CN112100828B - Electric heating system control method considering load quasi-dynamic characteristic of heating power network - Google Patents
Electric heating system control method considering load quasi-dynamic characteristic of heating power network Download PDFInfo
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Abstract
The invention discloses an electric heating system control method considering the load quasi-dynamic characteristic of a heating power network. The energy conversion equipment realizes the conversion among electricity, heat and gas according to the demand condition of various energy sources in the system, thereby improving the overall efficiency and flexibility of the system. The virtual energy storage capacity of the hot water pipe network plays a role in supplementing and enhancing the existing energy storage device, and reduces the operation energy consumption and the cost.
Description
Technical Field
The invention relates to an electric heating gas system control method considering the quasi-dynamic characteristic of the load of a heating power network, and belongs to the field of electric heating gas system control.
Background
The existing electric heating gas system is not independent from each other, but is a complex electric heating gas system formed by coupling a plurality of energy sources. The multi-energy-source coupled electric heating and gas system generally comprises electric subsystems, heat subsystems and gas subsystems in the same region and conversion equipment for coupling energy networks, the operation of each energy subsystem is constrained by the operation state of the energy subsystem coupled with the energy subsystem (such as natural gas node pressure, regional heating pipeline flow and the like), and meanwhile, along with the gradual increase of the permeability of the multi-energy-source coupled equipment, the change of the operation state can greatly influence the energy flow. The diversity of energy coupling promotes the cooperative optimization of energy flow, and is favorable for improving the comprehensive utilization rate of energy. However, the existing electric heating gas system does not utilize the energy storage capacity of the hot water pipe network, so that the power consumption of each subsystem in the system is still large.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides an electric heating system control method considering the quasi-dynamic characteristic of the load of a heating power network, which reduces the power consumption of the electric heating system.
The technical scheme is as follows: the invention adopts the technical scheme that the electric heating system control method considering the load quasi-dynamic characteristic of a heating power network comprises the following steps:
1) Performing virtual energy storage modeling on a heating power subsystem in the electric heating system;
2) Modeling a photovoltaic power generation system, a ground source heat pump, a gas boiler, a heat collection device and a heat exchanger of an electric heating system;
3) Establishing a room virtual energy storage control model considering the thermal comfort of a load user, and giving out a power consumption optimization objective function and constraint conditions of the electric heating system after virtual energy storage in the heating power subsystem;
4) And solving the objective function.
The virtual energy storage charging and discharging power of the hot water pipe network in the step 1) is as follows:
H＝Q t,HR -Q t,load
in the above formula, H is the virtual energy storage charge-discharge power of the hot water pipe network, the value is that the hot water pipe network is in the "energy storage" state when the value is positive, and the value is that the hot water pipe network is in the "energy discharge" state when the value is negative; q t,HR 、Q t,load Respectively the thermal power at the heat source and the heat exchange power at the thermal load in the t period.
The photovoltaic cell output power of the photovoltaic power generation system in the step 2) is as follows:
in the above formula, P PV Is the output power of the photovoltaic cell; g t Is the intensity of light, wherein G STC The illumination intensity under standard test conditions; t is STC Is the photovoltaic cell temperature; p STC Is the maximum output power; k is a temperature dependent coefficient, generally-0.47 c,t Is the temperature of the photovoltaic cell.
The thermal power Q generated by the ground source heat pump in the step 2) GSHP The following were used:
Q GSHP ＝P GSHP ·COP GSHP
in the above formula, P GSHP For ground source heat pump consumptionElectric power, COP GSHP The heat conversion efficiency of the ground source heat pump.
The relationship between the natural gas consumption of the gas-fired boiler and the heating power in the step 2) is as follows:
in the above formula, f GB Is the consumption of natural gas per unit time; q GB The heating power of the gas boiler; eta GB The heating efficiency is improved; h ng Indicating the heating value of natural gas.
The heat power entering the heat exchanger from the heat collecting device in the step 2) is
Q GT Indicating the thermal output of the gas turbine, i.e. having
The relationship between the inlet thermal power and the outlet thermal power of the heat exchanger is
In the above formula, Q hr Outlet heat power, epsilon, for the heat-collecting means hr Is the effective heat transfer rate of the heat exchanger.
In the step 3), the minimum converted power of each system in the region is taken as an objective function:
in the formula, P fuel Purchasing power for the gas network; p grid Purchasing power for a power grid; p steam grid Purchasing power for steam networks, P hw grid Power is purchased for the hot water network.
And the constraint conditions in the step 3) comprise power balance constraint, energy supply equipment output constraint and hot water pipe network constraint.
The power balance constraints include electrical power balance, thermal power balance, and gas power balance.
The powered device output constraints include being:
P k,min ≤P k (t)≤P k,max
in the formula P k (t) means the kth device contribution at time t; p k,max And P k,min The maximum and minimum output force of the kth device, respectively.
The hot water pipe network constraint comprises quasi-dynamic characteristic constraint, mixing constraint, supply and return water temperature constraint and mass flow constraint.
Has the advantages that: the invention takes the virtual energy storage capacity of a hot water pipe network into account in an electric heating system, and is provided with energy conversion equipment. The energy conversion equipment realizes the conversion among electricity, heat and gas according to the demand condition of various energy sources in the system, thereby improving the overall efficiency and flexibility of the system. The virtual energy storage capacity of the hot water pipe network plays a role in supplementing and enhancing the existing energy storage device, and reduces the operation energy consumption and the cost.
Drawings
FIG. 1 is a schematic structural view of an electric heating system according to the present invention;
FIG. 2 is a graph showing the time-varying change of various types of loads;
FIG. 3 is a graph of gas turbine electrical power optimization results;
FIG. 4 is a schematic diagram of the power purchased by the power grid in a day;
FIG. 5 is a schematic diagram of thermal output of each device in scene 1;
FIG. 6 is a schematic diagram of thermal output of each device in scene 2;
FIG. 7 is a schematic diagram of hot water source output in scenario 1;
FIG. 8 is a schematic diagram of hot water source output in scenario 2;
fig. 9 is a charging and discharging power curve diagram of a hot water pipe network in scene 2;
FIG. 10 is a graph of the temperature change of a hot water pipe network in scene 2;
FIG. 11 is a schematic diagram of actual gas usage of scenario 2;
FIG. 12 is a schematic diagram of transmission delay;
FIG. 13 is a schematic view of a fluidic mass of the present invention.
Detailed Description
As shown in fig. 1, the existing electric hot gas system consists of an electric, a thermal, and a gas subsystem and a conversion device for coupling between energy networks, wherein the thermal subsystem includes a hot water system and a steam system. Besides, a photovoltaic power generation system is installed as distributed energy equipment, and a ground source heat pump and a gas boiler are installed as energy conversion equipment. The ground source heat pump and the gas boiler realize energy conversion among electricity, heat and gas according to the demand condition of various energy sources in the system. The electric energy generated by the photovoltaic power generation system and the gas turbine is transmitted to the load side to meet the electricity utilization requirement of a user. The thermal subsystem transfers heat from the heat source to the load side in the form of hot water or steam through a hot water network and a steam network to meet the heat load demand of the user. The heat in the electric heating gas system of the embodiment is mainly provided by a gas turbine, a gas boiler and a ground source heat pump, and can be purchased from a heat supply network if the shortage exists. The gas network delivers natural gas from the gas source to the load side to meet the gas load demand of the user, and simultaneously supplies the natural gas to the gas turbine and the gas boiler in the system. And energy conversion is carried out among all subsystems through a gas turbine, a gas boiler and a ground source heat pump.
The virtual energy storage charging and discharging power of the hot water pipe network is as follows:
H＝Q t,HR -Q t,load
in the above formula, H is the virtual energy storage charging and discharging power of the hot water pipe network, the value is that the hot water pipe network is in the "energy storage" state when the value is positive, and the value is that the hot water pipe network is in the "energy discharge" state when the value is negative; q t,HR 、Q t,load Respectively the thermal power at the heat source and the heat exchange power at the thermal load in the t period.
And then modeling the photovoltaic power generation system, the ground source heat pump, the gas-fired boiler, the heat collection device and the heat exchanger.
Light intensity approximate clothes of photovoltaic power generation system in a period of timeFrom the Beta distribution, its probability density function f (G) t ) Comprises the following steps:
in the above formula, G t Is the intensity of light at time t, G max Alpha and Beta are shape parameters of Beta distribution, gamma is Gamma function.
After obtaining the average value mu and the standard deviation sigma of the illumination intensity according to the data statistical analysis, alpha and beta can be respectively expressed as
The temperature of the photovoltaic cell is difficult to directly measure, and can be obtained by estimating through an empirical formula in combination with the illumination intensity and the ambient temperature, and the relationship is
In the above formula, T c,t Is the temperature of the photovoltaic cell; t is a unit of amd,t Is ambient temperature.
Under the condition of known illumination intensity and temperature, the output power of the photovoltaic cell is as follows:
in the above formula, P PV Is the output power of the photovoltaic cell; g t Is the intensity of light, wherein G STC Is the illumination intensity under standard test conditions; t is STC Is the photovoltaic cell temperature; p STC Is the maximum output power; k is a temperature dependent coefficientGenerally, it is-0.47.
The ground source heat pump converts underground heat energy into heating heat energy by using electric energy. The model of the ground source heat pump is as follows:
Q GSHP ＝P GSHP ·COP GSHP (6)
in the above formula, Q GSHP The heat power generated by the ground source heat pump; p GSHP Electric power consumed for a ground source heat pump; COP GSHP The heat conversion efficiency of the ground source heat pump.
The relationship between the natural gas consumption of a gas boiler and its heating power is as follows:
in the above formula, f GB Is the consumption of natural gas per unit time; q GB The heating power of the gas boiler; eta GB The heating efficiency is improved; h ng Indicating the heating value of natural gas.
The heat collecting device mixes high-temperature steam generated by the gas turbine and the gas boiler, so that the stability of heat entering a pipeline is improved, and the control and management are facilitated. The heat collecting device in this embodiment ignores the medium mixing time, and considers that the outlet thermal power of the heat collecting device is close to the inlet thermal power at the same time. The heat collection efficiency in the mixing process is eta collector The thermal power entering the steam network from the heat collector is
The heat power entering the heat exchanger from the heat collecting device is
Q GT Indicating the thermal output of the gas turbine, i.e. having
The heat exchanger has certain heat loss in the heat exchange process, and the relationship between the inlet heat power and the outlet heat power is
In the above formula, Q hr Outlet heat power, epsilon, for the heat-collecting means hr Is the effective heat transfer rate of the heat exchanger.
In addition, the embodiment also considers the situation that heat is directly purchased from the outer net. The heat generated by the ground source heat pump and the heat collection device and the heat directly purchased in the hot water network enter the heat exchange initial station together to be mixed and transferred with the primary pipe network, the process also generates heat loss, and the relationship between the inlet heat power and the outlet heat power is as follows:
Q HR ＝ε HR (Q hr +Q GSHP +Q hw grid ) (10)
in the formula, Q HR The heat power transmitted to the primary pipe network for the heat exchange station; epsilon HR The total effective heat transfer degree of the heat exchange initial station; q hw grid Heat purchased for the hot water network.
And then, considering a power consumption optimization model and constraint conditions of the electric heating system after virtual energy storage in the heating power subsystem. The overall stability of the electric and hot gas system, the peak clipping and valley filling of electric energy and the reduction of the pressure of a power grid are comprehensively considered, and the minimum converted power of each system in an area is taken as a target function:
in the formula, P fuel Purchasing power for the gas network; p is grid Purchasing power for a power grid; p steam grid Purchasing power for steam networks, P hw grid Power is purchased for the hot water network.
The constraint conditions comprise power balance constraint, output constraint of energy supply equipment and hot water pipe network constraint.
(1) Power balance constraint
The power balance constraints in the system mainly comprise electric power balance, thermal power balance and gas power balance.
The electric, steam, hot water and gas loads of the whole electric heating system in the t period are respectively P user (t)、Q usersteam (t)、Q user hw (t)、f user (t) then there are
P PV (t)+P GT (t)+P grid (t)-P GSHP (t)＝P user (t) (12)
Q load (t)＝Q user hw (t) (13)
f grid (t)-f GT (t)-f GB (t)＝f user (t) (14)
(2) Output constraint of energy supply equipment
Kth device contribution constraint:
P k,min ≤P k (t)≤P k,max (15)
in the formula P k (t) means the kth device contribution at time t; p is k,max And P k,min The maximum and minimum output force of the kth device, respectively.
(3) Hot water pipe network restraint
The hot water pipe network constraint considers the quasi-dynamic characteristic constraint, the mixing constraint, the supply and return water temperature constraint and the mass flow constraint of heat in the pipeline.
Wherein the quasi-dynamic constraint is shown in FIG. 12 if the inlet temperature is at time t 1 Change, then the outlet temperature will be at time t 2 Change, transmission delay s equals t 2 -t 1 The duration of which is determined by the length of the pipe and the mass flow rate, the transmission delay changes when the mass flow rate changes.
In a heating hot water pipe network, the length of a pipeline of a primary pipe network is longer, so that the transmission delay is also larger. And the secondary pipe network is responsible for heat distribution inside the heat users, so the transmission delay is smaller due to the shorter length. The embodiment only considers the time delay characteristic of the primary pipe network.
Under normal conditions, a steady-state hydraulic model is adopted to describe continuous and stable flow of hot water in a pipe network, and if elevations of all nodes are the same, q represents mass flow in a pipeline, and q represents mass flow in the pipeline in And q is out Representing the mass flow into and out of the node, respectively, the following holds:
∑q out ＝∑q in
the flow mixing at the node is considered sufficient and satisfies the principle of conservation of energy, modeled by the following equation. T is in And T out Representing the temperature of the fluid entering and leaving the node, respectively, and the temperature of the fluid leaving the same node is the same, the following holds:
(∑q out )T out ＝(∑q in T in )
taking into account the transmission delay s of the pipe j In the present embodiment, the quasi-dynamic characteristics of thermal energy transmission in the pipeline are analyzed by using a node method, a cycle is divided into N consecutive equal time intervals Δ t, and a fluid mass block (FWM) is used to describe hot water entering the pipeline at an inlet in each time interval Δ t. While assuming that the conduits are in a steady state for a period of time, i.e. the physical quantities in the fluid mass are unchanged.
In the hot water pipeline shown in FIG. 13, the time of FIG. 13 is denoted as t, and the first section of the fluid mass on the right side of the inlet of the pipeline is denoted as FWM 1 The first stage fluid mass FWM 1 Enters the pipeline at the time t-delta t. Each fluid mass within the tube is numbered FWM in sequence from inlet to outlet 1 、FWM 2 To FWM K And K is the number of fluid blocks in the pipeline. Considering that the transmission delay of the hot water pipe may not be an integer multiple of Δ t, assume a transmission delay s j Between s 1 And s 2 Wherein s is 1 ＝(K-1)Δt，s 2 K Δ t. The fluid mass flowing out of the outlet of the pipeline is formed by FWM K And FWM K+1 Are composed together, as shown by the shaded portion in fig. 13. FWM in FIG. 13 K,2 Is FWM K At s 2 -s j Part of the time flowing out of the pipeline, FWM K+1,1 Is FWM K+1 At s j -s 1 The fraction of time that flows out of the pipeline. It is apparent that the mass of fluid flowing out of the pipe during the Δ t period is FWM K,2 And FWM K+1,1 And (4) summing. The hot water temperature at the outlet of the pipe is calculated according to the following formula:
in the formula (I), the compound is shown in the specification,
the temperature of hot water at the outlet side of a pipeline j at the time t is shown in unit ℃, the upper mark in represents the inlet side of the pipeline, and the upper mark out represents the outlet side of the pipeline; q. q.s K And q is K+1 Respectively fluid mass FWM K And FWM K+1 Mass flow of (2) in kg/s.
For convenience of calculation, order
C 1 And C 2 Representing the weight coefficients. The outlet temperature of the pipe j during the s period
Can be expressed as:
the fluid mass FWM of the K-th section is due to the heat lost by the hot water when it is transported in the pipe K The exit temperature at the outlet side can be expressed as:
in the formula, k j Is the heat loss coefficient of the pipeline, W/(m DEG C); c. C w The specific heat capacity of water is 4.2 kJ/(kg. DEG C); l j Is the length of the pipeline in m; t is am Is the temperature of the environment in which the pipe is located, in degrees c.
Kth section fluid mass FWM K And a K +1 th section fluid mass FWM K+1 The temperatures at the outlet were:
in the above formula
Thus the outlet temperature of pipe j during s
Can be further simplified into:
the above formula describes the quasi-dynamic change process of heat energy transport in the hot water pipeline based on considering the time delay characteristic, and is suitable for all water supply and return pipelines in the hot water pipeline network.
When the mass flow in the pipe is known, C 1 、C 2 、C 3 And C 4 Is constant and the outlet temperature of the pipeline is a linear combination of the temperatures of the inlet at different moments.
Mixing constrained to
(∑q out )T out ＝(∑q in T in ) (17)
q in And q is out Representing mass flow into and out of the node, respectively; t is a unit of in And T out Representing the temperature of the fluid entering and leaving the node, respectively.
The supply and return water temperature constraint ranges can be respectively expressed as
In the above formula, the first and second carbon atoms are,
respectively designing an upper limit and a lower limit of unit for the water supply temperature in a water supply pipeline;
the upper limit and the lower limit of the return water temperature in the return water pipeline are respectively designed. T is t supply Is the temperature of the water supply, T t return The return water temperature is given in units of ℃.
Likewise, the mass flow q in the pipe t Also has an upper and lower limit, and the mass flow constraint range can be expressed as
q min ≤q t ≤q max (20)
And (4) considering the comfort requirement of a user, and adding a room virtual energy storage control model. When the artificial ventilation behavior is not considered, the room temperature does not suddenly change immediately when the room heating power changes, which is also the manifestation of the room thermal inertia. According to the heat balance principle, a room temperature quasi-dynamic change general model can be obtained, which is mainly related to outdoor temperature and heating power, and the formula is as follows:
in the formula, Q ra,t Heating power for a period of t; eta air Is the air heat transfer coefficient; delta t is the scheduling time period duration; t is c Is a scheduling period.
And the heat comfort feeling of the user is considered, the heat load demand at the side of the user can be optimized, and the energy conservation of the system is facilitated. The PMV (PredictedMean Voice) index of human comfort is summarized by professor Fanger through an experimental method to voting results of voting on a large number of participants, can represent the thermal comfort of most people, and influences of the PMV index are mainly environmental factors and human body factors, so that the thermal comfort is divided into 7 grades for more intuitively describing the comfort degree of the environment where the PMV index is located, and the following table is shown:
TABLE 1-1 PMV-PPD comfort level values
As can be seen from table 1-1, the larger the absolute value of the PMV value is, the more uncomfortable and less comfortable the thermal sensation is, and the thermal sensation is most comfortable when the PMV value is 0.
Because the PMV index considers a plurality of factors such as clothing thermal resistance, human body metabolic rate, average radiation temperature, air flow rate, air humidity and the like, the calculation needs to be carried out through iteration and is complex, the PMV index is calculated by adopting a simplified formula which is as follows
Because the virtual energy storage of the room is considered, the heating hot water load is not a constant consistent with the predicted hot water load in the day, but needs to be obtained by optimization calculation, so the balance constraint of the hot water load does not need to be considered, and the constraint can be replaced by the temperature constraint corresponding to the PMV value and is expressed as
t a,min ≤t a ≤t a,max (23)
In the formula, t a,min Minimum allowed room temperature, ° c; t is t a,max Is the maximum allowable room temperature, c.
If the optimization is simply carried out from the energy-saving perspective, the optimization result is likely to have the condition that the room temperature is always at the lowest value, the optimized heat load requirement is minimum, and the reliability of the heat supply quality cannot be ensured, so that the total heat load and the predicted total heat load in the set optimization period are kept unchanged.
In the formula, Q load after,t The water heating load in each time interval after optimization is kW; q load before,t To optimize the hot water load for each time period (i.e., the predicted hot water load from the day onwards), kW.
The objective function and the constraint condition are both linear constraints, and the existing complete solving method can be used for solving. The present embodiment uses CPLEX to solve, and comprises three steps of defining variables, objective functions and constraint conditions. Specifically, the CPLEX can rapidly solve the linear programming problem based on a built-in simplex method and an interior point algorithm, and the output conditions of each device such as a gas turbine, a gas boiler and a ground source heat pump are obtained.
Examples of the design
And selecting energy consumption data of the electric heating system in a certain area in one day in winter for analysis and research. Relevant parameters of energy supply equipment such as a gas turbine, a gas boiler, a ground source heat pump, a photovoltaic generator set and the like, a heat collection device and a heat exchange device are shown in tables 1-2. The length of a water supply and return pipeline of a primary pipe network in the regional hot water pipe network is 2km, the pipe diameter is 0.3m, a quality adjusting mode is adopted, the stable mass flow is 80kg/s, the operating temperature range of the water supply pipeline is 60-130 ℃, and the operating temperature range of the water return pipeline is 30-90 ℃. According to relevant regulations in the heating design specifications, the PMV ranges are set as follows: PMV is more than or equal to-1 and less than or equal to +1, and the upper limit and the lower limit of the room temperature are respectively 28.9 ℃ and 23.2 ℃ respectively, and the air heat conduction coefficient eta is calculated air Take 0.18.
TABLE 1-2 associated plant parameters
The predicted change curves of the electrical, thermal and pneumatic loads from day to day are shown in fig. 2. In the optimization process, various loads are converted according to kilowatt as a unit for convenient calculation.
To verify the effectiveness of this embodiment, two scenarios were constructed in the electric heating system for comparison: the virtual energy storage characteristic of a hot water pipe network is not considered in the scene 1; scene 2 considers the virtual energy storage of the hot water pipe network.
(1) Power supply situation
In the electric gas-electric-heat gas system, the optimization results of the electric power of the gas turbine under the scenes 1 and 2 are shown in fig. 3, wherein the electric power fluctuation of the gas turbine in the scene 1 is small, and the electric power fluctuation of the gas turbine in the scene 2 is large. Compared with scenario 1, in scenario 2, the electric power of the gas turbine is increased by 21.2% in the peak period, increased by 3.6% in the normal period, and decreased by 19.9% in the valley period. Changes in the electrical power of the gas turbine also affect its thermal output. It can be seen that the electric power is significantly increased during the peak period, while the electric power is greatly decreased during the valley period.
Fig. 4 shows the power purchasing amounts from the power grid in the scenarios 1 and 2, and both the scenarios 1 and 2 show that the electric power of the gas turbine is reduced and the power purchasing amount is increased in the low power consumption period, and the electric power of the gas turbine is increased and the power purchasing amount is reduced in the high power consumption period, which shows the response to the power consumption load. However, the trend of the scene 2 is more obvious, and compared with the scene 1, the electricity purchasing quantity of the scene 2 is reduced by 82.5% in the peak period of electricity utilization, is reduced by 31.1% in the normal period, and is improved by 80.1% in the valley period. It is clear that the overall peak clipping and valley filling functions are better in scenario 2.
(2) Heating situation
In the electric heating gas system of this embodiment, after the heat that gas boiler and gas turbine produced passed through heat collection device, partly used hot water as the medium through the heat exchanger and got into the hot water pipe network in order to satisfy hot water load demand, another part got into steam system in order to satisfy steam load demand. The heat generated by the ground source heat pump enters the hot water system by taking hot water as a medium. The thermal output of each heating device in the two scenarios is shown in fig. 5 and 6.
Comparing fig. 5 and 6, it can be seen that the thermal output conditions of the gas-fired boiler in the two scenarios are not much different, while the overall output of the ground source heat pump is less and always operates in the valley period. The thermal output of the gas turbine is the same as the electric power change trend in each scene, and compared with scene 1, the thermal output of the gas turbine in scene 2 is increased by 21.2% in the electricity utilization peak period, increased by 3.6% in the normal time period and decreased by 19.9% in the valley time period.
In terms of heat output, the total heat output of scene 1 at each moment is balanced with the total heat load of the system, while scene 2 is higher than the total heat load of the system during high power utilization period and lower than the total heat load during low power utilization period. Under two kinds of scenes, steam heating power corresponds to the moment of load demand in the steam system, and the hot water supply condition in the hot water system is different due to the virtual energy storage characteristic of the hot water pipe network. In terms of the hot water system, the sources of the hot water supply are the heat exchanger and the ground source heat pump, the heat generated by the heat exchanger and the heat generated by the ground source heat pump enter the hot water pipe network through the primary pipe network heat exchange initial station, and the system hot water source output optimization results of the scene 1 and the scene 2 are respectively shown in fig. 7 and fig. 8.
Comparing fig. 7 and 8, the sum of the hot water source output of the system in scene 1 and scene 2 within one day has no obvious change, but the hot water source output of the system in scene 1 is synchronous with the hot water load moment, the trend of the result curve is completely consistent with that of the load curve, and the hot water source output of the system in scene 2 is asynchronous with the hot water load. In general, the output of the hot water source of the system is lower than the hot water load in the electricity consumption valley period in scene 2, and the heat energy stored in the pipe network at the earlier stage makes up the shortage of partial heat supply demand in the period; the hot water source output of the system is higher than the hot water load during the electricity consumption peak period, the high heat energy is stored in a hot water pipe network, the hot water source output and the hot water load of the system are not constantly balanced, and the cross-period transfer is realized.
The charging and discharging power of the virtual energy storage of the hot water pipe network in the scene 2 is shown in fig. 9. In fig. 9, a positive power indicates that the hot water pipe network is in the energy storage stage, and a negative power indicates that the hot water pipe network is in the energy discharge stage. In the electricity peak period, the system improves the output of the gas turbine, and the surplus heat is stored in a hot water pipe network in the form of hot water; in the time period of power consumption, the system reduces the output of the gas turbine, and the hot water pipe network emits the originally stored heat to compensate the shortage of heat supply of system equipment. The virtual energy storage of hot water pipe network is generally in the energy storage of power consumption peak hour, and the energy is put in the time of power consumption trough hour.
The temperature change in the primary pipe network in scenario 2 is shown in fig. 10. As can be seen from the analysis of fig. 10, the temperature in the pipe is closely related to the charge/discharge energy state. In the electricity consumption valley period, the output of a system hot water source is smaller than the heat load, the hot water pipe network is in the energy release stage, the temperature of supply and return water in the pipeline is integrally reduced, and the larger the energy release power is, the faster the temperature is reduced; in the peak period of electricity utilization, the output of a hot water source of the system is greater than the heat load, the hot water pipe network is in the energy storage stage, the temperature of supply and return water in the pipeline is integrally increased, and the higher the energy storage power is, the faster the temperature is increased.
(3) By gas conditions
Because the 'gas-heat' and 'gas-electricity' coupling equipment is considered in the electric heating gas system, the actual requirements of natural gas are greatly increased in two scenes, and the gas utilization condition of the system is analyzed by taking the scene 2 as an example. The actual gas usage of scenario 2 is shown in fig. 11.
As can be seen from fig. 11, after the electric heating gas system considering the virtual energy storage characteristic of the hot water pipe network is operated, the total gas utilization load is greatly increased because the gas turbine and the gas boiler are operated as two main coupling devices, and a significant peak-to-valley difference is formed compared with the original gas load trend. Compared with the electric load, the peak-to-valley difference characteristic of the optimized total gas load is similar to the peak-to-valley difference characteristic of the electric load.
Claims (10)
1. A control method of an electric heating system considering the quasi-dynamic characteristics of the load of a heating power network is characterized by comprising the following steps:
1) Performing virtual energy storage modeling on a heating power subsystem in the electric heating system;
2) Modeling a photovoltaic power generation system, a ground source heat pump, a gas boiler, a heat collection device and a heat exchanger of an electric heating system;
3) Establishing a room virtual energy storage control model considering PMV (thermal comfort) indexes of load users, and giving an optimization objective function and constraint conditions of the power consumption of the electric heating and air system after virtual energy storage in a heating subsystem;
wherein, the PMV index of the thermal comfort of the user is as follows:
setting the total heat load and the predicted total heat load in the optimization period to keep unchanged;
in the formula, Q load after，t The unit is kW for the optimized hot water load in each time period; q load before，t The hot water load of each time interval after optimization, namely the hot water load predicted in the day ahead, is in kW;
4) And solving the objective function.
2. The electric heating and air system control method considering the quasi-dynamic characteristics of the thermal power network load according to claim 1, wherein the virtual energy storage charging and discharging power of the hot water pipe network of the thermal power subsystem in the step 1) is as follows:
H＝Q t，HR -Q t，load
in the above formula, H is the virtual energy storage charging and discharging power of the hot water pipe network, the value is that the hot water pipe network is in the "energy storage" state when the value is positive, and the value is that the hot water pipe network is in the "energy discharge" state when the value is negative; q t，HR 、Q t，load Respectively the thermal power at the heat source and the heat exchange power at the thermal load in the t period.
3. The method for controlling an electric heating and air system considering the quasi-dynamic characteristics of the load of the thermal power grid as claimed in claim 1, wherein the photovoltaic cell output power of the photovoltaic power generation system in the step 2) is as follows:
in the above formula, P PV Is the output power of the photovoltaic cell; g t Is the intensity of light, wherein G STC The illumination intensity under standard test conditions; t is STC Is the photovoltaic cell temperature; p STC Is the maximum output power; k is a temperature correlation coefficient, and is-0.47 c，t Is the temperature of the photovoltaic cell;
the thermal power Q generated by the ground source heat pump in the step 2) GSHP The following were used:
Q GSHP ＝P GSHP ·COP GSHP
in the above formula, P GSHP Electric power, COP, consumed by ground source heat pumps GSHP The heat conversion efficiency of the ground source heat pump.
4. The method for controlling an electric heating and air system considering the load quasi-dynamic characteristics of a thermal power grid as claimed in claim 1, wherein the relationship between the natural gas consumption of the gas boiler and the heating power in the step 2) is as follows:
in the above formula, f GB Is the consumption of natural gas per unit time; q GB The heating power of the gas boiler; eta GB The heating efficiency is improved; h ng Indicating the heating value of natural gas.
5. The method as claimed in claim 1, wherein the thermal power entering the heat exchanger from the heat collecting device in step 2) is the thermal power
Q GT Indicating the thermal output of the gas turbine, i.e. having
The relationship between the inlet thermal power and the outlet thermal power of the heat exchanger is
In the above formula, Q hr Outlet heat power, epsilon, for the heat-collecting means hr Is the effective heat transfer rate of the heat exchanger.
6. An electric heating and air heating system control method considering the quasi-dynamic characteristics of the load of a thermal power grid as claimed in claim 1, wherein the minimum of the converted power of each system in the area is taken as an objective function in the step 3):
in the formula, P fuel Purchasing power for the gas network; p grid Purchasing power for a power grid; p steam grid Purchasing power for steam networks, P hw grid Power is purchased for the hot water network.
7. The method of claim 1, wherein the constraints of step 3) include power balance constraints, power plant output constraints, and hot water network constraints.
8. An electric heating and gas system control method taking into account thermal network charge quasi-dynamics as in claim 7 wherein said power balance constraints include electric power balance, thermal power balance and gas power balance.
9. The method of claim 7, wherein the power plant output constraints comprise:
P k，min ≤P k (t)≤P k，max
in the formula P k (t) means the kth device contribution at time t; p k，max And P k，min The maximum and minimum output of the kth device, respectively.
10. The method of claim 7, wherein the hot water network constraints comprise a quasi-dynamic constraint, a mixing constraint, a supply and return water temperature constraint, and a mass flow constraint.
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