Intelligent centralized control chain distributed multi-energy optimization scheduling device and method

Abstract

The present invention discloses an intelligent centralized control chain distributed multi-energy optimization scheduling device and method, which relates to the field of energy management technology. The method includes collecting energy samples and establishing energy characteristic indicators, and performing energy classification according to these indicators; collecting historical power load data and load influencing factor data, and using a multi-time scale power load forecasting model to obtain a power load forecasting sequence; collecting power supply and grid data of the power system, calculating the power supply capacity, and obtaining the power supply and demand balance according to the power load forecasting sequence; and finally forming a multi-energy optimization scheduling strategy according to the supply and demand balance and energy classification. The device includes an energy classification module, a load forecasting module, a power supply and demand balance calculation module, an optimization scheduling module, and a real-time monitoring and dynamic adjustment module, which are used to implement the above method. By classifying and optimizing the scheduling of energy, energy utilization efficiency is improved, operating costs are reduced, and environmental pollution is reduced.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and more specifically, to an intelligent centralized control chain distributed multi-energy optimization scheduling device and method. Background Art

[0002] With the rapid development of social economy, energy demand is increasing, and energy shortage, environmental pollution and other problems are becoming increasingly prominent. How to efficiently use energy, reduce energy consumption, reduce environmental pollution and achieve sustainable development has become the focus of people's attention.

[0003] In the field of energy management, there have been some related research and patents. For example, the Chinese patent application with publication number CN107292480A discloses a method for predicting the long-term load characteristics of a county power grid. Through an objective, scientific, comprehensive and accurate prediction method, it focuses on the prediction process of the main indicators of the typical daily load characteristics of the county power grid and the verification method of the prediction results, which can provide a certain reference for power market analysts and power grid planners to accurately grasp the trend of changes in regional long-term load characteristics. In addition, the Chinese patent with publication number CN115313377A provides a method and system for predicting power load, which uses a graph neural network combined with historical data and power plan data to divide each power area into units to form a graph structure, predict the power generation and power consumption of the power area unit in the future, and realize short-term load forecasting analysis in a new energy environment.

[0004] However, the traditional centralized energy management model has problems such as low energy utilization efficiency and inflexible management, which makes it difficult to meet the optimization and scheduling needs of the energy system. Existing technologies have not yet been able to solve the problem of how to classify energy according to energy characteristic indicators, combine power load forecasting and power supply and demand balance analysis, and form a multi-energy optimization scheduling strategy. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides an intelligent centralized control chain distributed multi-energy optimization scheduling device and method.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] Intelligent centralized control chain distributed multi-energy optimization scheduling method, including:

[0008] Step S1000: Collect energy samples, establish energy characteristic indicators, and classify energy according to the energy characteristic indicators; the energy characteristic indicators include energy quality indicators, energy environment indicators, cost-effectiveness ratio and renewability;

[0009] Step S2000: Collect historical power load data and load influencing factor data, and obtain a power load forecast sequence based on the historical power load data, load influencing factor data and a pre-built multi-time scale power load forecasting model; the load influencing factor data includes weather data, date type, economic and social development indicators and policy and regulatory information; the multi-time scale power load forecasting model includes a short-term power load forecasting model and a medium- and long-term power load forecasting model; the power load forecasting sequence is the power load forecast value for each future period; each period T m The time granularity is R m , the power load forecast sequence contains N m Power load forecast values, where N m =T m / R m ;

[0010] Step S3000: Collect power supply and grid data of the power system, calculate the power supply capacity of the power system according to the power supply and grid data, and obtain the power supply and demand balance according to the power supply capacity and power load forecast sequence;

[0011] Step S4000: Form a multi-energy optimization scheduling strategy based on the balance of power supply and demand and energy classification.

[0012] Furthermore, the step S1000 includes:

[0013] Step S1100, collecting energy samples, obtaining the energy content, energy volume, actual energy utilization rate, ambient temperature and energy temperature of the energy samples, and calculating energy quality indicators according to the energy content, energy volume, actual energy utilization rate, ambient temperature and energy temperature, the energy quality indicators including energy density and energy grade;

[0014] Step S1200, obtaining the CO of energy samples 2 Emissions, emissions of each pollutant and energy use, based on CO 2 Emissions, emissions of each pollutant and energy usage, calculate energy and environmental indicators, which include carbon emission intensity and pollutant emission coefficient;

[0015] Step S1300, obtaining the energy output and total cost of the energy sample, and calculating the cost-effectiveness ratio according to the energy output, the actual energy utilization rate and the total cost;

[0016] Step S1400, obtaining the renewable energy amount, total energy amount and environmental cost of the energy sample, and calculating the renewability of the energy according to the renewable energy amount, total energy amount, environmental cost and total cost;

[0017] Step S1500, calculate the comprehensive energy score according to the energy density and energy grade in the energy quality index, the carbon emission intensity and pollutant emission coefficient in the energy environment index, as well as the cost-effectiveness ratio and renewability, and classify the energy according to the comprehensive energy score.

[0018] Furthermore, the step S1100 includes:

[0019] Step S1110, collecting energy samples, obtaining the energy content and energy volume of the energy samples, and dividing the energy content by the energy volume to obtain energy density;

[0020] Step S1120, obtaining the actual energy utilization rate, ambient temperature and energy temperature of the energy sample, and calculating the energy grade according to the actual energy utilization rate, ambient temperature and energy temperature;

[0021] Based on the actual energy utilization rate, ambient temperature and energy temperature, the methods for calculating energy grade include:

[0022] ;

[0023] in:

[0024] : Energy grade;

[0025] : Ambient temperature;

[0026] : Energy temperature;

[0027] : Actual energy utilization rate;

[0028] The calculation method of actual energy utilization rate includes:

[0029] ;

[0030] in:

[0031] : Production efficiency;

[0032] : Transmission efficiency;

[0033] : conversion efficiency;

[0034] : Terminal usage efficiency.

[0035] Furthermore, the step S1200 includes:

[0036] Step S1210, obtain CO 2Emissions and energy use, CO 2 Emissions divided by energy use yield carbon intensity;

[0037] Step S1220, obtaining the emission of each pollutant, and calculating the pollutant emission coefficient according to the emission of each pollutant and the energy usage;

[0038] Based on the amount of each pollutant emitted and the amount of energy used, the methods for calculating the pollutant emission coefficient include:

[0039] ;

[0040] in:

[0041] : Pollutant emission coefficient;

[0042] : No. The weight coefficient of each pollutant;

[0043] : No. The amount of pollutants emitted;

[0044] : Energy usage;

[0045] : Total number of pollutant types;

[0046] : Index of pollutant types.

[0047] Furthermore, the step S1500 includes:

[0048] Step S1510, calculating the comprehensive energy score according to the energy density and energy grade in the energy quality index, the carbon emission intensity and pollutant emission coefficient in the energy environment index, as well as the cost-effectiveness ratio and renewability;

[0049] The methodology for calculating the energy composite score includes:

[0050] ;

[0051] in:

[0052] : Comprehensive energy score;

[0053] : Energy density;

[0054] : The maximum value of energy density among all energy samples;

[0055] : Energy grade;

[0056] : The maximum value of energy grade among all energy samples;

[0057] : Carbon emission intensity;

[0058] : The maximum carbon emission intensity among all energy samples;

[0059] : Pollutant emission coefficient;

[0060] : The maximum value of the pollutant emission coefficient among all energy samples;

[0061] : cost-effectiveness ratio;

[0062] : The maximum value of the cost-effectiveness ratio among all energy samples;

[0063] : Renewability;

[0064] : The maximum value of renewable energy among all energy samples;

[0065] : Weight coefficient of energy density;

[0066] : Weight coefficient of energy grade;

[0067] : Weight coefficient of carbon emission intensity;

[0068] : Weight coefficient of pollutant emission coefficient;

[0069] : weight coefficient of cost-effectiveness ratio;

[0070] : Weight coefficient of reproducibility, satisfying ;

[0071] Step S1520, collecting the energy comprehensive scores of all samples, sorting all the energy comprehensive scores from low to high, calculating the percentiles of the energy comprehensive scores, and dividing the energy levels according to the percentiles;

[0072] Level A: Energy with a comprehensive score greater than or equal to the 90th percentile;

[0073] Level B: Energy with a comprehensive score greater than or equal to the 70th percentile and less than the 90th percentile;

[0074] Level C: Energy with a comprehensive score greater than or equal to the 50th percentile and less than the 70th percentile;

[0075] Level D: Energy with a comprehensive score greater than or equal to the 30th percentile and less than the 50th percentile;

[0076] Level E: Energy sources with a composite score less than the 30th percentile.

[0077] Furthermore, the step S2000 includes:

[0078] Step S2100, obtaining a short-term power load forecast sequence according to historical power load data, weather data, date type and a pre-built short-term power load forecast model;

[0079] Step S2200, obtaining a medium- and long-term power load forecast sequence based on historical power load data, economic and social development indicators, policy and regulatory information, and a pre-built medium- and long-term power load forecast model.

[0080] Furthermore, the step S3000 includes:

[0081] Step S3100, setting a time period for supply and demand balance analysis according to the time period of the power load forecast sequence;

[0082] Step S3200, collecting power supply and grid data of the power system during the supply and demand balance analysis period, the power supply and grid data including a list of power generation equipment, maintenance plan and grid parameters;

[0083] Step S3300, according to the list of power generation equipment, the maintenance plan and the grid parameters, calculate the total output of the traditional power generation units and the total output of the new energy power generation equipment by time period; sum the total output of the traditional power generation units and the total output of the new energy power generation equipment to calculate the total power supply capacity;

[0084] Step S3400, determining the grid constraint conditions according to the power generation equipment list and the grid parameters, adjusting the total power supply capacity according to the grid constraint conditions, and obtaining the constrained total power supply capacity;

[0085] Step S3500, calculating the balance degree of power supply and demand according to the constrained total power supply capacity and the power load forecast sequence.

[0086] Furthermore, the method for calculating the total output of a traditional generator set by time period includes:

[0087] ;

[0088] in:

[0089] : The total output of the traditional generator set in the i-th period;

[0090] N: total number of traditional generator sets;

[0091] : Rated output of the jth traditional generator set;

[0092] : The available hours of the jth traditional generator set in the i-th period;

[0093] : The operating efficiency coefficient of the jth traditional generator set in the i-th period;

[0094] : The failure rate of the jth traditional generator set in the i-th period;

[0095] The method for calculating the total output of the new energy power generation equipment by time period includes:

[0096] ;

[0097] in:

[0098] : The total output of new energy power generation equipment in the i-th period;

[0099] : the rated capacity of the e-th new energy power generation equipment;

[0100] : Wind speed and irradiance The expected value of the output characteristic function under ;

[0101] M: total number of new energy power generation equipment;

[0102] : wind speed in the ith period;

[0103] : irradiance in the ith period;

[0104] Methods for calculating total power supply capacity include:

[0105] ;

[0106] in, is the total power supply capacity in the i-th period.

[0107] Furthermore, the step S4000 includes:

[0108] Step S4100, when the power supply and demand balance is greater than the excess threshold, generating a power supply reduction strategy;

[0109] Step S4200, when the power supply and demand balance is less than the shortage threshold, generating a power supply increase strategy;

[0110] Step S4300, when the power supply and demand balance is greater than or equal to the shortage threshold and less than or equal to the surplus threshold, maintain the current energy output power;

[0111] Step S4400, real-time monitoring of the balance between power supply and demand, and dynamic adjustment of the output power of each level of energy.

[0112] Furthermore, the step S4100 includes:

[0113] Step S4110, reducing the output power of the energy of level E until the power supply and demand balance is less than or equal to the excess threshold; if the output power of the energy of level E has been reduced to the minimum and the power supply and demand balance is still greater than the excess threshold, executing step S4120;

[0114] Step S4120, reducing the output power of the level D energy until the power supply and demand balance is less than or equal to the excess threshold; if the output power of the level D energy has been reduced to the minimum and the power supply and demand balance is still greater than the excess threshold, executing step S4130;

[0115] Step S4130, reducing the output power of the level C energy until the power supply and demand balance is less than or equal to the excess threshold; if the output power of the level C energy has been reduced to the minimum and the power supply and demand balance is still greater than the excess threshold, executing step S4140;

[0116] Step S4140, successively reducing the output power of higher-level energy sources until the balance degree of power supply and demand approaches zero;

[0117] The step S4200 includes:

[0118] Step S4210, increasing the output power of the energy source of level A until the balance degree of power supply and demand is greater than or equal to the shortage threshold; if the output power of the energy source of level A has been increased to the maximum and the balance degree of power supply and demand is still less than the shortage threshold, executing step S4220;

[0119] Step S4220, increasing the output power of the Class B energy until the power supply and demand balance is greater than or equal to the shortage threshold; if the output power of the Class B energy has been increased to the maximum and the power supply and demand balance is still less than the shortage threshold, executing step S4230;

[0120] Step S4230, increasing the output power of the Class C energy until the power supply and demand balance is greater than or equal to the shortage threshold; if the output power of the Class C energy has been increased to the maximum and the power supply and demand balance is still less than the shortage threshold, executing step S4240;

[0121] Step S4240, increasing the output power of lower-level energy sources in sequence until the balance between power supply and demand approaches zero.

[0122] An intelligent centralized control chain distributed multi-energy optimization dispatching device, which is used to implement the above-mentioned intelligent centralized control chain distributed multi-energy optimization dispatching method, comprises:

[0123] Energy classification module: used to collect energy samples, establish energy characteristic indicators, and classify energy according to the energy characteristic indicators; the energy characteristic indicators include energy quality indicators, energy environment indicators, cost-effectiveness ratio and renewability;

[0124] Load forecasting module: used to collect historical power load data and load influencing factor data, and obtain the power load forecasting sequence based on the historical power load data, load influencing factor data and the pre-built multi-time scale power load forecasting model;

[0125] Power supply and demand balance calculation module: used to collect power supply and grid data of the power system, calculate the power supply capacity of the power system based on the power supply and grid data, and obtain the power supply and demand balance based on the power supply capacity and power load forecast sequence;

[0126] Optimization and dispatching module: used to form a multi-energy optimization and dispatching strategy based on the balance of power supply and demand and energy classification;

[0127] Real-time monitoring and dynamic adjustment module: used to monitor the balance of power supply and demand in real time and dynamically adjust the output power of each level of energy.

[0128] Compared with the prior art, the present invention has the following beneficial effects:

[0129] Improve energy utilization efficiency: Through comprehensive analysis and classification of energy samples, as well as accurate prediction of power load and optimization of scheduling strategies, the present invention can effectively improve energy utilization efficiency and reduce energy waste.

[0130] Realize intelligent scheduling of multi-energy systems: The present invention introduces a multi-time scale power load forecasting model and a real-time monitoring mechanism, which can dynamically adjust the output power of each level of energy according to actual demand, thereby realizing intelligent scheduling of multi-energy systems and ensuring the balance of power supply and demand.

[0131] Environmentally friendly energy management: By calculating the carbon emission intensity and pollutant emission coefficient of energy, the present invention can reduce environmental pollution and promote low-carbon development while optimizing energy scheduling.

[0132] Reduce operating costs: By calculating the cost-effectiveness ratio and renewability of energy, the present invention can give priority to the use of high-efficiency and low-cost energy while ensuring power supply reliability, thereby reducing overall operating costs.

[0133] Enhance the robustness and flexibility of the system: By real-time monitoring of the balance between electricity supply and demand and dynamically adjusting energy output, the present invention can quickly respond to changes in electricity demand, enhance the robustness and flexibility of the power grid, and avoid power shortages or surpluses caused by imbalances in supply and demand.

[0134] Assist in the promotion and application of renewable energy: Through comprehensive scoring and grading of different energy sources, the present invention can give priority to the dispatch of renewable energy, promote the application of renewable energy in the power system, and promote the optimization and transformation of the energy structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0135] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0136] Figure 1 It is a flow chart of the distributed multi-energy optimization scheduling method of the intelligent centralized control chain in the present invention;

[0137] Figure 2 It is a flow chart of a method for forming a multi-energy optimization scheduling strategy in the distributed multi-energy optimization scheduling method of the intelligent centralized control chain of the present invention;

[0138] Figure 3 This is a functional module diagram of the intelligent centralized control chain distributed multi-energy optimization scheduling device in the present invention. DETAILED DESCRIPTION

[0139] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0140] Example 1

[0141] See also Figure 1As shown, this embodiment provides a distributed energy storage management method of an intelligent centralized control chain, including:

[0142] Step S1000: Collect energy samples, establish energy characteristic indicators, and classify energy according to the energy characteristic indicators; the energy characteristic indicators include energy quality indicators, energy environment indicators, cost-effectiveness ratio and renewability;

[0143] Furthermore, step S1000 includes:

[0144] Step S1100, collecting energy samples, obtaining the energy content, energy volume, actual energy utilization rate, ambient temperature and energy temperature of the energy samples, and calculating energy quality indicators according to the energy content, energy volume, actual energy utilization rate, ambient temperature and energy temperature, the energy quality indicators including energy density and energy grade;

[0145] Furthermore, step S1100 includes:

[0146] Step S1110, collecting energy samples, obtaining the energy content and energy volume of the energy samples, and dividing the energy content by the energy volume to obtain energy density;

[0147] Step S1120, obtaining the actual energy utilization rate, ambient temperature and energy temperature of the energy sample, and calculating the energy grade according to the actual energy utilization rate, ambient temperature and energy temperature;

[0148] Based on the actual energy utilization rate, ambient temperature and energy temperature, the methods for calculating energy grade include:

[0149] ;

[0150] in:

[0151] : Energy grade;

[0152] : Ambient temperature;

[0153] : Energy temperature;

[0154] : Actual energy utilization rate;

[0155] The actual energy utilization rate includes production efficiency, transmission efficiency, conversion efficiency and terminal use efficiency. The calculation methods of the actual energy utilization rate include:

[0156] ;

[0157] in:

[0158] : Production efficiency;

[0159] : Transmission efficiency;

[0160] : conversion efficiency;

[0161] : Terminal utilization efficiency;

[0162] Specifically, energy samples refer to specific energy instances or data sets used for analysis and management. Energy density refers to the energy contained in a unit volume of energy, which is used to measure the energy storage capacity of different energy sources. The higher the energy density, the more energy can be stored in the same volume. For fossil fuels, the energy contained in the energy is obtained by consulting documents such as fuel supply contracts and procurement records, and the energy volume is obtained by measuring the physical dimensions of storage facilities (such as oil storage tanks, coal bunkers, etc.) on the spot. For renewable energy, the energy contained in the energy is estimated by consulting technical parameters such as the power of photovoltaic modules and the rated power of wind turbines, and combined with actual operating data (such as sunshine time, wind speed, etc.), and the energy volume is obtained by measuring power generation equipment (such as photovoltaic arrays, wind turbine towers, etc.). For electrochemical energy storage, the energy contained in the energy is calculated by consulting the technical parameters of the battery (such as capacity, voltage, etc.), and the energy volume is obtained by measuring the physical dimensions of the battery pack.

[0163] Energy grade refers to the proportion of available energy to total energy, which reflects the quality of energy and evaluates the availability of energy. The higher the energy grade, the greater the proportion of available energy and the higher the energy utilization efficiency. Energy temperature refers to the thermodynamic temperature of energy, that is, the temperature related to the heat content and available work of energy. In thermodynamic systems such as heat engines or heat pumps, energy temperature refers to the temperature of the high-temperature heat source from which the system absorbs heat. For example, in a steam turbine, the temperature of the steam before entering the turbine can be regarded as the energy temperature. For the combustion process, energy temperature can refer to the temperature of the flame or combustion products generated when the fuel is burned. For example, the flame temperature generated by natural gas during the combustion process can be regarded as the energy temperature. In some thermodynamic cycles (such as Rankine cycle, Carnot cycle, etc.), the temperature of the working fluid at high temperature can be regarded as the energy temperature. For example, in the Rankine cycle, the temperature of the high-temperature and high-pressure steam generated in the boiler. For radiation energy (such as solar energy), energy temperature can refer to the blackbody radiation temperature of the radiation source. For example, the temperature of the surface of the sun can be regarded as the energy temperature of solar energy.

[0164] The actual energy utilization rate reflects the energy loss and efficiency of each link in the process from production to final use. Obtaining this parameter usually requires comprehensive consideration of multiple factors, including production efficiency, transmission loss, conversion efficiency and terminal use efficiency. Production efficiency refers to the efficiency of energy conversion from raw materials to usable forms, which can be estimated through field data, historical records and technical manuals. Transmission efficiency refers to the proportion of energy loss during transmission, which can be estimated through transmission loss data of power grids, pipeline systems, etc. Conversion efficiency refers to the efficiency of energy conversion from one form to another, which can be estimated through efficiency data of conversion equipment (such as generators, transformers, gas turbines, etc.). Terminal use efficiency refers to the efficiency of end users in using energy, which can be estimated through efficiency data of terminal equipment (such as motors, heaters, lighting equipment, etc.).

[0165] Step S1100 can accurately calculate the energy density and energy grade of energy by systematically collecting energy samples and obtaining key parameters such as energy contained in energy, energy volume, actual energy utilization rate, ambient temperature and energy temperature. These indicators can fully reflect the quality and utilization potential of energy. Energy with high energy density has more advantages in transportation, storage and other aspects, which is conducive to the efficient use of energy; energy with high energy grade has a large proportion of available energy, is more efficient in the process of energy utilization, and is more conducive to energy conservation and emission reduction.

[0166] Step S1200, obtaining the CO of energy samples 2 Emissions, emissions of each pollutant and energy use, based on CO 2 Emissions, emissions of each pollutant and energy usage, calculate energy and environmental indicators, which include carbon emission intensity and pollutant emission coefficient;

[0167] Further, step S1200 includes:

[0168] Step S1210, obtain CO 2 Emissions and energy use, CO 2 Emissions divided by energy use yield carbon intensity;

[0169] Step S1220, obtaining the emission of each pollutant, and calculating the pollutant emission coefficient according to the emission of each pollutant and the energy usage;

[0170] Based on the amount of each pollutant emitted and the amount of energy used, the methods for calculating the pollutant emission coefficient include:

[0171] ;

[0172] in:

[0173] : Pollutant emission coefficient;

[0174] : No. The weight coefficient of each pollutant;

[0175] : No. The amount of pollutants emitted;

[0176] : Energy usage;

[0177] : Total number of pollutant types;

[0178] : Index of pollutant types;

[0179] Specifically, carbon emission intensity refers to the amount of CO2 produced per unit of energy used. 2 Emissions are used to assess the carbon emission level of the energy use process. The lower the carbon emission intensity, the more environmentally friendly the energy is. For fossil fuels, CO is obtained through emission monitoring equipment after fuel combustion. 2 Emissions and energy usage are obtained through fuel consumption records. Renewable energy is generally considered to have a low carbon emission intensity, but it will generate a certain amount of CO during the manufacturing and installation of equipment. 2 Emissions, these data can be obtained through life cycle assessment methods. Energy usage refers to the total amount of energy used within a certain period of time or under specific conditions. Energy meters (such as electricity meters and gas meters) are used to record the actual energy usage. The pollutant emission coefficient refers to the weighted emissions of various pollutants during the use of energy. It comprehensively evaluates the pollutant emission level of the energy. The lower the emission coefficient, the cleaner and more environmentally friendly the energy. For fossil fuels, the emissions of various pollutants generated during the combustion process are obtained through emission monitoring equipment. For renewable energy, pollutant emissions are mainly concentrated in the equipment manufacturing, transportation and installation stages. These data can be obtained through life cycle assessment methods. The weight coefficient is used to reflect the importance of different pollutants to the environment and health, and can be determined through environmental impact assessments, relevant laws and regulations and standards, and expert consultation methods. Step S1200 systematically obtains the CO of energy samples. 2 Emissions, emissions of each pollutant, and energy usage can accurately calculate the carbon emission intensity and pollutant emission coefficient of energy. These indicators help to comprehensively evaluate the environmental impact of energy. Selecting energy with low carbon emission intensity helps reduce greenhouse gas emissions and alleviate climate change pressure. Preferring energy with low pollutant emission coefficients helps reduce harm to the environment and promote the development of clean energy.

[0180] Step S1300, obtaining the energy output and total cost of the energy sample, and calculating the cost-effectiveness ratio according to the energy output, the actual energy utilization rate and the total cost;

[0181] ;

[0182] in:

[0183] : Total cost, which can be obtained through financial records and market price data;

[0184] : cost-effectiveness ratio;

[0185] : Energy output;

[0186] : Actual energy utilization rate;

[0187] The total cost includes production cost, transmission cost, conversion cost and terminal use cost. The calculation method of the total cost includes:

[0188] ;

[0189] in:

[0190] : production cost;

[0191] : Transmission cost;

[0192] : switching costs;

[0193] : Terminal use cost;

[0194] Specifically, energy output refers to the total amount of energy that can actually be utilized, which can be obtained through the technical parameters and actual operation data of energy production equipment. For example, for power plants, it can be obtained through the output power data of generators; for fossil fuels, it can be obtained by calculating the available energy generated by the combustion process. Production costs are the costs in the energy production process, such as raw material costs, labor costs, etc., which can be obtained through financial records and market price data. Transmission costs are the costs in the energy transmission process, which can be obtained through the operation and maintenance cost data of the power grid or pipeline system. Conversion costs are the costs in the energy conversion process, which can be obtained through the operation and maintenance cost data of the conversion equipment. Terminal use costs are the costs when the end user uses energy, which can be obtained through the operation and maintenance cost data of the terminal equipment. Step S1300 can accurately calculate the cost-effectiveness ratio BCR of energy. By calculating BCR, the economic benefits of energy can be intuitively evaluated, which helps to select efficient and low-cost energy solutions. Energy solutions with high BCR are more attractive in practical applications and can help decision makers make the best decision among multiple energy options.

[0195] Step S1400, obtaining the renewable energy amount, total energy amount and environmental cost of the energy sample, and calculating the renewability of the energy according to the renewable energy amount, total energy amount, environmental cost and total cost;

[0196] Methods for calculating the renewable nature of energy sources based on the amount of renewable energy, total energy, environmental cost and total cost include:

[0197] ;

[0198] in:

[0199] : Renewability;

[0200] : Amount of renewable energy;

[0201] : total energy;

[0202] : Environmental costs;

[0203] The environmental cost includes carbon emission cost and pollutant emission cost. The calculation method of environmental cost includes:

[0204] ;

[0205] in:

[0206] : Carbon emission cost;

[0207] : No. The weight coefficient of each pollutant;

[0208] : No. The cost of emitting the pollutants;

[0209] : Total number of pollutant types;

[0210] Specifically, renewable energy refers to the total amount of energy obtained from renewable energy (such as solar energy, wind energy, hydropower, etc.), which is obtained by consulting the production capacity data and actual operation data of renewable energy equipment, for example, the power generation data of photovoltaic modules, the power generation data of wind turbines, etc. The total energy includes the sum of renewable energy and non-renewable energy, which is obtained through energy metering equipment records. For example, the total power generation of an integrated energy system includes the power generation of fossil fuels and renewable energy. Carbon emission costs It refers to the CO generated per unit of energy used. 2 The cost of emissions can be obtained through data from the carbon emissions trading market or environmental impact assessment data. The weight coefficient of the pollutant reflects the relative importance of the impact of different pollutants on the environment and can be determined based on scientific research, environmental impact assessments, and policies and regulations. Step S1400 accurately calculates the renewable RI of energy. By calculating the RI, the sustainability of energy can be evaluated, which helps to select environmentally friendly and sustainable energy solutions. Energy solutions with high RI have less impact on the environment, can promote the development of clean energy, and reduce harm to the environment.

[0211] Step S1500, calculating the energy comprehensive score according to the energy density and energy grade in the energy quality index, the carbon emission intensity and pollutant emission coefficient in the energy environment index, as well as the cost-effectiveness ratio and renewability, and classifying the energy according to the energy comprehensive score;

[0212] Further, step S1500 includes:

[0213] Step S1510, calculating the comprehensive energy score according to the energy density and energy grade in the energy quality index, the carbon emission intensity and pollutant emission coefficient in the energy environment index, as well as the cost-effectiveness ratio and renewability;

[0214] The methodology for calculating the energy composite score includes:

[0215] ;

[0216] in:

[0217] : Comprehensive energy score;

[0218] : Energy density;

[0219] : The maximum value of energy density among all energy samples;

[0220] : Energy grade;

[0221] : The maximum value of energy grade among all energy samples;

[0222] : Carbon emission intensity;

[0223] : The maximum carbon emission intensity among all energy samples;

[0224] : Pollutant emission coefficient;

[0225] : The maximum value of the pollutant emission coefficient among all energy samples;

[0226] : cost-effectiveness ratio;

[0227] : The maximum value of the cost-effectiveness ratio among all energy samples;

[0228] : Renewability;

[0229] : The maximum value of renewable energy among all energy samples;

[0230] : Weight coefficient of energy density;

[0231] : Weight coefficient of energy grade;

[0232] : Weight coefficient of carbon emission intensity;

[0233] : Weight coefficient of pollutant emission coefficient;

[0234] : weight coefficient of cost-effectiveness ratio;

[0235] : Weight coefficient of reproducibility, satisfying ;

[0236] Step S1510 calculates the comprehensive energy score by comprehensively considering multiple key indicators such as energy density, energy grade, carbon emission intensity, pollutant emission coefficient, cost-effectiveness ratio and renewability, so that the comprehensive score is comprehensive and can cover the quality, environmental impact, economic benefits and sustainability of energy. By using weight coefficients, the relative importance of each indicator can be adjusted according to specific needs, making the scoring method flexible and adaptable. Each indicator is standardized to eliminate dimensional differences, ensure the comparability between different indicators, and ensure the fairness and scientificity of the scoring results. The implementation of step S1510 can not only accurately evaluate the overall performance of energy, provide a scientific basis for energy selection and optimization, but also provide important references for decision makers in energy planning, investment and management, and help achieve energy efficient utilization and environmental protection goals. In addition, the systematic scoring mechanism helps to quickly identify efficient and environmentally friendly energy solutions, improve the efficiency of energy management and scheduling, greatly improve the comprehensiveness, accuracy and practicality of energy evaluation, and provide strong support for energy management and optimization.

[0237] Step S1520, collecting the energy comprehensive scores of all samples, sorting all the energy comprehensive scores from low to high, calculating the percentiles of the energy comprehensive scores, and dividing the energy levels according to the percentiles;

[0238] Level A: Energy with a comprehensive score greater than or equal to the 90th percentile;

[0239] Level B: Energy with a comprehensive score greater than or equal to the 70th percentile and less than the 90th percentile;

[0240] Level C: Energy with a comprehensive score greater than or equal to the 50th percentile and less than the 70th percentile;

[0241] Level D: Energy with a comprehensive score greater than or equal to the 30th percentile and less than the 50th percentile;

[0242] Level E: Energy with a comprehensive score less than the 30th percentile;

[0243] Step S1520 can effectively distinguish the advantages and disadvantages of different energy samples, providing a scientific basis for energy management. Through percentile division, the energy level is more refined and intuitive, which is convenient for decision makers to quickly identify energy samples that are efficient, low-emission, cost-effective and highly renewable. At the same time, this method has high flexibility and can adjust the standards for energy level division according to specific application scenarios and needs. In addition, the comprehensive score sorting and percentile calculation methods are simple and easy, can process large amounts of data, and improve the efficiency and accuracy of energy evaluation. Overall, step S1520 provides a systematic and standardized means for energy evaluation and optimization, which helps to achieve efficient use of energy and sustainable development.

[0244] Step S1000 collects energy samples and establishes energy characteristic indicators. This step can comprehensively and systematically evaluate the characteristics of different energy samples. Energy characteristic indicators include energy quality indicators, energy environmental indicators, cost-effectiveness ratio and renewability, which cover multiple key aspects of energy. Energy quality indicators can reflect the energy density and energy grade of energy, which helps to determine the physical properties and utilization efficiency of energy; energy environmental indicators evaluate the impact of energy on the environment through carbon emission intensity and pollutant emission coefficient; cost-effectiveness ratio provides a quantitative basis for economy, ensuring that energy selection is economically feasible; and renewability indicators promote the development of sustainable energy. The comprehensive use of these indicators for energy classification can not only improve the scientificity and accuracy of energy management, but also promote the use of clean energy and efficient energy, and ultimately achieve the dual goals of environmental protection and economic benefits.

[0245] Step S2000: Collect historical power load data and load influencing factor data, and obtain a power load forecast sequence based on the historical power load data, load influencing factor data and a pre-built multi-time scale power load forecasting model; the load influencing factor data includes weather data, date type, economic and social development indicators and policy and regulatory information; the multi-time scale power load forecasting model includes a short-term power load forecasting model and a medium- and long-term power load forecasting model; the power load forecasting sequence is the power load forecast value for each future period; each period T m The time granularity is R m , the power load forecast sequence contains N m Power load forecast values, where N m =T m / R m ;

[0246] Historical power load data is automatically recorded and stored by power load monitoring equipment (such as smart meters, etc.). Weather data includes temperature, humidity, wind speed, etc. Weather conditions directly affect power demand. For example, temperature changes affect the use of air conditioning and heating equipment, and humidity and wind speed also affect power consumption. Real-time and historical weather data can be obtained by subscribing to meteorological services or using public meteorological data APIs; date types include weekdays, weekends, and holidays. Different types of dates have different power demand patterns. For example, power demand on weekdays and weekends is usually different, and power load on holidays also has special changes. Date types are pre-defined and stored in the database by programming means, or obtained using the calendar API. Economic and social development indicators such as GDP, population, and industrial structure directly affect overall power demand. For example, GDP growth is usually accompanied by an increase in power demand, and population growth will also lead to an increase in power consumption. Economic and social development indicators can be obtained by accessing official statistical databases, annual reports, or using economic data APIs.

[0247] Furthermore, step S2000 includes:

[0248] Step S2100, obtaining a short-term power load forecast sequence according to historical power load data, weather data, date type and a pre-built short-term power load forecast model;

[0249] The short-term power load forecast sequence is the power load forecast sequence for each future period (such as 15 minutes, 1 hour, 24 hours, 48 ​​hours, etc.);

[0250] Methods for constructing short-term power load forecasting models include:

[0251] A first sample data set is obtained, which includes historical power load data, historical weather data, historical date types, and real power load data. The first sample data set is divided into a sample training set and a sample test set, and a time series model is constructed. The historical power load data, historical weather data, and historical date types in the sample training set are used as input data of the model, and the real power load data in the sample training set is used as output data of the model. The model is trained to obtain an initial short-term power load forecasting model for predicting short-term power load series. The initial short-term power load forecasting model is tested using the sample test set, and the initial short-term power load forecasting model that satisfies a preset error value is output as the final short-term power load forecasting model. The time series model is preferably a long short-term memory neural network.

[0252] Step S2100 can accurately obtain the short-term power load forecast sequence by combining historical power load data, weather data, date type and other factors, using the pre-built short-term power load forecast model. Factors such as weather data and date type significantly affect the short-term power load. By incorporating these factors, the model can more accurately reflect the actual load changes. The short-term forecast model can quickly respond to load changes, provide timely data support for power dispatching, and ensure the smooth operation of the power system. Accurate short-term load forecasting helps power companies optimize the short-term configuration of power resources, reduce operating costs, and improve economic benefits.

[0253] Step S2200, obtaining a medium- and long-term power load forecast sequence based on historical power load data, economic and social development indicators, policy and regulatory information, and a pre-built medium- and long-term power load forecast model;

[0254] The medium- and long-term power load forecast sequence is the power load forecast sequence for each future period (such as one week, one month, one year, etc.);

[0255] Methods for constructing medium- and long-term power load forecasting models include:

[0256] A second sample data set is obtained, wherein the second sample data set includes historical power load data, economic and social development indicators, policy and regulatory information, and real power load data. The second sample data set is divided into a sample training set and a sample test set, and a regression model is constructed. The historical power load data, economic and social development indicators, and policy and regulatory information in the sample training set are used as input data of the regression model, and the real power load data in the sample training set are used as output data of the regression model. The model is trained to obtain an initial regression model for predicting medium- and long-term power load series. The initial regression model is tested using the sample test set, and a regression model that satisfies a preset error value is output as the final medium- and long-term power load prediction model.

[0257] Step S2200 uses a pre-built medium- and long-term power load forecasting model to obtain a medium- and long-term power load forecasting sequence by combining historical power load data, economic and social development indicators, policy and regulatory information, and other factors. Taking policy and regulatory information into consideration enables the model to adapt to changes in load demand caused by future policy changes and enhances the foresight and adaptability of the forecast. Combined with economic and social development indicators, the forecast results can be synchronized with social and economic development, providing stable power guarantee for economic and social development.

[0258] Step S2000 can effectively predict the power load demand in each period of the future by comprehensively considering the historical power load data and various load influencing factor data and using the pre-built multi-time scale power load forecasting model. The combined use of the short-term power load forecasting model and the medium- and long-term power load forecasting model can not only cope with short-term load fluctuations, but also provide an accurate basis for medium- and long-term power planning. The comprehensiveness of the load influencing factor data, including weather data, date types, economic and social development indicators, and policy and regulatory information, ensures the accuracy and reliability of power load forecasting. This method can effectively improve the dispatching efficiency of the power system, optimize the allocation of power resources, reduce the risk of power supply, and ensure the safe and stable operation of the power system.

[0259] Step S3000: Collect power supply and grid data of the power system, calculate the power supply capacity of the power system according to the power supply and grid data, and obtain the power supply and demand balance according to the power supply capacity and power load forecast sequence;

[0260] Furthermore, step S3000 includes:

[0261] Step S3100, setting a time period for supply and demand balance analysis according to the time period of the power load forecast sequence;

[0262] For example, the analysis period corresponding to the short-term power load forecast sequence is 1 week to 1 month in the future. Accordingly, the daily power supply and demand data in the next week to 1 month are counted to identify the time period of short-term supply and demand imbalance and evaluate the possible power shortage or surplus. The analysis period corresponding to the medium- and long-term power load forecast sequence is 1 month to 1 year in the future. Accordingly, the monthly power supply and demand data in the next month to 1 year are counted to evaluate the medium- and long-term supply and demand trends and identify potential supply and demand imbalance risks.

[0263] Step S3200, collecting power supply and grid data of the power system during the supply and demand balance analysis period, the power supply and grid data including a list of power generation equipment, maintenance plan and grid parameters;

[0264] The list of power generation equipment includes the capacity, quantity, commissioning time, technical parameters, etc. of various types of power generation equipment (thermal power, hydropower, wind power, photovoltaic power, nuclear power, etc.); the maintenance plan includes the maintenance time, maintenance capacity, maintenance reasons, etc. of each power generation equipment; the power grid parameters are the capacity, voltage level, line length, line loss rate, network loss rate, maximum load rate, etc. of each transmission line and substation. Among them, the list of power generation equipment is obtained through the statistical ledger of the energy department or power company; the maintenance plan is obtained through the production management system of the power generation company; the power grid parameters are obtained through the SCADA system and energy management system of the power grid dispatching department.

[0265] Step S3300, according to the list of power generation equipment, the maintenance plan and the grid parameters, calculate the total output of the traditional power generation units and the total output of the new energy power generation equipment by time period; sum the total output of the traditional power generation units and the total output of the new energy power generation equipment to calculate the total power supply capacity;

[0266] Methods for calculating the total output of traditional generator sets by time period include:

[0267] ;

[0268] in:

[0269] : The total output of the traditional generator set in the i-th period;

[0270] N: total number of traditional generator sets;

[0271] : Rated output of the jth traditional generator set;

[0272] : The available hours of the jth traditional generator set in the i-th period;

[0273] : The operating efficiency coefficient of the jth traditional generator set in the i-th period;

[0274] : The failure rate of the jth traditional generator set in the i-th period;

[0275] It represents the product of the rated output of the generator set and the available hours, that is, the theoretically available power generation. Used to adjust the actual operating efficiency of the generator set, because the actual output is usually lower than the rated output. Taking into account the failure rate, Make sure to deduct unavailable output from the calculation. Solving for the total output of traditional generator sets requires knowing the rated output of each generator set, the number of available hours per period, operating efficiency, and failure rate. These data are usually obtained through historical data and forecasts.

[0276] Methods for calculating the total output of renewable energy power generation equipment by time period include:

[0277] ;

[0278] in:

[0279] : The total output of new energy power generation equipment in the i-th period;

[0280] : the rated capacity of the e-th new energy power generation equipment;

[0281] : Wind speed and irradiance The expected value of the output characteristic function under ;

[0282] M: total number of new energy power generation equipment;

[0283] : wind speed in the ith period;

[0284] : irradiance in the ith period;

[0285] The output of renewable energy power generation is affected by natural conditions (such as wind speed and irradiance). By predicting these conditions and using characteristic functions To estimate the actual output. Use the expected value The randomness of wind speed and irradiance is taken into account, making the calculation results more representative. To solve the total output of renewable energy power generation equipment, the rated capacity of renewable energy power generation equipment and the predicted wind speed and irradiance data are required. It can be obtained by fitting the curve graph provided by the equipment manufacturer or the measured data. and It can be obtained through meteorological service providers, professional meteorological service companies and public meteorological data platforms. Some companies specialize in providing high-precision weather forecast services, and you can subscribe to these services to obtain forecast data.

[0286] Methods for calculating total power supply capacity include:

[0287] ;

[0288] in, is the total power supply capacity in the i-th period; the total power supply capacity integrates the output of traditional power generation and new energy power generation.

[0289] Through step S3300, the output of traditional power generation and new energy power generation can be systematically integrated to comprehensively evaluate the supply capacity of the power system at different times. Specifically, traditional generator sets usually include power generation equipment such as coal, gas, and nuclear power, and their output can be accurately calculated based on the rated power, actual operating status, and maintenance plan of the equipment. New energy power generation equipment includes photovoltaic, wind power, hydropower, etc., and its output is greatly affected by factors such as weather and environment, and needs to be dynamically calculated in combination with real-time data and prediction models. Adding the output of the two can obtain the total power supply capacity, thereby providing a reliable basis for power grid dispatching and ensuring the stable operation and optimal configuration of the power system. In addition, this step can help identify potential power gaps or surpluses, take countermeasures in advance, and improve the resilience and economic benefits of the system. Through this comprehensive evaluation method, not only can resource allocation be optimized and the overall efficiency of the power system be improved, but also the coordinated development of traditional energy and new energy can be promoted, and the transformation of energy structure and sustainable development can be promoted.

[0290] Step S3400, determining the grid constraint conditions according to the power generation equipment list and the grid parameters, adjusting the total power supply capacity according to the grid constraint conditions, and obtaining the constrained total power supply capacity;

[0291] The grid constraints are: ;

[0292] in:

[0293] : Active power flow on the kth transmission line in the ith period;

[0294] : Rated transmission capacity of the kth transmission line;

[0295] L: total number of transmission lines;

[0296] k: index of the transmission line;

[0297] According to the grid constraints, the total power supply capacity is adjusted, and the method for obtaining the total power supply capacity after the constraints includes:

[0298] ;

[0299] ;

[0300] in:

[0301] : Total power supply capacity after constraints;

[0302] : refers to the transmission margin of the kth transmission line in the i-th period, that is, the part where the actual power flow exceeds its rated transmission capacity;

[0303] when hour, , indicating that the actual current is within the safe range and no adjustment is required;

[0304] when hour, , indicating that the actual power flow exceeds the rated transmission capacity and needs to be adjusted.

[0305] Step S3400 determines the grid constraints according to the list of power generation equipment and grid parameters, and adjusts the total power supply capacity based on these constraints to obtain the total power supply capacity after constraints, thereby ensuring the safety and reliability of the power system in actual operation. Such adjustments can take into account the physical limitations and operation constraints of the power grid, such as line capacity, substation capacity, stability requirements, etc., thereby avoiding overload and potential failure risks of the power system and ensuring the stable operation of the power grid.

[0306] Step S3500, calculating the balance degree of power supply and demand according to the total power supply capacity after constraints and the power load forecast sequence;

[0307] According to the total power supply capacity after constraints and the power load forecast sequence, the method for calculating the power supply and demand balance includes:

[0308] ;

[0309] in:

[0310] BA: balance between electricity supply and demand;

[0311] :The first Power load forecast value;

[0312] : The index of the power load forecast value in the power load forecast sequence;

[0313] N m : The total number of power load forecast values ​​in the power load forecast sequence;

[0314] If BA>0, it means that the power supply capacity is greater than the load demand and there is a power surplus; if BA=0, it means that the supply and demand are balanced; BA<0, it means that the power supply capacity is less than the load demand and there is a power gap.

[0315] Step S3500 calculates the balance of power supply and demand based on the total power supply capacity after constraints and the power load forecast sequence, which helps to dynamically evaluate the supply and demand status of the power system under different load conditions. Through this step, the power supply can be accurately predicted and adjusted to ensure that the power supply can meet the demand under peak load or sudden demand conditions and reduce the risk of power shortage. At the same time, this calculation of supply and demand balance can help optimize the allocation of power resources, improve energy utilization efficiency, and reduce the operating cost of the power system.

[0316] Step S3000 collects power supply and grid data of the power system, calculates the power supply capacity of the power system, and obtains the balance of power supply and demand based on the power supply capacity and power load forecast sequence, thereby realizing comprehensive monitoring and evaluation of the supply and demand status of the power system. This process can effectively identify potential supply and demand imbalance problems in the power system, take timely measures to adjust, and ensure the stable operation of the power system. In addition, by accurately calculating the power supply capacity and load forecast sequence, it is possible to optimize the allocation of power resources, improve the efficiency of power grid operation, reduce energy waste, and enhance the economic and environmental benefits of the power system. This step not only helps to ensure the reliability and safety of power supply, but also provides a scientific basis for energy management and scheduling, and supports the development of smart grids and the efficient use of renewable energy.

[0317] Step S4000: forming a multi-energy optimization scheduling strategy according to the balance of power supply and demand and energy classification;

[0318] For further information, see Figure 2 , step S4000 includes:

[0319] Step S4100, when the power supply and demand balance is greater than the excess threshold, generating a power supply reduction strategy;

[0320] Furthermore, step S4100 includes:

[0321] Step S4110, reducing the output power of the energy of level E until the power supply and demand balance is less than or equal to the excess threshold; if the output power of the energy of level E has been reduced to the minimum and the power supply and demand balance is still greater than the excess threshold, executing step S4120;

[0322] Step S4120, reducing the output power of the level D energy until the power supply and demand balance is less than or equal to the excess threshold; if the output power of the level D energy has been reduced to the minimum and the power supply and demand balance is still greater than the excess threshold, executing step S4130;

[0323] Step S4130, reducing the output power of the level C energy until the power supply and demand balance is less than or equal to the excess threshold; if the output power of the level C energy has been reduced to the minimum and the power supply and demand balance is still greater than the excess threshold, executing step S4140;

[0324] Step S4140, successively reducing the output power of higher-level energy sources until the balance degree of power supply and demand approaches zero;

[0325] Step S4100 achieves a dynamic balance between power supply and demand by orderly adjusting the output power of different levels of energy. When the balance between power supply and demand is greater than the excess threshold, the system first reduces the output power of the lower-efficiency level E energy to minimize the impact on high-efficiency energy and improve overall energy efficiency. If the output power of level E energy has dropped to the minimum, the system will reduce the output power of higher-level energy in turn. This method not only ensures the stability and continuity of power supply, but also optimizes the energy structure, reduces energy waste, and improves the level of refinement of energy management, ultimately helping to achieve energy conservation, emission reduction and environmental protection goals.

[0326] Step S4200, when the power supply and demand balance is less than the shortage threshold, generating a power supply increase strategy;

[0327] Further, step S4200 includes:

[0328] Step S4210, increasing the output power of the energy source of level A until the balance degree of power supply and demand is greater than or equal to the shortage threshold; if the output power of the energy source of level A has been increased to the maximum and the balance degree of power supply and demand is still less than the shortage threshold, executing step S4220;

[0329] Step S4220, increasing the output power of the Class B energy until the power supply and demand balance is greater than or equal to the shortage threshold; if the output power of the Class B energy has been increased to the maximum and the power supply and demand balance is still less than the shortage threshold, executing step S4230;

[0330] Step S4230, increasing the output power of the Class C energy until the power supply and demand balance is greater than or equal to the shortage threshold; if the output power of the Class C energy has been increased to the maximum and the power supply and demand balance is still less than the shortage threshold, executing step S4240;

[0331] Step S4240, increasing the output power of energy sources of lower levels in sequence until the balance degree of power supply and demand approaches zero;

[0332] Step S4200 increases the output power of different levels of energy step by step to ensure that when the balance between power supply and demand is lower than the shortage threshold, the system can respond and adjust the power supply quickly and effectively. First, by giving priority to increasing the output power of level A energy, high-quality, high-efficiency, and low-environmental-impact energy can be used to maximize the quality and efficiency of power supply. When level A energy has reached the maximum output power but still cannot meet the demand, the system gradually turns to level B and level C energy, and uses the second-highest quality energy to supplement in turn to ensure power supply stability and reliability. Finally, by increasing the output power of lower-level energy in turn, the system can flexibly respond to fluctuations in power demand, avoid power supply crises caused by power shortages, and ensure the safe and stable operation of the power grid. At the same time, this step-by-step adjustment strategy helps to optimize the utilization of energy resources, reduce the waste of high-quality energy, and reduce environmental load, so as to achieve a balance between the economic benefits and environmental benefits of energy.

[0333] Step S4300, when the power supply and demand balance is greater than or equal to the shortage threshold and less than or equal to the surplus threshold, maintain the current energy output power;

[0334] Step S4400, real-time monitoring of the balance between power supply and demand, and dynamic adjustment of the output power of each level of energy.

[0335] Step S4000 forms a multi-energy optimization scheduling strategy based on the balance of power supply and demand and energy classification, which can effectively improve the operating efficiency and stability of the power system. Specifically, when the balance of power supply and demand is greater than the surplus threshold, a power supply reduction strategy is generated to avoid energy waste and reduce operating costs; when the balance of power supply and demand is less than the shortage threshold, a power supply increase strategy is generated to ensure the reliability of power supply and meet user needs; and when the balance of power supply and demand is between the shortage threshold and the surplus threshold, the current energy output power is maintained to maintain the stable operation of the system. Through these measures, the rational allocation and optimal utilization of resources can be achieved, the economic and environmental benefits of the power system can be improved, and sustainable development can be achieved.

[0336] Example 2

[0337] This embodiment provides an intelligent centralized control chain distributed multi-energy optimization scheduling device based on embodiment 1, such as Figure 3 As shown, including:

[0338] Energy classification module: used to collect energy samples, establish energy characteristic indicators, and classify energy according to the energy characteristic indicators; the energy characteristic indicators include energy quality indicators, energy environment indicators, cost-effectiveness ratio and renewability;

[0339] Load forecasting module: used to collect historical power load data and load influencing factor data, and obtain the power load forecasting sequence based on the historical power load data, load influencing factor data and the pre-built multi-time scale power load forecasting model;

[0340] Power supply and demand balance calculation module: used to collect power supply and grid data of the power system, calculate the power supply capacity of the power system based on the power supply and grid data, and obtain the power supply and demand balance based on the power supply capacity and power load forecast sequence;

[0341] Optimization and dispatching module: used to form a multi-energy optimization and dispatching strategy based on the balance of power supply and demand and energy classification;

[0342] Real-time monitoring and dynamic adjustment module: used to monitor the balance of power supply and demand in real time and dynamically adjust the output power of each level of energy.

[0343] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0344] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0345] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0346] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0347] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0348] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

[0349] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Intelligent centralized control chain distributed multi-energy optimization scheduling method, characterized in that: include: Step S1000: Collect energy samples, establish energy characteristic indicators, and perform energy classification according to the energy characteristic indicators; The energy characteristic indicators include energy quality indicators, energy environment indicators, cost-effectiveness ratio and renewability; Step S2000: Collect historical power load data and load influencing factor data, and obtain a power load forecast sequence based on the historical power load data, load influencing factor data and a pre-built multi-time scale power load forecasting model; the load influencing factor data includes weather data, date type, economic and social development indicators and policy and regulatory information; The multi-time scale power load forecasting model includes short-term power load forecasting model and medium- and long-term power load forecasting model; the power load forecasting sequence is the power load forecast value for each period in the future; each period T m The time granularity is R m , the power load forecast sequence contains N m Power load forecast values, where N m =T m / R m ; Step S3000: Collect power supply and grid data of the power system, calculate the power supply capacity of the power system according to the power supply and grid data, and obtain the power supply and demand balance according to the power supply capacity and power load forecast sequence; Step S4000: forming a multi-energy optimization scheduling strategy according to the balance of power supply and demand and energy classification; The step S1000 includes: Step S1100, collecting energy samples, obtaining the energy content, energy volume, actual energy utilization rate, ambient temperature and energy temperature of the energy samples, and calculating energy quality indicators according to the energy content, energy volume, actual energy utilization rate, ambient temperature and energy temperature, the energy quality indicators including energy density and energy grade; Step S1200, obtaining CO2 emissions, each pollutant emission and energy usage of the energy sample, and calculating energy environment indicators according to the CO2 emissions, each pollutant emission and energy usage, the energy environment indicators including carbon emission intensity and pollutant emission coefficient; Step S1300, obtaining the energy output and total cost of the energy sample, and calculating the cost-effectiveness ratio according to the energy output, the actual energy utilization rate and the total cost; Step S1400, obtaining the renewable energy amount, total energy amount and environmental cost of the energy sample, and calculating the renewability of the energy according to the renewable energy amount, total energy amount, environmental cost and total cost; Step S1500, calculate the comprehensive energy score according to the energy density and energy grade in the energy quality index, the carbon emission intensity and pollutant emission coefficient in the energy environment index, as well as the cost-effectiveness ratio and renewability, and classify the energy according to the comprehensive energy score.

2. The intelligent centralized control chain distributed multi-energy optimization scheduling method according to claim 1 is characterized in that: The step S1100 includes: Step S1110, collecting energy samples, obtaining the energy content and energy volume of the energy samples, and dividing the energy content by the energy volume to obtain energy density; Step S1120, obtaining the actual energy utilization rate, ambient temperature and energy temperature of the energy sample, and calculating the energy grade according to the actual energy utilization rate, ambient temperature and energy temperature; Based on the actual energy utilization rate, ambient temperature and energy temperature, the methods for calculating energy grade include: ； in: : Energy grade; : Ambient temperature; : Energy temperature; : Actual energy utilization rate; The calculation methods of actual energy utilization rate include: ； in: : Production efficiency; : Transmission efficiency; : conversion efficiency; : Terminal usage efficiency.

3. The intelligent centralized control chain distributed multi-energy optimization scheduling method according to claim 1 is characterized in that: The step S1200 includes: Step S1210, obtaining CO2 emissions and energy usage, and dividing CO2 emissions by energy usage to obtain carbon emission intensity; Step S1220, obtaining the emission of each pollutant, and calculating the pollutant emission coefficient according to the emission of each pollutant and the energy usage; Based on the amount of each pollutant emitted and the amount of energy used, the methods for calculating the pollutant emission coefficient include: ； in: : Pollutant emission coefficient; : No. The weight coefficient of each pollutant; : No. The amount of pollutants emitted; : Energy usage; : Total number of pollutant types; : Index of pollutant types.

4. The intelligent centralized control chain distributed multi-energy optimization scheduling method according to claim 1 is characterized in that: The step S1500 includes: Step S1510, calculating the comprehensive energy score according to the energy density and energy grade in the energy quality index, the carbon emission intensity and pollutant emission coefficient in the energy environment index, as well as the cost-effectiveness ratio and renewability; Step S1520, collecting the energy comprehensive scores of all samples, sorting all the energy comprehensive scores from low to high, calculating the percentiles of the energy comprehensive scores, and dividing the energy levels according to the percentiles; Level A: Energy with a comprehensive score greater than or equal to the 90th percentile; Level B: Energy with a comprehensive score greater than or equal to the 70th percentile and less than the 90th percentile; Level C: Energy with a comprehensive score greater than or equal to the 50th percentile and less than the 70th percentile; Level D: Energy with a comprehensive score greater than or equal to the 30th percentile and less than the 50th percentile; Level E: Energy sources with a composite score less than the 30th percentile.

5. The intelligent centralized control chain distributed multi-energy optimization scheduling method according to claim 1 is characterized in that: The step S2000 includes: Step S2100, obtaining a short-term power load forecast sequence according to historical power load data, weather data, date type and a pre-built short-term power load forecast model; Step S2200, obtaining a medium- and long-term power load forecast sequence based on historical power load data, economic and social development indicators, policy and regulatory information, and a pre-built medium- and long-term power load forecast model.

6. The intelligent centralized control chain distributed multi-energy optimization scheduling method according to claim 1 is characterized in that: The step S3000 includes: Step S3100, setting a time period for supply and demand balance analysis according to the time period of the power load forecast sequence; Step S3200, collecting power supply and grid data of the power system during the supply and demand balance analysis period, the power supply and grid data including a list of power generation equipment, maintenance plan and grid parameters; Step S3300, according to the list of power generation equipment, the maintenance plan and the grid parameters, calculate the total output of the traditional power generation units and the total output of the new energy power generation equipment by time period; sum the total output of the traditional power generation units and the total output of the new energy power generation equipment to calculate the total power supply capacity; Step S3400, determining the grid constraint conditions according to the power generation equipment list and the grid parameters, adjusting the total power supply capacity according to the grid constraint conditions, and obtaining the constrained total power supply capacity; Step S3500, calculating the balance degree of power supply and demand according to the constrained total power supply capacity and the power load forecast sequence.

7. The intelligent centralized control chain distributed multi-energy optimization scheduling method according to claim 6 is characterized in that: The method for calculating the total output of a traditional generator set by time period includes: ； in, is the total output of traditional generator sets in the i-th period, N is the total number of traditional generator sets, is the rated output of the jth traditional generator set, is the available hours of the jth traditional generator set in the i-th period, is the operating efficiency coefficient of the jth traditional generator set in the i-th period, is the failure rate of the jth traditional generator set in the i-th period; The method for calculating the total output of the new energy power generation equipment by time period includes: ； in, is the total output of new energy power generation equipment in the i-th period, is the rated capacity of the e-th new energy power generation equipment, Wind speed and irradiance The expected value of the output characteristic function under , M is the total number of new energy power generation equipment, is the wind speed in the ith period, is the irradiance in the ith period; Methods for calculating total power supply capacity include: ； in, is the total power supply capacity in the i-th period.

8. The intelligent centralized control chain distributed multi-energy optimization scheduling method according to claim 1 is characterized in that: The step S4000 includes: Step S4100, when the power supply and demand balance is greater than the excess threshold, generating a power supply reduction strategy; Step S4200, when the power supply and demand balance is less than the shortage threshold, generating a power supply increase strategy; Step S4300, when the power supply and demand balance is greater than or equal to the shortage threshold and less than or equal to the surplus threshold, maintain the current energy output power; Step S4400, real-time monitoring of the balance between power supply and demand, and dynamic adjustment of the output power of each level of energy.

9. The intelligent centralized control chain distributed multi-energy optimization scheduling method according to claim 8 is characterized in that: The step S4100 includes: Step S4110, reducing the output power of the energy of level E until the power supply and demand balance is less than or equal to the excess threshold; if the output power of the energy of level E has been reduced to the minimum and the power supply and demand balance is still greater than the excess threshold, executing step S4120; Step S4120, reducing the output power of the level D energy until the power supply and demand balance is less than or equal to the excess threshold; if the output power of the level D energy has been reduced to the minimum and the power supply and demand balance is still greater than the excess threshold, executing step S4130; Step S4130, reducing the output power of the level C energy until the power supply and demand balance is less than or equal to the excess threshold; if the output power of the level C energy has been reduced to the minimum and the power supply and demand balance is still greater than the excess threshold, executing step S4140; Step S4140, successively reducing the output power of higher-level energy sources until the balance degree of power supply and demand approaches zero; The step S4200 includes: Step S4210, increasing the output power of the energy source of level A until the balance degree of power supply and demand is greater than or equal to the shortage threshold; if the output power of the energy source of level A has been increased to the maximum and the balance degree of power supply and demand is still less than the shortage threshold, executing step S4220; Step S4220, increasing the output power of the Class B energy until the power supply and demand balance is greater than or equal to the shortage threshold; if the output power of the Class B energy has been increased to the maximum and the power supply and demand balance is still less than the shortage threshold, executing step S4230; Step S4230, increasing the output power of the Class C energy until the power supply and demand balance is greater than or equal to the shortage threshold; if the output power of the Class C energy has been increased to the maximum and the power supply and demand balance is still less than the shortage threshold, executing step S4240; Step S4240, increasing the output power of lower-level energy sources in sequence until the balance between power supply and demand approaches zero.

10. An intelligent centralized control chain distributed multi-energy optimization scheduling device, which is used to implement the intelligent centralized control chain distributed multi-energy optimization scheduling method according to any one of claims 1 to 9, characterized in that: include: Energy classification module: used to collect energy samples, establish energy characteristic indicators, and classify energy according to the energy characteristic indicators; the energy characteristic indicators include energy quality indicators, energy environment indicators, cost-effectiveness ratio and renewability; Load forecasting module: used to collect historical power load data and load influencing factor data, and obtain the power load forecasting sequence based on the historical power load data, load influencing factor data and the pre-built multi-time scale power load forecasting model; Power supply and demand balance calculation module: used to collect power supply and grid data of the power system, calculate the power supply capacity of the power system based on the power supply and grid data, and obtain the power supply and demand balance based on the power supply capacity and power load forecast sequence; Optimization and dispatching module: used to form a multi-energy optimization and dispatching strategy based on the balance of power supply and demand and energy classification; Real-time monitoring and dynamic adjustment module: used to monitor the balance of power supply and demand in real time and dynamically adjust the output power of each level of energy.

Images