Photovoltaic on-grid power distribution method and device, electronic equipment and storage medium

Abstract

The invention provides a photovoltaic on-grid power distribution method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a plurality of candidate distribution strategies for photovoltaic on-grid power, and calculating the performance evaluation result of each candidate distribution strategy for a plurality of performance indexes; screening the plurality of candidate allocation strategies based on the performance evaluation result to obtain a Pareto optimal solution set; calculating a score fluctuation index of each candidate allocation strategy in the Pareto optimal solution set based on the performance evaluation result; and determining a target allocation strategy from the Pareto optimal solution set based on the score fluctuation index. According to the method, secondary screening is carried out on the Pareto optimal solution set by introducing the score fluctuation index, a photovoltaic on-grid power distribution strategy which is stable and strongest in robustness under different decision preferences can be determined, and the objectivity and engineering application value of a decision result are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic technology, and in particular to a method, apparatus, electronic device, and storage medium for distributing photovoltaic grid-connected electricity. Background Technology

[0002] With the energy structure shifting towards cleaner and lower-carbon energy, renewable energy sources, represented by distributed photovoltaic (PV) power, have experienced rapid development. Distributed PV systems typically adopt a grid connection model of full grid connection or "self-consumption with surplus grid connection," where users can sell surplus electricity to the market or participate in market transactions to generate revenue after meeting their own electricity needs. To promote the consumption and development of distributed PV, the electricity market system has become increasingly complex, resulting in a situation where multiple electricity sales channels coexist, such as a mechanism market with guaranteed purchase, a spot market with price fluctuations, and a green electricity certificate (green certificate) market that reflects environmental value.

[0003] Against this backdrop, how to formulate a reasonable grid-connected power allocation strategy for distributed photovoltaic users in order to balance the interests of users, power sales companies, and policy guidance has become an urgent technical problem to be solved. Summary of the Invention

[0004] This invention provides a photovoltaic grid-connected power distribution method, device, electronic device, and storage medium to overcome the deficiencies in the prior art.

[0005] This invention provides a method for allocating photovoltaic power to the grid, comprising: Multiple candidate allocation strategies for photovoltaic grid-connected electricity are obtained, and the performance evaluation results of each candidate allocation strategy for multiple performance indicators are calculated. Based on the performance evaluation results, multiple candidate allocation strategies are screened to obtain a Pareto optimal solution set; Based on the performance evaluation results, calculate the score fluctuation index of each candidate allocation strategy in the Pareto optimal solution set; Based on the score fluctuation index, the target allocation strategy is determined from the Pareto optimal solution set.

[0006] According to a photovoltaic power grid-connected power allocation method provided by the present invention, the step of calculating the score fluctuation index of each candidate allocation strategy in the Pareto optimal solution set based on the performance evaluation results includes: Obtain multiple sets of weight combinations for multiple performance metrics; Based on the weight combinations of each group, the performance evaluation results of each candidate allocation strategy in the Pareto optimal solution set are weighted and calculated to obtain multiple strategy scores for each candidate allocation strategy. Based on the multiple strategy scores of each candidate allocation strategy, the score fluctuation index of each candidate allocation strategy is calculated.

[0007] According to a photovoltaic power grid connection allocation method provided by the present invention, the step of screening multiple candidate allocation strategies based on the performance evaluation results to obtain a Pareto optimal solution set includes: Based on the performance evaluation results, a Pareto optimal solution is determined from multiple candidate allocation strategies using a non-dominated sorting algorithm; The Pareto optimal solutions are combined into the Pareto optimal solution set.

[0008] According to a photovoltaic power grid connection allocation method provided by the present invention, the calculation of the performance evaluation results of each of the candidate allocation strategies for multiple performance indicators includes: Calculate the performance evaluation results of each candidate allocation strategy for the user annualized net return index, market value realization index, and policy signal conversion rate index.

[0009] According to a photovoltaic power grid connection allocation method provided by the present invention, the calculation of the performance evaluation results of each of the candidate allocation strategies for multiple performance indicators includes: Collect photovoltaic parameters; wherein, the photovoltaic parameters include photovoltaic output parameters, user load parameters, photovoltaic cost parameters, and electricity price policy parameters; Based on the photovoltaic parameters, the performance evaluation results of each candidate allocation strategy for each performance index are calculated.

[0010] According to a photovoltaic power grid connection allocation method provided by the present invention, the step of obtaining multiple candidate allocation strategies for photovoltaic power grid connection includes: Obtain a pre-established multi-objective power allocation strategy model; wherein, the multi-objective power allocation strategy model includes power allocation strategy models with mechanism priority, market priority, green certificate priority and dynamic adaptive allocation as objectives respectively, and the dynamic adaptive allocation is adaptive allocation in mechanism market, spot market and green certificate market. By iterating through different combinations of configuration parameters in each of the power allocation strategy models, multiple candidate allocation strategies are obtained.

[0011] According to a photovoltaic power grid-connected power allocation method provided by the present invention, the step of determining a target allocation strategy from the Pareto optimal solution set based on the scoring fluctuation index includes: Based on the aforementioned score fluctuation index, target allocation strategies are determined from the Pareto optimal solution set, with the objectives of prioritizing mechanisms, prioritizing markets, prioritizing green certificates, and dynamically adaptive allocation, respectively.

[0012] The present invention also provides a photovoltaic grid-connected power distribution device, comprising: The first calculation module is configured to obtain multiple candidate allocation strategies for photovoltaic grid-connected electricity and calculate the performance evaluation results of each candidate allocation strategy for multiple performance indicators. The filtering module is configured to filter multiple candidate allocation strategies based on the performance evaluation results to obtain a Pareto optimal solution set; The second calculation module is configured to calculate the score fluctuation index of each candidate allocation strategy in the Pareto optimal solution set based on the performance evaluation results. The determination module is configured to determine the target allocation strategy from the Pareto optimal solution set based on the score fluctuation index.

[0013] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the photovoltaic grid-connected power distribution method as described above.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the photovoltaic grid-connected power distribution method as described above.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the photovoltaic grid-connected power distribution method as described above.

[0016] The present invention provides a photovoltaic (PV) grid-connected power allocation method, apparatus, electronic device, and storage medium. It calculates the performance evaluation results of candidate allocation strategies for multiple performance indicators, and filters these strategies based on the performance evaluation results to obtain a Pareto optimal solution set. Based on the performance evaluation results, it calculates the score fluctuation index of each candidate allocation strategy in the Pareto optimal solution set, and determines the target allocation strategy based on the score fluctuation index. By introducing a score fluctuation index to perform a secondary screening of the Pareto optimal solution set, this invention can determine a robust PV grid-connected power allocation strategy that is stable under different decision preferences, greatly improving the objectivity of the decision-making results and its engineering application value. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the photovoltaic grid-connected power allocation method provided by the present invention.

[0019] Figure 2 A schematic diagram of the preference weight scoring result space provided in this embodiment of the invention.

[0020] Figure 3 This is a schematic diagram of the photovoltaic grid-connected power distribution device provided by the present invention.

[0021] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0023] The data involved in this application (including but not limited to data used for analysis, data stored, data displayed, etc.) are all information and data authorized by the user or fully authorized by all parties. The collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals shall be provided for users to choose to authorize or refuse.

[0024] Existing allocation schemes typically require dynamically distributing grid-connected photovoltaic power across different markets based on real-time electricity prices, policy requirements, and user load information in order to maximize overall benefits.

[0025] Traditional control methods mostly employ fixed, static allocation strategies. For example, they may rely entirely on fixed priorities (such as always prioritizing guaranteed purchase) or make decisions based solely on a single price signal. These models lack flexibility and cannot adapt to dynamic changes in electricity market rules, pricing mechanisms, or policy orientations, making it difficult for the system to achieve optimal coordinated dispatch and revenue distribution in volatile market environments.

[0026] Meanwhile, existing decision-making models often evaluate benefits from the perspective of a single entity, such as simply maximizing the user's return on investment or unilaterally emphasizing market-side adoption costs. This evaluation system fails to comprehensively quantify the inherent connections and potential conflicts among different stakeholders. For example, strategies that increase user benefits may increase the subsidy costs for electricity retailers, while mandatory high-proportion guaranteed purchases may reduce users' enthusiasm for participating in market transactions. This one-sided evaluation method makes it difficult for the selected strategies to gain acceptance from all parties, lacking fairness and systematicity.

[0027] Furthermore, some existing technologies employ weighted scoring methods to select solutions when dealing with multi-objective optimization problems. However, the setting of weights often relies on the decision-maker's subjective experience, resulting in strong subjectivity. When external conditions such as electricity prices and policies fluctuate, even small changes in weights can lead to drastic changes in the final recommended strategy, resulting in unstable and unreliable decision outcomes. This makes the selected strategy insufficiently adaptable to real-world uncertainties and difficult to implement effectively in engineering practice. Therefore, this invention provides a photovoltaic grid-connected power allocation method, apparatus, electronic device, and storage medium to solve the above problems.

[0028] Figure 1 This is a flowchart illustrating a photovoltaic power grid connection allocation method according to an exemplary embodiment. For example... Figure 1 As shown in an exemplary embodiment, the photovoltaic grid-connected power distribution method includes steps 110 to 140, which are described in detail below.

[0029] Step 110: Obtain multiple candidate allocation strategies for photovoltaic grid-connected electricity, and calculate the performance evaluation results of each candidate allocation strategy for multiple performance indicators.

[0030] In this embodiment of the invention, multiple candidate allocation strategies for photovoltaic grid-connected electricity are obtained, and the performance evaluation results of each candidate allocation strategy under multiple preset performance indicators are calculated.

[0031] Candidate allocation strategies can be understood as a set of rules used to determine how surplus photovoltaic power is allocated across different markets or channels. These strategies may have different optimization objectives or priority logics. For example, some strategies may be designed to prioritize meeting policy requirements to ensure revenue stability, while others may be designed to prioritize participating in market transactions with fluctuating prices to pursue higher economic returns.

[0032] In a specific scenario, multiple different types of strategy models can be preset, and by adjusting the parameters inside the model, such as the allocation ratio of different markets and the sensitivity coefficient of price response, a large number of specific candidate allocation strategies can be generated, thus forming a broad strategy space for subsequent screening.

[0033] Performance metrics are evaluation standards used to quantitatively assess the merits of a candidate allocation strategy from different dimensions. Since distributed photovoltaic grid-connected operation involves multiple stakeholders, performance metrics should also be multi-dimensional.

[0034] In one possible implementation, performance metrics may include, but are not limited to: economic metrics used to measure the economic attractiveness of the strategy to photovoltaic investors, such as annualized returns to users; value metrics used to measure the operational impact of the strategy on the market, such as the degree of value realization on the market side; and policy metrics used to measure the degree of responsiveness of the strategy to macroeconomic policies, such as the degree of completion of the guaranteed purchase policy for renewable energy.

[0035] The process of calculating performance evaluation results is usually accomplished through a simulation model. Specifically, based on a set of input data, such as typical photovoltaic power output curves, user load curves, and market electricity price signals, each candidate allocation strategy can be simulated and run. The allocation behavior of the photovoltaic grid-connected electricity is recorded throughout the simulation period, and the specific evaluation value of the candidate allocation strategy on each performance index is obtained according to the preset performance index calculation formula, forming a multi-dimensional performance evaluation result vector.

[0036] Step 120: Based on the performance evaluation results, multiple candidate allocation strategies are screened to obtain a Pareto optimal solution set.

[0037] In this embodiment of the invention, based on the performance evaluation results obtained in the previous step, multiple candidate allocation strategies are screened to obtain a Pareto optimal solution set.

[0038] The Pareto optimal solution set, also known as the Pareto Frontier in this field, is the set of all non-dominated solutions. A candidate allocation strategy is called a non-dominated solution because no other candidate allocation strategy in the policy space is superior to or equal to it on all performance metrics, and strictly superior to it on at least one performance metric. In other words, each candidate allocation strategy in the Pareto optimal solution set represents an optimal trade-off; any improvement to it (enhancing one metric) necessarily comes at the expense of at least one other metric. The process of selecting this solution set can be achieved by traversing all candidate allocation strategies and comparing and eliminating them based on dominance relationships.

[0039] Step 130: Based on the performance evaluation results, calculate the score fluctuation index of each candidate allocation strategy in the Pareto optimal solution set.

[0040] In this embodiment of the invention, based on the previously calculated performance evaluation results, the score fluctuation index of each candidate allocation strategy in the Pareto optimal solution set is further calculated.

[0041] The rating volatility metric is used to quantify the performance stability of a candidate allocation strategy when faced with different decision preferences. The performance evaluation result of a candidate allocation strategy is a multi-dimensional vector. In actual decision-making, different decision-makers may have different preferences for different performance metrics; for example, users are more concerned with revenue, while electricity retailers (i.e., the market side) are more concerned with profit. The rating volatility metric measures whether the overall rating of a candidate allocation strategy fluctuates drastically due to changes in decision-maker preferences. Its calculation principle can be summarized as follows: for a given Pareto optimal strategy, simulate multiple different decision preference scenarios, and comprehensively rate it under each scenario, thus obtaining a series of rating values. Then, use statistical methods to calculate the dispersion of this series of rating values; this dispersion is the rating volatility metric. A lower volatility metric indicates that the candidate allocation strategy has better universality and compatibility.

[0042] Step 140: Based on the score fluctuation index, determine the target allocation strategy from the Pareto optimal solution set.

[0043] In this embodiment of the invention, the final target allocation strategy is determined from the Pareto optimal solution set based on the calculated score fluctuation index.

[0044] The process of determining the target allocation strategy involves a secondary decision-making process within the Pareto-optimal solution set based on pre-defined rules. Since the rating volatility index reflects the robustness of candidate allocation strategies, a preferred decision rule is to select the candidate allocation strategy whose rating volatility index satisfies a specific condition. For example, this condition could be having the minimum rating volatility index, or the rating volatility index being less than a pre-defined threshold. The target allocation strategy determined in this way is not only Pareto optimal but also, among all Pareto-optimal strategies, the candidate allocation strategy that is least sensitive to changes in decision preferences and exhibits the most stable performance.

[0045] This invention calculates the performance evaluation results of candidate allocation strategies for multiple performance indicators, and filters these strategies based on the performance evaluation results to obtain a Pareto optimal solution set. Based on the performance evaluation results, it calculates the score fluctuation index of each candidate allocation strategy in the Pareto optimal solution set, and then determines the target allocation strategy based on the score fluctuation index. This invention introduces a score fluctuation index to perform a secondary screening of the Pareto optimal solution set, enabling the determination of a robust photovoltaic power allocation strategy that is stable under different decision preferences, greatly improving the objectivity of the decision and its engineering application value.

[0046] In an exemplary embodiment of the present invention, the calculation of the performance evaluation results of each of the candidate allocation strategies for multiple performance metrics includes: Collect photovoltaic parameters; wherein, the photovoltaic parameters include photovoltaic output parameters, user load parameters, photovoltaic cost parameters, and electricity price policy parameters; Based on the photovoltaic parameters, the performance evaluation results of each candidate allocation strategy for each performance index are calculated.

[0047] In this embodiment of the invention, the collected photovoltaic parameters include the following types of data: Photovoltaic power output parameters refer to the power generation curves of a photovoltaic array at different time points (e.g., with a 1-hour resolution). These data can be derived from simulations of standardized photovoltaic power output models for typical meteorological years (such as summer and winter) in the target area, or from historical monitoring data of actual photovoltaic power plants.

[0048] User load parameters refer to the power consumption curve of the photovoltaic user themselves. These data typically represent the electricity consumption patterns of residential or commercial users in different seasons and time periods, and are used to calculate the self-consumption ratio of photovoltaic power generation and the surplus power available for grid connection.

[0049] Photovoltaic cost parameters include the unit investment cost and annualized operation and maintenance cost of photovoltaics, which are essential cost items when calculating the annualized net income of users.

[0050] Electricity pricing policy parameters are a complex set of market and policy signals, including time-of-use electricity prices in the spot market (peak, flat, and valley prices); the equivalent price of green electricity certificates that reflect environmental value; the price of policy-guaranteed purchase mechanisms and their scope of application; and the retail and transmission / distribution prices of electricity sales companies.

[0051] After collecting the aforementioned photovoltaic parameters, each candidate allocation strategy will be simulated based on these parameters, and its performance evaluation results under various performance indicators (such as annualized net income per user, market value realization, etc.) will be calculated.

[0052] This embodiment ensures that the calculation and evaluation of the entire allocation scheme are based on specific and realistic technical and economic parameters by clearly defining the fundamental step of data acquisition. This data-driven approach guarantees the accuracy and reliability of the simulation results, enabling the final target allocation strategy to truly adapt to the actual physical system and market environment, and thus has strong practical guiding significance.

[0053] In an exemplary embodiment of the present invention, obtaining a plurality of candidate allocation strategies for photovoltaic grid-connected electricity includes: Obtain a pre-established multi-objective power allocation strategy model; wherein, the multi-objective power allocation strategy model includes power allocation strategy models with mechanism priority, market priority, green certificate priority and dynamic adaptive allocation as objectives respectively, and the dynamic adaptive allocation is adaptive allocation in mechanism market, spot market and green certificate market. By iterating through different combinations of configuration parameters in each of the power allocation strategy models, multiple candidate allocation strategies are obtained.

[0054] In this embodiment of the invention, a pre-established power allocation strategy model library containing multiple optimization orientations is provided. Specifically, the multi-objective power allocation strategy model may include at least the following four types of models: The electricity allocation strategy model prioritizing the mechanism emphasizes meeting the policy-mandated purchase ratio to achieve the goals of revenue stabilization and risk mitigation. Based on the candidate allocation strategy model, in each time period, the candidate allocation strategy first calculates the amount of electricity to be allocated to the policy-driven purchase channel according to the preset mechanism ratio, and then introduces a price-driven function to distribute the remaining electricity. Its corresponding expression is: ; ; ; ; ; ; in, for Power generation of distributed photovoltaic systems during specific time periods; This refers to the electricity generated by distributed photovoltaic power generation. The proportion of distributed photovoltaic power used for its own purposes; Under the mechanism-first strategy, the electricity volume should be allocated to policy-based procurement channels; The preset mechanism ratio; Remaining battery level; Under the mechanism-first strategy, the price-driven function is based on the price difference between the market and green certificates; This is the price sensitivity coefficient, used to adjust the strategy's responsiveness to price differences; , These are the market electricity price and the equivalent electricity price for green certificate trading, respectively. , These are the electricity volumes that should be allocated to market trading and green certificate trading, respectively.

[0055] The market-first electricity allocation strategy model simulates user behavior aimed at maximizing short-term economic benefits, prioritizing the participation of distributed photovoltaic (PV) users in the spot market. It prioritizes assessing the price attractiveness of the spot market, allocating the majority of electricity there, with the remaining electricity then redistributed between the mechanism market and the green certificate market based on policy ratios and price advantages. Its corresponding mathematical expression is: ; ; ; ; ; in, For market attractiveness function; For mechanism pricing.

[0056] The electricity allocation strategy model prioritizing green certificates simulates user behavior highly sensitive to the value of green environments, and its logic is similar to that of the market-first strategy. In each time period, it first assesses the relative advantage of the equivalent price of green certificates compared to the average price from other channels, dynamically determines the proportion that should be prioritized for allocation to the green certificate market, and then optimizes the allocation between the mechanism and market channels based on the mechanism proportion and price advantage. Its corresponding mathematical expression is: ; ; ; ; ; in, This is a scoring function for the attractiveness of green certificates.

[0057] To further enhance the responsiveness of electricity allocation strategies to uncertain price signals, a dynamic adaptive allocation strategy model is designed to overcome the limitations of fixed priorities and achieve a dynamic balance between policy transformation and market opportunities. Instead of using fixed priorities, it first introduces a dynamic mechanism driven by price differences in each time period. This mechanism adjusts its value based on the real-time advantage of the randomized electricity price relative to the market and the lowest green certificate price. Based on this, it calculates the amount of electricity to be allocated to the policy-driven procurement channel, and the remaining electricity is distributed between the spot market and the green certificate market. The corresponding mathematical expression is: ; ; ; ; ; ; ; in, Price difference driver; The proportion of dynamic mechanisms driven by price differences.

[0058] After obtaining these power allocation strategy models, the configuration parameters in each model (e.g., the mechanism ratio parameter in the mechanism-first strategy, the price sensitivity parameter in the dynamic adaptive strategy, etc.) are systematically iterated within their respective reasonable value ranges. By instantiating different combinations of these parameters, hundreds or thousands of specific, executable candidate allocation strategies can be generated.

[0059] This embodiment constructs a policy model library containing various basic logics (preferred and adaptive), and combines it with a parameter traversal method to systematically and automatically generate a candidate policy space with broad coverage and diversity. This provides rich and high-quality input for subsequent Pareto selection and robust decision-making, which is a key prerequisite for ensuring that a globally optimal and robust allocation policy can be found in the end.

[0060] In an exemplary embodiment of the present invention, the step of filtering multiple candidate allocation strategies based on the performance evaluation results to obtain a Pareto optimal solution set includes: Based on the performance evaluation results, a Pareto optimal solution is determined from multiple candidate allocation strategies using a non-dominated sorting algorithm; The Pareto optimal solutions are combined into the Pareto optimal solution set.

[0061] In this embodiment of the invention, based on the performance evaluation results of all candidate allocation strategies, a non-dominated sorting algorithm is used to determine all Pareto optimal solutions. The non-dominated sorting algorithm is a classic algorithm in the field of multi-objective optimization. Its core idea is to compare any two candidate allocation strategies in the candidate strategy set and determine their dominance relationship based on their superiority or inferiority across all performance metrics. If candidate allocation strategy A is not inferior to candidate allocation strategy B in all performance metrics, and is strictly superior to candidate allocation strategy B in at least one performance metric, then candidate allocation strategy A is said to dominate candidate allocation strategy B.

[0062] In determining the Pareto optimal solution, for each candidate allocation strategy, two values ​​are calculated: one is the number N of other candidate allocation strategies it dominates. p Secondly, the number S dominated by how many other candidate allocation strategies.p Total number of people controlled, S p Candidate allocation strategies with a value of 0 are solutions that are not dominated by any other candidate allocation strategies; together they constitute the Pareto optimal solution.

[0063] Then, all the Pareto optimal solutions found are combined into a set, which is the Pareto optimal solution set.

[0064] This embodiment employs a non-dominated sorting algorithm to ensure that the process of selecting Pareto optimal solutions from a massive pool of candidate allocation strategies is systematic and mathematically rigorous. This allows for the objective and efficient identification of all optimal candidate allocation strategies for which no better trade-off exists, providing a high-quality, unbiased pool of candidate strategies for subsequent robust selection, thereby improving the quality of the final decision result of the entire method.

[0065] In an exemplary embodiment of the present invention, the calculation of the performance evaluation results of each of the candidate allocation strategies for multiple performance metrics includes: Calculate the performance evaluation results of each candidate allocation strategy for the user annualized net return index, market value realization index, and policy signal conversion rate index.

[0066] In this embodiment of the invention, the multiple performance indicators include at least: annualized net revenue per user, market value realization, and policy signal conversion rate.

[0067] The annualized net income per user (NPU) metric is used to evaluate the economic viability of candidate allocation strategies from the user's (i.e., the photovoltaic owner's) perspective. NPU is the core indicator for evaluating the economic feasibility of distributed photovoltaic (PV) projects, directly reflecting the investment drivers of distributed PV. The user's income comes from electricity cost savings and revenue from electricity sales resulting from self-consumption, while costs primarily consider operation and maintenance costs. Its mathematical expression is: ; ; ; ; in, This refers to the electricity consumed by distributed photovoltaic power generation. The annualized profit of distributed photovoltaic power; The annualized cost of distributed photovoltaic power; This represents the annualized net income of distributed photovoltaic power. To extrapolate the simulation period returns to the annualized factor for the whole year; This represents the total time period of the simulation cycle; The self-consumption electricity price for distributed photovoltaic power; The average market price; The annualized operation and maintenance cost of the photovoltaic system; This refers to the unit investment cost of a photovoltaic system. This represents the maximum installed capacity of distributed photovoltaic power.

[0068] As shown in the aforementioned formula, the annualized net income for users can be obtained by subtracting the total cost from the total revenue of photovoltaic power generation within one year. The total revenue includes two parts: the savings from purchasing electricity from power retailers due to self-consumption, and the revenue from selling surplus electricity to different markets (such as the grid market, spot market, and green certificate market). The total cost mainly includes the system's annualized investment cost (equipment depreciation) and operation and maintenance costs.

[0069] The market value realization index is used to systematically evaluate the actual conversion efficiency of the potential commercial value of distributed photovoltaic power under the current market mechanism. Its mathematical expression is: ; in, As a measure of market value realization; The transmission and distribution price for electricity sales companies; This refers to the retail electricity price charged by electricity sales companies.

[0070] As can be seen from the above formula, the market value realization index does not measure the profit of a single entity, but rather the degree to which the intrinsic market value of photovoltaic power as a commodity is ultimately recognized and realized by the market after physical transmission and policy regulation. This market value realization index mainly consists of three parts: the grid access fee charged to the electricity fed into the grid, the profit from the purchase and sale price difference of market-based electricity, and the subsidy cost borne by the mechanism-mandated electricity.

[0071] To reflect the degree to which the selected strategy supports the guaranteed purchase of renewable energy during operation, a policy signal conversion rate indicator is defined to measure the extent to which the signals of the mechanism-based electricity pricing system are accepted and implemented in the market. This indicator reflects the proportion of electricity actually allocated to the mechanism-based purchase market by a strategy within a complete evaluation period, relative to the total theoretically available electricity base for participation in mechanism-based purchase. A higher indicator indicates a more proactive response to policy. Its mathematical expression is: ; in, For policy signal conversion rate; This is the base amount of electricity that can be used for mechanism allocation.

[0072] This embodiment constructs a comprehensive performance evaluation system that reflects the interests of multiple parties by setting core performance indicators across three dimensions: users, market, and policy. This makes strategy evaluation no longer one-sided but systematic, providing quantitative evidence for finding fair and reasonable equilibrium solutions among different conflicting interests, thereby significantly improving the fairness and acceptability of the final strategy.

[0073] In an exemplary embodiment of the present invention, calculating the score fluctuation index of each candidate allocation strategy in the Pareto optimal solution set based on the performance evaluation results includes: Obtain multiple sets of weight combinations for multiple performance metrics; Based on the weight combinations of each group, the performance evaluation results of each candidate allocation strategy in the Pareto optimal solution set are weighted and calculated to obtain multiple strategy scores for each candidate allocation strategy. Based on the multiple strategy scores of each candidate allocation strategy, the score fluctuation index of each candidate allocation strategy is calculated.

[0074] In this embodiment of the invention, to eliminate the calculation bias caused by the different physical dimensions and numerical ranges of the aforementioned three performance indicators, it is necessary to perform dimensionless processing on the performance evaluation results in the Pareto optimal solution set. A sorting-based linear normalization method is used to uniformly map the numerical values ​​of the performance evaluation results corresponding to each performance indicator to the [0,1] interval, ensuring the comparability of each performance evaluation result in subsequent weighted calculations. The normalized performance evaluation results are denoted as follows: , and .

[0075] To incorporate the value preferences of different stakeholders, a linear weighted scoring function of the following form is constructed: ; in, , and The preference weight parameters representing user-side, market-side, and policy conversion outcomes, respectively, satisfy the following constraints: ; We need to obtain multiple weight combinations for various performance metrics. These weight combinations are vectors with the same dimension as the number of performance metrics. Each element in the vector represents the importance of the corresponding performance metric in the overall evaluation, and the sum of all elements is usually normalized to 1. For example, if there are three performance metrics, one set of weight combinations ( , , The range could be [0.6, 0.2, 0.2], which represents a decision preference that leans towards the first indicator.

[0076] To comprehensively evaluate the robustness of candidate allocation strategies, multiple weight combinations need to be generated. These combinations should be representative and able to cover various possible decision preference scenarios, such as user-first, market profit-first, policy-first, and various equilibrium scenarios. By adjusting the weight combinations, the solution with the highest weighted score can be selected as the recommended strategy for different decision orientations (such as user-first, market profit-first, or policy transformation-first).

[0077] Based on the obtained weight combinations, the performance evaluation results of each candidate allocation strategy in the Pareto optimal solution set are weighted and calculated to obtain multiple strategy scores for each candidate allocation strategy.

[0078] To further enhance the robustness of strategy selection and overcome the subjective dependence of traditional weighted methods on weight setting, a weighted scoring standard deviation index is introduced. This index selects the optimal solution with strong stability and high preference compatibility from the solution set with similar scores, thereby enhancing the adaptability and operability of multi-objective optimization results in practical applications.

[0079] Specifically, for a candidate allocation strategy in the Pareto optimal solution set, its performance evaluation result is a multi-dimensional vector, such as [index value A, index value B, index value C]. Using the first set of weights […] , , We then perform a weighted summation to obtain the first policy score S1 = ×Indicator value A+ ×Indicator value B+ ×Indicator value C.

[0080] For example, a prefecture-level city is selected as a typical region for case analysis. The standardized hourly photovoltaic output curves and residential load curves for typical summer (August) and winter (December) seasons in this region are used as the basic input data for the subsequent electricity allocation strategy model, with a time resolution of 1 hour. The market-based feed-in tariff follows a time-of-use pricing mechanism; the equivalent price of green electricity certificates is set at 0.32 yuan / kWh; the retail price of electricity purchased by users from electricity retailers is 0.65 yuan / kWh; the user-side self-consumption price is 0.35 yuan / kWh; and the transmission and distribution price at the distribution network level is 0.2145 yuan / kWh. Distributed photovoltaic power adopts a self-consumption, surplus power-to-grid operation mode; the local self-consumption ratio of photovoltaic power generation is 60%; the system absorption rate is assumed to be 85%; and the unit investment and operation and maintenance costs of the system are 3800 yuan / kWh and 125 yuan / kWh, respectively.

[0081] The mechanism-based electricity volume is set with reference to the provincial distributed photovoltaic surplus power grid connection capacity, non-hydro renewable energy consumption indicators, and the affordability of electricity users in a certain prefecture-level city; the upper limit of the mechanism-based electricity price is set with reference to the provincial desulfurized coal-fired power price. Based on the distributed photovoltaic installed capacity, output, market participation, and other factors, the range of the mechanism-based electricity price is determined to be 0.25 yuan / kWh to 0.4153 yuan / kWh, and the range of the mechanism-based proportion is 10% to 100%.

[0082] For example, Figure 2 As shown, in mechanism priority (i.e. Figure 2 In the case of user benefit preference, , , The values ​​are set to 0.7, 0.15, and 0.15 respectively. The mechanism electricity price is set to 0.25 and the mechanism ratio is set to 10%. The three indicators are then calculated and stored as the first row of the indicator result space. The mechanism electricity price is then set to 0.25 and the mechanism ratio is set to 15%. The three indicator values ​​are then calculated and stored as the second row of the indicator result space. This process is repeated until the mechanism electricity price is set to 0.41 and the mechanism ratio is set to 100%. Then the next candidate allocation strategy is traversed, and finally the indicator result space of 4 strategies × mechanism electricity price × mechanism ratio is formed.

[0083] Then, under the first weight combination, the three indicator values ​​in the indicator result space are weighted and summed to obtain the strategy score, which is stored as: [Strategy Name, Mechanism Electricity Price, Mechanism Ratio, Indicator A, Indicator B, Indicator C, Weight 1, Strategy Score].

[0084] Similarly, in market-first (i.e.) Figure 2 (i.e., grid profit preference type), green certificate priority (i.e.) Figure 2 In the context of guaranteed power preference type), dynamic adaptive (i.e.) Figure 2 The strategy scores are calculated sequentially under the weight combinations corresponding to the equilibrium preference type (in the text), and finally summarized as follows: Figure 2The space of preference weight scores is shown as 4 preferences × 4 strategies × mechanism electricity price × mechanism ratio. Based on the multiple strategy scores corresponding to each candidate allocation strategy, the score volatility index of that candidate allocation strategy is calculated. The purpose of this step is to quantify the dispersion of these strategy scores. For example, statistical indicators such as variance, standard deviation, or range (the difference between the maximum and minimum values) can be calculated for these score values. This calculated statistical indicator is the score volatility index of the candidate allocation strategy. For example, sorting the score results column in weight combination 1 by standard deviation, and selecting the solution with the lowest standard deviation, measures the score volatility of the candidate solution under different weight settings. This result can be interpreted as follows: if prioritizing the protection of user profits is desired, the mechanism electricity price and mechanism ratio shown in the results should be set, and the electricity allocation strategy shown in the results should be selected. For example, from the Pareto optimal solutions of each type of electricity allocation strategy (mechanism priority, market priority, etc.), the five solutions with the lowest standard deviation can be selected as the final recommended scheme.

[0085] This embodiment provides a scheme for repeated scoring based on weight combination and quantifying the dispersion of scoring results. This method can simulate the performance of candidate allocation strategies under different decision preferences and characterize their stability with an objective numerical value, thereby providing a reliable basis for determining the candidate allocation strategy with optimal robustness.

[0086] In an exemplary embodiment of the present invention, determining the target allocation strategy from the Pareto optimal solution set based on the score fluctuation index includes: Based on the aforementioned score fluctuation index, target allocation strategies are determined from the Pareto optimal solution set, with the objectives of prioritizing mechanisms, prioritizing markets, prioritizing green certificates, and dynamically adaptive allocation, respectively.

[0087] In this embodiment of the invention, in certain application scenarios, decision-makers may not only want to know which power allocation strategy is the best, but also what the best choice is under different strategic orientations. Therefore, in this embodiment, when selecting target allocation strategies from the Pareto optimal solution set, the candidate allocation strategies in the Pareto optimal solution set can first be grouped according to the power allocation strategy model to which they belong. For example, all Pareto optimal solutions generated by power allocation strategy models with mechanism priority as the objective are grouped into one group, all Pareto optimal solutions generated by power allocation strategy models with market priority as the objective are grouped into another group, and so on, forming four Pareto optimal strategy subsets that correspond one-to-one with the power allocation strategy models.

[0088] Then, within each subset, a screening scheme based on the rating volatility index is applied independently. Specifically: Within the Pareto optimal strategy subset prioritizing mechanisms, the candidate allocation strategy with the smallest score volatility is identified and designated as the target allocation strategy prioritizing mechanisms. This strategy represents the most stable performance among all options favoring risk aversion and policy compliance.

[0089] Similarly, within the Pareto optimal strategy subset prioritizing the market, we find the candidate allocation strategy with the smallest score volatility index, which serves as the target allocation strategy for prioritizing the market.

[0090] In the Pareto optimal strategy subset prioritizing green certificates, the candidate allocation strategy with the smallest score fluctuation index is found and used as the target allocation strategy with green certificate priority as the objective.

[0091] In the subset of dynamically adaptive Pareto optimal policies, the candidate allocation policy with the smallest score fluctuation is found and used as the target allocation policy for dynamically adaptive allocation.

[0092] By performing the above steps, the final output is no longer a single objective allocation strategy, but a recommendation list containing multiple objective allocation strategies. For example, the output might be a set of optimal strategies under four different orientations.

[0093] This embodiment provides decision-makers with richer and more profound decision support by classifying and filtering the Pareto optimal solution set. Decision-makers can not only obtain a globally robust strategy, but also clearly see what the best choices are under different strategic emphases. This allows them to make more flexible final decisions that align with actual business needs based on their higher-level strategic intentions, greatly enhancing the practicality and operability of the method.

[0094] In this invention, considering the different needs of users, the market, and policy transformation, four switchable power allocation strategies are constructed to achieve dynamic combination and flexible switching. This significantly improves adaptability to different market rules and policy orientations, overcoming the shortcomings of traditional single-strategy and static configuration, and realizing collaborative scheduling and revenue optimization in multi-market environments. Simultaneously, a multi-objective evaluation system is constructed with user revenue, electricity sales company profits, and policy signal conversion rate as its core. Combined with non-dominated ranking and weighted scoring mechanisms, a scientific Pareto optimal solution selection process is formed. Compared to traditional methods that rely solely on economic indicators or empirical rules for strategy selection, this invention comprehensively quantifies various influencing factors, significantly improving the fairness and stability of strategy recommendations. Moreover, this invention introduces a standard deviation screening mechanism to ensure that the selected strategy has low volatility and high compatibility under multiple preference weights, effectively solving the problem that traditional scoring systems are greatly affected by subjective weight settings, and improving the robustness of the strategy. This mechanism has good adaptability to uncertainties such as policy changes and electricity price fluctuations, and has broad prospects for engineering implementation and widespread application.

[0095] The photovoltaic grid-connected power distribution device provided by the present invention will be described below. The photovoltaic grid-connected power distribution device described below can be referred to in correspondence with the photovoltaic grid-connected power distribution method described above. It should be noted that the device provided in the following embodiments and the method provided in the above embodiments belong to the same concept, and the specific way in which each module and unit performs its operation has been described in detail in the method embodiments, and will not be repeated here.

[0096] In one exemplary embodiment of the present invention, please refer to Figure 3 , Figure 3 This is an exemplary embodiment of a photovoltaic grid-connected power distribution device, which includes the following modules.

[0097] The first calculation module 310 is configured to obtain multiple candidate allocation strategies for photovoltaic grid-connected electricity and calculate the performance evaluation results of each candidate allocation strategy for multiple performance indicators. The filtering module 320 is configured to filter multiple candidate allocation strategies based on the performance evaluation results to obtain a Pareto optimal solution set; The second calculation module 330 is configured to calculate the score fluctuation index of each of the candidate allocation strategies in the Pareto optimal solution set based on the performance evaluation results. The determination module 340 is configured to determine the target allocation strategy from the Pareto optimal solution set based on the score fluctuation index.

[0098] In an exemplary embodiment of the present invention, the second computing module 330 includes: The first acquisition submodule is configured to acquire multiple sets of weight combinations for multiple performance metrics; The first calculation submodule is configured to perform weighted calculations on the performance evaluation results of each candidate allocation strategy in the Pareto optimal solution set based on the weight combinations of each group, so as to obtain multiple strategy scores for each candidate allocation strategy. The second calculation submodule is configured to calculate the score fluctuation index of each candidate allocation strategy based on the multiple strategy scores of each candidate allocation strategy.

[0099] In an exemplary embodiment of the present invention, the screening module 320 includes: The first determining submodule is configured to determine the Pareto optimal solution from multiple candidate allocation strategies based on the performance evaluation results using a non-dominated sorting algorithm. The combination submodule is configured to combine the Pareto optimal solutions into the Pareto optimal solution set.

[0100] In an exemplary embodiment of the present invention, the first computing module 310 includes: The third calculation submodule is configured to calculate the performance evaluation results of each of the candidate allocation strategies for the user's annualized net return index, market value realization index, and policy signal conversion rate index.

[0101] In an exemplary embodiment of the present invention, the first computing module 310 includes: The data acquisition submodule is configured to acquire photovoltaic parameters; wherein, the photovoltaic parameters include photovoltaic output parameters, user load parameters, photovoltaic cost parameters, and electricity price policy parameters. The fourth calculation submodule is configured to calculate the performance evaluation results of each candidate allocation strategy for each performance index based on the photovoltaic parameters.

[0102] In an exemplary embodiment of the present invention, the first computing module 310 includes: The second acquisition submodule is configured to acquire a pre-established multi-objective power allocation strategy model; wherein, the multi-objective power allocation strategy model includes power allocation strategy models with mechanism priority, market priority, green certificate priority and dynamic adaptive allocation as objectives respectively, and the dynamic adaptive allocation is adaptive allocation in mechanism market, spot market and green certificate market. The traversal submodule is configured to traverse different combinations of configuration parameters in each of the power allocation strategy models to obtain multiple candidate allocation strategies.

[0103] In an exemplary embodiment of the present invention, the determining module 340 includes: The second determining submodule is configured to determine, based on the scoring fluctuation index, target allocation strategies from the Pareto optimal solution set, with the objectives of prioritizing mechanisms, prioritizing markets, prioritizing green certificates, and dynamically adaptive allocation, respectively.

[0104] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include a processor 410, a communications interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communications interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a photovoltaic grid-connected power allocation method, which includes: acquiring multiple candidate allocation strategies for photovoltaic grid-connected power, and calculating the performance evaluation results of each candidate allocation strategy for multiple performance indicators; Based on the performance evaluation results, multiple candidate allocation strategies are screened to obtain a Pareto optimal solution set; Based on the performance evaluation results, calculate the score fluctuation index of each candidate allocation strategy in the Pareto optimal solution set; Based on the score fluctuation index, the target allocation strategy is determined from the Pareto optimal solution set.

[0105] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0106] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program, the computer program being able to be stored on a non-transitory computer-readable storage medium, the computer program being executed by a processor, the computer being able to execute the photovoltaic grid-connected power allocation method provided by the above methods, the method including: obtaining a plurality of candidate allocation strategies for photovoltaic grid-connected power, and calculating the performance evaluation results of each of the candidate allocation strategies for a plurality of performance indicators; Based on the performance evaluation results, multiple candidate allocation strategies are screened to obtain a Pareto optimal solution set; Based on the performance evaluation results, calculate the score fluctuation index of each candidate allocation strategy in the Pareto optimal solution set; Based on the score fluctuation index, the target allocation strategy is determined from the Pareto optimal solution set.

[0107] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the photovoltaic grid-connected power allocation method provided by the above methods, the method comprising: obtaining a plurality of candidate allocation strategies for photovoltaic grid-connected power, and calculating the performance evaluation results of each of the candidate allocation strategies for a plurality of performance indicators; Based on the performance evaluation results, multiple candidate allocation strategies are screened to obtain a Pareto optimal solution set; Based on the performance evaluation results, calculate the score fluctuation index of each candidate allocation strategy in the Pareto optimal solution set; Based on the score fluctuation index, the target allocation strategy is determined from the Pareto optimal solution set.

[0108] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0109] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for allocating photovoltaic power to the grid, characterized in that, include: Multiple candidate allocation strategies for photovoltaic grid-connected electricity are obtained, and the performance evaluation results of each candidate allocation strategy for multiple performance indicators are calculated. Based on the performance evaluation results, multiple candidate allocation strategies are screened to obtain a Pareto optimal solution set; Based on the performance evaluation results, calculate the score fluctuation index of each candidate allocation strategy in the Pareto optimal solution set; Based on the score fluctuation index, the target allocation strategy is determined from the Pareto optimal solution set.

2. The photovoltaic grid-connected power allocation method according to claim 1, characterized in that, The calculation of the score fluctuation index for each candidate allocation strategy in the Pareto optimal solution set based on the performance evaluation results includes: Obtain multiple sets of weight combinations for multiple performance metrics; Based on the weight combinations of each group, the performance evaluation results of each candidate allocation strategy in the Pareto optimal solution set are weighted and calculated to obtain multiple strategy scores for each candidate allocation strategy. Based on the multiple strategy scores of each candidate allocation strategy, the score fluctuation index of each candidate allocation strategy is calculated.

3. The photovoltaic grid-connected power allocation method according to claim 1, characterized in that, The step of filtering multiple candidate allocation strategies based on the performance evaluation results to obtain a Pareto optimal solution set includes: Based on the performance evaluation results, a Pareto optimal solution is determined from multiple candidate allocation strategies using a non-dominated sorting algorithm; The Pareto optimal solutions are combined into the Pareto optimal solution set.

4. The photovoltaic grid-connected power allocation method according to claim 1, characterized in that, The calculation of the performance evaluation results of each of the candidate allocation strategies for multiple performance metrics includes: Calculate the performance evaluation results of each candidate allocation strategy for the user annualized net return index, market value realization index, and policy signal conversion rate index.

5. The photovoltaic grid-connected power allocation method according to claim 1, characterized in that, The calculation of the performance evaluation results of each of the candidate allocation strategies for multiple performance metrics includes: Collect photovoltaic parameters; wherein, the photovoltaic parameters include photovoltaic output parameters, user load parameters, photovoltaic cost parameters, and electricity price policy parameters; Based on the photovoltaic parameters, the performance evaluation results of each candidate allocation strategy for each performance index are calculated.

6. The photovoltaic grid-connected power allocation method according to any one of claims 1 to 5, characterized in that, The process of obtaining multiple candidate allocation strategies for photovoltaic grid-connected electricity includes: Obtain a pre-established multi-objective power allocation strategy model; wherein, the multi-objective power allocation strategy model includes power allocation strategy models with mechanism priority, market priority, green certificate priority and dynamic adaptive allocation as objectives respectively, and the dynamic adaptive allocation is adaptive allocation in mechanism market, spot market and green certificate market. By iterating through different combinations of configuration parameters in each of the power allocation strategy models, multiple candidate allocation strategies are obtained.

7. The photovoltaic grid-connected power allocation method according to claim 6, characterized in that, The step of determining the target allocation strategy from the Pareto optimal solution set based on the score fluctuation index includes: Based on the aforementioned score fluctuation index, target allocation strategies are determined from the Pareto optimal solution set, with the objectives of prioritizing mechanisms, prioritizing markets, prioritizing green certificates, and dynamically adaptive allocation, respectively.

8. A photovoltaic grid-connected power distribution device, characterized in that, include: The first calculation module is configured to obtain multiple candidate allocation strategies for photovoltaic grid-connected electricity and calculate the performance evaluation results of each candidate allocation strategy for multiple performance indicators. The filtering module is configured to filter multiple candidate allocation strategies based on the performance evaluation results to obtain a Pareto optimal solution set; The second calculation module is configured to calculate the score fluctuation index of each candidate allocation strategy in the Pareto optimal solution set based on the performance evaluation results. The determination module is configured to determine the target allocation strategy from the Pareto optimal solution set based on the score fluctuation index.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the photovoltaic grid-connected power distribution method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the photovoltaic grid-connected power distribution method as described in any one of claims 1 to 7.

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