A capacity configuration method for off-grid solar-storage-diesel system based on investment recovery analysis

Abstract

The present invention relates to a capacity configuration method for an off-grid photovoltaic, energy storage and diesel system based on investment recovery analysis, and belongs to the field of optimization design of new energy systems. The present invention constructs a capacity configuration model of photovoltaic, energy storage and diesel engines through load demand data, regional lighting data and equipment cost parameters provided by users, and realizes the configuration design of the capacity of the photovoltaic, energy storage and diesel system in combination with the economic analysis of the investment recovery period. The method optimizes the operation time of photovoltaic-supported loads and the operation time of energy storage-supported loads by utilizing the particle swarm optimization algorithm PSO, generates a two-dimensional curve of the investment payback period, and intuitively displays the economy of different configuration schemes. The present invention solves the problems of insufficient flexibility of capacity configuration models and incomplete economic analysis in the prior art, and provides scientific and detailed decision support for the design of off-grid high-efficiency photovoltaic, energy storage and diesel hybrid systems.

Description

Technical Field

[0001] The present invention belongs to the field of new energy system optimization and data analysis, and specifically relates to a capacity configuration method for an off-grid solar-storage-diesel system based on investment recovery analysis. Background Art

[0002] In recent years, with the rapid development of global energy structure transformation and renewable energy utilization, off-grid photovoltaic energy storage diesel (PV-storage-diesel) systems have become an important way to solve power supply problems in remote areas and special scenarios (such as mining areas, islands, and areas without electricity) due to their flexibility and adaptability. However, for off-grid photovoltaic energy storage diesel hybrid energy systems, how to reasonably design the ratio of photovoltaic and energy storage capacity based on user load requirements, regional sunshine conditions, and system cost data to clarify the initial investment payback period is a key issue in the current technical field.

[0003] At present, the industry's research on photovoltaic, energy storage, and diesel hybrid systems is mostly focused on theoretical optimization capacity, while there is little research on off-grid photovoltaic, energy storage, and diesel capacity and actual calculation. Reference [Liu Xinyue. Research on Capacity Configuration and Control Strategy of Photovoltaic, Energy Storage, and Diesel Isolated Microgrid [D]. Three Gorges University, 2018. DOI:10.27270 / d.cnkgsxau.2018.000116.], a genetic algorithm (GA) was used to solve the capacity optimization model of photovoltaic, energy storage, and diesel isolated microgrid. By simulating the genetic evolution process, a global search for the capacity optimization problem was achieved. Although the genetic algorithm can find the global optimal solution, it has the problem of slow convergence speed. In practical applications, the calculation efficiency is low and it is difficult to meet the needs of rapid optimization of large-scale off-grid systems. At the same time, the research mainly focuses on the mathematical model of the algorithm, which does not fully combine the actual system cost, equipment performance, and user needs, and lacks dynamic adaptation to user loads and regional sunshine resources. Reference [Xiao Weichao, Yang Han, Jiang Qifeng, et al. Capacity configuration analysis of off-grid photovoltaic storage system based on Homer Pro [J]. Energy Research and Management, 2024, 16(03): 98-103+111. DOI: 10.16056 / j.2096-7705.2024.03.015.], based on Homer Pro microgrid software, analyzes the photovoltaic storage diesel capacity ratio relationship under the monthly minimum effective light time and the annual average effective light time, focusing on the ratio analysis of photovoltaic and energy storage capacity, and does not make an in-depth discussion on the role and influence of diesel engine capacity configuration. The input parameters are relatively simple, mainly including the monthly minimum light time, the annual average light time and the power consumption characteristics. The final output is only the adjustment coefficient of the capacity ratio. This study only solves the problem of the closeness between theoretical calculation and software results, and does not further optimize the economy of the capacity configuration plan, nor does it provide user-oriented decision support.

[0004] Therefore, although the existing capacity optimization methods for solar-storage-diesel systems can realize theoretical research and software simulation of capacity configuration to a certain extent, there is still much room for improvement in economic analysis, dynamic adaptability, user decision support and practical promotion. Summary of the invention

[0005] In view of the above problems, the present invention proposes a capacity configuration method for an off-grid solar-storage-diesel system based on investment recovery analysis, which solves the deficiencies of the existing technology in economic analysis, dynamic adaptability and user decision support. The highlight of this method is its high user participation and strong dynamic adaptability. Users can actively input key parameters according to their needs, and the system will calculate the investment recovery curves of systems of different sizes accordingly. This not only ensures the flexibility of the method, but also can adapt to changing application scenarios.

[0006] By extending the original algorithm, we can generate a solution set of investment cost and payback period, and then draw intuitive curves. These curves provide users with a clear basis for decision-making while ensuring economic efficiency. Users can see at a glance the relationship between investment and return under different configuration schemes, so as to make more informed choices.

[0007] The calculation method of the present invention has high adaptability. It simplifies the operation process. The user only needs to input a few key parameters, and the system can output a series of investment and payback time curves of the photovoltaic-storage-diesel configuration scheme that meet actual needs. This simple and easy-to-use operation interface effectively solves the practicality and promotion problems of the existing technology and ensures the wide application of the method.

[0008] In addition, the present invention is extremely practical and expansible. It can not only meet the needs of the current market, but also, due to its flexibility and adaptability, can easily cope with the challenges brought by future technological development and market changes. Whether it is providing power support in remote areas or conducting cost-benefit analysis in the commercial field, the present invention can provide strong support.

[0009] In summary, the present invention not only solves the deficiencies of the prior art, but also demonstrates broad prospects and far-reaching impact in the field of photovoltaic energy storage diesel hybrid system through its innovative methodology and practical application. It not only improves the efficiency and accuracy of decision-making, but also provides users with a practical, flexible and easy-to-operate tool to achieve the optimal energy solution.

[0010] The present invention combines user load data, regional sunshine resource data and system cost parameters, and adopts a particle swarm optimization (PSO) collaborative optimization model to dynamically optimize the capacity of photovoltaic and energy storage after determining the power of diesel generators, and generate an investment return cycle curve. This helps users achieve a balance between the economy and reliability of the photovoltaic-storage-diesel system, and provides users with an intuitive decision-making basis.

[0011] A capacity configuration method for an off-grid solar-storage-diesel system based on investment recovery analysis comprises the following steps:

[0012] S1 takes the following data as input: daytime load power Night load power The average daily effective sunshine duration in the user's area is H, and the cost of diesel generator electricity Photovoltaic system costs , Energy Storage System Cost and the unit installation cost of diesel engines , the photovoltaic system cost , Energy Storage System Cost and the unit installation cost of diesel engines The cost of the photovoltaic system includes the initial purchase cost and the subsequent operation and maintenance costs. , Energy Storage System Cost All are costs per unit capacity;

[0013] Calculate the maximum power of the diesel generator according to the following formula ;

[0014]

[0015] Where n is a number greater than 1, which is used to make the maximum power of the diesel generator Can be greater than the daytime load power Night load power The maximum value of , in order to meet the redundancy when the power suddenly increases;

[0016] S2 builds an optimization model.

[0017] S21 defines the optimization variable: the time for photovoltaic to support load operation , the time that energy storage supports load operation ;

[0018] S22 Construction investment payback period As the objective function:

[0019] =

[0020] in: is the total investment cost, = + , photovoltaic capacity = , energy storage capacity ; R is the annual cost savings, R= + , is the efficiency of the photovoltaic system;

[0021] S23 defines constraints:

[0022] (1) Power balance: power output of the solar-storage-diesel system Greater than or equal to the power required by the load :

[0023] -

[0024] When the system is photovoltaic powered: = , = ; When the system is powered by energy storage: = During the day, = ; At night, = ;

[0025] (2) Optimize the value range constraints of variables: ,

[0026] S3 uses the particle swarm optimization algorithm PSO to search for a solution set that satisfies the constraints and the objective function. The solution set includes several groups and Combination of; based on each group and The combination of and investment payback period .

[0027] S4 is based on the total investment cost obtained in S3 , with investment cost The coordinate axes are drawn to obtain a curve relationship diagram, which includes several investment cost The relationship curve of each group and The combination of corresponds to a relationship curve.

[0028] Preferably, in step S1, =1.2.

[0029] Preferably, in step S1, the average daily effective sunshine duration H in the area where the user is located is obtained from the NASA POWER database.

[0030] Preferably, in step S1, the electricity cost of the diesel generator is The calculation method is as follows:

[0031] = .

[0032] Preferably, step S3 specifically includes:

[0033] Initialize the following parameters: Number of particles , maximum number of iterations , learning factor and , inertia weight ;

[0034] Each particle represents a group and Combination, randomly generate the initial position of each particle within the value range of the optimization variable; randomly generate the initial velocity of the particle;

[0035] Calculate the payback period for each particle As a fitness function;

[0036] The particle velocity and particle position are iteratively updated according to the following formula:

[0037]

[0038]

[0039] in: is a random number between 0 and 1. is the historical optimal position of particle i, is the global optimal position.

[0040] Preferably, after step S4, the following steps are also included:

[0041] S5 Investment cost based on budget , from several investment costs Select the target curve from the relationship curve, and get the corresponding and Combination, according to the following formula to get the maximum power of the diesel generator , Photovoltaic capacity and energy storage capacity : ; = ; .

[0042] Preferably, in step S5, the target curve corresponds to The value is greater than 1

[0043] Beneficial effects of the present invention:

[0044] 1) The optimization method of the present invention can maximize the utilization of photovoltaic power generation and energy storage systems, reduce dependence on diesel power generation, thereby reducing the total operating cost of the system and improving the utilization efficiency of clean energy, providing an effective solution for energy conservation and emission reduction of photovoltaic-storage-diesel systems.

[0045] 2) The present invention introduces and expands the particle swarm algorithm, dynamically adjusts the search range and the distribution of solutions during the multivariable optimization process, and can efficiently generate a solution set that meets the system constraints while exploring the economic feasibility of different capacity configurations.

[0046] 3) The present invention can generate an intuitive decision curve, so that users can quickly judge the advantages and disadvantages of different capacity configuration schemes by observing the curve, especially when environmental conditions (such as light resources) or load demand changes, it can still adaptively provide the most suitable solar storage configuration curve for the current situation.

[0047] 4) The present invention combines the technical characteristics of photovoltaics, energy storage, and diesel engines to propose an optimization design method suitable for off-grid photovoltaic-storage-diesel systems. Compared with the existing technology, the present invention not only has significant advantages in economic analysis, but also can be flexibly adjusted according to the needs of different scenarios, providing users with efficient, economical and practical energy configuration solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The flowchart of the capacity configuration method of the off-grid solar-fuel storage system based on the investment recovery analysis of the present invention;

[0049] Figure 2 It is the flow chart of PSO algorithm;

[0050] Figure 3 It is the investment return cycle curve. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and beneficial effects of the present invention more clearly described, the present invention will be further described in detail below with reference to the accompanying drawings and specific examples.

[0052] like Figure 1 The present invention is a flow chart of a capacity configuration method of an off-grid solar-storage-diesel system based on investment recovery analysis, comprising the following steps:

[0053] S1: Data input.

[0054] S11 input daytime load power ( =10kw) and night load power ( =5kw). Calculate the total load demand and find the maximum power according to the load time period. Here we assume that the daytime is from 8am to 4pm and the nighttime is from 4pm to 8am. Analyze the power data entered by the user and find the time period with the highest power.

[0055] S12 Input the average daily effective sunshine duration (e.g. H=5). You can query from NASA POWER database (abroad) or other public data platforms. Input the photovoltaic system ( ,For example =3100 yuan / kw), energy storage system ( ,For example = 1200 yuan / kw) and the unit installation cost of diesel engines ( ,For example =3100 yuan / kw), and the unit installation cost of photovoltaic, energy storage and diesel generation includes the initial purchase cost and the subsequent operation and maintenance costs.

[0056] S13 calculates the maximum power of the diesel generator and the electricity cost of the diesel generator ( ). According to step S11, the maximum power of the load can be obtained, and the power of the diesel engine can be calculated. In this step, we use the maximum load power to calculate the power of the diesel generator to avoid extreme situations where the photovoltaic system cannot be used at all and the diesel generator can be used as the only micro power source to power the load.

[0057] .2

[0058] =

[0059] In the S1 data input stage, we give users the autonomy to manually set the load level during the day and night, the sunshine duration, and the unit installation cost of photovoltaic, energy storage and diesel power generation systems. This design not only provides great flexibility for diverse usage scenarios, but also greatly enhances the scalability and dynamic adaptability of our method. Through load input, we can accurately calculate the power demand for a day. This data is crucial for evaluating the rationality of the initial investment cost. For example, if the load is small, the initial investment cost should be correspondingly low; on the contrary, in the face of a large load, we can appropriately increase the initial investment amount while considering the long-term return on investment. Without the specific input of the load, our analysis will become a purely theoretical model, and its output results may be far from the actual application scenario. Given the differences in installation costs in different regions, setting the unit installation cost as a variable parameter can make the decision curve we generate more accurate and ensure that the output decision plan is closer to the actual needs of users. In addition, by allowing users to input the sunshine duration and the unit cost of various energy systems, our system can generate more personalized and accurate energy solutions. This design not only improves the practicality of the solution, but also enables users to make more economical investment choices based on their own conditions.

[0060] S2: Build an optimization model.

[0061] S21 defines optimization variables:

[0062] : PV supports load operation time (hours).

[0063] : Time (hours) that energy storage supports load operation.

[0064] S22 defines the objective function:

[0065] Investment payback period :

[0066] =

[0067] in:

[0068] is the total investment cost, = + ;

[0069] In the formula, They represent: photovoltaic capacity and energy storage capacity respectively;

[0070] Photovoltaic capacity = ;

[0071] Energy storage capacity ;

[0072] R is the annual cost savings, R= + ;

[0073] is the efficiency of the photovoltaic system;

[0074] 365 represents the number of days in a year.

[0075] There are two main reasons why the payback period is used as the objective function here: First, the investment period is a critical factor for investors. Keeping the investment cost unchanged, the shorter the payback period, the faster the investor can recover his initial investment. This is crucial for evaluating the attractiveness and risk of an investment, because a shorter payback period is usually associated with lower risk. Second, this function is derived from the total investment cost and the annual cost savings, and these two values ​​are directly related to what we require. It can better reflect the actual benefits of the investment, allowing investors to more accurately predict and evaluate the financial feasibility of the investment. In this way, investors can more intuitively understand the payback period of the investment and make more informed investment decisions.

[0076] S23 constraints:

[0077] In order to obtain a feasible solution, the following constraints need to be met during the entire system operation due to the operational and physical limitations of the target system.

[0078] (1) Power balance: In any configuration, the power provided to the load should meet the principle of accuracy and reliability, that is, the power output of the solar-storage-diesel system must be greater than or equal to the power required by the load.

[0079] -

[0080] in, Indicates the actual power output of the photovoltaic and energy storage system. Since the maximum power of the diesel generator has been determined, it is sufficient to control the output power of photovoltaic and energy storage to be greater than the load. Therefore, when the system is powered by photovoltaic power, = , the same applies to energy storage = . is the power of the load at the current moment. If it is powered by photovoltaic power, = If it is energy storage power supply, it can be determined by calculating the time period from 8 am to t o'clock. yes still .

[0081] (2) Optimize the value range constraints of variables:

[0082]

[0083]

[0084] Among them, H is the duration of light exposure.

[0085] Using the above power balance as a constraint, our core goal is to ensure that the configured system can stably and continuously provide the required power. These constraints are the basis for system design, and they ensure that the reliability and stability of the power supply will not be affected under any circumstances. The value range constraints of the optimization variables effectively define the search range of the algorithm. Within the framework of these constraints, we pursue not only the robust operation of the system, but also the optimization of the configuration. This means that while ensuring the power supply is secure, we further seek the most cost-effective solution. We fine-tune the system configuration through precise calculations and intelligent optimization algorithms to achieve comprehensive improvements in economy, efficiency and sustainability.

[0086] S3: Algorithm optimization search.

[0087] We use the particle swarm optimization algorithm (PSO) to search for a solution set that satisfies the constraints and the objective function, such as Figure 2 This is the schematic diagram of the algorithm.

[0088] The PSO algorithm is an optimization algorithm based on swarm intelligence. It initializes random particles and then iterates. In the process of continuous iteration, it finds the desired individual extreme value and global extreme value, eventually reaches the maximum number of iterations, and then stops iterating. In the process of iteration, the solution set that meets the constraints is recorded. The main process is as follows:

[0089] Step 1: Set the relevant parameters of the algorithm.

[0090] Including particle number ( = ), the maximum number of iterations is ( =100), learning factor ( =0.5)、 ( =0.5)、Inertia weight ( =0.9). As optimization variables. Initially set the population position and population speed: Initially set to random values. Initial random generation within the defined search range ( , ), the speed range is ([-0.5,0.5]).

[0091] Step 2: Substitute the initialized population into the fitness function (objective function) and calculate the fitness of each particle (the function value of the objective function).

[0092] Step 3: Iteration.

[0093] After calculating the fitness, the individual optimal value should be updated ( ) and the global optimum ( ), check whether the constraints are met, and redefine a space to record the individual optimal ( ), and calculate C and T through its value. Then determine whether it converges. If it does not converge, update the speed and position to enter the next iteration until the last iteration is completed. The formula is as follows:

[0094]

[0095]

[0096] in:

[0097] A random number between 0 and 1.

[0098] is the historical optimal position of particle i.

[0099] is the global optimal position of the current iteration.

[0100] In this critical step, we choose the particle swarm optimization algorithm (PSO) as our preferred tool because the next iteration of PSO is determined by the particles themselves and the global optimum, which provides us with a way to record the optimal position of the particles and provides data support for the subsequent generation of curves. In addition, the PSO algorithm is applicable to various types of optimization problems, which means that it can be more easily expanded. Therefore, after calculating the fitness using the objective function, it is easier to use the local optimal expansion to calculate C and T, reduce the probability of mismatch, and be more convenient and efficient when checking the constraints of power balance.

[0101] S4: Generate a two-dimensional curve.

[0102] From the solution set output by the algorithm, extract , , , The data can be plotted , The curve relationship between Figure 3 It is a curve graph generated based on the extracted data. The input parameters of this curve graph are based on the example given above.

[0103] S5: Investment costs based on budget , from several investment costs and investment payback period Select the target curve from the relationship curve, and get the corresponding and Combination, according to the following formula to get the maximum power of the diesel generator , Photovoltaic capacity and energy storage capacity : ; = ; .

[0104] , , , The solution set is the most suitable solution set under the constraints. According to the input load power, the relationship between the investment cost and the investment payback period of this load is generated. The second-order parameters affected are directly proportional, and according to the ratio of light storage, the capacity of photovoltaics is larger than that of energy storage, so in the process of drawing the graph, The value is from 0 to 5 (the lighting duration in the example is 5), and select one The value of remains unchanged, let Plot a curve from 0 to 24.

[0105] In the graph, we can observe that when The values ​​of t all increase by 0.5 each time. This is because when the algorithm is optimized, each time t increases by 0.5, the payback period can be shorter and the investment cost can be lower while satisfying the constraints. However, when the values ​​of t are all less than 1, the photovoltaic capacity is too small and the energy storage capacity may far exceed the photovoltaic capacity, which is not of much reference value in practical applications. Therefore, when providing decision-making references for users, we should focus on those curves that are directly proportional, that is, the part where the value of t is greater than 1. In these curves, we can clearly observe the intuitive relationship between investment and payback period. After determining the investment cost and payback period, we can infer the capacity of photovoltaic and energy storage based on the values ​​of the two, and thus determine the capacity configuration of the three in the photovoltaic, energy storage and diesel system.

Claims

1. A capacity configuration method for an off-grid solar-storage-diesel system based on investment recovery analysis, characterized in that The method comprises the following steps: S1: Get the following data as input: daytime load power Night load power The average daily effective sunshine duration in the user's area is H, and the cost of diesel generator electricity Photovoltaic system costs , Energy Storage System Cost and the unit installation cost of diesel engines , the photovoltaic system cost , Energy Storage System Cost and the unit installation cost of diesel engines The cost of the photovoltaic system includes the initial purchase cost and the subsequent operation and maintenance costs. , Energy Storage System Cost All are costs per unit capacity; Calculate the maximum power of the diesel generator according to the following formula ; ； Where n is a number greater than 1, which is used to make the maximum power of the diesel generator Can be greater than the daytime load power Night load power The maximum value of S2: Build optimization model; S21 defines the optimization variable: the time for photovoltaic to support load operation , the time that energy storage supports load operation ; S22 Construction investment payback period As the objective function: = ； in: is the total investment cost, = + , photovoltaic capacity = , energy storage capacity ; R is the annual cost savings, R= + , is the efficiency of the photovoltaic system; S23 defines constraints: (1) Power balance: power output of the solar-storage-diesel system Greater than or equal to the power required by the load : - ； When the system is photovoltaic powered: = , = ; When the system is powered by energy storage: = During the day, = ; At night, = ; (2) Optimize the value range constraints of variables: , ; S3: Use the particle swarm optimization algorithm PSO to search for a solution set that satisfies the constraints and the objective function. The solution set includes several groups and Combination of; based on each group and The combination of and investment payback period ; S4: Total investment cost based on S3 , with investment cost The coordinate axes are drawn to obtain a curve relationship diagram, which includes several investment cost The relationship curve of each group and The combination of corresponds to a relationship curve.

2. A capacity configuration method for an off-grid solar-storage-diesel system based on investment recovery analysis as claimed in claim 1, characterized in that: In step S1, =1.

2.

3. The capacity configuration method of an off-grid solar-storage-diesel system based on investment recovery analysis as claimed in claim 1, characterized in that: In step S1, the average daily effective sunlight duration H in the area where the user is located is obtained from the NASA POWER database.

4. The capacity configuration method of an off-grid solar-storage-diesel system based on investment recovery analysis as claimed in claim 1, characterized in that: In step S1, the electricity cost of the diesel generator is The calculation method is as follows: = 。 5. The capacity configuration method of an off-grid solar-storage-diesel system based on investment recovery analysis as claimed in claim 1, characterized in that: Step S3 specifically includes: Initialize the following parameters: Number of particles , maximum number of iterations , learning factor and , inertia weight ; Each particle represents a group and Combination, randomly generate the initial position of each particle within the value range of the optimization variable; randomly generate the initial velocity of the particle; Calculate the payback period for each particle As a fitness function; The particle velocity and particle position are iteratively updated according to the following formula: ； ； in: is a random number between 0 and 1. is the historical optimal position of particle i, is the global optimal position.

6. The capacity configuration method of an off-grid solar-storage-diesel system based on investment recovery analysis as claimed in claim 1, characterized in that: The step S4 further includes the following steps: S5: Investment costs based on budget , select the target curve from several relationship curves between investment cost C and investment payback period T, and obtain the corresponding and Combination, according to the following formula to get the maximum power of the diesel generator , Photovoltaic capacity and energy storage capacity : ; = ; .

7. A capacity configuration method for an off-grid solar-storage-diesel system based on investment recovery analysis as claimed in claim 6, characterized in that: In step S5, the target curve corresponds to The value of is greater than 1.

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