CN117974365A - Multi-objective operation optimization method and system for electric heating comprehensive energy coupling system - Google Patents
Multi-objective operation optimization method and system for electric heating comprehensive energy coupling system Download PDFInfo
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Abstract
The invention discloses a multi-objective operation optimization method and a multi-objective operation optimization system for an electric heating comprehensive energy coupling system, which relate to the technical field of comprehensive energy, and comprise the following steps: acquiring predicted meteorological data of a current period, and fitting to generate comprehensive parameters of a system environment; the comprehensive parameters of the system environment and the historical heat load values of the system in a plurality of continuous periods are input into a heat load dynamic prediction model based on CNN and LSTM, the heat load predicted value is output, and real-time dynamic correction is carried out on the heat load predicted value; based on the corrected thermal load predicted value, constructing a multi-objective system operation optimization model taking the lowest running cost, carbon emission and energy consumption of the electrothermal comprehensive energy coupling system as objective functions; and carrying out model solving by using a non-dominant sorting genetic algorithm to obtain an optimal operation optimization scheme, and reasonably distributing the heat load proportion of each heat supply module in the current period system. The invention reasonably matches and fully utilizes light energy and geothermal resources, achieves the purposes of energy conservation and emission reduction, and effectively reduces the running cost.
Description
Technical Field
The invention relates to the technical field of comprehensive energy, in particular to a multi-objective operation optimization method and system of an electric heating comprehensive energy coupling system.
Background
In order to meet the low-carbon development requirement, the heat supply industry is taken as an energy consumption large household, and the development is towards energy conservation, low carbon and green at present. According to the energy-saving and carbon-reducing requirements, the energy structure of the heating system is optimized, the energy utilization rate is improved, and it is urgent to construct a safe, multi-element, efficient and environment-friendly urban heating system. At present, under the condition that the heat supply of a boiler is taken as a main heat source, various energy forms such as a photovoltaic system, a middle-deep layer ground source heat pump, an air source heat pump, photo-thermal energy and the like are introduced, and the heat supply of the boiler becomes one of the development trends. Considering that deep geothermal resources are rich, the thermal stability performance is better, geothermal heat supply is more economical and environment-friendly than other renewable energy sources, and the initial investment is higher, but the operation cost and the maintenance cost are relatively lower; photovoltaic is to directly convert light energy into electric energy by utilizing the photovoltaic effect of a semiconductor, the electric energy conversion efficiency of the existing photovoltaic panel can reach more than 20%, the production and operation technology of solar photovoltaic power generation equipment is mature and widely used, and the electric energy of a photovoltaic power generation system can be directly used for the electricity utilization of comprehensive energy coupling system equipment. Therefore, the heating system form of the electric heating comprehensive energy coupling system is the first choice of the form of the urban central heating and clean heating system, can ensure the stable operation of the heating system, and simultaneously improves the green electricity utilization ratio.
However, when the electric heating comprehensive energy coupling system actually operates, the heat load of each heat supply module in the system is always constant, so that when the weather condition changes, the operation parameters of each heat supply module cannot be adjusted according to the actual condition, and further the output heat load of each heat supply module cannot be reasonably allocated, so that various energy sources such as light energy resources, geothermal resources and the like cannot be fully utilized, resource waste is caused, the energy consumption and the cost of different heat supply modules are different, and the conditions of high operation cost and high energy consumption of the whole system are caused.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a multi-objective operation optimization method and a multi-objective operation optimization system for an electric heating comprehensive energy coupling system, which are used for analyzing the operation state of the system and predicting the heat load required to be provided by the system on the basis of fully grasping the operation state of the system, so as to construct a multi-objective system operation optimization model with the lowest operation cost, carbon emission and energy consumption of the electric heating comprehensive energy coupling system as objective functions, and the operation states of heat supply modules in the system are adjusted by solving the optimal operation optimization strategy, so that new energy sources such as light energy resources, geothermal resources and the like in the system are reasonably and fully utilized, the purposes of energy conservation and emission reduction are achieved, and the operation cost is reduced.
In a first aspect, the invention provides a multi-objective operation optimization method for an electrothermal integrated energy coupling system.
A multi-objective operation optimization method of an electrothermal integrated energy coupling system comprises the following steps:
acquiring predicted meteorological data of a current period, and fitting to generate comprehensive parameters of a system environment;
The comprehensive parameters of the system environment of the current period and the historical system heat load values of a plurality of continuous periods are input into a CNN and LSTM-based heat load dynamic prediction model, the heat load predicted value of the current period is output, and the heat load predicted value is dynamically corrected in real time;
based on the corrected thermal load predicted value, constructing a multi-objective system operation optimization model taking the lowest running cost, carbon emission and energy consumption of the electrothermal comprehensive energy coupling system as objective functions;
and carrying out model solving by using a non-dominant sorting genetic algorithm to obtain an optimal operation optimization scheme, and reasonably distributing the heat load proportion of each heat supply module in the current period system.
In a second aspect, the invention provides a multi-objective operation optimization system of an electrothermal integrated energy coupling system.
A multi-objective operation optimization system of an electrothermal integrated energy coupling system, comprising:
The weather data acquisition and preprocessing module is used for acquiring the predicted weather data of the current period and generating the comprehensive parameters of the system environment in a fitting way;
the thermal load prediction module is used for inputting the comprehensive parameters of the system environment of the current period and the historical system thermal load values of a plurality of continuous periods into a thermal load dynamic prediction model based on CNN and LSTM, outputting the thermal load predicted value of the current period and carrying out real-time dynamic correction on the thermal load predicted value;
The system operation optimization model construction module is used for constructing a multi-objective system operation optimization model taking the lowest electric heating comprehensive energy coupling system operation cost, carbon emission and energy consumption as objective functions based on the corrected thermal load predicted value;
And the system operation optimization module is used for carrying out model solving by utilizing a non-dominant sorting genetic algorithm to obtain an optimal operation optimization scheme, and reasonably distributing the heat load proportion of each heat supply module in the current period system.
The one or more of the above technical solutions have the following beneficial effects:
1. The invention provides a multi-objective operation optimization method and a multi-objective operation optimization system for an electric heating comprehensive energy coupling system, which are used for analyzing system operation data and predicting heat load required to be provided by the system on the basis of fully grasping the system operation state, so as to construct a multi-objective system operation optimization model with the lowest operation cost, carbon emission and energy consumption of the electric heating comprehensive energy coupling system as objective functions, and the operation states of heat supply modules in the system are adjusted by solving an optimal operation optimization strategy, so that new energy sources such as light energy resources, geothermal resources and the like in the system are reasonably and fully utilized, the purposes of energy conservation and emission reduction are achieved, and the operation cost is reduced.
2. In the optimization method provided by the invention, the influence of weather conditions on the heat load required by a user and the influence on photovoltaic power generation are considered, wherein the photovoltaic power generation capacity is preferentially consumed by an electricity enterprise and the surplus power of the surplus power load is connected with the internet, and the comprehensive electricity price of the self-power consumption and the surplus power internet surfing is related to the actual consumption rate and is changed along with the change of the consumption rate; by considering the dynamically-changed comprehensive electricity price, an objective function of a multi-objective system operation optimization model is constructed, so that the constructed model can be more attached to the actual condition of operation of the electric heating comprehensive energy coupling system, the operation cost is optimized in real time, the cost minimization target is realized, and meanwhile, the operation cost, the carbon emission and the energy consumption can be balanced to be minimized, and the optimal operation optimization is realized.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a flow chart of a multi-objective operation optimization method of an electric heating comprehensive energy coupling system according to an embodiment of the invention;
Fig. 2 is a schematic structural diagram of an electrothermal integrated energy coupling system according to an embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary only for the purpose of describing particular embodiments and is intended to provide further explanation of the invention and is not intended to limit exemplary embodiments according to the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Furthermore, it will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, steps, operations, devices, components, and/or groups thereof.
Example 1
The embodiment provides a multi-objective operation optimization method of an electric heating comprehensive energy coupling system, which can achieve the effects of energy conservation and carbon reduction while ensuring the stable operation of the system and effectively reduce the operation cost of the whole system. The method provided by the embodiment is applied to an electric heating comprehensive energy coupling system, wherein the system is a heat supply system based on coupling of a gas boiler unit and a water source heat pump unit which are powered by photovoltaic power generation and commercial power, and as shown in fig. 2, the system comprises a photovoltaic power generation device, the water source heat pump unit, the gas boiler unit and a comprehensive energy intelligent group control cabinet (called intelligent group control cabinet for short).
In the system, in the daytime, the photovoltaic power generation device is used for generating power, and the water source heat pump unit and the gas boiler unit are driven to operate by the electric energy of photovoltaic power generation. Further, main electric equipment such as water source heat pump unit, gas boiler unit still connects municipal power supply electrically, when photovoltaic power generation did not satisfy the power consumption demand of main electric equipment, by municipal power supply to guarantee the power supply stability of system. The system generates power through the photovoltaic power generation device and combines the commercial power to supply power for the whole system, and the comprehensive energy intelligent group control cabinet distributes electric energy according to the heat loads of the water source heat pump unit and the gas boiler unit, regulates and controls the operation of the gas boiler unit and the water source heat pump unit, so that the purpose of stable heat supply is achieved. In addition, when the photovoltaic power generation device is used for generating electricity and supplying power, when the generated energy exceeds the power consumption of the system, the exceeded power is accessed to the grid main grid frame.
In addition, on the basis of the system, a specific optimization regulation method is provided, namely: the method comprises the steps of utilizing an integrated energy intelligent group control cabinet and on-site main equipment to conduct data acquisition and control command issuing, adjusting the water outlet temperature or instantaneous flow of each heat supply module (namely a water source heat pump unit, a gas boiler unit and the like), so as to achieve the aim of load prediction and distribution, firstly, establishing a system heat load dynamic prediction model through an intelligent prediction algorithm, predicting a heat load value of the next period, then, constructing a multi-objective operation optimization model based on the economical efficiency, the carbon emission and the energy consumption of the integrated energy coupling system according to the predicted heat load value, conducting multi-objective optimization, determining the final optimal heat load distribution scheme through a solving model, further, utilizing the integrated energy intelligent group control cabinet to conduct reasonable heat load proportion distribution, and then achieving the aim of optimizing and controlling the integrated energy coupling system according to the control parameters such as the operation power, the water outlet temperature and the like of the heat load adjustment unit, and achieving the effects of energy conservation and consumption reduction.
The multi-objective operation optimization method of the electric heating comprehensive energy coupling system provided by the embodiment, as shown in fig. 1, comprises the following steps:
acquiring predicted meteorological data of a current period, and fitting to generate comprehensive parameters of a system environment;
The comprehensive parameters of the system environment of the current period and the historical system heat load values of a plurality of continuous periods are input into a CNN and LSTM-based heat load dynamic prediction model, the heat load predicted value of the current period is output, and the heat load predicted value is dynamically corrected in real time;
based on the corrected thermal load predicted value, constructing a multi-objective system operation optimization model taking the lowest running cost, carbon emission and energy consumption of the electrothermal comprehensive energy coupling system as objective functions;
and carrying out model solving by using a non-dominant sorting genetic algorithm to obtain an optimal operation optimization scheme, and reasonably distributing the heat load proportion of each heat supply module in the current period system.
The multi-objective operation optimization method of the electric heating comprehensive energy coupling system provided by the embodiment is described in more detail through the following matters.
S1, obtaining predicted meteorological data of a current period, and fitting to generate comprehensive parameters of a system environment.
In this embodiment, the predicted meteorological data of the current period including the outdoor temperature, the outdoor wind speed and the solar radiation illuminance (which may be the average value of the current period) are obtained by the data of the local meteorological department, and the system environment comprehensive parameters are calculated. The system environment comprehensive parameter is an equivalent temperature value obtained by considering outdoor wind speed and solar radiation illuminance on the basis of outdoor temperature, and the three have the following relational expression:
；
In the above-mentioned method, the step of, Is the system environment comprehensive parameter of tau period, and is in DEG C; /(I)Is the average outdoor temperature in tau period, deg.c; /(I)For the average solar radiation illuminance of τ period,/>；/>The average outdoor wind speed of tau period is m/s; a and b are corresponding fitting coefficients, and can be obtained through regression fitting of historical meteorological data.
And S2, inputting the comprehensive parameters of the system environment of the current period and the historical system heat load values of a plurality of continuous periods into a CNN and LSTM-based heat load dynamic prediction model, outputting the heat load predicted value of the current period, and carrying out real-time dynamic correction on the heat load predicted value.
In this embodiment, a system thermal load dynamic prediction model based on a Convolutional Neural Network (CNN) and a long-short-term memory network model (LSTM) is constructed and trained, and the system thermal load in the current period is predicted by using the thermal load dynamic prediction model, that is: integrating system environment parameters of current periodAnd the historical heat load values of 24 continuous periods before the current period are input into the heat load dynamic prediction model, the input system environment comprehensive parameters of the current period output a first predicted value through a CNN network, the input historical heat load values of 24 continuous periods before the current period output a second predicted value through an LSTM network, and the final heat load predicted value of the current period is output based on the weighted sum of the first predicted value and the second predicted value. The prediction values obtained by the two prediction modes are weighted to obtain a more comprehensive prediction value, so that the accuracy of final prediction is improved.
The training process of the system thermal load dynamic prediction model comprises the following steps:
Acquiring historical system environment comprehensive parameters of a plurality of continuous periods and corresponding historical heat load values thereof, and constructing a training data set;
Taking corresponding historical system environment comprehensive parameters and historical heat load values as training samples, inputting the training samples into a CNN (computer network) for training, and learning the association relation between the system environment comprehensive parameters and the heat load values; taking historical heat load values of a plurality of continuous periods as training samples, inputting the training samples into an LSTM network for training, and learning the dependency relationship contained in continuous time series data of the continuous heat load values; and carrying out iterative training by combining the loss function based on the weighted sum of the two network output heat load predicted values and the actual heat load value until the loss value is smaller than a set value or the maximum iteration number is reached, and completing training of the model.
Further, according to the heat load prediction result, the difference value between the heat load prediction value of the previous period and the actual heat load value is adopted to dynamically correct the heat load prediction value of the current period in real time so as to ensure the accuracy of heat load distribution of a subsequent system.
In this embodiment, the period duration is set to 1-2 hours. Considering that the heating system has thermal inertia, namely after the system is adjusted, the heating system needs to be fed back to the heat user after two hours, therefore, the embodiment predicts the heat load demand of the user side after two hours in advance through a mode of predicting and adjusting in advance, and adjusts the operation parameters of each heating module or unit in the system in advance, and can transfer the required heat load to the heat user side after two hours, thereby timely meeting the user demand, and realizing the effects of energy conservation, emission reduction and cost reduction.
And step S3, constructing a multi-objective system operation optimization model taking the lowest running cost, carbon emission and energy consumption of the electric heating comprehensive energy coupling system as an objective function based on the corrected thermal load predicted value.
After the heat load required by the system and the corrected heat load are determined based on the step S1 and the step S2, the optimal load distribution scheme is determined by multi-objective optimization based on three dimensions of the economical efficiency, the carbon emission and the primary energy consumption of the comprehensive energy coupling system. The optimization objective function of the multi-objective model is as follows:
(1) Running cost
The comprehensive electricity price of each unit in the current operation period is calculated by combining the photovoltaic electricity generation amount, the electricity consumption amount and the peak-to-valley electricity price, and the operation cost of the system is calculated according to the comprehensive electricity price, and is as follows:
；
In the above-mentioned method, the step of, Is the running cost; p is the control period; k is the running number of the heat pump unit; n is the running total number of the heat pump units; c b is the running cost of the gas boiler for each 1GJ heat produced; c g,k is the heat quantity of 1GJ produced, and the running cost of the water source heat pump with comprehensive electricity price is considered; w b is the heat load born by the gas boiler in the control period, GJ; w g,k is the thermal load born by the geothermal pump unit in the control period, GJ.
Calculating the generated energy of photovoltaic power generation and the power consumption of a system according to actual conditions to obtain the light Fu Xiaona rate; and calculating and obtaining comprehensive electricity price based on the photovoltaic absorption rate and the peak-valley electricity price. The calculation formula of the photovoltaic absorption rate R is as follows:
R=Pu/Pg；
in the above formula, pu is the electricity consumption and kWh; pg is the generated energy, kWh.
Further, the power generation amount and the power consumption amount in the control period can be calculated according to the following formula:
the electricity consumption Pu is:
；
in the above formula, j is the jth power consumption device (M power consumption devices in total) in the system; rated power for equipment, kW; /(I) A correction factor for taking into account the actual operating output of the device; /(I)To control the period duration, h.
The generated energy Pg is as follows:
；
In the above-mentioned method, the step of, To control the average illumination intensity in a period,/>The illumination intensity can be obtained through measurement of an illumination intensity measuring instrument; /(I)Is the efficiency of the photovoltaic module; /(I)Is the area of the photovoltaic component,/>. In another embodiment, in practical application, if no actual measurement condition exists, the calculation can be performed by referring to the illumination intensity of the typical weather year or similar time, and the calculation can also be performed by weather factors such as weather cloudy and sunny.
Then, the comprehensive electricity price in the control period is calculated as follows: control of integrated electricity price in period = period electricity price in periodThe periodic time-division light Fu Xiaona rate + the online electricity price/>(1-Light Fu Xiaona rate in this period).
By calculating the comprehensive electricity price in the mode, the problem of different photovoltaic power generation energy supply under different conditions is considered, so that the system operation cost based on the comprehensive electricity price calculation is more accurate, and the accurate rationality of the subsequent optimization adjustment strategy solution is further ensured.
(2) Carbon emission
；
In the above-mentioned method, the step of,Carbon emission, kg; q b is the carbon emission of the gas boiler per 1GJ heat produced, kg; q g,k is the carbon emission of the water source heat pump per 1GJ heat produced, kg.
Taking the carbon emission amount into consideration emission sources of all system material equipment, traversing a carbon emission factor library according to the carbon emission sources, and obtaining carbon emission factors corresponding to the carbon emission sources; and inputting the data related to the carbon emission source and the corresponding carbon emission factors into a system to obtain the carbon emission amount of the unit heat production.
Since the energy consumption type of the system is mainly electric power consumption, the carbon emission amount calculation formula is:
；
in the formula, j is the j-th power consumption equipment in the system, and q is the carbon emission quantity for producing unit heat and kg; kgCO 2/KWh;Qc,j is the power consumption per unit heat produced by the jth device in the system, which is the power carbon emission factor.
(3) Energy consumption
；
In the above-mentioned method, the step of,Is energy consumption amount tce; s b is the standard coal quantity converted from the gas consumption of the gas boiler per 1GJ heat produced, tce; s g,k is the standard coal quantity converted by the power consumption of the water source heat pump per 1GJ heat production, tce. In this embodiment, the energy consumption is used as an index parameter for energy conservation, and the lower the energy consumption is, the more energy is saved.
By setting the multi-objective function and considering the operation cost, the carbon emission and the energy consumption minimization, the optimal solution of the model obtained by solving is the optimal solution of the three factors, so that the optimal effect of all the three factors is achieved, and the condition that the load distribution obtained under a single factor cannot meet the minimum optimization targets of other factors is avoided.
The optimization constraint conditions of the multi-objective model are as follows:
(1) Heat supply demand constraints
；
In the above-mentioned method, the step of,To control the total heat load of the heating system in the period, MW; /(I)To control the corrected heat load, MW, of the heating system in the cycle.
(2) Heat supply capability constraints
Aiming at the water source heat pump unit, when the outdoor temperature is higher in the initial cold period and the final cold period, the required load is smaller, and the requirement can be met by the first-stage direct heat exchange utilization stage of the geothermal water; with further reduction of the outdoor temperature, the heat load demand is increased, and the heat is borne by the primary heat exchange heat pump unit, the secondary heat exchange heat pump unit and the gas boiler unit. When the heat supply requirement is smaller, the water source heat pump unit can realize lower cost, energy consumption and carbon emission relatively, so that only geothermal heat supply is adopted at the moment, and a gas boiler is not used independently; when the heat supply demand increases, a water source heat pump unit and a gas boiler unit are needed, and the heat load of each heat supply module is regulated and controlled according to specific conditions.
Therefore, geothermal heat supply capacity should satisfy the following constraints:
QGRP1,k≤Wg,k≤QGRP, all, k；
QGRP1,k+0.3QGRP2,k≤Wg,k≤QGRP,all,k；
in the above formula, Q GRP1 is the primary heat exchange amount, MW; q GRP2 is the secondary heat exchange quantity, MW; q GRP,all is the total heat supply capacity of the primary and secondary utilization stages, MW.
Considering that the high-efficiency operation of the boiler needs to be ensured and the heating capacity cannot exceed the maximum heating capacity of the equipment, the output of the boiler needs to meet the following constraint:
0.1QBRL≤Wb≤QBRL；
in the above formula, Q BRL is the rated power of the boiler and MW.
(3) Operation constraint of circulating water pump
The minimum running water flow of the circulating water pump cannot be lower than 0.5 times of rated water, and the maximum running water flow cannot be higher than 1.2 times of rated water, so that the running of the circulating water pump is required to meet the following constraint:
0.5LR≤L≤1.2/>LR；
in the above formula, L is the running water flow of the circulating water pump, m 3/h;LR is the rated flow of the circulating water pump, and m 3/h.
In addition, it should be noted that, in the above formula, the parameter W b、Wg,k is the heat load borne by the gas boiler unit and the geothermal pump unit in the control period to be solved, and other parameter data can be obtained according to the actual running condition of the system.
And S4, carrying out model solving by using a non-dominant sorting genetic algorithm to obtain an optimal operation optimization scheme, and reasonably distributing the heat load proportion of each heat supply module in the current period system.
In this example, a non-dominant ordered genetic algorithm (NSGA-II) was used for model solving. The NSGA-II algorithm refers to a rapid non-dominant multi-objective optimization algorithm with elite retention strategy, and is a relatively basic multi-objective optimization algorithm based on Pareto optimal solution.
Specifically, taking a solution of the model as an individual in a population, initializing the population individual, and solving by adopting a non-dominant ordering genetic algorithm to obtain a Pareto (Pareto) optimal solution set of the model; and then, determining an optimal regulation and control scheme by adopting a superior-inferior solution distance method (TOPSIS), namely, forward normalizing an original solution set matrix, normalizing the forward normalized matrix, finally, giving weight, calculating a comprehensive score, wherein the score is between 0 and 1, and the scheme with the highest score is the optimal scheme. Furthermore, the weights of the three optimization targets in the algorithm can be input into different proportions according to actual conditions in the system.
After the optimal solution, the heat loads of the water source heat pump unit and the gas boiler unit of the optimal scheme are determined, and according to the heat loads, the geothermal well exploitation flow, the water outlet temperature of the water source heat pump, the start-stop condition of the gas boiler and the like can be calculated, so that the optimal regulation and control scheme of each unit in the system is obtained, and the set value of the control equipment is regulated and controlled according to the optimal regulation and control mode, such as regulating the water outlet temperature of the water source heat pump unit, regulating the instantaneous flow of main pipelines of the water source heat pump unit and the gas boiler unit by regulating the operation frequency of the circulating water pump and the like, so that the optimal operation of the electric heating comprehensive energy coupling system is realized.
Example two
The embodiment provides a multi-objective operation optimization system of an electric heating comprehensive energy coupling system, which comprises:
The weather data acquisition and preprocessing module is used for acquiring the predicted weather data of the current period and generating the comprehensive parameters of the system environment in a fitting way;
the thermal load prediction module is used for inputting the comprehensive parameters of the system environment of the current period and the historical system thermal load values of a plurality of continuous periods into a thermal load dynamic prediction model based on CNN and LSTM, outputting the thermal load predicted value of the current period and carrying out real-time dynamic correction on the thermal load predicted value;
The system operation optimization model construction module is used for constructing a multi-objective system operation optimization model taking the lowest electric heating comprehensive energy coupling system operation cost, carbon emission and energy consumption as objective functions based on the corrected thermal load predicted value;
And the system operation optimization module is used for carrying out model solving by utilizing a non-dominant sorting genetic algorithm to obtain an optimal operation optimization scheme, and reasonably distributing the heat load proportion of each heat supply module in the current period system.
The steps involved in the second embodiment correspond to those of the first embodiment of the method, and the detailed description of the second embodiment can be found in the related description section of the first embodiment.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented by general-purpose computer means, alternatively they may be implemented by program code executable by computing means, whereby they may be stored in storage means for execution by computing means, or they may be made into individual integrated circuit modules separately, or a plurality of modules or steps in them may be made into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
While the present invention has been described in connection with the preferred embodiments, it should be understood that the present invention is not limited to the specific embodiments, but is set forth in the following claims.
Claims (10)
1. A multi-objective operation optimization method of an electric heating comprehensive energy coupling system is characterized by comprising the following steps:
acquiring predicted meteorological data of a current period, and fitting to generate comprehensive parameters of a system environment;
The comprehensive parameters of the system environment of the current period and the historical system heat load values of a plurality of continuous periods are input into a CNN and LSTM-based heat load dynamic prediction model, the heat load predicted value of the current period is output, and the heat load predicted value is dynamically corrected in real time;
based on the corrected thermal load predicted value, constructing a multi-objective system operation optimization model taking the lowest running cost, carbon emission and energy consumption of the electrothermal comprehensive energy coupling system as objective functions;
and carrying out model solving by using a non-dominant sorting genetic algorithm to obtain an optimal operation optimization scheme, and reasonably distributing the heat load proportion of each heat supply module in the current period system.
2. The method for optimizing multi-objective operation of an electrothermal integrated energy coupling system according to claim 1, wherein the meteorological data comprises outdoor temperature, outdoor wind speed and solar radiation illuminance; fitting and generating system environment comprehensive parameters according to the acquired meteorological data, wherein the formula is as follows:
；
In the above-mentioned method, the step of, Is the system environment comprehensive parameter of tau period, and is in DEG C; /(I)Is the average outdoor temperature in tau period, deg.c; For the average solar radiation illuminance of τ period,/> ；/>The average outdoor wind speed of tau period is m/s; a and b are fitting coefficients.
3. The method for optimizing multi-objective operation of an electrothermal integrated energy coupling system according to claim 1, wherein dynamically correcting the predicted thermal load value in real time comprises:
and carrying out real-time dynamic correction on the thermal load predicted value of the current period by utilizing the difference value between the thermal load predicted value of the previous period and the actual thermal load value.
4. The multi-objective operation optimization method of the electric heating integrated energy coupling system according to claim 1, wherein the operation cost of the electric heating integrated energy coupling system comprises the operation cost of a gas boiler unit and the operation cost of a water source heat pump unit considering integrated electricity price; and calculating the comprehensive electricity price of each unit running in the current period based on the photovoltaic power generation amount, the power consumption amount and the peak-to-valley electricity price.
5. The method for optimizing multi-objective operation of an electrothermal integrated energy coupling system according to claim 1, wherein constraints of the multi-objective system operation optimization model include: and (3) heat supply demand constraint, heat supply capacity constraint and circulating water pump operation constraint.
6. The method for optimizing multi-objective operation of an electrothermal integrated energy coupling system according to claim 1, wherein the model solving by using a non-dominant ordered genetic algorithm to obtain an optimal operation optimization scheme comprises:
taking the solution of the multi-target system operation optimization model as an individual in the population, initializing the population individual, and solving by adopting a non-dominant ordering genetic algorithm to obtain a pareto optimal solution set of the model;
And determining an optimal operation optimization scheme by adopting a good-bad solution distance method, namely, firstly normalizing an obtained original solution set matrix, normalizing the positive normalized matrix, then giving weight and calculating comprehensive scores, wherein the scheme with the highest score is the optimal operation optimization scheme.
7. The multi-objective operation optimization method of the electric heating comprehensive energy coupling system according to claim 1, wherein the electric heating comprehensive energy coupling system is a heat supply system based on coupling of a gas boiler unit and a water source heat pump unit of photovoltaic power generation and commercial power energy supply, and comprises a photovoltaic power generation device, a water source heat pump unit, a gas boiler unit and a comprehensive energy intelligent group control cabinet; the photovoltaic power generation device and municipal power supply are connected into the comprehensive energy intelligent group control cabinet together, and the comprehensive energy intelligent group control cabinet regulates and controls the operation of the gas boiler unit and the water source heat pump unit according to the heat loads of the water source heat pump unit and the gas boiler unit obtained by solving.
8. A multi-objective operation optimization system of an electrothermal integrated energy coupling system is characterized by comprising:
The weather data acquisition and preprocessing module is used for acquiring the predicted weather data of the current period and generating the comprehensive parameters of the system environment in a fitting way;
the thermal load prediction module is used for inputting the comprehensive parameters of the system environment of the current period and the historical system thermal load values of a plurality of continuous periods into a thermal load dynamic prediction model based on CNN and LSTM, outputting the thermal load predicted value of the current period and carrying out real-time dynamic correction on the thermal load predicted value;
The system operation optimization model construction module is used for constructing a multi-objective system operation optimization model taking the lowest electric heating comprehensive energy coupling system operation cost, carbon emission and energy consumption as objective functions based on the corrected thermal load predicted value;
And the system operation optimization module is used for carrying out model solving by utilizing a non-dominant sorting genetic algorithm to obtain an optimal operation optimization scheme, and reasonably distributing the heat load proportion of each heat supply module in the current period system.
9. The electrothermal integrated energy coupling system multi-objective operation optimization system of claim 8, wherein the meteorological data comprises outdoor temperature, outdoor wind speed, and solar irradiance.
10. The electrothermal integrated energy coupling system multi-objective operation optimization system according to claim 8, wherein the operation cost of the electrothermal integrated energy coupling system includes the operation cost of a gas boiler unit and the operation cost of a water source heat pump unit taking the integrated electricity price into consideration; and calculating the comprehensive electricity price of each unit running in the current period based on the photovoltaic power generation amount, the power consumption amount and the peak-to-valley electricity price.
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