Evaluation method of comprehensive micro energy network planning scheme taking new energy as main body

Abstract

The invention relates to the field of power system planning, and particularly discloses an evaluation method of a comprehensive micro energy network planning scheme taking new energy as a main body, which comprises the steps of constructing an evaluation index system of the comprehensive micro energy network planning scheme taking the new energy as the main body, and enabling a comprehensive micro energy network taking the new energy as the main body to participate in an electricity-hydrogen-heat market; respectively utilizing a BWM method and a CRITIC method to obtain subjective weight and objective weight of each evaluation index in the evaluation index system; according to the obtained subjective and objective weights, obtaining a combined weight of each evaluation index by using a least square method; according to the evaluation index value of each planning scheme of the comprehensive micro energy network taking new energy as a main body and the combination weight of each evaluation index, utilizing an improved cloud matter element model to obtain a comprehensive score of each planning scheme; and determining the planning scheme with the highest comprehensive score as a final planning scheme for the comprehensive micro energy grid taking the new energy as the main body to participate in the electricity-hydrogen-heat market. According to the invention, the accuracy and comprehensiveness of evaluation results can be improved.

Description

Technical Field

[0001] The present invention relates to the field of power system planning, and in particular to an evaluation method for a comprehensive micro-energy network planning scheme with new energy as the main body. Background Art

[0002] As an innovative energy solution, the integrated micro-energy network is gaining more and more attention. By integrating multiple energy technologies and intelligent control technologies, the integrated micro-energy network can achieve efficient, flexible and sustainable use of energy, which is of great significance for reducing dependence on traditional energy, improving the stability of the energy system and responding to climate change.

[0003] However, the planning and implementation of integrated micro-energy networks has always been a difficult problem because it involves multiple energy forms, energy storage technologies, and complex system interactions. To this end, it is crucial to provide an evaluation method to guide the planning and implementation of integrated micro-energy networks. Summary of the invention

[0004] To this end, the present invention provides an evaluation method for a comprehensive micro-energy network planning scheme with new energy as the main body, in an effort to solve or at least alleviate the above problems.

[0005] According to one aspect of the present invention, there is provided an evaluation method for a planning scheme of an integrated micro-energy network with new energy as the main body, comprising: constructing an evaluation index system for the planning scheme of the integrated micro-energy network with new energy as the main body, wherein the integrated micro-energy network with new energy as the main body participates in the electricity-hydrogen-heat joint market; respectively obtaining the subjective weight and the objective weight of each evaluation index in the evaluation index system by using the best-worst method and the index correlation method; obtaining the combined weight of each evaluation index by using the least squares method according to the obtained subjective weight and objective weight of each evaluation index; obtaining the comprehensive score of each planning scheme by using the improved cloud-matter-element model that considers both the clarity and fuzziness of the level boundary according to the evaluation index values ​​of each planning scheme of the integrated micro-energy network with new energy as the main body and the obtained combined weight of each evaluation index, and determining the planning scheme with the highest comprehensive score as the final planning scheme for the integrated micro-energy network with new energy as the main body to participate in the electricity-hydrogen-heat joint market.

[0006] Optionally, in the evaluation method of the comprehensive micro-energy network planning scheme with new energy as the main body according to the present invention, the improved cloud-matter element model includes:

[0007]

[0008] Among them, E xqg 、E nqg , H eqg They represent the cloud expectation value, cloud entropy and cloud super entropy of the evaluation index q corresponding to level g, and c qg,max 、cqg,min They respectively represent the maximum and minimum values ​​of the numerical range of the evaluation index q at level g.

[0009] Optionally, in the evaluation method of the comprehensive micro-energy network planning scheme with new energy as the main body according to the present invention, the evaluation index system includes evaluation indicators at two levels, namely, primary and secondary levels, and uses an improved cloud-matter element model to obtain a comprehensive score for each planning scheme, including: using the improved cloud-matter element model to obtain the cloud expectation value, cloud entropy and cloud super entropy of each secondary evaluation indicator at each level; according to the secondary evaluation indicator values ​​of each planning scheme, the combined weights of each secondary evaluation indicator, and the cloud expectation value, cloud entropy and cloud super entropy of each secondary evaluation indicator at each level, through multiple Monte Carlo simulations, multiple scores for each planning scheme are obtained; the average value of the multiple scores of each planning scheme is calculated, and the average value is used as the comprehensive score of each planning scheme.

[0010] Optionally, in the evaluation method of the planning scheme of the comprehensive micro-energy network with new energy as the main body according to the present invention, in each Monte Carlo simulation, the score of each planning scheme is obtained in the following way: for each planning scheme, according to the value of each secondary evaluation indicator and the cloud expectation value, cloud entropy and cloud super entropy of each secondary evaluation indicator at each level, the cloud correlation of each secondary evaluation indicator of the planning scheme corresponding to each level is obtained; according to the obtained cloud correlation of each secondary evaluation indicator corresponding to each level and the combined weight of each secondary evaluation indicator, the cloud correlation of each first-level evaluation indicator of the planning scheme corresponding to each level is obtained; based on the obtained cloud correlation of each secondary evaluation indicator corresponding to each level and the combined weight of each secondary evaluation indicator, the cloud correlation of each first-level evaluation indicator of the planning scheme corresponding to each level is obtained; based on the obtained cloud correlation of each first-level evaluation indicator corresponding to each level, the score of the planning scheme is obtained.

[0011] Optionally, in the evaluation method of the comprehensive micro-energy network planning scheme with new energy as the main body according to the present invention, for each planning scheme, according to the value of each secondary evaluation index and the cloud expectation value, cloud entropy and cloud super entropy of each secondary evaluation index at each level, the cloud correlation degree of each secondary evaluation index corresponding to each level of the planning scheme is obtained, and the cloud correlation degree of each secondary evaluation index corresponding to each level is obtained by the following formula:

[0012]

[0013] Among them, P ijg (x ij ) represents the cloud correlation degree of the second-level evaluation index j of planning scheme i corresponding to level g, x ij represents the value of the secondary evaluation index j of planning scheme i, E xjg represents the expected value of the cloud of level g corresponding to the secondary evaluation index j, E′ njg It represents the cloud entropy E corresponding to level g with the secondary evaluation index j njg is the mean value, and the cloud super entropy H of the secondary evaluation index j corresponding to level g ejgis a normal random number with standard deviation .

[0014] Optionally, in the evaluation method of the comprehensive micro-energy network planning scheme with new energy as the main body according to the present invention, in obtaining the cloud correlation of each level of each first-level evaluation indicator corresponding to each level of the planning scheme according to the obtained cloud correlation of each level of each second-level evaluation indicator and the combined weight of each second-level evaluation indicator, the cloud correlation of each level of each first-level evaluation indicator corresponding to each level is obtained by the following formula:

[0015]

[0016] in, It represents the cloud correlation degree of the first-level evaluation index l corresponding to the level g of the second-level evaluation index j of planning scheme i, P ijg (x ij ) represents the cloud correlation degree of the second-level evaluation index j of planning scheme i corresponding to level g, u j represents the combined weight of the secondary evaluation index j, l J Represents the total number of secondary evaluation indicators in the first-level evaluation indicator l.

[0017] Optionally, in the evaluation method of the integrated micro-energy network planning scheme with new energy as the main body according to the present invention, in obtaining the score of the planning scheme based on the cloud association of each level corresponding to each first-level evaluation indicator obtained, the score of the planning scheme is obtained by the following formula:

[0018]

[0019] Among them, Q i represents the score of planning scheme i, u l represents the combined weight of the first-level evaluation index l, represents the score of the first-level evaluation index l of planning scheme i, It represents the cloud correlation degree of level g corresponding to the first-level evaluation index l of the planning scheme i after normalization, G represents the total number of levels, and L represents the total number of first-level evaluation indicators.

[0020] Optionally, in the evaluation method of the comprehensive micro-energy network planning scheme with new energy as the main body according to the present invention, the first-level evaluation indicators in the evaluation index system include economic indicators, energy utilization indicators, social indicators, market indicators and reliability indicators; and, the second-level evaluation indicators under the economic indicators include: initial investment cost, operation and maintenance cost, network transmission loss cost, dynamic investment recovery period and total internal and external benefits of the system; the second-level evaluation indicators under the energy utilization indicators include: comprehensive energy utilization rate, energy sufficiency rate and renewable energy penetration rate; the second-level evaluation indicators under the social indicators include: employment benefits, user energy satisfaction and leading level; the second-level evaluation indicators under the market indicators include: market coupling degree, system market power and market price abnormality; the second-level evaluation indicators under the reliability indicators include: system outage duration, system energy supply response time and energy storage equivalent discharge times.

[0021] According to another aspect of the present invention, there is provided a computing device, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be suitable for execution by the at least one processor, and the program instructions include instructions for executing an evaluation method for a comprehensive micro-energy network planning scheme based on new energy according to the present invention.

[0022] According to another aspect of the present invention, a readable storage medium storing program instructions is provided. When the program instructions are read and executed by a computing device, the computing device executes an evaluation method for a comprehensive micro-energy network planning scheme based on new energy according to the present invention.

[0023] According to the evaluation method of the comprehensive micro-energy network planning scheme with new energy as the main body of the present invention, by integrating subjective and objective weighting methods and adopting the least squares method to optimize the combination of weights, the differences between subjective and objective evaluations can be effectively balanced, so that the accuracy of the combined weights of evaluation indicators can be ensured. In addition, by adopting an improved cloud-matter element model that takes into account the clarity and fuzziness of level boundaries to obtain a comprehensive score for the planning scheme, the uncertainty and fuzziness caused by evaluation indicators with strong correlation can be reduced. Therefore, the present invention can effectively improve the accuracy of the evaluation results.

[0024] In addition, the present invention establishes an evaluation index system from five dimensions: economy, energy utilization, sociality, market and reliability. Through this multi-dimensional evaluation system, the accuracy and comprehensiveness of the evaluation results can be further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] To achieve the above and related purposes, certain illustrative aspects are described herein in conjunction with the following description and accompanying drawings, which indicate various ways in which the principles disclosed herein can be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. The above and other purposes, features and advantages of the present disclosure will become more apparent by reading the following detailed description in conjunction with the accompanying drawings. Throughout the present disclosure, the same reference numerals generally refer to the same parts or elements.

[0026] Figure 1 A structural block diagram of a computing device 100 according to an embodiment of the present invention is shown;

[0027] Figure 2 A flow chart of an evaluation method 200 for a comprehensive micro-energy network planning scheme based on new energy sources according to an embodiment of the present invention is shown;

[0028] Figure 3 A schematic diagram showing a relationship curve between fuzzy acceptance and calculation denominator according to an embodiment of the present invention;

[0029] Figure 4 A schematic diagram showing a weight calculation result of a primary evaluation index according to an embodiment of the present invention;

[0030] Figure 5 A schematic diagram showing a weight calculation result of a secondary evaluation index according to an embodiment of the present invention;

[0031] Figure 6 The "3E n ” principle, “50% correlation” principle and schematic diagram of the normal distribution of cloud matter element model level under optimized cloud entropy. DETAILED DESCRIPTION

[0032] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0033] In the face of the complexity of the planning and implementation of the integrated micro-energy network, the present invention integrates new energy technologies such as photovoltaic power generation and wind power generation, and provides an evaluation method for the planning scheme of an integrated micro-energy network with new energy as the main body, so as to guide decision makers to make more accurate and effective planning decisions, making the planning and implementation of the integrated micro-energy network more scientific and reasonable, thereby promoting the development of the integrated micro-energy network in a more efficient, stable and sustainable direction.

[0034] The evaluation method of the comprehensive micro-energy network planning scheme with new energy as the main body of the present invention can be executed in a computing device. Figure 1 A block diagram of the physical components (i.e., hardware) of a computing device 100 is shown. In a basic configuration, the computing device 100 includes at least one processing unit 102 and a system memory 104. According to one aspect, depending on the configuration and type of the computing device, the processing unit 102 can be implemented as a processor. The system memory 104 includes, but is not limited to, volatile storage (e.g., random access memory), non-volatile storage (e.g., read-only memory), flash memory, or any combination of such memories. According to one aspect, the system memory 104 includes an operating system 105 and a program module 106, and the program module 106 includes an evaluation module 120, which is configured to execute the evaluation method 200 of the comprehensive micro-energy network planning scheme based on new energy as the main body of the present invention.

[0035] According to one aspect, operating system 105 is suitable for controlling the operation of computing device 100, for example. Furthermore, examples are practiced in conjunction with graphics libraries, other operating systems, or any other application programs, and are not limited to any particular application or system. Figure 1 This basic configuration is illustrated in FIG. 1 by those components within dashed line 108. According to one aspect, computing device 100 has additional features or functionality. For example, according to one aspect, computing device 100 includes additional data storage devices (removable and / or non-removable), such as magnetic disks, optical disks, or tapes. Such additional storage Figure 1 1 is illustrated by removable storage 109 and non-removable storage 110.

[0036] As stated above, according to one aspect, program modules are stored in the system memory 104. According to one aspect, the program modules may include one or more application programs, and the present invention does not limit the type of application programs, for example, the application programs may include: email and contact applications, word processing applications, spreadsheet applications, database applications, slide show applications, drawing or computer-aided applications, web browser applications, etc.

[0037] According to one aspect, the examples may be practiced on a circuit comprising discrete electronic components, a packaged or integrated electronic chip containing logic gates, a circuit utilizing a microprocessor, or a single chip containing electronic components or a microprocessor. Figure 1Each or many components shown in can be integrated into a system on a chip (SOC) on a single integrated circuit to practice examples. According to one aspect, such a SOC device may include one or more processing units, a graphics unit, a communication unit, a system virtualization unit, and various application functions, all of which are integrated (or "burned") into a chip substrate as a single integrated circuit. When operated via SOC, the functions described in this article can be operated via a dedicated logic integrated with other components of the computing device 100 on a single integrated circuit (chip). Embodiments of the present invention can also be practiced using other technologies capable of performing logical operations (such as AND, OR, and NOT), including but not limited to mechanical, optical, fluid, and quantum technologies. In addition, embodiments of the present invention can be practiced in a general-purpose computer or in any other circuit or system.

[0038] According to one aspect, the computing device 100 may also have one or more input devices 112, such as a keyboard, a mouse, a pen, a voice input device, a touch input device, etc. Output devices 114 may also be included, such as a display, a speaker, a printer, etc. The aforementioned devices are examples and other devices may also be used. The computing device 100 may include one or more communication connections 116 that allow communication with other computing devices 118. Examples of suitable communication connections 116 include, but are not limited to: RF transmitter, receiver and / or transceiver circuits; Universal Serial Bus (USB), parallel and / or serial ports.

[0039] The term computer-readable medium as used herein includes computer storage media. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (e.g., computer-readable instructions, data structures, or program modules). System memory 104, removable storage 109, and non-removable storage 110 are all examples of computer storage media (i.e., memory storage). Computer storage media may include random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage, cassettes, tapes, disk storage or other magnetic storage devices, or any other products that can be used to store information and can be accessed by computer device 100. According to one aspect, any such computer storage medium can be a part of computing device 100. Computer storage media do not include carrier waves or other propagated data signals.

[0040] According to one aspect, communication media is implemented by computer readable instructions, data structures, program modules, or other data in a modulated data signal (e.g., a carrier wave or other transport mechanism), and includes any information delivery media. According to one aspect, the term "modulated data signal" describes a signal that has one or more characteristics set or changed in a manner that encodes information in the signal. By way of example and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0041] Figure 2 A flowchart of an evaluation method 200 for a comprehensive micro-energy network planning scheme based on new energy sources according to an embodiment of the present invention is shown. The method 200 is suitable for use in a computing device (e.g. Figure 1 Executed in the computing device 100 shown in FIG. Figure 2 As shown, the method begins at 210 .

[0042] In 210, an evaluation index system for the planning scheme of an integrated micro-energy grid with new energy as the main body is constructed. The new energy in the integrated micro-energy grid with new energy as the main body may specifically include photovoltaic equipment and wind power equipment. Among them, considering the diversity and complexity of the market in which the integrated micro-energy grid (IMEG) participates, according to an embodiment of the present invention, the evaluation index system for the planning scheme of the integrated micro-energy grid can be constructed from five dimensions of economy, energy utilization, sociality, market and reliability in accordance with the principles of representativeness, dynamism, comparability, multi-facetedness and independence. Specifically, an evaluation index system for the planning scheme of an integrated micro-energy grid with new energy as the main body (for the sake of ease of description, the integrated micro-energy grid with new energy as the main body is referred to as IMEG below) is constructed with economy, energy utilization, sociality, market and reliability as the first-level evaluation indicators. Among them, in some implementations, each first-level evaluation indicator may further include one or more second-level evaluation indicators.

[0043] Specifically, the secondary evaluation indicators under the economic indicators may include initial investment cost, operation and maintenance cost, network transmission loss cost, dynamic investment payback period and total internal and external benefits of the system; the secondary evaluation indicators under the energy utilization indicators may include comprehensive energy utilization rate, energy adequacy rate and renewable energy penetration rate; the secondary evaluation indicators under the social indicators may include employment benefits, user energy satisfaction and leading level; the secondary evaluation indicators under the market indicators may include market coupling degree, system market power and market price abnormality; the secondary evaluation indicators under the reliability indicators may include system outage duration, system energy supply response time and energy storage equivalent discharge times. Table 1 below shows the evaluation indicator system of a comprehensive micro-energy network planning scheme with new energy as the main body according to an embodiment of the present invention.

[0044] Table 1

[0045]

[0046] Next, each evaluation index and how to obtain each evaluation index are explained in turn.

[0047] 1. Economic indicators: The economic performance of the system is considered from the perspective of the planned investment of IMEG's participation in the market planning program and the internal and external operation of the system. The economic evaluation indicators mainly include two parts: system cost and benefit. The following is an explanation of the various sub-indicators (i.e., second-level evaluation indicators) contained in this first-level evaluation indicator.

[0048] (1) Initial investment cost refers to the sum of all costs involved in the early stage of IMEG construction, mainly including equipment investment cost, loan interest during the construction period and labor cost, which can be specifically expressed as follows.

[0049]

[0050] In the formula, A 11 Represents the initial investment cost, N, M CS , R rg Represent the number of equipment categories, total loan amount and labor cost respectively, a n represents the number of devices in category n, c n It represents the average price of equipment of category n, including equipment purchase cost and installation cost. r and h represent the annual interest rate of the loan granted by the bank for the project and the construction time of IMEG respectively.

[0051] (2) Operation and maintenance costs refer to the costs incurred by IMEG in participating in different markets and operating within the system. From the perspective of the internal and external cost structure of the system, this indicator is divided into market operation and maintenance costs and internal operation and maintenance costs, which can be specifically expressed as the following formula.

[0052]

[0053] In the formula, A 12 represents the operation and maintenance cost, c fn represents the market operation and maintenance cost per unit time of equipment of category n, including labor cost, fuel cost, maintenance cost and other planned operation costs; c vn T represents the system internal operation and maintenance cost per unit time of equipment of category n. n Indicates the operation and maintenance period of equipment of category n.

[0054] (3) Network transmission loss cost refers to the cost incurred when IMEG participates in market transactions and transportation. It can be obtained from the loss and price of various energy sources in each period, and can be specifically expressed as the following formula.

[0055]

[0056] In the formula, A 13 represents the network transmission loss cost, N e Represents the amount of energy, P loss,e (t) represents the network transmission loss of the e-th energy at time t, O e (t) represents the price of the e-th energy at time t.

[0057] (4) Dynamic payback period refers to the time (in years) required for the total revenue from IMEG operation to offset the total life cycle cost. It is used to indicate the ability of different planning schemes to recover the total cost. The specific information can be obtained through the following formula.

[0058]

[0059] In the formula, A 14 represents the dynamic payback period, T HSQ Indicates the year in which the cumulative net cash flow begins to become positive, C LC represents the cumulative net cash flow of the previous year (10,000 yuan), C CY Represents the net cash flow for the year (10,000 yuan).

[0060] (5) Total internal and external benefits of the system, mainly including IMEG’s benefits under the market environment and internal operating benefits in one year. Market benefits include electricity market benefits, hydrogen energy market benefits, thermal energy market benefits, government subsidies and incentive benefits, etc. The annual benefits of the system’s internal loads include electricity, thermal and hydrogen load benefits, which can be specifically expressed as follows.

[0061] A 15 =R D +R R +R Q +R ZF +R NB

[0062] In the formula, A 15 represents the total internal and external benefits of the system, R D represents the electricity market revenue, R R represents the hydrogen energy market revenue, R Q represents the heat market revenue, R ZF represents government subsidies and incentive benefits, R NB Represents the annual internal income of the system.

[0063] 2. Energy utilization index: The energy utilization assessment of IMEG's participation in market planning is a process of quantitative analysis of energy efficiency and market energy utilization, involving multiple aspects. The following is an explanation of the various sub-indicators included in this first-level evaluation index.

[0064] (1) Comprehensive energy utilization rate refers to the ratio of input energy to output energy of the heat storage tank, fuel cell and electric heat pump in the IMEG system. The larger the ratio, the higher the energy utilization efficiency. The comprehensive energy utilization efficiency can be improved by reasonably allocating energy. In some embodiments, the comprehensive energy utilization rate can be specifically expressed as the following formula.

[0065]

[0066] In the formula, B 11 represents the comprehensive utilization rate of energy, O CD Represents the hydrogen energy supplied to the fuel cell, O CR Indicates the heat supplied to the heat storage tank, O CQ Indicates the power consumption of the electric heat pump, ε CD Indicates the fuel cell output energy efficiency, which can be 80%; ε CR Indicates the heat storage and release efficiency of the heat storage tank, and the specific value can be 92%, ε CQ represents the electrothermal conversion coefficient, and its specific value can be 94%. CD , ε CR and ε CQ The above value is just an example and the present invention does not limit it.

[0067] (2) Energy adequacy ratio, which reflects the degree of matching between energy supply and energy demand when IMEG operates in the market participation mode. The higher the value, the higher the degree of IMEG's participation in external market transactions. Regarding this indicator, in some embodiments, it can be obtained specifically through the following formula.

[0068]

[0069] In the formula, B 12 Indicates energy adequacy ratio, ΔW E , ΔW H , ΔW CThey represent the load deviation in the form of electricity, heat, and hydrogen when IMEG participates in external market transactions, respectively. E , W H , W C They respectively represent the total load required by the energy consumption terminals in the system in the form of electricity, heat and hydrogen.

[0070] (3) Renewable energy penetration rate refers to the proportion of renewable energy consumption in the entire energy supply system. The higher the index value, the better the energy utilization effect. Regarding this index, in some embodiments, it can be specifically obtained by the following formula.

[0071]

[0072] In the formula, B 13 represents the renewable energy penetration rate, W E , W H , W C Respectively represent the total load required by the energy consumption terminals in the system in the form of electricity, heat, and hydrogen, W S , W W Represent the internal power supply energy of solar energy and wind energy system, W BUY represents the total amount of electricity sold to the external market, φ RE Indicates the proportion of renewable energy.

[0073] 3. Social indicators: Indicates the impact of IMEG on society. The following is an explanation of the various sub-indicators included in this first-level evaluation indicator.

[0074] (1) Employment benefit refers to the product of the number of jobs created by the IMEG planning projects each year and the average income per employee, which can be expressed as the following formula.

[0075] C 11 =C JY P JY

[0076] In the formula, C 11 represents employment benefit, C JY represents the average income per employed person, P JY Indicates the number of jobs generated by IMEG's planned projects.

[0077] (2) User energy satisfaction refers to the user satisfaction with the IMEG planning project in the region obtained by collecting and analyzing a large amount of user evaluation data in the region through questionnaire surveys.

[0078] (3) Leading level, which mainly reflects the leading level of IMEG-ES planning projects at home and abroad, directly determines whether the projects can obtain policy and financial support in the subsequent stages. Specifically, respondents can evaluate the leading level from three dimensions: innovation, scalability, and representativeness.

[0079] 4. Market indicators: These are important features that reflect IMEG’s planning schemes in different markets. The following is an explanation of the various sub-indicators included in this first-level evaluation indicator.

[0080] (1) Market coupling refers to the ratio of the time when the clearing prices in different regions are the same to the total market transaction time. The more time when the market clearing prices in different regions are the same, the better the inter-regional power liquidity, the higher the market coupling, and the less likely the market transmission channel capacity is to be blocked. Regarding this indicator, in some embodiments, it can be obtained by the following formula.

[0081]

[0082] Where D 11 represents the market coupling degree, φ UIE represents the time when the clearing prices in different regions are the same, γ CO2 Represents the total time of market transactions in different regions.

[0083] (2) System market power refers to the ability of IMEG planning to unilaterally control market prices. This ability allows IMEG to manipulate prices to a certain extent to exceed the competitive level of other participants, thereby increasing its own profits. Regarding this indicator, in some embodiments, it can be specifically obtained by the following formula.

[0084]

[0085] Where D 12 represents the system market power, E Xv Indicates the energy of IMEG to participate in the market, E Nv Represents the total energy of the regional market.

[0086] (3) Market price anomaly refers to the ratio of the time of market price anomaly after IMEG participates in market clearing to the total market transaction time, where it is assumed that the market clearing price is abnormal when it exceeds or is lower than 50% of the market price in the past month. Regarding this indicator, in some embodiments, it can be obtained specifically by the following formula.

[0087]

[0088] Where D 13 Indicates the market price abnormality, T YCJG It indicates the time when the market price anomaly occurs after IMEG-ES participates in the market clearing, TJYSJ Indicates the total time that the IMEG system has participated in market transactions in the past month.

[0089] 5. Reliability index: It represents the reliability of IMEG's participation in market planning to ensure the safe and reliable operation of IMEG. The following is an explanation of the various sub-indicators included in this first-level evaluation index.

[0090] (1) System outage duration refers to the average outage duration suffered by each user supplied by IMEG in one year, which can be specifically expressed as the following formula.

[0091]

[0092] In the formula, E 11 Indicates the duration of system outage, N α,β represents the number of users at load point β in the αth layer network, λ α,β Indicates the power outage time of the αth layer network load point β.

[0093] (2) System energy supply response time refers to the time required for IMEG to receive instructions and the system to fully output power, which can be specifically expressed as the following formula.

[0094] E 12 =t c +t N

[0095] In the formula, E 12 Indicates the system energy supply response time, t c It indicates the time from receiving the command to the power station starting to output power, t N Indicates the time from starting to fully exert force.

[0096] (3) The number of equivalent discharges of energy storage refers to the ratio of the total amount of heat released by the heat storage tank during one year of operation to the rated capacity of the heat storage. The larger the value, the more frequent the heat storage charge and discharge, the more reasonable the configuration capacity of the heat storage tank, and the higher the system reliability. Regarding this indicator, in some embodiments, it can be obtained specifically through the following formula.

[0097]

[0098] In the formula, E 13 Indicates the equivalent discharge times of energy storage, O EES It represents the total heat release of the heat storage tank during one year of operation, O ESD Indicates the rated capacity of the thermal storage tank (kWh).

[0099] The above is the evaluation index system of the integrated micro-energy network planning scheme with new energy as the main body. In some embodiments, IMEG can participate in the electricity market, hydrogen energy market and thermal energy market at the same time, that is, IMEG participates in the electricity-hydrogen-heat joint market. Next, based on the constructed evaluation index system, the evaluation method of IMEG's participation in the electricity-hydrogen-heat joint market planning scheme is explained.

[0100] In 220, the best-worst method and the indicator correlation method are used to obtain the subjective weight and the objective weight of each evaluation indicator in the evaluation indicator system.

[0101] The best-worse-method (BWM) is a subjective weighting method based on pairwise comparison, which is used to determine the relative importance of the index. Its operating principle is to first determine the best / worst index in the evaluation index system, and then compare the relative importance of the best index and other indexes, and other indexes and the worst index. From the perspective of method efficiency, the time complexity of BWM is much lower than that of the traditional hierarchical analysis process, which means that BWM can greatly reduce the time cost. Moreover, as the number of indicators increases, the computational advantage of the BWM method becomes more obvious. The following is an explanation of how to obtain the subjective weights of each evaluation index using the BWM method.

[0102] First, the first-level evaluation indicators with the greatest and least impact on the IMEG planning scheme are determined from all the first-level evaluation indicators, and the first-level evaluation indicator with the greatest impact is used as the best first-level evaluation indicator, and the first-level evaluation indicator with the least impact is used as the worst first-level evaluation indicator; and for each first-level evaluation indicator, the second-level evaluation indicators with the greatest and least impact on the IMEG planning scheme are determined from all its second-level evaluation indicators, and the second-level evaluation indicator with the greatest impact is used as the best second-level evaluation indicator under the first-level evaluation indicator, and the second-level evaluation indicator with the least impact is used as the worst second-level evaluation indicator under the first-level evaluation indicator. The best and worst evaluation indicators can be determined by experts.

[0103] Then, based on the determined optimal first-level evaluation indicators and the worst first-level evaluation indicators, an evaluation matrix of the optimal first-level evaluation indicators and an evaluation matrix of the worst first-level evaluation indicators are constructed; and, for each first-level evaluation indicator, based on the determined optimal second-level evaluation indicators and the worst second-level evaluation indicators, an evaluation matrix of the optimal second-level evaluation indicators and an evaluation matrix of the worst second-level evaluation indicators under the first-level evaluation indicator are constructed.

[0104] Regarding the construction of the evaluation matrix of the optimal first-level evaluation index, specifically, the score of the optimal first-level evaluation index relative to each first-level evaluation index is determined to characterize the importance of the optimal first-level evaluation index relative to each first-level evaluation index. In some embodiments, a score of 1 to 9 can be used to characterize the importance of the optimal first-level evaluation index relative to each first-level evaluation index. Furthermore, for the importance of the optimal first-level evaluation index relative to each first-level evaluation index, a score of 1 can be set to represent that the optimal first-level evaluation index is equally important to the first-level evaluation index (that is, if two evaluation indicators are equally important, the scores of both are 1), and the higher the score, the greater the importance of the optimal first-level evaluation index relative to the first-level evaluation index. The score can be given by an expert. So far, the score of the optimal first-level evaluation index relative to all first-level evaluation indicators is obtained. Next, each score obtained is used as an element to form the evaluation matrix of the optimal first-level evaluation index. The following gives the evaluation matrix A of the optimal first-level evaluation index s: s An example of .

[0105] A s =(a s1 ,a s2 ,…,a sl ,…,a sL )

[0106] In the formula, a s1 、a s2 、a sl 、a sL They respectively represent the scores of the optimal first-level evaluation index s relative to the first first-level evaluation index, the second first-level evaluation index, the lth first-level evaluation index (also referred to as the first-level evaluation index l) and the Lth first-level evaluation index, and L represents the total number of first-level evaluation indicators.

[0107] Regarding the construction of the evaluation matrix of the worst first-level evaluation index, specifically, the score of each first-level evaluation index relative to the worst first-level evaluation index is determined to characterize the importance of each first-level evaluation index relative to the worst first-level evaluation index. In some embodiments, a score of 1 to 9 can be used to characterize the importance of each first-level evaluation index relative to the worst first-level evaluation index. Furthermore, for the importance of each first-level evaluation index relative to the worst first-level evaluation index, a score of 1 can be set to represent that the first-level evaluation index is equally important to the worst first-level evaluation index (that is, if two evaluation indicators are equally important, the scores of both are 1). The higher the score, the greater the importance of the first-level evaluation index relative to the worst first-level evaluation index. The score can be given by an expert. So far, the scores of all first-level evaluation indicators relative to the worst first-level evaluation index are obtained. Next, each score obtained is used as an element to form the evaluation matrix of the worst first-level evaluation index. The following gives the evaluation matrix A of the worst first-level evaluation index y:y An example of .

[0108] A y =(a 1y ,a 2y ,…,a ly ,…,a Ly ) T

[0109] In the formula, a 1y 、a 2y 、a ly 、a Ly They respectively represent the scores of the first first-level evaluation index, the second first-level evaluation index, the lth first-level evaluation index and the Lth first-level evaluation index relative to the worst first-level evaluation index y, and L represents the total number of first-level evaluation indicators.

[0110] Regarding the construction of the evaluation matrix of the best secondary evaluation indicator and the evaluation matrix of the worst secondary evaluation indicator under each first-level evaluation indicator, please refer to the construction of the evaluation matrix of the best first-level evaluation indicator and the evaluation matrix of the worst first-level evaluation indicator respectively, which will not be repeated here.

[0111] Finally, according to the evaluation matrix of the best first-level evaluation indicators and the worst first-level evaluation indicators, the subjective weights of each first-level evaluation indicator are generated. And according to the evaluation matrix of the best second-level evaluation indicators and the worst second-level evaluation indicators under each first-level evaluation indicator, the subjective weights of each second-level evaluation indicator under each first-level evaluation indicator are generated.

[0112] Regarding the subjective weight of each primary evaluation index, according to an embodiment of the present invention, it can be obtained by solving the following formula.

[0113]

[0114] In the formula, represents the optimal solution of the subjective weight of the first-level evaluation index, w s 、w y 、w l They represent the subjective weights of the best first-level evaluation index s, the worst first-level evaluation index y, and the first-level evaluation index l, respectively. sl represents the score of the optimal first-level evaluation index s relative to the first-level evaluation index l, a ly represents the score of the first-level evaluation index l relative to the worst first-level evaluation index y, and L represents the total number of first-level evaluation indicators. In some embodiments, its value can be 5.

[0115] Regarding the subjective weights of each secondary evaluation index under each primary evaluation index, according to an embodiment of the present invention, they can be obtained by solving the following formula.

[0116]

[0117] In the formula, represents the optimal solution of the subjective weight of the secondary evaluation index under the primary evaluation index l, w d 、w f 、w j They represent the subjective weights of the best secondary evaluation index d, the worst secondary evaluation index f, and the secondary evaluation index j, respectively. dj represents the score of the optimal secondary evaluation index d relative to the secondary evaluation index j, a jf represents the score of the secondary evaluation index j relative to the worst secondary evaluation index f, l J Represents the total number of secondary evaluation indicators in the primary evaluation indicator l. Regarding the optimal secondary evaluation indicator d, the worst secondary evaluation indicator f and the secondary evaluation indicator j in this formula, it is explained here that they refer to the optimal secondary evaluation indicator d, the worst secondary evaluation indicator f and the secondary evaluation indicator j under the primary evaluation indicator l. In addition, in some embodiments, the value of l can be specifically A, B, C, D, E, which represent economic indicators, energy utilization indicators, social indicators, market indicators and reliability indicators respectively. Further, these five primary indicators can correspond to the first primary evaluation indicator, the second primary evaluation indicator, the third primary evaluation indicator, the fourth primary evaluation indicator and the fifth primary evaluation indicator respectively.

[0118] Regarding the above two formulas, in some embodiments, MATLAB can be used to solve them, which will not be repeated here. So far, the subjective weights of each evaluation index (including primary evaluation index and secondary evaluation index) are obtained. Next, the method of obtaining the objective weights of each evaluation index using the Criteria Importance Through Intercrieria Correlation (CRITIC) is described.

[0119] First, construct an evaluation index matrix. According to one embodiment of the present invention, the secondary evaluation index values ​​(i.e., the values ​​of the secondary evaluation indexes) of each planning scheme of the integrated micro-energy network with new energy as the main body can be used as a column vector. In some embodiments, the evaluation index matrix can be represented as X. In this way, when there are I planning schemes and each planning scheme has J secondary evaluation indicators, the evaluation index matrix X can be represented as:

[0120]

[0121] In the formula, x jiIt represents the value of the secondary evaluation index j of IMEG planning scheme i, where the value of i is 1, 2…, I, the value of j is 1, 2…, J, I represents the total number of IMEG planning schemes, and J represents the total number of secondary evaluation indicators.

[0122] Then, each evaluation index value in the evaluation index matrix is ​​standardized to obtain a standardized evaluation index matrix. According to an embodiment of the present invention, each evaluation index value in the evaluation index matrix can be standardized by the following formula.

[0123]

[0124] In the formula, x′ ji It represents the standardized value of the secondary evaluation index j of IMEG planning scheme i, max(x j )、min(x j ) respectively represent the maximum and minimum values ​​of the jth row in the evaluation index matrix (i.e. the maximum and minimum values ​​of the secondary evaluation index j of all planning schemes).

[0125] Among them, when using the above formula to standardize the values ​​of each evaluation index in the evaluation index matrix, specifically, for the positive index, use For the reverse index, use In addition, in some embodiments, the standardized evaluation index matrix can be expressed as X′=(x′ ji ) J×I .

[0126] After obtaining the standardized evaluation index matrix, the comparability of each secondary evaluation index and the contradiction of each secondary evaluation index are obtained according to the standardized evaluation index matrix, which can be specifically as follows.

[0127] The comparability of each secondary evaluation index is obtained through the following formula:

[0128]

[0129] In the formula, f j Indicates the contrast of the secondary evaluation index j. The larger the value, the greater the numerical difference of the index, the stronger the evaluation, the more information it can reflect, and the more weight needs to be allocated; It represents the average value of the jth row in the standardized evaluation index matrix (i.e. the average value of the secondary evaluation index j of all planning schemes after standardization).

[0130] The contradiction of each secondary evaluation index is obtained through the following formula:

[0131]

[0132] In the formula, ρ j Indicates the contradiction of the secondary evaluation index j, which represents the correlation between the secondary evaluation index j and other secondary rating indicators. The stronger the correlation, the higher the correlation. j The smaller it is, the lower the weight; kj The correlation coefficient between the secondary evaluation index k and the secondary evaluation index j may be, in some embodiments, the Pearson correlation coefficient.

[0133] Next, based on the comparison and contradiction of each secondary evaluation index, the information carrying capacity of each secondary evaluation index is obtained by using the index correlation method. In some embodiments, the information carrying capacity of each secondary evaluation index can be obtained by the following formula:

[0134] F j =ρ j f j

[0135] In the formula, F j It represents the information carrying capacity of the secondary evaluation index j. The larger the value is, the greater the role of the secondary evaluation index j in the entire evaluation index system.

[0136] Finally, according to the information carrying capacity of each secondary evaluation indicator, the objective weight of each secondary evaluation indicator is obtained. In some embodiments, the objective weight of each secondary evaluation indicator can be obtained by the following formula.

[0137]

[0138] In the formula, r j represents the objective weight of the secondary evaluation index j, F k Represents the information carrying capacity of the secondary evaluation index k.

[0139] After obtaining the objective weights of the secondary evaluation indicators, according to one embodiment of the present invention, the objective weights of the primary evaluation indicators can be obtained by summing the objective weights of all the secondary evaluation indicators under the primary evaluation indicators. That is, the objective weight of each primary evaluation indicator is the sum of the objective weights of all the secondary evaluation indicators under the primary evaluation indicator.

[0140] So far, the subjective weight and objective weight of each evaluation index are obtained. Next, the combined weight is calculated based on the obtained subjective weight and objective weight. Among them, in order to reduce subjective arbitrariness while achieving consistency between subjective and objective weighting, this embodiment adopts the least squares comprehensive weight method, which is as follows.

[0141] In 230 , based on the obtained subjective weight and objective weight of each evaluation index, a least square method is used to obtain a combined weight of each evaluation index.

[0142] Specifically, first, a combined weight acquisition model is constructed with the goal of minimizing the sum of squares of the first deviation and the second deviation. The first deviation is the deviation between the combined weight and the subjective weight, and the second deviation is the deviation between the combined weight and the objective weight. That is, a combined weight acquisition model is established with the goal of minimizing the sum of squares of the deviations of "the deviation between the combined weight and the subjective weight" and "the deviation between the combined weight and the objective weight". According to one embodiment of the present invention, the combined weight acquisition model may include an objective function and constraints, as follows.

[0143] The objective function is:

[0144]

[0145] The constraints are:

[0146]

[0147] u j ≥0,j=1,2,3…,J

[0148] Where H(u) is the objective function of the combined weight, which represents the difference between the combined weight and the subjective and objective weights (i.e., subjective weight and objective weight), and u j represents the combined weight of the secondary evaluation index j, w j represents the subjective weight of the secondary evaluation index j, r j represents the objective weight of the secondary evaluation index j, x′ ji It represents the standardized value of the secondary evaluation index j of IMEG planning scheme i, I represents the total number of IMEG planning schemes, and J represents the total number of secondary evaluation indicators.

[0149] Then, the obtained subjective weight and objective weight of each secondary evaluation index are input into the constructed model, and the solution is performed to obtain the combined weight of each secondary evaluation index. After obtaining the combined weight of each secondary evaluation index, according to one embodiment of the present invention, the combined weight of each primary evaluation index can be obtained by summing the combined weights of all secondary evaluation indicators under each primary evaluation index. That is, the combined weight of each primary evaluation index is the sum of the combined weights of all secondary evaluation indicators under the primary evaluation index.

[0150] At this point, the combined weights of the evaluation indicators are obtained. Next, enter 240, according to the evaluation indicator values ​​of each planning scheme of the integrated micro energy network and the obtained combined weights of each evaluation indicator, use the improved cloud matter element model that considers both the clarity and fuzziness of the level boundary to obtain the comprehensive score of each planning scheme, and determine the planning scheme with the highest comprehensive score as the final planning scheme for the integrated micro energy network to participate in the electricity-hydrogen-heat joint market.

[0151] In the cloud-matter-element model, clouds are composed of a large number of cloud droplets, each of which is fuzzy and random. n When cloud entropy is calculated using the "3E" principle, the judgment of the level boundary is very strict, while when cloud entropy is calculated using the "50% correlation" principle, the judgment of the level boundary is very vague. Based on this, this implementation proposes an improved cloud matter-element model, which is based on the "3E n The "principle" and "50% correlation" principles are used to optimize and improve cloud entropy, while taking into account the clarity and fuzziness of the level boundaries, making the evaluation results more reasonable. The following is an explanation of the improved cloud matter-element model.

[0152] Specifically, the calculation formula of cloud entropy is: d determines the blurriness of the cloud model. The larger the value, the higher the blurriness. min ,c max The probability outside the range is defined as the fuzzy acceptance degree z of the evaluator. Through Monte Carlo simulation, multiple sets of values ​​about (z, d) are obtained. The curve is shown in Figure 3 As shown. Figure 3 It can be seen that the fuzzy acceptance z is negatively correlated with the denominator d and decreases as the d value increases.

[0153] After obtaining the curve, this embodiment first performs a 4th-order polynomial mathematical calculation on the curve to obtain the following expression z(d):

[0154] z(d)=0.0012d 4 -0.0066d 3 +0.0382d 2 -0.3035d+1.021

[0155] Then, the fuzzy acceptance is set to 10%, and the cloud entropy is optimized to obtain d = 3.2. So far, the improved cloud matter-element model is obtained, as follows.

[0156]

[0157] Among them, E xqg 、E nqg , H eqg They represent the cloud expectation value, cloud entropy and cloud super entropy of the evaluation index q corresponding to level g, respectively, and C qg,max , C qg,minThey represent the maximum and minimum values ​​of the numerical range of the evaluation index q under level g. In addition, two points are explained here. First, the evaluation index q can be a primary evaluation index or a secondary evaluation index. Second, the classification of the levels can be set by experts. For example, the numerical range can be divided into five different levels: excellent, good, medium, general and poor. Of course, this is only an example and the present invention is not limited to this.

[0158] Next, the comprehensive scores of various planning schemes for the integrated micro-energy network are obtained by using the improved cloud-matter element model according to the evaluation index values ​​of the various planning schemes and the combined weights of the evaluation indexes.

[0159] In the first step, the improved cloud-matter-element model is used to obtain the cloud expected value, cloud entropy and cloud super entropy of each secondary evaluation index at each level, as follows.

[0160]

[0161] In the formula, E xjg 、E njg , H ejg They represent the cloud expectation value, cloud entropy and cloud super entropy of the secondary evaluation index j corresponding to level g, c jg,max 、c jg,min They respectively represent the maximum and minimum values ​​of the numerical range of the secondary evaluation index j at level g.

[0162] In the second step, according to the values ​​of each secondary evaluation index of each planning scheme, the combined weights of each secondary evaluation index, and the cloud expected value, cloud entropy, and cloud super entropy of each secondary evaluation index at each level, multiple Monte Carlo simulations are performed to obtain multiple scores for each planning scheme. In each Monte Carlo simulation, the scores of each planning scheme can be obtained in the following way.

[0163] First, for each planning scheme, according to the values ​​of each secondary evaluation index and the cloud expected value, cloud entropy and cloud super entropy of each secondary evaluation index at each level, the cloud correlation of each secondary evaluation index of the planning scheme corresponding to each level is obtained. In some embodiments, the cloud correlation of each secondary evaluation index of each planning scheme corresponding to each level can be obtained by the following formula.

[0164]

[0165] Where P ijg (x ij ) represents the cloud correlation degree of the second-level evaluation index j of planning scheme i corresponding to level g, x ij represents the value of the secondary evaluation index j of planning scheme i, E xjg represents the expected value of the cloud of level g corresponding to the secondary evaluation index j, E′ njgIt represents the cloud entropy E corresponding to level g with the secondary evaluation index j njg is the mean value, and the cloud super entropy H of the secondary evaluation index j corresponding to level g ejg is a normal random number with standard deviation .

[0166] Next, according to the obtained cloud correlations of each level corresponding to each secondary evaluation indicator of each planning scheme and the combined weights of each secondary evaluation indicator, the cloud correlations of each level corresponding to each primary evaluation indicator of each planning scheme are obtained. According to one embodiment of the present invention, the cloud correlations of each level corresponding to each primary evaluation indicator of each planning scheme can be obtained by the following formula.

[0167]

[0168] In the formula, It represents the cloud correlation degree of the first-level evaluation index l corresponding to the level g of the second-level evaluation index j of planning scheme i, P ijg (x ij ) represents the cloud correlation degree of the second-level evaluation index j of planning scheme i corresponding to level g, u j represents the combined weight of the secondary evaluation index j, l J Represents the total number of secondary evaluation indicators in the first-level evaluation indicator l.

[0169] Finally, based on the obtained cloud correlations of each level corresponding to each first-level evaluation index of each planning scheme, the score of each planning scheme is obtained. According to one embodiment of the present invention, the cloud correlations of each level corresponding to each first-level evaluation index of each planning scheme can be normalized first, as shown in the following formula.

[0170]

[0171] In the formula, It represents the cloud correlation degree of level g corresponding to the first-level evaluation index l of the planning scheme i after normalization, It indicates the cloud correlation degree of level g corresponding to the first-level evaluation index l of planning scheme o. They respectively represent the maximum and minimum values ​​of the cloud correlation degree of the first-level evaluation index l corresponding to level g of all planning schemes.

[0172] Next, the score of each planning scheme is obtained by the following formula.

[0173]

[0174] In the formula, Q i represents the score of planning scheme i, u l represents the combined weight of the first-level evaluation index l, represents the score of the first-level evaluation index l of planning scheme i, It represents the cloud correlation degree of the first-level evaluation index l corresponding to the level g of the planning scheme i after normalization, G represents the total number of levels, and L represents the total number of first-level evaluation indicators. Among them, when G = 5, The value range of is [1,5]; when and The maximum value is 5; and The minimum value is 1.

[0175] The third step is to calculate the average of multiple scores of each planning scheme and use it as the comprehensive score of each planning scheme. Specifically, for each planning scheme, the average of its scores obtained from multiple simulations is calculated, and then this average is used as the comprehensive score of the planning scheme.

[0176] Furthermore, in some embodiments, the scoring results obtained by multiple simulations are statistically analyzed to provide a 90% confidence interval V i , as shown in the following formula.

[0177] V i =[F i -1.628G i ,F i +1.628G i ]

[0178] In the formula, F i , G i Respectively represent Q i The mean and standard deviation are 1.628, which is the quantile corresponding to the 90% confidence interval of the normal distribution.

[0179] At this point, the comprehensive scores of each planning scheme have been obtained. Next, the planning scheme with the highest comprehensive score can be determined as the final planning scheme for the integrated micro-energy network with new energy as the main body to participate in the electricity-hydrogen-heat joint market by sorting the comprehensive scores in descending order.

[0180] Among them, regarding the various planning schemes for the comprehensive micro-energy network with new energy as the main body to participate in the electricity-hydrogen-heat joint market, it is explained here that it can be composed of multiple equipment including photovoltaics, wind power, electric heat pumps, fuel cells, alkaline electrolyzers, heat storage tanks, hydrogen storage tanks, and batteries.

[0181] The above is the evaluation method of the comprehensive micro-energy network planning scheme with new energy as the main body of the present invention. Further, in order to better understand the evaluation method, the present invention also gives an example of building an IMEG project in a certain area.

[0182] 1. Basic data

[0183] The planned construction and operation period of the project is 25 years. Specifically, the maximum electricity, heat and hydrogen loads within IMEG are 3.2MW, 2.6MW and 0.8MW respectively, the typical daily average wind speed in four seasons is 7.54m / s, and the maximum light intensity is 800W / m 2 The average daily electricity market clearing price in the region during the four seasons is 0.7 yuan / kWh, the heat market clearing price is 0.7 yuan / kWh, and the hydrogen market clearing price is 2.4 yuan / m 3 Based on the above regional resource conditions, 10 planning and investment plans were designed, as shown in Table 2.

[0184] Among the different market planning schemes for IMEG participation shown in Table 2, Schemes 1, 2, 3 and 4 compare the advantages and disadvantages of investing in fuel cells, electric heat pumps and batteries when participating in the external electricity-hydrogen-heat joint market at the same time, Schemes 5, 6 and 7 compare the advantages and disadvantages of investing in fuel cells and electric heat pumps when participating in the external electricity-heat joint market at the same time, Schemes 8, 9 and 10 compare the advantages and disadvantages of investing in fuel cells and electric heat pumps when participating in the external electricity-hydrogen joint market at the same time, Schemes 1, 5 and 8 compare the advantages and disadvantages of participating in different markets when using fuel cells and electric heat pump equipment for heating at the same time, Schemes 3, 6 and 9 compare the advantages and disadvantages of participating in different markets when only electric heat pump equipment is used for heating, and Schemes 4, 7 and 10 compare the advantages and disadvantages of participating in different markets when only fuel cell equipment is used for heating. In addition, Table 3 also shows the energy supply of the equipment participating in the market in each scheme.

[0185] Table 2

[0186]

[0187] Table 3

[0188]

[0189] IMEG's equipment for the electricity-heat-hydrogen joint market planning includes wind turbines, photovoltaic generators, fuel cells, electric heat pumps, alkaline electrolyzers, batteries, heat storage tanks and hydrogen storage tanks. The equipment capacity planning data under each plan is shown in Table 4 below, and the economic and technical data of each equipment is shown in Table 5 below.

[0190] Table 4

[0191]

[0192] Table 5

[0193]

[0194] 2. Calculation of subjective and objective combined weights

[0195] The IMEG evaluated in this example is a grid-connected system that sells electricity, heat, and hydrogen to the external market. Assume that the annual average electricity market clearing price of IMEG is 0.7 yuan / kWh, the heat market clearing price is 0.7 yuan / kWh, and the hydrogen market clearing price is 2.4 yuan / m 3 The system sells electricity at 0.8 yuan / kWh, heat at 0.6 yuan / kWh, and hydrogen at 2.4 yuan / m 3 The specific values ​​of each indicator can be calculated using the above indicator formula based on the given planning scheme data and the constructed indicator evaluation system of IMEG's participation in the market planning scheme. The following Table 6 shows the calculation results of the initial indicators under each scheme.

[0196] Table 6

[0197]

[0198] 1) Calculation of subjective weight using the BWM method

[0199] The first-level evaluation indicators of the IMEG participation market planning program constructed in this implementation include: economic evaluation indicators, energy utilization evaluation indicators, social evaluation indicators, market evaluation indicators and reliability evaluation indicators, a total of 5 first-level indicators. Five experts were invited to form a scoring team to compare the IMEG evaluation indicators. Each expert compared and scored according to 1-9, and then the BWM method was used to calculate the subjective weight.

[0200] ① Calculation of primary indicator weights

[0201] By inviting 5 experts to compare and score the 5 first-level indicators, it was found that economic efficiency (A1) was the best criterion and social efficiency (C1) was the worst criterion. The specific results of the pairwise comparison of the first-level indicators are shown in Table 7 below.

[0202] Table 7

[0203]

[0204] ② Secondary indicator weight calculation

[0205] The economic indicators include initial investment cost, operation and maintenance cost, network transmission loss cost, dynamic investment payback period and total internal and external benefits of the system. By inviting 5 experts to compare and score the 5 economic indicators, the total internal and external benefits of the system (A 15 ) is the optimal criterion, network transmission loss cost (A 13 ) is the worst criterion, and the specific comparison of economic indicators in pairs is shown in Table 8 below.

[0206] Table 8

[0207]

[0208]

[0209] Energy utilization indicators include three evaluation indicators: comprehensive energy utilization rate, energy adequacy rate and renewable energy penetration rate. By inviting five experts to compare and score the three energy utilization indicators, the comprehensive energy utilization rate (B 11 ) is the optimal criterion, renewable energy penetration rate (B 13 ) is the worst criterion, and the specific comparison of energy utilization indicators in pairs is shown in Table 9.

[0210] Table 9

[0211]

[0212] Social indicators include employment benefits, user energy satisfaction and leading level. By inviting five experts to compare and score the three social indicators, the user energy satisfaction (C 12 ) is the optimal criterion, leading level (C 13 ) is the worst criterion. The specific comparison of social indicators in pairs is shown in Table 10 below.

[0213] Table 10

[0214]

[0215] The market indicators include market coupling, system market power and market price anomaly. By inviting five experts to compare and score the three market indicators, the system market power (D 12 ) is the optimal criterion, market price abnormality (D 13 ) is the worst criterion. The specific comparison of market indicators in pairs is shown in Table 11 below.

[0216] Table 11

[0217]

[0218] The reliability index includes three evaluation indicators: system outage duration, system energy supply response time and energy storage equivalent discharge times. By inviting five experts to compare and score the three reliability indicators, the system energy supply response time (E 12 ) is the optimal criterion, the equivalent discharge times of energy storage (E 13 ) is the worst criterion. The specific comparison of market indicators in pairs is shown in Table 12.

[0219] Table 12

[0220]

[0221] The above comparison results are organized into a matrix form. Table 13 below shows the judgment matrix of the secondary indicators obtained by the organization.

[0222] Table 13

[0223]

[0224] After completing the pairwise comparison of the secondary indicators, MATLAB is used to solve the following linear programming function based on the indicator judgment matrix at each level to generate the weight coefficients of all indicators.

[0225] Calculation of primary indicator weights:

[0226]

[0227] Secondary indicator weight calculation:

[0228]

[0229] The subjective weights of indicators at all levels are obtained through calculation, as shown in Table 14 below.

[0230] Table 14

[0231]

[0232] After sorting out the calculation contents, we can see that among the 17 secondary indicators, the initial investment cost (A 11 ), total internal and external benefits of the system (A 15 ), comprehensive energy utilization rate (B 11 ), user energy satisfaction (C 12 ) have a weight of more than 0.1, the highest among the 17 secondary indicators, indicating that these four indicators are the most important for selecting IMEG to participate in the market planning program; and the equivalent discharge times of energy storage (E 13 ), leading level (C 13 ), network transmission loss cost (A 13 ), renewable energy penetration rate (B 13 ) The weights of the four indicators are lower than 0.03, which is the lowest among the 17 indicators, indicating that IMEG is not important in selecting market planning programs.

[0233] 2) CRITIC method objective weight calculation

[0234] By using the indicator data of each scheme in Table 6, an evaluation matrix X consisting of 17 indicators of 10 evaluation schemes can be constructed. ji ) 17×10 , where i = 1, 2, ..., 10; j = 1, 2, ..., 17. Then the objective indicator evaluation matrix of the CRITIC method is obtained as follows:

[0235]

[0236] By performing standardized calculations on the secondary indicator data, the standardized indicator calculation results are obtained, as shown in Table 15 below.

[0237] Table 15

[0238]

[0239]

[0240] After completing the standardized calculation of the secondary indicators, the objective weights were calculated using the CRITIC model and solved using MATLAB to obtain the final objective indicator weight results as shown in Table 16 below.

[0241] Table 16

[0242]

[0243] 3) Least squares method combined weight calculation

[0244] Based on the least squares model, MATLAB is used to calculate the final weight. Among them, the weight of the first-level index combination is as follows: Figure 4 As shown in Table 17 and Figure 5 shown.

[0245] Table 17

[0246]

[0247]

[0248] As can be seen from Table 17, there are certain differences between the BWM method and the CRITIC weighting results, which shows that the use of a reasonable combination method to combine indicator weights is of great value. The combined weighting method of the "least squares method" is used to solve the comprehensive weights of different indicators, which are all between the maximum and minimum values ​​of the BWM method and the CRITIC method. The results show that the least squares combined weighting method proposed in this embodiment is scientific, comprehensive and fair.

[0249] from Figure 5 It can be seen that the combined weights of most secondary indicator layers are concentrated between 3% and 10%, and the values ​​are relatively average. Among them, the initial investment cost and the total internal and external benefits of the system in the economic indicators have higher weights, indicating that the initial investment and operating benefits of IMEG's participation in the electricity-hydrogen-heat market planning scheme are more important; while the weights of the energy storage equivalent discharge times, leading level and renewable energy penetration rate are lower, mainly because these two indicators have little impact on IMEG's planning scheme for participating in the market and are relatively less important.

[0250] 3. Improve the comprehensive evaluation results of the cloud-physical element model

[0251] 1) Establishment and analysis of cloud-physical element model

[0252] Taking system market power as an example, Figure 6 From left to right, they show the n The cloud-matter-element model grade is normally distributed under the "principle", "50% correlation" principle and optimized cloud entropy, and the value range is divided into five different levels: excellent, good, medium, general and poor.

[0253] pass Figure 6 It can be seen that the use of "3E n The cloud-matter-element model that determines cloud entropy by the "50% correlation" principle is very clear in the division of grade boundaries, which reflects that the evaluator uses very strict evaluation criteria; while the cloud-matter-element model that determines cloud entropy by the "50% correlation" principle is relatively vague in the division of grade boundaries, that is, the index belonging to a certain numerical level is prone to shift to the adjacent level. The cloud-matter-element model obtained by the optimized cloud entropy calculation method properly balances the clarity and ambiguity at the grade boundaries, providing evaluators with a quantitative tool when dealing with the degree of ambiguity.

[0254] 2) Overall evaluation results of the planning scheme

[0255] Using the improved cloud-matter-element model, we can obtain three numerical eigenvalues ​​of the secondary evaluation indicators at five evaluation levels. The results are shown in Tables 18, 19 and 20 below.

[0256] Table 18

[0257]

[0258]

[0259] Table 19

[0260]

[0261] Table 20

[0262]

[0263] Based on the above improved cloud-matter-element model, the overall scores of each scheme with a 90% confidence interval of the comprehensive score are calculated through 3000 Monte Carlo simulations, and the results are shown in Table 21. As can be seen from Table 21, the improved cloud-matter-element model ranks the 10 schemes from best to worst as follows: 6, 1, 10, 5, 3, 8, 2, 4, 7, 9.

[0264] Table 21

[0265]

[0266] In summary, according to the evaluation method of the comprehensive micro-energy network planning scheme with new energy as the main body of the present invention, by integrating the subjective and objective weighting methods and using the least squares method to optimize the combination of weights, the differences between subjective and objective evaluations can be effectively balanced, so that the accuracy of the combined weights of the evaluation indicators can be ensured. In addition, by adopting the improved cloud-matter element model that takes into account the clarity and fuzziness of the level boundaries to obtain the comprehensive score of the planning scheme, the uncertainty and fuzziness caused by the evaluation indicators with strong correlation can be reduced. Therefore, the present invention can effectively improve the accuracy of the evaluation results. In addition, the present invention is an evaluation index system established from five dimensions: economy, energy utilization, sociality, market and reliability. Through this multi-dimensional evaluation system, the accuracy and comprehensiveness of the evaluation results can be further improved.

[0267] The various techniques described herein may be implemented in combination with hardware or software, or a combination thereof. Thus, the method and apparatus of the present invention, or some aspects or parts of the method and apparatus of the present invention may be in the form of program codes (i.e., instructions) embedded in a tangible medium, such as a removable hard disk, a USB flash drive, a floppy disk, a CD-ROM, or any other machine-readable storage medium, wherein when the program is loaded into a machine such as a computer and executed by the machine, the machine becomes a device for practicing the present invention.

[0268] In the specification provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be put into practice without these specific details. In some instances, known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this specification. In addition, unless otherwise specified, the use of ordinal numbers "first", "second", "third" or the like to describe common objects only represents different instances involving similar objects, and is not intended to imply that the objects described in this way must have a given order in time, space, ordering or in any other way.

[0269] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments may be envisioned within the scope of the invention thus described. In addition, it should be noted that the language used in this specification is selected primarily for readability and teaching purposes, rather than for explaining or defining the subject matter of the present invention. Therefore, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is illustrative, not restrictive, with respect to the scope of the present invention, which is defined by the appended claims.

Claims

1. An evaluation method for a comprehensive micro-energy network planning scheme based on new energy, comprising: Constructing an evaluation index system for planning schemes of integrated micro-energy networks with new energy as the main body, wherein the integrated micro-energy network participates in the electricity-hydrogen-heat joint market; The subjective weight and the objective weight of each evaluation index in the evaluation index system are obtained by using the best and worst method and the index correlation method respectively; According to the subjective weight and objective weight of each evaluation index, the combined weight of each evaluation index is obtained by using the least square method; According to the evaluation index values ​​of each planning scheme of the integrated micro-energy network and the combined weights of the evaluation indexes, an improved cloud-matter-element model that simultaneously considers the clarity and fuzziness of the level boundaries is used to obtain the comprehensive scores of each planning scheme, and the planning scheme with the highest comprehensive score is determined as the final planning scheme for the integrated micro-energy network to participate in the electricity-hydrogen-heat joint market.

2. The method of claim 1, wherein: The improved cloud-matter element model includes: Among them, E xqg 、E nqg , H eqg They represent the cloud expectation value, cloud entropy and cloud super entropy of the evaluation index q corresponding to level g, and c qg,max 、c qg,min They respectively represent the maximum and minimum values ​​of the numerical range of the evaluation index q at level g.

3. The method according to claim 1 or 2, wherein: The evaluation index system includes two levels of evaluation indexes, primary and secondary, and the improved cloud-matter element model is used to obtain a comprehensive score for each planning scheme, including: The improved cloud-matter-element model is used to obtain the cloud expected value, cloud entropy and cloud super entropy of each secondary evaluation index at each level. According to the values ​​of each secondary evaluation index of each planning scheme, the combined weights of each secondary evaluation index, and the cloud expected value, cloud entropy, and cloud super entropy of each secondary evaluation index at each level, multiple Monte Carlo simulations are performed to obtain multiple scores for each planning scheme. The average of the multiple scores of each planning scheme is calculated and used as the comprehensive score of each planning scheme.

4. The method of claim 3, wherein: In each Monte Carlo simulation, the score of each planning scheme is obtained in the following way: For each planning scheme, according to the values ​​of each secondary evaluation index and the cloud expected value, cloud entropy and cloud super entropy of each secondary evaluation index at each level, obtain the cloud correlation degree of each secondary evaluation index corresponding to each level of the planning scheme; According to the obtained cloud correlations of each level corresponding to each secondary evaluation indicator and the combined weights of each secondary evaluation indicator, the cloud correlations of each level corresponding to each primary evaluation indicator of the planning scheme are obtained; Based on the obtained cloud correlations of each first-level evaluation indicator corresponding to each level, the score of the planning scheme is obtained.

5. The method of claim 4, wherein: For each planning scheme, according to the values ​​of each secondary evaluation index and the cloud expected value, cloud entropy and cloud super entropy of each secondary evaluation index at each level, the cloud correlation of each secondary evaluation index corresponding to each level of the planning scheme is obtained, and the cloud correlation of each secondary evaluation index corresponding to each level is obtained by the following formula: Among them, P ijg (x ij ) represents the cloud correlation degree of the second-level evaluation index j of planning scheme i corresponding to level g, x ij represents the value of the secondary evaluation index j of planning scheme i, E xjg represents the expected value of the cloud of level g corresponding to the secondary evaluation index j, E′ njg It represents the cloud entropy E corresponding to level g with the secondary evaluation index j njg is the mean value, and the cloud super entropy H of the secondary evaluation index j corresponding to level g ejg is a normal random number with standard deviation .

6. The method according to claim 4 or 5, wherein: In the process of obtaining the cloud correlation degree of each level corresponding to each level of each secondary evaluation indicator and the combined weight of each secondary evaluation indicator, the cloud correlation degree of each level corresponding to each primary evaluation indicator of the planning scheme is obtained. The cloud correlation degree of each level corresponding to each primary evaluation indicator is obtained by the following formula: in, It represents the cloud correlation degree of the first-level evaluation index l corresponding to the level g of the second-level evaluation index j of planning scheme i, P ijg (x ij ) represents the cloud correlation degree of level g corresponding to the secondary evaluation index j of planning scheme i, u j represents the combined weight of the secondary evaluation index j, l J Represents the total number of secondary evaluation indicators in the first-level evaluation indicator l.

7. The method according to any one of claims 4 to 6, wherein: In obtaining the score of the planning scheme based on the cloud correlation of each level corresponding to each first-level evaluation indicator, the score of the planning scheme is obtained by the following formula: Among them, Q i represents the score of planning scheme i, u l represents the combined weight of the first-level evaluation index l, represents the score of the first-level evaluation index l of planning scheme i, It represents the cloud correlation degree of level g corresponding to the first-level evaluation index l of the planning scheme i after normalization, G represents the total number of levels, and L represents the total number of first-level evaluation indicators.

8. The method according to any one of claims 1 to 7, wherein: The first-level evaluation indicators in the evaluation indicator system include economic indicators, energy utilization indicators, social indicators, market indicators and reliability indicators; The secondary evaluation indicators under the economic indicators include: initial investment cost, operation and maintenance cost, network transmission loss cost, dynamic investment payback period and total internal and external benefits of the system; The secondary evaluation indicators under the energy utilization index include: comprehensive energy utilization rate, energy adequacy rate and renewable energy penetration rate; The secondary evaluation indicators under the social indicators include: employment benefits, user energy satisfaction and leading level; The secondary evaluation indicators under the market indicators include: market coupling, system market power and market price anomaly; The secondary evaluation indicators under the reliability index include: system outage duration, system energy supply response time and energy storage equivalent discharge times.

9. A computing device comprising: at least one processor; as well as A memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, and the program instructions include instructions for executing the method according to any one of claims 1 to 8.

10. A readable storage medium storing program instructions, when the program instructions are read and executed by a computing device, the computing device executes the method according to any one of claims 1 to 8.

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