Energy storage capacity configuration method based on state of charge and battery life

Abstract

The invention discloses an energy storage capacity configuration method based on state of charge and battery life, which comprises the following steps: constructing a charge-discharge state quantity coefficient adjustment model, and setting a state of charge control strategy based on the charge-discharge state quantity coefficient adjustment model; establishing an incidence relation between the battery cycle index and the discharge depth, and constructing a battery energy storage life model according to the conversion coefficient and the incidence relation; constructing a comprehensive cost minimization objective function based on the charge state control strategy and the battery energy storage life model, establishing a comprehensive operation constraint of the wind and light energy storage system, and obtaining a capacity configuration model based on the comprehensive cost minimization objective function and the comprehensive operation constraint; and solving the capacity configuration model by using an ant lion algorithm to obtain power PB and capacity EB configured by the energy storage system. The invention relates to the technical field of energy storage system capacity configuration, and solves the technical problem of unreasonable energy storage configuration caused by difficulty in unification of economy and technicality in an existing energy storage capacity configuration method.

Description

Technical Field

[0001] The present invention belongs to the field of new energy technology and relates to energy storage capacity configuration technology, specifically to an energy storage capacity configuration method based on charge state and battery life. Background Art

[0002] With the acceleration of global energy transformation, especially the large-scale development of renewable energy such as wind power and photovoltaics, the flexibility and stability of the power system are facing unprecedented challenges. As an important means to solve this problem, energy storage technology and its capacity configuration method are increasingly valued. The energy storage system can release energy during peak power demand and store energy during low power demand, thereby effectively smoothing out power grid fluctuations and improving the overall operating efficiency of the power system.

[0003] A scientific and reasonable energy storage capacity configuration method is the cornerstone for reducing the operating costs of the power system and optimizing resource allocation. It avoids unnecessary idleness and waste of resources, while ensuring that demand can be responded to quickly at critical moments to avoid imbalances in supply and demand. In addition, a reasonable configuration strategy can significantly improve the efficiency and life of the energy storage system. By reducing unnecessary frequent charge and discharge cycles, protecting the battery from damage and extending its service life, it reduces long-term maintenance costs and improves the overall economic benefits of the system.

[0004] However, the existing energy storage capacity configuration methods have some shortcomings in terms of economic and technical considerations. On the one hand, economic considerations require that energy storage configuration must be cost-effective to avoid unnecessary waste of resources; on the other hand, technical considerations require that the energy storage system be able to meet the actual operating needs of the power grid and have sufficient regulation capabilities and reliability. However, the existing energy storage configuration methods often find it difficult to find an ideal balance between the two. They either overemphasize economic considerations and result in insufficient technical performance, or over-pursue technological advancement and result in excessive configuration costs. In addition, when the energy storage system frequently overcharges and over-discharges, or is in an abnormal working state of charge for a long time, the battery life will be greatly shortened, increasing the cost of the energy storage system. Existing methods often ignore the impact of the state of charge and battery life on the performance of the energy storage system, making it difficult to control the charging and discharging power, resulting in inaccurate capacity configuration results, affecting the stability of the power grid, and making it difficult to cope with abnormal power fluctuations under emergencies or extreme conditions. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a method for configuring energy storage capacity based on state of charge and battery life, which is used to solve the technical problem that it is difficult to unify the economic and technical aspects in the existing energy storage capacity configuration method, resulting in unreasonable energy storage configuration.

[0006] To achieve the above object, the present invention provides a method for configuring energy storage capacity based on state of charge and battery life, comprising:

[0007] S1, constructing a charge-discharge state quantity coefficient adjustment model, and setting a state of charge control strategy based on the charge-discharge state quantity coefficient adjustment model;

[0008] S2, establishing a correlation equation between the number of battery cycles and the depth of discharge, and constructing a battery energy storage life model based on the conversion coefficient and the correlation equation;

[0009] S3, constructing a comprehensive cost minimization objective function based on the state of charge control strategy and the battery energy storage life model, establishing comprehensive operation constraints of the wind-solar energy storage system, and obtaining a capacity configuration model based on the comprehensive cost minimization objective function and the comprehensive operation constraints;

[0010] S4, using the ant lion algorithm to solve the capacity configuration model, and obtain the power P of the energy storage system configuration B and capacity E B .

[0011] Based on the above technical steps, the present invention improves the frequency regulation performance and economy of wind and solar energy storage systems in high-penetration new energy power systems. By constructing a charge and discharge state coefficient adjustment model and setting a charge state control strategy, the battery safety is effectively protected, the battery service life is extended, and the performance of the energy storage system is optimized; the correlation between the number of battery cycles and the depth of discharge is established, and a battery energy storage life model is constructed in combination with the conversion coefficient to accurately predict the battery life, providing a basis for the reasonable arrangement of the energy storage system maintenance plan and reducing the operating cost; through the comprehensive cost minimization objective function, the ant lion algorithm is used to quickly solve the lowest cost energy storage configuration plan and optimize the energy storage configuration; through iterative solution and verification, it is ensured that the configuration plan meets the needs of the power system and meets all operating constraints, which improves the reliability and accuracy of the solution results, thereby achieving the optimal configuration of the energy storage capacity.

[0012] Furthermore, the charge and discharge state quantity coefficient adjustment model is: Where λ(t) represents the energy storage charging and discharging power state coefficient at time t, SOC ext Indicates the maximum or minimum value of the operating range, SOC ext With SOC opp Indicates one of the maximum or minimum values ​​in the operating range and adjusts the SOC according to the charge and discharge status of the energy storage system ext With SOC opp SOC(t) represents the state of charge of the battery energy storage system at time t.

[0013] Furthermore, the SOC is adjusted according to the charge and discharge state of the energy storage system. ext With SOC opp The values ​​of include:

[0014] (1) When the energy storage system is in the charging state, SOC ext is the minimum value of the operating range, SOC opp is the maximum value during the operation;

[0015] (2) When the energy storage system is in the discharge state, SOC ext is the maximum value of the operating range, SOC opp is the minimum value of the operating range.

[0016] By constructing a charging and discharging state quantity coefficient adjustment model, the energy storage charging and discharging state quantity coefficient λ(t) at time t can be quickly reduced, effectively realizing the pre-control of the charging and discharging power of the energy storage system.

[0017] Furthermore, setting a state of charge control strategy based on the charge and discharge state quantity coefficient adjustment model includes:

[0018] A1, judging whether the state of charge value of the current energy storage system is within the preset normal range, and whether the charge and discharge state coefficient λ(t) is 1; if yes, the energy storage system is charged and discharged normally; if no, jump to A2;

[0019] A2, judging whether the current state of charge value of the energy storage system is within the pre-over-discharge range or the over-discharge range; if yes, the original charge value is kept unchanged and the process jumps to A3; if no, it indicates that the current state of charge value of the energy storage system is within the pre-over-charge range or the over-charge range, the original discharge value is kept unchanged and the process jumps to A4;

[0020] A3, by calculating the formula P' B (t) = λ(t)P B (t) / η d Get the adjusted energy storage power generation power P' at time t B (t); where P B (t) represents the power generation of energy storage at time t, η d Indicates the discharge efficiency.

[0021] A4, by calculating the formula P' B (t) = λ(t)P B (t)η c Get the adjusted energy storage power generation power P' at time t B (t); where η c Indicates charging efficiency.

[0022] By precisely controlling the charging and discharging process, the energy storage system can keep the battery state of charge within the optimal range while meeting the frequency regulation requirements of the power system, which helps to improve the response speed and regulation accuracy of the energy storage system. By setting corresponding control strategies according to different state of charge values, the problem of battery performance degradation due to extreme charging and discharging conditions is avoided, ensuring the stability and reliability of the energy storage system during long-term operation.

[0023] Furthermore, the correlation equation between the battery cycle number and the discharge depth is: Among them, D a is the actual discharge depth, D r is the rated depth of discharge, N a is the actual number of cycles, N r is the rated number of cycles, α1 and α2 are the fitting coefficients of the correlation formula;

[0024] It should be noted that D a and N a The data are obtained through experiments.

[0025] Furthermore, the battery energy storage life model is obtained according to the conversion coefficient and the association relationship, including:

[0026] B1, sort out the conversion coefficient and the association relationship to obtain the conversion coefficient K DOD The expression is Among them, the conversion factor is the ratio of the rated number of cycles to the actual number of cycles;

[0027] B2, according to the discharge power formula E of battery energy storage r =N r D r E B , calculate the effective discharge capacity E of the energy storage battery at the rated discharge depth within one year as: Among them, E r is the total discharge capacity, E B is the capacity, N(d) is the number of discharge processes of the energy storage battery on the dth day, K DOD(d) is the conversion factor of the energy storage battery on the dth day, P B(d) is the discharge power of the energy storage battery on the dth day, Δt is N(d), K DOD(d) and P B(d) The sampling interval time point;

[0028] B3, the annual unit life of the battery Y is measured based on the ratio of the total discharge capacity of the battery discharged at the rated discharge depth during its life cycle to the effective discharge capacity of the battery discharged at the rated discharge depth within one year. r , the battery energy storage life model is obtained as:

[0029] By calculating the battery conversion coefficient in detail and taking into account the impact of different discharge depths on the battery cycle life, the prediction of the battery energy storage life model is made more accurate, which helps to more realistically reflect the battery loss in actual operation and provide a reliable basis for the optimal configuration of energy storage capacity.

[0030] Furthermore, the comprehensive cost minimization objective function is: minC = C a +C b +C w +C no Where, C a represents the initial investment cost of wind-solar-storage system construction, C b represents the daily operation and maintenance cost of the wind-solar energy storage system, C w represents the cost of wind and solar curtailment in the wind-solar energy storage system, C no It indicates that the wind, solar and energy storage system penalties are insufficient for costs;

[0031] The C a The calculation method is: C a =K p P B +K E E B ; Among them, K p and K E Respectively represent the unit power cost and unit electricity cost of the wind-solar-energy storage system, P B Indicates the rated power of the energy storage configuration;

[0032] The C b The calculation method is: Among them, F is the restriction coefficient, r is the discount rate, and Y r is the annual unit life of the battery, obtained from the battery energy storage life model, K K represents the annual operation and maintenance cost per unit power capacity of the wind-solar energy storage system, K V The annual operation and maintenance cost per unit capacity of the wind-solar energy storage system;

[0033] The C w The calculation method is: C w =K W ∑(P BWS (t)×Δt); where K W P represents the service unit price of a frequency regulation by the wind-solar-energy storage system. BWS (t) represents the reserved standby power for wind, solar and energy storage participating in primary frequency regulation at time t;

[0034] The C no The calculation method is: C no =KNO ΔT∑[P WT (t)-P BWS (t)-P B (t)]; where K NO represents the penalty coefficient for insufficient frequency regulation of wind, solar and energy storage systems, P WT (t) represents the reference value of the wind-solar-energy storage system dispatching power in the corresponding time, P' B (t) represents the energy storage power generation power adjusted at time t, which is obtained by adjusting the state of charge control strategy.

[0035] Furthermore, the establishment of comprehensive operation constraints of the wind-solar-energy storage system includes:

[0036] C1, establish the operating constraints of the wind-solar energy storage system: 0≤P W (t)≤P WN ; Among them, P WN Represents the rated installed capacity of the wind-solar energy storage system, P W (t) represents the power generation of the wind-solar-energy storage system at time t;

[0037] C2, establishes the operating constraints of battery energy storage as: and Among them, E Bmin Indicates the lower limit of the energy storage battery, E0 indicates the initial power of the energy storage battery, λ1 indicates the charge state coefficient, λ2 indicates the discharge state coefficient, and when charging: λ1=1,λ2=0, when discharging: λ1=0,λ2=1;

[0038] C3, combining the operation constraints of the wind-solar energy storage system and the battery energy storage to obtain a comprehensive operation constraint.

[0039] By constructing a comprehensive cost minimization objective function that includes initial construction costs, daily maintenance costs, wind and solar power curtailment costs, and penalty costs for insufficient frequency regulation, we can comprehensively consider various cost factors of the wind, solar, and energy storage systems, thereby optimizing the configuration and operation strategy of the energy storage system and minimizing the overall cost. It also combines the operating constraints of the wind, solar, and energy storage systems with the operating constraints of battery energy storage to ensure that the energy storage system meets specific technical requirements and restrictions during operation, thereby improving the operating efficiency and stability of the system.

[0040] Furthermore, the using of the ant lion algorithm to solve the capacity configuration model includes:

[0041] E1, set the data collection cycle and initial state of charge value;

[0042] E2, collecting parameter data related to the charge and discharge state coefficient adjustment model, the battery energy storage life model and the comprehensive cost minimization objective function during the data collection period; wherein the parameter data includes: rated discharge depth D r 、Actual number of cycles N a 、N number of cycles at rated discharge depth r 、Battery charging efficiency η c 、Battery discharge efficiency η d , fitting coefficient α1, fitting coefficient α2, initial power of energy storage battery E0, unit power cost K of wind and solar energy storage system p 、The unit electricity cost of wind-solar energy storage system K E , Annual operation and maintenance cost per unit power capacity of wind and solar energy storage system K K , Annual operation and maintenance cost per unit capacity of wind and solar energy storage system K V , the unit price of a frequency regulation service performed by a wind-solar-energy storage system is K W 、The penalty coefficient K for insufficient frequency regulation of wind, solar and energy storage systems NO ;

[0043] E3, set the parameters of the ant lion algorithm, and use the ant lion algorithm to calculate according to the parameters of the ant lion algorithm to solve the power P of the energy storage system configuration B and capacity E B ; Wherein, the parameters of the antlion algorithm include: the number of ants, the number of antlions and the maximum number of iterations;

[0044] E4, determine the power P B and the capacity E B Whether it meets the comprehensive operation constraints; if yes, complete the energy storage system configuration; if no, adjust the parameters of the ant lion algorithm and repeat E3 to E4.

[0045] By using the Antlion algorithm to solve the rated power and capacity of the energy storage configuration, it is possible to find the optimal energy storage configuration solution based on actual operating data and the comprehensive cost minimization objective function, thereby improving the economy and practicality of the energy storage system, and helping to reduce the phenomenon of wind and solar power abandonment, improve the utilization rate and absorption capacity of renewable energy, and thus promote the sustainable development of renewable energy.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The battery energy storage life model is constructed based on the relationship between the number of battery cycles and the depth of discharge. It can more accurately predict the service life of the battery, provide an important basis for the long-term planning and operation and maintenance management of energy storage capacity, and effectively avoid the problem of battery replacement or overuse caused by inaccurate predictions. The ant lion algorithm is combined with the comprehensive cost minimization objective function to comprehensively consider the costs of each link of the wind and solar energy storage system, from the initial investment in construction, daily maintenance, wind and solar power abandonment losses, to the punitive costs that may be caused by insufficient frequency regulation. It is verified through iteration to ensure that the configuration scheme meets the needs of the power system and all operating constraints, and realizes the optimal configuration of the energy storage capacity, which not only improves the solution efficiency and the reliability of the configuration scheme, but also significantly improves the economy of the energy storage system. At the same time, by constructing a charge and discharge state coefficient adjustment model and setting a sophisticated state of charge control strategy, the battery is effectively protected from damage under extreme charge and discharge conditions, the battery service life is extended, and the battery is ensured to fluctuate within the optimal state of charge range, thereby improving the safety and stability of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 A schematic flow chart of a method for configuring energy storage capacity based on state of charge and battery life provided by the present invention;

[0050] Figure 2 A logic judgment block diagram of a method for configuring energy storage capacity based on state of charge and battery life provided by the present invention;

[0051] Figure 3 A schematic diagram of a state of charge model provided by the present invention;

[0052] Figure 4 A constraint relationship diagram between the comprehensive cost and the energy storage configuration power and capacity provided by the present invention;

[0053] Figure 5 A frequency waveform diagram of a system under cost constraints provided by the present invention;

[0054] Figure 6 The present invention provides a frequency waveform diagram of the system under the conditions of state of charge, battery life and cost constraints. DETAILED DESCRIPTION

[0055] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0056] See also Figure 1-Figure 6 , an embodiment of the present invention provides a method for configuring energy storage capacity based on state of charge and battery life, comprising:

[0057] S1, constructing a charge and discharge state quantity coefficient adjustment model, and setting a charge state control strategy based on the charge and discharge state quantity coefficient adjustment model;

[0058] S2, establishing a correlation equation between the number of battery cycles and the depth of discharge, and constructing a battery energy storage life model based on the conversion coefficient and the correlation equation;

[0059] S3, construct a comprehensive cost minimization objective function based on the battery energy storage life model, establish the comprehensive operation constraints of the wind and solar energy storage system, and obtain a capacity configuration model based on the comprehensive cost minimization objective function and comprehensive operation constraints;

[0060] S4, use the ant lion algorithm to solve the capacity configuration model and obtain the power P of the energy storage system configuration B and capacity E B .

[0061] In the operation and management of battery energy storage systems, the key constraint is to reasonably set and limit the battery's state of charge. Specifically, it is to divide and define the different state of charge intervals of energy storage batteries during operation in detail, and form a clear state of charge model to better manage and optimize battery use, extend battery life, and improve the efficiency and reliability of energy storage systems;

[0062] The state of charge model in this embodiment is as follows Figure 3 As shown, the upper and lower limits of the state of charge are SOC max and SOC min , the warning range limit of overcharge and overdischarge is SOC Y2 and SOC X2 ,; then, the normal range of state of charge is [SOC X1 ,SOC Y1 ], the pre-over discharge range is [SOC X2 ,SOC X1 ), the over discharge range is [SOC min ,SOC X2 ), the pre-overcharge range is (SOC Y1 ,SOCY2 ], the overcharge range is (SOC Y2 ,SOC max ]; and the corresponding settings are shown in the following table;

[0063] <![CDATA[SOC max ]]> 1 <![CDATA[SOC Y2 ]]> 0.95 <![CDATA[SOC Y1 ]]> 0.8 <![CDATA[SOC X2 ]]> 0.2 <![CDATA[SOC X1 ]]> 0.1 <![CDATA[SOC min ]]> 0.05

[0064] The pre-control of the battery energy storage system is adjusted by adjusting the charge and discharge state quantity coefficient. When the state of charge runs to a certain range limit, the system will control the charge and discharge behavior of the battery by adjusting the charge and discharge state quantity coefficient. In this embodiment, the charge and discharge state quantity coefficient adjustment model is: Where λ(t) represents the energy storage charging and discharging power state coefficient at time t, SOC(t) represents the state of charge of the battery energy storage system at time t, and SOC ext With SOC opp Indicates the maximum or minimum value of the operating range, SOC opp Represents SOC ext The corresponding maximum or minimum value in the corresponding interval is related to the charge and discharge state of the energy storage system; specifically, when the energy storage system is in the charging state, SOC ext is the minimum value of the operating range, SOC opp is the maximum value during operation; when the energy storage system is in the discharge state, SOC ext is the maximum value of the operating range, SOC opp is the minimum value of the operating range;

[0065] By constructing a charge and discharge state quantity coefficient adjustment model, the energy storage charge and discharge state quantity coefficient λ(t) at time t can be quickly reduced, effectively realizing the pre-control of the charge and discharge power of the energy storage system;

[0066] The charge and discharge state coefficient adjustment model is a key component of the state of charge control strategy. By adjusting the charge and discharge coefficient, the control strategy can achieve fine control of the battery charge and discharge power. Specifically:

[0067] When the state of charge value of the energy storage system is within the normal range, when the charge and discharge state coefficient λ(t) = 1, the energy storage system performs normal charging and discharging;

[0068] When the state of charge value of the energy storage system is within the pre-over-discharge range or over-discharge range, the system will maintain the original charge value unchanged. The discharge is adjusted according to the mathematical model: P' B (t) = λ(t)P B (t) / η d , reduce the speed of the state of charge decrease, and obtain the energy storage power generation power P' adjusted at time t B (t); where P B(t) represents the power generation of energy storage at time t, η d Indicates discharge efficiency;

[0069] When the energy storage system's state of charge is in the pre-overcharge range or overcharge range, the system will maintain the original discharge value unchanged. Charging is adjusted according to the mathematical model: P' B (t) = λ(t)P B (t)η c , reduce the speed of the state of charge increase, and obtain the energy storage power generation power P' adjusted at time t B (t); where η d Indicates the discharge efficiency.

[0070] The energy storage life of a battery is mainly affected by multiple factors such as discharge times and depth, temperature, charge / discharge rate, storage time, electrolyte impurities, external environment, etc., among which discharge depth and discharge rate are the key factors that determine the battery life. In order to more accurately calculate the service life of the energy storage battery, in this embodiment, the experimental data of the number of cycles of lithium iron phosphate batteries are fitted by a data analysis method, and the correlation between the number of battery cycles and the depth of discharge is obtained as follows: Among them, D a is the actual discharge depth, D r is the rated depth of discharge, N a is the actual number of cycles, N r is the rated number of cycles, α1 and α2 are the fitting coefficients of the correlation equation;

[0071] Secondly, in the actual operation of the wind-solar-storage system, the charging and discharging process will show irregular and random fluctuation characteristics. Therefore, the discharge depth does not always remain constant. In order to more accurately study the discharge amount of each discharge stage, it is necessary to establish a battery energy storage life model based on the conversion coefficient and the correlation between the number of battery cycles and the discharge depth;

[0072] Specifically, in this embodiment, the conversion coefficient is defined as the ratio of the rated number of cycles to the actual number of cycles; then the conversion coefficient K is obtained according to the correlation between the number of battery cycles and the depth of discharge. DOD The expression is:

[0073] Next, the effective discharge of battery energy storage at the rated depth is certain, and its discharge power formula is: E r =N r D r E B , then the effective discharge capacity E of the energy storage battery at the rated discharge depth in one year is: Among them, E r is the total discharge capacity, EB is the capacity, N(d) is the number of discharge processes of the energy storage battery on the dth day, K DOD(d) is the conversion factor of the energy storage battery on the dth day, P B(d) is the discharge power of the energy storage battery on the dth day, Δt is N(d), K DOD(d) and P B(d) The sampling interval time point;

[0074] Finally, the annual unit life of the battery is measured by the ratio of the total discharge capacity of the battery at the rated discharge depth during its life cycle to the effective discharge capacity of the battery at the rated discharge depth within one year. r , the battery energy storage life model is obtained as:

[0075] The battery energy storage life model provides information about the cycle life of the battery at different depths of discharge, which is crucial for evaluating the long-term operating costs of the battery energy storage system. In the capacity configuration model of the wind and solar energy storage system, the comprehensive cost minimization objective function is closely related to the initial construction cost of the system, daily maintenance costs, wind and solar curtailment costs, and system frequency regulation penalty costs; among them, the system daily maintenance costs are directly affected by the battery energy storage life. If the battery cycle life is longer, then the battery replacement frequency will be lower, and the system daily maintenance costs will be reduced accordingly. Therefore, the battery energy storage life model provides a reliable basis for the subsequent optimization configuration of energy storage capacity.

[0076] The optimal configuration of wind, solar and storage capacity is a complex issue involving multiple factors, including the selection of energy storage technology, the determination of energy storage capacity, the layout and control strategy of the energy storage system, etc. At the same time, factors such as the economics and operational benefits of wind farms and photovoltaic power generation must also be considered. On this basis, the core goal of the optimal configuration of wind farm energy storage is to ensure the stable operation of the power system frequency, while meeting the needs of coordination between the wind, solar and storage systems and grid dispatch, and achieving optimal economy. To achieve this goal, in this embodiment, a comprehensive cost minimization objective total function is constructed that combines the initial construction cost of the wind, solar and storage system, daily maintenance costs, wind and solar abandonment costs, and penalty costs for insufficient frequency regulation: minC = C a +C b +C w +C no ;in,

[0077] C a represents the initial investment cost of wind-solar-energy storage system construction, and its expression is: C a =K p P B +K E E B ; Among them, K p and K ERespectively represent the unit power cost and unit electricity cost of the wind-solar-energy storage system, P B Indicates the rated power of the energy storage configuration;

[0078] C b It represents the daily operation and maintenance cost of the wind-solar energy storage system, and its expression is: Among them, F is the restriction coefficient, r is the discount rate, and Y r K is the annual unit life of the battery K represents the annual operation and maintenance cost per unit power capacity of the wind-solar energy storage system, K V The annual operation and maintenance cost per unit capacity of the wind-solar energy storage system;

[0079] C w It represents the cost of wind and solar curtailment in the wind-solar energy storage system, and its expression is C w =K W ∑(P BWS (t)×Δt); where K W P represents the service unit price of a frequency regulation by the wind-solar-energy storage system. BWS (t) represents the reserved standby power for wind, solar and energy storage participating in primary frequency regulation at time t;

[0080] C no It represents the insufficient penalty cost of the wind-solar-energy storage system, and its expression is C no =K NO ΔT∑[P WT (t)-P BWS (t)-P B (t)]; where K NO represents the penalty coefficient for insufficient frequency regulation of wind, solar and energy storage systems, P WT (t) represents the dispatching power reference value of the wind-solar-energy storage system within the corresponding time.

[0081] By analyzing the operating characteristics of the wind-solar energy storage system and battery energy storage, the operating constraints of the wind-solar energy storage system can be derived as follows: 0≤P W (t)≤P WN ; Among them, P WN Represents the rated installed capacity of the wind-solar energy storage system, P W (t) represents the power generation of the wind-solar-storage system at time t;

[0082] The operating constraints of the energy storage system mainly include charging and discharging power limits, upper and lower limits of the state of charge, continuous operation time limits, and other environmental conditions. These constraints together ensure the safe and efficient operation of the energy storage system. Considering the state of charge and battery life, the operating constraints of battery energy storage are: and and Among them, E Bminrepresents the lower limit of the energy storage battery, E0 represents the initial power of the energy storage battery, η c Represents the charging efficiency, η d represents the discharge efficiency, λ1 and λ2 represent the charge and discharge state coefficients, and when charging: λ1=1,λ2=0, when discharging: λ1=0,λ2=1.

[0083] After constructing the capacity configuration model of the wind-solar-energy storage system based on the comprehensive cost minimization objective function and constraint conditions, the ant lion algorithm is used to solve the rated power P of the energy storage configuration in the model. B And the capacity E B Specifically,

[0084] First, determine the data collection cycle; then set the initial state of charge value, and collect parameter data related to the charge and discharge state coefficient adjustment model, battery energy storage life model, and comprehensive cost minimization objective function during the data collection cycle; including: rated discharge depth D r 、Actual number of cycles N a 、N number of cycles at rated discharge depth r , battery charging efficiency η c 、Battery discharge efficiency η d , fitting coefficient α1, fitting coefficient α2, initial power of energy storage battery E0, unit power cost K of wind and solar energy storage system p 、The unit electricity cost of wind-solar energy storage system K E , Annual operation and maintenance cost per unit power capacity of wind and solar energy storage system K K , Annual operation and maintenance cost per unit capacity of wind and solar energy storage system K V , the unit price of a frequency regulation service performed by a wind-solar-energy storage system is K W 、The penalty coefficient K for insufficient frequency regulation of wind, solar and energy storage systems NO ;

[0085] Then set the number of ants, ant lions and the maximum number of iterations of the ant lion algorithm;

[0086] Finally, according to the charge and discharge state coefficient adjustment model, battery energy storage life model and comprehensive cost minimization objective function, the capacity configuration model of the wind and solar energy storage system is established, and the ant lion algorithm is used for calculation to solve the power P of the energy storage system configuration. B And the capacity E B .

[0087] In this embodiment, it is assumed that the total installed capacity of wind farms and photovoltaic power generation in a certain place is 15MW each, and the life of the energy storage battery is 12 years. Set the sampling interval Δt to 1min and the cycle to 24h; set the wind, photovoltaic and energy storage power ratio P of the primary frequency regulation reserved for the sampling interval Δt to BWS(t) is set to 10%; Table 1 is the collected parameter data related to the battery state of charge strategy, battery energy storage life model and comprehensive cost minimization objective function, and the comprehensive cost and energy storage configuration power P are obtained. B And the capacity E B The constraint relationship diagram is as follows Figure 4 As shown; Based on this relationship diagram, the ant lion algorithm can be used to further solve the energy storage configuration scheme that minimizes the comprehensive cost; When the number of iterations is set to 300, the number of ant lions is 50, and only the capacity configuration of the cost is considered, the result obtained by using the ant lion algorithm is as follows: The optimal energy storage capacity is 11.3MWh; Then, the charge and discharge state coefficient adjustment model and the state of charge control strategy are used to adjust the charge and discharge power coefficient to comprehensively consider the state of charge, battery life and cost constraints, and obtain the final energy storage capacity E B The energy storage configuration power is 10.2MW. B 3.5mw;

[0088] The system frequencies of the above two configuration methods are collected within 50 seconds, and the system load suddenly increases by 20% at the second second, and the frequency waveform of the system under the condition of considering the cost constraint and the frequency waveform of the system under the condition of considering the state of charge, battery life and cost constraint are obtained; Figure 5 and Figure 6 It can be seen that the energy storage capacity configuration that comprehensively considers the state of charge and cost constraints is 10.2MW, which is 9.73% less than the capacity configuration method proposed by considering only the cost constraints. However, when the load suddenly increases by 20%, it can still ensure that the lowest point of the system frequency is not less than 49.95Hz. Compared with the cost constraint conditions, the lowest point of the frequency deviation under the energy storage capacity configuration method is increased by 30%, and the maximum frequency change rate is about 0.1Hz / s. It can be seen that the stability of the system has been significantly improved.

[0089] Therefore, the energy storage capacity configuration method that comprehensively considers the state of charge and cost constraints not only reduces the demand for energy storage capacity, but also improves the frequency stability of the system. This method has higher economy and feasibility in practical applications and can better meet the needs of the power system.

[0090] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0091] Working principle of the present invention:

[0092] The present invention pre-controls the charging and discharging behavior of the energy storage battery by constructing a charge and discharge state quantity coefficient adjustment model and setting a charge state control strategy based on the model; constructs a battery energy storage life model by analyzing the relationship between the number of battery cycles and the discharge depth, and considering other factors that affect the battery life, which is used to evaluate the cycle life of the battery at different discharge depths; constructs a comprehensive cost minimization objective function based on the initial construction cost, daily maintenance cost, wind and solar abandonment cost, and insufficient frequency regulation penalty cost of the wind and solar energy storage system; establishes a capacity configuration model of the wind and solar energy storage system based on the battery energy storage life model and the comprehensive cost minimization objective function, considering the operating constraints of the system; solves the capacity configuration model with the ant lion algorithm to obtain the power and capacity of the energy storage system configuration. The algorithm searches for the energy storage configuration scheme that minimizes the comprehensive cost through iterative calculation; adjusts the charge and discharge power coefficient of the energy storage system through the charge and discharge state quantity coefficient adjustment model and the charge state control strategy, and obtains the final energy storage capacity configuration scheme by comprehensively considering the charge state and cost constraints.

[0093] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for configuring energy storage capacity based on state of charge and battery life, characterized in that: include: S1, constructing a charge-discharge state quantity coefficient adjustment model, and setting a state of charge control strategy based on the charge-discharge state quantity coefficient adjustment model; S2, establishing a correlation equation between the number of battery cycles and the depth of discharge, and constructing a battery energy storage life model based on the conversion coefficient and the correlation equation; S3, constructing a comprehensive cost minimization objective function based on the state of charge control strategy and the battery energy storage life model, establishing comprehensive operation constraints of the wind-solar energy storage system, and obtaining a capacity configuration model based on the comprehensive cost minimization objective function and the comprehensive operation constraints; S4, using the ant lion algorithm to solve the capacity configuration model, and obtain the power P of the energy storage system configuration B and capacity E B .

2. The method for configuring energy storage capacity based on state of charge and battery life according to claim 1, characterized in that: The charge and discharge state quantity coefficient adjustment model is: Where λ(t) represents the energy storage charging and discharging power state coefficient at time t, SOC ext With SOC opp Indicates one of the maximum or minimum values ​​in the operating range and adjusts the SOC according to the charge and discharge status of the energy storage system ext With SOC opp SOC(t) represents the state of charge of the battery energy storage system at time t.

3. The method for configuring energy storage capacity based on state of charge and battery life according to claim 2, characterized in that: The SOC is adjusted according to the charge and discharge state of the energy storage system ext With SOC opp The values ​​of include: (1) When the energy storage system is in the charging state, SOC ext is the minimum value of the operating range, SOC opp is the maximum value during the operation; (2) When the energy storage system is in the discharge state, SOC ext is the maximum value of the operating range, SOC opp is the minimum value of the operating range.

4. The method for configuring energy storage capacity based on state of charge and battery life according to claim 1, characterized in that: Setting a state of charge control strategy based on the charge and discharge state quantity coefficient adjustment model includes: A1, determine whether the current state of charge value of the energy storage system is within the normal range, and whether the charge and discharge state coefficient λ(t) is 1; if yes, the energy storage system is charged and discharged normally; if no, jump to A2; A2, determine whether the current state of charge value of the energy storage system is within the pre-over-discharge range or the over-discharge range; if yes, keep the original charge value unchanged and jump to A3; if no, the current state of charge value of the energy storage system is within the pre-over-charge range or the over-charge range, keep the original discharge value unchanged and jump to A4; A3, adjusting the discharge based on the charge and discharge state quantity coefficient adjustment model to obtain the adjusted energy storage power generation power P' at time t B (t) is: P' B (t) = λ(t)P B (t) / η d ; where P B (t) represents the power generation of energy storage at time t, η d Indicates discharge efficiency; A4, based on the numerical model, adjust the charging to obtain the adjusted energy storage power generation power P' at time t B (t) is: P' B (t) = λ(t)P B (t)η c ; where η c Indicates charging efficiency.

5. The method for configuring energy storage capacity based on state of charge and battery life according to claim 1, characterized in that: The correlation equation between the battery cycle number and the discharge depth is: Among them, D a is the actual discharge depth, D r is the rated depth of discharge, N a is the actual number of cycles, N r is the rated number of cycles, and α1 and α2 are the fitting coefficients of the correlation formula.

6. The method for configuring energy storage capacity based on state of charge and battery life according to claim 1, characterized in that: The battery energy storage life model is obtained according to the conversion coefficient and the association relationship, including: B1, according to the definition of the conversion coefficient and the association relationship, the conversion coefficient K is obtained DOD The expression is The conversion factor is defined as the ratio of the rated number of cycles to the actual number of cycles; B2, according to the discharge power formula E of battery energy storage r =N r D r E B , calculate the effective discharge capacity E of the energy storage battery at the rated discharge depth within one year as: Among them, E r is the total discharge capacity, E B is the capacity, N(d) is the number of discharge processes of the energy storage battery on the dth day, K DOD(d) is the conversion factor of the energy storage battery on the dth day, P B(d) is the discharge power of the energy storage battery on the dth day, Δt is N(d), K DOD(d) and P B(d) The sampling interval time point; B3, the annual unit life of the battery Y is measured based on the ratio of the total discharge capacity of the battery discharged at the rated discharge depth during its life cycle to the effective discharge capacity of the battery discharged at the rated discharge depth within one year. r , the battery energy storage life model is obtained as:

7. The method for configuring energy storage capacity based on state of charge and battery life according to claim 1, characterized in that: The comprehensive cost minimization objective function is: minC = C a +C b +C w +C no Where, C a represents the initial investment cost of wind-solar-storage system construction, C b represents the daily operation and maintenance cost of the wind-solar energy storage system, C w represents the cost of wind and solar curtailment in the wind-solar energy storage system, C no It indicates that the wind, solar and energy storage system penalties are insufficient for costs; The C a The calculation method is: C a =K p P B +K E E B ; Among them, K p and K E Respectively represent the unit power cost and unit electricity cost of the wind-solar-energy storage system, P B Indicates the rated power of the energy storage configuration; The C b The calculation method is: Among them, F is the restriction coefficient, r is the discount rate, and Y r is the annual unit life of the battery, obtained from the battery energy storage life model, K K represents the annual operation and maintenance cost per unit power capacity of the wind-solar energy storage system, K V The annual operation and maintenance cost per unit capacity of the wind-solar energy storage system; The C w The calculation method is: C w =K W ∑(P BWS (t)×Δt); where K W P represents the service unit price of a frequency regulation by the wind-solar-energy storage system. BWS (t) represents the reserved standby power for wind, solar and energy storage participating in primary frequency regulation at time t; The C no The calculation method is: C no =K NO ΔT∑[P WT (t)-P BWS (t)-P' B (t)]; where K NO represents the penalty coefficient for insufficient frequency regulation of wind, solar and energy storage systems, P WT (t) represents the reference value of the wind-solar-energy storage system dispatching power in the corresponding time, P' B (t) represents the energy storage power generation power adjusted at time t, which is obtained by adjusting the state of charge control strategy.

8. The method for configuring energy storage capacity based on state of charge and battery life according to claim 1, characterized in that: The comprehensive operation constraints of the wind-solar-energy storage system are established, including: C1, establish the operating constraints of the wind-solar energy storage system: 0≤P W (t)≤P WN ; Among them, P WN Represents the rated installed capacity of the wind-solar energy storage system, P W (t) represents the power generation of the wind-solar-energy storage system at time t; C2, establishes the operating constraints of battery energy storage as: and Among them, E Bmin Indicates the lower limit of the energy storage battery, E0 indicates the initial power of the energy storage battery, λ1 indicates the charge state coefficient, λ2 indicates the discharge state coefficient, and when charging: λ1=1,λ2=0, when discharging: λ1=0,λ2=1; C3, combining the operation constraints of the wind-solar energy storage system and the battery energy storage to obtain a comprehensive operation constraint.

9. The method for configuring energy storage capacity based on state of charge and battery life according to claim 1, characterized in that: The using the ant lion algorithm to solve the capacity configuration model includes: E1, set the data collection cycle and initial state of charge value; E2, collecting parameter data related to the charge and discharge state coefficient adjustment model, the battery energy storage life model and the comprehensive cost minimization objective function during the data collection period; wherein the parameter data includes: rated discharge depth D r 、Actual number of cycles N a 、N number of cycles at rated discharge depth r , battery charging efficiency η c 、Battery discharge efficiency η d , fitting coefficient α1, fitting coefficient α2, initial power of energy storage battery E0, unit power cost K of wind and solar energy storage system p 、The unit electricity cost of wind-solar energy storage system K E , Annual operation and maintenance cost per unit power capacity of wind and solar energy storage system K K , Annual operation and maintenance cost per unit capacity of wind and solar energy storage system K V , the unit price of a frequency regulation service performed by a wind-solar-energy storage system is K W 、The penalty coefficient K for insufficient frequency regulation of wind, solar and energy storage systems NO ; E3, set the parameters of the ant lion algorithm, and use the ant lion algorithm to calculate according to the parameters of the ant lion algorithm to solve the power P of the energy storage system configuration B and capacity E B ; Wherein, the parameters of the antlion algorithm include: the number of ants, the number of antlions and the maximum number of iterations; E4, determine the power P B and the capacity E B Whether it meets the comprehensive operation constraints; if yes, complete the energy storage system configuration; if no, adjust the parameters of the ant lion algorithm and repeat E3 to E4.

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