Wind and light storage micro-grid capacity optimization method based on particle swarm optimization

Abstract

The invention discloses a wind and light storage micro-grid capacity optimization method based on a particle swarm algorithm, and relates to the field consistent with the specification, and the content in a bracket is deleted during writing, and the method comprises the following steps: S1, building a micro-grid system model; s2, setting constraint conditions; s3, constructing a target function; s4, particle swarm optimization is carried out; according to the method, through the efficient global optimization capability and the multi-constraint condition adaptability of the particle swarm algorithm, an accurate distributed power supply and energy storage mathematical model is constructed, the annual maximum economic benefit is taken as a target, optimization is carried out in combination with regional wind and light resource characteristics and load demands, the investment economy of the micro-grid is remarkably improved, and meanwhile, the installation and maintenance cost is reduced; by reasonably proportioning the energy storage capacity and a wind-solar complementary strategy, the power supply reliability and stability of the system are enhanced; renewable energy sources are fully consumed, and the environmental benefits are remarkable; cooperative improvement of economy, reliability, environmental benefits and regional suitability is realized.

Description

Technical Field

[0001] The present invention relates to the technical field, and specifically to a method for optimizing the capacity of a wind-solar-storage microgrid based on the particle swarm optimization algorithm. Background Art

[0003] In existing research, genetic algorithms, comprehensive evaluation methods, etc. have been used for optimizing the capacity of microgrids, aiming to balance the output smoothness and cost. However, traditional methods have problems such as a single objective function, low computational efficiency, or insufficient adaptability when dealing with complex constraint conditions (such as the charge-discharge depth limit of energy storage devices and the carbon emission constraint of diesel generators) and regional-specific resource characteristics (such as seasonal fluctuations in wind and solar resources and time-of-day load differences). For example, in regions where wind and solar resources vary significantly with seasons (such as the coal mining subsidence area in northern Shanxi, where the wind speed is high but the sunlight is insufficient from March to May, and the sunlight is sufficient but the wind speed is low in some months from April to October), and under the time-of-day load characteristics where the load depends on photovoltaic power during the day and energy storage at night, existing solutions have not fully combined such dynamic data with actual constraints, and it is difficult to achieve the global economic optimum of the wind-solar-storage capacity while taking into account the installation cost, maintenance cost, and environmental benefits.

[0004] In addition, the microgrid system involves distributed power generation modeling (such as the power model of wind turbines based on wind speed segmentation and the output model of photovoltaic modules affected by temperature and irradiance), the operation control of energy storage devices (such as the state-of-charge constraint of lead-carbon batteries), and multi-objective optimization solutions, which pose higher requirements for the global optimization ability and constraint handling efficiency of the algorithm. The insufficient adaptability of existing technologies in complex scenarios leads to the optimization results being difficult to meet the actual engineering requirements.

[0005] In view of this, a method for optimizing the capacity of a wind-solar-storage microgrid based on the particle swarm optimization algorithm is provided to overcome the above problems. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for optimizing the capacity of a wind-solar-storage microgrid based on the particle swarm optimization algorithm to solve the problems raised in the above background art.

[0007] To solve the above technical problems, a method for optimizing the capacity of a wind-solar-storage microgrid based on the particle swarm optimization algorithm provided by the present invention includes the following steps:

[0008] S1. Establish a microgrid system model: Construct a microgrid structure including wind turbines, photovoltaic modules, and energy storage devices, and determine the mathematical models of each distributed power generation and energy storage device. The mathematical models include:

[0009] Wind turbine power model: Calculate the output power according to wind speed segmentation;

[0010] Photovoltaic module power model: Consider the influence of irradiance intensity and temperature;

[0011] Charge storage model of energy storage device: Describes the charge and discharge states of the energy storage system;

[0012] S2. Set constraint conditions: Establish the charge and discharge power constraints, state of charge constraints, and system carbon emission constraints of the energy storage system;

[0013] S3. Construct the objective function: With the annual maximum economic benefit as the goal, taking the installed capacity of wind turbines, the installed capacity of photovoltaic systems, the installed capacity of energy storage, and the grid-connected exchange power as independent variables, comprehensively considering the utilization efficiency of distributed power sources, the utilization efficiency of energy storage, and the conversion coefficient of mains exchange power;

[0014] S4. Optimize using the particle swarm optimization algorithm: Initialize the particle swarm parameters, calculate the particle fitness through iteration, update the inertia weight and particle positions, and perform boundary processing in combination with the constraint conditions until the optimal capacity ratio of wind, light, and energy storage is found.

[0015] Furthermore, the power model of the wind turbine is shown in Equation (1):

[0016]

[0017] Where: v c i is the cut-in wind speed, 3 m / s; v r is the rated wind speed, 12 m / s; v co is the cut-out wind speed, 20 m / s; P r is the rated output power, kW.

[0018] Furthermore, the power model of the photovoltaic module is shown in Equation (2):

[0019] P PV = P STC + β G [1 + k(t c - t sc )] (2);

[0020] Where: P PST is the rated power of the PV module under standard conditions; β G is the current irradiance reference ratio; k is the irradiance ratio gain; t c is the surface temperature of the working point cell; t sc is the reference temperature.

[0021] Furthermore, the charge storage model of the energy storage device is shown in Equations (3) and (4):

[0022]

[0023]

[0024] Where: E B,min and E B,max are the upper and lower limits of the energy storage electrical energy; is the conversion efficiency of the PCS; is the bidirectional energy conversion efficiency between the lead-carbon battery and the PCS; E WP,t is the electrical energy generated by the distributed power source over time t; E L,t is the electrical energy consumed by the power supply load over time t.

[0025] Furthermore, the constraint conditions include:

[0026] Battery charging constraint conditions:

[0027]

[0028] Battery discharging constraint conditions:

[0029]

[0030] Where: are respectively the electrical energy absorbed and released by the energy storage battery pack over time t;

[0031] The state of charge constraint is used to describe the charge and discharge depth of the battery, that is, its charge and discharge are controlled within the charge and discharge thresholds, and its constraint form:

[0032] E B,min ≤E B,t ≤E B,max (7);

[0034] Since there is a diesel generator in this system, the carbon emissions generated during its power generation become a non-negligible problem, so the CO2 emissions during the power generation stage are considered as a constraint condition, and its carbon emission constraint form:

[0035] E3x3δ3≤e max (8);

[0037] Where: E3 is the annual power generation of the diesel generator; x3 is the number of diesel generators; δ3 is the carbon emission factor of using diesel power generation; e max is the peak value that the system can tolerate for CO2 generation.

[0038] Furthermore, the objective function is as shown in formula (9):

[0039]

[0040] Where: P gi(t) is the grid-connected exchange power; c1 is the utilization efficiency of distributed power sources affected by weather; c t is the energy storage utilization efficiency; c 2 is the conversion coefficient of mains power exchange power.

[0041] Furthermore, the optimization process of the particle swarm algorithm includes:

[0042] Initializing parameters: setting the range of distributed power source installed capacity, the range of energy storage converter power, the maximum number of iterations, the number of particles, and the dimension of the search space;

[0043] Particle initialization: generating random particles as the initial solution, corresponding to the installed capacity of wind turbines, photovoltaic installations, and energy storage parameters;

[0044] Fitness calculation: calculating the particle fitness according to the objective function;

[0045] Iterative update: updating the particle velocity and position, adjusting the inertia weight; handling boundary conditions until the maximum number of iterations is reached or the convergence condition is satisfied.

[0046] Furthermore, in parameter initialization, the range of distributed installed capacity is 0 - 100kW, the grid power range is 1 - 50kW, the energy storage bidirectional converter power range is -30 - 30kW, the maximum number of iterations is 500, the number of particles is 800, and the dimension of the search space is 86.

[0047] Furthermore, the principles of the capacity ratio of wind, light, and storage include:

[0048] Combining with regional load characteristics: giving priority to using photovoltaic and wind power generation, and the energy storage system satisfies the basic load power supply during island operation;

[0049] Considering terrain environment and weather data: optimizing the layout of distributed power sources to reduce the wind and light abandonment rate;

[0050] Integrating installation cost and maintenance cost: balancing the capacity ratio of each power source to maximize economic benefits.

[0051] Compared with the prior art, the beneficial effects of the present invention are:

[0052] 1. High-efficiency global optimization and multi-constraint adaptation: Using the particle swarm algorithm (PSO) to optimize the capacity ratio of wind, light, and storage, this algorithm has strong global search ability and fast convergence speed, can effectively handle multi-variable models including wind turbines, photovoltaic modules, and energy storage devices, and combines complex conditions such as energy storage charge and discharge power constraints, state of charge constraints, and carbon emission constraints to achieve the search for the optimal solution under multiple objectives, and is more suitable for capacity optimization in multi-constraint scenarios of microgrids than traditional algorithms.

[0053] 2. Significantly improved economy: Taking the annual maximum economic benefit as the objective function, comprehensively considering the utilization efficiency of distributed power sources, the utilization efficiency of energy storage, and the power conversion coefficient of mains power exchange, combining the regional wind and solar resource database with load characteristics (such as peak electricity consumption periods and seasonal wind and solar fluctuations), reducing the curtailment rate of wind and light by optimizing the ratio, and reducing the dependence on mains power. The case study shows that this method can achieve an annual power generation income of 21,000 yuan, significantly improving the return on investment of the microgrid and balancing the installation costs (differentiated costs of photovoltaic, wind power, and energy storage) and long-term operation and maintenance costs.

[0054] 3. Enhanced reliability and stability: By establishing accurate mathematical models of distributed power sources (such as the segmented power model of wind turbines and the temperature and irradiance response model of photovoltaic modules) and the charge storage model of energy storage, ensuring the stable operation of the microgrid in both grid-connected and island modes. For example, the capacity design of the energy storage system meets the full-power output for 2 consecutive hours during island operation, effectively coping with emergencies such as mains power interruption; the strategy of giving priority to photovoltaic power supply during the day and supplementing with energy storage at night, combined with the dynamic regulation of wind turbines, improves the power supply reliability of the system.

[0055] 4. Optimization of environmental benefits and resource utilization: Making full use of the idle land in special areas such as coal mining subsidence areas, reducing the dependence on fossil energy through the collaborative power generation of wind, solar, and energy storage. The case shows that this method can achieve an annual average saving of 8.056 tons of standard coal and a reduction of 21.375 tons of carbon dioxide emissions, meeting the requirements of the "dual carbon" goal. At the same time, aiming at the seasonal differences in regional wind and solar resources (such as rich wind resources from March to May and sufficient sunlight from April to October), by dynamically adjusting the power utilization efficiency coefficient, maximizing the consumption of renewable energy and reducing resource waste.

[0056] 5. Regional specificity adaptation and engineering practicability: Combining the terrain environment, meteorological data (wind speed, irradiance intensity), and load types (such as office and residential electricity) in typical areas such as the coal mining subsidence area in northern Shanxi, simplifying the constraint conditions and formulating the capacity ratio principle (such as the maximum output power of energy storage being slightly greater than the basic power of the load and avoiding sending excess electricity to the grid), making the optimization results more in line with the actual engineering needs. This method can be extended to areas with similar wind and solar resource distributions and load characteristics, providing a replicable theoretical and data basis for the energy-saving transformation of intelligent microgrids.

[0057] In summary, through algorithm innovation, model precision improvement, and multi-objective collaborative optimization, this method has achieved a comprehensive improvement in the economy, reliability, environmental benefits, and engineering adaptability of the wind-solar-storage microgrid, with significant technological progress and practical application value. Brief Description of the Drawings

[0058] Figure 1 It is a schematic diagram of the basic structure of the microgrid in a method for optimizing the capacity of a wind-solar-storage microgrid based on the particle swarm algorithm of the present invention;

[0059] Figure 2 It is the simplified optimization flowchart of the POS algorithm in a method for optimizing the capacity of a wind-solar-storage microgrid based on the particle swarm algorithm according to the present invention;

[0060] Figure 3 It is the influence diagram of the ratio of wind power and PV installed capacity on the annual power generation income in a method for optimizing the capacity of a wind-solar-storage microgrid based on the particle swarm algorithm according to the present invention;

[0061] Figure 4 It is the monthly average power generation diagram of distributed power sources in a method for optimizing the capacity of a wind-solar-storage microgrid based on the particle swarm algorithm according to the present invention. Specific embodiments

[0062] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0063] Please refer to Figures 1 - 4 , the present invention provides a technical solution:

[0064] Refer to Figure 1 -

[0065] Figure 4 As shown in, an embodiment of a method for optimizing the capacity of a wind-solar-storage microgrid based on the particle swarm algorithm:

[0066] 1. Microgrid model:

[0067] Under the background of comprehensively managing coal mining subsidence areas and building large-scale wind-solar-storage energy bases in northern Shanxi, the regional intelligent microgrid is transformed. Distributed power sources are installed on fixed hills, and three-phase cables and communication cables need to be led to the distribution room. On the basis of the original power distribution design, wind turbines, PV distributed power sources, bidirectional converters, energy storage systems, and energy management control systems are added to reduce project investment while ensuring the maximum utilization of new energy. The PCS is responsible for controlling the charging and discharging of the energy storage battery to the microgrid busbar. Figure 1 It is the basic structure of the microgrid. In the system, distributed power sources are connected to the 0.4 kV busbar through circuit breakers. The busbar is connected to the load, and voltage and frequency data on both sides of the common connection point of the microgrid are collected through PT1 and PT2. Each circuit of the distribution cabinet is equipped with three-phase current transformers (CTs) for collecting current data of each circuit. Such data is summarized and accessed into the microgrid central controller through IEC104 communication messages. The central controller sends a power distribution signal to the power source controller according to the power demand of the load.

[0068] 2. Distributed and energy storage system modeling:

[0069] 2.1 Mathematical model of wind turbine:

[0070] For the energy-saving transformation of the microgrid, the main investments include the input of PV panels, wind turbines, energy storage systems, and energy management control systems. The power generation power P of the wind turbine Wt is affected by the wind speed v t and can be expressed by formula (1):

[0071]

[0072] In the formula: v ci is the cut-in wind speed, 3 m / s; v r is the rated wind speed, 12 m / s; v co is the cut-out wind speed, 20 m / s; P r is the rated output power, kW.

[0073] 2.2 Mathematical model of PV module:

[0074] The actual power generation power P of the PV module PV can be described by formula (2):

[0075] P PV = P STC + β G [1 + k(t c - t sc )] (2)

[0076] In the formula: P PST is the rated power of the PV module under the standard environment (temperature is 25 °C, irradiation intensity is 1 MW / m 2 , air mass is 1.5), kW; β G is the current irradiation intensity reference ratio; k is the irradiation ratio gain, taking 0.0045; t c is the surface temperature of the working point battery, °C; t sc is the reference temperature, 25 °C.

[0077] 2.3 Mathematical model of energy storage element:

[0078] The lead-carbon battery is selected as the main energy storage element, which has the advantages of fast response, long life, fast charge and discharge time, etc. Its charge storage model is:

[0079]

[0080]

[0081] In the formula: E B,minis the minimum energy storage electrical energy, 10 kW·h; E B,max is the maximum energy storage electrical energy, 60 kW·h; the charge absorbed or released by the energy storage battery over time t can be represented by |ΔE t |; is the conversion efficiency of the PCS; is the bidirectional energy conversion efficiency between the lead-carbon battery and the PCS; E WP,t is the electrical energy generated by the distributed power source over time t, kW·h; E L,t is the electrical energy consumed by the power supply load over time t, kW·h.

[0082] 2.4. Constraints:

[0083] The service life and charge-discharge depth of the storage battery are related to the charge-discharge frequency. In addition to serving as an emergency and maintenance power supply, it is also responsible for power compensation of the microgrid to improve economic benefits. The storage battery is located inside the office community, facilitating its regular maintenance to extend the service life after commissioning.

[0084] This system uses a lead-carbon storage battery as the chemical energy storage device. It is recommended that its charge-discharge power does not exceed 20% of the rated capacity. When the wind turbine and PV power generation power are greater than the load power at time t, the storage battery is charged

[0085] Charge constraint conditions for the storage battery:

[0086]

[0087] Discharge constraint conditions for the storage battery:

[0088]

[0089] In the formula: are the electrical energies absorbed and released by the energy storage battery pack over time t, kW·h.

[0090] The state of charge constraint is used to describe the charge-discharge depth of the storage battery, that is, its charge and discharge are controlled within the charge-discharge threshold. Its constraint form:

[0091] E B,min ≤E B,t ≤E B,max (7)

[0093] Since there is a diesel generator in this system, the carbon emissions generated during its power generation become a problem that cannot be ignored. Therefore, the CO2 emissions during the power generation stage are considered as a constraint condition. Its carbon emission constraint form:

[0094] E3x3δ3≤e max (8)

[0096] Where: E3 is the annual power generation of the diesel generator, kW·h; x3 is the number of diesel generators; δ3 is the carbon emission factor for power generation using diesel generators; e max is the peak value that the system can tolerate for generating CO2.

[0097] 3. Microgrid economic benefit modeling:

[0098] The economy of the microgrid is reflected in many aspects. During the preliminary design stage of the project, it is estimated, comprehensively considering the economic value and environmental protection value of the system. After determining the transformation structure of the intelligent microgrid and the power source models of each, it is necessary to determine the capacity ratio of the wind turbines, PV, and energy storage in the microgrid. In the northern Shanxi region, its wind energy and light energy resources vary with seasons. In this paper, a database is established for the wind speed and irradiation intensity in this area, combined with the load power consumption situation, and a time-of-use electricity price mechanism is introduced. With the maximum annual average profit creation as the objective function, the PSO algorithm is used to calculate the optimal ratio scheme of wind, light, and storage.

[0099] 3.1 Objective function:

[0100] The changes in wind and light resources and load vary greatly in different months. In this paper, the annual maximum economic benefit is obtained on a monthly basis, which is greatly affected by the installed capacity of different types of power sources and the exchange power at the grid connection point.

[0101] Using the PSO algorithm to solve the maximum economic benefit model, with the installed capacity P of the wind turbine Wt , the installed capacity P of PV power generation PVt , the installed capacity E of energy storage Bt , and the grid-connected exchange power P gt as independent variables and the annual maximum economic benefit F as the dependent variable, then:

[0102]

[0103] Where: p gi (t) is 0 during island operation; c1 is the utilization efficiency of distributed power sources affected by weather; c t is the utilization efficiency of energy storage; c2 is the conversion coefficient of mains power exchange power, depending on the instantaneous power generation and load conditions, and taking 10% here.

[0104] Taking the installed capacity of distributed power sources and energy storage and the grid-connected exchange power in Equation (9) as the optimization variables of the algorithm, then:

[0105]

[0106] Where: {x}

[0107] To find N different optimal solutions for different types of power sources, the optimization types include all distributed power sources, energy storage systems, and exchange power, where the energy storage system can adjust the charge and discharge capacity under PCS control.

[0108] 3.2. Optimization process:

[0109] The input items are the monthly load average, the upfront cost of the microgrid, and the maintenance cost during operation. Under the conditions of the proposed installed capacity range, the charge and discharge constraints of the chemical energy storage battery, the state of charge constraint, and the carbon emission constraint, the PSO algorithm is used to find the optimal solution for the capacity ratio of wind, light, and storage. Table 1 lists the specific parameters.

[0110] Table 1: Power economic optimization parameters:

[0111] Distributed power source Parameter Distributed installed capacity range / kW 0～100 Grid power range / kW 1～50 Energy storage bi - directional converter power range / kW -30～30 Maximum number of iterations 500 Search space dimension 86 Number of particles 800

[0112] First, parameter initialization is performed. The power economic optimization parameters are assigned initial values as variables. The wind turbine and PV installed capacity are used as population individuals. 800 irregular particles are used to find the initial wind turbine and PV installed capacity, and the weight coefficients c1, c2, c of each power source capacity and exchange power are updated. t , after boundary processing through the constraint conditions, enter the next iteration for optimization, as Figure 2 shown.

[0113] 3.2. Principles for selecting the capacity of wind, light, and storage:

[0114] In addition to the economic indicators of the microgrid connected to the grid, the method for selecting the capacity ratio of the microgrid also needs to consider the following aspects comprehensively.

[0115] (1) The wind turbine and PV equipment are installed in hilly areas. Considering reducing noise, reducing the occupied area, lightning protection, and the large fluctuations of wind resources in different months, the installed capacity of the wind turbine should not be too large.

[0116] (2) The peak electricity consumption in the region is from 08:00 to 20:00. Therefore, the energy storage is mainly used at night, the PV power generation is mainly used during the day, and the wind turbine is used as a supplement.

[0117] (3) This project can operate in island mode. The energy storage can independently supply power to the load when the PV and wind energy conditions are extremely poor. Therefore, the maximum output power of the energy storage should be slightly greater than the basic power of the load.

[0118] (4) This project is mainly for self-use. When connected to the grid, the phenomenon of sending surplus electricity to the grid and abandoning wind and light should be avoided. Therefore, the installed capacity of the PV should be determined according to the average irradiance intensity.

[0119] 4. Case study:

[0120] 4.1. Statistics of the power generation of the renovation items:

[0121] The microgrid system needs to comprehensively consider the load and the impact of the local environment. First, the regional load is statistically analyzed, and the specific data is shown in Table 2. As can be seen from Table 2, the peak load power is 36.55 kW, and the expected always-on load is 20.00 kW. According to the principle of "this project can operate in island mode, and the energy storage can independently supply power to the load when the PV and wind energy conditions are extremely poor. Therefore, the maximum output power of the energy storage should be slightly greater than the basic power of the load", considering that when operating in island mode, to ensure that the microgrid has the ability to supply power to the load, the installed capacity of the energy storage is 60.00 kW, and the PCS is selected as 30.00 kW to ensure that when the PCS outputs at full power, the energy storage system can continuously operate for 2 h and serve as an emergency power supply.

[0122] Table 2: Regional load statistics:

[0123] Equipment type Power / kW Quantity / unit Power / kW Computer 0.25 16 4.00 Air conditioner 2.00 5 10.00 Lighting 0.04 50 2.00 Printer 1.50 2 3.00 TV set 0.50 10 5.00 Electric water heater 2.00 1 2.00 Refrigerator 0.05 1 0.05 Computer room equipment 4.00 1 4.00 Monitoring equipment 1.00 1 1.00 Other equipment 5.50 1 5.50 Total 36.55

[0124] The power generation of wind turbines and PV is affected by the weather. In this project, small meteorological monitors are set up near the hills to collect data. Table 3 shows the local monthly irradiance value and the wind speed data at a height of 24 m above the ground.

[0125] Table 3: Solar irradiance and wind speed data:

[0126]

[0127]

[0128] As can be seen from Table 3, strong wind weather occurs frequently from March to May throughout the year, the wind resources are relatively rich, but there are more cloudy days and the irradiance is poor; the solar irradiance resources are rich from April to October. Adjust the utilization efficiency c1 of the distributed power source weather impact in Equation (9) through Table 3.

[0129] Equipment incurs corresponding costs due to the installed capacity and later maintenance. When considering economy, the installation cost and maintenance cost need to be taken into account. Table 4 shows the average installation cost of wind-solar-storage in 2021. As can be seen from Table 4, the installation cost and maintenance cost of PV are relatively high, followed by wind turbines, and the investment cost of energy storage is the lowest.

[0130] Table 4: Installation costs of different power source types:

[0131]

[0132] 4.2. Economic analysis of the optimal configuration of microgrid power sources:

[0133] Taking the load, wind speed, and light data in Tables 3 and 4 as basic parameters, with energy storage charging and discharging, state of charge, and carbon emissions as constraint conditions, and annual power generation revenue as the optimization objective solution, a database of the ratio of wind turbine to PV installed capacity is established. The PSO algorithm is used for optimization, and the optimization results are as Figure 3 shown.

[0134] 4.3 Economic Analysis:

[0135] The primary goal of this project is to ensure continuous power supply for the basic load of regional users. As analyzed in Section 4.2, the proportion of energy storage installed capacity is relatively large, followed by PV power generation, and the wind turbine is the smallest. A ratio of 5:30:45 for the capacity (kW) of wind, PV, and energy storage is more appropriate, and the annual power generation revenue is approximately 21,000 yuan. The operation mode of the microgrid is as follows.

[0136] Under grid-connected conditions, the electricity consumption is large during the day and evening. The PCS and distributed power sources complement each other, reducing the exchange power consumed from the municipal power grid. At this time, all the electricity generated by the distributed power sources is in the state of self-use. At midnight, the electricity consumption of the community load is very small, and the wind turbine and the municipal power grid charge the energy storage battery through the PCS.

[0137] (2) When the municipal power grid is cut off, the sunlight is sufficient during the day, and the charge and discharge of the energy storage system remain balanced, providing a constant voltage and constant frequency power supply point for the system. The distributed power sources follow the power generation. In the evening, the electricity consumption is large. Since the PV power generation is 0 at this time and the wind turbine has large fluctuations, the energy storage serves as the main power source to continue power supply.

[0138] Figure 4 is the average monthly power generation of the distributed power source. As Figure 4 can be seen, in this mode of installed capacity ratio, it conforms to the principle of selecting the capacity of wind, PV, and energy storage described in Section 3.3. It makes full use of the abundant sunlight resources in the region, the exchange power reaches the lowest, and the wind turbine only appropriately supplements the load. Under the conditions of ensuring reliability and economy indicators, it conforms to taking into account making full use of renewable resources to obtain the maximum economic benefits, as shown in Table 5.

[0139] Table 5: Project Economic Benefits:

[0140] Index Data Average annual power generation / (MW·h) 25.5 Average annual standard coal saved / t 8.056 <![CDATA[Average annual CO2 emission reduction / t]]> 21.375

[0141] 5. Summary:

[0142] In small-scale wind-PV-energy storage microgrid projects, the carbon emissions of wind turbines and PVs during power generation are relatively small. Most of the materials of wind turbines can be recycled after retirement. The comprehensive benefits of electrochemical energy storage are affected by its service life. This paper fully evaluates the regional load situation, designs the transformation structure of the microgrid grid, uses the PSO algorithm to optimize the ratio of wind-PV-energy storage capacity, and the following conclusions can be drawn through case analysis.

[0143] (1) The PSO optimization algorithm is proposed. Using regional wind speed, light intensity, and load power as reference data, it can achieve the global optimal position with the maximum economic profit as the objective function, thereby obtaining the actual installed capacity ratio of wind, light, and storage in the microgrid.

[0144] (2) Compared with similar models for seeking the maximum comprehensive economic benefit of the microgrid, the PSO algorithm adopted in this paper is simple to apply. Verified by actual projects in coal mining subsidence areas, the data conforms to the reality, providing theoretical and practical references for the energy-saving transformation project of the intelligent microgrid in the comprehensive management area of coal mining subsidence in northern Shanxi.

Claims

1. A method for optimizing the capacity of a wind-solar-storage microgrid based on the particle swarm algorithm, characterized in that, It includes the following steps: S1. Establish a microgrid system model: Construct a microgrid structure including wind turbines, photovoltaic modules, and energy storage devices, and determine the mathematical models of each distributed power source and energy storage device. The mathematical models include: Wind turbine power model: Calculate the output power in segments according to the wind speed; Photovoltaic module power model: Consider the influence of irradiation intensity and temperature; Energy storage device charge storage model: Describe the charge and discharge states of the energy storage system; S2. Set constraint conditions: Establish the charge and discharge power constraints, state of charge constraints of the energy storage system, and system carbon emission constraints; S3. Construct an objective function: With the annual maximum economic benefit as the goal, take the installed capacity of wind turbines, photovoltaic installed capacity, energy storage installed capacity, and grid-connected exchange power as independent variables, and comprehensively consider the utilization efficiency of distributed power sources, the utilization efficiency of energy storage, and the mains exchange power conversion coefficient; S4. Optimize using the particle swarm algorithm: Initialize the particle swarm parameters, calculate the fitness of particles through iterative calculations, update the inertia weight and particle positions, and perform boundary processing in combination with the constraint conditions until the optimal ratio of wind, light, and energy storage capacities is obtained.

2. The capacity optimization method of a wind-solar-storage microgrid based on the particle swarm optimization algorithm according to claim 1, characterized in that: The wind turbine power model is shown in Equation (1): Where: v c i is the cut-in wind speed, 3 m / s; v r is the rated wind speed, 12 m / s; v co is the cut-out wind speed, 20 m / s; P r is the rated output power, kW.

3. A capacity optimization method for a wind-solar-storage microgrid based on a particle swarm algorithm according to claim 1, characterized in that: The photovoltaic module power model is shown in Equation (2): P PV = P STC + β G [1 + k(t c - t sc )] (2); Where: P PST is the rated power of the PV module under standard conditions; β G is the current irradiation intensity reference ratio; k is the irradiation ratio gain; t c is the surface temperature of the working point cell; t sc is the reference temperature.

4. The capacity optimization method of a wind-solar-storage microgrid based on the particle swarm algorithm according to claim 1, wherein: The energy storage device charge storage model is shown in Equations (3) and (4): where: E B,min and E B,max are the upper and lower limits of the stored electrical energy; is the conversion efficiency of the PCS; is the bidirectional energy conversion efficiency between the lead-carbon battery and the PCS; E WP,t is the electrical energy generated by the distributed power source over time t; E L,t is the electrical energy consumed by the power supply load over time t.

5. The capacity optimization method of a wind-solar-storage microgrid based on the particle swarm algorithm according to claim 1, characterized in that: The constraint conditions include: Battery charging constraint conditions: Battery discharging constraint conditions: Wherein: are the electric energies absorbed and released by the energy storage battery pack with time t respectively; The state of charge constraint is used to describe the charge and discharge depth of the battery, that is, its charge and discharge are controlled within the charge and discharge thresholds. Its constraint form: E B,min ≤E B,t ≤E B,max (7)； Since a diesel generator is installed in this system, the carbon emissions generated during its power generation become a problem that cannot be ignored. Therefore, the CO2 emissions during the power generation stage are considered as a constraint condition. Its carbon emission constraint form: E3x3δ3≤e max (8)； Where: E3 is the annual power generation of the diesel generator; x3 is the number of diesel generators; δ3 is the carbon emission factor for power generation using diesel generators; e max is the peak value that the system can withstand for CO2 generation.

6. The capacity optimization method of a wind-solar-storage microgrid based on the particle swarm algorithm according to claim 1, wherein: The objective function is shown in Equation (9): Where: P gi (t) is the grid-connected exchange power; c1 is the utilization efficiency of distributed power sources affected by weather; c t is the energy storage utilization efficiency; c2 is the conversion coefficient of mains exchange power.

7. A capacity optimization method for a wind-solar-storage microgrid based on the particle swarm algorithm according to claim 1, characterized in that: The process of optimizing using the particle swarm algorithm includes: Initialize parameters: Set the installed capacity range of distributed power sources, the power range of the energy storage converter, the maximum number of iterations, the number of particles, and the search space dimension; Particle initialization: Generate random particles as the initial solution, corresponding to the installed capacity of wind turbines, photovoltaic installed capacity, and energy storage parameters; Fitness calculation: Calculate the fitness of particles according to the objective function; Iterative update: Update the particle velocity and position, adjust the inertia weight; perform boundary condition processing until the maximum number of iterations is reached or the convergence condition is satisfied.

8. The capacity optimization method of a wind-solar-storage microgrid based on the particle swarm algorithm according to claim 7, characterized in that: In parameter initialization, the distributed installed capacity range is 0 - 100 kW, the grid power range is 1 - 50 kW, the power range of the energy storage bidirectional converter is - 30 - 30 kW, the maximum number of iterations is 500, the number of particles is 800, and the search space dimension is 86.

9. A method for optimizing the capacity of a wind-solar-storage microgrid based on a particle swarm algorithm according to claim 1, characterized in that: The principles for the ratio of wind, light, and energy storage capacities include: Combine with regional load characteristics: Give priority to using photovoltaic and wind power generation, and the energy storage system meets the basic load power supply during island operation; Consider terrain environment and weather data: Optimize the layout of distributed power sources to reduce the curtailment rate of wind and light; Comprehensively consider the installation cost and maintenance cost: Balance the capacity ratio of each power source to maximize economic benefits.

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