Multi-energy complementary system rolling operation planning method based on energy storage health state prediction

Abstract

The invention belongs to the technical field of multi-energy complementary power generation, and discloses an energy storage health state prediction-based multi-energy complementary system rolling operation planning method, which comprises a multi-energy complementary system consisting of a photovoltaic subsystem, a wind turbine subsystem and a lithium battery energy storage subsystem, and is characterized in that the multi-energy complementary system is connected with a power grid; mutual power transmission between the two is realized; the method further comprises a wind and light energy storage capacity attenuation double-layer optimization model composed of an upper-layer system operation optimization model and a lower-layer energy storage capacity attenuation prediction model. The photovoltaic subsystem, the wind turbine subsystem and the energy storage subsystem are utilized to form a multi-energy complementary system, a wind and light energy storage capacity attenuation double-layer optimization model is established, and by researching a system distribution and battery capacity attenuation mechanism, the economy and renewable energy consumption rate of the combined system are optimal, and the energy utilization rate of the wind and light energy storage system is optimized. Therefore, operation planning for the multi-energy complementary system is realized.

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] At present, the development and utilization of wind and solar energy has become an increasingly important research focus. However, wind and solar energy have the characteristics of "randomness", "volatility" and "intermittency", which makes it difficult to meet the requirements of grid connection and thus forced to abandon wind and solar power. In addition, when wind power and photovoltaic power are connected to the grid, they often cause impacts on the grid due to their instability, which can cause a lot of economic losses in serious cases. Therefore, in order to weaken the impact of the "randomness" and "volatility" of wind and solar power on the grid connection of the system, it is an effective solution to configure energy storage batteries in wind and photovoltaic power stations. However, in actual engineering, energy storage batteries often have the problem that the energy storage life cannot reach the designed life. This is because as the system's operating years increase, the capacity of the energy storage battery will continue to decay due to factors such as discharge depth, temperature, charge and discharge rate, and number of cycles, resulting in the energy storage life failing to meet the design standard. In addition, in the later stage of the life cycle of the energy storage battery, there will be a problem that the capacity decay cannot meet the regulation demand, which will not only affect the absorption of surplus wind and solar power, but also cause large-scale abandonment of wind and solar power. In serious cases, it will also cause impacts on the grid, resulting in serious economic losses.

[0003] In the case of energy storage capacity attenuation, if this factor is ignored and the operation strategy formulated at the beginning is continued, the energy storage battery will frequently operate in an unhealthy battery state of charge (SOC) condition, which will aggravate the attenuation of the energy storage capacity. Therefore, it is necessary to adjust the charging and discharging operation strategy of the energy storage battery in the system in a timely manner. However, the adjustment of this operation strategy will be restricted by the capacity configuration and attenuation of the energy storage battery, and it is often difficult to achieve the desired results. Therefore, how to accurately clarify this two-way influence mechanism and build a reasonable optimization operation strategy on this basis, so that it can not only meet the needs of high-quality power transmission of wind and solar multi-energy complementary power stations, but also maximize the service life of energy storage batteries, is one of the problems that need to be solved urgently in this field; in addition, how to quantify the game between the increase in investment cost caused by energy storage configuration considering capacity attenuation and the improvement in economic benefits brought by a more reasonable and stable energy storage system is also a problem that needs to be solved urgently in this field.

[0004] In response to the above problems, a 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 and system obtain parameter data related to the energy storage capacity of a large-scale new energy multi-energy complementary system and screen the parameter data to obtain 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 solutions of the outer optimization model and the inner optimization model, and the energy storage capacity of the large-scale new energy multi-energy complementary system is optimized according to the optimal solutions. The Chinese patent (publication number: CN116845932A) discloses a method for optimizing the configuration of shared energy storage in a multi-microgrid integrated energy system. Considering the complementarity of cold, heat and electricity multi-energy flows, a mathematical model of a shared energy storage integrated energy system is proposed; a two-layer optimization configuration model for shared energy storage in a multi-microgrid system is constructed. The upper layer takes the lowest daily operating cost of the shared energy storage power station as the objective function, and the maximum charging and discharging power and capacity of the energy storage equipment as the decision variables; the lower layer takes the lowest total energy cost of the multi-microgrid as the objective function, and the output of each device, the purchase and sale of electricity, etc. are used as decision variables; after simplifying the two-layer model using KKT conditions, linear programming is used to solve it; multiple scenarios are compared and the effect of energy storage configuration is analyzed. The Chinese patent (publication number: CN116090664A) discloses a multi-energy complementary optimization system based on park energy storage and wind, solar, gas, electricity and water. By setting up a park energy storage module, a multi-energy acquisition module, a data analysis module, a scheduling optimization module and a multi-energy optimization platform, the multi-energy complementary optimization system can reasonably call the electric energy of each power generation area, reasonably call according to the actual situation, not evenly distribute, and multi-energy complementarity and reasonable optimization. These methods / systems all focus on the initial configuration of energy storage capacity and the optimization of operating strategies among wind power, photovoltaics, and energy storage in multi-energy complementary systems, and this optimization often ignores the impact of capacity decay. In addition, after the capacity configuration and operation strategy optimization plan are completed, the energy storage capacity decay and economic analysis during system operation are mostly concentrated on the static changes from the beginning of energy storage configuration to the end of its life, and rarely involve dynamic, year-by-year rolling changes, which is not conducive to the detailed research of multi-energy complementary systems. Summary of the invention

[0005] The present invention aims to provide a rolling operation planning method for a multi-energy complementary system based on the prediction of the health status of energy storage, which uses a photovoltaic subsystem, a wind turbine subsystem and an energy storage subsystem to form a multi-energy complementary system, and establishes a double-layer optimization model for the attenuation of wind and solar energy storage capacity. By studying the system allocation and battery capacity attenuation mechanism, the economic performance of the combined system and the optimal renewable energy consumption rate are achieved, thereby realizing the operation planning for the multi-energy complementary system. The problem in the background technology is solved.

[0006] In order to achieve the above object, the present invention provides the following technical solutions: A rolling operation planning method for a multi-energy complementary system based on energy storage health status prediction, the method comprising: A multi-energy complementary system consisting of a photovoltaic subsystem, a wind turbine subsystem and a lithium battery energy storage subsystem, which is connected to the power grid to achieve mutual transmission of electricity between the two. The photovoltaic subsystem and the wind turbine subsystem generate electricity in a complementary manner to meet the basic load, and the lithium battery energy storage subsystem is responsible for adjusting the gap between power generation and load, storing excess electricity generated by the entire system, and discharging when there is a power shortage. A two-layer optimization model of wind-solar-energy storage capacity attenuation consists 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 a 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 iterations, and real-time two-way scheduling to obtain the final result.

[0007] Furthermore, in the upper-level system operation optimization model, the system revenue is improved under the premise of ensuring that the load is met, and the battery throughput, state of charge, number of cycles and charge and discharge rate are provided to the lower-level energy storage capacity attenuation prediction model to calculate the battery attenuation rate. The total revenue from the wind and photovoltaic power generation and the electricity sold by the battery power supply is deducted from the wind and photovoltaic system operation and maintenance costs, power abandonment and power shortage penalties, power purchase costs caused by power shortages, battery operation costs and depreciation costs. The total difference is the system operation daily revenue; among them, the total objective function with the largest system operation daily revenue is:

[0008] 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, C 1 For wind power, C 2 is the operation and maintenance coefficient of photovoltaic, yuan; P w , P pv are wind power and photovoltaic grid-connected power, MW; P d , P dl , P la are the battery discharge, abandoned power and power shortage, MW; P f , P g The wind turbine and photovoltaic power generation on the same day; B y is the daily operation and maintenance cost of the battery, RMB / MWh.

[0009] Furthermore, in the upper system operation optimization model, the system energy balance is ensured by constraints on power balance, energy conservation, wind power access, energy storage battery capacity and charging and discharging, and energy storage battery SOC; Among them, according to the total power supply is equal to the load size, the function of the power balance constraint is:

[0010] 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:

[0011] In the formula, 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; The function of wind power grid power constraint is:

[0012] Where P wmin , P wmax , P pvmin , P pvmax are the minimum and maximum values ​​of wind power and photovoltaic power respectively; The energy storage battery capacity and charge and discharge constraint functions are:

[0013]

[0014]

[0015] 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 the energy storage battery SOC constraint is:

[0016] In the formula, SOC min , SOC maxRespectively represent the minimum and maximum values ​​of SOC; The battery is not charged and discharged at the same time:

[0017] In the formula, B c , B d It is a binary variable of the battery charging and discharging status, with a value of 0 / 1.

[0018] 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 system wind and solar power abandonment and power shortage, 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: firstly, 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.

[0019] Furthermore, the comprehensive attenuation model includes a cycle attenuation model of energy storage battery capacity, a calendar attenuation model of energy storage battery capacity, and a comprehensive capacity attenuation model of energy storage battery; Among them, the energy storage battery capacity cycle attenuation model is:

[0020] In the formula, Crate is the charge and discharge rate; T is the temperature; Ah battery throughput is related to the number of cycles and the depth of discharge; Energy storage battery capacity calendar decay model:

[0021] 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; Comprehensive capacity attenuation model of energy storage battery:

[0022] Among them, a, b, c, d, e, f, g, and h are fitting parameters.

[0023] 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:

[0024] In the formula, is the daily maintenance cost of the battery; C BA is the battery investment cost, C ZHThe cost of battery replacement; in, It is divided into daily operation and maintenance cost and daily depreciation cost, and its function is:

[0025] In the formula, 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 MWh; 0.08 is the operation and maintenance coefficient; Investment cost BA The function is:

[0026] 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:

[0027] In the formula, 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.

[0028] Furthermore, the interactive mechanism of the two-layer optimization model of wind, solar and energy storage capacity attenuation is as follows: the upper 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 system operation optimization model are substituted into the lower energy storage capacity attenuation prediction model to calculate the battery attenuation, and use it to simulate the battery attenuation for 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 the next year's calculation. This is repeated iteratively and rolled out until the battery capacity decays to 80%, that is, the battery is replaced and the attenuation calculation starts again.

[0029] This process is repeated until the battery capacity decays to 80%, at which time the battery is replaced and the decay calculation starts again.

[0030] The beneficial effects of the technical solution are: 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 and solar power with battery energy storage. The rapid response capability and stability of battery energy storage will greatly improve the system's wind and solar power consumption rate, 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 in 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.

[0031] 2. The rolling operation planning method of 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 method 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, the calendar attenuation and cycle attenuation of the energy storage battery are comprehensively considered to construct a more accurate capacity attenuation model, 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.

[0032] 3. The rolling operation planning method of a multi-energy complementary system based on the prediction of the health status of energy storage provided by the present invention clarifies the impact of the decline in regulation capacity caused by the attenuation of energy storage capacity on the abandonment of wind and solar power generation systems, power supply stability, and system operation scheduling decisions. It clarifies the impact of capacity attenuation on system stability and economy caused by the energy storage battery possibly working in an unhealthy SOC state due to the operation strategy formulated without considering the attenuation of energy storage capacity; relevant penalty costs are introduced to quantify the above influencing factors, providing a feasible reference solution for energy storage operation and replacement cost evaluation in actual engineering applications; 4. The rolling operation planning method of the multi-energy complementary system based on the prediction of the health status of energy storage provided by the present invention reduces the impact of energy storage capacity decay on the safety and economy of the wind and solar power generation system by rationally configuring the energy storage capacity and formulating an operation strategy that takes capacity decay into consideration. The scheme is verified by using the wind and solar output and power load data of a typical day in a certain area in the west to obtain the annual rolling changes in energy storage capacity decay and system economic benefits, and a comparative analysis is conducted with the existing scheme to verify the advancement of the patented scheme in terms of long-term system benefits.

[0033] In summary, the method provided by the present invention can firstly configure a more reasonable energy storage capacity for the wind-solar complementary power station, reducing the situation where the capacity cannot meet the regulation demand due to capacity decay; secondly, the method provides a more accurate energy storage capacity decay model, which helps to improve the accuracy of energy storage configuration cost estimation and formulate a more reasonable operation scheduling plan; finally, the optimization scheduling method proposed by the method can effectively reduce the wind and solar abandonment rate, improve the stability of power transmission, extend the service life of the energy storage battery, and maximize the economic benefits of the multi-energy complementary system over the entire life cycle; In addition, compared with the existing solutions for estimating capacity decay and economic benefits, the present invention fully considers the impact of the actual operating conditions of energy storage batteries on the power regulation, power response and other aspects of new energy bases. It not only provides the dynamic rolling change curve of energy storage capacity decay year by year and the dynamic change of economic benefits of multi-energy complementary systems year by year, but also gives the dynamic change of wind and solar power abandonment rate and power shortage of new energy bases year by year with the decay of energy storage capacity, which can provide investors and operators with more complete and detailed operation status of the system throughout its life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a structural diagram of a wind-solar-battery-storage multi-energy complementary system in Embodiment 1 of the present invention; Figure 2 This is a structural diagram of a double-layer optimization model for wind-solar energy storage capacity attenuation in Example 1 of the present invention; Figure 3 This is an electric power balance diagram of a wind-solar-storage multi-energy complementary system under ideal conditions in Example 2 of the present invention; Figure 4 This is an electric power balance diagram of a wind-solar-storage multi-energy complementary system considering attenuation conditions in Example 2 of the present invention; Figure 5 This is a graph showing changes in SOH of lithium batteries at different DODs over ten years in Example 2 of the present invention; 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

[0035] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments: Example 1

[0036] A rolling operation planning method for a multi-energy complementary system based on energy storage health status prediction, the method comprising: 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 complement each other to generate electricity 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 when there is a power shortage. The structure of the multi-energy complementary system is as follows: Figure 1 As shown; A double-layer optimization model for wind-solar energy storage capacity attenuation 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, repeated iterations, and real-time two-way scheduling to obtain the final result; the structure of the double-layer optimization model for wind-solar energy storage capacity attenuation is as follows Figure 2 shown.

[0037] In this embodiment, the designed wind-solar-energy storage capacity attenuation multi-energy complementary system model aims to optimize the daily revenue of the system operation and minimize the comprehensive operating cost of the lithium battery, and is based on the typical daily wind-solar output and power load data of a certain area in the west, including operation and maintenance costs, wind and solar abandonment penalty costs, power shortage penalty costs, etc., a simulation program is written in the MATLAB platform, and a particle swarm algorithm is selected for solving; In this embodiment, the comprehensive energy system configuration includes: 360MW wind turbines, 210MW photovoltaic generators, 115.2MWh energy storage battery packs, the decision variables are the power output of each component of the system, and the optimization variable is the system operating cost.

[0038] In the upper-level system operation optimization model, the system revenue is improved under the premise of ensuring that the load is met, and the battery throughput, state of charge, number of cycles and charge and discharge rate are provided to the lower-level energy storage capacity attenuation prediction model to calculate the battery attenuation rate. The total revenue from the wind and photovoltaic power generation and the electricity sold by the battery power supply is deducted from the wind and photovoltaic system operation and maintenance costs, power abandonment and power shortage penalties, power purchase costs caused by power shortages, battery operation costs and depreciation costs. The total difference is the system operation daily revenue; among them, the total objective function with the largest system operation daily revenue is:

[0039] 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, C 1 For wind power, C2 is the operation and maintenance coefficient of photovoltaic, yuan; P w , P pv are wind power and photovoltaic grid-connected power, MW; P d , P dl , P la are the battery discharge, abandoned power and power shortage, MW; P f , P g The wind turbine and photovoltaic power generation on the same day; B y is the daily operation and maintenance cost of the battery, RMB / MWh; 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, charging and discharging, and energy storage battery SOC; Among them, according to the total power supply is equal to the load size, the function of the power balance constraint is:

[0040] 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:

[0041] In the formula, 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; The function of wind power grid power constraint is:

[0042] Where P wmin , P wmax , P pvmin , P pvmax are the minimum and maximum values ​​of wind power and photovoltaic power respectively; The energy storage battery capacity and charge and discharge constraint functions are:

[0043]

[0044]

[0045] Where V Lmin 、V Lmax Respectively represent the minimum and maximum values ​​of the energy storage battery capacity; Pdmin , 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 the energy storage battery SOC constraint is:

[0046] In the formula, SOC min , SOC max Respectively represent the minimum and maximum values ​​of SOC; The battery is not charged and discharged at the same time:

[0047] In the formula, B c , B d It is a binary variable of the battery charging and discharging status, with a value of 0 / 1.

[0048] The lower-level energy storage capacity attenuation prediction model is used to formulate and optimize the charging and discharging strategy of the energy storage battery, ensure the stability of power transmission, reduce the probability of wind and solar power abandonment and power shortage in the system, and reduce the capacity attenuation of the energy storage battery and the comprehensive operating cost of the lithium battery. The specific method is: firstly, 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; 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:

[0049] In the formula, Crate is the charge and discharge rate; T is the temperature; Ah battery throughput is related to the number of cycles and the depth of discharge; Energy storage battery capacity calendar decay model:

[0050] 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; Comprehensive capacity attenuation model of energy storage battery:

[0051] 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.

[0052] In the lower-level energy storage capacity attenuation prediction model, the objective function for the lowest comprehensive cost of energy storage battery operation is:

[0053] In the formula, is the daily maintenance cost of the battery; C BA is the battery investment cost, C ZH The cost of battery replacement; in, It is divided into daily operation and maintenance cost and daily depreciation cost, and its function is:

[0054] In the formula, C e =1500000 yuan / MWh is the unit capacity investment cost of the lithium battery energy storage system; E=115.2 MWh is the initial capacity of the energy storage system; 0.08 is the operation and maintenance coefficient; Investment cost BA The function is:

[0055] 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:

[0056] In the formula, 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; The interactive mechanism of the two-layer optimization model of wind, solar and energy storage capacity attenuation is as follows: the upper 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 system operation optimization model are substituted into the lower energy storage capacity attenuation prediction model to calculate the battery attenuation, and use it to simulate the battery attenuation for 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 the next year's calculation. This is repeated iteratively and rolled out until the battery capacity decays to 80%, that is, the battery is replaced and the attenuation calculation starts again.

[0057] Example 2 Without considering the battery capacity attenuation, a case analysis 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.

[0058] Considering the attenuation of energy storage capacity, after the system has been in operation for ten years, the power balance diagram is as follows: Figure 4 shown.

[0059] 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, 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 abandonment, causing economic losses to the system.

[0060] For energy storage systems, a comparative analysis is conducted on the impact of energy storage capacity attenuation on system economy at different depths of discharge (DOD) and different capacity configurations. Finally, recommendations on energy storage configuration and operation strategies are given through comparative analysis.

[0061] 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.

[0062] The operation quantification is summarized as shown in Table 1 and Table 2: Table 1 System daily power shortage, power abandonment and service life under different DOD DOD 60% DOD 80% DOD 100% DOD Abandoned electricity 188.84 MWh 176.719 MWh 164.59 MWh Lack of power 188.63 MWh 166.747 MWh 144.863 MWh Use life 6 5 5 Table 2 System daily power shortage, power abandonment and service life under different energy storage capacity configurations

[0063] Depend on Figure 5 and Figure 6 As shown in Table 1 and Table 2, the capacity of the energy storage battery decays faster as the DOD range increases. At 60% DOD, the battery will be replaced at the end of the sixth year, while at 80% and 100% DOD, the battery will be replaced at the end of the fifth year, especially at 100% DOD, where the battery decays fastest. Since the system is a wind, solar and storage multi-energy complementary system, in order to cope with the uncertainty of wind and solar output and the complex and variable load, the energy storage battery needs to be frequently charged and discharged to adjust the system power and energy balance, so it will decay faster than the daily use of the battery capacity. If the corresponding upper and lower limits of the state of charge are not set reasonably, the battery capacity will decay more rapidly, the daily income of the entire system will continue to decrease, the cost of battery replacement will increase, and the system economy will deteriorate.

[0064] Depend on Figure 5 and Figure 6 It can be seen that at 303.1 K and 80% DOD, the 85.5 MWh battery capacity decays most rapidly, while the battery capacity decay rate is in the middle under the 115.2 MWh configuration, and the decay rate is the slowest under the 142.5 MWh configuration. Because the energy storage battery capacity is large and the system has strong peak-to-valley-filling capability under the 142.5 MWh configuration, the battery does not need to work at high frequency and for a long time under unhealthy SOC conditions, and the working curve is relatively stable. In addition, the larger battery capacity will lead to a relatively reduced number of battery daily cycles, so its decay is the slowest.

[0065] In summary, the dynamic attenuation of batteries should be fully considered for the energy storage capacity configuration and operation strategy design of the multi-energy complementary system. Based on the annual rolling data of parameters such as throughput, shelving time, and temperature, combined with the energy storage capacity attenuation prediction model, the annual rolling attenuation of energy storage batteries is calculated. The data shows that in the fifth year, the battery capacity has decayed to below 80% under some working conditions. At this time, the system's regulation ability decreases, and the amount of abandoned electricity and power shortages increase, reducing the economic benefits of the entire new energy base. After considering battery attenuation and configuring the system with a larger capacity energy storage, the amount of abandoned electricity and power shortages is significantly reduced, and the energy storage life is significantly increased.

[0066] In addition, the battery DOD setting should be adjusted in time. 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 show that under the same capacity configuration, the higher the DOD, the shorter the battery life. Dynamically adjusted DOD can avoid the battery working under sub-healthy SOC conditions as much as possible and extend the life of the energy storage battery. Considering the capacity attenuation to configure the energy storage capacity will increase the investment cost, 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 the energy storage battery, thereby improving the economic benefits of the new energy base. 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.

[0067] The above is only an embodiment of the present invention, and the common knowledge such as the known 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, several modifications and improvements can be made without departing from the technical solution of the present invention, which should also be regarded as the protection scope 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 specification 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, which is connected to the power grid to achieve mutual transmission of electricity between the two. The photovoltaic subsystem and the wind turbine subsystem generate electricity in a complementary manner to meet the basic load, and the lithium battery energy storage subsystem is responsible for adjusting the gap between power generation and load, storing excess electricity generated by the entire system, and discharging when there is a power shortage. A two-layer optimization model of wind-solar-energy storage capacity attenuation 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 a 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 iterations, and real-time two-way scheduling to obtain the final result.

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: In the upper-level system operation optimization model, the system revenue is improved under the premise of ensuring that the load is met, and the battery throughput, state of charge, number of cycles and charge and discharge rate are provided to the lower-level energy storage capacity attenuation prediction model to calculate the battery attenuation rate. The total revenue from the wind and photovoltaic power generation and the electricity sold by the battery power supply is deducted from the wind and photovoltaic system operation and maintenance costs, power abandonment and power shortage penalties, power purchase costs caused by power shortages, battery operation costs and depreciation costs. The total difference is the system operation daily revenue; among them, the total objective function with the largest system operation daily revenue is: ； 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 power, MW; P d , P dl , P la are the battery discharge, abandoned power and power shortage, MW; P f , P g The wind turbine and photovoltaic power generation on the same day; B 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: In the upper system operation optimization model, the system energy balance is ensured by constraints on power balance, energy conservation, wind power access, energy storage battery capacity and charging and discharging, and energy storage battery SOC; Among them, according to the total power supply is 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: ； In the formula, 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; The function of wind power grid power constraint is: ； Where P wmin , P wmax , P pvmin , P pvmax are the minimum and maximum values ​​of wind power and 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 the energy storage battery SOC constraint is: ； In the formula, SOC min , SOC max Respectively represent the minimum and maximum values ​​of SOC; The battery is not charged and discharged at the same time: ； In the formula, B c , B d It is a binary variable of the battery charging and discharging status, with a value of 0 / 1.

4. The rolling operation planning method of a multi-energy complementary system based on energy storage health status prediction according to claim 3 is characterized in that: 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 system wind and solar power abandonment and power shortage, and at the same time reduce the capacity attenuation of energy storage batteries and the comprehensive operating costs of lithium batteries; the specific method is: firstly, 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.

5. The rolling operation planning method of a multi-energy complementary system based on energy storage health status prediction according to claim 4 is characterized in that: 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: ； In the formula, Crate is the charge and discharge rate; T is the temperature; Ah battery throughput is related to the number of cycles and the 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, day; 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 battery: ； Among them, a, b, c, d, e, f, g, and h are fitting parameters.

6. The rolling operation planning method of a multi-energy complementary system based on energy storage health status prediction according to claim 5 is characterized in that: In the lower-level energy storage capacity attenuation prediction model, the objective function for the lowest comprehensive cost of energy storage battery operation is: ； In the formula, 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: ； In the formula, 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 MWh; 0.08 is the operation and maintenance coefficient; Investment cost 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: ； In the formula, 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.

7. The rolling operation planning method of a multi-energy complementary system based on energy storage health status prediction according to claim 6 is characterized in that: The interactive mechanism of the two-layer optimization model of wind, solar and energy storage capacity attenuation is as follows: the upper 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 system operation optimization model are substituted into the lower energy storage capacity attenuation prediction model to calculate the battery attenuation, and use it to simulate the battery attenuation for 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 the next year's calculation. This is repeated iteratively and rolled out until the battery capacity decays to 80%, that is, the battery is replaced and the attenuation calculation starts again.

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