New energy power station energy storage type selection and constant volume method and system

Abstract

The invention discloses a new energy power station energy storage type selection and capacity determination method. The method comprises the steps that S1, a data acquisition module acquires power grid side data and power generation side data; s2, determining parameters of each energy storage battery in an energy storage type selection and constant volume module, and constructing an energy storage battery model; s3, constructing a multi-working-condition operation model of the energy storage system; s4, constructing a particle swarm optimization model, and performing multi-working-condition economical efficiency optimization on each energy storage battery model; s5, searching the optimal model and capacity configuration information, and inputting the optimal model and the capacity configuration information into an economical efficiency calculation module; and S6, calculating and outputting an economic index of the energy storage system of the new energy power station. According to the method, the economical efficiency of the energy storage system is improved, and the internal return rate, the payback period and the net present value indexes of the energy storage system constructed by the new energy power station are calculated. According to the method, the model selection problem and the capacity configuration problem of energy storage construction of the new energy power station can be solved, the economical efficiency of energy storage configuration is optimized, and guidance is provided for energy storage system configuration of the new energy power station.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy storage, and in particular to a method and system for selecting and sizing energy storage in a new energy power station. Background Art

[0002] With the development of wind power and photovoltaic power generation technology and the increase in installed capacity, the impact of large-scale grid connection of new energy power on power quality has become an issue that cannot be ignored. Energy storage systems have the advantages of fast response and flexible layout. They can improve the power quality problems caused by the volatility and randomness of new energy in new power systems and provide auxiliary services such as frequency regulation and peak regulation for the power grid. Therefore, it is necessary to configure energy storage in new energy sites to promote the large-scale application of advanced energy storage technology.

[0003] In order to improve the auxiliary service capabilities of new energy stations and reduce the assessment power caused by unstable electricity, it is necessary to reasonably select and configure the capacity of the energy storage system to improve the overall economic benefits of new energy and energy storage systems. In the research on energy storage selection and capacity determination, the existing results optimize the configuration of energy storage by analyzing the output of new energy stations. However, in previous studies, there were few economic analyses and selections of various electrochemical energy storage batteries for new energy stations, and there was no consideration of the power grid's output assessment of new energy power stations and the multi-condition capacity configuration for providing frequency regulation and peak load regulation auxiliary services. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method for selecting and sizing energy storage in a new energy power station, the method comprising: Step S1: The data acquisition module collects grid-side data and power generation-side data; Step S2: Determine the parameters of each energy storage battery in the energy storage selection and capacity determination module, and construct an energy storage battery model; Step S3: construct a multi-operating condition operation model of the energy storage system; Step S4: construct a particle swarm optimization model to optimize the economic performance of each energy storage battery model under multiple operating conditions; Step S5: Find the most optimal model and capacity configuration information and input it into the economic calculation module; Step S6: Calculate and output the economic performance index of the energy storage system of the new energy power station.

[0005] Furthermore, the grid-side data collected by the data acquisition module includes historical grid frequency data and peak-shaving instruction data; the power generation side data includes historical new energy actual power data and predicted power data.

[0006] Furthermore, the energy storage battery parameters include energy storage system unit price, battery cell unit price, energy storage system efficiency, charge and discharge depth, number of cycles, annual attenuation rate, and residual value rate; The constraints of the energy storage battery model include energy storage power constraints, energy storage charge and discharge rate constraints, and energy storage charge state constraints: The charging and discharging power of the energy storage system in practical applications should be within the allowable range: ; in, They are t The discharging and charging power of the energy storage system at each moment; is the maximum charging and discharging power of the energy storage system; They are t The discharge and charge variables at the moment are 0-1 variables; The energy storage charge and discharge rate constraints are: ; in, E is the capacity of the energy storage system; is the energy storage rate. In a system with a storage time of 2 hours, ; The energy storage system should be operated at a reasonable state of charge: ; in, They are t Moment and t -1 state of charge at time; is the charging and discharging efficiency of the energy storage system; It is the charging and discharging period of the energy storage system; SOC min , SOC max They are the lower and upper limits of the state of charge of the energy storage system respectively.

[0007] Furthermore, the multi-operating condition operation model of the energy storage system takes into account the adjustment of output fluctuations of new energy power stations, participation in frequency regulation auxiliary services and participation in peak load regulation auxiliary services; The total benefit of the energy storage system participating in multi-operating conditions is expressed as: ; in, C r1 , C r2 , C r3 The revenues from energy storage regulating the output of new energy, participating in frequency regulation ancillary services, and participating in peak load regulation ancillary services are respectively; C Investment cost for batteries; C B The replacement cost of energy storage; C MT For operation and maintenance costs; The benefits of energy storage regulation of new energy output are composed of reduced power forecast assessment benefits and reduced active power change assessment benefits: ; in, C r11 To predict and assess the benefits of energy storage and regulation of new energy power; the calculation method of prediction accuracy is: ; in, P Mi for i The actual power at the moment; P Pi for i The predicted value at the moment; C ap is the available capacity of the new energy power station; n is the number of samples during the power generation period; For the day-ahead power forecast, the wind farm’s 0-24h day-ahead power forecast accuracy should be greater than or equal to 83%. If it is less than 83%, the assessment power will be calculated as (83%-day-ahead power forecast accuracy) × wind farm rated capacity × 1h; the photovoltaic power station’s 0-24h day-ahead power forecast accuracy should be greater than or equal to 85%. If it is less than 85%, the assessment power will be calculated as (85%-day-ahead power forecast accuracy) × photovoltaic power station rated capacity × 1.5h; For ultra-short-term power forecast, the accuracy of ultra-short-term power forecast of wind farm in the fourth hour shall be greater than or equal to 87%. If it is less than 87%, (87% - ultra-short-term accuracy) × rated capacity of wind farm × 1h shall be calculated as the assessment power. The accuracy of ultra-short-term power forecast of photovoltaic power station in the fourth hour shall be greater than or equal to 90%. If it is less than 90%, (90% - ultra-short-term accuracy) × rated capacity of photovoltaic power station × 1.5h shall be calculated as the assessment power. C r11 According to (accuracy rate daily assessment power before energy storage configuration - accuracy rate daily assessment power after energy storage configuration) Electricity price calculation; The active power change of new energy stations meets the requirements of power system safety and stability; when the rated capacity of the wind farm is less than 30MW, the maximum limit of active power change in 10 minutes is 10MW, and the maximum limit of active power change in 1 minute is 3MW; when the rated capacity of the wind farm is greater than or equal to 30MW and less than or equal to 150MW, the maximum limit of active power change in 10 minutes is 1 / 3 of the rated capacity of the power station, and the maximum limit of active power change in 1 minute is 1 / 10 of the rated capacity of the power station; when the rated capacity of the wind farm is greater than 150MW, the maximum limit of active power change in 10 minutes is 50MW, and the maximum limit of active power change in 1 minute is 15MW; the maximum limit of active power change in 1 minute of the photovoltaic power station is 1 / 10 of the rated capacity of the power station; If the power change exceeds the limit within 10 minutes, the power consumption will be calculated according to the following formula: ; If the power change in 1 minute exceeds the limit, the power consumption is calculated according to the following formula: ; in, P i,c for i The power change value that exceeds the limit during the time period; P lim is the power variation limit; C r12 According to (active power fluctuation daily assessment power before energy storage configuration - active power fluctuation daily assessment power after energy storage configuration) Electricity price calculation; Energy storage participation in frequency regulation ancillary services revenue C r2 Compensation for small disturbances C r21 Compensation with large disturbance benefits C r22 composition: ; Maximum frequency deviation of power grid For small disturbances, compensation will be given to the grid-connected entities that meet the small disturbance performance index requirements. The compensation standards are: ; in, The rated capacity of the grid-connected entity; C 0.1 hour; like : ; in, Monthly pass rate for primary frequency modulation: ; is the contribution rate of each qualified small disturbance event, and the contribution rate of a frequency modulation K The calculation method is: ; in, is the actual contribution of primary frequency modulation, is the theoretical contribution of primary frequency modulation, and the calculation method is: ; in, t 0 It is the moment when the system frequency exceeds the dead zone of the primary frequency regulation action of the grid-connected entity; t 1 It is the moment when the system frequency enters the dead zone of the primary frequency regulation action of the grid-connected main body; Δ f ( t )correspond t The value of the grid frequency exceeding the dead zone at the moment, high frequency is positive and low frequency is negative; The rated frequency of the power grid is 50Hz; It is the primary frequency regulation coefficient of the grid-connected entity; for t The actual active power generated by the grid-connected entity at any given moment; P 0 for t 0 The actual active power generated by the grid-connected entity at any given moment; Maximum frequency deviation of the power grid |Δ f |≥0.1Hz is considered a large disturbance. Compensation will be given to the grid-connected entities that meet the large disturbance performance index requirements. The compensation standards are: ; ; in, The actual amount of electricity contributed to the frequency regulation of each qualified large disturbance event for the grid-connected entity; is the contribution rate of each large disturbance qualified event; Energy storage participation in frequency regulation ancillary services revenue Profit from peak-to-valley spread Peak load compensation income Composition: The power dispatching agency calls upon the system peak load regulation needs and issues a dispatching plan, including dispatching instructions, requiring the energy storage power station to compensate for the charging power when it enters the charging state. The specific compensation standard is 400 (yuan / MWh): ; ; ; in, is the peak-valley electricity price series; is the energy storage discharge quantity sequence; The energy storage charging sequence.

[0008] Furthermore, the particle swarm optimization model is constructed by a particle swarm optimization algorithm, and the particle swarm optimization algorithm searches for the optimal solution by simulating iterative position adjustment of individual particles in the search space.

[0009] Furthermore, the particle swarm optimization algorithm includes: Initialize the particle swarm: set the particle swarm system parameters, including the particle swarm size, particle dimension, number of iterations, inertia weight, and learning factor; randomly generate a group of particles, each particle represents a potential solution; randomly distribute the particles in the search space, and randomly generate the position and velocity of each particle; Evaluate particle fitness: For each particle, calculate the fitness value, i.e., the value of the objective function, through the objective function; the higher the fitness value, the closer the particle is to the optimal solution; Update the individual optimal position: If the fitness value of the particle's current position is higher than the fitness value of the individual optimal position, the current position is set as the individual optimal position; Update the global optimal position: In the particle swarm, find the particle with the highest fitness value, and take the position of the particle with the highest fitness value as the global optimal position. The global optimal position represents the best solution in the entire swarm; Update particle speed and position: Each particle updates its own speed and position based on individual experience and collaboration with the group; Iteration termination: When the set number of iterations is reached, the algorithm stops iterating; Output result: The particle swarm algorithm outputs the solution corresponding to the global optimal position, which is the optimal solution to the problem; The individual experience is that particles tend to move along the direction of the best solution they have found, so as to reach the individual optimal position faster; The group collaboration is that particles are also attracted by the global optimal position in the group so as to reach the global optimal position faster; The speed is updated as follows: ; in, V i ( t +1) and V i ( t ) are particles i In iteration t +1 with t The speed of the second; ω is the inertia weight;c 1 , c 2 They are individual learning factor and group learning factor respectively; rand 1 , rand 2 All are random numbers in [0,1]; pbest i For particles i The individual optimal position of gbest is the global optimal position; X i ( t ) is a particle i In iteration t The position of the next time; The location is updated as follows: .

[0010] Furthermore, the particle swarm optimization algorithm obtains the optimal power or capacity of each energy storage system and the annual net profit under multiple working conditions; selects the energy storage system with the largest net profit, and imports the energy storage system parameters and capacity configuration results into the economic calculation and analysis module.

[0011] Furthermore, the step S6 specifically includes, the economic calculation module calculating the economic indicators of the energy storage system, including the internal rate of return, investment payback period, and net present value of the energy storage project; The internal rate of return is a method used to evaluate the financial benefits of project investment; the internal rate of return is the discount rate when the total present value of capital inflows is equal to the total present value of capital outflows, that is, the net present value is equal to zero, and is calculated through the project cash flow; Project cash flow = net cash flow from operating activities - net cash flow from investing activities, where net cash flow from operating activities = cash inflow - cash outflow, net cash flow from investing activities = (equipment cost + battery replacement) (1 + equipment VAT rate); Among them, the cash inflow is composed of the annual operating income of the energy storage system and the residual value in the last year. Cash inflow = (operating income + residual value) (1 + output tax rate), residual value = equipment cost Residual value rate; The cash outflow part is the maintenance cost of the energy storage system; Cash outflows include maintenance and operating costs; Cash outflow = (maintenance costs + operating costs) (1 + input tax rate), maintenance cost = equipment cost Energy storage system maintenance rates; Net cash flows from investing activities include equipment costs and battery replacement. When the remaining capacity of the battery at the end of the period is less than the cut-off capacity, the battery cell needs to be replaced; The payback period is the number of years required for the total income obtained from the investment project after it is put into production to reach the total investment amount of the investment project; the said payback period is calculated using the project cash flow; Payback period = cumulative number of years of negative project cash flow + | cumulative cash flow of the year before the project cash flow becomes positive | / cash flow of the year when the project cash flow becomes positive; Net present value is the difference between the present value of cash inflows and the value of cash outflows.

[0012] On the other hand, the present invention also provides a new energy storage type selection and capacity determination system, the system comprising a data acquisition module, an energy storage type selection and capacity determination module, and an economic calculation module; The data acquisition module includes a data acquisition device on the power generation side and a data acquisition device on the power grid side; the data acquisition device on the power generation side collects historical output data of new energy power stations; the data acquisition device on the power grid side collects historical frequency data and peak load instruction data; The energy storage selection and capacity determination module includes a multi-operating condition operation module and a particle swarm optimization module; the multi-operating condition operation module is used to construct a multi-operating condition operation model of the energy storage system, simulating the operating conditions of the energy storage system in regulating the output fluctuation of the new energy power station, participating in the frequency regulation auxiliary service and participating in the peak regulation auxiliary service; the particle swarm optimization module is used to construct a particle swarm optimization model, taking the maximum power, capacity and charge and discharge power of the energy storage as variables, and solving the optimal energy storage selection and configuration scheme when the annual net income of multiple operating conditions is maximized; The economic calculation module is used to receive the optimal energy storage model and capacity configuration data output by the energy storage selection and capacity determination module, calculate and output the economic indicators of the energy storage system; the economic indicators of the energy storage system include the internal rate of return, investment payback period and net present value of the energy storage project.

[0013] On the other hand, the present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement a method for selecting and sizing energy storage in a new energy power station as described above.

[0014] Beneficial effects of the present invention: 1. Aiming at the assessment problems of poor power quality and unstable output of new energy power stations in new power systems, the present invention uses particle swarm optimization method to solve the optimal configuration, power and capacity of energy storage systems under multiple working conditions such as adjusting output fluctuations of new energy power stations, participating in frequency regulation auxiliary services and participating in peak regulation auxiliary services, so as to improve the economy of energy storage systems and calculate the internal rate of return, investment payback period and net present value indicators of energy storage systems for new energy power station construction.

[0015] 2. The method provided by the present invention can solve the selection and capacity configuration problems of energy storage in the construction of new energy power stations, optimize the economic efficiency of energy storage configuration, and provide guidance for the configuration of energy storage systems in new energy power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a new energy storage type selection and capacity determination method provided by the present invention; Figure 2 This is the overall workflow of the photovoltaic power station energy storage selection and capacity determination system in the embodiment of the present invention; Figure 3 The actual power curve of the photovoltaic power station and the day-ahead predicted power curve in the embodiment of the present invention; Figure 4 is a flow chart of a particle swarm optimization algorithm in an embodiment of the present invention; Figure 5 It is a comparison of photovoltaic output curves regulated by energy storage in an embodiment of the present invention; Figure 6 It is the energy storage SOC state when the energy storage adjusts the photovoltaic output in the embodiment of the present invention; Figure 7 It is the energy storage charging and discharging state when the energy storage regulates the photovoltaic output in the embodiment of the present invention; Figure 8 A new energy storage type selection and capacity determination system provided by the present invention; Fig. 9 A schematic diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0017] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limitations of the present invention. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0018] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0019] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0020] like Figure 1 As shown, the first purpose of the embodiment of the present invention is to provide a method for selecting and sizing energy storage in a new energy power station, including: S100, obtain the historical data of the output of new energy power plants, grid frequency, and peak load regulation instructions, and construct a data set; S200, respectively inputting energy storage system unit price, battery cell unit price, energy storage system efficiency, charge and discharge depth, number of cycles, annual attenuation rate, and residual value rate parameters of the candidate energy storage batteries, considering energy storage power constraints, energy storage charge and discharge rate constraints, and energy storage state of charge constraints, to construct models of each energy storage battery; S300, based on the energy storage battery models, construct a multi-operating condition operation model of an energy storage system that takes into account adjusting output fluctuations of new energy power stations, participating in frequency regulation auxiliary services, and participating in peak load regulation auxiliary services; S400, based on the data set and the multi-operating condition operation model of the energy storage system, a particle swarm algorithm is used to solve the optimal solution for the multi-operating condition operation economy, and the optimal energy storage model and capacity configuration result are obtained; S500, outputting the optimal energy storage model and capacity configuration information to an economic calculation module, so as to obtain the internal rate of return, investment payback period, and net present value economic indicators of the energy storage system of the new energy power station.

[0021] The method of the present invention optimizes the capacity configuration of the energy storage system under multiple working conditions such as regulating photovoltaic power, participating in frequency regulation auxiliary services, and participating in peak regulation auxiliary services through a particle swarm algorithm, and compares the configurations of several energy storage systems to perform the most economical optimization.

[0022] Furthermore, the economic parameters of the energy storage system such as internal rate of return, net present value, and payback period under the optimal configuration are calculated to provide guidance for the configuration of energy storage systems in new energy power stations.

[0023] The complete technical solution of the present invention is described in detail below with reference to the accompanying drawings and embodiments: Figure 2 This is the overall workflow of the photovoltaic power station energy storage selection and capacity determination system in the embodiment of the present invention.

[0024] In order to overcome the shortcomings of the existing technology, Figure 2 The present invention provides a method for selecting and sizing energy storage in a new energy power station, comprising the following steps: Step S1, the data acquisition module acquires grid-side data and power generation-side data.

[0025] Figure 3It is the actual power curve of the photovoltaic power station and the day-ahead predicted power curve in the embodiment of the present invention.

[0026] In this embodiment, the grid-side data collected by the data collection module includes historical grid frequency data and peak load instruction data; Figure 3 As shown, the power generation side data includes the historical power data of the photovoltaic power station.

[0027] Step S2, determining the parameters of each energy storage battery in the energy storage selection and capacity determination module, and constructing an energy storage battery model.

[0028] In this embodiment, lithium iron phosphate battery, sodium sulfur battery, all-vanadium flow battery, lead carbon battery are candidate battery types for photovoltaic power station, and each energy storage battery parameter includes energy storage system unit price, battery cell unit price, energy storage system efficiency, charge and discharge depth, number of cycles, annual attenuation rate and residual value rate. The specific values ​​of each energy storage battery parameter are shown in Table 1.

[0029] Table 1 Parameters of various energy storage batteries parameter Lithium iron phosphate battery Sodium-sulfur battery All-vanadium flow battery Lead Carbon Battery System price 1600 yuan / kWh 2000 yuan / kWh 2600 yuan / kWh 1000 yuan / kWh Cell price 500 yuan / kWh 500 yuan / kWh / 500 yuan / kWh System efficiency 86% 80% 68% 85% Depth of charge and discharge 90% 100% 100% 70% Cycle times 6000 4500 15000 3500 Annual decay rate 0.02 / 0.01 / Residual value rate 3% 10% 30% 20% In this embodiment, the energy storage battery model constraints include energy storage power constraints, energy storage charge and discharge rate constraints, and energy storage charge state constraints: The charging and discharging power of the energy storage system in practical applications should be within the allowable range: ; in, P dis,t , P chr,t They are t The discharging and charging power of the energy storage system at each moment; P max is the maximum charging and discharging power of the energy storage system; ξ dis,t , ξ chr,t They are t The discharge and charge variables at the moment are 0-1 variables.

[0030] Energy storage charge and discharge rate constraints: ; in, E is the capacity of the energy storage system; C rate is the energy storage rate. In a system with a storage time of 2 hours, C rate =0.5.

[0031] The energy storage system should be operated at a reasonable state of charge: ; Among them, SOCt , SOC t-1 They are t Moment and t -1 state of charge at time; η is the charging and discharging efficiency of the energy storage system; Δ t It is the charging and discharging period of the energy storage system; SOC min , SOC max They are the lower and upper limits of the state of charge of the energy storage system respectively.

[0032] Step S3, constructing a multi-operating condition operation model of the energy storage system; In this embodiment, the multi-operating condition operation model of the energy storage system takes into account regulating the output fluctuation of the photovoltaic power station, participating in the frequency regulation auxiliary service and participating in the peak regulation auxiliary service.

[0033] The total benefit of the energy storage system participating in multi-condition operation can be expressed as: ; in, C r1 , C r2 , C r3 The benefits of energy storage regulating photovoltaic output, participating in frequency regulation auxiliary services, and participating in peak load regulation auxiliary services; C Investment cost for batteries; C B The replacement cost of energy storage; C MT For operation and maintenance costs.

[0034] The benefits of energy storage regulation of photovoltaic output are composed of reduced power forecast assessment benefits and reduced active power change assessment benefits: ; in, C r11 The prediction and assessment of photovoltaic power for energy storage regulation is carried out. The calculation method of prediction accuracy is: ; in, P Mi for i The actual power at the moment; P Pi for i The predicted value at the moment; C ap is the available capacity of the PV power station; n is the number of samples during the power generation period.

[0035] For the day-ahead power forecast, the power forecast accuracy of the photovoltaic power station for 0-24 hours the next day should be greater than or equal to 85%. If it is less than 85%, the assessment power will be calculated as (85% - day-ahead power forecast accuracy) × rated capacity of the photovoltaic power station × 1.5 hours; For ultra-short-term power forecast, the accuracy of the ultra-short-term power forecast of the photovoltaic power station in the 4th hour should be greater than or equal to 90%. If it is less than 90%, the assessment power will be calculated as (90%-ultra-short-term accuracy) × rated capacity of the photovoltaic power station × 1.5h.

[0036] C r11 According to (accuracy rate daily assessment power before energy storage configuration - accuracy rate daily assessment power after energy storage configuration) Electricity price calculation.

[0037] The active power change of new energy stations should meet the requirements of power system safety and stability. The maximum limit of active power change of photovoltaic power station in one minute is 1 / 10 of the rated capacity of the power station.

[0038] If the power change in 1 minute exceeds the limit, the power consumption is calculated according to the following formula: ; in, P i,c for i The power change value that exceeds the limit during the time period; P lim is the power variation limit.

[0039] C r12 According to (active power fluctuation daily assessment power before energy storage configuration - active power fluctuation daily assessment power after energy storage configuration) Electricity price calculation.

[0040] Energy storage participation in frequency regulation ancillary service revenue C r2 Compensation for small disturbances C r21 Compensation with large disturbance benefits C r22 composition: ; Maximum grid frequency deviation |Δ f |<0.1Hz is a small disturbance. Compensation will be given to the grid-connected entities that meet the small disturbance performance index requirements. The compensation standard is: ; in, The rated capacity of the grid-connected entity; C 0.1 hour.

[0041] If |Δf |<0.06Hz: ; in, Monthly pass rate for primary frequency modulation: ; is the contribution rate of each qualified small disturbance event, and the contribution rate of a frequency modulation K The calculation method is: ; in, is the actual contribution of primary frequency modulation, is the theoretical contribution of primary frequency modulation, and the calculation method is: ; in, t 0 It is the moment when the system frequency exceeds the dead zone of the primary frequency regulation action of the grid-connected entity; t 1 It is the moment when the system frequency enters the dead zone of the primary frequency regulation action of the grid-connected main body; Δ f ( t )correspond t The value of the grid frequency exceeding the dead zone at the moment, high frequency is positive and low frequency is negative; f n The rated frequency of the power grid is 50Hz; K c It is the primary frequency regulation coefficient of the grid-connected entity; P t for t The actual active power generated by the grid-connected entity at any given moment; P 0 for t 0 The actual active power generated by the grid-connected entity at any moment.

[0042] Maximum grid frequency deviation |Δ f |≥0.1Hz is considered a large disturbance. Compensation will be given to the grid-connected entities that meet the large disturbance performance index requirements. The compensation standards are: ; ; in, The actual amount of electricity contributed to the frequency regulation of each qualified large disturbance event for the grid-connected entity; is the contribution rate of each large disturbance qualified event.

[0043] Energy storage participation in frequency regulation ancillary service revenue Profit from peak-to-valley spread Peak load compensation income Composition: The power dispatching agency calls on the system peak load regulation needs and issues a dispatching plan (including dispatching instructions) requiring the energy storage power station to be compensated for its charging power when it enters the charging state. The specific compensation standard is 400 (yuan / MWh): ; ; ; in, C buy is the peak-valley electricity price series; E dis is the energy storage discharge quantity sequence; E chr The energy storage charging sequence.

[0044] Step S4, constructing a particle swarm optimization model to perform multi-operating condition economic optimization on each energy storage battery model.

[0045] Figure 4 It is a flow chart of the particle swarm optimization algorithm in an embodiment of the present invention.

[0046] In this embodiment, the particle swarm optimization algorithm is a high-precision, fast-converging optimization algorithm based on swarm intelligence, which searches for the optimal solution by simulating the iterative position adjustment of individual particles in the search space. The steps of the particle swarm algorithm to solve the global optimal solution are as follows: Step S4-1, initialize the particle swarm: set the particle swarm system parameters, including particle swarm size, particle dimension, number of iterations, inertia weight, and learning factor. In this embodiment, the particle swarm size is 100, the particle dimension is 2, the number of iterations is 1000, the inertia weight is 0.4, and the individual learning factor and the group learning factor are both 1. A group of particles is randomly generated, each particle represents a potential solution. These particles are randomly distributed in the search space, and the position and velocity of each particle are randomly generated; Step S4-2, evaluate particle fitness: for each particle, calculate its fitness value, i.e., the value of the objective function, through the objective function. In this embodiment, the objective function is to maximize the total benefit of the energy storage system participating in multi-operating conditions. The higher the fitness value, the closer the particle is to the optimal solution, i.e., the solution when the total benefit of the energy storage system participating in multi-operating conditions is maximized; Step S4-3, update the individual optimal position: if the fitness value of the current position of the particle is higher than the fitness value of the individual optimal position, set the current position as the individual optimal position; Step S4-4, update the global optimal position: in the particle swarm, find the particle with the highest fitness value and take its position as the global optimal position, which represents the best solution in the entire swarm; Step S4-5, updating particle speed and position: each particle updates its own speed and position based on individual experience and collaboration with the group.

[0047] Individual experience: Particles tend to move in the direction of the best solution they have found in order to reach their individual optimal position faster.

[0048] Group collaboration: Particles are also attracted to the global optimal position in the group in order to reach the global optimal position faster.

[0049] The speed is updated as follows: ; in, V i ( t +1) and V i ( t ) are particles i In iteration t +1 with t The speed of the second; ω is the inertia weight; c 1 , c 2 They are individual learning factor and group learning factor respectively; rand 1 , rand 2 All are random numbers in [0,1]; pbest i For particles i The individual optimal position of gbest is the global optimal position; X i ( t ) is a particle i In iteration t The position of the next time.

[0050] The location is updated as follows: ; Step S4-6, iteration termination: when the set number of iterations is reached, the algorithm stops iterating; Step S4-7, output result: The particle swarm algorithm outputs the solution corresponding to the global optimal position, that is, the optimal capacity configuration result of the energy storage system. The energy storage regulation photovoltaic output result after particle swarm optimization and the operating status are as follows Figure 5-Figure 7 shown.

[0051] Figure 5 It is a comparison of photovoltaic output curves regulated by energy storage in an embodiment of the present invention; Figure 6It is the energy storage SOC state when the energy storage adjusts the photovoltaic output in the embodiment of the present invention; Figure 7 It is the energy storage charging and discharging state when the energy storage regulates the photovoltaic output in the embodiment of the present invention; The optimal capacity configuration results and total benefits of lithium iron phosphate batteries, sodium sulfur batteries, all-vanadium flow batteries, and lead-carbon batteries participating in multi-operating condition regulation are shown in Table 2.

[0052] Table 2 Participation in multi-condition capacity configuration and total benefits parameter Lithium iron phosphate battery Sodium-sulfur battery All-vanadium flow battery Lead Carbon Battery unit Power / Capacity 4.8095 / 9.6189 3.3520 / 6.7039 2.7700 / 11.0799 4.6898 / 9.3796 MW / MWh cost 121.7452 124.4106 158.2912 134.1779 Ten thousand yuan Net income 153.9478 102.8959 99.4706 125.7397 Ten thousand yuan

[0053] It can be seen from the above table that in this embodiment, the net benefit of configuring lithium iron phosphate batteries is the largest, higher than other types of energy storage batteries, and the optimal capacity configuration of lithium iron phosphate batteries is 4.8095MW / 9.6189MWh.

[0054] Step S5, finding the most optimal model and capacity configuration information and inputting it into the economic calculation module.

[0055] In this embodiment, the particle swarm optimization algorithm obtains the optimal power / capacity of each energy storage system and the annual net income under multiple working conditions. The lithium iron phosphate battery with the maximum net income is selected as the energy storage system configured for the photovoltaic power station, and the capacity configuration is 4.8095MW / 9.6189MWh. The energy storage system parameters and capacity configuration results are imported into the economic calculation and analysis module.

[0056] Step S6, calculate and output the economic performance index of the energy storage system of the new energy power station.

[0057] In this embodiment, the economic calculation module calculates the economic indicators of the energy storage system, including the internal rate of return, investment payback period and net present value of the energy storage project.

[0058] The internal rate of return is the discount rate at which the total present value of capital inflows is equal to the total present value of capital outflows, that is, the net present value is equal to zero, and it needs to be calculated through the project cash flow.

[0059] Project cash flow = net cash flow from operating activities - net cash flow from investing activities, where net cash flow from operating activities = cash inflow - cash outflow, net cash flow from investing activities = (equipment cost + battery replacement) (1 + equipment VAT rate). In this embodiment, the equipment VAT rate is 13%.

[0060] Among them, the cash inflow is mainly composed of the annual operating income of the energy storage system and the residual value in the last year. Cash inflow = (operating income + residual value) (1 + output tax rate), residual value = equipment cost Residual value rate. The cash outflow is the maintenance cost of the energy storage system. In this embodiment, the output tax rate is 13%, the residual value rate is 3%, and the maintenance fee rate is 1%.

[0061] Cash outflow is mainly composed of maintenance costs and operating costs. Cash outflow = (maintenance costs + operating costs) (1 + input tax rate), maintenance cost = equipment cost Energy storage system maintenance rate: In this embodiment, the input tax rate is 13% and the energy storage system maintenance rate is 1%.

[0062] The net cash flow from investing activities is composed of equipment cost and battery replacement. When the remaining capacity of the battery at the end of the period is less than the cut-off capacity, the battery cell needs to be replaced.

[0063] In this embodiment, the cash flow of the project configured with lithium iron phosphate batteries is shown in Table 3.

[0064] Table 3 Cash flow of projects equipped with lithium iron phosphate batteries years 1 2 3 4 5 6 7 8 9 10 Cash flow (10,000 yuan) -1490 249.4 247.0 244.5 242.0 239.5 236.9 233.8 230.0 -301 years 11 12 13 14 15 16 17 18 19 Cash flow (10,000 yuan) 251.8 249.4 247.0 244.5 242.0 239.5 236.9 233.8 282.2 The payback period is the number of years required for the total income obtained after the investment project is put into production to reach the total investment of the investment project. It is calculated using the project cash flow. Payback period = the number of years when the cumulative project cash flow is negative + |the cumulative cash flow of the year before the project cash flow becomes positive| / the cash flow of the year when the project cash flow becomes positive.

[0065] Net present value is the difference between the present value of cash inflows and the value of cash outflows.

[0066] In this embodiment, based on 19 years of operation, the economic calculation module obtains that the internal rate of return of the lithium iron phosphate battery is 12.92%, the investment payback period is 7.13 years, and the net present value is 23.589 million yuan.

[0067] Figure 8 A new energy storage type selection and capacity determination system is provided by the present invention.

[0068] like Figure 8 As shown, the second purpose of the embodiment of the present invention is to provide a new energy storage selection and capacity determination system, including a data acquisition module, an energy storage selection and capacity determination module and an economic calculation module.

[0069] The data acquisition module includes a power generation side data acquisition device and a grid side data acquisition device. In the embodiment of the present invention, the power generation side data acquisition device collects historical output data and power forecast data of the photovoltaic power station, and the grid side data acquisition device collects historical frequency data and peak load instruction data.

[0070] The energy storage selection and sizing module includes a multi-operating condition operation module and a particle swarm optimization module. In this embodiment, a multi-operating condition operation model of the energy storage system is constructed in the multi-operating condition operation module to simulate the operating conditions of the energy storage system in regulating the output fluctuation of the photovoltaic power station, participating in the frequency regulation auxiliary service and participating in the peak regulation auxiliary service; a particle swarm optimization model is constructed in the particle swarm optimization module, and the maximum power, capacity and charge and discharge power of the energy storage are used as variables to solve the optimal energy storage selection and configuration plan when the annual net income of multiple operating conditions is maximized.

[0071] In this embodiment, the economic calculation module receives the optimal energy storage model and capacity configuration data output by the energy storage selection and capacity determination module, calculates and outputs the economic indicators of the energy storage system, including the internal rate of return, investment payback period and net present value of the energy storage project, and provides guidance for the configuration of the energy storage system in the new energy power station.

[0072] Fig. 9 A schematic diagram of an electronic device provided by the present invention.

[0073] like Fig. 9 As shown, the third purpose of an embodiment of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for selecting and sizing energy storage in a new energy power station when executing the computer program.

[0074] The above embodiments describe the technical solutions of the present invention in detail. Obviously, the present invention is not limited to the described embodiments. Based on the embodiments of the present invention, people familiar with the technical field can also make various changes accordingly, but any changes that are equivalent or similar to the present invention belong to the scope of protection of the present invention.

[0075] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

Claims

1. A method for selecting and sizing energy storage in a new energy power station, characterized in that: The method comprises: Step S1: The data acquisition module collects grid-side data and power generation-side data; Step S2: Determine the parameters of each energy storage battery in the energy storage selection and capacity determination module, and construct an energy storage battery model; Step S3: construct a multi-operating condition operation model of the energy storage system; Step S4: construct a particle swarm optimization model to optimize the economic performance of each energy storage battery model under multiple operating conditions; Step S5: Find the most optimal model and capacity configuration information and input it into the economic calculation module; Step S6: Calculate and output the economic performance index of the energy storage system of the new energy power station.

2. A method for selecting and determining the capacity of energy storage in a new energy power station according to claim 1, characterized in that: The grid-side data collected by the data acquisition module include historical grid frequency data and peak-shaving instruction data; the power generation side data include historical new energy actual power data and predicted power data.

3. A method for selecting and determining the capacity of energy storage in a new energy power station according to claim 1, characterized in that: The energy storage battery parameters include energy storage system unit price, battery cell unit price, energy storage system efficiency, charge and discharge depth, number of cycles, annual attenuation rate, and residual value rate; The constraints of the energy storage battery model include energy storage power constraints, energy storage charge and discharge rate constraints, and energy storage charge state constraints: The charging and discharging power of the energy storage system in practical applications should be within the allowable range: ； in They are t The discharging and charging power of the energy storage system at each moment; is the maximum charging and discharging power of the energy storage system; They are t The discharge and charge variables at the moment are 0-1 variables; The energy storage charge and discharge rate constraints are: ； in, is the capacity of the energy storage system; is the energy storage rate. In a system with a storage time of 2 hours, ; The energy storage system should be operated at a reasonable state of charge: ； in, They are t Moment and t -1 state of charge at time; is the charging and discharging efficiency of the energy storage system; It is the charging and discharging period of the energy storage system; They are the lower and upper limits of the state of charge of the energy storage system respectively.

4. A method for selecting and determining the capacity of energy storage in a new energy power station according to claim 1, characterized in that: The multi-operating condition operation model of the energy storage system takes into account the adjustment of output fluctuations of new energy power stations, participation in frequency regulation auxiliary services and participation in peak load regulation auxiliary services; The total benefit of the energy storage system participating in multi-operating conditions is expressed as: ； in, The revenues from energy storage regulating the output of new energy, participating in frequency regulation auxiliary services, and participating in peak load regulation auxiliary services; Investment cost for batteries; The replacement cost of energy storage; For operation and maintenance costs; The benefits of energy storage regulation of new energy output are composed of reduced power forecast assessment benefits and reduced active power change assessment benefits: ； in, To predict and assess the benefits of energy storage and regulation of new energy power; the calculation method of prediction accuracy is: ； in, for The actual power at the moment; for i The predicted value at the moment; is the available capacity of the new energy power station; n is the number of samples during the power generation period; For the day-ahead power forecast, the wind farm’s 0-24h day-ahead power forecast accuracy should be greater than or equal to 83%. If it is less than 83%, the assessment power will be calculated as (83%-day-ahead power forecast accuracy) × wind farm rated capacity × 1h; the photovoltaic power station’s 0-24h day-ahead power forecast accuracy should be greater than or equal to 85%. If it is less than 85%, the assessment power will be calculated as (85%-day-ahead power forecast accuracy) × photovoltaic power station rated capacity × 1.5h; For ultra-short-term power forecast, the accuracy of ultra-short-term power forecast of wind farm in the fourth hour shall be greater than or equal to 87%. If it is less than 87%, (87% - ultra-short-term accuracy) × rated capacity of wind farm × 1h shall be calculated as the assessment power. The accuracy of ultra-short-term power forecast of photovoltaic power station in the fourth hour shall be greater than or equal to 90%. If it is less than 90%, (90% - ultra-short-term accuracy) × rated capacity of photovoltaic power station × 1.5h shall be calculated as the assessment power. According to (accuracy rate daily assessment power before energy storage configuration - accuracy rate daily assessment power after energy storage configuration) Electricity price calculation; The active power change of new energy stations meets the requirements of power system safety and stability; when the rated capacity of the wind farm is less than 30MW, the maximum limit of active power change in 10 minutes is 10MW, and the maximum limit of active power change in 1 minute is 3MW; when the rated capacity of the wind farm is greater than or equal to 30MW and less than or equal to 150MW, the maximum limit of active power change in 10 minutes is 1 / 3 of the rated capacity of the power station, and the maximum limit of active power change in 1 minute is 1 / 10 of the rated capacity of the power station; when the rated capacity of the wind farm is greater than 150MW, the maximum limit of active power change in 10 minutes is 50MW, and the maximum limit of active power change in 1 minute is 15MW; the maximum limit of active power change in 1 minute of the photovoltaic power station is 1 / 10 of the rated capacity of the power station; If the power change exceeds the limit within 10 minutes, the power consumption will be calculated according to the following formula: ； If the power change in 1 minute exceeds the limit, the power consumption is calculated according to the following formula: ； in, for i The power change value that exceeds the limit during the time period; is the power variation limit; According to (active power fluctuation daily assessment power before energy storage configuration - active power fluctuation daily assessment power after energy storage configuration) Electricity price calculation; Energy storage participation in frequency regulation ancillary services revenue Compensation for small disturbances Compensation with large disturbance benefits composition: ； Maximum frequency deviation of power grid For small disturbances, compensation will be given to the grid-connected entities that meet the small disturbance performance index requirements. The compensation standards are: ； in, The rated capacity of the grid-connected entity; C 0.1 hour; like : ； in, Monthly pass rate for primary frequency modulation: ； is the contribution rate of each qualified small disturbance event, and the contribution rate of a frequency modulation K The calculation method is: ； in, is the actual contribution of primary frequency modulation, is the theoretical contribution of primary frequency modulation, and the calculation method is: ； in, It is the moment when the system frequency exceeds the dead zone of the primary frequency regulation action of the grid-connected entity; It is the moment when the system frequency enters the dead zone of the primary frequency regulation action of the grid-connected main body; correspond t The value of the grid frequency exceeding the dead zone at the moment, high frequency is positive and low frequency is negative; The rated frequency of the power grid is 50Hz; It is the primary frequency regulation coefficient of the grid-connected entity; for t The actual active power generated by the grid-connected entity at any given moment; for The actual active power generated by the grid-connected entity at any given moment; Maximum frequency deviation of power grid For large disturbances, compensation will be given to the grid-connected entities that meet the large disturbance performance index requirements. The compensation standards are: ； ; in, The actual amount of electricity contributed to the frequency regulation of each qualified large disturbance event for the grid-connected entity; is the contribution rate of each large disturbance qualified event; Energy storage participation in frequency regulation ancillary services revenue Profit from peak-to-valley spread Peak load compensation income Composition: The power dispatching agency calls upon the system peak load regulation needs and issues a dispatching plan, including dispatching instructions, requiring the energy storage power station to compensate for the charging power when it enters the charging state. The specific compensation standard is 400 (yuan / MWh): ； ； ； in, is the peak-valley electricity price series; is the energy storage discharge quantity sequence; The energy storage charging sequence.

5. A method for selecting and determining the capacity of energy storage in a new energy power station according to claim 1, characterized in that: The particle swarm optimization model is constructed by a particle swarm optimization algorithm, and the particle swarm optimization algorithm is to find the optimal solution by simulating the iterative position adjustment of individual particles in the search space.

6. A method for selecting and determining the capacity of energy storage in a new energy power station according to claim 5, characterized in that: The particle swarm optimization algorithm includes: Initialize the particle swarm: set the particle swarm system parameters, including the particle swarm size, particle dimension, number of iterations, inertia weight, and learning factor; randomly generate a group of particles, each particle represents a potential solution; randomly distribute the particles in the search space, and randomly generate the position and velocity of each particle; Evaluate particle fitness: For each particle, calculate the fitness value, i.e., the value of the objective function, through the objective function; the higher the fitness value, the closer the particle is to the optimal solution; Update the individual optimal position: If the fitness value of the particle's current position is higher than the fitness value of the individual optimal position, the current position is set as the individual optimal position; Update the global optimal position: In the particle swarm, find the particle with the highest fitness value, and take the position of the particle with the highest fitness value as the global optimal position. The global optimal position represents the best solution in the entire swarm; Update particle speed and position: Each particle updates its own speed and position based on individual experience and collaboration with the group; Iteration termination: When the set number of iterations is reached, the algorithm stops iterating; Output result: The particle swarm algorithm outputs the solution corresponding to the global optimal position, which is the optimal solution to the problem; The individual experience is that particles tend to move along the direction of the best solution they have found, so as to reach the individual optimal position faster; The group collaboration is that particles are also attracted by the global optimal position in the group so as to reach the global optimal position faster; The speed is updated as follows: ； in, and Particles i In iteration t +1 with t The speed of the second; is the inertia weight; c 1. c 2 are individual learning factors and group learning factors; rand 1. rand 2 are all random numbers in [0,1]; pbest i For particles i The individual optimal position of gbest is the global optimal position; For particles In iteration The position of the next time; The location is updated as follows: 。 7. A method for selecting and determining the capacity of energy storage in a new energy power station according to claim 6, characterized in that: The particle swarm optimization algorithm obtains the optimal power or capacity of each energy storage system and the annual net profit under multiple working conditions; selects the energy storage system with the largest net profit, and imports the energy storage system parameters and capacity configuration results into the economic calculation and analysis module.

8. A method for selecting and determining the capacity of energy storage in a new energy power station according to claim 1, characterized in that: The step S6 specifically includes: the economic calculation module calculates the economic indicators of the energy storage system, including the internal rate of return, investment payback period, and net present value of the energy storage project; The internal rate of return is a method used to evaluate the financial benefits of project investment; the internal rate of return is the discount rate when the total present value of capital inflows is equal to the total present value of capital outflows, that is, the net present value is equal to zero, and is calculated through the project cash flow; Project cash flow = net cash flow from operating activities - net cash flow from investing activities, where net cash flow from operating activities = cash inflow - cash outflow, net cash flow from investing activities = (equipment cost + battery replacement) (1 + equipment VAT rate); Among them, the cash inflow is composed of the annual operating income of the energy storage system and the residual value in the last year. Cash inflow = (operating income + residual value) (1 + output tax rate), residual value = equipment cost Residual value rate; The cash outflow part is the maintenance cost of the energy storage system; Cash outflows include maintenance and operating costs; Cash outflow = (maintenance costs + operating costs) (1 + input tax rate), maintenance cost = equipment cost Energy storage system maintenance rates; Net cash flows from investing activities include equipment costs and battery replacement. When the remaining capacity of the battery at the end of the period is less than the cut-off capacity, the battery cell needs to be replaced; The payback period is the number of years required for the total income obtained from the investment project after it is put into production to reach the total investment amount of the investment project; the said payback period is calculated using the project cash flow; Payback period = cumulative number of years of negative project cash flow + | cumulative cash flow of the year before the project cash flow becomes positive | / cash flow of the year when the project cash flow becomes positive; Net present value is the difference between the present value of cash inflows and the value of cash outflows.

9. A new energy storage type selection and capacity determination system, characterized in that: The system includes a data acquisition module, an energy storage selection and capacity determination module, and an economic calculation module; The data acquisition module includes a data acquisition device on the power generation side and a data acquisition device on the power grid side; the data acquisition device on the power generation side collects historical output data of new energy power stations; the data acquisition device on the power grid side collects historical frequency data and peak load instruction data; The energy storage selection and capacity determination module includes a multi-operating condition operation module and a particle swarm optimization module; The multi-operating condition operation module is used to construct a multi-operating condition operation model of the energy storage system, simulating the operating conditions of the energy storage system in regulating the output fluctuation of the new energy power station, participating in the frequency regulation auxiliary service and participating in the peak regulation auxiliary service; the particle swarm optimization module is used to construct a particle swarm optimization model, taking the maximum power, capacity and charge and discharge power of the energy storage as variables, and solving the optimal energy storage selection and configuration scheme when the annual net profit of multiple operating conditions is maximized; The economic calculation module is used to receive the optimal energy storage model and capacity configuration data output by the energy storage selection and capacity determination module, calculate and output the economic indicators of the energy storage system; the economic indicators of the energy storage system include the internal rate of return, investment payback period and net present value of the energy storage project.

10. An electronic device, characterized in that: It includes a memory and a processor, the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement a new energy power station energy storage selection and sizing method as described in any one of claims 1-8.

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