Analysis method and device for multi-energy use self-consistent system

Abstract

The application provides an analysis method and device for a multi-energy self-consistent system, which comprises the following steps: taking minimization of total operation cost of the multi-energy self-consistent system as an optimization target, and constructing an operation optimization model based on the multi-energy self-consistent system; solving the operation optimization model according to system information of the multi-energy self-consistent system and a preset model solver to obtain operation results of each energy supply device and each energy storage device in the multi-energy self-consistent system; calculating an energy self-consistent rate measurement index of the multi-energy self-consistent system based on the operation results, constructing a fuzzy evaluation factor set by combining the energy self-consistent rate measurement index and a fuzzy comprehensive evaluation method, and establishing a fuzzy comprehensive evaluation matrix by combining a self-consistent level membership function of the energy self-consistent rate measurement index; and determining a comprehensive evaluation result of the multi-energy self-consistent system according to the fuzzy comprehensive evaluation matrix and a weight coefficient of the energy self-consistent rate measurement index.

Description

Technical Field

[0001] This invention relates to the field of energy technology, and in particular to an analysis method and apparatus for a multi-energy self-consistent system. Background Technology

[0002] In related technologies, most energy self-sufficiency rate calculation methods focus on the supply and demand of total electricity consumption in microgrids, i.e., the ratio of total power generation to total electricity demand within a statistical period. They fail to calculate the time-of-use self-sufficiency level for each period within the statistical period, making it difficult to reflect the self-sufficiency capability of the energy self-sufficiency system at different times. Furthermore, they do not consider the support provided by power exchange with the main grid for the self-sufficiency capability of the system, excessively exaggerating the energy self-sufficiency rate of the system in islanded operation. On the other hand, existing energy self-sufficiency rate calculation methods mostly focus on the single energy type of electricity self-sufficiency in microgrids, without considering the energy losses caused by various heterogeneous energy conversions within the integrated energy system and the self-sufficiency level of supply and demand for multiple energy types. Moreover, with continuous technological development, the types of energy used in low-carbon, multi-energy self-sufficient systems, primarily based on new energy sources, will continue to increase. Traditional energy self-sufficiency rate calculation methods for electricity are no longer sufficient to meet the calculation needs of these new energy self-sufficient systems. Summary of the Invention

[0003] This invention provides an analysis method and apparatus for multi-energy self-consistent systems, in order to solve the problem of how to better analyze multi-energy self-consistent systems.

[0004] This invention provides an analysis method for a multi-energy self-consistent system, comprising:

[0005] With minimizing the total operating cost of the multi-energy self-consistent system as the optimization objective, an operation optimization model based on the multi-energy self-consistent system is constructed.

[0006] Based on the system information of the multi-energy self-consistent system, the operation optimization model is solved using a preset model solver to obtain the operating results of each energy supply device and each energy storage device in the multi-energy self-consistent system; wherein, the system information includes: the installed capacity information of the energy supply device, the energy storage capacity information of the energy storage device, the internal load information of the system, the energy conversion efficiency of the energy conversion device, and the energy price.

[0007] Based on the above operating results, the energy self-sufficiency rate of the multi-energy self-sufficiency system is calculated. The energy self-sufficiency rate includes: total energy consumption self-sufficiency rate, internal total energy consumption self-sufficiency rate, internal self-sufficiency time percentage, internal capacity shortage rate, and internal peak shaving shortage rate.

[0008] Based on the energy self-consistency rate measurement index, a fuzzy evaluation factor set is constructed by combining the fuzzy comprehensive evaluation method. At the same time, a fuzzy comprehensive evaluation matrix is ​​established by combining the self-consistency level membership function of the energy self-consistency rate measurement index.

[0009] The comprehensive evaluation result of the multi-energy self-consistency system is determined based on the weight coefficients of the fuzzy comprehensive evaluation matrix and the energy self-consistency rate calculation index. The weight coefficients are obtained by analyzing the energy self-consistency rate calculation index using the analytic hierarchy process.

[0010] According to the analysis method of a multi-energy self-consistent system provided by the present invention, the operation optimization model is specifically as follows:

[0011]

[0012] Constraints:

[0013] Power balance constraints:

[0014]

[0015] Electricity storage balance constraints:

[0016]

[0017] E k,min ≤E k,t ≤E k,max

[0018] E k,1 =E k,T

[0019] Type m energy balance constraint:

[0020]

[0021] Energy storage balance constraint of type m:

[0022]

[0023] E m,k,min ≤E m,k,t ≤E m,k,max

[0024] E m,k,1 =E m,k,T

[0025] Multi-energy conversion constraints:

[0026] P m,i,t =η m,a Q m,i,t

[0027] Qm,j,t =η m,b P m,j,t

[0028] Power output constraints of various types of power supply equipment:

[0029] 0≤P n,t ≤P n,max

[0030] P m,i,min ≤P m,i,t ≤P m,i,max

[0031] Q m,j,min ≤Q m,j,t ≤Q m,j,max

[0032] P k,min ≤P k,t ≤P k,max

[0033] Q m,k,min ≤Q m,k,t ≤Q m,k,max

[0034] in, These are the off-grid electricity price and on-grid electricity price at time t, respectively. These are the purchase price and sales price of the m-th type of energy at time t, respectively; E k,t Let η be the remaining electrical energy of the energy storage device at time t. e c η e d These represent charging efficiency and discharging efficiency, respectively. k,min E k,max These represent the lower and upper limits of the storage capacity of power storage devices; E m,k,t Let η be the energy storage capacity of the m-th type of energy storage device at time t. m c η m d These are the charging efficiency and the discharging efficiency, respectively, E m,k,min E m,k,max These represent the lower and upper limits of the energy storage capacity for the m-th type of energy storage device, respectively; η m,a η m,b , respectively, are the energy conversion coefficients for power generation and production of the m-th energy source.

[0035] According to the analysis method of a multi-energy self-consistent system provided by the present invention, the method for calculating the total energy consumption self-consistency rate is as follows:

[0036] Power generation and supply capacity W of new energy units at time t N,t for:

[0037]

[0038] The power output W of the m-th type energy conversion unit at time t m,t for:

[0039]

[0040] The power supply W of the multi-energy storage device at time t B,t for:

[0041]

[0042] The total load W of all types of energy in the system at time t Lt for:

[0043]

[0044] The total energy consumption self-consistency rate is η. total :

[0045]

[0046] Where N represents the set of new energy generating units within the system; P n,t Let P be the power generation of the new energy unit n at time t; E be other energy generators in the system, such as fuel cells and gas turbines; G be other energy production devices in the system, such as water electrolysis for hydrogen production and hydrogen methanation devices; P m,i,t Q m,j,t Let Q represent the power generation and energy generation rate of the m-th type energy generator i and the m-th type energy production device j at time t, respectively; m,i,t P m,j,t Let α represent the energy consumption rate and power consumption of energy generator i (type m) and energy production device j (type m) at time t, respectively; m Let be the energy equivalent conversion coefficient between electrical energy and the m-th type of energy; B is the set of multiple energy storage devices within the system, such as electrochemical energy storage and hydrogen storage tanks; M is the set of other energy types within the system, such as hydrogen energy and thermal energy; P c k,t P d k,t Q represents the charging power and discharging power of the energy storage device at time t, respectively. c m,k,t Q d m,k,t Let P be the charging rate and discharging rate of the m-th type of energy storage device at time t, respectively; L,t Q m,L,t These represent the electrical load of the self-consistent energy system at time t and the energy load of the m-th energy type, respectively; T is the number of statistical periods for the measured data.

[0047] According to the analysis method of a multi-energy self-consistent system provided by the present invention, the method for calculating the internal total self-consistency rate is as follows:

[0048] Calculate the internal power supply P at time t elec,t：

[0049]

[0050] Calculate the internal power supply Q of energy sources other than electrical energy at time t. m,t；

[0051]

[0052] Internal total self-consistency rate η in,total：

[0053]

[0054] According to the analysis method of a multi-energy self-consistent system provided by the present invention, the method for calculating the proportion of internal self-consistent time is as follows:

[0055] Calculate the part-time self-consistency rate η within time t. in,t：

[0056]

[0057] Calculate the proportion of internal self-consistency time h in；

[0058]

[0059] According to the analysis method of a multi-energy self-consistent system provided by the present invention, the method for calculating the proportion of internal self-consistent time is as follows:

[0060] Calculate the energy deficit E of the energy-consistent system during the statistical period. short：

[0061]

[0062] Calculate the surplus energy E of the energy-consistent system during the statistical period. surplus：

[0063]

[0064] The computing system's energy output is insufficient (energy E) short_pr：

[0065] E short_pr =max(E shor tE surplus ,0)

[0066] Calculate the internal production deficit rate η of the system pr :

[0067]

[0068] According to the analysis method of a multi-energy self-consistent system provided by the present invention, the method for calculating the internal peak-shaving insufficiency rate is specifically as follows:

[0069] The computing system's energy peak shaving is insufficient. short_ad：

[0070] E short_ad =E short -E short_pr

[0071] Calculate the internal peak shaving insufficiency rate η of the computing system ad：

[0072]

[0073] The present invention also provides an analysis device for a multi-energy self-consistent system, comprising:

[0074] A construction module is used to construct an operation optimization model based on the multi-energy self-consistent system with the optimization objective of minimizing the total operating cost of the multi-energy self-consistent system.

[0075] The first analysis module is used to solve the operation optimization model based on the system information of the multi-energy self-consistent system and in conjunction with a preset model solver, to obtain the operation results of each energy supply device and each energy storage device in the multi-energy self-consistent system; wherein, the system information includes: the installed capacity information of the energy supply device, the energy storage capacity information of the energy storage device, the internal load information of the system, the energy conversion efficiency of the energy conversion device, and the energy price.

[0076] The calculation module is used to calculate the energy self-sufficiency rate of the multi-energy self-sufficiency system based on the running results. The energy self-sufficiency rate calculation indicators include: total energy consumption self-sufficiency rate, internal total energy consumption self-sufficiency rate, internal self-sufficiency time percentage, internal capacity shortage rate, and internal peak shaving shortage rate.

[0077] The second analysis module is used to construct a fuzzy evaluation factor set based on the energy self-consistency rate measurement index and the fuzzy comprehensive evaluation method, and to establish a fuzzy comprehensive evaluation matrix based on the self-consistency level membership function of the energy self-consistency rate measurement index.

[0078] The third analysis module is used to determine the comprehensive evaluation result of the multi-energy self-consistency system based on the fuzzy comprehensive evaluation matrix and the weight coefficients of the energy self-consistency rate calculation index. The weight coefficients are obtained by analyzing the energy self-consistency rate calculation index using the analytic hierarchy process.

[0079] 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 steps of the analysis method for a multi-energy self-consistent system as described above.

[0080] 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 program to implement the analysis method of the multi-energy self-consistent system as described above.

[0081] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the analysis method for a multi-energy self-consistent system as described above.

[0082] This invention provides an analysis method and apparatus for a multi-energy self-sufficient system. By considering the energy consumption characteristics, technical and economic properties of internal components, and energy flow characteristics of the multi-energy self-sufficient system, and optimizing its operation with the goal of minimizing operating costs, it proposes an energy self-sufficiency rate calculation index system based on the results of economic operation optimization. This system includes total energy consumption self-sufficiency rate, internal total energy consumption self-sufficiency rate, internal self-sufficiency time percentage, internal capacity shortage rate, and internal peak-shaving shortage rate. It calculates and analyzes the self-sufficiency rate of the multi-energy self-sufficient system from multiple perspectives, including total energy consumption self-sufficiency, internal self-sufficiency, and peak-shaving self-sufficiency, and judges the energy self-sufficiency level of the system based on a fuzzy comprehensive evaluation method. It can measure the self-sufficiency level of the system without relying on a larger system for energy input and output. It can measure the system's self-sufficiency capability from different perspectives, including capacity shortage and peak-shaving shortage. Compared with existing self-sufficiency rate calculation methods, it can more comprehensively and accurately evaluate the system's self-sufficiency level from multiple perspectives, thus providing targeted guidance for the planning and optimization of capacity and peak-shaving equipment in self-sufficient systems. Attached Figure Description

[0083] 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.

[0084] Figure 1A schematic flowchart of the analysis method for a multi-energy self-consistent system provided in the embodiments of this application;

[0085] Figure 2 A schematic diagram of the analysis device structure for a multi-energy self-consistent system provided in an embodiment of this application;

[0086] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0087] 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.

[0088] Figure 1 This is a schematic diagram of the analysis method for a multi-energy self-consistent system provided in the embodiments of this application, such as... Figure 1 As shown, it includes:

[0089] Step 110: With minimizing the total operating cost of the multi-energy self-consistent system as the optimization objective, construct an operation optimization model based on the multi-energy self-consistent system.

[0090] Step 120: Based on the system information of the multi-energy self-consistent system, the operation optimization model is solved using a preset model solver to obtain the operating results of each energy supply device and each energy storage device in the multi-energy self-consistent system; wherein, the system information includes: the installed capacity information of the energy supply device, the energy storage capacity information of the energy storage device, the internal load information of the system, the operating parameters such as the energy conversion efficiency of the energy conversion device, and the energy price;

[0091] Step 130: Based on the operation results, calculate the energy self-sufficiency rate of the multi-energy self-sufficiency system. The energy self-sufficiency rate calculation indicators include: total energy consumption self-sufficiency rate, internal total energy consumption self-sufficiency rate, internal self-sufficiency time ratio, internal capacity shortage rate, and internal peak shaving shortage rate.

[0092] Step 140: Based on the energy self-consistency rate measurement index, construct a fuzzy evaluation factor set using the fuzzy comprehensive evaluation method, and simultaneously establish a fuzzy comprehensive evaluation matrix by combining the self-consistency level membership function of the energy self-consistency rate measurement index.

[0093] Step 150: Determine the comprehensive evaluation result of the multi-energy self-consistency system based on the weight coefficients of the fuzzy comprehensive evaluation matrix and the energy self-consistency rate calculation index. The weight coefficients are obtained by analyzing the energy self-consistency rate calculation index using the analytic hierarchy process.

[0094] The multi-energy self-sufficient system described in the embodiments of this application may specifically include various types of generator sets, energy conversion equipment, electrical energy storage, hydrogen storage tanks and other energy storage devices.

[0095] In this embodiment, the system information of the multi-energy self-consistent system can specifically refer to various types of generator sets within the multi-energy self-consistent system, such as wind power, photovoltaic, fuel cells, and electricity-to-hydrogen devices. It can also refer to the installed capacity of energy conversion equipment and the distribution of new energy resources, the energy storage capacity of energy storage devices such as electric energy storage and hydrogen storage tanks, the electrical load and other energy type loads within the self-consistent system, the energy conversion efficiency of the energy conversion equipment, and may also include the energy price corresponding to the system, which may include the purchase price of energy and the price of selling surplus energy.

[0096] In this embodiment of the application, the total operating cost f of the self-sufficient energy system is considered, taking into account the purchase costs of various types of energy and the revenue from sending out surplus energy. op With the minimum as the optimization objective, an operational optimization model based on a multi-energy self-consistent system is constructed.

[0097]

[0098] Constraints:

[0099] Power balance constraints:

[0100]

[0101] Electricity storage balance constraints:

[0102]

[0103] E k,min ≤E k,t ≤E k,max

[0104] E k,1 =E k,T

[0105] Type m energy balance constraint:

[0106]

[0107] Energy storage balance constraint of type m:

[0108]

[0109] E m,k,min ≤E m,k,t ≤E m,k,max

[0110] E m,k,1 =E m,k,T

[0111] Multi-energy conversion constraints:

[0112] P m,i,t =η m,a Q m,i,t

[0113] Q m,j,t =η m,b P m,j,t

[0114] Power output constraints of various types of power supply equipment:

[0115] 0≤P n,t ≤P n,max

[0116] P m,i,min ≤P m,i,t ≤P m,i,max

[0117] Q m,j,min ≤Q m,j,t ≤Q m,j,max

[0118] P k,min ≤P k,t ≤P k,max

[0119] Q m,k,min ≤Q m,k,t ≤Q m,k,max

[0120] in, These are the off-grid electricity price and on-grid electricity price at time t, respectively. These are the purchase price and sales price of the m-th type of energy at time t, respectively; E k,t Let η be the remaining electrical energy of the energy storage device at time t. e c η e d These represent charging efficiency and discharging efficiency, respectively. k,min E k,max These represent the lower and upper limits of the storage capacity of power storage devices; E m,k,t Let η be the energy storage capacity of the m-th type of energy storage device at time t. m c η m dThese are the charging efficiency and the discharging efficiency, respectively, E m,k,min E m,k,max These represent the lower and upper limits of the energy storage capacity for the m-th type of energy storage device, respectively; η m,a η m,b , respectively, are the energy conversion coefficients for power generation and production of the m-th energy source.

[0121] In this embodiment of the application, after the construction of the operation optimization model is completed, the model can be further optimized and solved in MATLAB software using commercial optimization solvers such as Gurobi, based on the system information of the multi-energy self-consistent system, to obtain the operation results of each energy supply device and energy storage element.

[0122] Then, based on the operating results, the energy self-sufficiency rate of the multi-energy self-sufficiency system is calculated.

[0123] In this application embodiment, in order to calculate the energy self-sufficiency rate of a multi-energy self-sufficiency system and evaluate the degree of system self-sufficiency, a comprehensive and accurate energy self-sufficiency rate calculation index is established, including total energy consumption self-sufficiency rate, internal total energy consumption self-sufficiency rate, internal self-sufficiency time ratio, internal capacity shortage rate, and internal peak shaving shortage rate.

[0124] In this embodiment of the application, the energy self-sufficiency rate refers to the proportion of energy demand that can be met by the total energy provided by the energy self-sufficiency system itself (such as wind power, photovoltaic, fuel cells, electricity-to-hydrogen devices, etc.). The calculation method for the total energy self-sufficiency rate of the energy self-sufficiency system is as follows:

[0125] Power generation and supply capacity W of new energy units at time t N,t for:

[0126]

[0127] The power output W of the m-th type energy conversion unit at time t m,t for:

[0128]

[0129] The power supply W of the multi-energy storage device at time t B,t for:

[0130]

[0131] The total load W of all types of energy in the system at time t L,t for:

[0132]

[0133] The total energy consumption self-consistency rate is η. total：

[0134]

[0135] Where N represents the set of new energy generating units within the system; P n,t Let P be the power generation of the new energy unit n at time t; E be other energy generators in the system, such as fuel cells and gas turbines; G be other energy production devices in the system, such as water electrolysis for hydrogen production and hydrogen methanation devices; P m,i,t Q m,j,t Let Q represent the power generation and energy generation rate of the m-th type energy generator i and the m-th type energy production device j at time t, respectively; m,i,t P m,j,t Let α represent the energy consumption rate and power consumption of energy generator i (type m) and energy production device j (type m) at time t, respectively; m Let be the energy equivalent conversion coefficient between electrical energy and the m-th type of energy; B is the set of multiple energy storage devices within the system, such as electrochemical energy storage and hydrogen storage tanks; M is the set of other energy types within the system, such as hydrogen energy and thermal energy; P c k,t P d k,t Q represents the charging power and discharging power of the energy storage device at time t, respectively. c m,k,t Q d m,k,t Let P be the charging rate and discharging rate of the m-th type of energy storage device at time t, respectively; L,t Q m,L,t These represent the electrical load of the self-consistent energy system at time t and the energy load of the m-th energy type, respectively; T is the number of statistical periods for the measured data.

[0136] In this embodiment of the application, the internal total self-sufficiency rate refers to the proportion of energy demand met by the internal basic energy supply facilities and internal peak-shaving devices of the energy self-sufficiency system during the entire statistical period. This indicator can be used together with the total energy self-sufficiency rate to measure whether the internal peak-shaving capacity of the system is sufficient. That is, when the total energy self-sufficiency rate is high and the internal total self-sufficiency rate is low, it means that the overall energy support capacity of the system is sufficient but the internal peak-shaving capacity is insufficient, and it is necessary to rely on the large system for peak-shaving capacity support.

[0137] The calculation method for the internal consistency rate is as follows:

[0138] Calculate the internal power supply P at time t elec,t：

[0139]

[0140] Calculate the internal power supply Q of energy sources other than electrical energy at time t. m,t；

[0141]

[0142] Internal total self-consistency rate η in,total：

[0143]

[0144] In this embodiment, the internal self-consistency time ratio refers to the proportion of self-consistency periods satisfied by the internal basic energy supply facilities and internal peak-shaving devices (such as fuel cells, energy storage batteries, hydrogen storage tanks, etc.) of the energy self-consistency system without relying on the main power grid for power exchange, gas transmission pipelines, etc. The calculation method for the internal self-consistency time ratio of the energy self-consistency system is as follows:

[0145] Calculate the part-time self-consistency rate η within time t. in,t：

[0146]

[0147] Calculate the proportion of internal self-consistency time h in ;

[0148]

[0149] In this embodiment, the internal energy deficit rate refers to the proportion of energy supply shortage caused by insufficient energy production capacity of the energy-self-sufficient system to the total energy demand. It can be used to measure the energy production self-sufficiency of the system. The calculation method of the internal energy deficit rate of the internal self-sufficient system is as follows:

[0150] Calculate the energy deficit E of the energy-consistent system during the statistical period. short That is, the total energy that a self-consistent system needs to input from an external system:

[0151]

[0152] in, Let be the input electrical power from the external system to the self-consistent system at time t, and the input power of the m-th type of energy, respectively; Δt is the statistical time interval.

[0153] Calculate the surplus energy E of the energy-consistent system during the statistical period. surplus：

[0154]

[0155] in, Let be the output electric power from the self-consistent system to the external system at time t, and the input power of the m-th type of energy, respectively.

[0156] The computing system's energy output is insufficient (energy E) short_prThe energy deficit caused by insufficient system capacity can be represented by the difference between the system's insufficient energy and surplus energy.

[0157] E short_pr =max(E short -E surplus ,0)

[0158] Calculate the internal production deficit rate η of the system pr :

[0159]

[0160] In this embodiment, the internal peak-shaving insufficiency rate refers to the proportion of insufficient energy supply caused by the insufficient peak-shaving capacity of the energy self-sufficient system to the total energy demand. It can be used to measure the peak-shaving self-sufficiency capacity of the system. The calculation method of the internal peak-shaving insufficiency rate of the energy self-sufficient system is as follows:

[0161] The computing system's energy peak shaving is insufficient. short_ad This refers to the energy deficit caused by the system's sufficient energy production capacity but insufficient peak-shaving capacity, which can be represented by the difference between the system's insufficient energy and the insufficient energy produced:

[0162] E short_ad =E short -E short_pr

[0163] Calculate the internal peak shaving insufficiency rate η of the computing system ad：

[0164]

[0165] In an optional embodiment, the analytic hierarchy process (AHP) is used to score and weight the energy self-sufficiency rate measurement indicators, quantifying the importance of each indicator.

[0166] Specifically, pairwise comparative analyses can be performed on each indicator in the energy self-consistency rate calculation index to determine the importance of each indicator. A 1-9 scale method is used to numerically represent the judgment matrix, thereby quantifying the importance of each indicator. The judgment matrix scale is shown in Table 1.

[0167] Table 1

[0168]

[0169] Based on the above judgment scaling method, construct the judgment matrix C = (C ij ) 5*5 :

[0170]

[0171] Find the eigenvector W = (W1, W2, ..., W5) of the judgment matrix, which represents the weight coefficients of each indicator:

[0172]

[0173] Calculate λ max Value:

[0174]

[0175] Calculate the consistency index CI and the random consistency ratio CR. If CR < 0.1, the constructed judgment matrix passes the consistency test. If it fails the consistency test, the judgment matrix needs to be reconstructed.

[0176]

[0177] In this embodiment of the application, a fuzzy comprehensive evaluation method is adopted to establish a factor set U = {u1, u2, u3, u4, u5} for evaluation indicators, where u1 is the total energy consumption self-consistency rate, u2 is the internal total energy consumption self-consistency rate, u3 is the proportion of internal self-consistency time, u4 is the internal capacity shortage rate, and u5 is the internal peak shaving shortage rate; and a comment set V = {v1, v2, v3}, where v1 is the low self-consistency level, v2 is the medium self-consistency level, and v3 is the high self-consistency level.

[0178] This invention selects a trapezoidal distribution function to determine the membership degree of each factor:

[0179]

[0180] In the formula r i,1 r i,2 r i,3 They are indicators x i The membership degree of the three levels of the comment set is used to determine the self-consistency level.

[0181] Calculate the fuzzy matrix R based on the membership functions of each factor:

[0182]

[0183] The comprehensive evaluation set M is obtained by combining the weight coefficients W of each indicator determined in the above steps:

[0184] M = WR = (m1, m2, m3)

[0185] Finally, the self-consistency rate of the multi-energy self-consistency system was determined according to the principle of maximum membership and the evaluation level was determined.

[0186] In this embodiment, by considering the energy consumption characteristics, technical and economic characteristics of internal components, and energy flow characteristics of a multi-energy self-sufficient system, and aiming to minimize the operating cost of the self-sufficient system, an energy self-sufficiency rate calculation index system based on the economic operation optimization results is proposed. This system includes total energy consumption self-sufficiency rate, internal total energy consumption self-sufficiency rate, internal self-sufficiency time percentage, internal capacity shortage rate, and internal peak shaving shortage rate. The self-sufficiency rate of the multi-energy self-sufficient system is calculated and analyzed from multiple perspectives, including total energy consumption self-sufficiency, internal self-sufficiency, and peak shaving self-sufficiency. The energy self-sufficiency level of the system is judged based on a fuzzy comprehensive evaluation method. This system can measure the self-sufficiency level of the system without relying on a large system for energy input and output. It can measure the system's self-sufficiency capability from different perspectives of capacity shortage and peak shaving shortage. Compared with existing self-sufficiency rate calculation methods, this system can more comprehensively and accurately evaluate the system's self-sufficiency level from multiple perspectives, thus providing targeted guidance for the planning and optimization of capacity and peak shaving equipment in self-sufficient systems.

[0187] The analysis device for the multi-energy self-consistent system provided by the present invention is described below. The analysis device for the multi-energy self-consistent system described below and the analysis method for the multi-energy self-consistent system described above can be referred to in correspondence.

[0188] Figure 2 This is a schematic diagram of the analysis device structure for a multi-energy self-consistent system provided in an embodiment of this application, as shown below. Figure 2 As shown, it includes:

[0189] The construction module 210 is used to construct an operation optimization model based on the multi-energy self-consistent system with the goal of minimizing the total operating cost of the multi-energy self-consistent system.

[0190] The first analysis module 220 is used to solve the operation optimization model based on the system information of the multi-energy self-consistent system and in conjunction with a preset model solver, to obtain the operation results of each energy supply device and each energy storage device in the multi-energy self-consistent system; wherein, the system information includes: the installed capacity information of the energy supply device, the energy storage capacity information of the energy storage device, the internal load information of the system, the energy conversion efficiency of the energy conversion device, and the energy price.

[0191] The calculation module 230 is used to calculate the energy self-sufficiency rate of the multi-energy self-sufficiency system based on the running results. The energy self-sufficiency rate calculation indicators include: total energy consumption self-sufficiency rate, internal total energy consumption self-sufficiency rate, internal self-sufficiency time percentage, internal capacity shortage rate, and internal peak shaving shortage rate.

[0192] The second analysis module 240 is used to construct a fuzzy evaluation factor set based on the energy self-consistency rate measurement index and the fuzzy comprehensive evaluation method, and at the same time, to establish a fuzzy comprehensive evaluation matrix based on the self-consistency level membership function of the energy self-consistency rate measurement index.

[0193] The third analysis module 250 is used to determine the comprehensive evaluation result of the multi-energy self-consistent system based on the fuzzy comprehensive evaluation matrix and the weight coefficients of the energy self-consistency rate calculation index. The weight coefficients are obtained by analyzing the energy self-consistency rate calculation index using the analytic hierarchy process.

[0194] In this embodiment, by considering the energy consumption characteristics, technical and economic characteristics of internal components, and energy flow characteristics of a multi-energy self-sufficient system, and aiming to minimize the operating cost of the self-sufficient system, an energy self-sufficiency rate calculation index system based on the economic operation optimization results is proposed. This system includes total energy consumption self-sufficiency rate, internal total energy consumption self-sufficiency rate, internal self-sufficiency time percentage, internal capacity shortage rate, and internal peak shaving shortage rate. The self-sufficiency rate of the multi-energy self-sufficient system is calculated and analyzed from multiple perspectives, including total energy consumption self-sufficiency, internal self-sufficiency, and peak shaving self-sufficiency. The energy self-sufficiency level of the system is judged based on a fuzzy comprehensive evaluation method. This system can measure the self-sufficiency level of the system without relying on a large system for energy input and output. It can measure the system's self-sufficiency capability from different perspectives of capacity shortage and peak shaving shortage. Compared with existing self-sufficiency rate calculation methods, this system can more comprehensively and accurately evaluate the system's self-sufficiency level from multiple perspectives, thus providing targeted guidance for the planning and optimization of capacity and peak shaving equipment in self-sufficient systems.

[0195] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 3 As shown, the electronic device may include a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other through the communication bus 340. The processor 310 can call logical instructions in the memory 330 to execute an analysis method for a multi-energy self-consistent system. This method includes: constructing an operation optimization model based on the multi-energy self-consistent system with the optimization objective of minimizing the total operating cost of the multi-energy self-consistent system;

[0196] Based on the system information of the multi-energy self-consistent system, the operation optimization model is solved using a preset model solver to obtain the operation results of each energy supply device and each energy storage device in the multi-energy self-consistent system; wherein, the system information includes: the installed capacity information of the energy supply device, the energy storage capacity information of the energy storage device and the internal load information of the system, the energy conversion efficiency of the energy conversion device and the energy price.

[0197] Based on the above operating results, the energy self-sufficiency rate of the multi-energy self-sufficiency system is calculated. The energy self-sufficiency rate includes: total energy consumption self-sufficiency rate, internal total energy consumption self-sufficiency rate, internal self-sufficiency time percentage, internal capacity shortage rate, and internal peak shaving shortage rate.

[0198] Based on the energy self-consistency rate measurement index, a fuzzy evaluation factor set is constructed by combining the fuzzy comprehensive evaluation method. At the same time, a fuzzy comprehensive evaluation matrix is ​​established by combining the self-consistency level membership function of the energy self-consistency rate measurement index.

[0199] The comprehensive evaluation result of the multi-energy self-consistency system is determined based on the weight coefficients of the fuzzy comprehensive evaluation matrix and the energy self-consistency rate calculation index. The weight coefficients are obtained by analyzing the energy self-consistency rate calculation index using the analytic hierarchy process.

[0200] Furthermore, the logical instructions in the aforementioned memory 330 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.

[0201] 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 analysis method of the multi-energy self-consistent system provided by the above methods, the method including: constructing an operation optimization model based on the multi-energy self-consistent system with the optimization objective of minimizing the total operating cost of the multi-energy self-consistent system;

[0202] Based on the system information of the multi-energy self-consistent system, the operation optimization model is solved using a preset model solver to obtain the operating results of each energy supply device and each energy storage device in the multi-energy self-consistent system; wherein, the system information includes: the installed capacity information of the energy supply device, the energy storage capacity information of the energy storage device, the internal load information of the system, the energy conversion efficiency of the energy conversion device, and the energy price.

[0203] Based on the above operating results, the energy self-sufficiency rate of the multi-energy self-sufficiency system is calculated. The energy self-sufficiency rate includes: total energy consumption self-sufficiency rate, internal total energy consumption self-sufficiency rate, internal self-sufficiency time percentage, internal capacity shortage rate, and internal peak shaving shortage rate.

[0204] Based on the energy self-consistency rate measurement index, a fuzzy evaluation factor set is constructed by combining the fuzzy comprehensive evaluation method. At the same time, a fuzzy comprehensive evaluation matrix is ​​established by combining the self-consistency level membership function of the energy self-consistency rate measurement index.

[0205] The comprehensive evaluation result of the multi-energy self-consistency system is determined based on the weight coefficients of the fuzzy comprehensive evaluation matrix and the energy self-consistency rate calculation index. The weight coefficients are obtained by analyzing the energy self-consistency rate calculation index using the analytic hierarchy process.

[0206] 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, implements an analysis method for a multi-energy self-consistent system provided by the above methods, the method comprising: constructing an operation optimization model based on the multi-energy self-consistent system with the optimization objective of minimizing the total operating cost of the multi-energy self-consistent system;

[0207] Based on the system information of the multi-energy self-consistent system, the operation optimization model is solved using a preset model solver to obtain the operating results of each energy supply device and each energy storage device in the multi-energy self-consistent system; wherein, the system information includes: the installed capacity information of the energy supply device, the energy storage capacity information of the energy storage device, the internal load information of the system, the energy conversion efficiency of the energy conversion device, and the energy price.

[0208] Based on the above operating results, the energy self-sufficiency rate of the multi-energy self-sufficiency system is calculated. The energy self-sufficiency rate includes: total energy consumption self-sufficiency rate, internal total energy consumption self-sufficiency rate, internal self-sufficiency time percentage, internal capacity shortage rate, and internal peak shaving shortage rate.

[0209] Based on the energy self-consistency rate measurement index, a fuzzy evaluation factor set is constructed by combining the fuzzy comprehensive evaluation method. At the same time, a fuzzy comprehensive evaluation matrix is ​​established by combining the self-consistency level membership function of the energy self-consistency rate measurement index.

[0210] The comprehensive evaluation result of the multi-energy self-consistency system is determined based on the weight coefficients of the fuzzy comprehensive evaluation matrix and the energy self-consistency rate calculation index. The weight coefficients are obtained by analyzing the energy self-consistency rate calculation index using the analytic hierarchy process.

[0211] 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.

[0212] 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.

[0213] 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. An analytical method for a multi-energy self-consistent system, characterized in that, include: With minimizing the total operating cost of the multi-energy self-consistent system as the optimization objective, an operation optimization model based on the multi-energy self-consistent system is constructed. Based on the system information of the multi-energy self-consistent system, the operation optimization model is solved using a preset model solver to obtain the operating results of each energy supply device and each energy storage device in the multi-energy self-consistent system; wherein, the system information includes: the installed capacity information of the energy supply device, the energy storage capacity information of the energy storage device, the internal load information of the system, the energy conversion efficiency of the energy conversion device, and the energy price. Based on the above operating results, the energy self-sufficiency rate of the multi-energy self-sufficiency system is calculated. The energy self-sufficiency rate includes: total energy consumption self-sufficiency rate, internal total energy consumption self-sufficiency rate, internal self-sufficiency time percentage, internal capacity shortage rate, and internal peak shaving shortage rate. Based on the energy self-consistency rate measurement index, a fuzzy evaluation factor set is constructed by combining the fuzzy comprehensive evaluation method. At the same time, a fuzzy comprehensive evaluation matrix is ​​established by combining the self-consistency level membership function of the energy self-consistency rate measurement index. The comprehensive evaluation result of the multi-energy self-consistency system is determined based on the weight coefficients of the fuzzy comprehensive evaluation matrix and the energy self-consistency rate calculation index. The weight coefficients are obtained by analyzing the energy self-consistency rate calculation index using the analytic hierarchy process. The method for calculating the total energy consumption self-sufficiency rate is as follows: Power generation and supply capacity of new energy units at time t W N,t for: ； Power supplied by the m-th type energy conversion unit at time t W m,t for: ； Power supplied by a multi-energy storage device at time t W B,t for: Total load of all types of energy in the system at time t W L,t for: The total energy consumption self-sufficiency rate is η total： Where N represents the set of new energy generating units within the system; P n,t Let be the power generation of the new energy unit n at time t; E be other energy generators in the system, including fuel cells and gas turbines; G be other energy production devices in the system, including water electrolysis to produce hydrogen and hydrogen methanation devices. P m,i,t , Q m,j,t Let i be the power generation capacity of the m-th type energy generator unit i and the m-th type energy production device j at time t, respectively. Q m,i,t , P m,j,t These represent the energy consumption rate and power consumption of energy generator i (type m) and energy production device j (type m) at time t, respectively. α m denoted as the energy equivalent conversion coefficient between electrical energy and the m-th type of energy; B represents the set of multiple energy storage devices within the system, including electrochemical energy storage and hydrogen storage tanks; M represents the set of other energy types within the system, including hydrogen energy and thermal energy. P c k,t , P d k,t These represent the charging power and discharging power of the energy storage device at time t, respectively. Q c m,k,t , Q d m,k,t Let be the charging rate and the discharging rate of the m-th type of energy storage device at time t, respectively. P L,t , Q m,L,t These represent the electrical load of the self-consistent energy system at time t and the energy load of the m-th energy source, respectively; T is the number of statistical periods for the measured data. The method for calculating the internal total self-consistency rate is as follows: Calculate the internal power supply at time t P elec,t： ； Calculate the internal power supply of energy sources other than electrical energy at time t. Q m,t； Internal total self-consistency rate η in,total： ； The calculation method for the proportion of internal self-consistency time is as follows: calculate t Partial time self-consistency rate within a given time period η in,t： Calculate the proportion of internal self-consistency time h in； ； The specific method for calculating the internal capacity shortage rate of the system is as follows: Calculate the energy deficit of the energy-consistent system during the statistical period. E short： Calculate the surplus energy of the energy-consistent system during the statistical period. E surplus： The computing system's energy production is insufficient. E short_pr： Internal capacity shortage rate of computing system η pr : ； in, Let be the input electrical power from the external system to the self-consistent system at time t, and the input power of the m-th type of energy, respectively; Δt is the statistical time interval. Let be the output electric power from the self-consistent system to the external system at time t, and the input power of the m-th type of energy, respectively. The calculation method for the internal peak shaving insufficiency rate is as follows: Computing system energy peak shaving insufficient energy E short_ad： Calculate the internal peak shaving insufficiency rate η ad： 。 2. The analysis method for a multi-energy self-consistent system according to claim 1, characterized in that, The aforementioned operational optimization model is specifically as follows: Constraints: Power balance constraints: Electricity storage balance constraints: Type m energy balance constraint: Energy storage balance constraint of type m: Multi-energy conversion constraints: Power output constraints of various types of power supply equipment: in, These are the off-grid electricity price and on-grid electricity price at time t, respectively. Let $t$ be the purchase price and the selling price of the $m$ type of energy at time $t$. E k,t Let be the remaining electrical energy of the energy storage device at time t. η e c , η e d These are charging efficiency and discharging efficiency, respectively. E k,min , E k,max These are the lower and upper limits of the storage capacity of power storage devices, respectively. E m,k,t Let m be the energy storage capacity of the m-th type of energy storage device at time t. η m c , η m d These are charging efficiency and discharging efficiency, respectively. E m,k,min , E m,k,max These are the lower and upper limits of the energy storage capacity for the m-th type of energy storage device, respectively. η m,a , η m,b , respectively, are the energy conversion coefficients for power generation and production of the m-th energy source.

3. An analysis device for a multi-energy self-consistent system, characterized in that, include: A construction module is used to construct an operation optimization model based on the multi-energy self-consistent system with the optimization objective of minimizing the total operating cost of the multi-energy self-consistent system. The first analysis module is used to solve the operation optimization model based on the system information of the multi-energy self-consistent system and in conjunction with a preset model solver, to obtain the operation results of each energy supply device and each energy storage device in the multi-energy self-consistent system; wherein, the system information includes: the installed capacity information of the energy supply device, the energy storage capacity information of the energy storage device, the internal load information of the system, the energy conversion efficiency of the energy conversion device, and the energy price. The calculation module is used to calculate the energy self-sufficiency rate of the multi-energy self-sufficiency system based on the running results. The energy self-sufficiency rate calculation indicators include: total energy consumption self-sufficiency rate, internal total energy consumption self-sufficiency rate, internal self-sufficiency time percentage, internal capacity shortage rate, and internal peak shaving shortage rate. The second analysis module is used to construct a fuzzy evaluation factor set based on the energy self-consistency rate measurement index and the fuzzy comprehensive evaluation method, and to establish a fuzzy comprehensive evaluation matrix based on the self-consistency level membership function of the energy self-consistency rate measurement index. The third analysis module is used to determine the comprehensive evaluation result of the multi-energy self-consistency system based on the weight coefficients of the fuzzy comprehensive evaluation matrix and the energy self-consistency rate calculation index. The weight coefficients are obtained by analyzing the energy self-consistency rate calculation index using the analytic hierarchy process. The method for calculating the total energy consumption self-sufficiency rate is as follows: Power generation and supply capacity of new energy units at time t W N,t for: ； Power supplied by the m-th type energy conversion unit at time t W m,t for: ； Power supplied by a multi-energy storage device at time t W B,t for: Total load of all types of energy in the system at time t W L,t for: The total energy consumption self-sufficiency rate is η total： Where N represents the set of new energy generating units within the system; P n,t Let be the power generation of the new energy unit n at time t; E be other energy generators in the system, including fuel cells and gas turbines; G be other energy production devices in the system, including water electrolysis to produce hydrogen and hydrogen methanation devices. P m,i,t , Q m,j,t Let i be the power generation capacity of the m-th type energy generator unit i and the m-th type energy production device j at time t, respectively. Q m,i,t , P m,j,t These represent the energy consumption rate and power consumption of energy generator i (type m) and energy production device j (type m) at time t, respectively. α m denoted as the energy equivalent conversion coefficient between electrical energy and the m-th type of energy; B represents the set of multiple energy storage devices within the system, including electrochemical energy storage and hydrogen storage tanks; M represents the set of other energy types within the system, including hydrogen energy and thermal energy. P c k,t , P d k,t These represent the charging power and discharging power of the energy storage device at time t, respectively. Q c m,k,t , Q d m,k,t Let be the charging rate and the discharging rate of the m-th type of energy storage device at time t, respectively. P L,t , Q m,L,t These represent the electrical load of the self-consistent energy system at time t and the energy load of the m-th energy source, respectively; T is the number of statistical periods for the measured data. The method for calculating the internal total self-consistency rate is as follows: Calculate the internal power supply at time t P elec,t： ； Calculate the internal power supply of energy sources other than electrical energy at time t. Q m,t； Internal total self-consistency rate η in,total： ； The calculation method for the proportion of internal self-consistency time is as follows: calculate t Partial time self-consistency rate within a given time period η in,t： Calculate the proportion of internal self-consistency time h in； ； The specific method for calculating the internal capacity shortage rate of the system is as follows: Calculate the energy deficit of the energy-consistent system during the statistical period. E short： Calculate the surplus energy of the energy-consistent system during the statistical period. E surplus： The computing system's energy production is insufficient. E short_pr： Internal capacity shortage rate of computing system η pr : ； in, Let be the input electrical power from the external system to the self-consistent system at time t, and the input power of the m-th type of energy, respectively; Δt is the statistical time interval. Let be the output electric power from the self-consistent system to the external system at time t, and the input power of the m-th type of energy, respectively. The calculation method for the internal peak shaving insufficiency rate is as follows: Computing system energy peak shaving insufficient energy E short_ad： Calculate the internal peak shaving insufficiency rate η ad： 。 4. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the analysis method for the multi-energy self-consistent system as described in any one of claims 1 to 2.

5. 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 steps of the analysis method for the multi-energy self-consistent system as described in any one of claims 1 to 2.

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