Optimization method for design and operation of a grid-connected photovoltaic energy storage system based on an accurate model

Abstract

This invention relates to an integrated optimization method for the design and operation of a photovoltaic-storage-charging system based on a precise model, comprising the following steps: constructing a precise photovoltaic output power prediction model; considering an orderly charging strategy for electric vehicles within constraints, and based on the precise photovoltaic output power prediction model, constructing an integrated optimization model for the design and operation of the photovoltaic-storage-charging system with the optimization objective of maximizing the system's net present value over the entire lifecycle of the system; solving the integrated optimization model to obtain the optimal design scheme and operation strategy. Compared with existing technologies, this invention has advantages such as high model accuracy, low model complexity, high feasibility of the optimization scheme, and good economic efficiency.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system design optimization, and in particular to an integrated optimization method for the design and operation of a photovoltaic-storage-charging system based on a precise model. Background Technology

[0002] In the construction of smart grids, the organic integration of electric vehicles and renewable energy promotes the application of both and helps improve the overall economic and environmental benefits of operation. Photovoltaic-energy storage-charging pile systems (PV-Storage-Charging systems) are a type of microgrid system with broad application prospects. PV-Storage-Charging systems can effectively improve the utilization rate of solar energy resources, alleviate pressure on the main grid, reduce carbon emissions, and enhance the economic efficiency of the system. With the promotion of grid parity for photovoltaic systems and the rapid development of the electric vehicle industry, PV-Storage-Charging systems have shown broad application prospects, and numerous demonstration projects and commercial applications based on PV-Storage-Charging systems are being built and applied around the world.

[0003] The implementation of design and operation optimization for photovoltaic-storage-charging systems is of great significance for improving the renewable energy consumption efficiency and overall economic efficiency of these systems, and for enabling their rapid deployment and application under different source-load characteristic scenarios. Due to the strong coupling between the design and operation of photovoltaic-storage-charging systems, the system design phase typically requires combining historical source-load characteristic data with operational strategy optimization to obtain a feasible optimal design solution. Therefore, the integrated optimization of the design and operation of photovoltaic-storage-charging systems has received increasing attention; however, its implementation remains challenging due to the difficulty in guaranteeing the reliability of optimization results. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and provide an integrated optimization method for the design and operation of a photovoltaic energy storage and charging system based on a precise model, which has high model accuracy, low model complexity, high feasibility of optimization schemes, and good economic efficiency.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] An integrated optimization method for the design and operation of a photovoltaic-storage-charging system based on a precise model includes the following steps:

[0007] Construct an accurate prediction model for photovoltaic output power;

[0008] Considering the orderly charging strategy for electric vehicles under constraints, and based on the accurate prediction model of photovoltaic output power, an integrated optimization model for the design and operation of the photovoltaic-storage-charging system is constructed with the optimization objective of maximizing the net present value of the system throughout the entire life cycle of the photovoltaic-storage-charging system.

[0009] Solve the ensemble optimization model to obtain the optimal design scheme and operation strategy.

[0010] Furthermore, the photovoltaic output power accurate prediction model uses a single diode R P Based on the model, and through the photovoltaic mechanism of the battery, the radial basis function neural network model, and historical weather data, the single diode R is analyzed. P The circuit parameters in the model are corrected.

[0011] Furthermore, the accurate photovoltaic output power prediction model is expressed as follows:

[0012]

[0013]

[0014] In the formula, I represents the output power of the photovoltaic system, and I and V represent the current and voltage of the photovoltaic cell, respectively. PV I S R S R P and a represent the photovoltaic cell's photocurrent, reverse saturation current, series internal resistance, shunt internal resistance, and ideality factor, respectively. Corrections are achieved based on the cell's photovoltaic mechanism, radial basis function neural network model, and historical weather data. T c k is the cell temperature of the photovoltaic cell. B is Boltzmann's constant, and q is electron volt constant.

[0015] Furthermore, the historical weather data includes hourly ambient temperature, solar radiation intensity, and wind speed over a year.

[0016] Furthermore, the formula for calculating the system's net present value is as follows:

[0017]

[0018] NPV is the system's net present value, AOP and TCI represent the system's annual operating profit and total construction investment, respectively, and CRF... l,i It is the capital recovery factor when the system life cycle is l years and the discount rate is i.

[0019] Furthermore, the constraints include power balance constraints, ordered charging constraints, design constraints, and operational constraints.

[0020] Furthermore, the ordered charging constraint is constructed based on historical data of electric vehicle charging characteristic distribution and stochastic simulation of electric vehicle / charging pile behavior characteristics, and its expression includes:

[0021]

[0022]

[0023]

[0024] In the formula, P t load Let be the power used to charge the electric vehicle at time t. Let be the output power of the c-th charging pile at time t, and Δt be the step size for system operation optimization. and P represents the arrival time, departure time, and charging amount of the v-th electric vehicle corresponding to the c-th charging pile. pile,min and P pile,max These represent the minimum and maximum output power of the charging station, respectively. c,t It is a 0-1 variable used to determine whether charging pile c is idle at time t.

[0025] Furthermore, the historical data on electric vehicle charging characteristics includes the time interval distribution of electric vehicles arriving at charging stations, the charging demand distribution, and the distribution of additional dwell time at charging stations.

[0026] Furthermore, the implementation steps of the stochastic simulation of the behavioral characteristics of the electric vehicle / charging station include:

[0027] Based on historical data on electric vehicle charging characteristics, an electric vehicle arrival time interval Δt is constructed. car Additional stay time Δt extra and charging power requirements e pile Distribution characteristics;

[0028] A large amount of characteristic data for the above-mentioned feature parameters was generated based on the Monte Carlo simulation method;

[0029] The key factors of electric vehicle charging behavior are calculated to construct the ordered charging constraints.

[0030] Furthermore, the constraints are linearized constraints.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. This invention constructs an integrated optimization model for the design and operation of photovoltaic-storage-charging systems based on a precise photovoltaic output power prediction model, which can obtain more feasible design schemes and operation strategies, and can be used for the design of photovoltaic-storage-charging systems in different scenarios.

[0033] 2. This invention considers an orderly charging strategy for electric vehicles. By optimizing the output power of each charging pile at each time and rationally allocating the charging load at each time, the peak-valley difference of the charging load can be effectively reduced, thereby improving the photovoltaic power generation absorption rate.

[0034] 3. This invention linearizes the nonlinear constraints in the optimization model, reducing the complexity of the optimization model and achieving a balance between model accuracy and computational complexity.

[0035] 4. This invention facilitates the rapid and accurate design of photovoltaic energy storage and charging systems in different regions and scenarios, and promotes the rapid application of photovoltaic energy storage and charging systems. Attached Figure Description

[0036] Figure 1 This is a schematic flowchart of the method of the present invention;

[0037] Figure 2 This is a schematic diagram of the arrival interval distribution of electric vehicles in the embodiment;

[0038] Figure 3 This is a schematic diagram of the daily average power of typical energy flow at different times in the embodiment;

[0039] Figure 4 This is a schematic diagram comparing the optimal capacity of each device based on different photovoltaic power prediction models in the embodiment;

[0040] Figure 5 This is a schematic diagram comparing the optimization results based on different electric vehicle charging strategies in the embodiments. Detailed Implementation

[0041] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0042] In the process of system design and operation integration optimization, the formulation of feasible optimal design schemes depends on the accurate description of the operating characteristics of system components such as source, load, grid, and storage. However, at the same time, excessively complex component characteristic models can make the solution of the design and operation integration optimization model too difficult. How to reasonably balance model accuracy and computational complexity, and thus achieve the optimal design and operation of the photovoltaic energy storage and charging system, is the creative contribution of this invention.

[0043] like Figure 1 As shown, this invention provides an integrated optimization method for the design and operation of a photovoltaic energy storage and charging system based on a precise model, comprising the following steps:

[0044] S1. Obtain historical data such as weather conditions and electric vehicle charging characteristics distribution at the location of the photovoltaic-storage-charging system, and collect data such as the economic and technical parameters of the main equipment and local time-of-use electricity prices.

[0045] Historical weather data includes hourly ambient temperature, solar radiation intensity, and wind speed over a year. Historical data on electric vehicle charging characteristics includes the time interval distribution of electric vehicles arriving at charging stations, the distribution of charging demand, and the distribution of extra time spent at charging stations.

[0046] The main equipment includes photovoltaic systems, energy storage systems, and transformers. Economic parameters include the unit capacity construction cost, aging cost, maintenance cost, and environmental cost of photovoltaic and energy storage systems. Technical parameters include the charging / discharging efficiency, maximum / minimum charging / discharging rate, and maximum / minimum remaining capacity of the energy storage system.

[0047] Local time-of-use electricity pricing includes peak, flat, and valley electricity prices for different seasons and their corresponding times.

[0048] S2. Construct an accurate prediction model for photovoltaic cell output power based on photovoltaic cell mechanism and data-driven methods, and correct the model parameters based on historical data.

[0049] The constructed photovoltaic cell output power accurate prediction model is based on a single diode R P The model was obtained using a hybrid modeling method combining the model and radial basis function neural networks. The model structure is as follows:

[0050]

[0051]

[0052]

[0053] x P,m =[G m ,T c,m ]

[0054]

[0055] I S =y2(G,T c )I S,STC (5)

[0056] a = a STC (6)

[0057]

[0058]

[0059] Among them, equations (1)-(2) represent the single diode R P The model, Equation (3) is the radial basis function neural network (RBF neural network) model, and Equations (4)-(8) are the single diode R based on the battery mechanism and the radial basis function neural network model. PThe circuit parameters in the model are corrected. Where, This represents the output power of the photovoltaic system at time t. I and V are the current and voltage of the photovoltaic cell, respectively; I PV I S R S R P and 'a' represent the photovoltaic cell's photocurrent, reverse saturation current, series internal resistance, shunt internal resistance, and ideality factor, respectively; T c The cell temperature of the photovoltaic cell; k B Boltzmann constant; q is electron volt constant; x P,m and y n,m y1, y2, and y3 are the input layer neurons and output layer neurons of the RBF neural network for the m-th historical data point, respectively; m is the historical data point number; h n,P σ n,P , and These represent the number of hidden layer nodes, kernel width, center point location, output layer weight factor, and weight factor constant bias, respectively; G is the illumination intensity; I... PV,STC I S,STC a STC R S,STC and R P,STC For single diode R under standard test conditions P The circuit parameters of the model; T c,STC and G STC The values ​​represent the operating temperature and solar radiation intensity of the photovoltaic cell under standard test conditions; μ represents the electrical parameters in the model, determined by the characteristics of the photovoltaic cell.

[0060] S3. Based on historical data of electric vehicle charging characteristic distribution, perform random simulation of electric vehicle / charging pile behavior characteristics to generate electric vehicle charging power characteristic data, and match electric vehicles and charging piles to obtain the arrival / departure time, charging amount and corresponding charging pile number of each electric vehicle that arrives at the photovoltaic-storage-charging system, which is used for subsequent orderly charging constraint construction.

[0061] The implementation steps for stochastic simulation of the behavioral characteristics of electric vehicles / charging stations are as follows:

[0062] (1) Based on historical data, construct the arrival time interval Δt of electric vehicles. car Additional stay time Δt extra and charging power requirements e pile Distribution characteristics.

[0063] (2) Generate a large amount of characteristic data of the above characteristic parameters based on the Monte Carlo simulation method.

[0064] (3) Then calculate the key factors of electric vehicle charging behavior, including the time t when the electric vehicle arrives at the charging station. a The time t is when you leave the charging station l and charging power requirements e pile Wherein, arrival time t a Based on the time interval Δt between the arrival and departure of the vehicle at the charging station car Generated by random simulation; departure time t l Then based on the arrival time t a Charging time and additional dwell time Δt after charging is complete extra The calculation yields the following expression:

[0065] t l =t a +Δt extra +e pile / P pile,max (9)

[0066] In the formula, P pile,max This represents the maximum charging power of the charging station.

[0067] S4. Based on the economic parameters of the main equipment and the time-of-use electricity price, calculate the net present value of the photovoltaic-storage-charging system throughout its entire life cycle, and use the maximization of the system's net present value as the optimization objective of the integrated optimization model for the design and operation of the photovoltaic-storage-charging system.

[0068] The method for calculating the system's net present value is as follows:

[0069]

[0070]

[0071] AOP=RC P -C D -C M -rE O (12)

[0072]

[0073]

[0074]

[0075]

[0076]

[0077] TCI = C I +rE I (18)

[0078]

[0079]

[0080] In the formula, NPV is the system's net present value; AOP and TCI represent the system's annual operating profit and total construction investment, respectively; CRF l,i The capital recovery factor is the discount rate i when the system lifespan is l years; R represents the system's annual revenue; C P C D and C M These represent the annual costs of purchasing electricity from the grid, equipment aging, and equipment maintenance, respectively; E O β is the annual carbon dioxide emission equivalent; r is the carbon trading price; Δt is the step size for system operation optimization; β sell and β load These are the prices sold to the grid and the prices charged to electric vehicles, respectively; P t sell and P t load These represent the power sold to the grid and the power charged to the electric vehicle at time t, respectively; α buy This is the basic monthly electricity cost; W VT It is the design capacity of the transformer, and also the maximum power demand P from the grid. buy,max ; and P t buy These are the time-of-use electricity price and power purchased from the grid at time t; P t dch It is the discharge power of the energy storage system at time t; Q is the investment cost per kWh of energy storage system capacity; cycle It is the number of complete charge-discharge cycles of the energy storage system throughout its entire life cycle; W PV It is the design capacity of the photovoltaic system; This represents the aging cost per kW of photovoltaic system capacity; N is the number of charging piles. This is the annual maintenance cost for each charging station; It is the annual maintenance cost of the power generation process of equipment q (including photovoltaic and energy storage systems). buy , and These represent the unit CO2 emission equivalents of buying electricity from the grid, recycling energy storage systems, and recycling photovoltaic systems, respectively; C I The economic cost of the construction process; E I The carbon dioxide emission equivalent during the construction process; The fixed cost of equipment q during the construction process; The unit variable cost representing an increase of 1 kW / kWh in system capacity; x qIt is a 0-1 variable, representing whether or not to construct the equipment q; It is the unit construction cost of the charging pile; β tra It is the unit cost of increasing the capacity of the transformer. and These are the fixed carbon emission equivalent of equipment q during the construction process and the unit carbon emission equivalent per 1kW / kWh capacity, respectively.

[0081] S5. Based on the technical parameters of the main equipment, construct the constraints of the integrated optimization model for the design and operation of the photovoltaic storage and charging system, and linearize the nonlinear constraints in the model.

[0082] The constraints of the integrated optimization model for the design and operation of the photovoltaic-storage-charging system include power balance constraints, orderly charging constraints, design constraints, and operational constraints.

[0083] (1) Power balance constraint

[0084] At any given moment during the operation of the photovoltaic energy storage and charging system, the input power of the bus should equal the output power, which can be described as:

[0085]

[0086] In the formula, η dch and η ch These represent the discharge efficiency and charging efficiency of the energy storage system, respectively; P t dch and P t ch These represent the discharge power and charging power of the energy storage system at time t, respectively.

[0087] The remaining energy S of the energy storage system at time t t Equal to the remaining energy S of the energy storage system at time t-1 t-1 The sum of the net charging capacity during that period can be described as:

[0088]

[0089] (2) Ordered charging constraint

[0090] The ordered charging strategy optimizes the output power of each charging pile at different times and rationally allocates the charging load at different times. This effectively reduces the peak-to-valley difference in charging load and improves the photovoltaic power generation absorption rate, making it an effective way to enhance the photovoltaic-storage-charging system. The constraints of ordered charging are as follows:

[0091]

[0092]

[0093]

[0094] In the formula, Let be the output power of the c-th charging pile at time t; and P represents the arrival time, departure time, and charging amount of the v-th electric vehicle corresponding to the c-th charging pile; pile,min and P pile,max These represent the minimum and maximum output power of the charging station, respectively; z c,t It is a 0-1 variable used to determine whether charging pile c is idle at time t.

[0095] (3) Design constraints

[0096] Due to space constraints, the design capacity of all equipment in the optical storage and charging system should be limited to a reasonable range, as shown below:

[0097]

[0098] In the formula, W q For the design capacity of device q, and These represent the minimum and maximum allowable design capacity of device q, respectively.

[0099] (4) Operational constraints

[0100] Electricity trading activities (including buying and selling electricity) between photovoltaic, energy storage, and charging systems and the main power grid should be limited to a reasonable range, which can be described as follows:

[0101]

[0102]

[0103] In the formula, P buy,min and P buy,max and represent the minimum and maximum power purchased from the grid, respectively; and and represent the minimum and maximum power sold to the grid, respectively; It is a 0-1 variable representing whether the system purchases electricity; It is a 0-1 variable indicating whether the system sells electricity.

[0104] For energy storage systems, the minimum / maximum charging power and discharging power are limited by the system capacity and charge / discharge rate, and can be described as follows:

[0105]

[0106]

[0107]

[0108] In the formula, It is a 0-1 variable characterizing whether the energy storage system discharges at time t. It is a 0-1 variable characterizing whether the energy storage system is charging at time t; charging and discharging of the energy storage system cannot occur simultaneously; γ ch,min and γ ch,max These represent the minimum and maximum charging rates of the energy storage system, respectively; γ dch,min and γ dch,max These represent the minimum and maximum discharge rates of the energy storage system, respectively.

[0109] The constraints (28)-(30) above are nonlinear constraints. In order to achieve a balance between model accuracy and computational complexity, these nonlinear constraints are linearized, which can be achieved using linearization methods such as the Big M method. In this embodiment, the linearized constraints are:

[0110]

[0111]

[0112]

[0113] S6. Based on the above optimization objectives and constraints, construct an integrated optimization model for the design and operation of the photovoltaic-storage-charging system, solve it, and output the optimal design scheme and operation strategy. Specifically, the design scheme comprises the optimal installed capacity of the photovoltaic system, energy storage system, and transformer. The operation strategy includes the time-of-use power output characteristics of the photovoltaic system, the time-of-use charging / discharging characteristics of the energy storage system, and the time-of-use power trading strategy between the system and the grid.

[0114] If the above methods are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0115] Example

[0116] This embodiment focuses on the resource and load characteristics of a DC charging station in a residential area of ​​a city, conducting case calculations. Specifically, this embodiment uses annual experimental data from a rooftop photovoltaic demonstration project for parameter estimation of the photovoltaic output power accurate prediction model. The demonstration project has approximately 1385.93 hours of effective sunshine per year, and the annual power generation of a single photovoltaic cell is approximately 289.68 kWh (rated power 310 W). The predicted RMSE of the proposed accurate model is 6.95 W, which is 40.4% lower than that of the commonly used simple linear model.

[0117] This embodiment assumes an arrival time interval Δt for the electric vehicle. car It follows an exponential distribution, with an additional dwell time Δt. extra and charging power requirements e pile It follows a normal distribution. In residential energy consumption scenarios, the arrival interval distribution of electric vehicles is as follows: Figure 2 As shown in Table 1, the relevant parameters for the random distribution of additional dwell time and charging power demand are shown in Table 2. There are 25 charging piles with a maximum charging power of 10kW. Based on the stochastic simulation model of electric vehicle / charging pile behavior characteristics, the photovoltaic-storage-charging system in this case can serve 29,604 electric vehicles annually, with a total load demand of approximately 755.20MWh. The time-of-use electricity price in this embodiment refers to the latest general industrial and commercial electricity price system of the city. The city implements a three-tiered pricing system of peak, valley, and flat rates, differentiating between summer and non-summer electricity prices. In this embodiment, the photovoltaic-storage-charging system can purchase electricity from the grid, but cannot sell surplus electricity back to the grid; surplus electricity is considered abandoned. The relevant parameters for the photovoltaic-storage-charging system's grid transaction in this embodiment are shown in Table 2. The main economic, technical, and environmental parameters of the energy storage system and photovoltaic system are shown in Table 3. In this model, the system lifespan is set to 20 years, the discount rate is 6%, and the unit dispatch time is 1 hour.

[0118] Table 1. Relevant parameters of charging piles

[0119]

[0120] Table 2 Parameters Related to Power Grid Transactions

[0121]

[0122] Table 3. Relevant parameters for energy storage and photovoltaic systems

[0123]

[0124]

[0125] Based on the above embodiments, the proposed integrated optimization model for the design and operation of the photovoltaic-storage-charging system was solved, and the optimization results and related evaluation indicators are shown in Table 4. In this embodiment, the optimal capacities of the photovoltaic system, energy storage system, and transformer are 221.61kW, 79.96kWh, and 114.70kVA, respectively. Economically, the net present value of the photovoltaic-storage-charging system over its entire lifecycle is 9.858 million yuan, with a payback period of approximately 2.35 years. Compared to a fully grid-powered system, the photovoltaic-storage-charging system can improve economic efficiency by approximately 15.67% over its entire lifecycle. Environmentally, compared to a fully grid-powered system, the photovoltaic-storage-charging system can reduce equivalent carbon emissions by approximately 37.14% over its entire lifecycle. In terms of energy utilization, the proposed photovoltaic-storage-charging system has an energy utilization rate as high as 97.55%, a photovoltaic curtailment rate of approximately 4.88%, and a daily cycle count of approximately 0.85 for the energy storage system, achieving a good balance between battery life and cost.

[0126] Table 4. Optimization Results and Relevant Evaluation Indicators of the Integrated Optimization Model for the Design and Operation of the Photovoltaic Storage and Charging System

[0127]

[0128]

[0129] The diurnal variation in photovoltaic (PV) system output power and time-of-use pricing significantly impact the operation strategy of PV-storage-charging systems. To analyze the system's energy flow characteristics at different times, this embodiment calculates the daily average power of each typical energy flow for each hour from 1:00 to 24:00, such as... Figure 3 As shown. From Figure 3It is evident that at any given time, the system's electrical load demand exceeds the photovoltaic (PV) power generation. The power shortfall is primarily met by purchasing electricity from the grid, with a portion supplied through the discharge of energy storage systems. Specifically, the grid purchases relatively little power during the periods of 9:00-11:00, 14:00-15:00, and 19:00-21:00. This is because these are peak periods for electricity purchase prices, resulting in higher energy costs, and the system mainly meets its power demand through energy storage or PV. PV system curtailment mainly occurs between 9:00-15:00, due to strong solar radiation and high PV power generation during this time; when the energy storage system is fully charged, excess PV energy cannot be stored. For the energy storage system, the charging process primarily occurs during the periods of 0:00-7:00 and 12:00-17:00. Between 0:00 and 7:00, when grid electricity prices are low, the energy storage system uses off-peak electricity purchased from the grid to charge. Between 12:00 and 17:00, photovoltaic power generation exceeds the system's load, and excess energy is temporarily stored through the energy storage system. The energy storage system discharges primarily during two periods: 8:00-11:00 and 18:00-22:00. Between 8:00 and 11:00, load demand gradually increases, but photovoltaic power generation is low and grid electricity prices are high. Part of the load demand is met through energy storage discharge. Between 18:00 and 22:00, the photovoltaic system does not generate electricity, time-of-use pricing is at its peak, and the cost of purchasing electricity from the grid is high; part of the load demand is met through energy storage discharge. In summary, the charging and discharging behavior of the energy storage system is closely related to the photovoltaic system's power generation capacity and time-of-use pricing.

[0130] This embodiment uses photovoltaic power prediction data from a linear model and the proposed precise model to implement integrated optimization of the photovoltaic-storage-charging system design and operation. The optimal capacities of the photovoltaic system, energy storage system, and transformer obtained are as follows: Figure 4 As shown.

[0131] Meanwhile, this embodiment uses measured photovoltaic power data from photovoltaic demonstration projects to perform integrated optimization of the photovoltaic-storage-charging system design and operation. The resulting design scheme serves as the standard scheme for the model. The closer the design scheme optimized from the predictive model is to the standard scheme, the more reliable the model is. Figure 4As can be seen, in the design scheme optimized based on the proposed accurate photovoltaic power prediction model, the optimal capacities of the photovoltaic system, energy storage system, and transformer are 221.84kW, 79.96kWh, and 114.70kVA, respectively, with relative errors of -0.99%, 0.44%, and 0.64% compared to the standard design scheme. In the design scheme based on the simple linear prediction model, the optimal capacities of the photovoltaic system, energy storage system, and transformer are 213.32kW, 82.44kWh, and 115.84kVA, respectively, with relative errors of -4.70%, 3.55%, and 1.64% compared to the standard design scheme. This demonstrates that the photovoltaic-energy storage-charging system obtained based on the proposed photovoltaic power prediction model has a high degree of agreement with the standard design scheme, with absolute values ​​of relative errors all less than 1%. However, designing the photovoltaic-energy storage-charging system based on a simple linear model could lead to significant design deviations, potentially rendering the system infeasible in subsequent operation. Therefore, by conducting integrated optimization of the design and operation of photovoltaic-storage-charging systems based on more accurate photovoltaic power prediction models, a design scheme closer to the optimal one can be obtained.

[0132] This embodiment optimizes the design and operation of a photovoltaic-storage-charging system based on three different electric vehicle charging strategies: maximum power strategy, average power strategy, and ordered charging strategy. By comparing their economic efficiency and design differences, it studies the impact of different electric vehicle charging strategies on the system design. Figure 5 As shown in the figure, the maximum power strategy refers to charging at the maximum power of the charging pile from the moment the vehicle arrives at the charging station, regardless of the vehicle's dwell time, until the vehicle's charging demand is met. The average power strategy refers to obtaining the average power by dividing the vehicle's charging demand by the dwell time, and charging the electric vehicle at the average power throughout the entire process until the vehicle leaves. As can be seen from the figure, the net present value (NPV) of the system based on the maximum power strategy, average power strategy, and ordered charging strategy are 9.036 million, 9.1135 million, and 9.858 million yuan, respectively. The NPV of the ordered charging strategy proposed in this invention is 9.10% and 8.17% higher than the previous two strategies, respectively. The optimal energy storage system capacities based on the maximum power strategy, average power strategy, and ordered charging strategy are 325.74, 312.60, and 79.96 kWh, respectively. The energy storage system capacity of the ordered charging strategy proposed in this invention is reduced by 74.42% and 75.60% compared to the previous two strategies, respectively. Therefore, compared with traditional electric vehicle charging strategies, the ordered charging strategy can significantly reduce the required energy storage system capacity, thereby improving system economy.

[0133] In summary, this embodiment focuses on the resource and load characteristics of a DC charging station in a residential area of ​​a certain city, conducts case calculations, and systematically analyzes the optimization results. The results show that the integrated optimization system for the design and operation of a photovoltaic-storage-charging system based on a precise model proposed in this invention has higher model accuracy and controllable computational complexity compared to existing optimization methods, effectively improving the system's economic efficiency.

[0134] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for integrated optimization of the design and operation of a photovoltaic-storage-charging system based on a precise model, characterized in that, Includes the following steps: Construct a precise prediction model for photovoltaic output power; Considering the orderly charging strategy for electric vehicles under constraints, and based on the accurate prediction model of photovoltaic output power, an integrated optimization model for the design and operation of the photovoltaic-storage-charging system is constructed with the optimization objective of maximizing the net present value of the system throughout the entire life cycle of the photovoltaic-storage-charging system. Solve the ensemble optimization model to obtain the optimal design scheme and operation strategy; The photovoltaic output power accurate prediction model uses a single diode. R P Based on the model, and through the photovoltaic mechanism of batteries, radial basis function neural network model, and historical weather data, the single diode was analyzed. R P The circuit parameters in the model are corrected; The constraints include power balance constraints, orderly charging constraints, design constraints, and operational constraints. The ordered charging constraints are constructed based on historical data of electric vehicle charging characteristic distribution and random simulation of electric vehicle / charging pile behavior characteristics. Specifically, random simulation of electric vehicle / charging pile behavior characteristics is performed based on historical data of electric vehicle charging characteristic distribution to generate electric vehicle charging power characteristic data. Electric vehicles and charging piles are then matched to obtain the arrival / departure time, charging amount, and corresponding charging pile number of each electric vehicle that arrives at the photovoltaic-storage-charging system, which are used for subsequent construction of ordered charging constraints.

2. The integrated optimization method for the design and operation of a photovoltaic energy storage and charging system based on a precise model as described in claim 1, characterized in that, The accurate photovoltaic output power prediction model is expressed as follows: In the formula, It is the output power of the photovoltaic system. I and V These represent the current and voltage of the photovoltaic cell, respectively. I PV , I S , R S , R P and a These are the photovoltaic cell's photocurrent, reverse saturation current, series internal resistance, shunt internal resistance, and ideality factor. Corrections are achieved based on the photovoltaic mechanism of the cell, a radial basis function neural network model, and historical weather data. T c The temperature of the photovoltaic cell. k B Boltzmann's constant, q is the electron volt constant.

3. The integrated optimization method for the design and operation of a photovoltaic energy storage and charging system based on a precise model as described in claim 1, characterized in that, The historical weather data includes hourly ambient temperature, solar radiation intensity, and wind speed over a year.

4. The integrated optimization method for the design and operation of a photovoltaic energy storage and charging system based on a precise model as described in claim 1, characterized in that, The formula for calculating the system's net present value is as follows: NPV The system's net present value. AOP and TCI These represent the system's annual operating profit and total construction investment, respectively. CRF l,i During the system lifecycle l Annual discount rate i The capital recovery factor at that time.

5. The integrated optimization method for the design and operation of a photovoltaic energy storage and charging system based on a precise model as described in claim 1, characterized in that, The ordered charging constraint expression includes: In the formula, In order to be in t The power of constantly charging electric vehicles. for t Time of the first c The output power of each charging station, Δ t The step size for system operation optimization. , and They correspond to the first c The first charging pile v The arrival time, departure time, and charge level of each electric vehicle. P pile,min and P pile,max These represent the minimum and maximum output power of the charging station, respectively. z c,t It is used for judgment t Instant charging station c Whether a 0-1 variable is idle.

6. The integrated optimization method for the design and operation of a photovoltaic energy storage and charging system based on a precise model as described in claim 1, characterized in that, The historical data on electric vehicle charging characteristics includes the time interval distribution of electric vehicles arriving at charging stations, the charging demand distribution, and the distribution of extra time spent at charging stations.

7. The integrated optimization method for the design and operation of a photovoltaic-storage-charging system based on a precise model as described in claim 1, characterized in that, The implementation steps for the stochastic simulation of the behavioral characteristics of the electric vehicle / charging station include: Based on historical data on electric vehicle charging characteristics, an electric vehicle arrival time interval Δ is constructed. t car Additional stay time Δ t extra and charging power requirements e pile Distribution characteristics; A large amount of characteristic data for the above-mentioned feature parameters was generated based on the Monte Carlo simulation method; The key factors of electric vehicle charging behavior are calculated to construct the ordered charging constraints.

8. The integrated optimization method for the design and operation of a photovoltaic energy storage and charging system based on a precise model as described in claim 1, characterized in that, The constraints are linearized constraints.

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