the data collected and analyzed in steps S11-S16, an internal data set DS of the energy storage power station is constructed. internal ;

[0087] DS internal ={C total ,E current ,S health ,D charge_discharge ,T env →P battery ,N demand_supply}.

[0088] S2: constructing a multidimensional decision model based on the particle swarm algorithm, and setting constraints and objective functions of the multidimensional decision model according to the internal data set of the energy storage power station.

[0089] Based on the internal data set DS of the energy storage power station internal Constructing a multi-dimensional decision model includes: using the internal data set of the energy storage power station to set the constraints and objective function of the multi-dimensional decision model, specifically, including the following sub-steps S21-S22:

[0090] S21: Based on the internal data set DS of the energy storage power station internal , set the constraints of the multi-dimensional decision model. The set constraints include: setting battery capacity constraints, charge and discharge rate constraints, and ambient temperature constraints. The details are as follows:

[0091] The battery capacity constraints are:

[0092] The constraints on the charge and discharge rates are:

[0093] The constraint condition of ambient temperature is: T min ≤T env ≤T max ;

[0094] Among them, C min and C max are the minimum and maximum values ​​of the battery capacity limit, respectively, charge and η discharge are the charging and discharging efficiencies, and are the charge and discharge amounts in time period t, Rate charge ,Rate discharge They are charging rate and discharging rate, Rate max is the maximum charge and discharge rate, T min and T max are the minimum and maximum temperature ranges of battery operation, T env is the ambient temperature.

[0095] S22: Setting the objective function F of the multidimensional decision model objective :

[0096]

[0097] Among them, P t is the electricity market price in time period t, and are the electricity sold and purchased in time period t, is the electricity purchase price in time period t, C fixed is a fixed operating cost, is the variable operating cost in time period t, Di is the number of charge and discharge cycles of the ith battery unit, and D max is the designed maximum number of cycles, and n is the total number of battery cells.

[0098] S23: Using the internal data set DS of the energy storage power station internalThe data in the energy storage power station is used as real-time input to the multi-dimensional decision-making model, so that the model reflects the current operation status of the energy storage power station and the external market environment. The internal data set DS of the energy storage power station internal :

[0099] DS internal (t) = {C total (t),E current (t),S health (t),D charge_discharge (t),T env (t)→P battery (t),N demand_supply (t)};

[0100] Battery health status health :

[0101]

[0102] Where Ti(t) is the operating temperature of the ith battery cell at time t, ΔV i (t) is the voltage fluctuation when the battery is discharged at time t, α ′ and β ′ is the attenuation coefficient for temperature and voltage fluctuations.

[0103] Energy demand and supply data within the energy storage power station N demand_supply (t):

[0104] N demand_supply (t) = Φ demand (t,Θ demand )-Ψ supply (t,Θ supply ,S renewable (t));

[0105] N demand_supply (t) represents the gap between electricity demand and supply at time t. If N demand_supply (t) is greater than zero, indicating that the power demand at the moment is greater than the supply, that is, there is a power gap or shortage. In this case, the energy storage power station can supplement the power supply through electricity to meet the power demand and balance the grid load.

[0106] On the contrary, if N demand_supply (t) is less than zero, indicating that the power demand at the current moment is less than the supply, that is, there is excess power. In this case, the energy storage power station can absorb the excess power through charging, improve energy utilization efficiency and balance the grid load.

[0107] Φ demand and supply are functions of demand and supply, including time t, demand parameter Θ demand, supply parameter Θ supply and renewable energy status S renewable (t).

[0108] S3: Execute the particle swarm algorithm, wherein each particle in the particle swarm represents a set of energy storage power station charging and discharging strategies, and when the particle swarm algorithm terminates, the optimal charging and discharging strategy is found. This step may include the following sub-steps:

[0109] S31: Initialization step, including initializing a particle group, where each particle represents a set of charging and discharging strategies, including charging and discharging time, charging amount and discharging amount parameters; and expressing the position vector of particle i as: X i =(x i1 ,v i2 ,...,x in ) ; The velocity vector of particle i is expressed as: V i =(v i1 ,v i2 ,...,v in ), where n is the number of decision variables;

[0110] S32: Setting the fitness F of the particle i By the objective function F objective It is calculated that, where the higher the fitness, the better the solution;

[0111] S33: Iteratively update the particle swarm based on the particle swarm optimization algorithm, specifically including the following:

[0112] Each particle updates its individual optimal position P using the following formula best,i and the global optimal position G best :

[0113]

[0114] The speed update formula and position update formula using the nonlinear dynamic weight factor w(t) and random factor are as follows, where the information exchange mechanism between particles is introduced into the speed update formula:

[0115] Speed ​​update formula:

[0116]

[0117] Position update formula:

[0118] Among them, c1c2c3 are learning factors that control the tendency of particles to move toward individual optimal and global optimal, r1, r2 and r3 are random numbers in the interval [0,1]. is the average position of the group, w(t) is the nonlinear dynamic weight, which changes with the number of iterations;

[0119] Where w(t) is calculated by the following formula:

[0120] Among them, w max and w min are the maximum and minimum values ​​of the weight, T max is the maximum number of iterations, and t represents the current number of iterations;

[0121] The constraint handling mechanism is introduced to allow particles to explore the solution space while satisfying all operational constraints. If any constraint is violated, the adjustment function will be used to correct it, pulling the particle position back to the feasible solution space or adjusting its position according to the degree of violation. The adjustment function Φ(X) used is:

[0122]

[0123] Among them, λ is the control factor, δ k is the weight that is dynamically adjusted according to the degree of satisfaction of constraint k, x best,k It is the local optimal solution under the constraint k;

[0124] S34: When the particle swarm algorithm is iterated to the preset maximum number of iterations T max , or the change of the global optimal solution is less than the predetermined threshold, select G best As a charging and discharging management strategy for energy storage power stations:

[0125]

[0126] Among them, G best represents the optimal charge and discharge management strategy, G is the set of all possible charge and discharge strategies, is the total operating cost calculated according to strategy g, and are the electricity sold and purchased at time t according to strategy g, C maintain (g) is the maintenance cost according to strategy g, L extension is the battery life extension function, which depends on the battery health status S under strategy g health (g), α, β, and γ are coefficients.

[0127] S4: managing the charging and discharging of the energy storage power station based on the optimal charging and discharging management strategy. This step may include the following sub-steps S41-S43:

[0128] S41: Automatically adjust the charging and discharging operations of the energy storage power station according to the optimal charging and discharging strategy. The specific operating parameters include the charging start time t charge,start , charging end time t charge,end , discharge start time t discharge,start and discharge end time t discharge,end And the corresponding charge E charge and discharge capacity E discharge , as follows:

[0129] The operating parameters for charge and discharge are calculated using the following formula:

[0130]

[0131] Among them, t charge,start is the charging start time, t charge,end is the charging end time, t discharge,start is the discharge start time, t discharge,end is the discharge end time, E charge and E discharge is the corresponding charge and discharge amount;

[0132] Based on the calculated charging and discharging operating parameters Perform charging and discharging of energy storage power stations.

[0133] S42: It has a dynamic adjustment mechanism that can dynamically adjust the charging and discharging operation parameters according to the real-time energy demand and supply, the charging and discharging amount obtained based on the electricity market price and price changes. The details are as follows:

[0134] According to the real-time energy demand and supply, the charge and discharge amount obtained based on the electricity market price and price changes, the charge and discharge amount adjustment parameter E is calculated using the following formula adjust (t):

[0135] E adjust (t) = E demand (t)-E supply (t)+ΔE market (P t ,ΔP t ),

[0136] Among them, E demand (t) is the real-time energy demand, E supply (t) is the real-time energy supply, P t is the electricity price in the electricity market, ΔP t is the change in electricity market price, ΔE market is the charge and discharge capacity;

[0137] Adjust parameter E based on the calculated charge and discharge amount adjust(t), adjust the charging and discharging of the energy storage power station.

[0138] S43: Applying a battery health management strategy, including adjusting the charge and discharge depth and the charge and discharge rate; wherein the charge and discharge depth and the charge and discharge rate are adjusted according to the battery health status and historical operation data.

[0139] In some embodiments, the charging and discharging strategy is optimized multiple times by using the improved particle swarm optimization algorithm within a preset operation period, such as one month. In each iteration, the fitness value of the particle (representing a possible charging and discharging strategy) is evaluated and updated to find the optimal solution and apply it.

[0140] In some examples of application of the present application, the energy storage power station charge and discharge management method based on the improved particle swarm provided in the present application is applied to an energy storage power station in a coastal city. The energy storage power station mainly balances the grid load and improves operational efficiency by storing electricity during the trough period and releasing electricity during the peak period. However, due to factors such as large price fluctuations in the electricity market and unstable output of renewable energy, the energy management efficiency of the power station is not ideal, and the battery life is shortened, and the operating cost remains high. After applying the method provided in the present application, the power station first collects relevant data including the power station energy storage capacity, current battery power, battery health status, historical charge and discharge mode data, ambient temperature and battery performance relationship data, and power station internal energy demand and supply forecast data. Through these data, a multi-dimensional decision-making model is constructed, which can take into account the fluctuations in electricity market prices, the uncertainty of renewable energy output, and the real-time health status of the battery.

[0141] Based on the method provided by this application, the charging cost is successfully reduced and the discharge income is increased. During the off-peak period, the power station intelligently determines the charging time and amount to ensure that electricity is purchased at the lowest cost; during the peak period, the power station releases electricity according to the high price of the electricity market to maximize the income.

[0142] In addition, before this, the average cycle life of the battery was about 5 years. Based on the solution of this application, it is expected that the average cycle life of the battery can be extended to 5.5 years. This application successfully extends the battery life by precisely controlling the depth and rate of charge and discharge, thereby significantly reducing long-term operation and maintenance costs.

[0143] This application optimizes the charging and discharging decision-making process of the energy storage power station to ensure that at any given time point, the operation of the energy storage power station is carried out under the comprehensive consideration of multiple factors such as energy demand, market electricity price and energy storage battery status, thereby improving energy utilization efficiency and being able to make dynamic adjustments according to electricity price fluctuations, thereby maximizing the economic benefits of the energy storage power station. Through the automated energy scheduling and optimization decision support system, energy demand fluctuations and market changes can be effectively responded to, and the high operational stability of the energy storage power station can be maintained.

[0144] In addition, this application introduces a battery health management strategy, which reduces the battery aging rate by intelligently adjusting the charge and discharge depth and charge and discharge rate, thereby effectively extending the battery life. And by automatically determining the optimal charge and discharge timing through the application of an improved particle swarm optimization algorithm, the intelligent and automated energy management of energy storage power stations is achieved.

[0145] In the above description, the numbers representing the steps, such as S110, S120, etc., do not necessarily mean that the steps will be executed in this manner. If permitted, the order of the steps can be interchanged or they can be executed simultaneously.

[0146] The term "comprising" as used in the description and claims should not be interpreted as being limited to what is listed thereafter; it does not exclude other elements or steps. Therefore, it should be interpreted as specifying the presence of the features, integers, steps or components mentioned, but does not exclude the presence or addition of one or more other features, integers, steps or components and groups thereof. Therefore, the expression "a device comprising means A and B" should not be limited to a device consisting of components A and B only.

[0147] References to "one embodiment" or "an embodiment" in this specification mean that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least one embodiment of the present application. Therefore, the phrases "in one embodiment" or "in an embodiment" appearing in various places in this specification do not necessarily refer to the same embodiment, but may refer to the same embodiment. In addition, in one or more embodiments, the particular features, structures, or characteristics can be combined in any appropriate manner, as would be apparent to one of ordinary skill in the art from this disclosure.

[0148] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may also include more other equivalent embodiments without departing from the concept of the present application, all of which belong to the scope of protection of the present application.

Claims

1. A method for charging and discharging management of energy storage power stations based on particle swarm algorithm, characterized in that: The following steps are involved: A. Obtain the operating data of the energy storage power station and build an internal data set of the energy storage power station; B. constructing a multidimensional decision model based on a particle swarm algorithm, and setting constraints and objective functions of the multidimensional decision model according to the internal data set of the energy storage power station; C. Execute the particle swarm algorithm, wherein each particle in the particle swarm represents a set of energy storage power station charging and discharging strategies, and when the particle swarm algorithm terminates, the optimal charging and discharging strategy is found; D. Manage the charging and discharging of the energy storage power station based on the optimal charging and discharging management strategy.

2. The method according to claim 1, characterized in that The step A of obtaining the operation data of the energy storage power station and constructing the internal data set of the energy storage power station includes: Get the total energy storage capacity C of all energy storage devices in the energy storage power station total 、Current battery level E current , Battery health status S health , historical charge and discharge mode data D charge_discharge , Ambient temperature and battery performance relationship data T env →P battery And the power station internal energy demand and supply data N demand_supply ; Construct the internal data set DS of the energy storage power station internal , which satisfies: DS internal ={C total ,E current ,S health ,D charge_discharge ,T env →P battery ,N demand_supply }。 3. The method according to claim 2, characterized in that The setting of the constraints and objective function of the multi-dimensional decision model in step B includes: Setting constraints includes: setting battery capacity constraints, charge and discharge rate constraints, and ambient temperature constraints, as follows: The battery capacity constraints are: The constraints on the charge and discharge rates are: The constraint condition of ambient temperature is: T min ≤T env ≤T max ; Among them, C min and C max are the minimum and maximum values ​​of the battery capacity limit, respectively, charge and η discharge are the charging and discharging efficiencies, and are the charge and discharge amounts in time period t, Rate charge ,Rate discharge They are charging rate and discharging rate, Rate max is the maximum charge and discharge rate, T min and T max are the minimum and maximum temperature ranges of battery operation, T env is the ambient temperature; Set the objective function F objective for: Among them, P t is the electricity market price in time period t, and are the electricity sold and purchased in time period t, is the electricity purchase price in time period t, C fixed is a fixed operating cost, is the variable operating cost in time period t, D i is the number of charge and discharge cycles of the ith battery cell, D max is the maximum number of charge and discharge cycles of the battery cell, n is the total number of battery cells, T is the total number of time periods t, and α, β, and γ are coefficients.

4. The method according to claim 3, characterized in that The execution of the particle swarm algorithm in step C includes: Each particle represents a set of charging and discharging strategies, including the charging and discharging time, charging amount and discharging amount parameters; and the position vector of particle i is expressed as: X i =(x i1 ,x i2 ,...,x in ) ; The velocity vector of particle i is expressed as: V i =(v i1 ,v i2 ,...,v in ), where n is the number of decision variables; Let the fitness of the particle F i By the objective function F objective It is calculated that, where the higher the fitness, the better the solution; Each particle updates its individual optimal position P using the following formula best,i and the global optimal position G best : The speed update formula and position update formula using the nonlinear dynamic weight factor w(t) and random factor are as follows, where the information exchange mechanism between particles is introduced into the speed update formula: Speed ​​update formula: Position update formula: Among them, c1c2c3 are learning factors that control the tendency of particles to move toward individual optimal and global optimal, r1, r2 and r3 are random numbers in the interval [0,1]. is the average position of the group, w(t) is the nonlinear dynamic weight, which changes with the number of iterations; Where w(t) is calculated by the following formula: Among them, w max and w min are the maximum and minimum values ​​of the weight, T max is the maximum number of iterations, and t represents the current number of iterations; The constraint handling mechanism is introduced to allow particles to explore the solution space while satisfying all operational constraints. If any constraint is violated, the adjustment function will be used to correct it, pulling the particle position back to the feasible solution space or adjusting its position according to the degree of violation. The adjustment function φ(X) used is: Among them, λ is the control factor, δ k is the weight that is dynamically adjusted according to the degree of satisfaction of constraint k, x best,k It is the local optimal solution under the constraint k; When the particle swarm algorithm is iterated to the preset maximum number of iterations T max , or the change of the global optimal solution is less than the predetermined threshold, select G best As a charging and discharging management strategy for energy storage power stations: Among them, G best represents the optimal charge and discharge management strategy, G is the set of all possible charge and discharge strategies, is the total operating cost calculated according to strategy g, and are the electricity sold and purchased at time t according to strategy g, C maintain (g) is the maintenance cost according to strategy g, L extension is the battery life extension function, which depends on the battery health status S under strategy g health (g), α, β, and γ are coefficients.

5. The method according to claim 4, characterized in that The step D of managing the charging and discharging of the energy storage power station includes: The operating parameters for charge and discharge are calculated using the following formula: Among them, t charge,start is the charging start time, t charge,end is the charging end time, t discharge,start is the discharge start time, t discharge,end is the discharge end time, E charge and E discharge is the corresponding charge and discharge amount; Based on the calculated charging and discharging operating parameters Perform charging and discharging of energy storage power stations.

6. The method according to claim 5, characterized in that The step D of managing the charging and discharging of the energy storage power station further includes: According to the real-time energy demand and supply, the charge and discharge amount obtained based on the electricity market price and price changes, the charge and discharge amount adjustment parameter E is calculated using the following formula adjust (t): E adjust (t)=E demand (t)-E supply (t)+ΔE market (P t ,ΔP t ), Among them, E demand (t) is the real-time energy demand, E supply (t) is the real-time energy supply, P t is the electricity price in the electricity market, ΔP t is the change in electricity market price, ΔE market is the charge and discharge capacity; Adjust parameter E based on the calculated charge and discharge amount adjust (t), adjust the charging and discharging of the energy storage power station.

7. The method according to claim 2, characterized in that The battery health status S health , calculated using the following formula based on the number of charge and discharge cycles, temperature history, and voltage fluctuations of each battery cell: Among them, D i is the number of charge and discharge cycles of the ith battery cell, D max is the maximum number of cycles designed for the battery, T i is the battery operating temperature, T ref is the reference temperature, ΔV i is the voltage fluctuation when the battery is discharged, and α and β are the attenuation coefficients.

8. The method according to claim 2, characterized in that The power station internal energy demand and supply data N demand_supply , calculated according to the following formula: N demand_supply =Φ demand (t,Θ demand )-Ψ supply (t,Θ supply ,S renewable ); Among them, φ demand (t,Θ demand ) is at time t and the demand parameter Θ demand The energy demand function under supply (t,Θ supply ,S renewable ) is at the same time, supply parameter Θ supply and renewable energy status S renewable The energy supply function under .

9. The method according to claim 1, characterized in that: Also includes: Applying a battery health management strategy includes adjusting the charge and discharge depth and the charge and discharge rate; wherein the charge and discharge depth and the charge and discharge rate are adjusted according to the battery health status and historical operation data.

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