User side optical storage integrated system full life cycle economy evaluation and capacity optimization configuration method

Abstract

The invention discloses a user side optical storage integrated system full life cycle economy evaluation and capacity optimization configuration method, and relates to the technical field of power system energy configuration. Establishing a full-life-cycle economic evaluation model of the photovoltaic power generation system and the energy storage system, wherein the full-life-cycle economic evaluation model covers the full life cycles of the photovoltaic power generation system and the energy storage system; a multi-target evaluation function with the capacity of a photovoltaic power generation system and the capacity of an energy storage system as decision variables and with the goals of saving the power utilization cost of a user, stabilizing load fluctuation and improving the photovoltaic power generation consumption rate of the user side is established, and then the capacity configuration problem of the user side photovoltaic power storage integrated system is optimized and solved through a sea image optimization algorithm. According to the invention, the economical efficiency of the optical storage integrated system can be effectively improved, the load fluctuation can be stabilized, the photovoltaic power generation consumption rate is improved, and the application prospect is wide.

Description

Technical Field

[0001] The present invention relates to the technical field of power system energy configuration, and specifically to a method for full life cycle economic evaluation and capacity optimization configuration of a user-side photovoltaic storage integrated system based on multi-objective evaluation indicators. Background Art

[0002] With the continuous increase in the penetration rate of new energy power generation, the existing absorption capacity of the power grid not only makes the absorption of new energy a difficult problem, but also brings potential challenges to the safe and stable operation of the power grid. Therefore, in order to ensure the safe and stable operation of the power grid, the role of the energy storage system is particularly important. At the same time, with the promotion of time-of-use electricity prices, it has become a trend for large industrial and commercial power users to use user-side photovoltaic storage systems to save electricity costs. The latest electricity price policy shows that the peak-to-valley difference between industrial and commercial electricity prices will further expand, and it is increasingly important for industrial and commercial users to use photovoltaic storage systems to save electricity bills. With reasonable system configuration and scheduling strategies, the user-side photovoltaic storage system can not only improve the absorption of new energy and reduce net load fluctuations, but also further save the electricity costs on the user side. Therefore, the user-side photovoltaic storage system that serves industrial and commercial loads and uses chemical batteries as energy storage means has broad development prospects.

[0003] The walrus optimization algorithm is a new type of meta-heuristic optimization algorithm that solves optimization problems by simulating the predation behavior of walruses in nature. The design inspiration mainly comes from the process of walruses eating, migrating, escaping and fighting predators. The algorithm implementation steps are mathematically modeled into three stages: exploration, development and migration. The performance evaluation in the optimization application shows that the search process has a high ability to balance exploration and development. It can be widely used in various fields, including but not limited to: In engineering design problems, it can be used to optimize design parameters to achieve better performance indicators. In the field of machine learning, it can be used to optimize the hyperparameters of the model and improve the prediction accuracy of the model. It can also be used for optimization problems in pattern recognition, classification and cluster analysis in data mining. In the power system, it can be used to optimize the load distribution and power dispatch of the power grid.

[0004] Based on this, it is particularly necessary to develop an economic evaluation method that considers the entire life cycle of the user-side photovoltaic and storage integrated system, and based on the economic evaluation of the entire life cycle of the photovoltaic and storage integrated system, propose a photovoltaic and storage capacity optimization configuration method taking into account the time-of-use electricity price background. Summary of the invention

[0005] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a method for economic evaluation and capacity optimization configuration of a user-side photovoltaic and storage integrated system throughout its life cycle, to conduct a more accurate and practical economic evaluation of the user-side photovoltaic and storage integrated system, and at the same time, to optimize the photovoltaic and storage capacity configuration of the user-side photovoltaic and storage integrated system with the goals of improving economy, smoothing load fluctuations and promoting photovoltaic power generation and consumption by using the walrus algorithm, so as to facilitate popularization and use.

[0006] In order to achieve the above-mentioned purpose, the present invention is implemented through the following technical solutions: a method for evaluating the economic performance of a user-side photovoltaic storage integrated system over its entire life cycle, wherein the entire life cycle of a relatively short-lived energy storage is selected as the time scale for evaluating the economic performance of the photovoltaic storage integrated system. Since the life of energy storage is much shorter than that of photovoltaic power generation, and the variation of energy storage capacity attenuation is relatively complex, the life of energy storage is used as the entire life cycle of the system for economic evaluation. In addition, since the attenuation rate of energy storage in the first year and that in the remaining years is quite different, if a simple proportional attenuation is used and the capacity after attenuation is taken for economic calculation, and the energy storage capacity is directly corrected using the attenuation coefficient at the end of the year, a large error will occur between the economic evaluation result and the actual engineering situation, making the evaluation result inaccurate. Therefore, a linear correction is made to the attenuation of energy storage capacity in each year, and the energy storage life is taken as ten years. The attenuation coefficient in each year after correction is expressed as:

[0007]

[0008] In formula (1), n ​​is the year, d1 is the attenuation rate of the energy storage battery at the end of the first year, d2 is the attenuation rate at the end of the remaining 9 years, and ESC is the percentage of the existing capacity of the energy storage system;

[0009] After the revision, the economic evaluation model is as follows:

[0010]

[0011] In formula (2), Rn1 and Rn2 are the photovoltaic power generation income and energy storage peak-valley arbitrage income in the nth year, respectively, C is the comprehensive cost, and V is the residual value. The residual value V takes into account the comprehensive factors of equipment age, usage status, technological progress and market demand;

[0012] The photovoltaic power generation income in the nth year and the peak-valley arbitrage income in the nth year are expressed as:

[0013] R n1 =PV high P high +PV mid P mid (3)

[0014] R n2 =ESC n ηDoD(N high Phigh +N mid P mid ) (4)

[0015] In formula (3) and (4), PV high With PV mid are the amount of electricity provided by photovoltaic power to user loads in different electricity price periods, P high With P mid They are the peak electricity price and the flat electricity price for electricity users under time-of-use electricity price; ESC n is the capacity percentage of the energy storage device in the nth year after attenuation correction in formula (1), N is the energy storage discharge capacity in the corresponding period, η is the efficiency parameter of the energy storage device during charging and discharging, and DoD is the depth of charge and discharge of the energy storage device;

[0016] The comprehensive cost C is divided into three parts for consideration: energy storage construction cost, operation and maintenance cost, installation, labor and site cost.

[0017] C=C ES +C O&M +C I&C (5)

[0018] In formula (5), C ES is the energy storage construction cost, C O&M is the energy storage operation, maintenance and installation cost, C I&C Labor and site costs.

[0019] Based on the economic evaluation method of the whole life cycle of the user-side photovoltaic and storage integrated system, a photovoltaic and storage capacity optimization configuration method considering the time-of-use electricity price is proposed. The steps are as follows:

[0020] S1. Establish a full life cycle economic evaluation model for photovoltaic power generation systems and energy storage systems: Under the constraints of each system, the photovoltaic system gives priority to powering the power load. When there is sufficient sunlight, it will choose to power the energy storage system or feed power to the grid according to the local electricity price policy and the charge state of the energy storage system; the energy storage system will power the load, either through photovoltaic charging or from the grid when the electricity price is low; the power load will receive the electricity generated by photovoltaics first and choose to be powered by the grid or the energy storage system according to the electricity price policy;

[0021] S2. The newly added photovoltaic storage system will change the original characteristic curve of the load. Reasonable photovoltaic storage system configuration can effectively smooth out load fluctuations and reduce the peak-shaving pressure of the power grid. When too much photovoltaic power generation cannot be absorbed by the power grid or stored by the energy storage system, it will lead to abandoned light, wasting construction resources and increasing unnecessary operation and maintenance costs. Therefore, on the basis of the proposed economic evaluation, the reduction of load fluctuation rate and the improvement of photovoltaic power generation utilization rate are comprehensively considered, and a multi-objective evaluation function is established with the photovoltaic power generation system capacity and the energy storage system capacity as decision variables, with the goal of saving user electricity costs, smoothing load fluctuations, and improving the photovoltaic power generation absorption rate on the user side.

[0022] S3. In order to coordinate the output of each unit and improve the economic efficiency of electricity consumption on the user side while meeting the power supply demand, it is necessary to formulate an appropriate electricity control strategy based on the electricity price policy and electricity cost; formulate a coordinated operation strategy at each moment based on the energy flow characteristics, user electricity cost, the operating status of each unit in the integrated photovoltaic storage system and system constraints;

[0023] S4. Taking the multi-objective evaluation function as the evaluation index, on the basis of the proposed coordinated operation strategy of each unit at each moment, the walrus algorithm is used to solve the photovoltaic and energy storage integrated system model to obtain the best coordinated output curve of each unit, and thus calculate the optimal configuration capacity of photovoltaic and energy storage.

[0024] Preferably, the mathematical models of each part in step S1 are constructed as follows:

[0025] ① Photovoltaic power generation model

[0026] The output energy of the photovoltaic power generation system is affected by solar radiation, photovoltaic laying area, and photoelectric conversion efficiency. In the actual system, the photovoltaic power generation is also affected by the efficiency of the components, the matching of components, the efficiency of the inverter, and the factors of the photovoltaic cell surface being blocked by pollutants. The service life of the photovoltaic cell is 25 years. The power generation of the photovoltaic cell is expressed as:

[0027] E T =T Ymax ×P R ×A pv ×k (6)

[0028] In formula (6), E T is the actual photovoltaic power generation, T Ymax is the annual peak sunshine hours, P R is the rated power per unit capacity, A pv is the amount of photovoltaic laying, k is the correction coefficient of theoretical power generation, taking into account various influencing factors and capacity attenuation during actual photovoltaic power generation;

[0029] ②Energy storage model

[0030] The energy stored in the energy storage at a certain moment is related to the charging and discharging state in the previous period. When the energy storage is working in the charging state at time t, the energy stored at time t+1 is expressed as:

[0031] E (t+1) =E (t) -η ch P ES(t) ΔT (7)

[0032] In formula (7), E (t) With E (t+1) are the energy states of the energy storage at time t and t+1 respectively, η ch is the energy storage charging efficiency; P ES(t) is the charging power of the energy storage from time t to time t+1, at this time P ES(t) <0; △T is the time interval between time t and time t+1;

[0033] When the energy storage is in the discharge state from time t to time t+1, the discharge power P ES(t) >0, energy storage state E at time t+1 (t+1) satisfy

[0034]

[0035] Preferably, the system constraint condition in step S1 is:

[0036] ① Rated power constraints of distribution transformers: To avoid additional transformer construction costs, the capacity variation range of the photovoltaic storage integrated system should meet the following requirements:

[0037] P tr.min ≤P load +P pv +P ES ≤P tr.max (9)

[0038] In formula (9), P tr.min It is the minimum rated power limit of the user's transformer, P tr.max is the maximum rated power limit of the user's transformer, P 1oad is the load power, P pv is the photovoltaic power generation power, P ES is the rated power of the energy storage system;

[0039] ② Power balance constraint: To ensure the safety of electricity consumption on the user side, the output power of the photovoltaic storage system must always be balanced with the load demand power and the power grid:

[0040] P grid,t +P pv,t +P ES,t =P load,t (10)

[0041] In formula (10), P grid,t is the grid output power at time t, P pv,t is the photovoltaic output power, P ES,t Energy storage output power, P load,t is the load power;

[0042] ③ Constraints on photovoltaic access capacity: The installation of photovoltaic power in industrial and commercial user parks is not only limited by the site, but also by the allowable access capacity of the transformer. When configuring photovoltaic power, its capacity is subject to the following constraints:

[0043] A pv,min ≤A pv ≤A pv,max (11)

[0044] In formula (11), A pv,max , A pv,min They are the maximum and minimum photovoltaic capacities that can be connected to the user side respectively;

[0045] ④ Photovoltaic output constraint: The output of photovoltaic power generation at any time period is related to the external factors of light intensity and temperature at that time, but the output power at any time is less than the maximum power generation power of the photovoltaic system at the current time.

[0046] 0≤P pv,t ≤P pv,max (12)

[0047] In formula (12), P pv,t is the photovoltaic output power at time t, P pv,max is the theoretical maximum power generation of photovoltaics;

[0048] ⑤ Energy storage charging and discharging state constraints: The charging and discharging power of the energy storage system at any time should meet the power constraints and energy storage battery charge state constraints:

[0049] P dis ≤P ES,t ≤P ch (13)

[0050] SOC min ≤SOC t ≤SOC max (14)

[0051] In formula (13) and (14), P dis and P ch They are the maximum discharge power and maximum charging power of energy storage; SOC t is the state of charge of the energy storage battery at time t, SOC max and SOC minThey are the maximum state of charge and minimum state of charge of the battery respectively;

[0052] ⑥ Energy storage charging and discharging time constraints Energy storage equipment reserves enough time in each charging and discharging cycle so that the equipment can complete the established charging and discharging goals. This can not only make full use of the energy storage capacity to maximize economic benefits, but also avoid damage to the service life of the energy storage battery caused by incomplete charging and discharging:

[0053]

[0054] In formula (15) and (16), T dis is the discharge time of the energy storage system, T ch is the charging time, P dis,N is the rated discharge power, P ch,N is the rated charging power.

[0055] Preferably, the step S2 constitutes a multi-objective evaluation function:

[0056] ① Comprehensive income:

[0057] The economic evaluation model proposed in S1 further derives the evaluation index of saving electricity cost:

[0058]

[0059] ②Net load fluctuation:

[0060] The newly added photovoltaic storage system changes the original characteristic curve of the load. Reasonable configuration of the photovoltaic storage system can effectively smooth the load fluctuation on the grid side and reduce the peak load regulation pressure of the grid. Considering the influence of the photovoltaic storage system on the user side, the net active load fluctuation is used as the evaluation index, and the expression is:

[0061]

[0062] In formula (18), P load (t), P pv (t), P ES (t) are load power, photovoltaic power generation power and energy storage charging and discharging power at time t respectively; is the average power of the load in a typical working day, T = 24h

[0063] ③ Photovoltaic power generation consumption:

[0064] When there are too many photovoltaic configurations, the excessive photovoltaic power generation cannot be absorbed by the grid or stored by the energy storage system. The photovoltaic system will reduce the power generation, resulting in abandoned light, wasting construction resources and increasing unnecessary operation and maintenance costs. Taking photovoltaic power generation absorption as the system pricing indicator, it is expressed as:

[0065]

[0066] In formula (19), W pv,total is the actual photovoltaic power generation, P pv,t is the photovoltaic output power at time t;

[0067] In summary, taking comprehensive revenue, net load fluctuation rate and photovoltaic power generation consumption rate as evaluation indicators, the following multi-objective evaluation function is constructed:

[0068] min F=[F1(x,y),F2(x,y),F3(x,y)] (20)

[0069] In formula (20), F1 is the evaluation index of electricity cost saved by the photovoltaic energy storage system, F2 is the evaluation index of net load fluctuation rate, and F3 is the evaluation index of photovoltaic power generation consumption rate; x and y are the configuration capacities of photovoltaic and energy storage, respectively, that is, the decision variables of the entire model.

[0070] As a preference, the specific process of formulating the coordinated operation strategy at each moment in step S3 is as follows:

[0071] S3.1. When the net load is positive, it means that the photovoltaic power generation output is less than the user load demand, and the energy storage SOC needs to be queried:

[0072] i. If the SOC is less than the lower limit, the energy storage cannot be discharged, and the user directly purchases electricity from the grid to support the load;

[0073] ⅱ. If the SOC is greater than the lower limit, further compare the time-of-use electricity price with the cost per kilowatt-hour, that is, the cost required for the energy storage unit to transfer a unit of electricity:

[0074] If the time-of-use electricity price is lower than the cost per kilowatt-hour and the SOC is less than or equal to the upper limit, electricity is purchased from the grid to support the load and charge the energy storage; otherwise, the energy storage has reached the upper limit and cannot be charged further, and the grid only supports the load;

[0075] If the time-of-use electricity price is higher than the cost per kilowatt-hour and the power shortage of the load demand is not higher than the rated discharge power of the energy storage, the load power consumption will be supported only by the energy storage; otherwise, the energy storage alone cannot meet the load power demand, and the power grid and energy storage will work together to support the load power consumption;

[0076] S3.2. When the net load is negative, it means that the photovoltaic power generation output is greater than the user load demand, and the energy storage SOC needs to be queried:

[0077] ⅰ. If the SOC is higher than the upper limit, the energy storage cannot be charged, resulting in abandoned light;

[0078] ii. If the SOC is lower than the upper limit and the surplus power of photovoltaic power generation is less than the rated charging power of energy storage, the energy storage can absorb the surplus power of photovoltaic power generation at this moment, and the photovoltaic power generation supports the load, and the surplus power is used to charge the energy storage; otherwise, the energy storage cannot completely absorb the surplus power of photovoltaic power generation, and some photovoltaic power generation will be abandoned while the photovoltaic power generation supports the load and charges the energy storage;

[0079] S3.3. The above process is executed repeatedly within 24 hours to obtain the whole-day control strategy and form a reasonable coordinated power supply mechanism for the user-side photovoltaic and storage integrated system.

[0080] Preferably, the specific steps of step S4 are as follows:

[0081] S4.1. Initialize the walrus population size and iteration times as well as load, energy storage output and photovoltaic output data;

[0082] S4.2. Calculate the fitness values ​​of all walruses according to the optimization objective function and select the best walrus;

[0083] S4.3. Simulate the behavior of walruses exploring unknown territories and update the Xth i Only the walrus position is taken, and the best objective function value is taken corresponding to the walrus X i P1 ;

[0084] S4.4. Simulate the walrus migration behavior to find a better living area, and update the Xth i Only the walrus position is taken, and the best objective function value is taken corresponding to the walrus X i P2 ;

[0085] S4.5. Simulate walrus escape and anti-predator behavior: Update the Xth i Only the walrus position is taken, and the best objective function value is taken corresponding to the walrus X i P3 ;

[0086] S4.6. Determine whether all walrus individuals have been traversed. If the traversal is complete, retain the optimal result, otherwise return to step S4.3;

[0087] S4.7. Determine whether the current number of iterations has reached the specified maximum value. If so, output the optimal output data of each unit. Otherwise, add 1 to the number of iterations and return to step S4.2.

[0088] Beneficial effects of the invention: The invention conducts a more accurate and practical economic evaluation of the user-side photovoltaic and storage integrated system, and at the same time uses the walrus algorithm to optimize the photovoltaic and storage capacity configuration of the user-side photovoltaic and storage integrated system with the goals of improving economy, smoothing load fluctuations and promoting photovoltaic power generation and consumption. It can not only effectively improve the economy of the photovoltaic and storage integrated system, but also smooth load fluctuations and improve the photovoltaic power generation and consumption rate, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments;

[0090] Figure 1 A flow chart of the method for optimizing the configuration of optical storage capacity of the present invention;

[0091] Figure 2 This is a flow chart of the intra-day coordinated operation control strategy of the photovoltaic storage system of the present invention;

[0092] Figure 3 This is a flow chart of the walrus optimization algorithm of the present invention;

[0093] Figure 4 This is a schematic diagram of energy flow of the integrated photovoltaic and energy storage system of the present invention;

[0094] Figure 5 It is the capacity decay diagram of each year after energy storage correction of the present invention;

[0095] Figure 6 are the output curves of each unit under different scenarios (a is the configuration without PV and storage, b is PV only, c is the configuration with energy storage only, d is the configuration after WaOA optimization, and e is the configuration after PSO optimization);

[0096] Figure 7 It is a net load curve diagram under various scenarios on the user side of the present invention. DETAILED DESCRIPTION

[0097] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0098] Reference Figure 1-7, this specific implementation adopts the following technical solutions: a method for evaluating the economic performance of the entire life cycle of a user-side photovoltaic storage integrated system. For the photovoltaic storage integrated system, the entire life cycle of the energy storage with a relatively short life span is selected as the time scale to evaluate the economic performance of the photovoltaic storage integrated system. Since the life span of the energy storage is much shorter than that of photovoltaics, and the change in the attenuation of the energy storage capacity is relatively complex, the life span of the energy storage is used as the economic performance evaluation of the system for the entire life cycle. In addition, since the attenuation rate of the energy storage in the first year and the remaining years is quite different, if a simple proportional attenuation is used and the capacity after the attenuation is taken for economic calculation, the energy storage capacity is directly corrected using the attenuation coefficient at the end of the year, which will lead to a large error between the economic evaluation result and the actual engineering situation, making the evaluation result inaccurate. Therefore, a linear correction is made to the attenuation of the energy storage capacity in each year, and the energy storage life is taken as ten years. The attenuation coefficient in each year after correction is expressed as:

[0099]

[0100] In formula (1), n ​​is the year, d1 is the attenuation rate of the energy storage battery at the end of the first year, d2 is the attenuation rate at the end of the remaining 9 years, and ESC is the percentage of the existing capacity of the energy storage system;

[0101] After the revision, the economic evaluation model is as follows:

[0102]

[0103] In formula (2), Rn1 and Rn2 are the photovoltaic power generation income and energy storage peak-valley arbitrage income in the nth year, respectively, C is the comprehensive cost, and V is the residual value. The residual value V takes into account the comprehensive factors of equipment age, usage status, technological progress and market demand;

[0104] The photovoltaic power generation income in the nth year and the peak-valley arbitrage income in the nth year are expressed as:

[0105] R n1 =PV high P high +PV mid P mid (3)

[0106] R n2 =ESC n ηDoD(N high P high +N mid P mid ) (4)

[0107] In formula (3) and (4), PV high With PV mid are the amount of electricity provided by photovoltaic power to user loads in different electricity price periods, P high With P midThey are the peak electricity price and the flat electricity price for electricity users under time-of-use electricity price; ESC n is the capacity percentage of the energy storage device in the nth year after attenuation correction in formula (1), N is the energy storage discharge capacity in the corresponding period, η is the efficiency parameter of the energy storage device during charging and discharging, and DoD is the depth of charge and discharge of the energy storage device;

[0108] The comprehensive cost C is divided into three parts for consideration: energy storage construction cost, operation and maintenance cost, installation, labor and site cost.

[0109] C=C ES +C O&M +C I&C (5)

[0110] In formula (5), C ES is the energy storage construction cost, C O&M is the energy storage operation, maintenance and installation cost, C I&C Labor and site costs.

[0111] Based on the economic evaluation method of the whole life cycle of the user-side photovoltaic and storage integrated system, a photovoltaic and storage capacity optimization configuration method considering the time-of-use electricity price is proposed. The steps are as follows:

[0112] S1. Establish a full life cycle economic evaluation model for photovoltaic power generation systems and energy storage systems: Under the constraints of each system, the photovoltaic system gives priority to powering the power load. When there is sufficient sunlight, it will choose to power the energy storage system or feed power to the grid according to the local electricity price policy and the charge state of the energy storage system; the energy storage system will power the load, either through photovoltaic charging or from the grid when the electricity price is low; the power load will receive the electricity generated by photovoltaics first and choose to be powered by the grid or the energy storage system according to the electricity price policy;

[0113] S2. The newly added photovoltaic storage system will change the original characteristic curve of the load. Reasonable photovoltaic storage system configuration can effectively smooth out load fluctuations and reduce the peak-shaving pressure of the power grid. When too much photovoltaic power generation cannot be absorbed by the power grid or stored by the energy storage system, it will lead to abandoned light, wasting construction resources and increasing unnecessary operation and maintenance costs. Therefore, on the basis of the proposed economic evaluation, the reduction of load fluctuation rate and the improvement of photovoltaic power generation utilization rate are comprehensively considered, and a multi-objective evaluation function is established with the photovoltaic power generation system capacity and the energy storage system capacity as decision variables, with the goal of saving user electricity costs, smoothing load fluctuations, and improving the photovoltaic power generation absorption rate on the user side.

[0114] S3. In order to coordinate the output of each unit and improve the economic efficiency of electricity consumption on the user side while meeting the power supply demand, it is necessary to formulate an appropriate electricity control strategy based on the electricity price policy and electricity cost; formulate a coordinated operation strategy at each moment based on the energy flow characteristics, user electricity cost, the operating status of each unit in the integrated photovoltaic storage system and system constraints;

[0115] S4. Taking the multi-objective evaluation function as the evaluation index, on the basis of the proposed coordinated operation strategy of each unit at each moment, the walrus algorithm is used to solve the photovoltaic and energy storage integrated system model to obtain the best coordinated output curve of each unit, and thus calculate the optimal configuration capacity of photovoltaic and energy storage.

[0116] It is worth noting that the mathematical models of each part in step S1 are constructed as follows:

[0117] ① Photovoltaic power generation model

[0118] The output energy of the photovoltaic power generation system is affected by solar radiation, photovoltaic laying area, and photoelectric conversion efficiency. In the actual system, the photovoltaic power generation is also affected by the efficiency of the components, the matching of components, the efficiency of the inverter, and the factors of the photovoltaic cell surface being blocked by pollutants. The service life of the photovoltaic cell is 25 years. The power generation of the photovoltaic cell is expressed as:

[0119] E T =T Ymax ×P R ×A pv ×k (6)

[0120] In formula (6), E T is the actual photovoltaic power generation, T Ymax is the annual peak sunshine hours, P R is the rated power per unit capacity, A pv is the amount of photovoltaic laying, k is the correction coefficient of theoretical power generation, taking into account various influencing factors and capacity attenuation during actual photovoltaic power generation;

[0121] ②Energy storage model

[0122] The energy stored in the energy storage at a certain moment is related to the charging and discharging state in the previous period. When the energy storage is working in the charging state at time t, the energy stored at time t+1 is expressed as:

[0123] E (t+1) =E (t) -η ch P ES(t) ΔT (7)

[0124] In formula (7), E (t) With E (t+1) are the energy states of the energy storage at time t and t+1 respectively, η chis the energy storage charging efficiency; P ES(t) is the charging power of the energy storage from time t to time t+1, at this time P ES(t) <0; △T is the time interval between time t and time t+1;

[0125] When the energy storage is in the discharge state from time t to time t+1, the discharge power P ES(t) >0, energy storage state E at time t+1 (t+1) satisfy

[0126]

[0127] It is worth noting that the system constraints in step S1 are:

[0128] ① Rated power constraints of distribution transformers: To avoid additional transformer construction costs, the capacity variation range of the photovoltaic storage integrated system should meet the following requirements:

[0129] P tr.min ≤P load +P pv +P ES ≤P tr.max (9)

[0130] In formula (9), P tr.min It is the minimum rated power limit of the user's transformer, P tr.max is the maximum rated power limit of the user's transformer, P 1oad is the load power, P pv is the photovoltaic power generation power, P ES is the rated power of the energy storage system;

[0131] ② Power balance constraint: To ensure the safety of electricity consumption on the user side, the output power of the photovoltaic storage system must always be balanced with the load demand power and the power grid:

[0132] P grid,t +P pv,t +P ES,t =P load,t (10)

[0133] In formula (10), P grid,t is the grid output power at time t, P pv,t is the photovoltaic output power, P ES,t Energy storage output power, P load,t is the load power;

[0134] ③ Constraints on photovoltaic access capacity: The installation of photovoltaic power in industrial and commercial user parks is not only limited by the site, but also by the allowable access capacity of the transformer. When configuring photovoltaic power, its capacity is subject to the following constraints:

[0135] Apv,min ≤A pv ≤A pv,max (11)

[0136] In formula (11), A pv,max , A pv,min They are the maximum and minimum photovoltaic capacities that can be connected to the user side respectively;

[0137] ④ Photovoltaic output constraint: The output of photovoltaic power generation at any time period is related to the external factors of light intensity and temperature at that time, but the output power at any time is less than the maximum power generation power of the photovoltaic system at the current time.

[0138] 0≤P pv,t ≤P pv,max (12)

[0139] In formula (12), P pv,t is the photovoltaic output power at time t, P pv,max is the theoretical maximum power generation of photovoltaics;

[0140] ⑤ Energy storage charging and discharging state constraints: The charging and discharging power of the energy storage system at any time should meet the power constraints and energy storage battery charge state constraints:

[0141] P dis ≤P ES,t ≤P ch (13)

[0142] SOC min ≤SOC t ≤SOC max (14)

[0143] In formula (13) and (14), P dis and P ch They are the maximum discharge power and maximum charging power of energy storage; SOC t is the state of charge of the energy storage battery at time t, SOC max and SOC min They are the maximum state of charge and minimum state of charge of the battery respectively;

[0144] ⑥ Energy storage charging and discharging time constraints Energy storage equipment reserves enough time in each charging and discharging cycle so that the equipment can complete the established charging and discharging goals. This can not only make full use of the energy storage capacity to maximize economic benefits, but also avoid damage to the service life of the energy storage battery caused by incomplete charging and discharging:

[0145]

[0146] In formula (15) and (16), T dis is the discharge time of the energy storage system, T chis the charging time, P dis,N is the rated discharge power, P ch,N is the rated charging power.

[0147] It is worth noting that the step S2 constitutes a multi-objective evaluation function:

[0148] ① Comprehensive income:

[0149] The economic evaluation model proposed in S1 further derives the evaluation index of saving electricity cost:

[0150]

[0151] ②Net load fluctuation:

[0152] The newly added photovoltaic storage system changes the original characteristic curve of the load. Reasonable configuration of the photovoltaic storage system can effectively smooth the load fluctuation on the grid side and reduce the peak load regulation pressure of the grid. Considering the influence of the photovoltaic storage system on the user side, the net active load fluctuation is used as the evaluation index, and the expression is:

[0153]

[0154] In formula (18), P load (t), P pv (t), P ES (t) are load power, photovoltaic power generation power and energy storage charging and discharging power at time t respectively; is the average power of the load in a typical working day, T = 24h

[0155] ③ Photovoltaic power generation consumption:

[0156] When there are too many photovoltaic configurations, the excessive photovoltaic power generation cannot be absorbed by the grid or stored by the energy storage system. The photovoltaic system will reduce the power generation, resulting in abandoned light, wasting construction resources and increasing unnecessary operation and maintenance costs. Taking photovoltaic power generation absorption as the system pricing indicator, it is expressed as:

[0157]

[0158] In formula (19), W pv,total is the actual photovoltaic power generation, P pv,t is the photovoltaic output power at time t;

[0159] In summary, taking comprehensive revenue, net load fluctuation rate and photovoltaic power generation consumption rate as evaluation indicators, the following multi-objective evaluation function is constructed:

[0160] minF=[F1(x,y),F2(x,y),F3(x,y)] (20)

[0161] In formula (20), F1 is the evaluation index of electricity cost saved by the photovoltaic energy storage system, F2 is the evaluation index of net load fluctuation rate, and F3 is the evaluation index of photovoltaic power generation consumption rate; x and y are the configuration capacities of photovoltaic and energy storage, respectively, that is, the decision variables of the entire model.

[0162] It is worth noting that the specific process of formulating the coordinated operation strategy at each moment in step S3 is as follows:

[0163] S3.1. When the net load is positive, it means that the photovoltaic power generation output is less than the user load demand, and the energy storage SOC needs to be queried:

[0164] i. If the SOC is less than the lower limit, the energy storage cannot be discharged, and the user directly purchases electricity from the grid to support the load;

[0165] ⅱ. If the SOC is greater than the lower limit, further compare the time-of-use electricity price with the cost per kilowatt-hour, that is, the cost required for the energy storage unit to transfer a unit of electricity:

[0166] If the time-of-use electricity price is lower than the cost per kilowatt-hour and the SOC is less than or equal to the upper limit, electricity is purchased from the grid to support the load and charge the energy storage; otherwise, the energy storage has reached the upper limit and cannot be charged further, and the grid only supports the load;

[0167] If the time-of-use electricity price is higher than the cost per kilowatt-hour and the power shortage of the load demand is not higher than the rated discharge power of the energy storage, the load power consumption will be supported only by the energy storage; otherwise, the energy storage alone cannot meet the load power demand, and the power grid and energy storage will work together to support the load power consumption;

[0168] S3.2. When the net load is negative, it means that the photovoltaic power generation output is greater than the user load demand, and the energy storage SOC needs to be queried:

[0169] ⅰ. If the SOC is higher than the upper limit, the energy storage cannot be charged, resulting in abandoned light;

[0170] ii. If the SOC is lower than the upper limit and the surplus power of photovoltaic power generation is less than the rated charging power of energy storage, the energy storage can absorb the surplus power of photovoltaic power generation at this moment, and the photovoltaic power generation supports the load, and the surplus power is used to charge the energy storage; otherwise, the energy storage cannot completely absorb the surplus power of photovoltaic power generation, and some photovoltaic power generation will be abandoned while the photovoltaic power generation supports the load and charges the energy storage;

[0171] S3.3. The above process is executed repeatedly within 24 hours to obtain the whole-day control strategy and form a reasonable coordinated power supply mechanism for the user-side photovoltaic and storage integrated system.

[0172] In addition, the step S4 is based on the optimization configuration of the optical storage capacity of the walrus algorithm:

[0173] The walrus optimization algorithm WaOA can quickly locate the area with better solutions in the search space by simulating the walrus's foraging, migration, escape and anti-predator behaviors. The mathematical description of WaOA includes four stages: variable initialization, exploration, migration and development. Each stage is described in detail as follows:

[0174] ①In the initialization phase, the optimization parameters such as walrus population and number of iterations are initialized.

[0175]

[0176] In formula (21), X represents the population of walrus; X i represents the i-th walrus; X i,d represents the value of the d-th dimension solution of the i-th walrus; D represents the total dimension of the solution included in the walrus. Next, the corresponding evaluation function values ​​of all walruses are calculated to obtain the evaluation function matrix shown in the following formula, and the walrus with the best evaluation adaptability is selected.

[0177]

[0178] In formula (22), F is the objective function vector, F i is the value of the objective function based on the i-th walrus.

[0179] ② In the exploration phase, the position of the i-th walrus is updated according to the candidate solution with the best adaptability in the previous phase. After the update, the walrus is And compare the evaluation results with the candidate solutions. If the updated position produces a better result (F P1 i <F), the candidate solution is updated, otherwise the candidate solution is retained.

[0180]

[0181] In formula (23): represents the new generated position of the i-th walrus in the first stage; Indicates the d-th dimension value of the newly generated walrus; rand i.d is a random number obtained from the interval [0,1]; SW d represents the dth dimension value of the optimal walrus; I i,d represents a randomly selected integer between 1 or 2; is the objective function value of the newly generated walrus.

[0182] ③ Migration phase, guiding the walruses in the search space to find new and better suitable areas in the search space, aiming to take into account the global search space and choose whether to update the candidate solution based on the adaptability evaluation of the new walrus.

[0183]

[0184] In formula (24), represents the new generated position of the i-th walrus in the second stage; Indicates the d-th dimension value of the newly generated walrus; X k,d is the d-th dimension value of the k-th walrus, k∈{1,…,N}, k is a randomly selected walrus different from the i-th one; is the objective function value of the newly generated walrus.

[0185] ④ Development stage, this stage aims to simulate the walrus escape and fight against predators to improve the local search ability, the mathematical description is shown in the following formula. If a new walrus position with better adaptability is generated in this stage, the candidate solution is updated, otherwise the candidate solution is retained.

[0186]

[0187] In formula (25), represents the new generated position of the i-th walrus in the third stage; Indicates the d-th dimension value of the newly generated walrus; l d 、u d They correspond to the lower and upper limits of the walrus d dimension respectively; Corresponding to the lower and upper limits of the iteration at time j; F i P3 is the objective function value of the newly generated walrus.

[0188] The specific steps for optimizing the configuration of optical storage capacity are as follows:

[0189] S4.1. Initialize the walrus population size and iteration times as well as load, energy storage output and photovoltaic output data;

[0190] S4.2. Calculate the fitness values ​​of all walruses according to the optimization objective function and select the best walrus;

[0191] S4.3. Simulate the behavior of walruses exploring unknown territories and update the Xth i Only the walrus position is taken, and the best objective function value is taken corresponding to the walrus X i P1 ;

[0192] S4.4. Simulate the walrus migration behavior to find a better living area, and update the Xth i Only the walrus position is taken, and the best objective function value is taken corresponding to the walrus X i P2 ;

[0193] S4.5. Simulate walrus escape and anti-predator behavior: Update the Xth i Only the walrus position is taken, and the best objective function value is taken corresponding to the walrus X iP3 ;

[0194] S4.6. Determine whether all walrus individuals have been traversed. If the traversal is complete, retain the optimal result, otherwise return to step S4.3;

[0195] S4.7. Determine whether the current number of iterations has reached the specified maximum value. If so, output the optimal output data of each unit. Otherwise, add 1 to the number of iterations and return to step S4.2.

[0196] The different configuration capacities of photovoltaic and energy storage correspond to the initial values ​​of each walrus. The multi-objective function is used as the evaluation function to calculate the corresponding fitness value of each walrus in each dimension and retain the best fitness value. After continuous iteration, the walrus value corresponding to the best fitness value is obtained after certain conditions are met, and the output curve of each unit under the coordinated operation mechanism of the integrated photovoltaic and energy storage system is generated, and finally the corresponding optimal photovoltaic and energy storage capacity configuration is calculated.

[0197] This specific implementation method is to accurately evaluate the economic benefits of the project and reasonably configure the capacity of the photovoltaic system and the energy storage system in the early stage of the construction of the photovoltaic and energy storage integrated system project. On the basis of clarifying the specific time scale of the entire life cycle and linearly correcting the energy storage capacity attenuation, a method for economic evaluation of the entire life cycle of the user-side photovoltaic and energy storage integrated system is proposed. It can fully reflect the economy of the user-side photovoltaic and energy storage integrated system throughout the entire life cycle and ensure that the economic evaluation result is not an accidental result in the short term. Compared with the economic evaluation method of the traditional photovoltaic and energy storage integrated system, the result is more accurate and closer to reality, suitable for typical industrial and commercial user application scenarios, and has broad market application prospects.

[0198] Example 1: A certain enterprise is used as an application case to verify the model involved in the present invention and the proposed optimization algorithm. The average time-of-use electricity price of the enterprise in the past five years is shown in Table 1. In the optimization process, the photovoltaic output characteristics take the output curve of a typical day in each season, and the photovoltaic storage system operates in coordination according to the mechanism described in. The multi-objective evaluation function is used as the optimization target; WaOA is used to optimize the capacity configuration of the user-side photovoltaic storage integrated system.

[0199] Table 1 Average price of time-sharing electricity for enterprises

[0200]

[0201]

[0202] In the initialization phase, the number of walruses is set to 300, the maximum number of iterations is set to 100, and the dimension is set to 24, that is, each day is divided into 24 time periods. The decision variables are the configuration capacity of photovoltaic and energy storage.

[0203] After solving the walrus optimization algorithm, the output curves of each unit on the user side after system optimization were obtained when only photovoltaics, only energy storage, and photovoltaic-storage coordination were deployed in the enterprise, among which the power grid purchase is the net load data observed on the grid side. From the calculation results, it can be seen that when only photovoltaics are deployed, the power grid and photovoltaics jointly support the load, and the amount of abandoned light is large at the peak of photovoltaic power generation; when only energy storage is deployed, energy storage is used for peak shaving and valley filling and peak-valley arbitrage; after optimizing the photovoltaic storage capacity using WaOA, the energy storage configuration is 10.157MWh and the photovoltaic laying amount is 10.986MW, which improves the new energy consumption rate but still has a small amount of abandoned light.

[0204] Combined with the time-of-use electricity prices in Table 1, the comparison of user daily income data and average electricity cost under different configurations is shown in Table 2. It can be seen from the results in Table 2 that the coordinated configuration of photovoltaic and storage can significantly improve the economic benefits of users.

[0205] In order to further verify the capacity configuration optimization strategy based on the walrus algorithm proposed in the present invention, the particle swarm algorithm PSO is used to optimize the capacity of the enterprise. After optimization, the enterprise needs to configure a storage capacity of 12.092MWh and a photovoltaic installation of 12.668MW. However, compared with the WaOA optimization result, the user's average annual net income is reduced to 8.2567 million yuan under the PSO optimization configuration result, and the average cost per kilowatt-hour is increased to 0.63kWh.

[0206] Table 2 User side electricity cost

[0207]

[0208]

[0209] The access to the photovoltaic storage system can not only save the user's electricity costs, but also smooth the load fluctuations by shaving the peak and filling the valley. The net load fluctuation rates in five scenarios can be calculated by load data: 21.87%, 16.80%, 10.88%, 9.31% and 14.05%. The results show that the user-side photovoltaic storage integrated system has the best improvement effect on net load fluctuations under the optimization configuration of the walrus algorithm.

[0210] Table 3 makes a quantitative comparison of the utilization rate of photovoltaic power generation in enterprises. From the comparison results, it can be seen that after the capacity configuration is optimized by PSO, the utilization rate of photovoltaic power generation in users is the best.

[0211] Table 3 User-side abandoned light rate

[0212] Capacity Configuration Photovoltaic power generation utilization rate / % Photovoltaic only 80.82 PSO optimizes PV and storage configuration 90.77 WaOA optimizes PV storage configuration 97.04

[0213] The present invention proposes a photovoltaic storage capacity configuration optimization method under the background of time-of-use electricity price. The photovoltaic storage integrated system is modeled, relevant conditions are constrained and a system coordinated operation strategy is formulated. Then a multi-objective evaluation model is established. Finally, the model is solved using the walrus algorithm to obtain the optimal configuration result. The calculation results show that this configuration optimization method can not only effectively improve the economic efficiency of the photovoltaic storage integrated system, but also smooth out load fluctuations and improve the photovoltaic power generation absorption rate.

[0214] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for evaluating the economic performance of a user-side photovoltaic and energy storage integrated system throughout its life cycle, characterized in that: The economic evaluation of the system is carried out with the life of energy storage as the whole life cycle, and a linear correction is made to the energy storage capacity attenuation in each year. The energy storage life is taken as ten years, and the attenuation coefficient in each year after correction is expressed as: In formula (1), n ​​is the year, d1 is the attenuation rate of the energy storage battery at the end of the first year, d2 is the attenuation rate at the end of the remaining 9 years, and ESC is the percentage of the existing capacity of the energy storage system; After the revision, the economic evaluation model is as follows: In formula (2), Rn1 and Rn2 are the photovoltaic power generation income and energy storage peak-valley arbitrage income in the nth year, respectively, C is the comprehensive cost, and V is the residual value. The residual value V takes into account the comprehensive factors of equipment age, usage status, technological progress and market demand; The photovoltaic power generation income in the nth year and the peak-valley arbitrage income in the nth year are expressed as: R n1 =PV high P high +PV mid P mid (3) R n2 = ESC n ηDoD(N high P high +N mid P mid ) (4) In formula (3) and (4), PV high With PV mid are the amount of electricity provided by photovoltaic power to user loads in different electricity price periods, P high With P mid They are the peak electricity price and the flat electricity price for electricity users under time-of-use electricity price; ESC n is the capacity percentage of the energy storage device in the nth year after attenuation correction in formula (1), N is the energy storage discharge capacity in the corresponding period, η is the efficiency parameter of the energy storage device during charging and discharging, and DoD is the depth of charge and discharge of the energy storage device; The comprehensive cost C is divided into three parts: energy storage construction cost, operation and maintenance cost, installation, labor and site cost. C=C ES +C O&M +C I&C (5) In formula (5), C ES is the energy storage construction cost, C O&M is the energy storage operation, maintenance and installation cost, C I&C Labor and site costs.

2. According to the method for evaluating the economic performance of the entire life cycle of a user-side photovoltaic and storage integrated system proposed in claim 1, a method for optimizing the configuration of photovoltaic and storage capacity is proposed, characterized in that: The steps are: S1. Establish a full life cycle economic evaluation model for photovoltaic power generation systems and energy storage systems: Under the constraints of each system, the photovoltaic system gives priority to powering the power load. When there is sufficient sunlight, it will choose to power the energy storage system or feed power to the grid according to the local electricity price policy and the charge state of the energy storage system; the energy storage system will power the load, either through photovoltaic charging or from the grid when the electricity price is low; the power load will receive the electricity generated by photovoltaics first and choose to be powered by the grid or the energy storage system according to the electricity price policy; S2. On the basis of the proposed economic evaluation, the reduction of load fluctuation rate and the improvement of photovoltaic power generation utilization rate are comprehensively considered, and a multi-objective evaluation function is established with the photovoltaic power generation system capacity and the energy storage system capacity as decision variables, with the goal of saving user electricity costs, smoothing load fluctuations, and improving the photovoltaic power generation absorption rate on the user side; S3. In order to coordinate the output of each unit and improve the economic efficiency of electricity consumption on the user side while meeting the power supply demand, it is necessary to formulate an appropriate electricity control strategy based on the electricity price policy and electricity cost; formulate a coordinated operation strategy at each moment based on the energy flow characteristics, user electricity cost, the operating status of each unit in the integrated photovoltaic storage system and system constraints; S4. Taking the multi-objective evaluation function as the evaluation index, on the basis of the proposed coordinated operation strategy of each unit at each moment, the walrus algorithm is used to solve the photovoltaic and energy storage integrated system model to obtain the best coordinated output curve of each unit, and thus calculate the optimal configuration capacity of photovoltaic and energy storage.

3. The method for optimizing the configuration of optical storage capacity according to claim 2, characterized in that: The mathematical models of each part in step S1 are constructed as follows: ① Photovoltaic power generation model The output energy of the photovoltaic power generation system is affected by solar radiation, photovoltaic laying area, and photoelectric conversion efficiency. In the actual system, the photovoltaic power generation is also affected by the efficiency of the components, the matching of components, the efficiency of the inverter, and the factors of the photovoltaic cell surface being blocked by pollutants. The service life of the photovoltaic cell is 25 years. The power generation of the photovoltaic cell is expressed as: E T =T Ymax ×P R ×A pv ×k (6) In formula (6), E T is the actual photovoltaic power generation, T Ymax is the annual peak sunshine hours, P R is the rated power per unit capacity, A pv is the amount of photovoltaic laying, k is the correction coefficient of theoretical power generation, taking into account various influencing factors and capacity attenuation during actual photovoltaic power generation; ②Energy storage model The energy stored in the energy storage at a certain moment is related to the charging and discharging state in the previous period. When the energy storage is working in the charging state at time t, the energy stored at time t+1 is expressed as: AND (t+1) =And (t) -η ch P ES(t) ΔT (7) In formula (7), E (t) With E (t+1) are the energy states of the energy storage at time t and t+1 respectively, η ch is the energy storage charging efficiency; P ES(t) is the charging power of the energy storage from time t to time t+1, at this time P ES(t) <0; △T is the time interval between time t and time t+1; When the energy storage is in the discharge state from time t to time t+1, the discharge power P ES(t) >0, energy storage state E at time t+1 (t+1) satisfy 4. The method for optimizing the configuration of optical storage capacity according to claim 2, characterized in that: The system constraints in step S1 are: ① Rated power constraints of distribution transformers: To avoid additional transformer construction costs, the capacity variation range of the photovoltaic storage integrated system should meet the following requirements: P tr.min ≤P load +P pv +P ES ≤P tr.max (9) In formula (9), P tr.min It is the minimum rated power limit of the user's transformer, P tr.max is the maximum rated power limit of the user's transformer, P 1oad is the load power, P pv is the photovoltaic power generation power, P ES is the rated power of the energy storage system; ② Power balance constraint: To ensure the safety of electricity consumption on the user side, the output power of the photovoltaic storage system must always be balanced with the load demand power and the power grid: P grid,t +P pv,t +P ES,t =P load,t (10) In formula (10), P grid,t is the grid output power at time t, P pv,t is the photovoltaic output power, P ES,t Energy storage output power, P load,t is the load power; ③ Constraints on photovoltaic access capacity: The installation of photovoltaic power in industrial and commercial user parks is not only limited by the site, but also by the allowable access capacity of the transformer. When configuring photovoltaic power, its capacity is subject to the following constraints: A pv,min ≤A pv ≤A pv,max (11) In formula (11), A pv,max , A pv,min They are the maximum and minimum photovoltaic capacities that can be connected to the user side respectively; ④ Photovoltaic output constraint: The output of photovoltaic power generation at any time period is related to the external factors of light intensity and temperature at that time, but the output power at any time is less than the maximum power generation power of the photovoltaic system at the current time. 0≤P pv,t ≤P pv,max (12) In formula (12), P pv,t is the photovoltaic output power at time t, P pv,max is the theoretical maximum power generation of photovoltaics; ⑤ Energy storage charging and discharging state constraints: The charging and discharging power of the energy storage system at any time should meet the power constraints and energy storage battery charge state constraints: P dis ≤P ES,t ≤P ch (13) SOC min ≤SOC t ≤SOC max (14) In formula (13) and (14), P dis and P ch They are the maximum discharge power and maximum charging power of energy storage; SOC t is the state of charge of the energy storage battery at time t, SOC max and SOC min They are the maximum state of charge and minimum state of charge of the battery respectively; ⑥ Energy storage charging and discharging time constraints Energy storage equipment reserves enough time in each charging and discharging cycle so that the equipment can complete the established charging and discharging goals. This can not only make full use of the energy storage capacity to maximize economic benefits, but also avoid damage to the service life of the energy storage battery caused by incomplete charging and discharging: In formula (15) and (16), T dis is the discharge time of the energy storage system, T ch is the charging time, P dis,N is the rated discharge power, P ch,N is the rated charging power.

5. The method for optimizing the configuration of optical storage capacity according to claim 2, characterized in that: The step S2 constitutes a multi-objective evaluation function: ① Comprehensive income: The economic evaluation model proposed in S1 further derives the evaluation index of saving electricity cost: ②Net load fluctuation: The newly added photovoltaic storage system changes the original characteristic curve of the load. Reasonable configuration of the photovoltaic storage system can effectively smooth the load fluctuation on the grid side and reduce the peak load regulation pressure of the grid. Considering the influence of the photovoltaic storage system on the user side, the net active load fluctuation is used as the evaluation index, and the expression is: In formula (18), P load (t), P pv (t), P ES (t) are load power, photovoltaic power generation power and energy storage charging and discharging power at time t respectively; is the average power of the load in a typical working day, T = 24h ③ Photovoltaic power generation consumption: When there are too many photovoltaic configurations, the excessive photovoltaic power generation cannot be absorbed by the grid or stored by the energy storage system. The photovoltaic system will reduce the power generation, resulting in abandoned light, wasting construction resources and increasing unnecessary operation and maintenance costs. Taking photovoltaic power generation absorption as the system pricing indicator, it is expressed as: In formula (19), W pv,total is the actual photovoltaic power generation, P pv,t is the photovoltaic output power at time t; In summary, taking comprehensive revenue, net load fluctuation rate and photovoltaic power generation consumption rate as evaluation indicators, the following multi-objective evaluation function is constructed: min F=[F1(x,y),F2(x,y),F3(x,y)] (20) In formula (20), F1 is the evaluation index of electricity cost saved by the photovoltaic energy storage system, F2 is the evaluation index of net load fluctuation rate, and F3 is the evaluation index of photovoltaic power generation consumption rate; x and y are the configuration capacities of photovoltaic and energy storage, respectively, that is, the decision variables of the entire model.

6. The method for optimizing the configuration of optical storage capacity according to claim 2, characterized in that: The specific process of step S3 formulating the coordinated operation strategy at each time is as follows: S3.

1. When the net load is positive, it means that the photovoltaic power generation output is less than the user load demand, and the energy storage SOC needs to be queried: i. If the SOC is less than the lower limit, the energy storage cannot be discharged, and the user directly purchases electricity from the grid to support the load; ⅱ. If the SOC is greater than the lower limit, further compare the time-of-use electricity price with the cost per kilowatt-hour, that is, the cost required for the energy storage unit to transfer a unit of electricity: If the time-of-use electricity price is lower than the cost per kilowatt-hour and the SOC is less than or equal to the upper limit, electricity is purchased from the grid to support the load and charge the energy storage; otherwise, the energy storage has reached the upper limit and cannot be charged further, and the grid only supports the load; If the time-of-use electricity price is higher than the cost per kilowatt-hour and the power shortage of the load demand is not higher than the rated discharge power of the energy storage, the load power consumption will be supported only by the energy storage; otherwise, the energy storage alone cannot meet the load power demand, and the power grid and energy storage will work together to support the load power consumption; S3.

2. When the net load is negative, it means that the photovoltaic power generation output is greater than the user load demand, and the energy storage SOC needs to be queried: ⅰ. If the SOC is higher than the upper limit, the energy storage cannot be charged, resulting in abandoned light; ii. If the SOC is lower than the upper limit and the surplus power of photovoltaic power generation is less than the rated charging power of energy storage, the energy storage can absorb the surplus power of photovoltaic power generation at this moment, and the photovoltaic power generation supports the load, and the surplus power is used to charge the energy storage; otherwise, the energy storage cannot completely absorb the surplus power of photovoltaic power generation, and some photovoltaic power generation will be abandoned while the photovoltaic power generation supports the load and charges the energy storage; S3.

3. The above process is executed repeatedly within 24 hours to obtain the whole-day control strategy and form a reasonable coordinated power supply mechanism for the user-side photovoltaic and storage integrated system.

7. The method for optimizing the configuration of optical storage capacity according to claim 2, characterized in that: The specific steps of step S4 are as follows: S4.

1. Initialize the walrus population size and iteration times as well as load, energy storage output and photovoltaic output data; S4.

2. Calculate the fitness values ​​of all walruses according to the optimization objective function and select the best walrus; S4.

3. Simulate the behavior of walruses exploring unknown territories and update the Xth i Only the walrus position is taken, and the best objective function value is taken corresponding to the walrus X i P1 ; S4.

4. Simulate the walrus migration behavior to find a better living area, and update the Xth i Only the walrus position is taken, and the best objective function value is taken corresponding to the walrus X i P2 ; S4.

5. Simulate walrus escape and anti-predator behavior: Update the Xth i Only the walrus position is taken, and the best objective function value is taken corresponding to the walrus X i P3 ; S4.

6. Determine whether all walrus individuals have been traversed. If the traversal is complete, retain the optimal result, otherwise return to step S4.3; S4.

7. Determine whether the current number of iterations has reached the specified maximum value. If so, output the optimal output data of each unit. Otherwise, add 1 to the number of iterations and return to step S4.2.

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