Optical storage and charging collaborative management evaluation method and device based on analytic hierarchy process

Abstract

The invention provides a collaborative management evaluation method and device for optical storage and charging based on an analytic hierarchy process, and relates to the field of power system optimization and management, and the method comprises the steps: constructing a judgment matrix corresponding to each level of index, and calculating a weight vector of each judgment matrix through a geometric averaging method, based on the membership degree value of each lowest-level index for each evaluation grade, obtaining a membership degree matrix of each lowest-level index; and based on the fuzzy evaluation matrix corresponding to each lowest-level index, sequentially and upwards calculating the fuzzy evaluation matrix corresponding to each index in each level according to a level sequence, and finally obtaining a final fuzzy evaluation matrix of the collaborative management evaluation system for optical storage and charging. According to the optical storage and charging collaborative management evaluation method and device based on the analytic hierarchy process provided by the invention, through the optical storage and charging collaborative management evaluation system based on the analytic hierarchy process, optimal scheduling and real-time monitoring of various resources in the system can be realized, the operation efficiency of the system is improved, and the operation cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system optimization and management, in particular to a photovoltaic storage and charging collaborative management evaluation method and device based on analytic hierarchy process. BACKGROUND

[0002] With the increasing global energy demand and the increasingly stringent environmental protection requirements, traditional fossil energy is gradually replaced by clean energy. Clean energy technologies such as photovoltaic power generation, energy storage systems and electric vehicles have become an important part of the power system, and more and more regional power grid systems begin to introduce distributed energy systems such as photovoltaic, energy storage and charging facilities to realize efficient use of clean energy.

[0003] However, due to the uncertainty and volatility of photovoltaic power generation, the complexity of the operation efficiency and load characteristics of energy storage systems and electric vehicle charging facilities, how to collaboratively manage and regulate the photovoltaic storage and charging system (photovoltaic, energy storage, electric vehicle charging and user load) becomes a major challenge in actual operation. The power system regulation method in the related art mainly targets a single type of energy facility, such as regulating photovoltaic power generation or energy storage systems alone, but ignores their synergistic effect. With the increasing proportion of distributed photovoltaic access, the complexity of power grid regulation is further aggravated, and the traditional dispatching mode is difficult to cope with power fluctuations and load changes, and cannot realize the overall optimization of the system.

[0004] Therefore, there is an urgent need for an evaluation method that can comprehensively evaluate the overall performance of the photovoltaic storage and charging system. SUMMARY

[0005] The purpose of the present application is to provide a photovoltaic storage and charging collaborative management evaluation method and device based on analytic hierarchy process, which can realize the optimal scheduling and real-time monitoring of various resources in the system, improve the operation efficiency of the system, reduce the operation cost, and minimize the impact on the environment, and ensure the reliable operation and sustainable development of the photovoltaic storage and charging system in the power grid.

[0006] The present application provides a photovoltaic storage and charging collaborative management evaluation method based on analytic hierarchy process, comprising: Based on the relative importance between the indexes in the pre-constructed photovoltaic storage and charging collaborative management evaluation system, a judgment matrix corresponding to indexes at each level is constructed to obtain a plurality of judgment matrices; the judgment matrix is used to represent the importance between any two indexes under the same parent index; the weight vector of each judgment matrix in the plurality of judgment matrices is calculated by using a geometric mean method, and membership is calculated based on the score of each lowest level index to obtain the membership value of each lowest level index for each evaluation level, and the membership matrix of each lowest level index is obtained based on the membership value of each lowest level index for each evaluation level; the fuzzy evaluation matrix corresponding to each index in each level is calculated in turn upwards based on the fuzzy evaluation matrix corresponding to each lowest level index according to the hierarchical order, and finally the final fuzzy evaluation matrix of the photovoltaic storage and charging collaborative management evaluation system is obtained; wherein the fuzzy evaluation matrix corresponding to each lowest level index is obtained by matrix multiplication based on the membership matrix of each lowest level index and the weight vector corresponding to the parent index to which each lowest level index belongs; the weight vector corresponding to the parent index to which each lowest level index belongs is the weight vector of the judgment matrix corresponding to the parent index; the final fuzzy evaluation matrix is used to represent the final evaluation result of the photovoltaic storage and charging collaborative management evaluation system.

[0007] Optionally, the photovoltaic storage and charging collaborative management evaluation system comprises a plurality of top-level indexes and a plurality of secondary indexes contained in each top-level index; the plurality of top-level indexes comprises an economic index, a regulation index, a reliability index and an environmental protection index.

[0008] Optionally, based on the relative importance between the indexes in the pre-constructed photovoltaic storage and charging collaborative management evaluation system, a judgment matrix corresponding to indexes at each level is constructed to obtain a plurality of judgment matrices, comprising: obtaining a plurality of to-be-judged indexes belonging to the same parent index in any target level, and the importance score between any two indexes in the plurality of to-be-judged indexes; generating the judgment matrix corresponding to the parent index to which the plurality of to-be-judged indexes belong according to the importance score between any two indexes in the plurality of to-be-judged indexes; wherein the target level is any level in the plurality of evaluation levels contained in the photovoltaic storage and charging collaborative management evaluation system; in the case that the target level is the top level, all indexes contained in the target level belong to the same parent index.

[0009] Optionally, the weight vector of each judgment matrix in the plurality of judgment matrices is calculated by using a geometric mean method, comprising: calculating the geometric mean value of each row of the target judgment matrix, and normalizing the geometric mean value of each row to obtain the weight corresponding to each row; based on the weight corresponding to each row, the weight vector of the target judgment matrix is obtained; wherein the target judgment matrix is any one of the plurality of judgment matrices.

[0010] Optionally, before the step of calculating the fuzzy evaluation matrix corresponding to each lowermost level index according to the hierarchical order and sequentially upwards, the method further comprises: calculating the maximum eigenvalue of the target judgment matrix based on the target judgment matrix and the weight vector of the target judgment matrix, and calculating a consistency index based on the maximum eigenvalue; calculating a consistency ratio based on the ratio of the consistency index and a random consistency index, and determining that the target judgment matrix is reasonable in the case that the consistency ratio is less than a preset ratio threshold.

[0011] Optionally, the step of calculating the fuzzy evaluation matrix corresponding to each lowermost level index according to the hierarchical order and sequentially upwards, and finally obtaining the final fuzzy evaluation matrix of the photovoltaic storage and charging collaborative management evaluation system comprises: multiplying the membership matrix of each lower level index belonging to the same target parent index with the weight vector corresponding to the target parent index to obtain a fuzzy evaluation vector of each lower level index; and generating the fuzzy evaluation matrix of the target parent index based on the fuzzy evaluation vector of each lower level index.

[0012] The application also provides a photovoltaic storage and charging collaborative management evaluation device based on the analytic hierarchy process, comprising: The construction module is configured to construct the judgment matrix corresponding to the indexes at each level based on the relative importance between the indexes in the photovoltaic storage and charging collaborative management evaluation system constructed in advance, and obtain a plurality of judgment matrices; the judgment matrix is used to represent the importance between any two indexes under the same parent index; the calculation module is configured to calculate the weight vector of each judgment matrix in the plurality of judgment matrices by using the geometric mean method, and calculate the membership value of each evaluation grade for each lowermost level index based on the score of each lowermost level index, and obtain the membership matrix of each lowermost level index based on the membership value of each evaluation grade for each lowermost level index; the evaluation module is configured to calculate the fuzzy evaluation matrix corresponding to each index in each level according to the hierarchical order and sequentially upwards based on the fuzzy evaluation matrix corresponding to each lowermost level index, and finally obtain the final fuzzy evaluation matrix of the photovoltaic storage and charging collaborative management evaluation system; wherein the fuzzy evaluation matrix corresponding to each lowermost level index is obtained by multiplying the membership matrix of each lowermost level index with the weight vector corresponding to the parent index to which each lowermost level index belongs; the weight vector corresponding to the parent index to which each lowermost level index belongs is the weight vector of the judgment matrix corresponding to the parent index; and the final fuzzy evaluation matrix is used to represent the final evaluation result of the photovoltaic storage and charging collaborative management evaluation system.

[0013] Optionally, the light storage and charging use cooperative management evaluation system comprises a plurality of top-level indexes and a plurality of sub-level indexes contained in each top-level index; and the plurality of top-level indexes comprise an economy index, a regulation index, a reliability index and an environmental protection index.

[0014] Optionally, the device further comprises an acquisition module; the acquisition module is configured to acquire a plurality of to-be-judged indexes belonging to a same parent index in any target level and an importance score between any two indexes in the plurality of to-be-judged indexes; and the construction module is specifically configured to generate a judgment matrix corresponding to the parent index to which the plurality of to-be-judged indexes belong according to the importance score between any two indexes in the plurality of to-be-judged indexes; wherein the target level is any level in a plurality of evaluation levels contained in the light storage and charging use cooperative management evaluation system; and in a case where the target level is a top level, all indexes contained in the target level belong to the same parent index.

[0015] Optionally, the calculation module is specifically configured to calculate a geometric mean value of each row of a target judgment matrix, and perform normalization processing on the geometric mean value of each row to obtain a weight corresponding to each row; and the calculation module is specifically further configured to obtain a weight vector of the target judgment matrix based on the weight corresponding to each row; wherein the target judgment matrix is any one of the plurality of judgment matrices.

[0016] Optionally, the device further comprises a verification module; the verification module is configured to calculate a maximum eigenvalue of the target judgment matrix based on the target judgment matrix and the weight vector of the target judgment matrix, and calculate a consistency index based on the maximum eigenvalue; and the verification module is further configured to calculate a consistency ratio based on a ratio of the consistency index to a random consistency index, and determine that the target judgment matrix is reasonable in a case where the consistency ratio is less than a preset ratio threshold.

[0017] Optionally, the evaluation module is specifically configured to perform matrix multiplication on a membership degree matrix of each sub-level index belonging to a same target parent index and a weight vector corresponding to the target parent index to obtain a fuzzy evaluation vector of each sub-level index; and the evaluation module is specifically further configured to generate a fuzzy evaluation matrix of the target parent index based on the fuzzy evaluation vector of each sub-level index.

[0018] The application further provides a computer program product comprising computer programs / instructions, which, when executed by a processor, implement the steps of the light storage and charging use cooperative management evaluation method based on the analytic hierarchy process according to any one of the above.

[0019] The application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the analytic hierarchy process-based photovoltaic power storage and charging collaborative management evaluation method according to any one of the above when executing the program.

[0020] The application further provides a computer readable storage medium, which stores a computer program, wherein the computer program is executable on a processor to implement the steps of the analytic hierarchy process-based photovoltaic power storage and charging collaborative management evaluation method according to any one of the above.

[0021] The analytic hierarchy process-based photovoltaic power storage and charging collaborative management evaluation method and device provided by the application first construct a judgment matrix corresponding to each level of indexes based on the relative importance between indexes in the pre-constructed photovoltaic power storage and charging collaborative management evaluation system, to obtain a plurality of judgment matrices; the judgment matrix is used to represent the importance between any two indexes under the same parent index; then, the weight vector of each judgment matrix in the plurality of judgment matrices is calculated by using the geometric mean method, and the membership degree is calculated based on the score of each lowest level index, to obtain the membership value of each lowest level index for each evaluation grade, and the membership matrix of each lowest level index is obtained based on the membership value of each lowest level index for each evaluation grade; finally, the fuzzy evaluation matrix corresponding to each index in each level is calculated in turn upwards based on the fuzzy evaluation matrix corresponding to each lowest level index, to finally obtain the final fuzzy evaluation matrix of the photovoltaic power storage and charging collaborative management evaluation system; wherein the fuzzy evaluation matrix corresponding to each lowest level index is obtained by matrix multiplication based on the membership matrix of each lowest level index and the weight vector corresponding to the parent index to which each lowest level index belongs; the weight vector corresponding to the parent index to which each lowest level index belongs is the weight vector of the judgment matrix corresponding to the parent index; and the final fuzzy evaluation matrix is used to represent the final evaluation result of the photovoltaic power storage and charging collaborative management evaluation system. In this way, the photovoltaic power storage and charging collaborative management evaluation system based on the analytic hierarchy process can realize the optimal scheduling and real-time monitoring of various resources in the system, improve the operation efficiency of the system, reduce the operation cost, and minimize the impact on the environment, to ensure the reliable operation and sustainable development of the photovoltaic power storage and charging system in the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the application or the prior art, the following will briefly introduce the drawings needed in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative effort.

[0023] Figure 1is a flowchart of a photovoltaic storage and charging collaborative management evaluation method based on the analytic hierarchy process provided in the present application; Figure 2 is an evaluation index diagram of each level in the photovoltaic storage and charging collaborative management evaluation system provided in the present application; Figure 3 is a structural diagram of a photovoltaic storage and charging collaborative management evaluation device based on the analytic hierarchy process provided in the present application; Figure 4 is a structural diagram of an electronic device provided in the present application. DETAILED DESCRIPTION

[0024] In order to make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0025] The terms "first", "second", and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally a category and do not limit the number of objects, for example, the first object can be one or more. In addition, "and / or" in the specification and claims indicates at least one of the connected objects, and the character " / ", generally indicates that the front and rear associated objects are in an "or" relationship.

[0026] The power system regulation method in the related art mainly targets a single type of energy facility, for example, separately regulating photovoltaic power generation or energy storage systems, but ignores the synergy between them. With the increasing proportion of distributed photovoltaic access, the complexity of power grid regulation is further intensified, and the traditional dispatching mode is difficult to cope with power fluctuations and load changes, and cannot achieve overall optimization of the system.

[0027] To address the aforementioned technical problems in related technologies, this application provides a photovoltaic-storage-charging-utilization collaborative management evaluation system and corresponding evaluation method based on the Analytic Hierarchy Process (AHP), which can comprehensively evaluate the overall performance of photovoltaic-storage-charging-utilization systems. It evaluates and optimizes systems involving photovoltaics, energy storage, electric vehicles, and user loads through four dimensions: economy, controllability, reliability, and environmental friendliness. The evaluation methods in related technologies are difficult to comprehensively evaluate photovoltaic-storage-charging-utilization systems due to unclear indicators or failure to consider multi-dimensional collaborative management. To scientifically evaluate the performance of photovoltaic-storage-charging-utilization systems, the AHP-based collaborative management evaluation method provided in this application combines the Analytic Hierarchy Process (AHP) with fuzzy comprehensive evaluation to perform weighted analysis and comprehensive evaluation of multiple factors in the system. This method can effectively handle the characteristics of photovoltaic power generation, energy storage systems, and electric vehicle charging facilities, constructing a scientific multi-dimensional evaluation system that ensures comprehensive management and optimal control of photovoltaic-storage-charging-utilization systems in terms of economy, controllability, reliability, and environmental friendliness. By using a analytic hierarchy process-based collaborative management and evaluation system for photovoltaic, energy storage, charging, and utilization, we can achieve optimized scheduling and real-time monitoring of various resources within the system, improve system operating efficiency, reduce operating costs, minimize environmental impact, and ensure the reliable operation and sustainable development of the photovoltaic, energy storage, charging, and utilization system in the power grid.

[0028] The following detailed description, in conjunction with the accompanying drawings, of the collaborative management and evaluation method for photovoltaic storage, charging, and utilization based on the analytic hierarchy process (AHP) provided in this application, through specific embodiments and application scenarios, will illustrate this method in detail.

[0029] like Figure 1 As shown in the embodiment of this application, a collaborative management and evaluation method for photovoltaic energy storage, charging, and utilization based on the analytic hierarchy process (AHP) is provided. This method may include the following steps 101 to 103: Step 101: Based on the relative importance of indicators in the pre-constructed collaborative management evaluation system for photovoltaic storage, charging and utilization, construct the judgment matrix corresponding to each level of indicators to obtain multiple judgment matrices.

[0030] The judgment matrix is ​​used to characterize the importance between any two indicators under the same parent indicator. The photovoltaic-storage-charging-utilization collaborative management evaluation system includes: multiple top-level indicators, and multiple secondary indicators contained in each top-level indicator; the multiple top-level indicators include: economic indicators, controllability indicators, reliability indicators, and environmental protection indicators.

[0031] For example, such as Figure 2As shown, the light storage and charging collaborative management evaluation system provided in the embodiment of the application is established from four dimensions of economic indicators, regulation indicators, reliability indicators and environmental protection indicators, 14 secondary indicators and 33 tertiary indicators are established, the particularity of photovoltaic power generation, energy storage system and electric vehicle charging facilities is comprehensively considered, weight analysis and calculation of each indicator are performed by using the analytic hierarchy process, and the final result of scoring calculation is given by combining the fuzzy comprehensive evaluation algorithm.

[0032] It should be noted that the term "charging and use" in the light storage and charging collaborative management evaluation system refers to charging piles, charging stations and users, that is, "charging" refers to charging piles or charging stations, and "use" refers to users.

[0033] Exemplarily, in the embodiment of the application, a suitable regulation model is selected, the specific values of each indicator are calculated, the weights of each indicator are determined by using the analytic hierarchy process, each indicator is scored comprehensively by combining the fuzzy comprehensive evaluation method, and the system evaluation result is calculated based on the scoring result.

[0034] Exemplarily, for the economic indicators, the indicator system includes 3 secondary indicators: energy storage cost, photovoltaic power generation loss amount proportion and electric vehicle cost, which are further subdivided into 6 tertiary indicators, and the specific calculation formula related to the indicators is explicitly given. For the regulation indicators, the indicator system includes 3 secondary indicators: photovoltaic grid-connected power fluctuation suppression effect, energy storage regulation capacity and electric vehicle regulation capacity, which are further subdivided into 14 tertiary indicators, and the specific calculation formula related to the indicators is explicitly given. For the reliability indicators, the indicator system includes 3 secondary indicators: system reliability, energy storage reliability and electric vehicle reliability. Each secondary indicator is further subdivided into 9 tertiary indicators, and the specific calculation formula related to the indicators is explicitly given. For the environmental protection indicators, the indicators include the following 3 secondary indicators: renewable energy contribution degree, pollutant emission reduction benefit and energy storage unit land area, and the specific calculation formula related to the indicators is explicitly given.

[0035] Exemplarily, the economic indicators are used to evaluate the economy of photovoltaic, energy storage and electric vehicle charging facilities in collaborative management. The indicator system includes three secondary indicators: energy storage cost, photovoltaic power generation loss amount proportion and electric vehicle cost, which are further subdivided into multiple tertiary indicators, for detailed analysis of system economy, energy storage economy and electric vehicle economy of the regulation system.

[0036] Exemplarily, the energy storage cost is composed of multiple tertiary indicators, including maximum demand capacity of energy storage battery, cycle charging and discharging times of battery, power loss rate of charging and discharging and proportion of station power consumption. The following is the specific evaluation and calculation method: maximum demand capacity of energy storage battery The indicator is used to evaluate the maximum battery capacity required by the system at peak usage. The calculation formula is as follows: Equation 1 In the formula, Pmax represents the maximum power of the system (unit: kW), and t represents the usage time (unit: min). The battery cycle number of charge and discharge This index is used to evaluate the service life of the battery, indicating the total number of times the battery can be charged and discharged. The charge and discharge power loss rate (unit: %), which is used to measure the efficiency of the battery system. The power loss rate is evaluated by calculating the difference between the input power and the output power, and the calculation formula is as follows: Equation 2 In the formula, P represents the input power (unit: kW), which is the power provided to the battery during charging. P represents the output power (unit: kW), which is the power provided to the load during discharging of the battery. Station power consumption rate (unit: %), which is used to evaluate the proportion of station equipment in the overall power consumption. The calculation formula is as follows: Equation 3 In the formula, P represents the total power consumed by the station equipment (unit: kWh), P represents the total power of the energy storage system (unit: kWh).

[0037] PV power generation loss ratio (unit: %) is used to evaluate the energy loss of the PV power generation system in actual operation. The calculation formula is as follows: Equation 4 In the formula, P represents the PV power generation (unit: kWh), P represents the light rejection rate (unit: %) Exemplarily, the cost of electric vehicles includes two parts: electric vehicle user compensation cost and charging station profit change. Electric vehicle user compensation cost (unit: yuan) is used to evaluate the compensation fee of electric vehicle users. The calculation formula is as follows: Equation 5 In the formula, P represents the number of electric vehicles, P represents the charging power of the electric vehicle (unit: kW), P represents the charging time (unit: min), and M represents the unit compensation cost (unit: yuan). Charging station profit change (unit: yuan) is used to evaluate the profit change of the charging station when providing charging services. The calculation formula is as follows: Equation 6 wherein, is the charging service revenue (unit: yuan), is the operating cost of the charging station (unit: yuan).

[0038] Exemplarily, the regulatory index part is used to evaluate the regulation capability of photovoltaic, energy storage and electric vehicle charging facilities in collaborative management. The index system includes three secondary indexes: photovoltaic grid-connected power fluctuation suppression effect, energy storage regulation capability and electric vehicle regulation capability, which are further subdivided into multiple tertiary indexes for detailed analysis of the responsiveness, accuracy and load adaptability of the regulation system. In this embodiment, the calculation formulas of the energy storage regulation capability and the electric vehicle regulation capability indexes are considered together.

[0039] Exemplarily, the photovoltaic grid-connected power fluctuation suppression effect (unit: %) is a measure of the degree of improvement in the absorption rate of photovoltaic power generation by the system after suppressing power fluctuations during grid connection. The calculation formula is as follows: Equation 7 wherein, is the photovoltaic consumption rate after fluctuation suppression (unit: %), is the photovoltaic consumption rate before fluctuation suppression (unit: %).

[0040] Exemplarily, the energy storage and electric vehicle regulation capability index is divided into seven tertiary indexes: regulation speed, regulation accuracy, response time, frequency regulation mileage, peak regulation amplitude, actual chargeable and dischargeable power and actual chargeable and dischargeable capacity. The regulation speed represents the power change rate of the energy storage or electric vehicle system in response to the regulation demand. The calculation formula is as follows: Equation 8 wherein, is the power change (unit: kW), is the time used for regulation power (unit: s). The regulation accuracy measures the accuracy of the energy storage or electric vehicle system in reaching the target power during regulation. The calculation formula is as follows: Equation 9 wherein, is the target power (unit: kW), is the actual output power (unit: kW). The response time (unit: s) represents the time delay of the energy storage or electric vehicle system from receiving the regulation signal to actually starting the response. The calculation formula is as follows: Equation 10 wherein, is the time to start the regulation (unit: s), is the time when the fluctuation is detected or the regulation instruction is received (unit: s). Frequency regulation mileage (unit: kW) represents the total amount of power covered by the energy storage or electric vehicle system during the regulation frequency process. The calculation formula is as follows: (Equation Eleven) wherein, is the power change amount of each frequency adjustment (unit: kW), and n is the regulation times. Peak shaving amplitude (unit: kW) represents the maximum output power that the energy storage or electric vehicle system can reach during peak shaving. The calculation formula is as follows: (Equation Twelve) wherein, is the output power during the regulation (unit: kW). Actual chargeable and dischargeable power (unit: kW) represents the power range that the energy storage or electric vehicle system can regulate under the current conditions. The calculation formula is as follows: (Equation Thirteen) wherein, is the maximum charge and discharge power (unit: kW), is the minimum charge and discharge power (unit: kW). Actual chargeable and dischargeable amount (unit: kWh) reflects the total regulation power of the energy storage or electric vehicle system in a specific period. The calculation formula is as follows: (Equation Fourteen) wherein, is the charge and discharge power at time t (unit: kW), is the time range of the charge and discharge cycle.

[0041] Exemplarily, the reliability index is used to evaluate the stability and safety of photovoltaic, energy storage system and electric vehicle charging facilities in collaborative management. The index system contains three secondary indexes: system reliability, energy storage reliability and electric vehicle reliability. Each secondary index is further divided into multiple tertiary indexes to analyze the reliability of the system in long-term operation in more detail.

[0042] Exemplarily, the system reliability index mainly includes three tertiary indexes: voltage stability, system mean time between failures and system mean time to repair. Voltage stability is used to evaluate the voltage fluctuation of the system during operation. The calculation formula is as follows: (Equation Fifteen) wherein, is the standard deviation of voltage fluctuation, is the average value of voltage (unit: kV). System mean time between failures represents the average normal working time of the system between failures, and evaluates the failure frequency of the system. The calculation formula is as follows: (Equation 16) wherein, is the total running time (unit: day), is the number of failures. System mean time to recovery represents the average time required for the system to recover from a failure state to a normal state. The calculation formula is as follows: (Equation 17) wherein, is the total recovery time (unit: s).

[0043] Exemplarily, the energy storage reliability mainly includes three third-level indicators of energy storage battery rated available power ratio, energy storage scheduling response success rate and energy storage failure time proportion. Energy storage battery rated available power ratio (unit: %) represents the proportion of actual available power of the energy storage battery to the rated power. The calculation formula is as follows: (Equation 18) wherein, is the actual available power of the energy storage battery (unit: kW), is the rated power of the energy storage battery (unit: kW). Energy storage scheduling response success rate (unit: %) represents the proportion of successful response of the energy storage system after receiving the scheduling instruction. The calculation formula is as follows: (Equation 19) wherein, is the number of successful responses to scheduling, is the total number of received scheduling instructions. Energy storage failure time proportion (unit: %) represents the proportion of time that the energy storage system cannot work normally due to failure during operation. The calculation formula is as follows: (Equation 20) wherein, is the failure time of the energy storage system (unit: min), is the total running time of the energy storage system (unit: min).

[0044] Exemplarily, the reliability of electric vehicles mainly includes three tertiary indicators: electric vehicle dispatch response success rate, maximum number of electric vehicles admitted, and average charging queue time. The electric vehicle dispatch response success rate (unit: %) represents the proportion of electric vehicles that successfully respond after receiving dispatch instructions. The calculation formula is as follows: (Formula Twenty-One) In the formula, is the number of successful responses to dispatch, is the total number of received dispatch instructions. Maximum number of electric vehicles admitted represents the maximum number of electric vehicles or charging demand that a charging facility can admit in a unit of time. The calculation formula is as follows: (Formula Twenty-Two) In the formula, is the total charging power of the charging station (unit: kW), is the available time of the charging station in a unit of time (unit: min), is the charging demand of each electric vehicle (unit: kWh). Average charging queue time (unit: min) represents the average queue waiting time of electric vehicles during charging peak periods. The calculation formula is as follows: (Formula Twenty-Three) In the formula, is the total charging power of the charging station (unit: kW), is the number of electric vehicles queuing in the statistical time.

[0045] Exemplarily, the environmental protection index is used to evaluate the environmental protection benefits of the system in the coordinated management of photovoltaic storage and charging, ensuring the friendliness of the system to the environment during operation. This index includes the following three secondary indicators: renewable energy contribution, pollutant emission reduction benefit, and energy storage unit land area.

[0046] Exemplarily, the renewable energy contribution (unit: %) is used to measure the proportion of renewable energy used by the system during operation. The calculation formula is as follows: (Formula Twenty-Four) In the formula, is the total energy from renewable energy sources in the system (unit: kWh), is the total energy required for system operation (unit: kWh). Pollutant emission reduction benefit is used to evaluate the amount of pollutant emissions reduced by the system during operation through the use of clean energy and the reduction of fossil energy use. The calculation formula is as follows: (Formula Twenty-Five) wherein, is the pollutant emission reduction coefficient of each unit of conventional energy replacement. The land area occupied by the energy storage unit (unit: m2 / kWh) The land area occupied by the energy storage unit is used to evaluate the land resource occupation efficiency of the energy storage facility. The calculation formula is as follows: (Formula Twenty-Six) wherein, is the total land area of the energy storage system (unit: m2), is the total capacity of the energy storage system (unit: kWh).

[0047] Exemplarily, in the step 101, first, a judgment matrix is constructed using an expert method. According to the common AHP scoring standard, the scoring method of 1, 3, 5, 7, 9 (representing equal importance, slightly important, obviously important, very important, and extremely important, respectively) and their reciprocals are used to give expert judgments on the importance of one index relative to another index. The results of the judgments are used to construct the judgment matrix A=(a ij ).

[0048] Specifically, in the step 101, the step of constructing the judgment matrix corresponding to each level of index can further include the following steps 101a1 and 101a2: Step 101a1, obtaining a plurality of to-be-judged indexes belonging to the same parent index in any target level, and an importance score between any two indexes in the plurality of to-be-judged indexes.

[0049] Step 101a2, generating a judgment matrix corresponding to the parent index to which the plurality of to-be-judged indexes belong, according to the importance score between any two indexes in the plurality of to-be-judged indexes.

[0050] Wherein, the target level is any level in the plurality of evaluation levels contained in the photovoltaic storage and charging collaborative management evaluation system; in the case that the target level is the uppermost level, all indexes contained in the target level belong to the same parent index.

[0051] Exemplarily, the four first-level indexes of economic index, regulation index, reliability index, and environmental protection index, the twelve second-level indexes of energy storage cost, photovoltaic power generation loss amount proportion, electric vehicle cost, photovoltaic grid-connected power fluctuation suppression effect energy storage regulation capacity, electric vehicle regulation capacity, and the twenty-nine third-level indexes of energy storage battery maximum capacity, battery cycle charging and discharging times, occupied electricity proportion, and electric vehicle user compensation cost, can be divided into different judgment matrices. For example, Figure 2As shown, this includes A11, A21, A22, A23, A24, A31, A32, A33, A34, A35, A36, and A37. The judgment matrices for each level of indicator are as follows: (Primary indicator) (Secondary Indicators) (Level 3 indicators) For example, taking the above A as an example... 11 and A 21 For example, A 11 Used to characterize the importance between any two of the four primary indicators, A 21 It is used to characterize the importance between any two of the three secondary indicators corresponding to the economic indicator.

[0052] Step 102: Calculate the weight vector of each judgment matrix in the plurality of judgment matrices using the geometric mean method, and calculate the membership degree based on the score of each lowest-level indicator to obtain the membership degree value of each lowest-level indicator for each evaluation level, and obtain the membership degree matrix of each lowest-level indicator based on the membership degree value of each lowest-level indicator for each evaluation level.

[0053] For example, in this embodiment of the application, the geometric mean method is used to calculate the weight vector of each judgment matrix. Specifically, in step 102 above, the step of calculating the weight vector of each judgment matrix among the plurality of judgment matrices using the geometric mean method may further include the following steps 102a1 and 102a2: Step 102a1: Calculate the geometric mean of each row of the target judgment matrix, and normalize the geometric mean of each row to obtain the weight corresponding to each row.

[0054] Step 102a2: Based on the weights corresponding to each row, obtain the weight vector of the target judgment matrix.

[0055] The target judgment matrix is ​​any one of the plurality of judgment matrices.

[0056] For example, for each row of the judgment matrix A i Calculate the geometric mean : (Formula 28) After that, for each Normalization is performed to obtain the weight of each row (Equation Twenty-Nine) wherein, k is the judgment matrix A in the first k row, n is the total number of rows of the judgment matrix A.

[0057] Exemplarily, after obtaining the weight of each row, the weight vector corresponding to the judgment matrix can be calculated, and the fuzzy evaluation matrix of each index can be calculated by using the weight vector and the membership matrix of each index.

[0058] Step 103, based on the fuzzy evaluation matrix corresponding to each lowest-level index, the fuzzy evaluation matrix corresponding to each index in each level is calculated in turn according to the hierarchical order, and finally the final fuzzy evaluation matrix of the photovoltaic storage and charging collaborative management evaluation system is obtained.

[0059] wherein, the fuzzy evaluation matrix corresponding to each lowest-level index is obtained by matrix multiplication based on the membership matrix of each lowest-level index and the weight vector corresponding to the parent index to which each lowest-level index belongs; the weight vector corresponding to the parent index to which each lowest-level index belongs is the weight vector of the judgment matrix corresponding to the parent index; and the final fuzzy evaluation matrix is used to represent the final evaluation result of the photovoltaic storage and charging collaborative management evaluation system.

[0060] Exemplarily, before calculating the fuzzy evaluation matrix of each index, the indexes are classified first, a part of the indexes can be directly calculated to obtain specific scores, and then converted into fuzzy comprehensive evaluation through membership calculation, and a part of the indexes need to be judged by experts in combination with specific parameters. The specific fuzzy comprehensive evaluation of the system can be obtained by comprehensively considering the two parts of indexes, and the specific evaluation result can be obtained by combining the analytic hierarchy process.

[0061] Exemplarily, in the embodiment of the present application, the direct scoring method is adopted for the charge and discharge power loss rate, the station power consumption ratio, the photovoltaic grid-connected power fluctuation suppression effect, the voltage stability, the energy storage dispatching response success rate and the electric vehicle dispatching response success rate. It is assumed that the reference value of each parameter is Zi, and the actual value Si is the score F=100*Si / Zi (the charge and discharge power loss rate and the station power consumption ratio are 100*Zi / Si). Through simulation of the actual situation, the direct calculation result is shown in Table 1 as follows: Table 1 Directly calculated parameter result

[0062] ​Exemplarily, the calculation of membership for the scores directly calculated is a key step in fuzzy comprehensive evaluation, which represents the belonging degree of a certain evaluation object under each evaluation grade. The membership is usually a value between 0 and 1, representing the matching degree of a certain index to a certain evaluation grade.

[0063] Exemplarily, the membership calculation is usually based on a membership function, which maps an actual value (such as a specific value of a certain index) to the membership value of different evaluation grades. For a given index, according to its value and the standard range of the pre-defined evaluation grade, its membership in each grade can be calculated.

[0064] Exemplarily, a triangular membership function is selected in the embodiments of the present application, and its calculation formula is as follows: for a given value, its membership μ(x) in a certain evaluation grade can be calculated by a triangular function. Assuming that the evaluation interval is 60-100, and the evaluation grades are: excellent: 90-100, good: 80-90, general: 70-80, pass: 60-70, and fail: 0-60.

[0065] For the "excellent" grade, the value range corresponding to the grade is [90, 100], and the triangular membership function can be set as follows: (Formula thirty-one) For the "good" grade, the value range corresponding to the grade is [80, 90], and the triangular membership function can be set as follows: (Formula thirty-two) For the "general" grade, the value range corresponding to the grade is [70, 80], and the triangular membership function can be set as follows: (Formula thirty-three) For the "pass" grade, the value range corresponding to the grade is [60, 70], and the triangular membership function can be set as follows: (Formula thirty-four) For the "fail" grade, the value range corresponding to the grade is [0, 60], and the triangular membership function can be set as follows: (Formula thirty-five) After the membership of each parameter is calculated, the results are shown in Table 2 as follows: Table 2 Membership calculation results of the direct scoring part

[0066] Exemplarily, for the remaining indicators, the expert scoring method is adopted, and a total of 20 experts vote for the evaluation grades (excellent, good, general, passing, and failing), and then the number of votes obtained by each evaluation grade is divided by the total number of 20 to obtain the membership degree of each grade, thereby forming a membership degree matrix. Combined with the membership degree calculation result of the direct scoring part, the expert scoring index fuzzy evaluation shown in Table 3 is obtained: Table 3 Fuzzy evaluation of expert scoring index

[0067] Exemplarily, after obtaining the membership degree matrix of each lowest-level indicator, the membership degree matrix of each lower-level indicator belonging to the same target parent indicator can be multiplied by the weight vector corresponding to the target parent indicator to obtain a fuzzy evaluation vector of each lower-level indicator. Then, based on the fuzzy evaluation vector of each lower-level indicator of the same parent, the fuzzy evaluation matrix of the parent is calculated.

[0068] Specifically, the above step 103 can further include the following step 103a1 and step 103a2: Step 103a1, multiply the membership degree matrix of each lower-level indicator belonging to the same target parent indicator by the weight vector corresponding to the target parent indicator to obtain a fuzzy evaluation vector of each lower-level indicator.

[0069] Step 103a2, generate a fuzzy evaluation matrix of the target parent indicator based on the fuzzy evaluation vector of each lower-level indicator.

[0070] Exemplarily, the membership degree matrix of each lower-level indicator R and the weight vector W are multiplied to obtain a fuzzy evaluation vector of each lower-level indicator: B = W * R (Formula Thirty-six) In the formula, B is the fuzzy vector of the evaluation, which represents the fuzzy evaluation of each indicator under different evaluation grades. For example, the fuzzy evaluation matrix of the secondary indicators calculated from the tertiary indicators is shown in Table 4 as follows: Table 4 Fuzzy evaluation of secondary indicators

[0071] The fuzzy evaluation matrix of the primary indicators is further calculated, which is specifically shown in Table 5 as follows: Table 5 Fuzzy evaluation of secondary indicators

[0072] Finally, the fuzzy evaluation matrix of the whole system is calculated by using the fuzzy evaluation matrix of each first-level index, and then the final fuzzy evaluation result is obtained. (Formula Thirty-seven) Exemplarily, the overall fuzzy evaluation matrix B = [0.36, 0.22, 0.18, 0.14, 0.10] is finally calculated, and according to this, it is judged that the system is excellent.

[0073] Optionally, in order to ensure the rationality of the judgment matrix, the judgment matrix can also be subjected to consistency check. That is, before the step 103, the method for evaluating the collaborative management of the photovoltaic storage and charging provided in the embodiment of the application based on the analytic hierarchy process can further include the following steps 104 and 105: Step 104, based on the target judgment matrix and the weight vector of the target judgment matrix, the maximum eigenvalue of the target judgment matrix is calculated, and a consistency index is calculated based on the maximum eigenvalue.

[0074] Step 105, based on the ratio of the consistency index and the random consistency index, a consistency ratio is calculated, and in the case that the consistency ratio is less than a preset ratio threshold, it is determined that the target judgment matrix is reasonable.

[0075] Exemplarily, in order to calculate the consistency index CI and the consistency ratio CR, first, the maximum eigenvalue needs to be calculated : (Formula Thirty) Wherein, represents the first result vector obtained by multiplying the judgment matrix A and the weight vector W . i

[0076] Then, the consistency index CI is calculated: (Formula Thirty-one) Finally, the consistency ratio CR is calculated: (Formula Thirty-two) Wherein, RI is the random consistency index, which depends on the order n of the judgment matrix. If CR < 0.1, the judgment matrix has consistency. The calculation results of the indexes at each level are shown in Table 6 as follows: Table 6 Weight calculation and consistency check results

[0077] ​The evaluation method for the photovoltaic storage and charging collaborative management based on the analytic hierarchy process provided in the embodiments of the present application firstly constructs a judgment matrix corresponding to each level of indexes based on the relative importance between the indexes in the pre-constructed evaluation system for the photovoltaic storage and charging collaborative management, to obtain a plurality of judgment matrices; the judgment matrix is used to represent the importance between any two indexes under the same parent index; then, the weight vector of each judgment matrix in the plurality of judgment matrices is calculated by using the geometric mean method, and the membership is calculated based on the score of each lowest level index, to obtain the membership value of each lowest level index for each evaluation level, and the membership matrix of each lowest level index is obtained based on the membership value of each lowest level index for each evaluation level; finally, the fuzzy evaluation matrix corresponding to each index in each level is calculated in turn upwards based on the fuzzy evaluation matrix corresponding to each lowest level index, to finally obtain the final fuzzy evaluation matrix of the evaluation system for the photovoltaic storage and charging collaborative management; wherein the fuzzy evaluation matrix corresponding to each lowest level index is obtained by matrix multiplication based on the membership matrix of each lowest level index and the weight vector corresponding to the parent index to which each lowest level index belongs; the weight vector corresponding to the parent index to which each lowest level index belongs is the weight vector of the judgment matrix corresponding to the parent index; the final fuzzy evaluation matrix is used to represent the final evaluation result of the evaluation system for the photovoltaic storage and charging collaborative management. In this way, the evaluation system for the photovoltaic storage and charging collaborative management based on the analytic hierarchy process can realize the optimal scheduling and real-time monitoring of various resources in the system, improve the operation efficiency of the system, reduce the operation cost, and maximize the reduction of the impact on the environment, to ensure the reliable operation and sustainable development of the photovoltaic storage and charging system in the power grid.

[0078] It should be noted that the evaluation method for the photovoltaic storage and charging collaborative management based on the analytic hierarchy process provided in the embodiments of the present application can be an evaluation device for the photovoltaic storage and charging collaborative management based on the analytic hierarchy process, or a control module in the evaluation device for the photovoltaic storage and charging collaborative management based on the analytic hierarchy process for executing the evaluation method for the photovoltaic storage and charging collaborative management based on the analytic hierarchy process. In the embodiments of the present application, the evaluation device for the photovoltaic storage and charging collaborative management based on the analytic hierarchy process is taken as an example to illustrate the evaluation device for the photovoltaic storage and charging collaborative management based on the analytic hierarchy process provided in the embodiments of the present application.

[0079] It should be noted that the evaluation method for the photovoltaic storage and charging collaborative management based on the analytic hierarchy process shown in each of the above methods is illustratively described by taking one of the drawings in the embodiments of the present application as an example. In the specific implementation, the evaluation method for the photovoltaic storage and charging collaborative management based on the analytic hierarchy process shown in each of the above methods can also be implemented in combination with any other drawings that can be combined as illustrated in the above embodiments, which will not be described herein again.

[0080] The following describes the photovoltaic-storage-charging-utilization collaborative management and evaluation device based on the analytic hierarchy process (AHP) provided in this application. The description below corresponds to the AHP-based collaborative management and evaluation method for photovoltaic-storage-charging-utilization described above.

[0081] Figure 3 A schematic diagram of the structure of the photovoltaic-storage-charging-utilization collaborative management and evaluation device based on the analytic hierarchy process provided in this application embodiment is shown below. Figure 3 As shown, it specifically includes: The construction module 301 is used to construct judgment matrices corresponding to each level of indicators based on the relative importance of indicators in the pre-constructed photovoltaic-storage-charging-utilization collaborative management evaluation system, resulting in multiple judgment matrices. These judgment matrices characterize the importance between any two indicators under the same parent level indicator. The calculation module 302 is used to calculate the weight vector of each judgment matrix using the geometric mean method, and to calculate the membership degree based on the score of each lowest-level indicator, obtaining the membership degree value of each lowest-level indicator for each evaluation level. Based on the membership degree value of each lowest-level indicator for each evaluation level, the module obtains the membership degree matrix of each lowest-level indicator. The evaluation module 3... 03. Based on the fuzzy evaluation matrix corresponding to each lowest-level indicator, the fuzzy evaluation matrix corresponding to each indicator in each level is calculated sequentially upwards in hierarchical order to finally obtain the final fuzzy evaluation matrix of the photovoltaic-storage-charging-utilization collaborative management evaluation system. The fuzzy evaluation matrix corresponding to each lowest-level indicator is obtained by matrix multiplication of the membership matrix of each lowest-level indicator and the weight vector corresponding to the parent indicator of each lowest-level indicator. The weight vector corresponding to the parent indicator of each lowest-level indicator is the weight vector of the judgment matrix corresponding to the parent indicator. The final fuzzy evaluation matrix is ​​used to characterize the final evaluation result of the photovoltaic-storage-charging-utilization collaborative management evaluation system.

[0082] Optionally, the photovoltaic-storage-charging-utilization collaborative management evaluation system includes: multiple top-level indicators, and multiple secondary indicators contained in each top-level indicator; the multiple top-level indicators include: economic indicators, controllability indicators, reliability indicators, and environmental protection indicators.

[0083] Optionally, the device further includes: an acquisition module; the acquisition module is used to acquire multiple indicators to be judged that belong to the same parent indicator in any target level, and the importance score between any two indicators among the multiple indicators to be judged; the construction module 301 is specifically used to generate a judgment matrix corresponding to the parent indicator to which the multiple indicators to be judged belong based on the importance score between any two indicators among the multiple indicators to be judged; wherein, the target level is any level among the multiple evaluation levels included in the optical storage charging and utilization collaborative management evaluation system; when the target level is the top level, all indicators included in the target level belong to the same parent indicator.

[0084] Optionally, the calculation module 302 is specifically configured to calculate the geometric mean of each row of the target judgment matrix, and normalize the geometric mean of each row to obtain a weight corresponding to each row; the calculation module 302 is also specifically configured to obtain a weight vector of the target judgment matrix based on the weight corresponding to each row; wherein the target judgment matrix is any one of the plurality of judgment matrices.

[0085] Optionally, the device further comprises a verification module; the verification module is configured to calculate a maximum eigenvalue of the target judgment matrix based on the target judgment matrix and the weight vector of the target judgment matrix, and calculate a consistency index based on the maximum eigenvalue; the verification module is also configured to calculate a consistency ratio based on a ratio of the consistency index to a random consistency index, and determine that the target judgment matrix is reasonable in a case where the consistency ratio is less than a preset ratio threshold.

[0086] Optionally, the evaluation module 303 is specifically configured to perform matrix multiplication on the membership matrix of each sub-level indicator belonging to the same target parent indicator and the weight vector corresponding to the target parent indicator to obtain a fuzzy evaluation vector of each sub-level indicator; the evaluation module 303 is also specifically configured to generate a fuzzy evaluation matrix of the target parent indicator based on the fuzzy evaluation vector of each sub-level indicator.

[0087] The application provides an evaluation device for photovoltaic storage and charging collaborative management based on an analytic hierarchy process, which first constructs a judgment matrix corresponding to each index based on the relative importance between indexes in a pre-constructed photovoltaic storage and charging collaborative management evaluation system, and obtains a plurality of judgment matrices; the judgment matrix is used to represent the importance between any two indexes under the same parent index; then, the weight vector of each judgment matrix in the plurality of judgment matrices is calculated by using a geometric mean method, and the membership degree is calculated based on the score of each lowest-level index, so as to obtain the membership value of each lowest-level index for each evaluation grade, and obtain the membership matrix of each lowest-level index based on the membership value of each lowest-level index for each evaluation grade; finally, the fuzzy evaluation matrix corresponding to each index in each level is calculated in turn upwards according to the hierarchical order based on the fuzzy evaluation matrix corresponding to each lowest-level index, and finally the final fuzzy evaluation matrix of the photovoltaic storage and charging collaborative management evaluation system is obtained; wherein the fuzzy evaluation matrix corresponding to each lowest-level index is obtained by matrix multiplication based on the membership matrix of each lowest-level index and the weight vector corresponding to the parent index to which each lowest-level index belongs; the weight vector corresponding to the parent index to which each lowest-level index belongs is the weight vector of the judgment matrix corresponding to the parent index; and the final fuzzy evaluation matrix is used to represent the final evaluation result of the photovoltaic storage and charging collaborative management evaluation system. In this way, the photovoltaic storage and charging collaborative management evaluation system based on the analytic hierarchy process can realize the optimal scheduling and real-time monitoring of various resources in the system, improve the operation efficiency of the system, reduce the operation cost, and maximize the reduction of the impact on the environment, so as to ensure the reliable operation and sustainable development of the photovoltaic storage and charging system in the power grid.

[0088] Figure 4 An example of a schematic diagram of the physical structure of an electronic device is shown in Figure 4As shown, the electronic device can include a processor 410, a communications interface 420, a memory 430, and a communications bus 440, wherein the processor 410, the communications interface 420, and the memory 430 complete mutual communication through the communications bus 440. The processor 410 can invoke a logical instruction in the memory 430 to execute an analytic hierarchy process-based photovoltaic storage and consumption coordination management evaluation method, which includes the following steps. First, based on the relative importance between indexes in a pre-constructed photovoltaic storage and consumption coordination management evaluation system, a judgment matrix corresponding to each level of index is constructed to obtain a plurality of judgment matrices. The judgment matrix is used to represent the importance between any two indexes under the same parent index. Then, the weight vector of each judgment matrix in the plurality of judgment matrices is calculated using a geometric mean method, and the membership degree is calculated based on the score of each lowest level index to obtain the membership value of each lowest level index for each evaluation grade, and the membership matrix of each lowest level index is obtained based on the membership value of each lowest level index for each evaluation grade. Finally, based on the fuzzy evaluation matrix corresponding to each lowest level index, the fuzzy evaluation matrix corresponding to each index in each level is calculated in turn according to the hierarchical order, and finally the final fuzzy evaluation matrix of the photovoltaic storage and consumption coordination management evaluation system is obtained. The fuzzy evaluation matrix corresponding to each lowest level index is obtained by matrix multiplication based on the membership matrix of each lowest level index and the weight vector corresponding to the parent index to which each lowest level index belongs. The weight vector corresponding to the parent index to which each lowest level index belongs is the weight vector of the judgment matrix corresponding to the parent index. The final fuzzy evaluation matrix is used to represent the final evaluation result of the photovoltaic storage and consumption coordination management evaluation system. In this way, through the analytic hierarchy process-based photovoltaic storage and consumption coordination management evaluation system, the optimization scheduling and real-time monitoring of various resources in the system can be realized, the operation efficiency of the system is improved, the operation cost is reduced, the impact on the environment is minimized, and the reliable operation and sustainable development of the photovoltaic storage and consumption system in the power grid are ensured.

[0089] Further, the logic instructions in the memory 430 described above can be implemented in the form of software functional units and sold or used as independent products, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0090] In another aspect, the present application also provides a computer program product, which comprises a computer program stored on a computer readable storage medium, and the computer program comprises program instructions, when the program instructions are executed by a computer, the computer can execute the light storage and use collaborative management evaluation method based on the analytic hierarchy process provided by the above method, which comprises the following steps: first, based on the relative importance between the indexes in the pre-constructed light storage and use collaborative management evaluation system, a judgment matrix corresponding to each level index is constructed, and a plurality of judgment matrices are obtained; the judgment matrix is used to represent the importance between any two indexes under the same parent index; then, the weight vector of each judgment matrix in the plurality of judgment matrices is calculated by using the geometric mean method, and the membership degree is calculated based on the score of each lowest level index, to obtain the membership value of each lowest level index for each evaluation level, and based on the membership value of each lowest level index for each evaluation level, the membership matrix of each lowest level index is obtained; finally, based on the fuzzy evaluation matrix corresponding to each lowest level index, the fuzzy evaluation matrix corresponding to each index in each level is calculated in turn according to the hierarchical order, and finally the final fuzzy evaluation matrix of the light storage and use collaborative management evaluation system is obtained; wherein the fuzzy evaluation matrix corresponding to each lowest level index is obtained by matrix multiplication based on the membership matrix of each lowest level index and the weight vector corresponding to the parent index of each lowest level index; the weight vector corresponding to the parent index of each lowest level index is the weight vector of the judgment matrix corresponding to the parent index; the final fuzzy evaluation matrix is used to represent the final evaluation result of the light storage and use collaborative management evaluation system. In this way, through the light storage and use collaborative management evaluation system based on the analytic hierarchy process, the optimization scheduling and real-time monitoring of various resources in the system can be realized, the operation efficiency of the system is improved, the operation cost is reduced, the impact on the environment is minimized, and the reliable operation and sustainable development of the light storage and use system in the power grid are ensured.

[0091] In another aspect, the application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the analytic hierarchy process-based photovoltaic storage and charging collaborative management evaluation method provided above, which comprises the following steps: firstly, based on the relative importance between indexes in the pre-constructed photovoltaic storage and charging collaborative management evaluation system, a judgment matrix corresponding to each level of indexes is constructed to obtain a plurality of judgment matrices; the judgment matrix is used to represent the importance between any two indexes under the same parent index; then, the weight vector of each judgment matrix in the plurality of judgment matrices is calculated by using the geometric mean method, and the membership degree is calculated based on the score of each lowest level index to obtain the membership value of each lowest level index for each evaluation grade, and the membership matrix of each lowest level index is obtained based on the membership value of each lowest level index for each evaluation grade; finally, the fuzzy evaluation matrix corresponding to each index in each level is calculated in turn from the bottom up based on the fuzzy evaluation matrix corresponding to each lowest level index, and finally the final fuzzy evaluation matrix of the photovoltaic storage and charging collaborative management evaluation system is obtained; wherein the fuzzy evaluation matrix corresponding to each lowest level index is obtained by matrix multiplication based on the membership matrix of each lowest level index and the weight vector corresponding to the parent index of each lowest level index; the weight vector corresponding to the parent index of each lowest level index is the weight vector of the judgment matrix corresponding to the parent index; and the final fuzzy evaluation matrix is used to represent the final evaluation result of the photovoltaic storage and charging collaborative management evaluation system. In this way, the photovoltaic storage and charging collaborative management evaluation system based on the analytic hierarchy process can realize the optimal scheduling and real-time monitoring of various resources in the system, improve the operation efficiency of the system, reduce the operation cost, and maximize the reduction of the impact on the environment, and ensure the reliable operation and sustainable development of the photovoltaic storage and charging system in the power grid.

[0092] The device embodiments described above are only schematic, wherein the units shown as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., they can be located in one place, or distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment. Those skilled in the art can understand and implement it without creative labor.

[0093] Those skilled in the art can clearly understand the implementation of the various embodiments by means of software and the necessary general hardware platform from the above description of the embodiments, and of course, the embodiments can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that contributes to the technical solutions can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0094] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A collaborative management and evaluation method for photovoltaic energy storage, charging, and utilization based on the analytic hierarchy process (AHP), characterized in that, include: Based on the relative importance of indicators in the pre-constructed collaborative management evaluation system for photovoltaic storage, charging and utilization, a judgment matrix corresponding to each level of indicators is constructed to obtain multiple judgment matrices. The judgment matrix is ​​used to characterize the importance between any two indicators under the same parent indicator; The weight vector of each judgment matrix in the plurality of judgment matrices is calculated using the geometric mean method, and the membership degree is calculated based on the score of each lowest-level indicator to obtain the membership degree value of each lowest-level indicator for each evaluation level. Based on the membership degree value of each lowest-level indicator for each evaluation level, the membership degree matrix of each lowest-level indicator is obtained. Based on the fuzzy evaluation matrix corresponding to each lowest-level indicator, the fuzzy evaluation matrix corresponding to each indicator in each level is calculated sequentially upwards in hierarchical order, and finally the final fuzzy evaluation matrix of the photovoltaic storage charging and utilization collaborative management evaluation system is obtained. The fuzzy evaluation matrix corresponding to each lowest-level indicator is obtained by multiplying the membership matrix of each lowest-level indicator with the weight vector corresponding to the parent indicator of each lowest-level indicator; the weight vector corresponding to the parent indicator of each lowest-level indicator is the weight vector of the judgment matrix corresponding to the parent indicator; the final fuzzy evaluation matrix is ​​used to characterize the final evaluation result of the photovoltaic-storage-charging-utilization collaborative management evaluation system.

2. The method according to claim 1, characterized in that, The photovoltaic, energy storage, charging and utilization collaborative management and evaluation system includes: multiple top-level indicators, and multiple secondary indicators contained in each top-level indicator; the multiple top-level indicators include: economic indicators, controllability indicators, reliability indicators and environmental protection indicators.

3. The method according to claim 1 or 2, characterized in that, Based on the relative importance of indicators in the pre-constructed collaborative management and evaluation system for photovoltaic energy storage and charging, a judgment matrix is ​​constructed corresponding to each level of indicator, resulting in multiple judgment matrices, including: Obtain multiple indicators to be judged that belong to the same parent indicator in any target level, and the importance score between any two indicators among the multiple indicators to be judged; Based on the importance score between any two of the plurality of indicators to be judged, a judgment matrix corresponding to the parent indicator to which the plurality of indicators to be judged belongs is generated. Wherein, the target level is any one of the multiple evaluation levels included in the photovoltaic-storage-charging-utilization collaborative management evaluation system; when the target level is the highest level, all indicators included in the target level belong to the same parent indicator.

4. The method according to claim 3, characterized in that, The step of calculating the weight vector of each judgment matrix in the plurality of judgment matrices using the geometric mean method includes: Calculate the geometric mean of each row of the target judgment matrix, and normalize the geometric mean of each row to obtain the weight corresponding to each row. Based on the weight corresponding to each row, the weight vector of the target judgment matrix is ​​obtained; The target judgment matrix is ​​any one of the plurality of judgment matrices.

5. The method according to claim 4, characterized in that, Before calculating the fuzzy evaluation matrix corresponding to each indicator in each level in hierarchical order based on the fuzzy evaluation matrix corresponding to each lowest-level indicator, the method further includes: Based on the target judgment matrix and its weight vector, the maximum eigenvalue of the target judgment matrix is ​​calculated, and a consistency index is calculated based on the maximum eigenvalue. Based on the ratio of the consistency index to the random consistency index, the consistency ratio is calculated. If the consistency ratio is less than a preset ratio threshold, the target judgment matrix is ​​determined to be reasonable.

6. The method according to claim 4 or 5, characterized in that, The fuzzy evaluation matrix corresponding to each lowest-level indicator is calculated sequentially upwards according to the hierarchical order, resulting in the final fuzzy evaluation matrix of the photovoltaic-storage-charging-utilization collaborative management evaluation system, including: Multiply the membership matrix of each subordinate indicator belonging to the same target parent indicator with the weight vector corresponding to the target parent indicator to obtain the fuzzy evaluation vector of each subordinate indicator. The fuzzy evaluation matrix of the target parent indicator is generated based on the fuzzy evaluation vector of each subordinate indicator.

7. A photovoltaic-storage-charging-utilization collaborative management and evaluation device based on the analytic hierarchy process, characterized in that, The device includes: The construction module is used to construct judgment matrices corresponding to each level of indicators based on the relative importance of indicators in the pre-constructed collaborative management evaluation system for photovoltaic storage, charging and utilization, thereby obtaining multiple judgment matrices; the judgment matrix is ​​used to characterize the importance between any two indicators under the same parent indicator; The calculation module is used to calculate the weight vector of each judgment matrix in the plurality of judgment matrices using the geometric mean method, and to calculate the membership degree based on the score of each lowest-level indicator to obtain the membership degree value of each lowest-level indicator for each evaluation level, and to obtain the membership degree matrix of each lowest-level indicator based on the membership degree value of each lowest-level indicator for each evaluation level. The evaluation module is used to calculate the fuzzy evaluation matrix corresponding to each indicator in each level in a hierarchical order based on the fuzzy evaluation matrix corresponding to each lowest level indicator, and finally obtain the final fuzzy evaluation matrix of the optical storage charging and utilization collaborative management evaluation system. The fuzzy evaluation matrix corresponding to each lowest-level indicator is obtained by multiplying the membership matrix of each lowest-level indicator with the weight vector corresponding to the parent indicator of each lowest-level indicator; the weight vector corresponding to the parent indicator of each lowest-level indicator is the weight vector of the judgment matrix corresponding to the parent indicator; the final fuzzy evaluation matrix is ​​used to characterize the final evaluation result of the photovoltaic-storage-charging-utilization collaborative management evaluation system.

8. The apparatus according to claim 7, characterized in that, The photovoltaic, energy storage, charging and utilization collaborative management and evaluation system includes: multiple top-level indicators, and multiple secondary indicators contained in each top-level indicator; the multiple top-level indicators include: economic indicators, controllability indicators, reliability indicators and environmental protection indicators.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the analytic hierarchy process-based collaborative management and evaluation method for photovoltaic storage, charging, and utilization as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the steps of the analytic hierarchy process-based collaborative management and evaluation method for photovoltaic storage, charging, and utilization as described in any one of claims 1 to 6.

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