CN113888132A

CN113888132A - An energy management system for large industrial enterprises

Info

Publication number: CN113888132A
Application number: CN202111181490.6A
Authority: CN
Inventors: 周炳; 韦磊; 李娜; 周游; 王冰; 佘家驹; 廖双乐; 马洲俊; 杨旭升; 张永浩; 周兴华; 赵洪刚; 杨晓亮
Original assignee: BEIJING JOIN BRIGHT DIGITAL POWER TECHNOLOGY CO LTD; State Grid Comprehensive Energy Service Group Co ltd; State Grid Jiangsu Electric Power Co Ltd
Current assignee: BEIJING JOIN BRIGHT DIGITAL POWER TECHNOLOGY CO LTD; State Grid Comprehensive Energy Service Group Co ltd; State Grid Jiangsu Electric Power Co Ltd
Priority date: 2021-10-11
Filing date: 2021-10-11
Publication date: 2022-01-04

Abstract

The invention relates to the technical field of energy management, and proposes an energy management system of a large-scale industrial enterprise, comprising an energy management center, an energy inspection unit, an energy consumption equipment unit, a production equipment unit, and a data acquisition system communicated and connected with the energy management center , the data acquisition system is also connected to the energy inspection unit and the production equipment unit in communication; the data acquisition system is used to collect the working status data of the energy consumption equipment unit and the production equipment unit, and send the working status data to the energy management center. Through the above technical solution, based on the data analysis method, using automation, information technology and centralized management mode, centralized and flat dynamic monitoring and digital management are implemented for the production, transmission and distribution and consumption links of the enterprise energy system, which solves the problem in the existing technology. Enterprise energy management is highly dependent on manual labor, and the data has the problem that human factors cannot truly reflect the situation.

Description

Energy management system of large-scale industrial enterprise

Technical Field

The invention relates to the technical field of energy management, in particular to an energy management system of a large-scale industrial enterprise.

Background

Energy management is the general term for scientific planning, organization, inspection, control and supervision of the overall process of production, distribution, conversion and consumption of energy. The content comprises the following steps: making a correct energy development policy and an energy-saving policy, continuously perfecting energy planning, energy laws and regulations and an energy control system, and well arranging the production and management of industrial energy and domestic energy; the method has the advantages of strengthening energy equipment management, carrying out technical transformation and updating on boilers, industrial kilns, various electrical appliances and the like in time, improving the energy utilization rate, carrying out energy quota management, calculating indexes of effective energy consumption and process loss, and checking each energy consumption quota layer by layer.

The problem of energy consumption is a problem that production enterprises in China need to attach importance, the existing enterprise energy management has strong manual dependence, and in the manual supervision process, human factors often exist in the authenticity of data, so that the data cannot be truly reflected. Meanwhile, the method is not beneficial to enterprises to find the problems of the production process and provide an improved scheme.

Disclosure of Invention

The invention provides an energy management system of a large-scale industrial enterprise, which is based on a data analysis method and solves the problems that the artificial dependence of enterprise energy management is strong and the data has human factors which can not truly reflect the situation in the prior art.

The technical scheme of the invention is as follows:

an energy management system of a large-scale industrial enterprise comprises an energy management center, and an energy patrol unit, an energy consumption equipment unit, a production equipment unit and a data acquisition system which are in communication connection with the energy management center; the data acquisition system is also in communication connection with the energy inspection unit and the production equipment unit; and the data acquisition system is used for acquiring the working state data of the energy consumption equipment unit and the production equipment unit and sending the working state data to the energy management center.

Furthermore, the energy management system also comprises an informatization system, wherein the enterprise informatization system comprises at least one of an enterprise resource planning system, a production informatization management system and an office system; and the energy management center exchanges information with the information system.

Furthermore, the energy management system also comprises an energy management terminal and an operation management terminal which are in communication connection with the energy management center, wherein the energy management terminal comprises equipment management, operation management and analysis, and the operation management terminal comprises energy system operation data; and the energy management terminal and the operation management terminal are used for acquiring real-time data, production actual performance information and production plan information from the energy management center.

Further, the energy routing inspection unit comprises at least one of power routing inspection equipment, gas routing inspection equipment and water channel routing inspection equipment; the energy consumption equipment unit comprises at least one of a transformer substation, a power distribution station, a water pump station and an air compression station; the production equipment unit comprises at least one of power plant energy production workshop equipment, electric power system production line equipment, steam system production line equipment and gas system production line equipment.

Further, the data acquisition system comprises a first group of data acquisition devices and a second group of data acquisition devices;

the first group of data acquisition equipment comprises a data acquisition gateway and a field intelligent instrument module, the field intelligent instrument module comprises at least one of an intelligent water meter, an intelligent gas meter, an intelligent electric meter and an intelligent heat meter, and the field intelligent instrument module is in communication connection with the data acquisition gateway;

the second group of data acquisition equipment comprises an interface server, a process control system and a data acquisition and monitoring control system, wherein the process control system and the data acquisition and monitoring control system are in communication connection with the interface server.

Further, the energy management center includes:

the energy plan scheduling module is used for receiving the production plan and the energy prediction result output by the information system, outputting an energy data plan, tracking the execution condition of the energy data plan and carrying out statistical analysis on various execution conditions of the energy data plan;

the energy cost management module is used for receiving basic data of energy and carrying out energy cost accounting;

the energy operation analysis module is used for acquiring the difference between any two of the energy performance data, the energy plan data and the energy prediction data within the preset time length, wherein the difference comprises a standard deviation or a variance, and outputting the comparative analysis of historical data and the comparative analysis of real-time data among the energy performance, the energy plan and the energy prediction within the preset time length;

the energy quality management module is used for receiving gas and/or water quality energy media and environmental detection and testing data, calculating the change rule of the environmental detection data of each energy medium under different conditions and outputting the future predicted change trend of each energy medium;

the energy equipment management module is used for monitoring equipment state, making an equipment maintenance plan and comprehensively inquiring equipment operation of the life cycle of energy equipment, wherein the energy equipment comprises at least one of wind power generation equipment, solar power generation equipment, hydroelectric power generation equipment, geothermal power generation equipment, thermal power generation equipment and nuclear power generation equipment;

the intelligent operation and maintenance module of the power system is used for receiving working state data output by the data acquisition system, wherein the working state data comprise energy consumption data, operation parameter data and environment monitoring data of a production workshop, and are combined with work orders and inspection work to realize online inspection, maintenance management, inspection management and emergency maintenance management of the power system;

the energy report management module is used for receiving the metering data and the management statistical data and outputting an energy report;

the energy balance analysis and prediction module is used for receiving a production operation plan, a maintenance and overhaul plan, historical production data and real-time data, predicting the conditions of natural gas supply quantity, whole plant power load and the like in the middle period or short period of the future according to the current situation of the current energy medium, and outputting a prediction curve;

the carbon emission and energy saving auxiliary decision module is used for realizing carbon emission level analysis and energy saving analysis;

and the direct power purchase auxiliary decision transaction module is used for receiving the current energy consumption situation and the product plan and outputting the daily, monthly, quarterly and annual power consumption load of the enterprise and the predicted value of the power consumption according to the historical power consumption and the load curve.

The working principle and the beneficial effects of the invention are as follows:

1. the invention takes an enterprise energy management system as a carrier, gives full play to data value based on a data analysis method, takes the purposes of improving energy utilization efficiency and reducing energy consumption cost as the fundamental purposes, and can realize PDCA closed-loop management of energy by technical means such as energy management, data mining, energy-saving transformation, demand response, micro-energy network and the like and assisted by energy management supervision and evaluation analysis, thereby realizing continuous improvement of energy consumption technology of enterprises, continuous optimization of energy management and continuous improvement of energy efficiency level and reducing the production cost of enterprises.

2. The enterprise energy management system can ensure that the management system, the operation system and the energy management system of the energy management and control center are effectively fused, the energy equipment, the process control, the production system and the energy plan are flatly managed longitudinally, and the transverse energy management system is fused with the production management system to realize cooperative management.

3. The enterprise energy management system of the invention utilizes automation, informatization technology and centralized management mode to implement centralized flat dynamic monitoring and digital management on production, transmission, distribution and consumption links of the enterprise energy system.

4. The enterprise energy management system of the invention mainly realizes the prior management and the optimized scheduling of energy media on the basis of realizing the basic energy management, establishes an energy load prediction and optimized scheduling model, and realizes the 'prior scheduling' and 'quantitative scheduling' based on the model.

The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.

Drawings

FIG. 1 is a block diagram of an energy management system according to the present invention;

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any inventive step, are intended to be within the scope of the present invention.

Example 1, as shown in fig. 1, this example proposes an energy management system for a large-scale industrial enterprise,

the energy management system comprises an energy management center, and an energy patrol unit, an energy consumption equipment unit, a production equipment unit and a data acquisition system which are in communication connection with the energy management center; the data acquisition system is also in communication connection with the energy inspection unit and the production equipment unit and is used for acquiring data of the energy consumption equipment unit and the production equipment unit and reporting the acquired data to a server corresponding to the energy management center.

The energy inspection unit comprises at least one of power inspection equipment, gas inspection equipment and water channel inspection equipment. The energy management center is interacted with the energy inspection units, and specifically, the energy management center sends a scheduling instruction to each energy inspection device, the plurality of energy inspection devices execute inspection operation according to the scheduling instruction, and the production plan and the energy prediction result output by the information system are fed back to the energy management center.

Wherein the water course inspection equipment specifically can be used to patrol and examine water drainage pipe network or water supply network, upload the position data of water course inspection equipment, patrol and examine time data and the state data of water drainage pipe network to energy management center. The status data for the drainage network may include a photograph of the pipe indicating whether the pipe has burst or broken. Through miniature camera machine and the transmission equipment that unmanned aerial vehicle carried, return the image of pipeline on the server at energy management center to can improve and patrol and examine efficiency, in time learn the condition of raceway beyond several kilometers. The state data of the drainage pipe network also comprises water pressure, flow direction, water quality information and filling rate of the pipeline which are acquired by polling a sensor arranged in the water channel polling equipment; and reporting the data to a server of the energy management center through a wireless data transmission gateway and/or the Internet.

The energy consumption equipment unit comprises at least one of energy consumption equipment such as a transformer substation, a power distribution station, a water pump station and an air compression station. The energy management center interacts with the energy consumption equipment units, and specifically, the energy management center sends an operation control instruction to each energy consumption equipment unit to control the working states of the energy consumption equipment units; the energy management center also sends a communication scheduling instruction to each energy consumption device; and the energy management center also receives the working state data of each energy consumption device reported by the data acquisition system.

The production equipment unit comprises at least one of equipment in an energy production workshop of a power plant, electric power system production line equipment, steam system production line equipment and gas system production line equipment. The energy management center interacts with the energy consumption equipment unit and is specifically operated to send a communication scheduling instruction to each production equipment; the energy management center also sends an energy adjustment instruction to each production device; and the energy management center also receives the working state data of each production device reported by the data acquisition system.

The data acquisition system comprises a first group of data acquisition equipment and a second group of data acquisition equipment;

the first group of data acquisition equipment comprises a data acquisition gateway and a field intelligent instrument module, wherein the field intelligent instrument module comprises at least one of an intelligent water meter, an intelligent gas meter, an intelligent electricity meter and an intelligent heat energy meter which are provided with communication interfaces but are not connected with a background system, and the field intelligent instrument module is in communication connection with the data acquisition gateway, is in communication with the energy management center through the data acquisition gateway and uploads the acquired data to a server of the energy management center;

the second group of data acquisition equipment is used for a data interface of the existing background system and comprises an interface server, a process control system and a data acquisition and monitoring control system, wherein the process control system and the data acquisition and monitoring control system are in communication connection with the interface server. The software program interface is compiled by adding an interface server to be in butt joint with each system for exchanging data, and the accessed data is uploaded to a server of the energy management center.

The informatization system includes at least one of an enterprise resource planning, ERP, a production informatization management system, MES, and an office system, OA. The energy management center exchanges information with an enterprise information system (ERP/MES/OA), realizes the issuing of production management information and the uploading of energy analysis information, and ensures the complete professional analysis function of the energy management center and/or the complete financial and energy index management function of the information system.

The energy management system of the embodiment has a plurality of functions as follows:

1. data acquisition function

The data acquisition refers to the collection of data meeting the application function requirements of the energy management center through an I/O (input/output), a communication interface, a special instrument or a third-party system, and the data comprises energy management system operation data, metering data, power public and auxiliary system state and fault information, production subject related to energy scheduling, production unit information and the like, so that the comprehensive monitoring and management requirements of the energy management system are met.

The data acquisition mode of the energy management system is mainly completed by adopting a data acquisition gateway and a database data communication mode of an energy management center. The data acquisition gateway is a core device of the data acquisition and control system of the energy management system, and various field energy process data and metering data including a variable power supply system, a power system, a steam system, a water system, a field production process unit, a metering system and the like are acquired and collated through the data acquisition gateway and then are accessed to the energy management system by adopting a communication protocol of a unified standard. The unattended station also transmits a control instruction issued by the energy management center to the PLC on site through the data acquisition gateway to realize remote control.

2. Energy monitoring function

The energy monitoring function mainly comprises conventional equipment monitoring, online management and adjustment, coordinated monitoring of a process system, a computer network system and the like, energy-saving requirements are met, energy-saving scheduling with remote monitoring as a core, flattened fault monitoring, analysis and processing and the like. The system monitors subsystems such as fuel gas, electric power, water, gas supply and the like, independently monitors the energy condition for the heavy-duty equipment, comprehensively monitors the workshop procedures, and remotely monitors and controls the unattended energy system.

3. Basic energy management function

The basic energy management is used as the supplement of the online balance scheduling and the online energy management of the energy management center, and comprises modules of energy plan management, energy performance management, energy production operation support, energy quality management, energy comparison analysis and the like. The modules simultaneously extract information such as production actual results, production plans and the like of an ERP/MES/OA system on the basis of real-time data of an energy management center, and the information is sent to an energy management terminal and an operation management terminal through system analysis and processing, is provided for energy management professionals and operation management professionals through a friendly design interface, and provides an integrated safety guarantee mechanism and a perfect basic management platform for the energy management center administrators from the integral perspective.

4. Information interaction with information systems (ERP, MES, OA)

The production management information is issued and the energy analysis information is uploaded by exchanging information with an enterprise information system (ERP/MES/OA), so that the complete professional analysis function of an energy management center and the complete financial and energy index management function of an information system are ensured.

5. Aided decision support

Energy consumption prediction support: according to the production, transfer (transportation) and maintenance plans of the product and the related quantities such as the unit consumption of the product, the energy supply and demand quantity in a period of time is predicted, and the energy management personnel is assisted to make a corresponding energy production and supply plan, so that the predictive energy supply and demand balance is achieved. Specifically, the unit consumption calculation of energy consumption and yield in the production and transportation process of the product is performed.

6. Energy balance and optimal scheduling

Energy balance: based on data of material movement and balance, energy production and balance and the like, the conditions of production operation, material movement, production execution, production statistics and the like of an enterprise are mastered in time; information integration and data sharing among all service levels are enhanced; the flexible production report format customization function is provided, so that the report function has good maintainability and expansibility. Data summarization and analysis can be conveniently performed based on production operation data. Realized in the form of a material and energy balance chart.

The energy balance report is one of important contents of energy management daily work, and the data analysis function of the energy management system can realize large-scale formula operation, realize the automation of the balance report and provide data support for energy balance scheduling.

The main energy balance comprises the purchase and use balance of primary energy, the balance of secondary energy, the material balance of the whole company and the like. According to actual needs, the energy balance can be managed in a partition mode, and regional or branch energy balance is provided.

Energy prediction and optimal scheduling: the method mainly completes functions of power load prediction, power consumption prediction, multi-medium balance optimization scheduling and the like, establishes a production and marketing prediction model of main energy media by using a data and control platform of an energy management center system, gives an energy system optimization scheduling scheme through energy comprehensive analysis, provides decision basis through scheduling and management of users, realizes balance optimization operation of an energy management system, and achieves the effects of energy conservation and consumption reduction.

7. Energy demand side management

Power demand side support: the system monitors energy supply and energy consumption of each workshop, outputs an alarm when a preset load critical is reached, and reminds related personnel of each factory to carry out production adjustment. And grading the energy loads, taking the unimportant non-productive energy loads as a power demand plan, and preferentially processing the part of the loads when responding to the demand side management, namely adjusting to temporarily shut down the unimportant non-productive energy loads.

The system monitors the water and gas production and use conditions of the energy production workshop of the power plant and each branch plant, reasonably allocates the supply and demand balance between production supply and use according to the order production condition, meets the production energy requirement and simultaneously ensures that the energy production facilities do not operate excessively.

Energy source mutual aid: according to the self-production planning condition of the enterprise and the data analysis of the peak, average and valley periods of energy consumption, the energy mutual-aid information is automatically optimized and configured, the energy mutual-aid index is formulated, and the energy regulation is implemented after the dispatching confirmation of the group company.

And (3) demand response: the traditional switching-off and power limiting mandatory control mode is changed into flexible control of an enterprise according to the operation rule of energy consumption equipment and the actual requirements of energy peak, flat and valley periods, so that the power utilization requirement of the enterprise is met, the peak and load regulation and reduction requirements advocated by power supply companies and governments are met, and the economic subsidy of the governments is enjoyed.

8. Carbon emissions decision support

And analyzing the energy consumption and carbon emission control level of the enterprise according to indexes issued by relevant government departments for standard coal consumption and carbon emission of the enterprise, wherein the indexes comprise monthly analysis, quarterly analysis and annual analysis, providing data support for carbon emission transactions of the enterprise, and helping the enterprise realize carbon emission decision transactions.

9. Direct power purchase transaction decision support

According to the current situation of energy consumption and a product plan, according to historical electric quantity and a load curve, the daily, monthly, quarterly and annual electric load and electric quantity of an enterprise are predicted in real time, and judgment basis is provided for adjusting the load peak value and the production process by applying a demand side management means; and analyzing historical power selling price curves of different power selling merchants, and providing decision support for direct power purchasing transactions of enterprises by combining the power predicting results of the enterprises so as to effectively reduce the production cost of the enterprises.

Example 2

In this embodiment, the energy management center includes:

1. energy plan scheduling module

The energy data planning system is used for making an energy data plan by referring to the results of production plans and energy forecast from the information system, tracking the execution condition of the energy data plan, and counting and analyzing the execution condition of various energy data plans so as to timely make adjustment according to a scheduling algorithm. The scheduling algorithm may include any one or more of the following: deep reinforcement learning algorithm, dynamic programming algorithm and particle swarm optimization algorithm.

The deep reinforcement learning algorithm shows remarkable performance in the aspect of strategy optimization. An Asynchronous dominant Actor-Critic algorithm (hereinafter referred to as A3C) is a new generation algorithm in the field of deep reinforcement learning, and the algorithm originates from an Actor-Critic algorithm and consists of an executor network and an evaluation network, and action selection and value judgment are realized by combining the two networks. The A3C algorithm creates a plurality of parallel local networks, and performs information interaction with the global network to realize parallel network parameter updating, and has the characteristics of high convergence speed and strong global optimization capability.

Dynamic Programming (DP) is a branch of operations research and is a mathematical method for solving the optimization process of a decision-making process.

Particle Swarm Optimization (PSO) is in turn translated into a Particle Swarm algorithm, or a Particle Swarm optimization algorithm. The method is a random search algorithm based on group cooperation and developed by simulating foraging behavior of a bird group.

2. Energy cost management module

The method comprises the steps of energy cost center formulation, energy cost center calculation, energy statistics and the like. And the energy cost accounting system is used for adopting a cost center mode, attributing the basic data of the energy to each cost center and carrying out energy cost accounting according to the cost centers.

3. Energy operation analysis module

The system is used for performing energy performance, energy planning, energy prediction and comparative analysis among the energy performance, the energy planning, the energy prediction and the energy prediction in time months, ten days, weeks and the like; and carrying out comparative analysis on historical data and real-time data. Specifically, the comparative analysis can be performed on different time scales, and the annual analysis can also be performed with historical data. And obtaining the difference degree between any two of the energy performance data, the energy plan data and the energy prediction data in a period of time, wherein the difference degree comprises standard deviation or variance.

The energy performance or the energy performance refers to the actual use condition of the energy; energy plan, which refers to a plan of monthly or yearly energy usage; energy prediction refers to the prediction of future energy usage. The energy prediction may be based on a pre-trained neural mesh model. The energy usage over a future period of time may also be predicted based on a linear regression equation.

4. Energy quality management module

The system is used for centralized management of energy media such as gas and/or water quality and detection and test data of environmental protection detection, and analyzing the change trend of the energy media and the environmental quality. The centralized management means that data are all unified on one platform to realize centralized management. The analyzing the trend of the energy medium and the environmental quality may specifically include: and analyzing the change rule of the environment detection data of each energy medium under different conditions, and predicting the future change condition or change trend of each energy medium.

5. Energy equipment management module

The management process for the life cycle of the energy equipment comprises at least one of equipment state monitoring, equipment maintenance plan making and equipment operation comprehensive inquiry. The energy devices are substantially identical to the energy consuming devices. Specifically, the energy plant includes any one or more of a wind power plant, a solar power plant, a hydroelectric power plant, a geothermal power plant, a thermal power plant, and a nuclear power plant. The above-mentioned equipment state monitoring includes: and monitoring whether the energy equipment has abnormal working conditions or fault alarm events.

6. Intelligent operation and maintenance module of power system

The data acquisition system is used for receiving the working state data of the energy consumption equipment unit and the production equipment unit acquired by the data acquisition system. The operating state data includes: energy consumption data, operation parameter data and environmental monitoring data of a production workshop. The real-time operation data is combined with daily work such as work orders and patrol, online early warning and offline maintenance can be achieved, and functions of online patrol, overhaul management, patrol management, rush repair management and the like of the power system in the industrial enterprise are achieved. Wherein, the operation parameter data can be obtained by an infrared thermal imaging technology and a transformer oil gas chromatography test.

The original traditional offline operation maintenance mode is improved into an online and offline combined operation maintenance mode, so that the electricity utilization safety, rapidness, money saving and worry saving are better realized for the energy utilization end, the real scientific electricity utilization is realized, and the requirements of production units on electricity utilization safety, convenience in service, high management efficiency and the like are met.

The power system intelligence operation and maintenance module includes:

and the energy consumption statistics submodule is used for carrying out statistics on the electricity consumption information, the gas consumption information, the water consumption information and the heat consumption information of a plurality of energy consumption devices and a plurality of production devices.

And the maintenance management submodule is used for respectively determining a preventive regular maintenance plan and a state maintenance plan and determining a final maintenance plan aiming at the power system of the industrial enterprise according to the preventive regular maintenance plan and the state maintenance plan.

Wherein, confirm preventative periodical maintenance plan, specifically: and determining the overhaul period or overhaul frequency aiming at the power equipment according to the acquired average service life and fault rate data of the power equipment, so as to overhaul according to the overhaul period or overhaul frequency.

Average life and failure rate data for the power equipment is determined from a power equipment failure rate bathtub curve. Namely, in the adaptation period (early failure period) of the power equipment investment and the near-end period (loss failure period) which is as fast as the service life, the failure rate is high, and the failure rate in the middle period (accidental failure period) is relatively stable. Through confirming reasonable maintenance frequency, be favorable to reducing the trouble risk of electrical equipment among the industrial enterprise to reduce electrical equipment outage time, reduce personnel's maintenance cost.

Wherein, the state maintenance plan is determined as follows: monitoring the current running state of the power equipment to obtain state monitoring data of the power equipment; according to the state monitoring data, adopting an artificial intelligence analysis technology (prediction algorithm) or an expert system to predict the working state of the power equipment in a specified time period in the future; if the working state of the power equipment in the future specified time period is normal, the power equipment is not overhauled in the future specified time period; and if the working state of the electric equipment in the future specified time period is abnormal, the electric equipment is overhauled in the future specified time period.

Namely, if the monitoring data or the monitoring index are normal (healthy), unnecessary maintenance can be avoided; if the operational data of the electrical equipment is abnormal, additional, or necessary, maintenance work may be scheduled. Wherein, the relevant algorithm for the state prediction of the power equipment can comprise: a time series method, a regression analysis method, a fuzzy prediction method, a gray prediction method, an artificial neural network method, a fault prediction algorithm based on a multi-output Support Vector Machine (SVM), and the like.

In this embodiment, the power equipment in the industrial enterprise may be a wind turbine generator system, the generator of which is a rotating machine, and the fault state of the generator is predicted, and the fault prediction methods that can be adopted are classified into three types: data-based prediction methods, model-based prediction methods, knowledge-based prediction methods. Data-based methods include autoregressive prediction, gray prediction, multi-level hierarchical methods, chaotic time series prediction, hidden markov models, machine learning (neural networks, support vector machines), statistical process monitoring methods, and the like. The fault prediction method based on the model comprises a fault prediction method based on a filter, a fault mechanism modeling method and the like. The method has the characteristic of going deep into the essential properties of the object, and can well track the change trend of the system. When the mathematical model of the object is accurate, an accurate fault prediction result can be obtained. The fault prediction method based on knowledge comprises an expert system, fuzzy logic and the like. The advantage of this type of approach is the ability to take advantage of existing expert knowledge and experience without the need to know very accurate mathematical models.

The Gray predictive Model (Gray Forecast Model) is a predictive method that builds a mathematical Model and makes predictions from a small amount of incomplete information. The fault prediction algorithm based on the multi-output Support Vector Machine (SVM) is a multi-output SVM fault prediction model, the input of the fault prediction model is a performance degradation data sequence of samples, each sample sequence is arranged in a time sequence, and the output is the reliability of a corresponding sample. The working principle of the fault prediction model is as follows: and fitting a nonlinear relation between the performance degradation data and the reliability by training the multi-output SVM, and predicting the reliability at the future moment by using a trained SVM fault prediction model.

According to the artificial intelligence equipment state prediction model, determining the abnormal state grade of the electric equipment in a specified time period in the future, and determining the maintenance grade of the electric equipment in the specified time period in the future according to the abnormal state grade. Examples are as follows: for each type of power transformation equipment, the failure can be roughly divided into four types according to the severity of the failure: catastrophic, fatal, borderline, and mild. For the classification of faults, taking a transformer as an example: first, catastrophic refers to a transformer being completely damaged; secondly, the fatality refers to that the performance of the transformer is reduced or seriously damaged, and the operation must be stopped immediately for maintenance; thirdly, performing the first step; criticality refers to a transformer that is only slightly damaged or has a slight degradation in performance; light faults mean that there is no influence on the normal operation of the transformer, but that maintenance time or the like needs to be specified in the plan.

Wherein, according to preventative regular maintenance plan and state maintenance plan, confirm the final maintenance plan to industrial enterprise's electric power system, specifically be: if the working state of the power equipment in a future specified time period is abnormal or a plurality of monitoring indexes exceed standards, the overhaul frequency and the overhaul grade in the undetermined specified time period are increased; optionally, adding corresponding overhaul items; and if the working state of the power equipment in the future specified time period is normal or a plurality of monitoring indexes are not out of standard, reducing the overhaul frequency and the overhaul grade in the undetermined specified time period. Optionally also reducing the corresponding service items.

By combining state maintenance and regular maintenance, the maintenance frequency can be effectively reduced, and the pertinence, the effect and the benefit of the maintenance are stronger.

7. Energy report management module

The energy management system is used for outputting energy report forms automatically or manually according to specified formats and time after necessary calculation and processing of metering and management statistical data of the energy management system. The above-described necessary calculation processing may include: data format, unit, etc. are processed.

8. Energy balance analysis and prediction module

The method is used for balance analysis and energy prediction of energy. The medium-short term online prediction is based on a production operation plan, a maintenance and overhaul plan, historical production data and real-time data. And (3) establishing an energy model by adopting different prediction algorithms, or predicting the short-term (1-8 hours) and medium-term (1-7 days) production and consumption of various energy mediums in the future according to historical data and/or field experience to be used as a reference for energy scheduling. And predicting the natural gas supply quantity, the whole plant power load and other conditions in the middle or short term in the future according to the trend condition of the current energy medium, providing a supply and demand prediction curve, and assisting a dispatcher to schedule an energy plan and dispatch in time.

9. Carbon emission and energy-saving auxiliary decision-making module

The carbon emission level analysis system is used for realizing carbon emission level analysis and can provide data support for future carbon emission transactions; and (4) energy-saving analysis and evaluation, data support is provided for energy-saving transaction, and energy mutual aid and transaction aid decision are realized. The energy-saving analysis and evaluation can include: the energy saving is calculated, the size and the type (equipment energy saving or management energy saving) are evaluated, the benefit can be brought, and the space is not further saved.

Specifically, the specific treatment process for realizing carbon emission monitoring and optimization comprises the following steps:

s1, acquiring continuous multi-month actual carbon emission data of the target enterprise;

s2, obtaining the predicted carbon emission of the target enterprise in the current year according to the actual carbon emission data of the target enterprise for a plurality of continuous months; for example, actual carbon emission data for a quarter is acquired, and annual predicted carbon emission is predicted based on the actual carbon emission data for a quarter.

Wherein, the acquisition of the annual predicted carbon emission amount can be adopted,

the time series prediction method specifically includes an ARAM time series prediction algorithm, a simple sequence-time average method, a weighted sequence-time average method, a moving average method, a weighted moving average method, a trend prediction method, an exponential smoothing method, and the like.

The statistical regression method specifically comprises the following steps: unary linear regression, multiple linear regression, orthogonal polynomial regression, differential regression, and the like. Specifically, a data curve is drawn by the statistical regression method based on actual carbon emission data of one quarter, and the annual predicted carbon emission is predicted based on the trend of change of the data curve (a line graph or a scatter graph).

The prediction algorithm specifically comprises a grey prediction algorithm, a neural network prediction algorithm, a fuzzy prediction algorithm, a Markov prediction algorithm and the like. Specifically, a neural network prediction model is obtained by adopting a certain sample training in advance, and then the predicted carbon emission of the target enterprise in the current year is obtained according to the continuous multi-month actual carbon emission data of the target enterprise and the pre-trained neural network prediction model. In a further example, a Long Short-Term Memory network (LSTM) advanced deep learning model may be employed to predict the predicted carbon emissions for the target enterprise for the year.

S3, determining a carbon emission difference according to the difference between the predicted carbon emission of the target enterprise in the current year and a preset carbon emission index;

s4, when the carbon emission difference is smaller than or equal to the preset first quota, sending a carbon emission credit purchased from the carbon emission trading market to the target enterprise; when the carbon emission difference is larger than a preset first quota, sending an emission reduction instruction to the target enterprise; the first quota is small in amount, allowing the business to obtain carbon emissions by trading.

Specifically, when the carbon emission difference is larger than a preset first quota and is smaller than a preset second quota, a first emission reduction instruction is sent to the target enterprise;

when the carbon emission difference is larger than or equal to a preset second quota and is smaller than a preset third quota, sending a second emission reduction instruction to the target enterprise;

and when the carbon emission difference is greater than or equal to a preset third quota and is smaller than a preset fourth quota, sending a third emission reduction instruction to the target enterprise.

And S5, generating corresponding recommended emission reduction measures according to the emission reduction instructions, and sending the corresponding recommended emission reduction measures to energy management personnel or technical management personnel.

Generating a first emission reduction measure corresponding to the first emission reduction instruction aiming at the first emission reduction instruction, wherein the first emission reduction measure comprises:

water-saving management measures, which instruct energy managers or technical managers to overhaul water utilization pipelines of a construction site (for example, adopting unmanned aerial vehicles for inspection) so as to reduce the waste phenomena of running, overflowing, dripping and leaking of the water utilization pipelines; the power saving management measures are used for instructing energy management personnel or technical management personnel to acquire the power consumption in the power distribution room of each power utilization place and carrying out check aiming at the power utilization places with the power consumption exceeding the standard; and the oil-saving management measures indicate energy management personnel or technical management personnel to acquire whether the machine equipment has oil leakage or not, and if so, the machine equipment is overhauled to prevent oil leakage accidents.

Generating, for the second reduction instruction, a second emission reduction measure corresponding to the second emission reduction instruction, the second emission reduction measure including, in addition to the first emission reduction measure: new energy power generation measures, which dictate the use of clean energy power generation equipment to replace traditional fossil energy power generation equipment; noise abatement measures which instruct noise pollution control by adopting measures such as sound insulation, shock absorption, noise absorption and the like; greening treatment measures which indicate that the air of the factory area of the industrial enterprise is purified by adopting a tree planting and afforestation mode at the periphery of the industrial enterprise.

Generating, for the third reduction instruction, a third emission reduction measure corresponding to the third emission reduction instruction, the third emission reduction measure further including, in addition to the second emission reduction measure: new process measures, which indicate: the improved production process can optimize the total production process. The method comprises the steps of adopting an energy-saving flow, optimizing process parameters (such as conversion rate, reflux ratio, circulation ratio and the like), improving the operation flexibility of the device, improving the reaction operation condition and reducing the energy consumption. Energy-saving equipment such as a high-efficiency fractionating tower, a heat exchanger, an air cooler, a pump, a compressor, a heating furnace and the like for mass transfer, heat exchange, rotation and the like is adopted, the production capacity of monomer equipment is improved, and energy conservation and consumption reduction are realized from the source. A power-down energy consumption measure indicating: the power consumption is reduced by adopting the variable frequency speed regulation technology of the motor. The power energy consumption mainly comprises electric power and steam consumption, and is a main part of the energy consumption of industrial enterprises. Based on the current situation that the load rate of devices of most industrial enterprises is low at present, the adoption of the variable frequency speed regulation technology is an effective way for energy conservation. The combination of devices is reasonably realized, and the optimization matching of cold and hot material flows is carried out in a larger range, so that the optimization of energy utilization is realized. Descaling, corrosion prevention and heat preservation measures, which indicate that: an anti-scaling agent is used to prevent the heat exchanger from scaling or slow down the scaling speed. In industrial enterprises, heat exchangers which run continuously are easy to have scaling phenomenon, so that the heat exchange efficiency is reduced. It is necessary to remove the scale by chemical cleaning or mechanical cleaning, and the use of an anti-scaling agent to prevent scaling or slow down the scaling rate is a simple and easy way.

10. Direct electricity purchasing auxiliary decision-making transaction module

The system is used for predicting the daily, monthly, quarterly and annual power loads and electric quantities of enterprises in real time according to the current energy consumption situation and the product plan and the historical electric quantity and load curve, and providing judgment basis for adjusting the load peak value and the production process by using a demand side management means; and analyzing historical power selling price curves of different power selling merchants, and providing decision support for direct power purchasing transactions of enterprises by combining the power predicting results of the enterprises so as to effectively reduce the production cost of the enterprises.

11. Demand side management module

The method and the device are used for guiding the user to optimize the energy utilization mode by taking effective measures, improving the energy utilization efficiency of the terminal, optimizing the resource allocation, improving and protecting the environment and realizing the energy utilization management activities carried out by the minimum-cost energy service. Specifically, taking effective measures may include: the loads are classified into class one loads, class two loads, and class three loads, and when load adjustment is required, an effective adjustment measure may be to temporarily shut down the class three non-productive loads, which are not important.

12. Solid energy storage heating equipment management module

The system is used for acquiring the working data of the solid energy storage heating equipment and controlling the working state of the solid energy storage heating equipment according to the working data. The solid energy storage heating equipment effectively charges and stores the low-ebb electricity, abandoned light electricity and abandoned wind electricity at night to supply heat and produce, and zero pollution and zero emission are realized during valley period storage and peak period use. The system can reduce the heat supply cost, greatly improve the energy utilization efficiency, and reduce the coal consumption and pollutant discharge, thereby realizing carbon neutralization in the fields of building and heating. The solid energy storage and heat supply equipment management module can be used for remotely regulating and controlling heat storage and heat utilization, heat storage is carried out by using low-price electricity in the electricity consumption valley, the load of a power grid is effectively balanced by shifting the peak and filling the valley, the efficiency and the utilization rate of the power grid are improved, and the heat storage temperature can reach over 800 ℃. Compared with natural gas and other similar electric heating products, the operation cost is about 30-50% lower, and the heat source stability is strong. Specifically, solid energy storage heating equipment management module specifically is used for in the millet electric time quantum, and control solid energy storage equipment changes the electric energy into heat energy storage, releases the heat energy of storage in order to provide enterprise's production required heat and hot water at production moment. In one example, the solid energy storage device can adopt a magnesium oxide energy storage device, wherein magnesium oxide is a refractory brick material, refractory bricks are stacked in an energy storage plant, a gap is reserved between the refractory bricks, a resistance wire penetrates through the gap, and a peripheral closed space (a boiler) is insulated by using aluminum silicate. The firebricks are heated to store energy by absorbing electric energy in the electricity valley. The heat energy stored in the refractory bricks is replaced by the wind-water heat exchange unit during the heat supply peak, so that the hot water for heating is changed into hot water for heating, and the hot water enters an enterprise heat supply pipe network.

Example 3

The embodiment provides a network design of an energy management system:

1. network architecture

The energy management center network adopts a star network structure. The backbone network utilizes the existing optical fiber network and is provided with a core switch and a node switch with multilayer routing functions; the field control equipment is connected into the fast optical fiber ring network through the node switch and uploaded to the server equipment through the backbone network. The whole network is also divided into a core layer, a distribution layer and an access layer. The project multiplexes the existing information-based optical fiber backbone communication network, network transmission media in secondary production units mainly use gigabit optical cables, and the tail ends use hundred-million optical cables and cables.

(1) Core network

The core switch is used for connecting devices such as an I/O server, a database server, a web server, an application server, an engineer station, an operation station, a network printer and the like. Is a central node of the entire network.

(2) Industrial Ethernet

The field industrial network backbone network is built, a star network topology is adopted, an industrial Ethernet switch with a network management function is adopted, and the design is kilomega speed. The node switches are respectively arranged in the transformer substations or the second-level unit office buildings of all the process units, and the node Ethernet switches are connected by single-mode optical fibers.

2. Network and IT system hardware description

I/O server x 2 (serving SCADA system, real-time report, etc.);

application, WEB server × 2 (for system development program, system maintenance, WEB release);

database server x 2 (for historical data storage);

disk array × 1;

core server x 2;

ring node server x 22;

star node servers × 26 (which will be appropriately increased or decreased depending on the specific geographical location and the like in implementation);

desktop server × 1 (connection of desktop devices such as an operation desk and an engineer station);

HMI (operating platform engineer station) × 28 (which is to be increased or decreased as appropriate depending on the particular situation in the case of implementation);

a GPS clock server (through a GPS clock, the whole system time is synchronized);

a power supply system: 20kW (UPS powered).

Designing power supply requirements:

the 380V double-path power supply is adopted to ensure the reliability of the system, and the 380V double-path power supply is additionally arranged in an energy management center power distribution room:

2 UPS: and a standard power distribution cabinet is additionally arranged to supply power to the energy center system.

UPS chooses according to whole power consumption condition: 20kVA

UPS emergency power supply air conditioner design with 20kVA capacity and 30kW of total consumption of 15 computers, 3 multiplied by 8DID large screens, 10 servers, 2 disk arrays, switches and matrixes and the like

The energy management center machine room requires to set up the precision air conditioner, chooses 2 commercial air conditioners for use.

Example 4

The embodiment proposes a safety design of the energy management system:

1. ground connection

The system ground uses the existing ground: the detailed requirements are as follows:

(1) the system should have good grounding to ensure personal safety and to prevent interference and lightning strikes.

(2) The working grounding resistance of the control equipment is less than 4 omega, and when the system adopts a comprehensive grounding network, the grounding resistance is less than 1 omega.

(3) A special grounding main line is adopted, and a copper core insulated wire or cable used by the special grounding main line has a core wire section not less than 25mm²。

(4) The grounding wire can not be in short grounding or mixed connection with a grounding wire of strong current and a zero line of a power grid, and the grounding wire can not form a closed loop.

(5) The ground wires leading from the control room to other devices of the system should be copper core insulated flexible wires.

(6) The grounding terminal of the three-core power socket in the system is connected with the grounding terminal of the system.

(7) When a cable enters a building in the system, the outer conductive shielding layer of the cable is grounded at a place close to the place where the cable enters the building.

2. Lightning protection design of system

The damage to electronic equipment caused by lightning strikes is, in large proportion, the damage to equipment caused by induction lightning striking the power supply. And power supply lightning protection is performed on the closed-circuit monitoring system.

The advantages of this embodiment are:

1. the no-load operation is reduced, and the utilization rate of equipment is improved. The running condition of the energy equipment is monitored on line through the energy management platform, the running condition of the equipment under the non-production condition is avoided, the no-load running of the equipment in the production process is monitored, the energy utilization efficiency is improved, and the energy waste is reduced. When the monitoring function of the power system monitors the operation and energy consumption of the power equipment, the power quality condition is diagnosed, the condition that the power operation has reactive power is found in time, and when the power is found to have low functional factor in the transmission process, the power quality is improved and the line loss in the transmission process is reduced.

2. Reduce the energy cost for the customer, create the value. By developing power demand response, energy management, load optimization and the like, the peak load of an enterprise can be reduced, the energy configuration of the enterprise is optimized, the basic electric charge of the enterprise is reduced, the investment of an internal power grid of the enterprise and the like is reduced, and meanwhile, the reliable and efficient power supply of the enterprise is guaranteed.

The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An energy management system of a large-scale industrial enterprise is characterized by comprising an energy management center, and an energy routing inspection unit, an energy consumption equipment unit, a production equipment unit and a data acquisition system which are in communication connection with the energy management center; the data acquisition system is also in communication connection with the energy inspection unit and the production equipment unit; and the data acquisition system is used for acquiring the working state data of the energy consumption equipment unit and the production equipment unit and sending the working state data to the energy management center.

2. The energy management system of a large industrial enterprise according to claim 1, further comprising an informatization system, wherein the enterprise informatization system comprises at least one of an enterprise resource planning, a production informatization management system, and an office system; and the energy management center exchanges information with the information system.

3. The energy management system of the large-scale industrial enterprise according to claim 2, further comprising an energy management terminal and an operation management terminal in communication connection with the energy management center, wherein the energy management terminal comprises equipment management, operation management and analysis, and the operation management terminal comprises energy system operation data; and the energy management terminal and the operation management terminal are used for acquiring real-time data, production actual performance information and production plan information from the energy management center.

4. The energy management system of a large industrial enterprise according to claim 1, wherein the energy inspection unit includes at least one of a power inspection device, a gas inspection device, and a water course inspection device; the energy consumption equipment unit comprises at least one of a transformer substation, a power distribution station, a water pump station and an air compression station; the production equipment unit comprises at least one of power plant energy production workshop equipment, electric power system production line equipment, steam system production line equipment and gas system production line equipment.

5. The energy management system of a large industrial enterprise of claim 1, wherein the data acquisition system comprises a first set of data acquisition devices and a second set of data acquisition devices;

6. The energy management system of large industrial enterprise according to claim 2, wherein said energy management center comprises:

7. The energy management system of large industrial enterprise according to claim 6, wherein the energy cost management module comprises energy cost center formulation, energy cost center calculation, and energy statistics.

8. The energy management system of large industrial enterprise according to claim 6, wherein the intelligent operation and maintenance module of power system comprises,

the energy consumption statistics submodule is used for counting the electricity consumption information, the gas consumption information, the water consumption information and the heat consumption information of the energy consumption equipment unit and the production equipment unit;

and the maintenance management submodule is used for determining a preventive regular maintenance plan and a state maintenance plan and determining a final maintenance plan aiming at the power system of the industrial enterprise according to the preventive regular maintenance plan and the state maintenance plan.

9. The energy management system of large industrial enterprise according to claim 6, wherein the carbon emission and energy saving aid decision module comprises,

s2, obtaining the predicted carbon emission of the target enterprise in the current year according to the actual carbon emission data of the target enterprise for a plurality of continuous months;

s4, when the carbon emission difference is smaller than or equal to a preset first quota, sending a command of purchasing carbon emission credit from the carbon emission trading market to the target enterprise; when the carbon emission difference is larger than a preset first quota, sending an emission reduction instruction to the target enterprise;

and S5, generating corresponding recommended emission reduction measures according to the emission reduction instructions, and sending the corresponding recommended emission reduction measures to target enterprise energy management personnel or technical management personnel.

10. The energy management system of the large-scale industrial enterprise according to claim 6, wherein the energy management center further comprises a solid energy storage and heat supply equipment management module, which is used for acquiring working data of the solid energy storage and heat supply equipment and controlling the working state of the solid energy storage and heat supply equipment according to the working data.