Rolling operation planning method for multi-energy complementary system based on energy storage health state prediction

Abstract

The present application belongs to the technical field of multi-energy complementary power generation, and discloses a rolling operation planning method for a multi-energy complementary system based on energy storage health state prediction, which comprises a multi-energy complementary system composed of a photovoltaic subsystem, a wind turbine subsystem and a lithium battery energy storage subsystem, the multi-energy complementary system being connected with a power grid to realize mutual transmission of power between the two; and further comprises a wind-solar-energy storage capacity attenuation double-layer optimization model composed of an upper system operation optimization model and a lower energy storage capacity attenuation prediction model. The present application utilizes the photovoltaic subsystem, the wind turbine subsystem and the energy storage subsystem to form a multi-energy complementary system, and establishes a wind-solar-energy storage capacity attenuation double-layer optimization model, realizes the optimal combination of system economy and renewable energy consumption rate by studying the system distribution and battery capacity attenuation mechanism, so as to realize operation planning for the multi-energy complementary system.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-energy complementary power generation, and in particular to a rolling operation planning method for a multi-energy complementary system based on energy storage health status prediction. Background Art

[0002] Currently, the development and utilization of wind and solar energy are becoming an increasing research focus. However, the characteristics of wind and solar energy are "random," "volatile," and "intermittent," making it difficult to meet grid access requirements and forcing curtailment. Furthermore, the instability of wind and solar power often impacts the grid when integrated, which can, in severe cases, cause significant economic losses. Therefore, to mitigate the impact of the "randomness" and "volatility" of wind and solar power on system integration, deploying energy storage batteries in wind and solar power plants is an effective solution. However, in practical applications, energy storage batteries often fail to meet their designed lifespan. This is because as the system ages, the capacity of the energy storage battery gradually degrades due to factors such as depth of discharge, temperature, charge and discharge rate, and number of cycles, resulting in a lifespan that falls short of the designed lifespan. Furthermore, in the later stages of the energy storage battery's lifespan, capacity degradation can make it impossible to meet regulation requirements. This not only impacts the absorption of excess wind and solar power, leading to large-scale curtailment of wind and solar power, but can also, in severe cases, impact the grid and cause significant economic losses.

[0003] If the energy storage capacity decays and this factor is ignored and the initially formulated operating strategy is continued, the energy storage battery will frequently operate in an unhealthy state of charge (SOC) condition, which will exacerbate the decay of the energy storage capacity. Therefore, it is necessary to timely adjust the charging and discharging operating strategy of the energy storage battery in the system. However, the adjustment of this operating strategy is constrained by the energy storage battery capacity configuration and decay status, and it is often difficult to achieve the desired results. Therefore, how to accurately clarify this two-way influencing mechanism and, based on this, construct a reasonable optimized operating strategy that can both meet the needs of high-quality power transmission from wind and solar hybrid power stations and maximize the service life of energy storage batteries is one of the issues that urgently need to be addressed in this field. In addition, how to quantify the game between the increased investment cost caused by energy storage configuration considering capacity decay and the improved economic benefits brought by a more reasonable and stable energy storage system is also an issue that urgently needs to be addressed in this field.

[0004] To solve the above problems, Chinese patent (publication number: CN116307032A) discloses a method and system for optimizing the energy storage capacity of a new energy multi-energy complementary system. The method includes obtaining parameter data related to the energy storage capacity of a large-scale new energy multi-energy complementary system, filtering the parameter data, and obtaining target parameter data for building an outer optimization model and an inner optimization model. Based on the target parameter data, the outer optimization model and the inner optimization model are built. Based on a double-layer optimization algorithm, the outer optimization model and the inner optimization model are solved to obtain the optimal solution of the outer optimization model and the inner optimization model. The energy storage capacity of the large-scale new energy multi-energy complementary system is optimized according to the optimal solution. Chinese patent (publication number: CN116845932A) discloses a shared energy storage optimization configuration method in a multi-microgrid integrated energy system. Considering the cold, heat, electricity, and multi-energy flow complementarity, a mathematical model of a shared energy storage integrated energy system is proposed. A multi-microgrid shared energy storage double-layer optimization configuration model is constructed. The upper layer takes the minimum daily operating cost of the shared energy storage power station as the objective function, and the maximum charge and discharge power and capacity of the energy storage device as the decision variables. The lower layer takes the minimum total energy consumption cost of the multi-microgrid as the objective function, and the output of each device, electricity purchase and sale behavior, etc. as the decision variables. After simplifying the double-layer model using KKT conditions, linear programming is used for solving. A multi-scenario comparison and energy storage configuration effect analysis is established. Chinese patent (publication number: CN116090664A) discloses a multi-energy complementary optimization system based on park energy storage and wind-solar-gas-electricity-water, which can reasonably call the electric energy of each power generation area according to the actual situation, and not evenly distribute, complement each other, and reasonably optimize. These methods / systems focus on the initial configuration of energy storage capacity and the optimization of the operation strategy between wind energy, photovoltaic, and energy storage in a multi-energy complementary system. However, this optimization often ignores the impact of capacity degradation. In addition, after the capacity configuration and operation strategy optimization scheme is completed, the analysis of energy storage capacity degradation and economic efficiency during system operation is mostly focused on the static changes from the beginning of energy storage configuration to the end of life, and there is little involvement in dynamic and rolling changes year by year, which is not conducive to the detailed study of multi-energy complementary systems. SUMMARY

[0005] The present application aims to provide a multi-energy complementary system rolling operation planning method based on energy storage health state prediction. A photovoltaic subsystem, a wind turbine subsystem, and an energy storage subsystem are used to form a multi-energy complementary system, and a wind-solar-energy storage capacity degradation double-layer optimization model is established. By studying the system distribution and battery capacity degradation mechanism, the joint system economy and renewable energy consumption rate are optimized, thereby realizing the operation planning of the multi-energy complementary system. The problems in the background art are solved.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A rolling operation planning method for a multi-energy complementary system based on energy storage health status prediction, the method comprising:

[0008] A multi-energy complementary system consisting of a photovoltaic subsystem, a wind turbine subsystem, and a lithium battery energy storage subsystem is connected to the power grid to enable power transmission between the two. The photovoltaic and wind turbine subsystems generate complementary power to meet the base load, while the lithium battery energy storage subsystem is responsible for adjusting the gap between power generation and load, storing excess power generated by the entire system and discharging it when power is insufficient.

[0009] A two-layer optimization model for wind, solar and energy storage capacity attenuation is composed of an upper-layer system operation optimization model and a lower-layer energy storage capacity attenuation prediction model. The output of the upper-layer system operation optimization model provides operating parameters for the operation optimization of the lower-layer energy storage capacity attenuation prediction model, and the lower-layer energy storage capacity attenuation prediction model provides an operation plan that takes energy storage capacity attenuation into account for the upper-layer system operation optimization model. The two are based on the real-time two-way scheduling strategy of the multi-energy complementary base. The upper-layer system operation optimization model and the lower-layer energy storage capacity attenuation prediction model are optimized through annual rolling optimization, repeated iterations, and real-time two-way scheduling to obtain the final result.

[0010] Furthermore, in the upper-level system operation optimization model, while ensuring that the load is met, the system revenue is improved, and the lower-level energy storage capacity attenuation prediction model is provided with battery throughput, state of charge, number of cycles, and charge and discharge rate to calculate the battery attenuation rate. The total revenue from wind and photovoltaic power generation and battery-powered electricity sales minus the wind and photovoltaic system operation and maintenance costs, power curtailment penalties, power purchase costs due to power shortages, battery operating costs, and depreciation costs is the total difference, which is the system's daily operating revenue. The total objective function for maximizing the system's daily operating revenue is:

[0011]

[0012] Where, C is the time-of-use electricity price, RMB / MWh; C dl is the power shortage penalty coefficient, C la is the power abandonment penalty coefficient, C1 is wind power, C2 is the photovoltaic operation and maintenance coefficient, RMB; P w 、P pv are wind power and photovoltaic grid-connected electricity, MW; P d 、P dl 、P la are the battery discharge capacity, abandoned capacity and power shortage, MW; P f 、P g B is the output of wind turbine and photovoltaic on the same day; y is the daily operation and maintenance cost of the battery, RMB / MWh.

[0013] Furthermore, in the upper-level system operation optimization model, the system energy balance is ensured by constraining power balance, energy conservation, wind power access, energy storage battery capacity and charging and discharging, and energy storage battery SOC;

[0014] According to the assumption that the total power supply is equal to the load size, the function of the power balance constraint is:

[0015]

[0016] Where, P load is the total electrical load, MW;

[0017] According to the total input equal to the total output, the function of the energy conservation constraint is:

[0018]

[0019] Where Q w , Q pv , Q c They are the total power generation of wind power and photovoltaic power, and the total battery charge, MW; Q load , Q cl , Q dl They are load power consumption, total battery discharge, and system power abandonment;

[0020] The function of wind power grid power constraint is:

[0021]

[0022] Where, P wmin 、P wmax 、P pvmin 、P pvmax are the minimum and maximum values ​​of wind power and photovoltaic power respectively;

[0023] The energy storage battery capacity and charge and discharge constraint functions are:

[0024]

[0025]

[0026]

[0027] Where V Lmin 、V Lmax Respectively represent the minimum and maximum values ​​of the energy storage battery capacity; P dmin 、P dmax Respectively represent the minimum and maximum values ​​of discharge power; P cmin 、P cmax Respectively represent the minimum and maximum values ​​of charging power;

[0028] The function of energy storage battery SOC constraint is:

[0029]

[0030] Where, SOC min , SOC max Respectively represent the minimum and maximum values ​​of SOC;

[0031] The battery is not charged and discharged at the same time:

[0032]

[0033] Where B c 、B d It is a binary variable indicating the battery charge and discharge status, and its value is 0 / 1.

[0034] Furthermore, the lower-level energy storage capacity attenuation prediction model is used to formulate and optimize the charging and discharging strategies of energy storage batteries, ensure the stability of power transmission, reduce the probability of wind and solar power abandonment and power shortage in the system, and at the same time reduce the capacity attenuation of energy storage batteries and reduce the comprehensive operating costs of lithium batteries; the specific method is: first, based on the model-data combination method, the Arrhenius semi-empirical formula is improved, the influencing factor of internal resistance is introduced, and the parameters are identified in combination with experimental data to obtain a more accurate capacity cycle attenuation model; then, the cycle and calendar attenuation of the battery are comprehensively considered to obtain a comprehensive attenuation model.

[0035] Furthermore, the comprehensive attenuation model includes a capacity cycle attenuation model for an energy storage battery, a capacity calendar attenuation model for an energy storage battery, and a comprehensive capacity attenuation model for an energy storage battery;

[0036] Among them, the energy storage battery capacity cycle attenuation model is:

[0037]

[0038] Where Crate is the charge and discharge rate; T is the temperature; Ah battery throughput is related to the number of cycles and depth of discharge;

[0039] Energy storage battery capacity calendar decay model:

[0040]

[0041] Where: Q loss-cal is the calendar decay rate, %; T is the battery storage temperature, K; t is the storage time, day; z is the exponential factor, which is 0.5; SOC is the ratio of the remaining power to the rated capacity;

[0042] Comprehensive capacity attenuation model of energy storage batteries:

[0043]

[0044] Among them, a, b, c, d, e, f, g, and h are fitting parameters.

[0045] Furthermore, in the lower-level energy storage capacity attenuation prediction model, the objective function for minimizing the comprehensive cost of energy storage battery operation is:

[0046]

[0047] Where, is the daily maintenance cost of the battery; C BA is the battery investment cost, C ZH is the battery replacement cost;

[0048] in, It is divided into daily operation and maintenance cost and daily depreciation cost, and its function is:

[0049]

[0050] Where C e is the unit capacity investment cost of the lithium battery energy storage system, RMB / MWh; E is the initial capacity of the energy storage system in MWh; 0.08 is the operation and maintenance coefficient;

[0051] Investment cost C BA The function is:

[0052]

[0053] Where N E N is the average annual charge and discharge times of the battery; B is the battery cycle life; P B is the rated power of the battery; E B is the rated capacity of the battery; U BP is the unit power cost of the battery; U BE is the unit energy cost of the battery;

[0054] Replacement cost C ZH The function is:

[0055]

[0056] Where, is the average annual reduction rate of battery cost, k is the number of battery replacements, n is the battery life, is the battery capacity.

[0057] Furthermore, the interaction mechanism of the two-layer optimization model of wind, solar and energy storage capacity attenuation is as follows: the upper-layer system operation optimization model inputs power generation, load and energy storage capacity data, and obtains the total daily operating income after MATLAB operation optimization. The battery throughput, state of charge, number of cycles and charge and discharge rate obtained by the upper-layer system operation optimization model are substituted into the lower-layer energy storage capacity attenuation prediction model to calculate the battery attenuation, and use this to simulate the battery attenuation for one year. The remaining capacity of the battery after attenuation calculated by the lower-layer energy storage capacity attenuation prediction model is substituted back into the upper-layer system operation optimization model for calculation in the next year. This process is repeated and rolled out until the battery capacity attenuates to 80%, that is, the battery is replaced and the attenuation calculation is restarted.

[0058] This process is repeated until the battery capacity decays to 80%, at which point the battery is replaced and the decay calculation starts again.

[0059] The beneficial effects of the technical solution are:

[0060] 1. The present invention provides a rolling operation planning method for a multi-energy complementary system based on the prediction of the health status of energy storage. The multi-energy complementary system combines wind, solar and battery energy storage. The rapid response capability and stability of battery energy storage will greatly improve the wind and solar absorption rate of the system, significantly reduce wind and solar power abandonment and power shortage, reduce system operating costs, improve power quality, and make power bundling and transmission more stable and reliable; at the same time, the daily cycle number of battery energy storage obtained after the operation of the wind and solar complementary system will also have a very large impact on the energy storage capacity attenuation model of the lower layer. The more daily cycle numbers, the faster the battery cycle energy storage decays, and the more the battery capacity decreases; conversely, the capacity obtained after attenuation of the lower layer will in turn affect the scheduling problem of the upper wind and solar multi-energy complementary system, and thus affect the system economy.

[0061] 2. The rolling operation planning method for a multi-energy complementary system based on the prediction of the health status of energy storage provided by the present invention adopts a data-driven approach to improve the Arrhenius semi-empirical formula, and introduces the influencing factor of internal resistance, so that the capacity cycle attenuation formula can more accurately describe the influencing factors such as temperature, internal resistance, number of cycles, and charge and discharge rate. At the same time, a more accurate capacity attenuation model is constructed by comprehensively considering the calendar attenuation and cycle attenuation of the energy storage battery, and the effectiveness of the model is verified in combination with relevant experimental data, providing a reference solution for predicting energy storage capacity attenuation in actual engineering.

[0062] 3. The multi-energy complementary system rolling operation planning method based on energy storage health state prediction provided by the application makes clear the influence of the adjustment capacity decline caused by energy storage capacity attenuation on wind and light power generation system wind and light abandonment, power supply stability, system operation scheduling decision, etc., clarifies the influence of capacity attenuation on system stability and economy caused by the operation strategy formulated without considering energy storage capacity attenuation, and quantifies the above influence factors by introducing relevant penalty costs, thereby providing a feasible reference scheme for energy storage operation and replacement cost evaluation in actual engineering applications.

[0063] 4. The multi-energy complementary system rolling operation planning method based on energy storage health state prediction provided by the application reduces the influence of energy storage capacity attenuation on the safety and economy of the wind and light power generation system by reasonably configuring the energy storage capacity and formulating an operation strategy considering capacity attenuation. The energy storage capacity attenuation and annual rolling change of system economic benefits are obtained by verifying the scheme with typical daily wind and light output and power load data of a certain region in the west, and compared and analyzed with the existing scheme, thereby verifying the advancement of the scheme of the application in system long-term benefits.

[0064] In summary, the method provided by the application can first configure more reasonable energy storage capacity for a wind and light complementary power station, reduce the situation that the capacity cannot meet the adjustment demand caused by capacity attenuation, secondly, the method provides a more accurate energy storage capacity attenuation model, which is helpful to improve the accuracy of energy storage configuration cost estimation and formulate a more reasonable operation scheduling scheme, and finally, the optimization scheduling method provided by the method can effectively reduce the wind and light abandonment rate, improve the stability of power transmission, prolong the service life of energy storage batteries, and maximize the economic benefits of the multi-energy complementary system in the whole life cycle.

[0065] In addition, compared with the existing scheme for estimating capacity attenuation and economic benefits, the application fully considers the influence of the actual operation condition of the energy storage battery on the power regulation and power response of the new energy base, not only provides the dynamic year-by-year rolling change curve of the energy storage capacity attenuation and the dynamic year-by-year change of the economic benefits of the multi-energy complementary system, but also gives the dynamic year-by-year change of the wind and light abandonment rate and the power shortage of the new energy base with the energy storage capacity attenuation, thereby providing more complete and more detailed system whole life cycle operation conditions for investors and operators. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 FIG. 1 is a structure diagram of a wind and light battery energy storage multi-energy complementary system in embodiment 1 of the application;

[0067] Figure 2 FIG. 2 is a structure diagram of a wind and light energy storage capacity attenuation double-layer optimization model in embodiment 1 of the application;

[0068] Figure 3This is the electric power balance diagram of the wind-solar-storage multi-energy complementary system under ideal conditions in Example 2 of the present invention;

[0069] Figure 4 This is the electric power balance diagram of the wind-solar-storage multi-energy complementary system considering the attenuation situation in Example 2 of the present invention;

[0070] Figure 5 This is a graph showing the SOH variation of lithium batteries under different DODs over ten years in Example 2 of the present invention;

[0071] Figure 6 This is a graph showing the SOH variation of the energy storage lithium battery under different configurations over ten years in Example 2 of the present invention. DETAILED DESCRIPTION

[0072] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments: Example 1

[0073] A rolling operation planning method for a multi-energy complementary system based on energy storage health status prediction includes:

[0074] The multi-energy complementary system consists of a photovoltaic subsystem, a wind turbine subsystem, and a lithium battery energy storage subsystem. The multi-energy complementary system is connected to the power grid to achieve mutual transmission of electricity between the two. Among them, the photovoltaic subsystem and the wind turbine subsystem generate complementary power to meet the basic load, and the lithium battery energy storage subsystem is responsible for adjusting the gap between power generation and load, storing the excess power generated by the entire system and discharging it when there is a power shortage. The structure of the multi-energy complementary system is as follows: Figure 1 As shown;

[0075] A wind-solar-energy storage capacity attenuation double-layer optimization model is composed of an upper-layer system operation optimization model and a lower-layer energy storage capacity attenuation prediction model; wherein, the output result of the upper-layer system operation optimization model provides operation parameters for the operation optimization of the lower-layer energy storage capacity attenuation prediction model, and the lower-layer energy storage capacity attenuation prediction model provides an operation plan that takes energy storage capacity attenuation into account for the upper-layer system operation optimization model; both are based on the real-time two-way scheduling strategy of the multi-energy complementary base, and the upper-layer system operation optimization model and the lower-layer energy storage capacity attenuation prediction model are optimized through annual rolling optimization, repeated iteration, and real-time two-way scheduling to obtain the final result; the structure of the wind-solar-energy storage capacity attenuation double-layer optimization model is as follows Figure 2 shown.

[0076] In this embodiment, a wind-solar-energy-storage capacity-decay multi-energy complementary system model is designed with the goal of maximizing the system's daily operating revenue and minimizing the comprehensive operating cost of lithium batteries. Based on typical daily wind-solar output and electricity load data from a certain western region, including operating and maintenance costs, penalty costs for curtailing wind and solar power, and power shortage penalty costs, a simulation program is written in the MATLAB platform, and a particle swarm algorithm is selected for solving the problem.

[0077] In this embodiment, the integrated energy system configuration includes: a 360MW wind turbine, a 210MW photovoltaic generator, and a 115.2MWh energy storage battery pack. The decision variable is the power output of each system component, and the optimization variable is the system operating cost.

[0078] In the upper-level system operation optimization model, system revenue is improved while ensuring that load is met. The lower-level energy storage capacity degradation prediction model is provided with battery throughput, state of charge, number of cycles, and charge and discharge rate to calculate the battery degradation rate. The total revenue from wind and photovoltaic power generation and battery-powered electricity sales is deducted from the wind and photovoltaic system operation and maintenance costs, power curtailment penalties, power purchase costs due to power shortages, battery operating costs, and depreciation costs. The total difference is the system's daily operating revenue. The total objective function for maximizing the system's daily operating revenue is:

[0079]

[0080] Where, C is the time-of-use electricity price, RMB / MWh; C dl is the power shortage penalty coefficient, C la is the power abandonment penalty coefficient, C1 is wind power, C2 is the photovoltaic operation and maintenance coefficient, RMB; P w 、P pv are wind power and photovoltaic grid-connected electricity, MW; P d 、P dl 、P la are the battery discharge capacity, abandoned capacity and power shortage, MW; P f 、P g B is the output of wind turbine and photovoltaic on the same day; y is the daily operation and maintenance cost of the battery, RMB / MWh;

[0081] In the upper-level system operation optimization model, the system energy balance is ensured by constraining power balance, energy conservation, wind power access, energy storage battery capacity and charging and discharging, and energy storage battery SOC;

[0082] According to the assumption that the total power supply is equal to the load size, the function of the power balance constraint is:

[0083]

[0084] Where, P load is the total electrical load, MW;

[0085] According to the total input equal to the total output, the function of the energy conservation constraint is:

[0086]

[0087] Where Q w , Qpv , Q c They are the total power generation of wind power and photovoltaic power, and the total battery charge, MW; Q load , Q cl , Q dl They are load power consumption, total battery discharge, and system power abandonment;

[0088] The function of wind power grid power constraint is:

[0089]

[0090] Where, P wmin 、P wmax 、P pvmin 、P pvmax are the minimum and maximum values ​​of wind power and photovoltaic power respectively;

[0091] The energy storage battery capacity and charge and discharge constraint functions are:

[0092]

[0093]

[0094]

[0095] Where V Lmin 、V Lmax Respectively represent the minimum and maximum values ​​of the energy storage battery capacity; P dmin 、P dmax Respectively represent the minimum and maximum values ​​of discharge power; P cmin 、P cmax Respectively represent the minimum and maximum values ​​of charging power;

[0096] The function of energy storage battery SOC constraint is:

[0097]

[0098] Where, SOC min , SOC max Respectively represent the minimum and maximum values ​​of SOC;

[0099] The battery is not charged and discharged at the same time:

[0100]

[0101] Where B c 、B d It is a binary variable indicating the battery charge and discharge status, and its value is 0 / 1.

[0102] The lower-level energy storage capacity decay prediction model is used to formulate and optimize the charging and discharging strategies of energy storage batteries, ensure the stability of power transmission, reduce the probability of wind and solar power curtailment and power shortages in the system, and simultaneously reduce the capacity decay of energy storage batteries and the overall operating costs of lithium batteries. The specific method is as follows: first, based on the model-data combination method, the Arrhenius semi-empirical formula is improved, the influencing factor of internal resistance is introduced, and the parameters are identified in combination with experimental data to obtain a more accurate capacity cycle decay model; then, the battery cycle and calendar decay are comprehensively considered to obtain a comprehensive decay model.

[0103] The comprehensive attenuation model includes the energy storage battery capacity cycle attenuation model, the energy storage battery capacity calendar attenuation model and the energy storage battery comprehensive capacity attenuation model;

[0104] Among them, the energy storage battery capacity cycle attenuation model is:

[0105]

[0106] Where Crate is the charge and discharge rate; T is the temperature; Ah battery throughput is related to the number of cycles and depth of discharge;

[0107] Energy storage battery capacity calendar decay model:

[0108]

[0109] Where: Q loss-cal is the calendar decay rate, %; T is the battery storage temperature, K; t is the storage time, day; z is the exponential factor, which is 0.5; SOC is the ratio of the remaining power to the rated capacity;

[0110] Comprehensive capacity attenuation model of energy storage batteries:

[0111]

[0112] In this embodiment, a=1.2918 10 -7 , b=-7.6878 10 -5 , c=0.0114, d=-6.7149 10 -3 , e=2.3467, f=154.9601, g=0.6898, h=-2.9467 10 3 , based on test data.

[0113] In the lower-level energy storage capacity attenuation prediction model, the objective function for minimizing the comprehensive cost of energy storage battery operation is:

[0114]

[0115] wherein, C is the daily maintenance cost of the battery; C BA C is the investment cost of the battery, ZH C is the replacement cost of the battery;

[0116] wherein, The daily operation and maintenance cost and the daily depreciation cost are divided into functions:

[0117]

[0118] wherein, C e =1500000 yuan / MWh is the investment cost of the lithium battery energy storage system per unit capacity; E=115.2 MWh is the initial capacity of the energy storage system MWh; 0.08 is the operation and maintenance coefficient;

[0119] The investment cost C BA is a function of:

[0120]

[0121] wherein, N E is the annual average number of charging and discharging times of the battery; N B is the cycle life of the battery; P B is the rated power of the battery; E B is the rated capacity of the battery; U BP is the unit power cost of the battery; U BE is the unit energy cost of the battery;

[0122] The replacement cost C ZH is a function of:

[0123]

[0124] wherein, is the annual average decline rate of the battery cost, k is the number of battery replacements, n is the battery life, is the battery capacity;

[0125] The interaction mechanism of the wind-solar- energy storage capacity attenuation double-layer optimization model is that the upper system operation optimization model inputs power generation, load and energy storage capacity data, obtains the daily operation total income after MATLAB operation optimization, and substitutes the battery throughput, state of charge, cycle number and charging and discharging rate obtained by the upper system operation optimization model into the lower energy storage capacity attenuation prediction model to calculate the battery attenuation amount, and simulate the attenuation of the battery in one year. The remaining capacity of the battery after attenuation calculated by the lower energy storage capacity attenuation prediction model is substituted back into the upper system operation optimization model for operation in the next year, and the iteration is repeated until the battery capacity is attenuated to 80%, that is, the battery is replaced, and the attenuation is calculated again.

[0126] Example 2

[0127] Without considering the battery capacity attenuation, a case study of the multi-energy complementary system is conducted. Taking the typical day wind and solar output and power load data of a certain area in the west as an example, the power balance diagram is obtained as follows: Figure 3 shown.

[0128] Considering the attenuation of energy storage capacity, after the system has been in operation for ten years, the electric power balance diagram is as follows: Figure 4 shown.

[0129] A comparative analysis of the operating conditions in the first and tenth years shows that as the capacity of the energy storage batteries decays, power shortages occur during peak hours of electricity consumption, and power abandonment becomes more frequent during low-consumption periods. This is because the operating strategy is not adjusted in a timely manner as the energy storage capacity decays, resulting in insufficient regulation capacity of the energy storage batteries, leading to large-scale power shortages and power abandonment, causing economic losses to the system.

[0130] For energy storage systems, a comparative analysis is conducted on the impact of energy storage capacity decay on system economics at different depths of discharge (DOD) and different capacity configurations. Finally, recommendations on energy storage configuration and operation strategies are given through comparative analysis.

[0131] The battery state of health (SOH) changes under different DODs are as follows: Figure 5 As shown; the battery SOH changes under different capacity configurations are as follows Figure 6 shown.

[0132] The operational quantification is summarized in Tables 1 and 2:

[0133] Table 1 Daily power shortage, power abandonment and service life of the system under different DOD

[0134] DOD 60% DOD 80% DOD 100% DOD Abandoned electricity 188.84 MWh 176.719 MWh 164.59 MWh Low power 188.63 MWh 166.747 MWh 144.863 MWh Use life 6 5 5

[0135] Table 2 System daily power shortage, power abandonment and service life under different energy storage capacity configurations

[0136]

[0137] Depend on Figure 5 and Figure 6As shown in Tables 1 and 2, the capacity of energy storage batteries decreases faster as the DOD range increases. At 60% DOD, the batteries will need to be replaced after the sixth year, while at 80% and 100% DOD, they will need to be replaced after the fifth year. The battery decay is particularly rapid at 100% DOD. Because this system is a wind, solar, and storage multi-energy system, to cope with the uncertainty of wind and solar output and the complex and variable load, the energy storage batteries need to be frequently charged and discharged to adjust the system's power and energy balance. Therefore, their capacity decay is more rapid than that of batteries used daily. If the corresponding upper and lower limits of the state of charge are not set appropriately, the battery capacity decay will be even faster, the daily revenue of the entire system will continue to decrease, the cost of battery replacement will increase, and the economic efficiency of the system will deteriorate.

[0138] Depend on Figure 5 and Figure 6 As can be seen, at 303.1 K and 80% DOD, the 85.5 MWh battery capacity decays most rapidly, while the 115.2 MWh configuration exhibits a moderate rate of decay and the 142.5 MWh configuration exhibits the slowest decay. This is because the 142.5 MWh configuration has a large energy storage battery capacity and strong system peak-shaving and valley-filling capabilities. The battery does not need to operate at high frequencies or for extended periods at unhealthy SOC conditions, resulting in a relatively smooth operating curve. Furthermore, the larger battery capacity results in a relatively reduced number of daily battery cycles, resulting in the slowest decay.

[0139] In summary, the dynamic degradation of batteries should be fully considered when designing energy storage capacity and operating strategies for multi-energy complementary systems. Based on annual rolling data on parameters such as throughput, idle time, and temperature, combined with a storage capacity degradation prediction model, the annual rolling degradation of energy storage batteries is calculated. The data shows that by the fifth year, battery capacity has decayed to below 80% under certain operating conditions. At this point, the system's regulatory capacity declines, power curtailment and power shortages increase, and the economic benefits of the entire new energy base are reduced. However, by considering battery degradation and configuring the system with a larger capacity energy storage system, power curtailment and power shortages are significantly reduced, and the energy storage life is significantly increased.

[0140] In addition, the battery DOD setting should be adjusted in a timely manner. Long-term and high-frequency full charging and discharging will accelerate the attenuation of battery life. The battery DOD setting should be adjusted according to the annual attenuation of energy storage capacity. Data shows that under the same capacity configuration, the higher the DOD, the shorter the battery life. Dynamically adjusted DOD can avoid the battery from operating under sub-healthy SOC conditions as much as possible, thereby extending the life of the energy storage battery. Considering capacity attenuation to configure energy storage capacity will increase investment costs, but a reasonable capacity configuration plan will increase the system's online power, reduce the frequency of power shortages, and maximize the operating life of energy storage batteries, thereby improving the economic benefits of new energy bases. This patent quantifies the game between the two and gives the dynamic changes in economic efficiency year by year. The data shows that a reasonable capacity configuration can bring better economic benefits.

[0141] The above is only an embodiment of the present invention, and common knowledge such as the specific technical solutions or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the description can be used to interpret the content of the claims.

Claims

1. A rolling operation planning method for a multi-energy complementary system based on energy storage health status prediction, characterized in that: The method comprises: A multi-energy complementary system consisting of a photovoltaic subsystem, a wind turbine subsystem, and a lithium battery energy storage subsystem is connected to the power grid to enable power transmission between the two. The photovoltaic and wind turbine subsystems generate complementary power to meet the base load, while the lithium battery energy storage subsystem is responsible for adjusting the gap between power generation and load, storing excess power generated by the entire system and discharging it when power is insufficient. A two-layer optimization model for wind, solar, and energy storage capacity decay, consisting of an upper-layer system operation optimization model and a lower-layer energy storage capacity decay prediction model. The output of the upper-layer system operation optimization model provides operating parameters for the operation optimization of the lower-layer energy storage capacity decay prediction model, and the lower-layer energy storage capacity decay prediction model provides an operation plan that takes energy storage capacity decay into account for the upper-layer system operation optimization model. Both are based on a real-time two-way scheduling strategy for multi-energy complementary bases. The upper-layer system operation optimization model and the lower-layer energy storage capacity decay prediction model are optimized annually, iterated repeatedly, and dispatched in real time in both directions to obtain the final result. In the upper-level system operation optimization model, system revenue is improved while ensuring load is met. The lower-level energy storage capacity degradation prediction model is provided with battery throughput, state of charge, number of cycles, and charge and discharge rate to calculate the battery degradation rate. The total revenue from wind and photovoltaic power generation and battery-powered electricity sales is deducted from the wind and photovoltaic system operation and maintenance costs, power curtailment penalties, power purchase costs due to power shortages, battery operating costs, and depreciation costs. The total difference is the system's daily operating revenue. In the upper-level system operation optimization model, the system energy balance is ensured by constraining power balance, energy conservation, wind power access, energy storage battery capacity and charging and discharging, and energy storage battery SOC; The lower-level energy storage capacity decay prediction model is used to formulate and optimize the charging and discharging strategies of energy storage batteries, ensure the stability of power transmission, reduce the probability of wind and solar power curtailment and power shortages in the system, and simultaneously reduce the capacity decay of energy storage batteries and the overall operating costs of lithium batteries. The specific method is as follows: first, based on the model-data combination method, the Arrhenius semi-empirical formula is improved, the influencing factor of internal resistance is introduced, and the parameters are identified in combination with experimental data to obtain a more accurate capacity cycle decay model; then, the battery cycle and calendar decay are comprehensively considered to obtain a comprehensive decay model. The comprehensive attenuation model includes the energy storage battery capacity cycle attenuation model, the energy storage battery capacity calendar attenuation model and the energy storage battery comprehensive capacity attenuation model; Among them, the energy storage battery capacity cycle attenuation model is: ； Where Crate is the charge and discharge rate; T is the temperature; Ah battery throughput is related to the number of cycles and depth of discharge; Energy storage battery capacity calendar decay model: ； Where: Q loss-cal is the calendar decay rate, %; T is the battery storage temperature, K; t is the storage time, days; z is the exponential factor, which is 0.5; SOC is the ratio of the remaining power to the rated capacity; Comprehensive capacity attenuation model of energy storage batteries: ； Among them, a, b, c, d, e, f, g, and h are fitting parameters; In the lower-level energy storage capacity attenuation prediction model, the objective function for minimizing the comprehensive cost of energy storage battery operation is: ； Where, is the daily maintenance cost of the battery; C BA is the battery investment cost, C ZH is the battery replacement cost; in, It is divided into daily operation and maintenance cost and daily depreciation cost, and its function is: ； Where C e is the unit capacity investment cost of the lithium battery energy storage system, RMB / MWh; E is the initial capacity of the energy storage system in MWh; 0.08 is the operation and maintenance coefficient; Investment cost C BA The function is: ； Where N E N is the average annual charge and discharge times of the battery; B is the battery cycle life; P B is the rated power of the battery; E B is the rated capacity of the battery; U BP is the unit power cost of the battery; U BE is the unit energy cost of the battery; Replacement cost C ZH The function is: ； Where, is the average annual reduction rate of battery cost, k is the number of battery replacements, n is the battery life, is the battery capacity.

2. The rolling operation planning method of a multi-energy complementary system based on energy storage health status prediction according to claim 1 is characterized in that: The total objective function for maximizing the daily profit of the system operation is: ； Where, C is the time-of-use electricity price, RMB / MWh; C dl is the power curtailment penalty coefficient, C la is the power shortage penalty coefficient, C1 is the operation and maintenance coefficient of wind power, C2 is the operation and maintenance coefficient of photovoltaic power, RMB; P w 、P pv are wind power and photovoltaic grid-connected electricity, MW; P d 、P dl 、P la are the battery discharge capacity, abandoned capacity and power shortage, MW; P f 、P g B is the output of wind turbine and photovoltaic on the same day; y is the daily operation and maintenance cost of the battery, RMB / MWh.

3. The rolling operation planning method of a multi-energy complementary system based on energy storage health status prediction according to claim 2 is characterized in that: According to the total power supply equal to the load size, the function of the power balance constraint is: ； Where, P load is the total electrical load, MW; According to the total input equal to the total output, the function of the energy conservation constraint is: ； Where, 、 、 They are the total wind power generation, total photovoltaic power generation, and total battery charging capacity, MW; 、 、 They are load power consumption, total battery discharge, and system power abandonment; The function of the power constraint of photovoltaic grid access is: ； Where, P pvmin 、P pvmax are the minimum and maximum values ​​of photovoltaic power respectively; The energy storage battery capacity and charge and discharge constraint functions are: ； Where V Lmin 、V Lmax Respectively represent the minimum and maximum values ​​of the energy storage battery capacity; P dmin 、P dmax Respectively represent the minimum and maximum values ​​of discharge power; P cmin 、P cmax Respectively represent the minimum and maximum values ​​of charging power; The function of energy storage battery SOC constraint is: ； Where, SOC min , SOC max Respectively represent the minimum and maximum values ​​of SOC; The battery is not charged and discharged at the same time: ； Where B c 、B d It is a binary variable indicating the battery charge and discharge status, and its value is 0 / 1.

4. The rolling operation planning method for a multi-energy complementary system based on energy storage health status prediction according to claim 3 is characterized in that: The interactive mechanism of the two-layer optimization model for wind, solar and energy storage capacity attenuation is as follows: the upper-layer system operation optimization model inputs power generation, load and energy storage capacity data, and obtains the total daily operating income after MATLAB operation optimization. The battery throughput, state of charge, number of cycles and charge and discharge rate obtained by the upper-layer system operation optimization model are substituted into the lower-layer energy storage capacity attenuation prediction model to calculate the battery attenuation, and use this to simulate the battery attenuation for one year. The remaining capacity of the battery after attenuation calculated by the lower-layer energy storage capacity attenuation prediction model is substituted back into the upper-layer system operation optimization model for calculation in the next year. This process is repeated and rolled out until the battery capacity attenuates to 80%, that is, the battery is replaced and the attenuation calculation is restarted.

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