Micro-grid wind and light storage capacity planning configuration method and system facing wind and light seasonal fluctuation

Abstract

The invention relates to the technical field of wind and light storage capacity planning, in particular to a wind and light seasonal fluctuation-oriented micro-grid wind and light storage capacity planning configuration method and system, and the method comprises the steps: constructing a micro-grid wind and light storage capacity planning model, and taking the minimum daily average cost of a micro-grid in a whole scheduling period as an optimization target, comprising new energy output and load fitting deviation punishment, electricity purchase cost and green evidence income; the new energy output and load fitting deviation penalty is determined through the new energy output and load fitting deviation and a corresponding penalty coefficient; the green evidence income is determined by combining wind power generation power, photovoltaic output power and green evidence selling related parameters; determining constraint conditions of the microgrid wind and light storage capacity planning model; and solving the model to obtain an energy storage optimal configuration scheme, and determining an optimization effect of the planning model in combination with different scenes. The optimization objective of the invention is to realize the minimum daily cost of the micro-grid on the premise of considering the fitting degree of the new energy output and the load.

Description

Technical Field

[0001] This invention relates to the field of wind, solar and energy storage capacity planning technology, and in particular to a method and system for planning and configuring wind, solar and energy storage capacity in microgrids to address seasonal fluctuations in wind and solar power. Background Technology

[0002] Wind turbines are devices that convert the kinetic energy of wind into electrical energy. Photovoltaic power generation is a technology that uses the photovoltaic effect of semiconductors to directly convert solar radiation energy into direct current (DC) electricity. Its fundamental and well-known technologies involve aerodynamics, electrical engineering, and materials science.

[0003] Operating Principles and Output Model: The output power of wind turbine generators is highly dependent on real-time wind speed. When the wind speed reaches the "cut-in wind speed," the unit begins generating electricity; as the wind speed increases, the output increases. When the "rated wind speed" is reached, the unit reaches its "rated capacity" or maximum output power. To protect the equipment, the unit will stop operating when the wind speed exceeds the "cut-out wind speed," thus there are upper and lower limits. The output power of photovoltaic arrays mainly depends on the solar irradiance and the surface temperature of the photovoltaic modules. The stronger the sunlight, the greater the output; however, excessively high temperatures will lead to a decrease in power generation efficiency, therefore, there are upper and lower limits.

[0004] When conducting capacity planning, the power output at a specific moment is usually simplified to be determined by the planned installed capacity and the normalized wind power output (per unit value) at that moment. However, its inherent intermittent, random, and volatile characteristics pose potential challenges to the safe and stable operation of the power system, and seasonal fluctuations in wind and solar power output are becoming increasingly significant. When large-scale renewable energy is integrated into the grid, it not only leads to difficulties in balancing the active power of the grid but also exacerbates the pressure on peak shaving and frequency regulation.

[0005] Against this backdrop, energy storage technology, as a key flexibility regulation resource for new power systems, coordinates seasonal fluctuations in wind and solar power output and fully utilizes renewable energy output, effectively reducing initial investment costs and providing crucial technical support for the safe and economical operation of high-proportion renewable energy power systems. However, current technologies lack solutions specifically for high-proportion renewable energy grid integration, increasingly significant curtailment and power fluctuations, and substantial seasonal fluctuations in wind and solar power output. Summary of the Invention

[0006] Objective: To overcome the shortcomings of existing technologies, this invention establishes a wind-solar-storage capacity planning model aimed at minimizing the daily cost of microgrids and reducing the mismatch between renewable energy output and load. By introducing a penalty for this mismatch, the model ensures that wind and solar output aligns as closely as possible with the load curve. Simultaneously, due to the flexible charging and discharging characteristics of energy storage, the model improves renewable energy absorption while minimizing the installed capacity of wind and solar power. Based on this, this invention proposes a method and system for planning and configuring wind, solar, and storage capacity in microgrids to address seasonal fluctuations in wind and solar power.

[0007] Technical solution: In a first aspect, the present invention provides a method for planning and configuring wind, solar, and energy storage capacity in microgrids to address seasonal fluctuations in wind and solar power. This method includes: Clustering algorithms were used to cluster the daily curve samples of photovoltaic and charging load throughout the year, and several representative typical operating days were extracted. A microgrid wind, solar, and storage capacity planning model is constructed based on typical operating days in different scenarios and seasons. The optimization objective is to minimize the average daily cost of the microgrid over an entire scheduling cycle, including the average daily investment cost, lifetime depreciation cost, renewable energy output and load mismatch penalty, electricity purchase cost, and green certificate revenue. The renewable energy output and load mismatch penalty is determined by the mismatch between renewable energy output and load and the corresponding penalty coefficient. The green certificate revenue is determined by combining wind power generation, photovoltaic power generation, and relevant parameters of green certificate sales. The constraints of the microgrid wind, solar and energy storage capacity planning model are determined. These constraints include power balance constraints, wind power output constraints, solar power output constraints, energy storage capacity constraints, and energy storage state of charge constraints. Solve the model to obtain the optimal energy storage configuration scheme, and determine the optimization effect of the planning model in combination with different scenarios.

[0008] Furthermore, including: The penalty for the mismatch between renewable energy output and load is determined by the mismatch between renewable energy output and load and the corresponding penalty coefficient, including: Determine the matching deviation between renewable energy output and load at a certain moment on the day, and use the current electricity load on the day to determine the mismatch coefficient between renewable energy output and load; The intermediate value of the fit is obtained by multiplying the misfit coefficient between the new energy output and the load by the fit deviation penalty coefficient between the new energy output and the load. The total optimization value is obtained by summing the intermediate value of the fit, the number of optimization days, and the number of optimization times within an optimization day. Multiply the total optimized value by the optimization time interval to obtain the total penalty, and calculate the penalty value for the deviation between the new energy output and the load based on the ratio of the total penalty to the number of optimization days.

[0009] Furthermore, including: The penalty for the deviation between the output of the new energy source and the load is expressed as follows: ; ; in, and for day The constant deviation between the output of new energy sources and the load. The mismatch coefficient between new energy output and load. The penalty coefficient for the deviation between new energy output and load matching is given, where D is the number of optimization days and T is the number of optimization times within one optimization day. for day The electrical load at any given time To optimize the time interval.

[0010] Furthermore, including: The revenue from green certificates is determined by combining wind power generation capacity, photovoltaic power output capacity, and relevant parameters of green certificate sales, including: Determine the wind power generation capacity and photovoltaic power output capacity at a certain moment on a given day, and calculate their combined value; Multiplying the composite value by the green certificate sales coefficient and the green certificate sales price per unit yields the green certificate revenue at a certain moment on the day; The total revenue is obtained by summing the green certificate revenue at a certain moment on the day, the number of optimization days, and the number of optimization times within an optimization day. The total revenue is divided into an intermediate value and an optimized time interval. The overall green certificate revenue of the microgrid is then determined by comparing the intermediate value with the optimized number of days.

[0011] Furthermore, including: The overall green certificate revenue of the microgrid is represented as follows: ; in, This represents the green certificate sales coefficient. The unit price for selling green certificates; for day Wind power generation capacity at any given time; for day Photovoltaic power output at any given time, where D represents the optimized number of days. To optimize the time interval, T is the number of optimization times within an optimization day.

[0012] Furthermore, including: The aforementioned average daily investment cost specifically refers to the investment cost of distributed power sources and energy storage, including: The total investment in wind power equipment, photovoltaic equipment, and energy storage equipment is summed and multiplied by an annualized factor to obtain the annual investment cost. The average daily investment cost of distributed power and energy storage is then determined based on the annual investment cost and the number of days in a year. Specifically, the total investment in wind power equipment is obtained by multiplying the construction cost of wind power equipment currently installed in the microgrid by the wind power capacity; the total investment in photovoltaic equipment is obtained by multiplying the construction cost of photovoltaic equipment by the photovoltaic capacity; and the total investment in energy storage equipment is obtained by multiplying the construction cost of energy storage equipment by the energy storage capacity.

[0013] Furthermore, including: The depreciation cost of the wind, solar, and energy storage equipment lifespan includes: the daily average lifespan depreciation cost of wind power equipment, the daily average lifespan depreciation cost of solar power equipment, and the daily average lifespan depreciation cost of energy storage equipment. The average daily life depreciation cost of the wind power equipment is calculated by multiplying the life depreciation coefficient of the wind power equipment by the wind power generation capacity at a certain moment on the day. The average daily life depreciation cost of the photovoltaic equipment is obtained by multiplying the difference between the life depreciation coefficient and the subsidy incentive coefficient of the photovoltaic equipment by the photovoltaic output power at a certain moment on the day. The daily average lifespan depreciation cost of energy storage equipment is obtained by multiplying the absolute value of the charging and discharging power of the energy storage at a certain moment on the day by the operation and maintenance cost coefficient of the energy storage equipment, and then combining it with the unit energy cycle loss cost coefficient of the energy storage.

[0014] Furthermore, including: The average daily lifespan depreciation cost of the energy storage device is expressed as follows:

[0015] ; ; In the formula, For the construction cost of energy storage equipment, For energy storage capacity, This refers to the unit energy cycle loss cost coefficient for energy storage. This refers to the number of cycles throughout the entire lifecycle of energy storage. This represents the operation and maintenance cost coefficient for energy storage devices. for day The absolute value of the charge / discharge power of the stored energy at any given time; if the charge / discharge power is negative, then... It becomes its opposite value.

[0016] Furthermore, including: The power balance constraint is expressed as: ; in, To optimize the day, To optimize time periods; for Wind power generation capacity at any given time; for Photovoltaic power output at any given time; They are respectively The constant discharge and charging power of stored energy; for The baseline electrical load at any given time; for The electrical load at any given time; To optimize daily The adjustment coefficient is an optimization variable, and its value is also regarded as the potential for subsequent user participation in demand response; To increase the flexibility of new energy processing, a formula with a slight deviation is set up, expressed as: ; in: , for day The constant deviation between the output of new energy sources and the load. This is the maximum deviation coefficient between the output and load of new energy sources.

[0017] On the other hand, the present invention also provides a microgrid wind-solar-storage capacity planning and configuration system for seasonal fluctuations in wind and solar power, the system comprising: The sample processing module is used to cluster the daily curve samples of photovoltaic and charging load throughout the year using a clustering algorithm to extract several representative typical operating days. The optimization objective determination module is used to construct a microgrid wind, solar, and storage capacity planning model based on typical operating days in different seasons under different scenarios. The optimization objective is to minimize the average daily cost of the microgrid over an entire scheduling cycle, including the average daily investment cost, life-cycle depreciation cost of wind, solar, and storage equipment, penalty for deviation between renewable energy output and load, electricity purchase cost, and green certificate revenue. The penalty for deviation between renewable energy output and load is determined by the deviation between renewable energy output and load and the corresponding penalty coefficient. The green certificate revenue is determined by combining wind power generation, solar power generation, and relevant parameters of green certificate sales. The constraint construction module is used to determine the constraint conditions of the microgrid wind, solar and energy storage capacity planning model. These constraint conditions include power balance constraints, wind power output constraints, photovoltaic power output constraints, energy storage capacity constraints and energy storage state of charge constraints. The optimization module is used to solve the above model, obtain the optimal energy storage configuration scheme, and determine the optimization effect of the planning model in combination with different scenarios.

[0018] Beneficial effects: Compared with the prior art, the present invention has the following advantages: This invention, after completing cost modeling and establishing corresponding constraints for each part, solves for the wind, solar, and energy storage capacity within the region. The optimization objective is to minimize the daily cost of the microgrid while considering the degree of alignment between renewable energy output and load.

[0019] This invention proposes a wind-solar-storage capacity planning model aimed at minimizing the daily cost of microgrids and reducing the mismatch between renewable energy output and load. By introducing a penalty for the mismatch between renewable energy output and load, the wind and solar output can be made to fit the load output curve as closely as possible. At the same time, due to the flexible charging and discharging characteristics of energy storage, the renewable energy absorption rate can be improved while minimizing the installed capacity of wind and solar power. Attached Figure Description

[0020] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein: Figure 1 A flowchart of a microgrid wind-solar-storage capacity configuration planning method for seasonal fluctuations in wind and solar power provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the clustering results of typical daily wind, solar load, and solar load data for four seasons provided in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the typical daily wind, solar, and energy storage output in four seasons under scenario 2 provided in this embodiment of the invention. Figure 4 This is a schematic diagram illustrating the variation of wind, solar, and energy storage installation costs with energy storage capacity, provided as an embodiment of the present invention. Figure 5 This is a schematic diagram illustrating the variation of wind, solar, and energy storage capacity with energy storage capacity, as provided in an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1: Refer to Figure 1 The diagram illustrates a flowchart of a microgrid wind-solar-storage capacity configuration planning method for seasonal fluctuations in wind and solar power proposed in this embodiment, which includes the following processes: Step 1: Construct a wind-solar-storage capacity planning model that considers the degree of matching between renewable energy output and load. Step 1.1: Construct a microgrid wind, solar, and energy storage capacity planning model with the objective of minimizing daily costs. This includes the average daily installed capacity cost, lifetime depreciation cost, penalty for deviation between renewable energy output and load, electricity purchase cost, and green certificate revenue, as detailed below: The optimization objective is to minimize the average daily cost of the microgrid over an entire scheduling cycle. The objective function is as follows: ; In the formula, These include the average daily investment cost of wind, solar, and energy storage equipment, the depreciation cost of wind, solar, and energy storage equipment over their lifespan, the penalty for deviation between renewable energy output and load, the cost of purchasing electricity, and the revenue from selling green certificates.

[0023] The investment cost expressions for wind, solar, and energy storage equipment are as follows: ; ; In the formula: For distributed power generation and energy storage investment costs, This is the annualized coefficient. The discount rate is... For the number of years in the plan; Construction costs for wind power, photovoltaic, and energy storage equipment; These represent the capacities for wind power, solar power, and energy storage, respectively.

[0024] The depreciation cost formulas for wind, solar, and energy storage equipment over their lifespan are as follows: Because the depreciation rates of wind, solar, and energy storage equipment are significantly affected by the characteristics of the resources themselves, depreciation costs need to be described separately. These are the average daily lifespan depreciation costs for wind power, photovoltaic power, and energy storage, respectively.

[0025] The expression for the daily average lifespan depreciation cost of wind power equipment is as follows: ; In the formula, This is the depreciation factor for the lifespan of wind power equipment. To optimize the time interval, D represents the number of optimization days, and T represents the number of optimization times within an optimization day. It's important to note that only days throughout the year that are used for optimization are considered optimization days, not necessarily 365 days.

[0026] The expression for the daily average lifespan depreciation cost of photovoltaic equipment is as follows: ; In the formula, These are the lifespan depreciation factor and subsidy incentive factor for photovoltaic equipment, respectively.

[0027] The expression for the daily average lifespan depreciation cost of energy storage equipment is as follows: ; ; In the formula, This refers to the unit energy cycle loss cost coefficient for energy storage. This refers to the number of cycles throughout the entire lifecycle of energy storage. This represents the operation and maintenance cost coefficient for energy storage devices. for day The absolute value of the charge / discharge power of the stored energy at any given time; if the charge / discharge power is negative, then... It becomes its opposite value.

[0028] The expression for the fit deviation penalty is as follows: ; ; in, and for day The positive and negative misalignment between the output of new energy sources and the load at all times will affect the deviation penalty cost, regardless of whether the deviation is positive or negative. It should be noted that, for the sake of ease of calculation and understanding, positive deviation is taken as positive and negative deviation is taken as negative. The total deviation can be calculated by subtracting the negative deviation from the positive deviation. This ensures that the misalignment coefficient is positive and will not have a negative sign, thus ensuring that the deviation penalty cost is a positive number. The mismatch coefficient between new energy output and load. This is the penalty coefficient for the deviation between new energy output and load matching. D represents the number of optimization days, and T represents the number of optimization times within one optimization day. for day The electrical load at any given time.

[0029] Furthermore, in this embodiment, the positive and negative deviations are the optimal values ​​of the decision variables obtained by the solver that satisfy the constraints, and the non-fit coefficient is in the first... The result obtained from the formula calculation is not necessarily the optimized result. The deviation penalty cost is calculated based on the deviation corresponding to the optimal value after optimization.

[0030] The above formulas, when incorporated into the model proposed in this invention, can incentivize new energy output to be as close to the load as possible, thereby reducing the problem of excessive source-load mismatch caused by seasonal fluctuations in wind and solar power, reducing the amount of electricity purchased by the system, and alleviating the pressure on the power grid to regulate peak and frequency during peak and valley periods.

[0031] The formula for electricity purchase cost is as follows: ; In the formula: Time-of-use electricity pricing; for day The power purchased from conventional generating units at all times; D represents the optimized daily quantity.

[0032] The formula for green certificate returns is as follows: ; In the formula, This represents the green certificate sales coefficient. The unit price for selling green certificates; for day Wind power generation capacity at any given time; for day Photovoltaic power output at any given time, where D represents the optimized number of days. To optimize the time interval, the green certificate revenue proposed in the example mainly involves obtaining corresponding green certificates for wind power and photovoltaic output, which can be sold in the green certificate market to generate profits and reduce the cost of the constructed model.

[0033] Step 2: Determine the constraints of the wind, solar and energy storage capacity planning model Step 2.1: The constraints include power balance constraints, wind power output constraints, photovoltaic power output constraints, energy storage capacity constraints, and energy storage state of charge constraints, as detailed below.

[0034] Power balance constraints are as follows ; In the formula, To optimize the day, To optimize time periods; for Wind power generation capacity at any given time; Photovoltaic power output; These represent the discharge and charging power of energy storage, respectively. As the baseline electrical load; For electrical load; To optimize daily The adjustment coefficient is an optimization variable, and its value is also regarded as the potential for subsequent user participation in demand response.

[0035] To increase the flexibility of new energy processing, a small amount of deviation is allowed: ; In the formula: This is the maximum deviation coefficient between the output and load of new energy sources.

[0036] To ensure the safe operation and lifespan protection of wind and solar power generation units, their output must always be maintained within a reasonable range. The constraints on wind and solar power output are as follows: ; In the formula: These are the normalized per-unit values ​​for wind power and solar power output, respectively. The installed capacity was optimized for wind power and photovoltaic power, respectively.

[0037] The installed capacity of wind power and photovoltaic power must meet the following constraints: ; ; In the formula: These represent the lower and upper limits of wind power installed capacity, respectively. These represent the lower and upper limits of photovoltaic installed capacity, respectively.

[0038] To ensure the safe operation and lifespan protection of the energy storage unit, its charging and discharging power must always be maintained within a reasonable range. The charging and discharging power and state constraints are as follows: ; ; In the formula: This refers to the charging / discharging state of energy storage. The range of values ​​is , The range of values ​​is .

[0039] The installed capacity of energy storage units must meet the following constraints: ; In the formula: These represent the lower and upper limits of energy storage installed capacity. To optimize the installed capacity of energy storage.

[0040] In addition, the state of charge (SOC) of the energy storage system must always be maintained within a reasonable range, and SOC constraints must be met, as follows: ; ; In the formula: Chemical energy storage Time and The amount of electricity stored at any given time; Self-discharge rate; These are the charge / discharge efficiencies, respectively. These represent the minimum and maximum storage capacities for chemical energy storage, respectively.

[0041] Step 3: Use the Big M method to handle nonlinear constraints.

[0042] Step 3.1: The above constraints on energy storage charging and discharging power and state are not linear constraints and cannot be solved directly. Therefore, the Big M method is used to linearize them:

[0043] In the formula, It has no practical meaning and is a 0-1 variable, where M is a sufficiently large constant, and The value of is changed to .

[0044] Furthermore, due to the change in the value range, the power balance constraints between new energy output and load should also be changed accordingly: ; Step 4: This invention uses Matlab to call the CPLEX solver to solve the above model, obtain the optimal energy storage configuration scheme, and further analyze the impact of wind, solar and energy storage capacity on the objective function.

[0045] Example Analysis: This invention employs the K-medoids clustering algorithm to cluster the daily curve samples of photovoltaic and charging loads throughout the year, extracting four representative typical operating days, and constructing a data input based on these days. Figure 2 As shown. Among them, Figure 2 (a) in the figure is a schematic diagram of the clustering results of typical spring day wind-light-load data. Figure 2 (b) in the figure is a schematic diagram of the clustering results of typical summer daytime wind-solar-load data. Figure 2 (c) in the figure is a schematic diagram of the clustering results of typical autumn daytime wind-light-load data. Figure 2 (d) in the figure is a schematic diagram of the clustering results of wind, solar and load data on a typical winter day.

[0046] After completing the cost modeling and corresponding constraints for each part, this invention solves for the wind, solar, and energy storage capacity within the region. The optimization objective is to minimize the daily cost of the microgrid while considering the degree of matching between renewable energy output and load.

[0047] This embodiment sets up two different scenarios to illustrate the effect of source-load matching. As shown in Table 1, scenarios 1 and 2 are the optimization results of wind, solar and energy storage configuration and operation before and after considering the matching of new energy output with load.

[0048] Table 1 Optimization Results of Wind-Solar-Storage Configuration and Operation Performance

[0049] Comparing scenarios 1 and 2, when the system considers the matching of renewable energy output with load, the system optimizes the configuration of 26.09MW of energy storage capacity in this region, significantly enhancing the system's ability to absorb renewable energy. Under the premise of meeting the source-load matching deviation, the total installed capacity of wind power and photovoltaic power across the grid increased by 6.58MW and 2.62MW respectively; the daily planning cost decreased by 86,639.15 yuan; compared to scenario 1, which did not consider source-load matching, the average curtailment rate decreased by 8.18%, and the renewable energy output-load mismatch coefficient further decreased from 31.39% to 14.71%. To address the impact of high renewable energy penetration on the power system, the four typical days fully utilize the energy storage scale, storing previously curtailed wind and solar power when renewable energy generation is high, and supplementing it when renewable energy generation is insufficient, better leveraging the energy time-shifting function of the energy storage system. With an energy storage capacity of 26.09MW, the optimal wind-solar ratio in this region is 2.58, and the wind-solar-storage ratio is 2.7:1:3.72.

[0050] The specific output details for typical days in the four seasons in Scenario 2 are as follows: Figure 3 As shown. Figure 3 (a) in the image shows the specific power generation situation on a typical day in spring in scenario 2. Figure 3 (b) shows the specific power generation situation on a typical summer day in scenario 2. Figure 3 (c) shows the specific power generation situation on a typical autumn day in Scenario 2. Figure 3 (d) in the diagram represents the specific power generation situation on a typical winter day in Scenario 2. Therefore, charging occurs between 7:00 and 10:00 in spring, while charging is typically done at night on typical summer, autumn, and winter days. This is because wind power output fluctuates greatly and is unevenly distributed in spring. Between 0:00 and 6:00, wind and solar resources are scarce, and the system, considering the cost of deviation penalties, chooses to purchase electricity to supplement the deficit during this period. The peak period for simultaneous wind and solar power output occurs between 7:00 and 10:00 on the same day. To balance source and load matching as much as possible, the system chooses to use energy storage to absorb the abundant wind and solar resources at this time. Its load regulation coefficient is the lowest among the four typical days, at 0.54.

[0051] The 180.1 MWh reduction in purchased electricity on a typical summer day compared to a typical spring day is due to the more stable wind power resources and the peak solar power resources in summer. The power output deviation shifts from 0:00 to 6:00 on typical spring days to 15:00 to 20:00, which is related to the characteristics of solar power output. Solar power output exhibits unique anti-peak-shaving characteristics; its output gradually decreases during the 14:00-20:00 time window due to reduced sunlight, showing an inverse peak-shaving characteristic negatively correlated with evening peak electricity demand. Since the model's optimization aims to reduce daily costs, the system tends to maximize the scale of renewable energy configuration. The algorithm strategically concentrates most of the allowable supply-demand deviations within the day into the aforementioned specific time period, at the cost of accommodating and integrating a larger scale of new energy power. Therefore, the system chooses to purchase electricity to make up for this shortfall between 15:00 and 20:00.

[0052] The overall power output on a typical autumn day is similar to that of a typical summer day. While wind and solar resources are abundant in autumn, they are not as plentiful as in summer. Consequently, the total electricity purchased increased by 71.97 MWh compared to a typical summer day. In contrast to a typical summer day, energy storage charging shifted entirely to the night. This is because wind resources are significantly higher in the evening and throughout the night on a typical autumn day compared to the daytime. The abundant wind power seamlessly bridged the gap left by daytime solar power decommissioning and effectively covered the evening peak load. Energy storage absorbed a total of 16.32 MWh between 8:00 PM and 6:00 AM the following day.

[0053] Winter's typical days exhibit the weakest wind and solar resources of the four seasons, while the load displays the classic "double-peak" characteristic. Because energy storage is planned according to the optimal economic conditions for the entire year, the already scarce wind and solar resources in winter are maximized through energy storage, achieving the highest absorption rate of 97.6% among the four typical days. This meets the "double-peak" load demand, and therefore the system does not need to purchase additional electricity from the external grid.

[0054] The size of energy storage capacity has a significant impact on wind power installed capacity, the degree of matching between renewable energy output and load, and system cost-effectiveness. This application conducts a sensitivity analysis on energy storage capacity.

[0055] Depend on Figure 4 This study reveals the relationship between the system's average daily total cost and energy storage capacity. The cost curve exhibits a U-shaped characteristic, with the system reaching its lowest average daily operating cost at an energy storage capacity of 26.09 MW. Below this value, increasing the energy storage capacity will reduce costs; above this value, the total cost rebounds due to the increased construction costs of energy storage.

[0056] Further analysis is needed to determine the coupling relationship between energy storage capacity and wind power installed capacity. Figure 5It is evident that the total cost is lowest when the system is at its optimal energy storage capacity (26.09MW), while the wind power installed capacity is already at a relatively high level. This is because wind power output is smoother and more stable than solar power, limiting the complementary effect between energy storage and wind power. Once energy storage reaches a certain scale, even further increases in energy storage capacity will not significantly increase wind power installed capacity. Unlike wind power, as energy storage capacity continues to rise, solar power installed capacity also increases. This is attributed to the distinct daily characteristics and strong fluctuations in solar power output, making the complementary and synergistic effect with energy storage more significant. Therefore, increasing energy storage capacity provides greater flexibility for solar power configuration. Finally, the relationship between energy storage capacity and electricity purchase cost is analyzed. Figure 4 When energy storage capacity is small, its increase is accompanied by an increase in wind and solar installed capacity and a significant decrease in system electricity purchase costs. This is because, within this range, the overall cost of wind and solar power generation is far lower than the cost of purchasing electricity from the grid. Increased energy storage enhances the availability of new energy sources, leading the system to prioritize the dispatch of relatively inexpensive new energy power. However, when energy storage capacity is large, the decrease in electricity purchase costs becomes limited. This is because the complementary effect of wind power and energy storage has reached saturation. At this point, increasing energy storage has little impact on wind power output, and the system can only rely on photovoltaics, which has greater complementary potential. Therefore, as photovoltaic installed capacity steadily increases, changes in electricity purchase costs become less significant.

[0057] Example 2: The present invention also provides a microgrid wind-solar-storage capacity planning and configuration system for seasonal fluctuations in wind and solar power, the system comprising: The sample processing module is used to cluster the daily curve samples of photovoltaic and charging load throughout the year using a clustering algorithm to extract several representative typical operating days. The optimization objective determination module is used to construct microgrid wind, solar, and storage capacity planning models based on different scenarios of typical operating days. The optimization objective is to minimize the average daily cost of the microgrid over an entire scheduling cycle. This includes the average daily investment cost of wind, solar, and storage equipment, lifetime depreciation cost, penalty for deviation between renewable energy output and load, electricity purchase cost, and green certificate revenue. The penalty for deviation between renewable energy output and load is determined by the deviation between renewable energy output and load and the corresponding penalty coefficient. The green certificate revenue is determined by combining wind power generation, solar power generation, and relevant parameters of green certificate sales. The constraint construction module is used to determine the constraint conditions of the microgrid wind, solar and energy storage capacity planning model. These constraint conditions include power balance constraints, wind power output constraints, photovoltaic power output constraints, energy storage capacity constraints and energy storage state of charge constraints. The optimization module is used to solve the above model, obtain the optimal energy storage configuration scheme, and determine the optimization effect of the planning model in combination with different scenarios.

[0058] The other technical features of the microgrid wind-solar-storage capacity planning and configuration system for wind and solar seasonal fluctuations described in this embodiment are similar to the technical features of the microgrid wind-solar-storage capacity planning and configuration method for wind and solar seasonal fluctuations, and will not be repeated here.

[0059] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0060] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0061] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0062] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0063] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0064] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0065] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0066] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0067] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0068] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for planning and configuring wind, solar, and energy storage capacity in microgrids to address seasonal fluctuations in wind and solar power, characterized in that, The method includes: Clustering algorithms were used to cluster the daily curve samples of photovoltaic and charging load throughout the year, and several representative typical operating days were extracted. A microgrid wind, solar, and storage capacity planning model is constructed based on typical operating days in different scenarios and seasons. The optimization objective is to minimize the average daily cost of the microgrid over an entire scheduling cycle, including the average daily investment cost, lifetime depreciation cost, renewable energy output and load mismatch penalty, electricity purchase cost, and green certificate revenue. The renewable energy output and load mismatch penalty is determined by the mismatch between renewable energy output and load and the corresponding penalty coefficient. The green certificate revenue is determined by combining wind power generation, photovoltaic power generation, and relevant parameters of green certificate sales. The constraints of the microgrid wind, solar and energy storage capacity planning model are determined. These constraints include power balance constraints, wind power output constraints, solar power output constraints, energy storage capacity constraints, and energy storage state of charge constraints. Solve the model to obtain the optimal energy storage configuration scheme, and determine the optimization effect of the planning model in combination with different scenarios.

2. The microgrid wind-solar-storage capacity planning and configuration method for seasonal fluctuations in wind and solar power according to claim 1, characterized in that, The penalty for the mismatch between renewable energy output and load is determined by the mismatch between renewable energy output and load and the corresponding penalty coefficient, including: Determine the matching deviation between renewable energy output and load at a certain moment on the day, and use the current electricity load on the day to determine the mismatch coefficient between renewable energy output and load; The intermediate value of the fit is obtained by multiplying the misfit coefficient between the new energy output and the load by the fit deviation penalty coefficient between the new energy output and the load. The total optimization value is obtained by summing the intermediate value of the fit, the number of optimization days, and the number of optimization times within an optimization day. Multiply the total optimized value by the optimization time interval to obtain the total penalty, and calculate the penalty value for the deviation between the new energy output and the load based on the ratio of the total penalty to the number of optimization days.

3. The microgrid wind-solar-storage capacity planning and configuration method for seasonal fluctuations in wind and solar power according to claim 2, characterized in that, The penalty for the deviation between the output of the new energy source and the load is expressed as follows: ; ; in, and for day The constant deviation between the output of new energy sources and the load. The mismatch coefficient between new energy output and load. The penalty coefficient for the deviation between new energy output and load matching is given, where D is the number of optimization days and T is the number of optimization times within one optimization day. for day The electrical load at any given time To optimize the time interval.

4. The microgrid wind-solar-storage capacity planning and configuration method for seasonal fluctuations in wind and solar power according to claim 1, characterized in that, The revenue from green certificates is determined by combining wind power generation capacity, photovoltaic power output capacity, and relevant parameters of green certificate sales, including: Determine the wind power generation capacity and photovoltaic power output capacity at a certain moment on a given day, and calculate their combined value; Multiplying the composite value by the green certificate sales coefficient and the green certificate sales price per unit yields the green certificate revenue at a certain moment on the day; The total revenue is obtained by summing the green certificate revenue at a certain moment on the day, the number of optimization days, and the number of optimization times within an optimization day. The total revenue is divided into an intermediate value and an optimized time interval. The overall green certificate revenue of the microgrid is then determined by comparing the intermediate value with the optimized number of days.

5. The microgrid wind-solar-storage capacity planning and configuration method for seasonal fluctuations in wind and solar power according to claim 4, characterized in that, The overall green certificate revenue of the microgrid is represented as follows: ； in, This represents the green certificate sales coefficient. The unit price for selling green certificates; for day Wind power generation capacity at any given time; for day Photovoltaic power output at any given time, where D represents the optimized number of days. To optimize the time interval, T is the number of optimization times within an optimization day.

6. The microgrid wind-solar-storage capacity planning and configuration method for seasonal fluctuations in wind and solar power according to claim 1, characterized in that, The aforementioned average daily investment cost specifically refers to the investment cost of distributed power sources and energy storage, including: The total investment in wind power equipment, photovoltaic equipment, and energy storage equipment is summed and multiplied by an annualized factor to obtain the annual investment cost. The average daily investment cost of distributed power and energy storage is then determined based on the annual investment cost and the number of days in a year. Specifically, the total investment in wind power equipment is obtained by multiplying the construction cost of wind power equipment currently installed in the microgrid by the wind power capacity; the total investment in photovoltaic equipment is obtained by multiplying the construction cost of photovoltaic equipment by the photovoltaic capacity; and the total investment in energy storage equipment is obtained by multiplying the construction cost of energy storage equipment by the energy storage capacity.

7. The microgrid wind-solar-storage capacity planning and configuration method for seasonal fluctuations in wind and solar power according to claim 1, characterized in that, The depreciation cost of the wind, solar, and energy storage equipment lifespan includes: the daily average lifespan depreciation cost of wind power equipment, the daily average lifespan depreciation cost of solar power equipment, and the daily average lifespan depreciation cost of energy storage equipment. The average daily life depreciation cost of the wind power equipment is calculated by multiplying the life depreciation coefficient of the wind power equipment by the wind power generation capacity at a certain moment on the day. The average daily life depreciation cost of the photovoltaic equipment is obtained by multiplying the difference between the life depreciation coefficient and the subsidy incentive coefficient of the photovoltaic equipment by the photovoltaic output power at a certain moment on the day. The daily average lifespan depreciation cost of energy storage equipment is obtained by multiplying the absolute value of the charging and discharging power of the energy storage at a certain moment on the day by the operation and maintenance cost coefficient of the energy storage equipment, and then combining it with the unit energy cycle loss cost coefficient of the energy storage.

8. The microgrid wind-solar-storage capacity planning and configuration method for seasonal fluctuations in wind and solar power according to claim 7, characterized in that, The average daily lifespan depreciation cost of the energy storage device is expressed as follows: ； ； In the formula, For the construction cost of energy storage equipment, For energy storage capacity, This refers to the unit energy cycle loss cost coefficient for energy storage. This refers to the number of cycles throughout the entire lifecycle of energy storage. This represents the operation and maintenance cost coefficient for energy storage devices. for day The absolute value of the charge / discharge power of the stored energy at any given time; if the charge / discharge power is negative, then... It becomes its opposite value.

9. The microgrid wind-solar-storage capacity planning and configuration method for seasonal fluctuations in wind and solar power according to claim 7, characterized in that, The power balance constraint is expressed as: ； in, To optimize the day, To optimize time periods; for Wind power generation capacity at any given time; for Photovoltaic power output at any given time; They are respectively The constant discharge and charging power of stored energy; for The baseline electrical load at any given time; for The electrical load at any given time; To optimize daily The adjustment coefficient is an optimization variable, and its value is also regarded as the potential for subsequent user participation in demand response; To increase the flexibility of new energy processing, a formula with a slight deviation is set up, expressed as: ; in: , for day The constant deviation between the output of new energy sources and the load. This is the maximum deviation coefficient between the output and load of new energy sources.

10. A microgrid wind-solar-storage capacity planning and configuration system for seasonal fluctuations in wind and solar power, characterized in that, The system includes: The sample processing module is used to cluster the daily curve samples of photovoltaic and charging load throughout the year using a clustering algorithm to extract several representative typical operating days. The optimization objective determination module is used to construct a microgrid wind, solar, and storage capacity planning model based on typical operating days in different seasons under different scenarios. The optimization objective is to minimize the average daily cost of the microgrid over an entire scheduling cycle, including the average daily investment cost, life-cycle depreciation cost of wind, solar, and storage equipment, penalty for deviation between renewable energy output and load, electricity purchase cost, and green certificate revenue. The penalty for deviation between renewable energy output and load is determined by the deviation between renewable energy output and load and the corresponding penalty coefficient. The green certificate revenue is determined by combining wind power generation, solar power generation, and relevant parameters of green certificate sales. The constraint construction module is used to determine the constraint conditions of the microgrid wind, solar and energy storage capacity planning model. These constraint conditions include power balance constraints, wind power output constraints, photovoltaic power output constraints, energy storage capacity constraints and energy storage state of charge constraints. The optimization module is used to solve the above model, obtain the optimal energy storage configuration scheme, and determine the optimization effect of the planning model in combination with different scenarios.

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