Nano-micro grid group multi-dimensional state sustainability evaluation index system construction method and system

Abstract

The invention discloses a method and a system for constructing a multi-dimensional state sustainability evaluation index system of a nano-micro grid group. The method comprises the following steps: constructing four key sustainability indexes of economy, environment, technology and society; the key sustainability indexes are subdivided into sub-indexes such as electric power cost, operation and maintenance cost, initial investment cost, return on investment, CO2 emission, land utilization, unsatisfied load, excess electric quantity and the like; the comprehensive value of economic, environmental, technical and social indicator priorities is calculated using a neural network expert system. In one embodiment, seven microgrid scenarios that integrate locally accessible resources are scored and ranked with iHOGA PRO + software to determine an appropriate configuration. Compared with the existing method, the method provided by the invention is helpful for related policy formulators and investors to better plan the micro-grid on the basis of sustainable development.

Description

Technical Field

[0001] This invention belongs to the field of microgrid operation technology, and in particular relates to a method and system for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups. Background Technology

[0002] Microgrids, as a complete power distribution system encompassing elements of "source-grid-load-storage," have expanded their application scenarios from initially solving power supply problems in remote areas (such as islands and rural mountainous regions) to include industrial park microgrids and urban area microgrids. While the introduction of new energy power generation technologies has effectively reduced carbon emissions, it has also brought new ecological and environmental problems, such as damage to birds from wind turbine blades, local climate change, and noise pollution affecting residents' lives. Therefore, in the development and application of new energy sources, in addition to considering resource constraints, environmental protection requirements must also be taken into account, such as avoiding damage to the growth and habitat of plants and animals and minimizing negative impacts on the living environment of residents.

[0003] Currently, microgrid planning and operation analysis mainly focus on technical feasibility and economics, with a lack of comprehensive environmental impact assessment. Traditional methods typically center on a single indicator (such as cost or carbon emissions), lacking a comprehensive consideration of multi-dimensional sustainability indicators. Furthermore, microgrids vary widely in scale and application scenarios, ranging from small "grids" containing only a single distribution substation to complex systems encompassing multiple distribution substation-level microgrids. This diversity complicates unified sustainability assessment, necessitating a comprehensive evaluation system that can take into account economic, environmental, technical, and social indicators.

[0004] In existing technologies, microgrid evaluation methods often rely on single software tools or simple weighted scoring, making it difficult to comprehensively reflect the system's sustainability. For example, some methods focus only on economic indicators (such as electricity costs and return on investment), neglecting environmental and social benefits; others lack dynamic assessments of future technological development. Furthermore, traditional methods often depend on subjective experience in the allocation of indicator weights and comprehensive evaluation processes, lacking scientific rigor and objectivity, resulting in insufficient credibility of the evaluation results.

[0005] Therefore, there is an urgent need for an innovative method and system for constructing a multi-dimensional state sustainability evaluation index system for nano-micro power grids to overcome the shortcomings of existing technologies. Summary of the Invention

[0006] This invention provides a method and system for constructing a multidimensional state sustainability evaluation index system for nano-micro power grids, which addresses the technical problem that the evaluation results often rely on subjective experience and lack scientific rigor and objectivity in the process of index weight allocation and comprehensive evaluation, resulting in insufficient credibility.

[0007] In a first aspect, the present invention provides a method for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups, comprising:

[0008] A sustainability evaluation index system for microgrids is constructed, which includes economic, environmental, technological, and social dimensions.

[0009] The economic dimension is further subdivided into sub-indicators of electricity cost, operation and maintenance cost, initial investment cost, and return on investment.

[0010] The environmental dimension is further subdivided into carbon dioxide emission sub-indicators and land use sub-indicators;

[0011] The aforementioned technical dimensions are further subdivided into unmet load sub-indicators, excess power sub-indicators, and future technology sub-indicators.

[0012] The social dimension is further subdivided into job creation sub-indicators, comfortable living sub-indicators, and ecosystem sub-indicators;

[0013] The overall priority of each key sustainability indicator is calculated using a neural network expert system;

[0014] The iHOGA PRO+ software was used to score and rank microgrid scenarios to determine the optimal configuration.

[0015] Secondly, this invention provides a system for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups, comprising:

[0016] The module is configured to build a sustainability evaluation index system for microgrids, which includes economic, environmental, technological and social dimensions.

[0017] The first differentiation module is configured to subdivide the economic dimension into sub-indicators of electricity cost, operation and maintenance cost, initial investment cost and return on investment.

[0018] The second differentiation module is configured to subdivide the environmental dimension into carbon dioxide emission sub-indicators and land use sub-indicators.

[0019] The third differentiation module is configured to subdivide the technical dimension into unmet load sub-indicators, excess power sub-indicators, and technical future sub-indicators.

[0020] The fourth differentiation module is configured to subdivide the social dimension into sub-indicators for job creation, comfortable living, and ecosystem.

[0021] The calculation module is configured to use a neural network expert system to calculate the overall priority of each key sustainability indicator;

[0022] The module was selected and configured to use iHOGA PRO+ software to score and rank microgrid scenarios, and the optimal configuration was determined.

[0023] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the method for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups according to any embodiment of the present invention.

[0024] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the method for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups according to any embodiment of the present invention.

[0025] This application presents a method and system for constructing a multi-dimensional state sustainability evaluation index system for microgrid clusters, including four key sustainability indicators: economic, environmental, technological, and social. These key sustainability indicators are further subdivided into sub-indicators such as electricity cost, operation and maintenance cost, initial investment cost, return on investment, CO2 emissions, land use, unmet load, and excess power. The comprehensive value of the priority of economic, environmental, technological, and social indicators is calculated using a neural network expert system. Seven microgrid scenarios integrating locally accessible resources were scored and ranked using iHOGAPRO+ software to determine suitable configurations. Compared to existing methods, this invention helps relevant policymakers and investors better plan microgrids based on sustainable development. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating a method for constructing a multidimensional state sustainability evaluation index system for nano-microgrid groups, as provided in an embodiment of the present invention;

[0028] Figure 2 This is a structural block diagram of a system for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups, provided in an embodiment of the present invention.

[0029] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Please see Figure 1 The flowchart illustrates a method for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups according to this application.

[0032] like Figure 1 As shown, the method for constructing a multi-dimensional state sustainability evaluation index system for nano- and micro-grid groups specifically includes the following steps:

[0033] Step S101: Construct a sustainability evaluation index system for microgrids, which includes economic, environmental, technological, and social dimensions.

[0034] In this step, the microgrid includes solar photovoltaic power generation modules, wind power generation modules, diesel power generation modules, battery storage modules, bidirectional converter modules, and loads;

[0035] The direct current is provided by the solar photovoltaic power generation module and the battery storage module, and is converted into alternating current through the bidirectional converter module;

[0036] The wind power generation module and the diesel generator module are directly connected to the AC bus, with the diesel generator module serving as a backup power source.

[0037] 1.1. Modeling of Solar Photovoltaic Power Generation Modules

[0038] The performance of solar photovoltaic (PV) modules varies with environmental conditions such as temperature and wind speed; for every 0.1% increase in the temperature coefficient, the annual energy output of the solar PV module increases; the PPV output of the PV module depends on the daytime solar irradiance and is estimated hourly using the following formula:

[0039] In the formula: Y PV f is the rated capacity of the photovoltaic power generation module array. PV As a depreciation factor, G PV For solar irradiation on photovoltaic power generation module arrays, G ref For incident solar irradiance; under standard test conditions, αP is the power temperature coefficient, T PV For the temperature of the photovoltaic power generation module cells, T refThe derating factor represents the battery temperature of the photovoltaic power generation module under standard test conditions of 25℃. The derating factor takes into account the reduction in output power caused by environmental conditions such as temperature and wind speed, including dust accumulation on the surface of the photovoltaic power generation module panel, aging, wiring loss, and shading.

[0040] The battery temperature Tc (°C) can be calculated from the energy balance of the photovoltaic power generation module using the following formula: (τα)I T =η PV I T +U L (T c -T a )

[0041] In the formula: τα is the effective transmittance-absorption coefficient of the photovoltaic power generation module array, η PV For the efficiency of photovoltaic power generation modules, U L T is the heat transfer coefficient. a For ambient temperature, I T It refers to the solar radiation shining on the photovoltaic array; the above formula can be rewritten as:

[0042]

[0043] The value was obtained under conditions of 800 W / m² solar radiation, 20°C ambient temperature, and no load (i.e., η). PV =0); the above formula can be rewritten as follows:

[0044]

[0045] In the formula, the subscript NOCT represents the parameters at the nominal operating battery temperature;

[0046] The final temperature of the photovoltaic module cells can be calculated using the following formula, assuming the value of (τα) is 0.9.

[0047]

[0048] Due to the varying effects of temperature on solar panels, a derating factor of 90% is considered for each panel.

[0049] The following formula gives the total power generation PTPV of the number of photovoltaic modules (N) at any time (t):

[0050] P TPV =N PV ×P PV (t)

[0051] 1.2 Wind Power Generation Module Modeling

[0052] The output of a wind turbine at each time step is determined using three steps: First, the wind speed at the turbine hub height is calculated; then, the turbine's output power is calculated using this wind speed and a standard air density; finally, the output value is adjusted to compensate for the actual air density; the wind speed v at any height (H) is determined by the following formula:

[0053]

[0054] In the formula: v ref The reference wind speed is recorded at the reference hub height (Href), and γ is the power-law exponent. The two-parameter Weibull distribution f(v) is widely used to characterize wind conditions, and its variation ranges between 0.10 and 0.25.

[0055]

[0056] In the formula: v is the wind speed, m / s; k is the Weibull shape factor, which has no unit; c is the Weibull scale parameter, m / s;

[0057] The cumulative distribution function F(v) is given by the following equation:

[0058]

[0059] The following formula combines the two Weibull parameters with the average wind speed Connecting them:

[0060]

[0061] In the formula: Γ is the gamma function;

[0062] Because wind speed varies greatly, the energy generated by wind turbines also varies greatly; the wind turbine output PWT is determined by the following formula.

[0063]

[0064] In the formula, v(t), v r v cut-in v cut-out The wind speed at time t, rated wind speed, cut-in wind speed, and cut-out wind speed are respectively, and P r This refers to the rated power of the turbine.

[0065] The total power generation of N wind turbines (NWT) (PTWT) can be expressed as:

[0066] P TWT =N WT ×P WT (t)

[0067] 1.3. Modeling of the Diesel Generator Module

[0068] Determine the diesel generator fuel consumption rate for a given time step:

[0069] F = F0.Y gen +F1.P gen

[0070] In the formula, F is the fuel consumption rate at the time step, F0 is the intercept coefficient of the diesel generator fuel curve, F1 is the slope of the diesel generator fuel curve, and Y... gen P is the rated capacity of the diesel generator. gen The output of the diesel engine at this time step.

[0071] If the diesel generator does not operate within a given time step, the fuel consumption for that time step is zero.

[0072] 1.4. Battery Storage Module Modeling

[0073] Lead-acid batteries store excess energy during charging and provide energy when resources are insufficient. The following formula gives the maximum storage capacity of the battery:

[0074]

[0075] In the formula: Q s Q(t) represents the available energy at the start of the time step and above the minimum charging state, Q(t) represents the total energy at the start of the time step, c represents the storage capacity ratio, Δt represents the storage rate constant, t represents the time step, and k = 1, 2, 3, ...;

[0076] The maximum discharge power of the battery can be calculated using the following formula:

[0077]

[0078] In the formula: Q max This represents the maximum storage capacity.

[0079] The following two formulas calculate the available battery energy during charging and discharging at each time step:

[0080]

[0081] In the formula: the battery energies at times t and t-1 are E respectively. b (t) and E b (t-1), where Σ is the self-discharge rate, σ represents the self-discharge rate, and η inv and η b E represents inverter efficiency and battery efficiency, respectively. G and E L These represent the total electricity generation and load from renewable energy sources, respectively.

[0082] The total electricity generation EG(t) from renewable energy sources is calculated using the following formula.

[0083] E G (t)=N PV ×P PV (t)+N WT ×P WT (t)

[0084] In the formula: the state of charge (BSOC) of the battery pack is expressed as:

[0085]

[0086] Where: Eb.max is the maximum available battery energy;

[0087] 1.5. Modeling the bidirectional conversion module

[0088] Since the solar photovoltaic power generation and battery storage modules provide DC power, while the demand is in AC mode, a bidirectional conversion module is used to convert DC power to AC power and vice versa; the size of the bidirectional conversion module is calculated based on the energy flow of the entire bus using the following formula:

[0089] P o (t)=η inv ×P i (t)

[0090] In the formula: P o (t) represents the inverter output power, P i (t) represents the inverter input power, η inv The conversion efficiency of the inverter;

[0091] 1.6. Setting Economic Parameters

[0092] a) Energy costs

[0093] The optimal scale is determined based on the lowest energy cost (CoE) of the microgrid architecture, which is calculated by the following formula:

[0094]

[0095] In the formula: C ACC C is the annual cost of capital. ARC For the replacement cost of one year, C AOM E is the annualized operating and maintenance cost. AES Annual electricity consumption;

[0096] b) Net Present Value Cost

[0097] On the other hand, the net present value cost (NPC) is calculated using the following formula, where CRF(i,n) is the capital recovery factor, which can be obtained using the following two formulas:

[0098]

[0099] In the formula: n is the number of years, i is the real annual interest rate, i' is the nominal interest rate, and f is the annual inflation rate;

[0100] c) Capacity shortage rate

[0101] Capacity shortage rate f cs The capacity shortage rate is equal to the total capacity shortage divided by the total electricity demand. The capacity shortage rate must be less than or equal to the system's maximum annual capacity shortage rate, calculated using the following formula.

[0102]

[0103] In the formula: E cs To meet the total capacity shortage (kWh / year) of electricity demand, ED emand Total electricity demand (kWh / year);

[0104] d) Failure to meet load ratio

[0105] The percentage of total annual electricity load not met due to insufficient power generation (f) Unmet This is called the unmet load ratio.

[0106]

[0107] In the formula: EU nmet Total unmet load (kWh / year);

[0108] e) Excess electricity fraction

[0109] The ratio f of total surplus electricity to total power generation Excess This is called the excess power fraction, and it is calculated at the end of each simulation using the following formula:

[0110]

[0111] In the formula: E Excess E represents the total surplus electricity (kWh / year). Prod Total power generation (kWh / year);

[0112] 2. Sustainability Evaluation Indicators

[0113] The four indicators for sustainable microgrids are economic, environmental, technological, and social.

[0114] 2.1. Economic Indicators

[0115] Electricity costs: The price of energy provided by a microgrid includes all expenditures incurred throughout its lifecycle, such as initial investment, operating and maintenance costs, and fuel pricing; it is also affected by typical factors such as efficiency, annual power generation, lifespan, and the type of energy involved.

[0116] Operation and maintenance costs: wages, fuel costs, engineering and consulting services, as well as expenses incurred in system maintenance and the purchase of spare parts;

[0117] Initial investment cost: All costs related to the construction and installation of the power plant, the purchase of equipment, engineering and consulting services, and any costs that may arise before the power plant is operational are included in the investment cost;

[0118] Return on investment: The cost savings per year compared to the initial investment are called the return on investment.

[0119] 2.2. Environmental Indicators

[0120] CO2 emissions: Most CO2 emissions come from the combustion of fossil fuels, which are composed of hydrocarbons;

[0121] Land use: In this invention, land demand is used as an environmental indicator; the land value after construction, operation, decommissioning, and resource installation is all included;

[0122] 2.3. Technical Specifications

[0123] Unmet loads: Demands that the microgrid system cannot meet;

[0124] Excess electricity: When demand is lower than generation, excess electricity is generated in the microgrid; in off-grid situations, excess electricity is discarded.

[0125] The future of technology: This involves what the future of a technology is, whether it will be replaced by other technologies, and whether the application of this technology will expand or be limited; since some technologies or fuels may be particularly resource-constrained, the availability and limitations of each technology must be considered;

[0126] 2.4. Social Indicators

[0127] Job creation: Each component of a microgrid has its own job creation factor; it improves the quality of life in the community while reducing unemployment; throughout the microgrid's lifecycle, many people are employed, either directly in activities such as manufacturing, installation, operation, and maintenance, or indirectly in jobs such as equipment, construction, and installation suppliers; wind power is available 24 / 7, but its maintenance costs are higher than solar power; therefore, wind turbine WTG will be able to provide more job opportunities.

[0128] A comfortable life: Communities would be more comfortable if electricity were uninterrupted; photovoltaic power generation has always been limited by the difficulty of storing electricity at night and on cloudy days, as sunlight cannot reach the batteries on cloudy days; noise pollution from wind turbines also disrupts the harmony of life; it has negative effects on people's mental health and environmental consequences; noise-induced hearing loss may be the result of long-term exposure to noise.

[0129] Ecosystems: Assessing the feasibility of renewable energy projects and their environmental impact; diesel and wind turbines are harmful to the environment; birds can also be hit by wind turbines;

[0130] Step S102, the economic dimension is further subdivided into sub-indicators of electricity cost, operation and maintenance cost, initial investment cost and return on investment.

[0131] Step S103: Subdivide the environmental dimension into carbon dioxide emission sub-indicators and land use sub-indicators.

[0132] Step S104: Subdivide the technical dimension into unmet load sub-indicators, excess power sub-indicators, and technical future sub-indicators.

[0133] Step S105: Subdivide the social dimension into sub-indicators for job creation, comfortable living, and ecosystem.

[0134] Step S106: Use a neural network expert system to calculate the overall priority of each key sustainability indicator.

[0135] Step S107: Use iHOGA PRO+ software to score and rank the microgrid scenario to determine the optimal configuration.

[0136] In this step, Case 1 is limited to diesel fuel, powered by a diesel generator; since there is no connection to renewable energy sources, the renewable portion of the system is zero; this system emits a large amount of CO2 into the atmosphere because diesel fuel has a high carbon content.

[0137] Case 2 is a hybrid solar photovoltaic / diesel / converter / battery system, which consists of solar photovoltaic, diesel generator, converter and battery;

[0138] Case 3 is a hybrid wind power / diesel / converter / battery microgrid system, which includes wind power generation, diesel generators, converters and batteries;

[0139] Case 4 includes all types of components;

[0140] Case 5 involves solar photovoltaics, inverters, and battery energy storage;

[0141] Case 6 involves wind power generation / converters / batteries, which has the highest electricity costs and generates excess electricity across all scenarios; this system is not cost-effective; similarly, the technology is not yet mature.

[0142] Case 7: Solar Photovoltaics / Wind Power / Inverter / Battery: Compared with other hybrid renewable energy sources, its energy cost is low; currently, researchers from different regions are trying to use solar and wind power to supply power to off-grid areas;

[0143] 4.2. Assigning values ​​to sub-indicators

[0144] The degree of preference can be used to assign values ​​to sustainability sub-indicators; a relative preference level between 0 and 5 is set, with 1 indicating a weak preference and 5 indicating the strongest preference for a specific technology; 0 indicates that the relevant components are missing in the microgrid.

[0145] Diesel generators require less space and scored 5 on the preference scale, although the technology is unlikely to be widely deployed in the future; solar photovoltaic (PV) is often considered to have a negative impact on land use; in scenarios where crops are not grown, the installation of solar PV panels is beneficial to land use and ecosystem balance; wind turbines provide readily available electricity, thus potentially reducing idle time and enabling a more comfortable lifestyle; however, it is well known that birds can die from impacts with wind turbines; therefore, it has certain negative impacts.

[0146] Ecological impact: Due to the intermittent nature of solar photovoltaic (PV) and wind, batteries and converters must be used simultaneously; these technologies have great future potential and create many jobs; therefore, batteries are valued at 5 points in terms of job creation.

[0147] The indicators are evaluated using the rank-sum ratio comprehensive evaluation method.

[0148] This method calculates the rank of the evaluation object to obtain a dimensionless statistic, then combines it with the weights of each indicator to obtain a rank-sum ratio composite value. Finally, the evaluation objects are directly ranked according to the magnitude of the rank-sum ratio composite value, thus making a comprehensive evaluation of the evaluation objects. The calculation process of this method is as follows:

[0149] 1) Ranking the raw data

[0150] First, the original data matrix S = (sij)m×n is constructed from n evaluation indicators of m evaluation objects. Then, the indicators are standardized to obtain the maximum indicators. Finally, the indicators are ranked from smallest to largest to obtain the rank matrix R = (rij)m×n.

[0151] Calculate the rank-sum ratio composite value: Calculate the rank-sum ratio composite value RSRj of the j-th evaluation object according to the following formula.

[0152]

[0153] In the formula: ωi is the weight of each indicator;

[0154] A comprehensive evaluation of the evaluation objects is conducted: the evaluation objects are ranked and a comprehensive assessment is made based on the rank-sum ratio comprehensive evaluation method;

[0155] The following is the approach to standardizing extremely large indicators by performing consistency processing on each indicator:

[0156] Sub-indicator values ​​are normalized between 0 and 1, used as system input, and aggregated values ​​are calculated according to priority; the numbers 1 and 0 represent the best and worst conditions, respectively; some sub-indicators must be maximized, while others must be minimized; return on investment, ecosystem, future of technology, job creation, and comfortable living must be maximized and are considered positive sub-indicators; on the other hand, electricity costs, initial investment costs, operation and maintenance costs, CO2 emissions, land use, excess electricity, and unmet load are minimized and presented as negative sub-indicators;

[0157] Comparison of microgrid scenarios based on grey relational analysis

[0158] Grey relational analysis was used to evaluate the priority-based sustainable microgrid scenario score.

[0159] In terms of economic priorities, wind turbines have high capital costs and require significant maintenance costs throughout their life cycle; although they do not produce CO2, the high cost of electricity will be a burden on residents.

[0160] In terms of environmental preferences, emissions sub-indicators were given significant weight compared to other indicators; solar photovoltaic (PV) power generation and wind turbines are considered environmentally friendly sources of electricity because they do not produce pollutants; stand-alone diesel generators, i.e., scenario one, are the worst case because they emit more carbon dioxide.

[0161] When evaluating the priority of microgrid technologies, solar energy is an emerging technology that will be widely used in the future; wind power combinations remain an untapped technology if skilled personnel are lacking; social weight is the most important factor in some regions; diesel generators are not socially acceptable and their pollution will have a negative impact on society.

[0162] Sustainability Analysis

[0163] A. Electrical Analysis

[0164] Based on iHOGA PRO+ software tests of microgrids in various scenarios, considering the rainy season, solar energy output in June and July is lower than in other months; in contrast, wind power output is extremely high; diesel generators are only used in a few months, and their utilization rate throughout the year is always quite low; wind turbines in areas with abundant wind power provide the most energy.

[0165] B. Financial Analysis

[0166] Most of the annual discounted cash outlay is spent at the beginning of the project; batteries are replaced after ten years due to exceeding output power; all other components except solar PV are replaced during the project; diesel generators are less expensive than wind turbines; PV and wind turbines do not require any fuel.

[0167] C. Emissions Analysis

[0168] Because of the use of renewable energy technologies, the solar photovoltaic (PV) / wind / diesel / converter / battery microgrid architecture reduces CO2 emissions and other gases significantly compared to a standalone diesel generator; one of the prerequisites for sustainable development initiatives is to move towards low-pollution emissions.

[0169] D. Sensitivity Analysis: The impact of energy costs, net present value, and operating and maintenance costs on solar radiation, wind speed, fuel prices, discount rates, and electricity demand on optimal sustainable microgrid configurations.

[0170] In summary, the method of this application includes four key sustainability indicators: economic, environmental, technological, and social. These key sustainability indicators are further subdivided into sub-indicators such as electricity cost, operation and maintenance cost, initial investment cost, return on investment, CO2 emissions, land use, unmet load, and excess electricity. The comprehensive value of the priority of economic, environmental, technological, and social indicators is calculated using a neural network expert system. Seven microgrid scenarios integrating locally accessible resources were scored and ranked using iHOGA PRO+ software to determine suitable configurations. Compared to existing methods, this invention helps relevant policymakers and investors better plan microgrids based on sustainable development.

[0171] Please see Figure 2 The diagram shows a structural block diagram of a system for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups according to this application.

[0172] like Figure 2 As shown, the system 200 for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups includes a construction module 210, a first differentiation module 220, a second differentiation module 230, a third differentiation module 240, a fourth differentiation module 250, a calculation module 260, and a determination module 270.

[0173] The system comprises the following components: a construction module 210, configured to construct a sustainability evaluation index system for a microgrid, which includes economic, environmental, technological, and social dimensions; a first differentiation module 220, configured to subdivide the economic dimension into sub-indicators of electricity cost, operation and maintenance cost, initial investment cost, and return on investment; a second differentiation module 230, configured to subdivide the environmental dimension into sub-indicators of carbon dioxide emissions and land use; a third differentiation module 240, configured to subdivide the technological dimension into sub-indicators of unmet load, excess electricity, and technological future; a fourth differentiation module 250, configured to subdivide the social dimension into sub-indicators of job creation, comfortable living, and ecosystem; a calculation module 260, configured to use a neural network expert system to calculate the comprehensive priority of each key sustainability indicator; and a determination module 270, configured to use iHOGA PRO+ software to score and rank the microgrid scenario to determine the optimal configuration.

[0174] It should be understood that Figure 2 The modules and references described in the document Figure 1 The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 2 The various modules in the document will not be described in detail here.

[0175] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the method for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups in any of the above method embodiments.

[0176] In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows:

[0177] A sustainability evaluation index system for microgrids is constructed, which includes economic, environmental, technological, and social dimensions.

[0178] The economic dimension is further subdivided into sub-indicators of electricity cost, operation and maintenance cost, initial investment cost, and return on investment.

[0179] The environmental dimension is further subdivided into carbon dioxide emission sub-indicators and land use sub-indicators;

[0180] The aforementioned technical dimensions are further subdivided into unmet load sub-indicators, excess power sub-indicators, and future technology sub-indicators.

[0181] The social dimension is further subdivided into job creation sub-indicators, comfortable living sub-indicators, and ecosystem sub-indicators;

[0182] The overall priority of each key sustainability indicator is calculated using a neural network expert system;

[0183] The iHOGA PRO+ software was used to score and rank microgrid scenarios to determine the optimal configuration.

[0184] Computer-readable storage media may include a program storage area and a data storage area. The program storage area may store an operating system and an application program required for at least one function. The data storage area may store data created during the use of the system based on the multi-dimensional state sustainability evaluation index system for nano-microgrid groups. Furthermore, the computer-readable storage medium may include high-speed random access memory and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely located relative to a processor. This remote memory can be connected to the multi-dimensional state sustainability evaluation index system for nano-microgrid groups via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0185] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby realizing the method for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid clusters as described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the multi-dimensional state sustainability evaluation index system for nano-microgrid clusters. The output device 340 may include a display screen or other display device.

[0186] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0187] In one implementation, the aforementioned electronic device is applied in a system for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups. As a client, it includes: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to:

[0188] A sustainability evaluation index system for microgrids is constructed, which includes economic, environmental, technological, and social dimensions.

[0189] The economic dimension is further subdivided into sub-indicators of electricity cost, operation and maintenance cost, initial investment cost, and return on investment.

[0190] The environmental dimension is further subdivided into carbon dioxide emission sub-indicators and land use sub-indicators;

[0191] The aforementioned technical dimensions are further subdivided into unmet load sub-indicators, excess power sub-indicators, and future technology sub-indicators.

[0192] The social dimension is further subdivided into job creation sub-indicators, comfortable living sub-indicators, and ecosystem sub-indicators;

[0193] The overall priority of each key sustainability indicator is calculated using a neural network expert system;

[0194] The iHOGA PRO+ software was used to score and rank microgrid scenarios to determine the optimal configuration.

[0195] 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., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0196] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups, characterized in that, include: A sustainability evaluation index system for microgrids is constructed, which includes economic, environmental, technological, and social dimensions. The economic dimension is further subdivided into sub-indicators of electricity cost, operation and maintenance cost, initial investment cost, and return on investment. The environmental dimension is further subdivided into carbon dioxide emission sub-indicators and land use sub-indicators; The aforementioned technical dimensions are further subdivided into unmet load sub-indicators, excess power sub-indicators, and future technology sub-indicators. The social dimension is further subdivided into job creation sub-indicators, comfortable living sub-indicators, and ecosystem sub-indicators; The overall priority of each key sustainability indicator is calculated using a neural network expert system; The iHOGA PRO+ software was used to score and rank microgrid scenarios to determine the optimal configuration.

2. The method for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups according to claim 1, characterized in that, The microgrid includes solar photovoltaic power generation modules, wind power generation modules, diesel power generation modules, battery storage modules, bidirectional converter modules, and loads; The direct current is provided by the solar photovoltaic power generation module and the battery storage module, and is converted into alternating current through the bidirectional converter module; The wind power generation module and the diesel generator module are directly connected to the AC bus, with the diesel generator module serving as a backup power source.

3. The method for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups according to claim 2, characterized in that, The output power of the solar photovoltaic power generation module is calculated using the following formula: In the formula, Y PV f is the rated capacity of the photovoltaic power generation module array. PV As a depreciation factor, G PV (t) represents the solar irradiance on the photovoltaic power generation module array at time t, and G ref For incident solar irradiance; under standard experimental conditions, α P T is the power temperature coefficient. PV (t) represents the temperature of the photovoltaic module's battery at time t, where T is the temperature of the battery. ref The temperature of the photovoltaic module battery is 25℃ under standard test conditions.

4. The method for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups according to claim 2, characterized in that, The output power of the wind power generation module is calculated using the following formula: In the formula, v(t), v r v cut-in v cut-out The wind speed at time t, rated wind speed, cut-in wind speed, and cut-out wind speed are respectively, and P r This refers to the rated power of the turbine.

5. The method for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups according to claim 2, characterized in that, The fuel consumption rate of the diesel generator module is calculated using the following formula: F=F0.Y gen +F1.P gen , In the formula, F is the fuel consumption rate at the time step, F0 is the intercept coefficient of the diesel generator fuel curve, F1 is the slope of the diesel generator fuel curve, and Y... gen P is the rated capacity of the diesel generator. gen The output of the diesel engine at this time step.

6. The method for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups according to claim 2, characterized in that, The state of charge of the battery storage module is calculated using the following formula: In the formula, F b，max For maximum usable battery energy, E b (t) represents the battery energy at time t.

7. The method for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups according to claim 1, characterized in that, in, The iHOGA PRO+ software was used to rank microgrid scenarios, specifically including: The microgrid scenarios are ranked using the rank-sum ratio comprehensive evaluation method, as follows: Construct the original data matrix and rank it; Calculate the combined rank-sum ratio of each evaluation object; The microgrid scenarios are ranked according to their comprehensive values.

8. The method for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups according to claim 1, characterized in that, The iHOGA PRO+ software was used to score the microgrid scenario and determine the optimal configuration, specifically: The grey relational degree of each scenario is calculated based on the weights of the economic, environmental, technological, and social dimensions. Determine the optimal configuration based on the degree of correlation.

9. A system for constructing a multi-dimensional state sustainability evaluation index system for nano-microgrid groups, characterized in that, include: The module is configured to build a sustainability evaluation index system for microgrids, which includes economic, environmental, technological and social dimensions. The first differentiation module is configured to subdivide the economic dimension into sub-indicators of electricity cost, operation and maintenance cost, initial investment cost and return on investment. The second differentiation module is configured to subdivide the environmental dimension into carbon dioxide emission sub-indicators and land use sub-indicators. The third differentiation module is configured to subdivide the technical dimension into unmet load sub-indicators, excess power sub-indicators, and technical future sub-indicators. The fourth differentiation module is configured to subdivide the social dimension into sub-indicators for job creation, comfortable living, and ecosystem. The calculation module is configured to use a neural network expert system to calculate the overall priority of each key sustainability indicator; The module was selected and configured to use iHOGA PRO+ software to score and rank microgrid scenarios, and the optimal configuration was determined.

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