CN118378834A - Energy-saving type energy comprehensive management system and method for optimizing energy scheduling - Google Patents
Energy-saving type energy comprehensive management system and method for optimizing energy scheduling Download PDFInfo
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Abstract
The invention relates to the technical field of energy management, in particular to an energy-saving type energy comprehensive management system and method for optimizing energy scheduling. According to the invention, through adopting a time sequence prediction technology to perform trend analysis on energy-saving energy production and demand data, energy demand fluctuation can be effectively predicted, energy distribution can be timely adjusted, the matching degree between energy supply and consumption is optimized, energy waste is remarkably reduced, energy use efficiency is improved, an external environment change is responded by utilizing a genetic algorithm, an energy supply strategy is optimized, the adaptability of a system to environment fluctuation is enhanced, the continuity and stability of energy supply are ensured, the overall management efficiency and economy of energy are remarkably improved, and meanwhile, the environmental sustainability target is supported.
Description
Technical Field
The invention relates to the technical field of energy management, in particular to an energy-saving type energy comprehensive management system and method for optimizing energy scheduling.
Background
The technical field of energy management aims at optimizing energy use efficiency and managing energy production and consumption, an intelligent system is adopted to monitor and control energy sources, so that waste is reduced, the energy use efficiency is improved, and key applications comprise energy monitoring, demand response, energy storage management, energy efficiency analysis and the like. The real-time energy optimization is realized through an advanced sensor network, data analysis software and an automatic control system. By analyzing the energy usage pattern, the energy demand can be predicted, the energy supply and consumption can be regulated, and the interaction with the external energy market can be optimized, so that the sustainable development goal can be supported.
The energy-saving comprehensive energy management system for optimizing energy scheduling is an advanced tool designed for improving energy utilization efficiency and optimizing energy resource distribution, and the energy distribution and consumption are automatically adjusted by analyzing data from different energy sources, so that the energy-saving effect is achieved. The main application comprises the steps of reducing energy waste, reducing operation cost, improving the reliability and stability of energy supply, supporting the aim of environmental sustainability, being particularly important in energy management of industrial, commercial and residential areas, and being applicable to the optimized operation of power grids, independent micro-grids and various energy fusion systems.
The existing energy-saving comprehensive energy management system has the functions of an intelligent monitoring and control system, but has the defects in the aspects of processing large-scale data and responding to external changes in real time, and is difficult to adapt to rapidly changing market demands and external environments, so that an energy distribution scheme cannot be perfectly matched with actual demands when being implemented, and the energy supply is excessive and insufficient, so that the economy and reliability of the system are affected. When the energy demand is predicted, the use of peak-valley electricity is difficult to effectively predict and manage due to lack of enough precision and foresight, the shortage of power supply is caused in the peak period of electric power or the energy waste is caused in the valley period, the response is not timely enough in the aspect of fault diagnosis, the fault treatment depends on manual intervention, and the lack of enough automatization and intellectualization means not only increases the operation cost, but also affects the overall stability and safety of an energy system, the efficiency and effect of the energy management technology are limited in actual operation, and the problem is overcome through higher-level data processing capacity and a more intelligent response mechanism.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides an energy-saving type energy comprehensive management system and method for optimizing energy scheduling.
In order to achieve the above purpose, the invention adopts the following technical scheme that the energy-saving type energy integrated management system for optimizing energy scheduling comprises:
The energy state real-time monitoring module is used for carrying out trend analysis on energy-saving energy production data and demand prediction data by adopting a time sequence prediction technology based on energy storage equipment of an energy station machine room to obtain energy-saving energy balance data;
the fault diagnosis and response module analyzes the deviation of the operation parameters of the energy storage equipment of the energy station machine room based on the energy-saving energy balance data to obtain a fault analysis result;
The energy supply adjustment module is used for carrying out influence analysis of external environment change on energy demand and adjustment of an energy supply strategy based on the fault analysis result, configuring output parameters of energy storage equipment of an energy station machine room, optimizing energy scheduling and obtaining optimized supply configuration;
The energy consumption calculation module performs optimization calculation on multiple types of energy consumption based on the optimized supply configuration by utilizing the internet of things technology, optimizes energy scheduling and obtains an energy-saving strategy scheme;
The energy efficiency feedback module collects optimized energy use data based on the energy saving strategy scheme, compares the energy use data with an energy saving target, and obtains an energy saving effect evaluation result;
And the intelligent machine room service module monitors the running state of the energy equipment based on the energy-saving effect evaluation result, and performs fault real-time error reporting and repair to obtain an unattended intelligent machine room maintenance scheme.
As a further aspect of the present invention, the energy-saving energy balance data includes an energy supply amount, a demand pre-measurement amount and a time period performance difference value, the fault analysis result includes an equipment abnormality type, an affected component name and a performance deviation index, the optimized supply configuration includes an adjusted power generation amount, an energy storage release speed and an energy distribution ratio, the energy-saving strategy scheme includes an energy-saving target set value, an adjusted equipment operation plan and a predetermined maintenance schedule, the energy-saving effect evaluation result includes an energy-saving achievement rate, an energy-saving total energy and a cost benefit analysis value, and the unattended machine room intelligent maintenance scheme includes fault automatic detection, a remote fault repair function and equipment maintenance period update.
As a further aspect of the present invention, the energy status real-time monitoring module includes:
the energy demand prediction submodule collects data of energy production capacity and predicted demand based on energy storage equipment of an energy station machine room, records output and input quantity of the energy storage equipment, and acquires an original energy data set by referring to an ID (identity) and a time stamp of the energy storage equipment;
the historical demand analysis submodule analyzes energy-saving energy supply and demand data based on the original energy data set, performs trend comparison analysis, starts an optimization cycle if the energy-saving energy supply is analyzed to not meet the demand, adjusts prediction parameters according to the historical data, and obtains a trend analysis result;
And the energy supply calculation submodule calculates energy supply and demand balance in a short period based on the trend analysis result, evaluates the shortage degree and puts forward an adjustment strategy if the energy supply is predicted to be in shortage, and obtains energy balance data by analyzing the energy supply and demand states of peak and valley periods.
As a further aspect of the present invention, the fault diagnosis and response module includes:
the parameter deviation analysis submodule analyzes real-time operation parameters of energy storage equipment of an energy station machine room based on the energy-saving energy balance data, calculates differences between the energy storage equipment and a standard reference model, and comprises temperature, voltage and current to generate an offset recognition result;
The fault identification sub-module compares the actually measured deviation with a set target threshold value based on the deviation identification result, if the deviation exceeds the threshold value, the fault identification sub-module is calibrated to be potential faults, and the performance stability of the energy storage equipment of the energy station machine room is evaluated to obtain a fault existence judgment result;
And the fault response sub-module starts a preset response alarm based on the fault existence judgment result, and comprises the steps of switching standby energy storage energy sources, adjusting operation parameters, reducing the load of the energy storage equipment, optimizing the operation efficiency of the energy storage equipment of the energy station machine room and generating a fault analysis result.
As a further aspect of the present invention, the energy supply adjustment module includes:
the environment analysis submodule analyzes external environment changes of the energy station machine room based on the fault analysis result, and if the external changes are detected to influence energy-saving energy requirements, the influence on energy-saving energy supply and demand balance is evaluated, and an environment influence evaluation result is obtained;
The energy allocation submodule adjusts an energy supply strategy if the analysis of the increase of the energy demand is based on the environmental impact evaluation result, designs new supply parameters, matches the energy demand which changes in real time, and acquires the adjusted supply strategy;
The energy demand change submodule adopts a genetic algorithm to optimize energy scheduling based on the adjusted supply strategy, configures the generated energy of an energy station machine room and the output parameters of energy storage equipment according to new energy-saving energy supply demands, and acquires the optimized supply configuration;
the formula of the genetic algorithm is as follows:
Where f (x) is a fitness function for evaluating benefits of the energy configuration scheme, w i represents a weight of the ith energy station in the total energy supply, x i is a supply parameter of the ith energy station, c i is a carbon emission coefficient of each energy station, p i is a peak period energy price, s i is a stability score of the energy station, and log (s i) is a logarithm of stability.
As a further aspect of the present invention, the energy consumption calculation module includes:
The key index monitoring submodule analyzes and records using data of multiple types of energy sources based on the optimized supply configuration, wherein the using data comprises electricity, water and fuel gas, and monitors key indexes of energy source consumption of an energy source station machine room in real time through the technology of the Internet of things to acquire energy source consumption data;
the consumption optimization submodule evaluates the efficiency of real-time energy-saving energy use based on the energy consumption data, adjusts supply configuration according to a preset energy-saving target, optimizes energy distribution and consumption modes and generates an optimized regulation and control scheme;
The strategy generation submodule designs energy-saving measures for optimizing energy scheduling based on the optimized regulation and control scheme, wherein the energy-saving measures comprise adjustment of the use period of peak-valley electricity and heat energy recovery, and the energy-saving measures are utilized to optimize energy cost effectiveness and energy scheduling so as to establish an energy-saving strategy scheme.
As a further aspect of the present invention, the energy efficiency feedback module includes:
The data comparison sub-module analyzes the energy consumption data, including the energy consumption of electric power, water and natural gas, after the implementation of the energy-saving strategy scheme based on the energy-saving strategy scheme, compares the energy consumption with an energy-saving target, and generates a change rate index;
The effect evaluation submodule analyzes the effect of the energy-saving measures based on the improvement degree index, quantifies the score of the energy-saving measures by calculating the energy-saving sources, the cost and the environmental influence, and performs priority ranking to obtain an energy-saving benefit analysis result;
And the strategy feedback sub-module establishes an optimization scheme aiming at real-time energy use based on the energy-saving benefit analysis result, wherein the optimization scheme comprises the steps of adjusting energy-saving energy configuration, optimizing energy storage equipment efficiency, redesigning a work flow, optimizing energy scheduling and establishing an energy-saving effect evaluation result.
As a further aspect of the present invention, the intelligent machine room service module includes:
The equipment operation monitoring submodule monitors the operation states of multi-energy equipment in an energy station machine room based on the energy-saving effect evaluation result, and key parameters including temperature, pressure and current are recorded and compared in real time to analyze the key parameters, so that energy storage equipment state data are obtained;
The real-time fault-reporting submodule judges whether the energy storage equipment fails or not through a set abnormal threshold value based on the state data of the energy storage equipment, and if the parameter is abnormal, a fault alarm is started and maintenance personnel are notified to generate a fault alarm record;
And the automatic maintenance submodule starts a preset maintenance flow based on the fault alarm record, comprises remote restarting, parameter adjustment and automatic submitting of maintenance requests, and establishes an unattended intelligent maintenance scheme of the machine room.
The energy-saving energy comprehensive management method for optimizing energy scheduling is executed based on the energy-saving energy comprehensive management system for optimizing energy scheduling and comprises the following steps of:
s1: based on energy storage equipment of an energy station machine room, comparing energy-saving energy supply and demand, predicting supply and demand trend in a future time period through historical data, calculating energy-saving energy supply and demand balance state, and adjusting energy supply strategies aiming at peak-valley time periods to generate energy-saving energy balance data;
s2: based on the energy-saving energy balance data, key operation parameters of the energy storage equipment are monitored, and compared with a standard reference model, key parameters of deviation are identified to obtain a fault analysis result;
S3: based on the fault analysis result, analyzing the energy supply and demand state according to the energy demand changing in real time, adjusting the energy supply strategy, optimizing the energy scheduling by using energy-saving measures, and obtaining an energy-saving strategy scheme;
s4: based on the energy-saving strategy scheme, analyzing the cost benefit of energy-saving measures, quantifying the scores of the energy-saving measures, and adjusting the energy-saving resource allocation through the priority ordering of the scores to generate an energy-saving effect evaluation result;
S5: based on the energy-saving effect evaluation result, potential energy storage equipment faults are identified, deviation evaluation is carried out on abnormal parameters, preset maintenance of the energy storage equipment is carried out through fault alarm, and an unattended intelligent maintenance scheme of the machine room is established.
Compared with the prior art, the invention has the advantages and positive effects that:
According to the invention, through adopting a time sequence prediction technology to perform trend analysis on energy-saving energy production and demand data, energy demand fluctuation can be effectively predicted, energy distribution can be timely adjusted, the matching degree between energy supply and consumption is optimized, energy waste is obviously reduced, and energy use efficiency is improved. The genetic algorithm is used for responding to external environment changes, optimizing an energy supply strategy, enhancing the adaptability of the system to environment fluctuation, ensuring the continuity and stability of energy supply, utilizing the internet of things technology to monitor and optimize energy consumption in real time, enabling energy scheduling to be more accurate, ensuring the high synchronization of energy supply and actual demands, and reducing the risk of excessive supply. By collecting the optimized energy use data and comparing the energy use data with the energy saving target, the actual effect of the energy saving measure is accurately estimated, real-time feedback is provided, continuous energy efficiency improvement is promoted, an efficient and quick-response energy management system is formed, the overall management efficiency and economy of the energy are remarkably improved, and meanwhile, the environmental sustainability target is supported.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a schematic diagram of a system framework of the present invention;
FIG. 3 is a flow chart of the energy status real-time monitoring module of the present invention;
FIG. 4 is a flow chart of a fault diagnosis and response module according to the present invention;
FIG. 5 is a flow chart of the energy supply adjustment module according to the present invention;
FIG. 6 is a flow chart of an energy consumption calculation module according to the present invention;
FIG. 7 is a flow chart of an energy efficient feedback module according to the present invention;
FIG. 8 is a flow chart of an intelligent machine room service module of the present invention;
FIG. 9 is a schematic diagram of the method steps of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate description of the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Example 1
Referring to fig. 1 to 2, the present invention provides a technical solution, an energy-saving integrated energy management system for optimizing energy scheduling, comprising:
The energy state real-time monitoring module is used for carrying out trend analysis on energy-saving energy production data and demand prediction data by adopting a time sequence prediction technology based on energy storage equipment of an energy station machine room to obtain energy-saving energy balance data;
The fault diagnosis and response module analyzes the deviation of the operation parameters of the energy storage equipment of the energy station machine room based on the energy-saving energy balance data, if the deviation exceeds a threshold value, the fault diagnosis and response module is calibrated as potential faults, and a preset response alarm is started to obtain a fault analysis result;
the energy supply adjustment module is used for carrying out influence analysis of external environment change on energy demand and adjustment of an energy supply strategy based on a fault analysis result, configuring output parameters of energy storage equipment of an energy station machine room, optimizing energy scheduling and obtaining optimized supply configuration;
The energy consumption calculation module performs optimization calculation on multiple types of energy consumption based on the optimized supply configuration by utilizing the internet of things technology, designs energy-saving measures for optimizing energy scheduling, and adjusts the use period of peak-valley electricity to obtain an energy-saving strategy scheme;
the energy efficiency feedback module collects optimized energy use data based on an energy saving strategy scheme, compares the energy use data with an energy saving target, calculates the saved energy, cost and environmental influence, and obtains an energy saving effect evaluation result;
and the intelligent machine room service module monitors the running state of the energy equipment based on the energy-saving effect evaluation result, and performs fault real-time error reporting and repair to obtain an unattended intelligent machine room maintenance scheme.
The energy-saving type energy balance data comprise energy supply quantity, demand pre-measurement and time period performance difference values, fault analysis results comprise equipment abnormality types, affected component names and performance deviation indexes, the optimized supply configuration comprises an adjusted generated energy, an energy storage release speed and an energy distribution proportion, the energy-saving strategy scheme comprises an energy-saving target set value, an adjusted equipment operation plan and a predetermined maintenance schedule, the energy-saving effect assessment results comprise an energy-saving achievement rate, saved total energy and a cost benefit analysis value, and the unattended intelligent maintenance scheme of the machine room comprises fault automatic detection, remote fault restoration functions and equipment maintenance period updating.
Referring to fig. 2 and 3, the energy status real-time monitoring module includes:
The energy demand prediction submodule collects data of energy production and predicted demand based on energy storage equipment of an energy station machine room, records output and input quantity of the energy storage equipment, and refers to an ID (identity) and a time stamp of the energy storage equipment to obtain an execution flow of an original energy data set as follows;
The energy demand prediction submodule is implemented by using a Scikit-Learn library of Python based on monitoring data of energy storage equipment of an energy station machine room and adopting a support vector machine regression algorithm, the characteristic variables are defined as output quantity and input quantity of the energy storage equipment, the target variables are energy production quantity and demand prediction quantity, RBF (radial basis function) is selected as a kernel function to process nonlinear problems, penalty strength of a C parameter of 1.0 control error item is set, precision tolerance of a model is defined by epsilon parameter of 0.1, optimal search of parameters is carried out by utilizing GRIDSEARCHCV, optimal performance of the model in various parameter combinations is ensured, future short-term energy demand quantity is predicted through the model, and an original energy dataset is generated.
The historical demand analysis submodule analyzes the energy-saving energy supply and demand data based on the original energy data set, performs trend comparison analysis, starts an optimization cycle if the energy-saving energy supply is analyzed to not meet the demand, adjusts prediction parameters according to the historical data, and obtains an execution flow of a trend analysis result as follows;
The historical demand analysis submodule is used for carrying out trend comparison on energy-saving energy supply and demand data based on an original energy data set by utilizing an incremental learning framework, a recursive least square algorithm is adopted, the algorithm is executed through a Scikit-Learn library of Python, model parameters are continuously updated by continuously learning new data without retraining the model, forgetting factor is set to be 0.9 to adjust the influence weight of old data, the sensitivity of the model to the new trend is kept, the energy-saving energy supply and demand relation is analyzed by using the dynamically adjusted model, the prediction parameters are adjusted according to analysis results, and a trend analysis result is generated.
The energy supply calculation submodule calculates energy supply and demand balance in a short period based on a trend analysis result, if the energy supply is predicted to be in shortage, the shortage degree is estimated, an adjustment strategy is provided, and the energy supply and demand states of a peak value period and a valley value period are analyzed to obtain an execution flow of energy-saving energy balance data as follows;
The energy supply calculation submodule calculates energy supply and demand balance in a short period by adopting a Bayesian optimization algorithm based on a trend analysis result, the algorithm is realized by a Scikit-Optimize library of Python, a loss function is defined as the difference between the predicted energy supply and the actual demand, an acq-func is set as EI (expected improvement quantity) as an acquisition function to Optimize model performance by selecting a Gaussian process as prior distribution, model parameters are dynamically adjusted, parameter setting is continuously optimized according to the prediction precision fed back by the model, and energy-saving energy balance data is generated.
Referring to fig. 2 and 4, the fault diagnosis and response module includes:
The parameter deviation analysis submodule analyzes real-time operation parameters of energy storage equipment of an energy station machine room based on energy-saving energy balance data, calculates differences between the parameters and a standard reference model, including temperature, voltage and current, and generates an execution flow of an offset recognition result as follows;
The parameter deviation analysis submodule adopts a statistical process control technology based on energy-saving energy balance data, utilizes Pandas libraries of Python to process the data, defines temperature, voltage and current as monitoring parameters, calculates the mean value and standard deviation between real-time operation parameters and a standard reference model, sets a control limit as three sigma (namely, the mean value plus and minus three times the standard deviation), and identifies parameter deviation and generates an offset recognition result by the technology if the real-time variation of the monitoring parameters exceeds the control limit.
The fault identification sub-module compares the actual measurement deviation with a set target threshold value based on the deviation identification result, if the deviation exceeds the threshold value, the fault identification sub-module is calibrated to be potential faults, the performance stability of the energy storage equipment of the energy station machine room is evaluated, and the execution flow for obtaining the fault existence judgment result is as follows;
The fault identification sub-module is realized through a Scikit-Learn library of Python based on an offset identification result by using a logistic regression algorithm, compares actual measurement deviation with a set target threshold, sets INDEPENDENT VARIABLE of a model as parameter deviation quantity, DEPENDENT VARIABLE as fault calibration state (0 is no fault and 1 is potential fault), defines model parameters, comprises regularization strength C as 1.0, selects the balance as l2 to optimize model stability, marks the model prediction result as 1 if the deviation exceeds the threshold, identifies the potential fault, evaluates the performance stability of energy storage equipment of an energy station machine room, and generates a fault existence judgment result.
The fault response submodule starts a preset response alarm based on a fault existence judgment result, comprises the steps of switching standby energy storage energy sources and adjusting operation parameters, reduces the load of the energy storage equipment, optimizes the operation efficiency of the energy storage equipment of an energy station machine room, and generates a fault analysis result, wherein the execution flow is as follows;
The fault response submodule is realized by using an Event-Driven Programming library of Python based on a fault existence judgment result by adopting an Event-driven programming method, an Event processing program is written to monitor the output of the fault identification submodule, if the fault calibration state is detected to be 1, a preset response alarm is automatically triggered, and the fault analysis result is generated by activating a standby energy storage energy system and adjusting the operation parameters of the energy storage equipment, such as reducing the charging current and adjusting the power output, so as to reduce the load of the energy storage equipment, optimize the operation efficiency of the energy storage equipment of an energy station machine room.
Referring to fig. 2 and 5, the energy supply adjustment module includes:
The environment analysis submodule analyzes external environment changes of the energy station machine room based on the fault analysis result, and if the external changes are detected to influence the energy-saving energy demand, the influence on the energy-saving energy supply and demand balance is evaluated, and an execution flow for acquiring an environment influence evaluation result is as follows;
The environment analysis submodule is realized by adopting a Statsmodels library of Python based on a fault analysis result and utilizing a time sequence prediction technology, energy-saving energy demand is defined as independent variables by defining external environment variables such as temperature, humidity and wind speed, a time sequence model such as SARIMA is set for model construction, parameter setting comprises seasonal difference, a seasonal period is selected as12 to represent month data, influence analysis between external environment change and energy demand is carried out, if obvious external change is detected to influence the energy-saving energy demand, specific influence of the change on energy-saving energy supply and demand balance is estimated, and an environment influence estimation result is generated.
The energy allocation submodule adjusts an energy supply strategy if the energy demand is analyzed to be increased based on the environmental impact evaluation result, designs new supply parameters, matches the energy demand which changes in real time, and obtains the execution flow of the adjusted supply strategy as follows;
The energy allocation submodule adopts a multi-objective optimization method based on an environmental impact evaluation result, uses Pyomo library implementation of Python, defines energy demand increase as one of optimization objectives, sets new supply parameters including generating capacity, generating efficiency and storage capacity as decision variables, adopts a linear programming model to adjust the parameters to match energy-saving energy demands changing in real time, and performs optimization design of a supply strategy by setting double objectives of cost minimization and supply maximization to generate an adjusted supply strategy.
The energy demand change submodule optimizes energy scheduling by adopting a genetic algorithm based on the adjusted supply strategy, configures the generated energy of an energy station machine room and the output parameters of energy storage equipment according to new energy-saving energy supply demands, and acquires the execution flow of the supply configuration after optimization as follows;
The energy demand change submodule adopts a genetic algorithm based on an adjusted supply strategy, is realized through a DEAP library of Python, defines output parameters of generated energy and energy storage equipment as a gene coding object, sets a fitness function as balance degree of energy supply and demand, selects a crossover rate of 0.7 and a mutation rate of 0.2 for algorithm parameter setting, operates the genetic algorithm for population iterative optimization, continuously evaluates and selects a configuration scheme which is most suitable for the current energy supply demand, and generates optimized supply configuration.
The formula of the genetic algorithm is as follows:
Where f (x) is a fitness function for evaluating benefits of the energy configuration scheme, w i represents a weight of the ith energy station in the total energy supply, xi is a supply parameter of the ith energy station, ci is a carbon emission coefficient of each energy station, p i is a peak period energy price, s i is a stability score of the energy station, and log (s i) is a logarithm of the stability.
The execution flow is as follows:
Determining a supply parameter xi of each energy station, covering the output of the generated energy and the energy storage equipment, calculating the weight wi of each energy station, reflecting the importance in the total energy supply, introducing a carbon emission coefficient ci, increasing the consideration of environmental factors in energy scheduling, determining a peak period energy price pi, adapting to market price change, evaluating the stability s i of each energy station, calculating the influence of the adjustment of log (s i) in the total fitness function, and searching for a parameter combination x which maximizes f (x) through iterative search of a genetic algorithm to obtain the optimized supply configuration.
Referring to fig. 2 and 6, the energy consumption calculation module includes:
The key index monitoring submodule analyzes and records using data of multiple types of energy sources based on the optimized supply configuration, the key index monitoring submodule monitors key indexes of energy source consumption of an energy source station machine room in real time through the technology of the Internet of things, and an execution flow for acquiring the energy source consumption data is as follows;
The key index monitoring submodule is based on optimized supply configuration, adopts the Internet of things technology, uses the MQTT library of Python to implement equipment communication, defines electricity, water and fuel gas as key energy types for monitoring, carries out real-time data acquisition in an energy station machine room by configuring an IoT device, sets data transmission frequency to be once per minute, encapsulates energy consumption data by adopting a JSON format, ensures the integrity and instantaneity of the data in the transmission process, monitors key indexes of energy consumption, and generates the energy consumption data.
The consumption optimization submodule evaluates the efficiency of real-time energy-saving energy use based on the energy consumption data, adjusts supply configuration according to a preset energy-saving target, optimizes energy distribution and consumption modes, and generates an execution flow of an optimized regulation scheme as follows;
the consumption optimization submodule is implemented through a Scikit-Learn library of Python based on energy consumption data and using a decision tree classification algorithm, takes real-time energy-saving energy use data as input, sets a target variable of a decision tree as an energy use efficiency level according to a preset energy-saving target, defines model parameters including a maximum tree depth max_depth to be 4 so as to control model complexity, sets a minimum division sample number min_samples_split to be 20 so as to prevent overfitting, and guides adjustment of supply configuration, optimizes energy allocation and consumption modes and generates an optimal regulation and control scheme by evaluating the use efficiency of different energy types.
The strategy generation submodule designs energy-saving measures for optimizing energy scheduling based on an optimized regulation and control scheme, wherein the energy-saving measures comprise adjustment of the use period of peak-valley electricity and heat energy recovery, the energy-saving measures are utilized to optimize energy cost effectiveness and energy scheduling, and an execution flow for establishing an energy-saving strategy scheme is as follows;
The strategy generation submodule is based on an optimized regulation and control scheme, implements by utilizing a linear programming technology through a PuLP library of Python, sets the aim of optimizing energy scheduling to minimize energy cost and maximize energy use efficiency, defines decision variables including peak-valley electricity use period and implementation degree of heat energy recovery, adopts a cost function and an energy-saving efficiency function as optimization targets, sets linear constraint conditions to ensure energy supply and demand balance, designs energy-saving measures, such as adjusting peak-valley time allocation of electric power use and implementing heat energy recovery scheme, optimizes energy cost benefit and energy scheduling, and establishes an energy-saving strategy scheme.
Referring to fig. 2 and 7, the energy efficiency feedback module includes:
the data comparison sub-module is based on an energy-saving strategy scheme, analyzes energy consumption data comprising energy consumption of electric power, water and natural gas after the energy-saving strategy scheme is implemented, compares the energy consumption with an energy-saving target, and generates an execution flow of a change rate index as follows;
The data comparison sub-module processes a data set through a Pandas library of Python by using a statistical analysis method based on an energy-saving strategy scheme, takes the implemented power, water and natural gas energy consumption data as a main analysis object, defines the comparison of the data and a preset energy-saving target as a core task, quantifies the use improvement of each energy source by using descriptive statistics by calculating the difference and percentage of the consumption of each energy source type and the target, such as average reduction percentage, standard deviation and the like, quantifies the improvement degree through indexes, and generates a change rate index.
The effect evaluation submodule analyzes the effect of the energy-saving measures based on the change progress index, quantifies the scores of the energy-saving measures by calculating the energy-saving sources, the cost and the environmental influence, and performs priority ranking to obtain an execution flow of an energy-saving benefit analysis result as follows;
The effect evaluation submodule adopts a utility function method based on an improvement degree index, uses NumPy library of Python to realize, takes the effect of energy-saving measures as an analysis focus, defines the energy-saving quantity and the cost-saving cost as input parameters of a utility function, sets environmental impact evaluation as a weight factor, calculates the comprehensive score of the energy-saving measures, reflects the economic and environmental protection dual values of the energy-saving effect, further ranks the different energy-saving measures in priority, and guides the subsequent energy policy formulation by using the ranking result to obtain the energy-saving benefit analysis result.
The strategy feedback submodule establishes an optimization scheme aiming at real-time energy use based on an energy-saving benefit analysis result, wherein the optimization scheme comprises the steps of adjusting energy-saving energy configuration, optimizing energy storage equipment efficiency and redesigning a work flow, optimizing energy scheduling, and establishing an execution flow of an energy-saving effect evaluation result as follows;
The strategy feedback submodule adopts a dynamic optimization strategy based on an energy-saving benefit analysis result, is implemented through a optimize module in a SciPy library of Python, sets energy-saving energy configuration, energy storage equipment efficiency and workflow as optimized variables, defines an optimization target to maximize energy-saving effect and minimize operation cost, adjusts the supply configuration of energy-saving energy and the operation parameters of the energy storage equipment through setting algorithm parameters such as step length and iteration times, optimizes the workflow of an energy station machine room to adapt to the change of the requirements of real-time energy use, optimizes energy scheduling, and establishes an energy-saving effect evaluation result.
Referring to fig. 2 and 8, the intelligent machine room service module includes:
The equipment operation monitoring submodule monitors the operation state of multi-energy equipment in an energy station machine room based on the energy-saving effect evaluation result, and key parameters including temperature, pressure and current are recorded and compared in real time to analyze the key parameters, and an execution flow for acquiring state data of the energy storage equipment is as follows;
the equipment operation monitoring sub-module adopts a real-time data acquisition and analysis technology based on an energy-saving effect evaluation result, uses an InfluxDB library of Python to store time sequence data, defines temperature, pressure and current as key parameters for monitoring, carries out real-time data acquisition on multi-energy equipment of an energy station machine room through an internet of things (IoT) device, records data once every 5 seconds, carries out data visualization by using Grafana, compares the real-time data with a historical performance baseline, analyzes fluctuation of the key parameters by setting a standard deviation and a mean control chart, and generates energy storage equipment state data.
The real-time fault-reporting submodule judges whether the energy storage equipment has faults or not through a set abnormal threshold value based on the state data of the energy storage equipment, if the parameter abnormality is detected, a fault alarm is started, maintenance personnel are notified, and an execution flow for generating a fault alarm record is as follows;
The real-time fault-reporting submodule is implemented through a Pandas library of Python based on the state data of the energy storage equipment by adopting a threshold analysis method, an abnormal threshold is set, for example, the upper temperature limit is 50 ℃, the upper pressure limit is 150psi and the upper current limit is 100A, real-time monitoring data are detected in real time, if any parameter is detected to exceed the preset threshold, the parameter is judged to be abnormal, a fault alarm system is immediately started, and the fault alarm record is generated by sending a notification to mobile equipment or a computer of maintainers through an MQTT protocol.
The automatic maintenance submodule starts a preset maintenance flow based on the fault alarm record, comprises remote restarting, parameter adjustment and automatic submitting of maintenance requests, and establishes an unattended intelligent maintenance scheme of the machine room as follows;
The automatic maintenance submodule is based on fault alarm records, adopts an automatic control technology, remotely executes equipment maintenance commands through a Paramiko library of Python, defines a maintenance flow, comprises remote restarting, parameter adjustment and automatic submitting maintenance requests, sets scripts to automatically execute related commands such as restarting commands or adjusting operation parameters on equipment, reduces the temperature setting by 5 ℃ or reduces the pressure by 10psi, simultaneously automatically submits requests to a maintenance system through a Web API, starts unattended maintenance response, minimizes equipment downtime and ensures efficient operation of an energy station machine room, and generates an unattended intelligent maintenance scheme of the machine room.
Referring to fig. 9, an energy-saving type energy integrated management method for optimizing energy scheduling is performed based on the energy-saving type energy integrated management system for optimizing energy scheduling, and includes the following steps:
s1: based on energy storage equipment of an energy station machine room, comparing energy-saving energy supply and demand, predicting supply and demand trend in a future time period through historical data, calculating energy-saving energy supply and demand balance state, and adjusting energy supply strategies aiming at peak-valley time periods to generate energy-saving energy balance data;
S2: based on energy-saving energy balance data, key operation parameters of the energy storage equipment are monitored, and compared with a standard reference model, key parameters of deviation are identified, so that a fault analysis result is obtained;
S3: based on the fault analysis result, analyzing the energy supply and demand state according to the energy demand changing in real time, adjusting the energy supply strategy, optimizing the energy scheduling by using energy-saving measures, and obtaining an energy-saving strategy scheme;
s4: based on the energy-saving strategy scheme, analyzing the cost benefit of the energy-saving measures, quantifying the scores of the energy-saving measures, and adjusting the energy-saving resource allocation through the priority ordering of the scores to generate an energy-saving effect evaluation result;
S5: based on the energy-saving effect evaluation result, potential energy storage equipment faults are identified, deviation evaluation is carried out on abnormal parameters, preset maintenance of the energy storage equipment is carried out through fault alarm, and an unattended intelligent maintenance scheme of the machine room is established.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.
Claims (9)
1. An energy-efficient integrated energy management system for optimizing energy scheduling, the system comprising:
The energy state real-time monitoring module is used for carrying out trend analysis on energy-saving energy production data and demand prediction data by adopting a time sequence prediction technology based on energy storage equipment of an energy station machine room to obtain energy-saving energy balance data;
the fault diagnosis and response module analyzes the deviation of the operation parameters of the energy storage equipment of the energy station machine room based on the energy-saving energy balance data to obtain a fault analysis result;
The energy supply adjustment module is used for carrying out influence analysis of external environment change on energy demand and adjustment of an energy supply strategy based on the fault analysis result, configuring output parameters of energy storage equipment of an energy station machine room, optimizing energy scheduling and obtaining optimized supply configuration;
The energy consumption calculation module performs optimization calculation on multiple types of energy consumption based on the optimized supply configuration by utilizing the internet of things technology, optimizes energy scheduling and obtains an energy-saving strategy scheme;
The energy efficiency feedback module collects optimized energy use data based on the energy saving strategy scheme, compares the energy use data with an energy saving target, and obtains an energy saving effect evaluation result;
And the intelligent machine room service module monitors the running state of the energy equipment based on the energy-saving effect evaluation result, and performs fault real-time error reporting and repair to obtain an unattended intelligent machine room maintenance scheme.
2. The energy-saving integrated management system for optimizing energy scheduling according to claim 1, wherein the energy-saving energy balance data includes an energy supply amount, a demand pre-measurement amount and a time period performance difference value, the fault analysis result includes an equipment abnormality type, an affected component name and a performance deviation index, the optimized supply configuration includes an adjusted power generation amount, an energy storage release speed and an energy distribution ratio, the energy-saving strategy scheme includes an energy-saving target set value, an adjusted equipment operation plan and a predetermined maintenance schedule, the energy-saving effect evaluation result includes an energy-saving achievement rate, an energy-saving total energy and a cost-benefit analysis value, and the unattended machine room intelligent maintenance scheme includes fault automatic detection, a remote fault repair function and equipment maintenance period update.
3. The energy-efficient integrated energy management system for optimizing energy scheduling of claim 1, wherein the energy status real-time monitoring module comprises:
the energy demand prediction submodule collects data of energy production capacity and predicted demand based on energy storage equipment of an energy station machine room, records output and input quantity of the energy storage equipment, and acquires an original energy data set by referring to an ID (identity) and a time stamp of the energy storage equipment;
the historical demand analysis submodule analyzes energy-saving energy supply and demand data based on the original energy data set, performs trend comparison analysis, starts an optimization cycle if the energy-saving energy supply is analyzed to not meet the demand, adjusts prediction parameters according to the historical data, and obtains a trend analysis result;
And the energy supply calculation submodule calculates energy supply and demand balance in a short period based on the trend analysis result, evaluates the shortage degree and puts forward an adjustment strategy if the energy supply is predicted to be in shortage, and obtains energy balance data by analyzing the energy supply and demand states of peak and valley periods.
4. The energy efficient integrated management system for optimizing energy scheduling of claim 1, wherein the fault diagnosis and response module comprises:
the parameter deviation analysis submodule analyzes real-time operation parameters of energy storage equipment of an energy station machine room based on the energy-saving energy balance data, calculates differences between the energy storage equipment and a standard reference model, and comprises temperature, voltage and current to generate an offset recognition result;
The fault identification sub-module compares the actually measured deviation with a set target threshold value based on the deviation identification result, if the deviation exceeds the threshold value, the fault identification sub-module is calibrated to be potential faults, and the performance stability of the energy storage equipment of the energy station machine room is evaluated to obtain a fault existence judgment result;
And the fault response sub-module starts a preset response alarm based on the fault existence judgment result, and comprises the steps of switching standby energy storage energy sources, adjusting operation parameters, reducing the load of the energy storage equipment, optimizing the operation efficiency of the energy storage equipment of the energy station machine room and generating a fault analysis result.
5. The energy-efficient integrated energy management system for optimizing energy scheduling of claim 1, wherein the energy supply adjustment module comprises:
the environment analysis submodule analyzes external environment changes of the energy station machine room based on the fault analysis result, and if the external changes are detected to influence energy-saving energy requirements, the influence on energy-saving energy supply and demand balance is evaluated, and an environment influence evaluation result is obtained;
The energy allocation submodule adjusts an energy supply strategy if the analysis of the increase of the energy demand is based on the environmental impact evaluation result, designs new supply parameters, matches the energy demand which changes in real time, and acquires the adjusted supply strategy;
The energy demand change submodule adopts a genetic algorithm to optimize energy scheduling based on the adjusted supply strategy, configures the generated energy of an energy station machine room and the output parameters of energy storage equipment according to new energy-saving energy supply demands, and acquires the optimized supply configuration;
the formula of the genetic algorithm is as follows:
Where f (x) is a fitness function for evaluating benefits of the energy configuration scheme, w i represents a weight of i th energy station in total energy supply, xi is a supply parameter of the i-th energy station, ci is a carbon emission coefficient of each energy station, p i is a peak period energy price, s i is a stability score of the energy station, and log (s i) is a logarithm of stability.
6. The energy-efficient integrated energy management system for optimizing energy scheduling of claim 1, wherein the energy consumption calculation module comprises:
The key index monitoring submodule analyzes and records using data of multiple types of energy sources based on the optimized supply configuration, wherein the using data comprises electricity, water and fuel gas, and monitors key indexes of energy source consumption of an energy source station machine room in real time through the technology of the Internet of things to acquire energy source consumption data;
the consumption optimization submodule evaluates the efficiency of real-time energy-saving energy use based on the energy consumption data, adjusts supply configuration according to a preset energy-saving target, optimizes energy distribution and consumption modes and generates an optimized regulation and control scheme;
The strategy generation submodule designs energy-saving measures for optimizing energy scheduling based on the optimized regulation and control scheme, wherein the energy-saving measures comprise adjustment of the use period of peak-valley electricity and heat energy recovery, and the energy-saving measures are utilized to optimize energy cost effectiveness and energy scheduling so as to establish an energy-saving strategy scheme.
7. The energy efficient integrated management system for optimizing energy scheduling of claim 1, wherein the energy efficiency feedback module comprises:
The data comparison sub-module analyzes the energy consumption data, including the energy consumption of electric power, water and natural gas, after the implementation of the energy-saving strategy scheme based on the energy-saving strategy scheme, compares the energy consumption with an energy-saving target, and generates a change rate index;
The effect evaluation submodule analyzes the effect of the energy-saving measures based on the improvement degree index, quantifies the score of the energy-saving measures by calculating the energy-saving sources, the cost and the environmental influence, and performs priority ranking to obtain an energy-saving benefit analysis result;
And the strategy feedback sub-module establishes an optimization scheme aiming at real-time energy use based on the energy-saving benefit analysis result, wherein the optimization scheme comprises the steps of adjusting energy-saving energy configuration, optimizing energy storage equipment efficiency, redesigning a work flow, optimizing energy scheduling and establishing an energy-saving effect evaluation result.
8. The energy-efficient integrated energy management system for optimizing energy scheduling of claim 1, wherein the intelligent machine room service module comprises:
The equipment operation monitoring submodule monitors the operation states of multi-energy equipment in an energy station machine room based on the energy-saving effect evaluation result, and key parameters including temperature, pressure and current are recorded and compared in real time to analyze the key parameters, so that energy storage equipment state data are obtained;
The real-time fault-reporting submodule judges whether the energy storage equipment fails or not through a set abnormal threshold value based on the state data of the energy storage equipment, and if the parameter is abnormal, a fault alarm is started and maintenance personnel are notified to generate a fault alarm record;
And the automatic maintenance submodule starts a preset maintenance flow based on the fault alarm record, comprises remote restarting, parameter adjustment and automatic submitting of maintenance requests, and establishes an unattended intelligent maintenance scheme of the machine room.
9. An energy-saving energy integrated management method for optimizing energy scheduling, characterized in that the energy-saving energy integrated management system for optimizing energy scheduling according to any one of claims 1 to 8 is executed, comprising the steps of:
Based on energy storage equipment of an energy station machine room, comparing energy-saving energy supply and demand, predicting supply and demand trend in a future time period through historical data, calculating energy-saving energy supply and demand balance state, and adjusting energy supply strategies aiming at peak-valley time periods to generate energy-saving energy balance data;
based on the energy-saving energy balance data, key operation parameters of the energy storage equipment are monitored, and compared with a standard reference model, key parameters of deviation are identified to obtain a fault analysis result;
Based on the fault analysis result, analyzing the energy supply and demand state according to the energy demand changing in real time, adjusting the energy supply strategy, optimizing the energy scheduling by using energy-saving measures, and obtaining an energy-saving strategy scheme;
Based on the energy-saving strategy scheme, analyzing the cost benefit of energy-saving measures, quantifying the scores of the energy-saving measures, and adjusting the energy-saving resource allocation through the priority ordering of the scores to generate an energy-saving effect evaluation result;
based on the energy-saving effect evaluation result, potential energy storage equipment faults are identified, deviation evaluation is carried out on abnormal parameters, preset maintenance of the energy storage equipment is carried out through fault alarm, and an unattended intelligent maintenance scheme of the machine room is established.
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