CN114218808A - Energy consumption optimization method for comprehensive energy system - Google Patents

Energy consumption optimization method for comprehensive energy system Download PDF

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CN114218808A
CN114218808A CN202111635559.8A CN202111635559A CN114218808A CN 114218808 A CN114218808 A CN 114218808A CN 202111635559 A CN202111635559 A CN 202111635559A CN 114218808 A CN114218808 A CN 114218808A
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李妍
汪德成
张群
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State Grid Jiangsu Electric Power Design Consultation Co ltd
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Abstract

The invention discloses an energy consumption optimization method of a comprehensive energy system, which comprises the following steps: carrying out year-round load simulation on the energy-using building by adopting a thermal balance method to obtain a time-by-time load in a preset time period; considering the operation modes and power characteristics of various devices, establishing a reliable mathematical model of each device and performing coupling connection of energy transmission on a simulation platform; making an equipment operation control strategy based on the load distribution characteristics and the equipment performance obtained in the first two steps, and analyzing main influence parameters of the system energy efficiency; and defining an optimization parameter variable, establishing an objective function running in a preset time period, and connecting an optimization platform to optimize system parameters. According to the energy consumption optimization method of the comprehensive energy system, each main device is modularized, energy is transmitted among the modules, and calculation is carried out in the modules, so that the setting of the constraint conditions of the composite energy system is simplified, the specific parameters of the single device in the complex system can be optimized, and reference is provided for the development of the energy digitization technology.

Description

Energy consumption optimization method for comprehensive energy system
Technical Field
The invention relates to the field of comprehensive energy, in particular to an energy consumption optimization method of a comprehensive energy system.
Background
With the rapid development of renewable energy sources and internet technologies, the coupling among various energy sources is further enhanced, a novel internet technology represented by the internet of things, big data and cloud computing provides possibility for the bidirectional flow of energy flows and information flows among a large number of energy nodes and information nodes, an energy-saving model adaptive to development targets of economy, energy and environment is established, and the building energy consumption can be effectively optimized.
However, research on the comprehensive energy system is mainly developed around the power system, research on heating subsystems with different time scales is relatively less, part of research is over-simplified for modeling heating in the comprehensive energy, the modeling is only used as input and output conditions for matching the power system, and influence of characteristics such as hysteresis of a heat supply network, thermal inertia, thermal hydraulic coupling and the like on the planning design and operation regulation process of the comprehensive energy system is difficult to break. Under the condition that the subsystems are coupled with each other, nonlinear and uncertain interferences can be generated among the subsystems in different operation modes, control strategies and application scenes, and great challenges are brought to modeling, simulation and engineering application of the whole system. Therefore, semi-physical modeling is carried out on key equipment in the comprehensive energy system, and system operation control strategy optimization is carried out by a digital means, so that important theoretical significance and engineering practical value are achieved for intelligent management and control of the novel distributed energy station and stable operation of a power grid.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an energy consumption optimization method of a comprehensive energy system, which adopts a semi-physical comprehensive energy system simulation platform to perform real-time rolling prediction and simulation calculation based on actual environmental data power data. The problem of large error of pure digital simulation caused by large fluctuation and randomness of renewable energy sources and more uncertain factors of building loads is solved, the optimization result is more real and more appropriate to the practical application, and the authenticity of system simulation and the reliability of economic evaluation are favorably improved. And the system can be configured according to the requirements of different types of users, power price schemes, configuration combinations and equipment types, and can provide consulting services of system design schemes, optimized operation strategies and system economy evaluation for different users.
The technical scheme adopted by the invention is as follows:
the energy consumption optimization method of the comprehensive energy system is characterized by comprising the following steps:
carrying out year-round load simulation on the energy-using building by adopting a thermal balance method to obtain a time-by-time load in a preset time period;
considering the operation modes and power characteristics of various devices, establishing a reliable mathematical model of each device and performing coupling connection of energy transmission on a simulation platform;
making an equipment operation control strategy based on the load distribution characteristics and the equipment performance obtained in the first two steps, and analyzing main influence parameters of the system energy efficiency;
and defining an optimization parameter variable, establishing an objective function running in a preset time period, and connecting an optimization platform to optimize system parameters.
The further technical scheme is as follows: firstly, carrying out annual load simulation and summarization on energy-using buildings by using energy consumption simulation software and adopting a thermal balance method; then, considering the operation modes and power characteristics of various devices, reasonably describing the characteristics by adopting a mathematical method, establishing a reliable mathematical model and performing coupling connection of energy transmission on a simulation platform; then, an equipment operation control strategy is formulated based on the load distribution characteristics and the equipment performance obtained in the first two steps, and main influence parameters of the system energy efficiency are analyzed; and finally, defining an optimized parameter variable, establishing an objective function running all the year round, and connecting an optimization platform to optimize system parameters.
Compared with other comprehensive energy system modeling optimization methods, the method has the following advantages:
according to the energy consumption optimization method of the comprehensive energy system, each main device is modularized, energy is transmitted among the modules, calculation is carried out in the modules, the setting of constraint conditions of the composite energy system is simplified, reproducibility and popularization are achieved, specific parameters of a single device in a complex system can be optimized based on the method, and reference is provided for the development of an energy digitization technology.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
FIG. 1 is a schematic diagram of a semi-physical modeling model according to the present invention;
FIG. 2 is a photovoltaic energy storage control logic in accordance with the present invention;
FIG. 3 is a flow chart of the optimization solution of the present invention.
In the figure: 1. load data; 2. a water pump; 3. a cold and hot unit; 4. meteorological parameters; 5. an inverter; 6. a storage battery; 7. a photovoltaic module; 8. a power grid; 9. heating and cooling season time; 10. a controller 1; 11. a controller 2; 12. and optimizing the interface.
Detailed Description
The present invention is described in terms of particular embodiments, other advantages and features of the invention will become apparent to those skilled in the art from the following disclosure, and it is to be understood that the described embodiments are merely exemplary of the invention and that it is not intended to limit the invention to the particular embodiments disclosed. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the modeling and optimizing method for the semi-physical integrated energy system provided by the invention comprises 4 steps, and the specific implementation steps are as follows:
the method comprises the following steps of firstly, carrying out annual load simulation and summarization on energy-using buildings by means of energy consumption simulation software and adopting a thermal balance method. And inputting the annual hourly load into an ideal load end module 1 in the graph, and calculating and outputting the hourly return water temperature of the load side through the module.
Figure BDA0003441993600000031
Wherein: t isout-return water temperature, deg.c; t isinTemperature of the water supply, DEG C；
Figure BDA0003441993600000032
-hourly load, kj/h;
Figure BDA0003441993600000037
-a hourly water supply flow, kg/h; cp-specific heat capacity of water, kj/kg.K.
And step two, considering the operation modes and power characteristics of various devices, establishing a reliable mathematical model of each device and packaging the reliable mathematical model into modules, setting required input and output interfaces for each module, and calculating and outputting specific parameters by the modules through input parameters and internal models to transmit the specific parameters to the input end of the next module. The main equipment models are as follows:
1) air source heat pump model
Figure BDA0003441993600000033
Figure BDA0003441993600000034
Figure BDA0003441993600000035
Figure BDA0003441993600000036
P+CAP＝MashpC|tw-tg|
Figure BDA0003441993600000041
Wherein: CAP is refrigerating capacity or heating capacity, and the control model output by the controller 1 determines whether the mode is a heating mode or a refrigerating mode; CAP (common Place Capacity)hThe heat quantity is generated; CAP (common Place Capacity)cThe refrigerating capacity; COP is coefficient of heating performance or cooling performanceA coefficient which determines whether the heating or cooling mode is selected according to the control model output by the controller 1; COPhIs a coefficient of heating performance; COPcIs the coefficient of refrigeration performance; t is tdThe outdoor dry bulb temperature is transmitted by the meteorological module; t is twThe temperature of the return water is transmitted by the load module; t is tgIs the temperature of the supplied water; a is1-a6、b1-b6、c1-c16、d1-d6Is a fitting coefficient; p is the power consumption of the air source heat pump; mashpIs the air source heat pump water flow; c is the specific heat of water.
Calculating the calculated water supply temperature t once per time step of the air source heat pump modelgTo the water pump module.
2) Water chiller model
CAPmax＝CAP0·CAPr
Figure BDA0003441993600000042
Figure BDA0003441993600000043
P0Pr1Pr2+PLR·CAPmax＝MwshpC|tw-tg′|
Figure BDA0003441993600000044
Figure BDA0003441993600000045
Pr2＝e3+e4PLR+e5PLR2
Wherein, CAPmaxThe refrigerating capacity is the refrigerating capacity in full load operation; CAP (common Place Capacity)0The refrigerating capacity is the refrigerating capacity when the compressor operates under the rated working condition; CAP (common Place Capacity)rFor the unit under actual conditionsCorrection coefficient of refrigerating capacity; COP is the energy efficiency of the water chilling unit; PLR is part load rate; q is the actual load quantity and is transmitted by the load module; p0The input power required by the heat pump unit under the rated working condition; pr1The heat pump set input power correction coefficient under full load; pr2The input power correction coefficient of the heat pump unit under partial load is obtained; r ismeThe ratio of the actual water flow to the rated water flow at the evaporator side is obtained; r ismcThe ratio of the actual water flow and the rated water flow at the condenser side is obtained; r isTeoThe ratio of the actual outlet water temperature at the evaporator side to the rated outlet water temperature; r isTciThe ratio of the actual backwater water temperature at the condenser side to the rated backwater water temperature is obtained;
e1-e5、f1-f4、g1-g4、h1-h4、i1-i4、j1-j2all the parameters are fitting parameters, and fitting coefficients are constants, are obtained according to the regression analysis of typical sample parameters, and can be modified according to special conditions; mwshpThe water flow is input by a water pump module; the above-mentioned nominal values are all constants, determined by the plant parameters.
The water outlet temperature t to be calculated is calculated once per time step by the water chiller modelg' an incoming water pump module.
The water pump module calculates the flow and temperature of the mixed water supplied by the two cold and heat sources and transmits the flow and temperature into the load module, and the load module calculates the return water temperature and transmits the return water temperature into the cold and heat source inlet to form a closed loop.
3) Photovoltaic panel model
By adopting a four-parameter model, the method comprises the following steps of,
Figure BDA0003441993600000051
Figure BDA0003441993600000052
Figure BDA0003441993600000053
i is current; i isoIs a diode reverse saturation current; i iso，refIs the reverse saturation current of the diode under the reference condition; i isLIs a single module photocurrent; i isL，refIs the single module photocurrent at the reference condition; v is a voltage; t iscIs the temperature; t isc，refIs the temperature at the reference condition; rsGamma, g, k are constants; gTThe total incident radiation is a function of the inclination angle theta of the photovoltaic panel and the solar radiation quantity transmitted by the meteorological module; gT，refIs the incident radiation at the reference condition. The photovoltaic panel module calculates the time-by-time power generation amount and transmits the time-by-time power generation amount to the inverter module, and the inverter determines the time-by-time current flow direction according to the time-by-time system power consumption, the photovoltaic power generation amount, the battery power amount and the built-in control logic.
4) Inverter model
The inverter module also undertakes the work of charge and discharge control besides the alternating current and direct current conversion function, the control mode is shown in figure 1, and the controller 2 collects and calculates the total time-by-time energy consumption of the heating power subsystem as an electric load signal and inputs the electric load signal to the inverter module as a junction of the cold, heat and electricity storage coupling. The inverter and the storage battery are operated jointly, and the electric quantity of the battery is used as an input signal FSOC of the inverter to determine whether to charge or discharge. As shown in fig. 2, if FSOC is between the set highest charging threshold FC and the set lowest discharging threshold FD, the charging and discharging mode is adjusted according to whether the photovoltaic power generation amount at that moment meets the electrical load, the photovoltaic power generation amount always meets the load demand preferentially, and surplus power is stored in the storage battery. When the current exceeds FC, the charging is stopped, and when the current falls below FD, the discharging is stopped. And the photovoltaic panel and the storage battery can not meet the load and are supplemented by a power grid.
And step three, formulating an equipment operation control strategy based on the load distribution characteristics and the equipment performance obtained in the first two steps, and analyzing main influence parameters of the system energy efficiency. The flow of the water pump is controlled through the programming of the controller 1, so that the group control of the cold and hot unit is realized.
And step four, as shown in fig. 3, after the optimization problem is determined, the connection optimization platform selects a proper optimization algorithm and defines an objective function, and optimization can be carried out. And the optimization parameters are defined in the unit equipment module which is written in the step two. And (3) constructing a whole-year target cost function by considering the time-of-use electricity price:
Figure BDA0003441993600000061
Figure BDA0003441993600000062
Figure BDA0003441993600000063
e-income from electricity sale; cpv-photovoltaic cost; cESS-energy storage costs; cgrid-cost of electricity purchase;
Figure BDA0003441993600000064
-the cost of the cold and hot set; ci-electricity price at time i; piThe system energy consumption (electrical load) at time i.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the scope of the present invention, and any person skilled in the art should be covered by the technical solutions of the present invention and the inventive concepts which are equivalent to or changed from the technical solutions of the present invention and the present invention.

Claims (5)

1. The energy consumption optimization method of the comprehensive energy system is characterized by comprising the following steps:
carrying out year-round load simulation on the energy-using building by adopting a thermal balance method to obtain a time-by-time load in a preset time period;
considering the operation modes and power characteristics of various devices, establishing a reliable mathematical model of each device and performing coupling connection of energy transmission on a simulation platform;
making an equipment operation control strategy based on the load distribution characteristics and the equipment performance obtained in the first two steps, and analyzing main influence parameters of the system energy efficiency;
and defining an optimization parameter variable, establishing an objective function running in a preset time period, and connecting an optimization platform to optimize system parameters.
2. The energy consumption optimization method of the integrated energy system according to claim 1, characterized in that: carrying out year-round load simulation on the energy-using building by adopting a thermal balance method to obtain a time-by-time load of a preset time period, wherein the method comprises the following steps:
acquiring typical building related parameters of an energy area building group, including outdoor meteorological parameters, building area, building floor height, building envelope, window-wall ratio, window glass materials, indoor lighting, electric equipment, personnel distribution and a time schedule;
the energy consumption simulation software is used for calculating the concrete building form, the influence of the building orientation, the window-wall ratio and the building plane layout is considered, the internal disturbance, the ventilation and various building enclosing components of the building are calculated, the material selection, the combination, the heat preservation and the heat insulation of the building enclosing structure are simulated and analyzed, and therefore the time-by-time cold load and the heat load of each building and the whole building group are obtained.
3. The energy consumption optimization method of the integrated energy system according to claim 1, characterized in that: considering the operation modes and power characteristics of various devices, establishing a reliable mathematical model of each device and performing coupling connection of energy transmission on a simulation platform, wherein the coupling connection comprises the following steps:
establishing an air source heat pump model, a water chiller model and a photovoltaic panel model:
reading the obtained hourly load of the preset time period, and calculating the hourly return water temperature twWill calculate the hourly return water temperature twThe outlet is transmitted into a water chilling unit and an air source heat pump module;
the air source heat pump model is as follows:
Figure FDA0003441993590000011
Figure FDA0003441993590000012
Figure FDA0003441993590000013
Figure FDA0003441993590000014
P+CAP＝MashpC|tw-tg|
Figure FDA0003441993590000021
wherein: CAP is refrigerating capacity or heating capacity; CAP (common Place Capacity)hThe heat quantity is generated; CAP (common Place Capacity)cThe refrigerating capacity; COP is heating coefficient or cooling coefficient; COPhIs a coefficient of heating performance; COPcIs the coefficient of refrigeration performance; t is tdThe outdoor dry bulb temperature is transmitted by the meteorological module; t is twThe gradual return water temperature; t is tgIs the temperature of the supplied water;
a1-a6、b1-b6、c1-c16、d1-d6is a fitting coefficient; p is the power consumption of the air source heat pump; mashpIs the air source heat pump water flow; c is the specific heat of water;
calculating the calculated water supply temperature t once per time step of the air source heat pump modelgA transfer water pump module;
the model of the water chiller is as follows:
CAPmax＝CAP0·CAPr
Figure FDA0003441993590000022
Figure FDA0003441993590000023
P0Pr1Pr2+PLR·CAPmax＝MwshpC|tw-tg′|
Figure FDA0003441993590000024
Figure FDA0003441993590000025
Pr2＝e3+e4PLR+e5PLR2
wherein, CAPmaxThe refrigerating capacity is the refrigerating capacity in full load operation; CAP (common Place Capacity)0The refrigerating capacity is the refrigerating capacity when the compressor operates under the rated working condition; CAP (common Place Capacity)rThe correction coefficient of the refrigerating capacity of the unit under the actual working condition is obtained; COP is the energy efficiency of the water chilling unit; PLR is part load rate; q is the actual load quantity and is transmitted by the load module; p0The input power required by the heat pump unit under the rated working condition; pr1The heat pump set input power correction coefficient under full load; pr2The input power correction coefficient of the heat pump unit under partial load is obtained; r ismeThe ratio of the actual water flow to the rated water flow at the evaporator side is obtained; r ismcThe ratio of the actual water flow and the rated water flow at the condenser side is obtained; r isTeoThe ratio of the actual outlet water temperature at the evaporator side to the rated outlet water temperature; r isTciThe ratio of the actual backwater water temperature at the condenser side to the rated backwater water temperature is obtained;
e1-e5、f1-f4、g1-g4、h1-h4、i1-i4、j1-j2are all fitting parameters, fitting systemThe number is a constant; mwshpThe water flow is input by a water pump module;
the water chilling unit model calculates once every time step and transmits the calculated water outlet temperature tg' into the water pump module;
calculating the flow and temperature of the mixed water from the two cold and heat sources according to the water pump module, and calculating the return water temperature to be transmitted into the cold and heat source inlet to form a closed loop;
photovoltaic panel model:
Figure FDA0003441993590000031
Figure FDA0003441993590000032
Figure FDA0003441993590000033
wherein I is current; i isoIs a diode reverse saturation current; i iso，refIs the reverse saturation current of the diode under the reference condition; i isLIs a single module photocurrent; i isL，refIs the single module photocurrent at the reference condition; v is a voltage; t iscIs the temperature; t isc，refIs the temperature at the reference condition; rsGamma, q, k are constants; gTAs a function of the total incident radiation, the inclination angle θ of the photovoltaic panel and the amount of solar radiation; gT，refIs the incident radiation at the reference condition;
the photovoltaic panel module calculates the time-by-time power generation amount and transmits the time-by-time power generation amount to the inverter, and the inverter determines the time-by-time current flow direction according to the time-by-time system power consumption amount, the photovoltaic power generation amount, the battery power amount and the built-in control logic.
4. The energy consumption optimization method of the integrated energy system according to claim 1, characterized in that:
based on the obtained load distribution characteristics and equipment performanceAn equipment operation control strategy is formulated, time-by-time energy consumption item statistics is carried out after 8760 hours of simulation operation in the whole year, and the main influence parameter of the system energy efficiency is MashpAnd MwshpAnd the inclination angle theta of the photovoltaic panel.
5. The energy consumption optimization method of the integrated energy system according to claim 1, characterized in that: defining an optimization parameter variable and establishing an objective function running in a preset time period, and connecting an optimization platform to optimize system parameters, wherein the optimization parameter variable comprises the following steps:
defining optimized parameter water pump flow MashpAnd MwshpDefining the inclination angle theta of the photovoltaic panel as a character type, carrying out initial assignment, calling a java program externally connected with an optimization tool box, and setting a variable type and a variable range;
establishing an objective function running in a preset time period:
Figure FDA0003441993590000041
E＝∫1 8760C1i·P1i
Cgrid＝∫1 8760C2i·P2i
in the formula, E is the income of electricity sale; cpvFor photovoltaic cost, about 300/m2；CESSFor energy storage cost, about 400/m3；CgridFor the cost of electricity purchase;
Figure FDA0003441993590000042
the cost of the cold and hot unit is determined by the actual equipment price; c1iThe price of electricity sold at the moment i; c2iThe price of the electricity purchased at the moment i; p1iThe electric quantity sold to the power grid for photovoltaic power generation at the moment i; p2iThe system energy consumption at the moment i;
and selecting a proper algorithm such as a PSO particle swarm algorithm in an optimization tool box, setting the maximum iteration number and the iteration precision of the optimization process, finally starting optimization, updating and optimizing variables after each step of calculation, and feeding back the variables to the system model for repeated operation until an optimal solution is found or the maximum value of the iteration times is reached.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118013626A (en) * 2024-02-06 2024-05-10 桂林电子科技大学 Performance parameter prediction method and device for auxiliary ground source heat pump system of cooling tower
CN118052153A (en) * 2024-04-16 2024-05-17 上海叁零肆零科技有限公司 Natural gas pipe network data optimization method, storage medium and electronic equipment

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118013626A (en) * 2024-02-06 2024-05-10 桂林电子科技大学 Performance parameter prediction method and device for auxiliary ground source heat pump system of cooling tower
CN118052153A (en) * 2024-04-16 2024-05-17 上海叁零肆零科技有限公司 Natural gas pipe network data optimization method, storage medium and electronic equipment

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