CN110716429A - Control method and device of combined cooling heating and power system, computer and storage medium - Google Patents

Control method and device of combined cooling heating and power system, computer and storage medium Download PDF

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CN110716429A
CN110716429A CN201910746147.8A CN201910746147A CN110716429A CN 110716429 A CN110716429 A CN 110716429A CN 201910746147 A CN201910746147 A CN 201910746147A CN 110716429 A CN110716429 A CN 110716429A
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heating
combined cooling
power
power system
network
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陈泽雄
高军伟
周仕杰
缪新招
关俊乐
冯欣桦
吕泉成
黄雅莉
邓明
龚小东
肖英豪
薛保国
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Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
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Guangzhou Power Supply Bureau Co Ltd
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    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
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Abstract

The invention relates to a control method, a control device, a computer and a storage medium of a combined cooling heating and power system, wherein the method comprises the steps of obtaining a target function of the minimum running cost of the combined cooling heating and power system in a preset time according to the cost data of the combined cooling heating and power system in the preset time; acquiring the operating characteristic constraint condition of a cold-hot electric power supply system; acquiring upper and lower limit constraints of variables of a cold, hot and electric power supply system as design variables; constructing a planning model of the combined cooling, heating and power system according to the objective function, the operating characteristic constraint condition and the design variable; calculating an optimized scheduling result of the combined cooling, heating and power system according to the planning model; and controlling the work distribution of the combined cooling heating and power system according to the optimized scheduling result. By implementing the embodiment of the invention, the operation cost of the combined cooling heating and power system can be reduced.

Description

Control method and device of combined cooling heating and power system, computer and storage medium
Technical Field
The invention relates to the technical field of energy system application, in particular to a control method and device of a combined cooling heating and power system, a computer and a storage medium.
Background
The combined cooling heating and power system is a total energy system which is established on the basis of an energy cascade utilization concept and integrates the processes of refrigeration, heating and power generation. A typical combined cooling, heating and power system generally consists of three parts, namely a power generation device, a refrigeration system and a heating system. The cost is generated in the actual application process of the cold-hot electricity supply system, and the existing cold-hot electricity supply system generates higher operation cost due to unreasonable work distribution.
Disclosure of Invention
Therefore, it is necessary to provide a method and an apparatus for controlling a combined cooling heating and power system, a computer, and a storage medium, for solving the problem that the existing combined cooling heating and power system has high operating costs due to unreasonable work allocation.
In one embodiment, a method for controlling a combined cooling, heating and power system is provided, the method including: according to the cost data of the combined cooling heating and power system in a preset time, obtaining a target function of the minimum running cost of the combined cooling and heating and power system in the preset time; acquiring operating characteristic constraint conditions of the cold-hot electric power supply system; acquiring upper and lower limit constraints of variables of the cold, hot and electric power supply system as design variables; constructing a planning model of the combined cooling heating and power system according to the objective function, the operating characteristic constraint condition and the design variable; calculating an optimized scheduling result of the combined cooling heating and power system according to the planning model; and controlling the work allocation of the combined cooling heating and power system according to the optimized scheduling result.
In one embodiment, the obtaining an objective function of the minimum operating cost of the combined cooling heating and power system in a preset time according to the cost data of the combined cooling heating and power system in the preset time includes: acquiring natural gas purchase cost data, power distribution network purchase cost data and energy storage device operation cost data of the combined cooling, heating and power system within preset time; and obtaining a target function of the minimum operating cost of the combined cooling heating and power system within the preset time according to the natural gas purchase cost data, the power distribution network purchase cost data and the energy storage device operating cost data.
In one embodiment, the natural gas combined cooling heating power system comprises a cooling and heating network, a gas supply network, a power supply network, an energy station and an energy storage device; the acquiring of the operating characteristic constraint condition of the cold-hot electric power supply system comprises the following steps: and acquiring the operating characteristic constraint conditions of the cooling and heating network, the gas supply network, the power supply network, the energy station and the energy storage device.
In one embodiment, the obtaining upper and lower limit constraints of the variables of the cold and hot electric power supply system as design variables includes: and acquiring the temperature of the cooling and heating network, the flow of the cooling and heating network, the pressure of the gas supply network, the power of the power supply network, the equipment variable of the energy station and the upper and lower limit constraints of a storage battery of the energy storage device as design variables.
In one embodiment, the calculating an optimized scheduling result of the combined cooling heating and power system according to the planning model includes: and calculating an optimal scheduling result of power supply and power storage, an optimal scheduling result of cold supply and cold storage and an optimal scheduling result of heat supply and heat storage of the combined cooling, heating and power supply system according to the planning model.
In one embodiment, the controlling the work allocation of the combined cooling heating and power system according to the optimized scheduling result includes: and controlling the active power output distribution, the cooling supply distribution and the heating supply distribution of the combined cooling heating and power system according to the optimized scheduling result.
In one embodiment, after obtaining the operating characteristic constraints of the cold-hot electric power supply system, the method further includes: carrying out linearization processing on the operating characteristic constraint condition to obtain a linear constraint condition; the obtaining of the planning model of the combined cooling heating and power system according to the objective function, the operating characteristic constraint condition and the design variable includes: and constructing a planning model of the combined cooling heating and power system according to the target function, the linear constraint condition and the design variable.
In one embodiment, a control device for a combined cooling heating and power system is provided, the device comprising: the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring a target function of the minimum running cost of the combined cooling heating and power system in a preset time according to the cost data of the combined cooling heating and power system in the preset time; the second acquisition module is used for acquiring the operating characteristic constraint condition of the cold-hot electric power supply system; the third acquisition module is used for acquiring upper and lower limit constraints of the cold, heat and electricity power supply system as design variables; the planning model construction module is used for constructing a planning model of the combined cooling heating and power system according to the objective function, the operating characteristic constraint condition and the design variable; the calculation module is used for calculating an optimized scheduling result of the combined cooling heating and power system according to the planning model; and the control module is used for controlling the work allocation of the combined cooling heating and power system according to the optimized scheduling result.
In an embodiment, the first obtaining module is configured to obtain natural gas purchase cost data, power distribution network purchase cost data, and energy storage device operation cost data of the combined cooling heating and power system in a preset time, and obtain an objective function of a minimum operation cost of the combined cooling heating and power system in the preset time according to the natural gas purchase cost data, the power distribution network purchase cost data, and the energy storage device operation cost data.
In one embodiment, the natural gas combined cooling heating power system comprises a cooling and heating network, a gas supply network, a power supply network, an energy station and an energy storage device; the second obtaining module is used for obtaining the operating characteristic constraint conditions of the cooling and heating network, the gas supply network, the power supply network, the energy station and the energy storage device.
In an embodiment, the third obtaining module is configured to obtain, as design variables, a temperature of the cooling and heating network, a flow rate of the cooling and heating network, a pressure of the gas supply network, a power of the power supply network, equipment variables of the energy station, and upper and lower limit constraints of a storage battery of the energy storage device.
In an embodiment, the calculation module is configured to calculate an optimal scheduling result of power supply and power storage, an optimal scheduling result of cooling and cooling storage, and an optimal scheduling result of heating and heat storage of the combined cooling and heating and power supply system according to the planning model.
In an embodiment, the control module is configured to control active power distribution, cooling capacity distribution, and heating capacity distribution of the combined cooling, heating, and power system according to the optimized scheduling result.
In an embodiment, the combined cooling, heating and power system further includes a linearization processing module, where the linearization processing module is configured to linearize the operating characteristic constraint condition to obtain a linear constraint condition; and the planning model construction module is used for constructing a planning model of the combined cooling heating and power system according to the objective function, the linear constraint condition and the design variable.
In one embodiment, a computer device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the steps of the method of any embodiment of the present application are implemented.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any of the embodiments of the application.
According to the control method, the control device, the control computer and the storage medium of the combined cooling heating and power system, the objective function of the minimum running cost is obtained according to the cost data of the combined cooling heating and power system in the preset time, the running characteristic constraint condition of the combined cooling and power system is obtained, the upper limit constraint and the lower limit constraint of the combined cooling and power system are obtained and serve as design variables, the planning model of the combined cooling and power system can be constructed according to the objective function, the running characteristic constraint condition and the design variables, the optimal scheduling result of the combined cooling and power system is calculated according to the planning model, and the work distribution of the combined cooling and power system is controlled according to the optimal scheduling result, so that the running cost of the combined cooling and power system is reduced.
Drawings
Fig. 1 is a schematic flowchart of a control method of a combined cooling, heating and power system according to an embodiment;
FIG. 2 is a piecewise linear graph of the relationship between the consumed power and the available thermal power of the circulating water pump according to an embodiment;
fig. 3 is a schematic structural diagram of a combined cooling, heating and power system according to an embodiment;
FIG. 4 is a diagram illustrating a total load of cold, heat, and electricity in the combined cooling, heating, and electricity system according to an embodiment;
FIG. 5 is a graph illustrating the total natural gas load in the cogeneration system according to one embodiment;
FIG. 6 is a predicted photovoltaic power plant output curve in the combined cooling, heating and power system according to an embodiment;
fig. 7 is a schematic structural diagram of a control device of the combined cooling, heating and power system according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature "under," "below," and "beneath" a second feature may be directly or obliquely under the first feature or may simply mean that the first feature is at a lesser elevation than the second feature. In the present invention, "upper" and "lower" merely indicate relative positions, and do not indicate absolute positions.
It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "upper," "lower," "left," "right," and the like as used herein are for illustrative purposes only and do not denote a unique embodiment.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In one embodiment, the present application discloses a method for controlling a combined cooling, heating and power system, the method including: according to the cost data of the combined cooling heating and power system in a preset time, obtaining a target function of the minimum running cost of the combined cooling and heating and power system in the preset time; acquiring operating characteristic constraint conditions of the cold-hot electric power supply system; acquiring upper and lower limit constraints of variables of the cold, hot and electric power supply system as design variables; constructing a planning model of the combined cooling heating and power system according to the objective function, the operating characteristic constraint condition and the design variable; calculating an optimized scheduling result of the combined cooling heating and power system according to the planning model; and controlling the work allocation of the combined cooling heating and power system according to the optimized scheduling result.
According to the control method of the combined cooling heating and power system, the objective function of the minimum running cost is obtained according to the cost data of the combined cooling, heating and power system in the preset time, the running characteristic constraint condition of the combined cooling, heating and power system is obtained, the upper limit constraint and the lower limit constraint of the combined cooling, heating and power system are obtained and serve as design variables, the planning model of the combined cooling, heating and power system can be constructed according to the objective function, the running characteristic constraint condition and the design variables, the optimal scheduling result of the combined cooling, heating and power system is calculated according to the planning model, and the work distribution of the combined cooling, heating and power system is controlled according to the optimal scheduling result, so that the running cost of the combined cooling, heating and power system is reduced.
In one embodiment, referring to fig. 1, a method for controlling a combined cooling, heating and power system includes:
step 110: according to the cost data of the combined cooling heating and power system in the preset time, obtaining the objective function of the minimum operating cost of the combined cooling heating and power system in the preset time.
Specifically, the combined cooling, heating and power system is a natural gas combined cooling, heating and power system. Specifically, the combined cooling heating and power system is a park microgrid system. Specifically, the natural gas combined cooling heating system comprises a cooling and heating network, an air supply network, a power supply network, an energy station and an energy storage device. Specifically, the cooling and heating network comprises a cooling network and a heating network. Specifically, the cost data comprises natural gas purchase cost data, power distribution network purchase cost data and energy storage device operation cost data. Specifically, the preset time is one day.
In an embodiment, the obtaining an objective function of the minimum operating cost of the combined cooling heating and power system in a preset time according to the cost data of the combined cooling heating and power system in the preset time includes: acquiring natural gas purchase cost data, power distribution network purchase cost data and energy storage device operation cost data of the combined cooling heating and power system within preset time; and obtaining a target function of the minimum operating cost of the combined cooling heating and power system in the preset time according to the natural gas purchase cost data, the power distribution network purchase cost data and the energy storage device operating cost data.
In one embodiment, the objective function is shown in equation (1) below:
Figure BDA0002165632020000051
in the formula, T is the total number of time periods within a preset time, for example, the preset time is one day, and the day is divided into 96 time periods, then T is 96, and each time period Δ T is 15 min; c. CgasThe unit price of the gas purchased by the natural gas,
Figure BDA0002165632020000052
injecting natural gas flow of a combined cooling heating and power system into the natural gas station; c. CgridIn order to purchase the electricity to the distribution network at a unit price,
Figure BDA0002165632020000053
injecting active power of a combined cooling heating and power system into the power distribution network;
Figure BDA0002165632020000054
for operating the energy storage device, cc、chAnd ceRespectively the running circulation loss cost of the cold storage tank, the heat storage tank and the storage battery,
Figure BDA0002165632020000055
is the cold accumulation/discharge power of the cold storage tank,
Figure BDA0002165632020000056
for the heat storage/release power of the heat storage tank,
Figure BDA0002165632020000057
is the charge/discharge power of the storage battery. The superscript t denotes the corresponding variable for the t period.
Step 120: and acquiring the operating characteristic constraint condition of the cold-hot electric power supply system.
The natural gas combined cooling heating system comprises a cooling and heating network, a gas supply network, a power supply network, an energy station and an energy storage device; the acquiring of the operating characteristic constraint condition of the cold-hot electric power supply system comprises the following steps: and acquiring the operating characteristic constraint conditions of the cooling and heating network, the gas supply network, the power supply network, the energy station and the energy storage device.
It should be noted that the problem of optimizing and scheduling the energy of the micro-grid in the natural gas combined cooling heating and power supply park is solved by adopting a continuous nonlinear programming algorithm, and in the obtained optimized scheduling scheme, the refrigerating capacity of the absorption refrigerator and the heating capacity of the heat exchange unit may not be in actual discrete gears, so that the scheme is difficult to implement practically. If the near integration is implemented, unbalanced cold/heat supply and out-of-limit of safety constraint of certain pipe networks can be caused; and moreover, the mixed integer nonlinear programming algorithm is adopted to solve the natural gas combined cooling heating and power supply park microgrid energy optimization scheduling problem, the mixed integer nonlinear programming problem is one of the most difficult problems to solve in the programming field and belongs to the NP-difficult problem, if a common mixed integer nonlinear programming solver such as an SBB is adopted to directly solve, the calculation speed is very low, and the optimal solution of the optimization problem can not be obtained frequently.
In order to obtain an optimal solution to the optimization problem, in an embodiment, after obtaining the operating characteristic constraints of the cold-hot power supply system, the method further includes: acquiring operating characteristic constraint conditions of the cold-hot electric power supply system; and carrying out linearization processing on the operating characteristic constraint condition to obtain a linear constraint condition.
In one embodiment, the operating characteristic constraints of the cooling and heating network include: the system comprises a load power model of a fan coil/heat exchanger of a cold/heat load node, a temperature drop and rise model of a water supply pipeline, a quality mixing model of pipeline fluid at the node, a temperature mixing model of the pipeline fluid at the node and a power consumption characteristic model of a circulating water pump in a network. The load power model of the fan coil/heat exchanger of the cooling/heating load node comprises a load power model of the fan coil of the cooling load node and a load power model of the heat exchanger of the heating load node, and the load power model of the fan coil of the cooling load node and the load power model of the heat exchanger of the heating load node have the same model and are described together in the application.
In one embodiment, the load power model of the fan coil/heat exchanger of the cooling/heating load node, the temperature drop and rise model of the water supply pipeline, the quality mixed model of the pipeline fluid at the node, the temperature mixed model of the pipeline fluid at the node, and the power consumption characteristic model of the circulating water pump in the network are sequentially shown in the following formula (2):
Figure BDA0002165632020000061
in the formula (I), the compound is shown in the specification,
Figure BDA0002165632020000062
load capacity of fan coil/heat exchanger, cwIs the specific heat capacity of the water,
Figure BDA0002165632020000063
is the cold/hot water flow through the fan coil/heat exchanger, sjThe load characteristic is used for representing the load property of a node j, wherein the load of the node j is +1 when the load of the node j is a cold load fan coil, and is-1 when the load of the node j is a heat load heat exchanger;the temperature of the inlet water/return water of the fan coil or the heat exchanger;
Figure BDA0002165632020000065
the temperature of the outlet water of the pipeline is shown,
Figure BDA0002165632020000066
is the inlet water temperature of the pipeline, Ta is the ambient temperature, lambda is the heat transfer coefficient of the pipeline per unit length, L is the length of the pipeline,
Figure BDA00021656320200000611
is the pipe flow;
Figure BDA0002165632020000067
for the flow of fluid into/out of the node,and
Figure BDA0002165632020000069
the temperature of each fluid flowing into the node before mixing and the temperature of the fluid flowing out of the node after mixing are respectively obtained;g is the gravitational acceleration, H is the lift of the water pump, rhowη p is the water density and the efficiency of the water pump.
As the temperature rise/temperature drop of the return pipeline of the cooling/heating network is very small, the return temperature is close to 12 ℃/50 ℃. Therefore, it can be assumed that the return water temperatures of the cooling/heating network are all fixed values as a first step of simplification. Taking a heating network as an example, in an embodiment, the process of linearizing the constraint condition of the operation characteristic of the cooling and heating network includes linearizing a load power model of a fan coil/heat exchanger of the cooling/heating load node and a power consumption characteristic model of a circulating water pump in the network.
Wherein the fan coil/heat exchanger of the cooling/heating load node is connectedThe process of the load power model linearization process is specifically as follows: defining the difference between the thermal power contained in the heat medium in the water supply pipeline and the thermal power contained in the corresponding heat medium in the water return pipeline as the available thermal power of the heat medium, the available thermal power phi contained in the heat medium flowing into the node i through the pipeline ijijIs as follows;
φij＝cwmij(Twi-Tr) Formula (3)
In the formula, mijThe flow between the pipes ij.
Obtained by a temperature-reducing model
Figure BDA0002165632020000071
Multiplication of both sides of the equation by cwmijAnd subtract cwmijTrTo obtain the available thermal power phi contained in the heat medium flowing out of the node j through the pipeline ijjiComprises the following steps:
the loss of usable thermal power in the recording network is delta phiijI.e. delta phiij＝φij-φjiObtaining:
Figure BDA0002165632020000073
wherein, the right side of the ≈ sign is Δ φijA first order taylor series expansion at L-0. T isswIs the water supply temperature of the heat supply side of the energy station.
Thus, the linearized expression of the heat supply network energy flow model is as follows:
Figure BDA0002165632020000074
the model decouples the available heat power of the pipe network and the heat medium flow and temperature in the pipeline, only comprises available heat power variables, and constraint conditions are linear, so that the model of the heat network is converted into a linear model. When the optimal solution of the heat supply network is obtained, the flow rate and the temperature of the heat medium are obtained by solving the following formula (7).
Figure BDA0002165632020000075
Similarly, the above method can be referred to for the linearization process of the cooling network.
The process of the load power model linearization processing of the fan coil/heat exchanger of the cooling/heating load node is as follows: the power consumption of the circulating water pump can be controlled by the available heat power phi contained in the heat medium of the node jiExpressed as the following equation (8):
by using piecewise linear approximation: dividing the function of each time interval into N segments to perform linear approximation, and introducing a discrete variable into each segment
Figure BDA0002165632020000082
And a continuous variableAs shown in fig. 2, the relationship between the consumed power and the available thermal power of the circulating water pump can be converted to the following equation (9):
Figure BDA0002165632020000084
in the formula: gamma rayiAnd ωiIs the slope and intercept of the ith segment, ziAnd zi+1For the head and tail end abscissas of each segment,
Figure BDA0002165632020000085
and
Figure BDA0002165632020000086
head and tail end ordinates corresponding to each segment.
In one embodiment, the operating characteristic constraints of the cooling and heating network include: the flow equation of the natural gas pipeline under the steady state condition and the power consumption characteristic model of the electrically driven compressor in the network.
In one embodiment, the flow equation of the natural gas pipeline under the steady state condition and the power consumption characteristic model of the electrically driven compressor in the network are sequentially shown in the following formula (10):
Figure BDA0002165632020000087
in the formula (I), the compound is shown in the specification,
Figure BDA0002165632020000088
for flow through the pipe between nodes i and j, KijFor the pipe constants, reference may be made to the documents [3-4 ]]And (4) calculating.
Figure BDA0002165632020000089
And
Figure BDA00021656320200000810
the pressures at nodes i and j are,
Figure BDA00021656320200000811
for characterizing the flow direction of natural gas when
Figure BDA00021656320200000812
Taking +1 when the current value is positive, or taking-1 when the current value is negative; HPtThe electrical power consumed for electrically driving the compressor,
Figure BDA00021656320200000813
is the inlet/outlet pressure of the compressor,is the inlet flow of the compressor, BkConstants related to compressor k efficiency, temperature, natural gas heating value; zkIs a constant related to the compressor k compression factor and the natural gas heating value.
In one embodiment, linearizing the operating characteristic constraints of the cooling and heating network comprises linearizing the flow equations of the natural gas pipeline at the steady state conditions and the power consumption characteristic models of the electrically driven compressors in the network.
The process of the flow equation linearization processing of the natural gas pipeline under the steady state condition is specifically as follows: first, auxiliary variables are defined
Figure BDA0002165632020000091
And
Figure BDA0002165632020000092
which is a binary variable. When in use
Figure BDA0002165632020000093
If so, the actual direction of the energy flow of the air network branch ij is from i to j; when in useThe actual direction of the energy flow of the time-air network branch ij is from j to i or no energy flow of the branch; when in use
Figure BDA0002165632020000095
Then, it means that the actual direction of energy flow of the air network branch ij is from j to i. Obviously, the gas flow direction of the actual pipeline is unique, and the following conditions are met:
Figure BDA0002165632020000096
the direction of gas flow
Figure BDA0002165632020000097
The equivalent is expressed as:
Figure BDA0002165632020000098
now, the two sides of the natural gas pipeline flow equation are squared to makeThen there are:
Figure BDA00021656320200000910
when in use
Figure BDA00021656320200000911
Figure BDA00021656320200000912
When it is, then
Figure BDA00021656320200000913
Comprises the following steps:
Figure BDA00021656320200000914
when in use
Figure BDA00021656320200000915
When the temperature of the water is higher than the set temperature,
Figure BDA00021656320200000917
comprises the following steps:
Figure BDA00021656320200000918
due to the fact that
Figure BDA00021656320200000919
Will be used in the model, and adopts the piecewise linearization method to pairProcessing, i.e. dividing the function of each time interval into N segments to carry out linear approximation, and introducing N discrete variables
Figure BDA00021656320200000921
And
Figure BDA00021656320200000922
the following were used:
in the formula: epsiloniAnd τiThe slope and intercept of the ith segment; y isiAnd yi+1For the head and tail end abscissas of each segment;
Figure BDA00021656320200000924
and
Figure BDA00021656320200000925
head and tail end ordinates corresponding to each segment.
The process of linearization processing of the power consumption characteristic model of the electrically driven compressor in the network is specifically as follows:
since it has already proceeded
Figure BDA00021656320200000926
Is equivalent to
Figure BDA00021656320200000927
Then
Figure BDA0002165632020000101
Will ktDiscretized, then HPtExpressed as continuous variables
Figure BDA0002165632020000102
And a discrete variable ktIntroducing binary variables
Figure BDA0002165632020000103
Indicating whether the electrically-driven compressor is operating in the ith pressure-regulating range, intermediate variable
Figure BDA0002165632020000104
And
Figure BDA0002165632020000105
the large M method is applied to convert the mixed integer linear constraint into a mixed integer linear constraint, and the method comprises the following steps:
Figure BDA0002165632020000106
in the formula (I), the compound is shown in the specification,is the total number of the pressure regulating gears.
In one embodiment, the constraints on the operational characteristics of the power supply network include power consumed by each electrical load node in the power supply network and power consumed by the cooling/heating network load-side circulating water pump and the air supply network load-side compressor and active and reactive balance equations of each electrical load node, wherein the power consumed by each electrical load node in the power supply network and the power consumed by the cooling/heating network load-side circulating water pump and the air supply network load-side compressor are shown in equation (16), and the active and reactive balance equations of each electrical load node are shown in equation (17).
In the formula (I), the compound is shown in the specification,
Figure BDA0002165632020000109
for the electric power consumed by the load node j,
Figure BDA00021656320200001010
the active/reactive power of the load node i when the circulating water pump and the compressor are not taken into account,and
Figure BDA00021656320200001012
the electric power consumed by the circulating water pumps on the cold load side and the hot load side respectively,
Figure BDA00021656320200001013
the power factor angle of the cold/hot load side circulating water pump,is the power factor angle of the compressor in the air supply grid.
Figure BDA00021656320200001015
In the formula, when i is injected into a microgrid node of a park for a power distribution network, the node is
Figure BDA00021656320200001016
Figure BDA00021656320200001017
Injecting reactive power of a microgrid in a park area into a power distribution network; when i is a storage battery node and the reactive output of the storage battery is assumed to be zero, discharging
Figure BDA00021656320200001018
When charging
Figure BDA0002165632020000111
Q sj0; when i is a photovoltaic power station node, assuming that the reactive output of the photovoltaic power station is zero, then
Figure BDA0002165632020000112
Figure BDA0002165632020000114
The photovoltaic power station active power output is achieved. Vi tIs the voltage amplitude of node i, Gij/BijFor the mutual conductance/susceptance between nodes i and j,
Figure BDA0002165632020000115
is the voltage phase angle difference between nodes i and j.
In one embodiment, linearizing the operating characteristic constraints of the power supply network comprises linearizing the active and reactive balance equations for the electrical load nodes.
The process of the active and reactive balance equation linearization processing of the electric load node is specifically as follows: for most scenarios in practical power systems, the bus voltage magnitude is about 1p.u., the absolute value of the phase difference between the lines rarely exceeds 30 °, mostly within 10 °. Thus cos θ can be assumedij≈1，sinθij≈θij＝θi-θj，V i1. Based on the above assumptions, the injected active and reactive power of the node can be transformed into an approximately linear equation as shown in (18):
in formula (II) to'ijRemoving the ground susceptance b for diagonal elementsiiThe latter nodes accommodate the matrix elements.
In conclusion, the active and reactive equilibrium equations are transformed into linear models. The equation (17) for the active and reactive balance of the electrical load node can be translated into a linear constraint as follows:
Figure BDA0002165632020000117
in one embodiment, the constraint conditions of the operating characteristics of the energy station include an energy conversion model of a natural gas combined cooling heating and power unit, an energy conversion model of an absorption refrigerator, an electric refrigerator, a heat exchange unit, an electric boiler and a gas boiler, and an equilibrium equation of various energy supplies of cooling, heating, electricity and gas in the energy station.
In one embodiment, the relationship between the efficiency of the gas-turbine generator set and the total active power output thereof adopts a cubic model, as shown in formula (20):
in the formula (I), the compound is shown in the specification,
Figure BDA0002165632020000119
is the efficiency of the gas generator set, a, b, c and d are the efficiency coefficients of the gas generator set,
Figure BDA00021656320200001110
is the total active output of the gas generator set
Figure BDA00021656320200001111
Ratio to the rated active output.
The thermal power of the natural gas consumed by the gas generator set can be calculated according to the power generation efficiency and the power generation powerAs shown in equation (21); the deducted power is the waste heat discharge power, and the waste heat power input by the hot water type and smoke type absorption refrigerators can be calculated
Figure BDA0002165632020000121
And
Figure BDA0002165632020000122
as shown in equations (22) and (23):
Figure BDA0002165632020000123
Figure BDA0002165632020000124
Figure BDA0002165632020000125
in the formula, alphawaterAnd alphasmokeIs the residual heat factor of the cylinder sleeve water and the flue gas.
In the aspect of a cooling unit, a hot water type absorption refrigerator and a flue gas type absorption refrigerator respectively utilize high-temperature cylinder sleeve water and exhaust smoke for refrigeration, an electric refrigerator utilizes electric energy for refrigeration, and the refrigeration power is as follows:
Figure BDA0002165632020000126
in the formula (I), the compound is shown in the specification,is the refrigeration power, COP, of the hot water type/flue gas type/electric refrigerator1、COP2And COP3Is a coefficient of thermal power, eta, corresponding to a refrigeratorhrs1/ηhrs2For the efficiency of the hot water/flue gas recovery,the electrical power consumed by the electrical refrigerator.
In the aspect of the heat supply unit, the heat exchange unit supplies heat through the low-temperature cylinder sleeve water that comes out of the recovery hot water type absorption refrigerator, and electric boiler and gas boiler then utilize electric energy and natural gas to make hot water, and its heating power is:
Figure BDA0002165632020000129
in the formula (I), the compound is shown in the specification,
Figure BDA00021656320200001210
heating power, eta, of heat exchanger unit/electric boiler/gas boilerhrs3Is the efficiency of the heat exchanger, ηH/ηgIn order to achieve the efficiency of the electric boiler/gas boiler,
Figure BDA00021656320200001211
the electric power consumed by supplying heat to the electric boiler,
Figure BDA00021656320200001212
natural gas flow rate, q, for heat supply to gas boilersgasIs the heat value of natural gas.
Cooling/heat balance equation inside the energy station:
in the formula (I), the compound is shown in the specification,
Figure BDA00021656320200001214
for the total cold/heat load demand,
Figure BDA00021656320200001215
the number of the absorption refrigerators is the number of the absorption refrigerators,the number of heat exchangers put into heat supply.
An active and reactive power balance equation of an internal power supply side of the energy station, namely a power balance equation of a generator-side bus of a gas turbine set:
Figure BDA00021656320200001217
in the formula (I), the compound is shown in the specification,
Figure BDA0002165632020000132
and
Figure BDA0002165632020000133
respectively the electric power consumed by the circulating water pump at the cold/heat source side of the energy station and the power factor angle.
Figure BDA0002165632020000134
And
Figure BDA0002165632020000135
the electric power consumed by the compressor for regulating the input air pressure of the gas engine set in the energy station and the power factor angle are respectively. Pc1、Pc2And PwRespectively single flue gas type and heatThe electric power consumed by the water type refrigerator and the heat exchanger unit,the reactive output of the gas generator set is obtained.
On the side of an air supply network in the energy station, the natural gas amount consumed by the gas generator set and the gas boiler jointly forms the gas load L of a node of the natural gas network at the energy stationt。
Figure BDA0002165632020000137
And (3) climbing restraint of power generation output of the gas turbine unit:
Figure BDA0002165632020000138
in the formula, rui/rdiThe active power output which can be increased/decreased at most in unit time is obtained.
The relation equation of the thermal power of the natural gas of the gas generator set and the total active power output of the gas generator set is shown as a formula (20) -a formula (21), and intermediate variables are used
Figure BDA0002165632020000139
Elimination to obtain
Figure BDA00021656320200001310
In one embodiment, the process of linearizing the operating characteristic constraints of the energy plant includes linearizing an equation relating the thermal power of the natural gas of the gas-turbine generator set to the total active power output of the gas-turbine generator set.
The process of the relation equation of the thermal power of the natural gas of the gas generator set and the total active power output of the gas generator set is specifically as follows: dividing the function of each time interval into N segments to perform linear approximation, and introducing a binary variable into each segment
Figure BDA00021656320200001311
And a continuous variable
Figure BDA00021656320200001312
The relationship between the thermal power of the natural gas of the gas generator set and the total active power output of the gas generator set can be converted into the formula (31).
In the formula: alpha is alphaiAnd betaiThe slope and intercept of the ith segment; l isiAnd Li+1For the head and tail end abscissas of each segment;
Figure BDA00021656320200001314
and
Figure BDA00021656320200001315
head and tail end ordinates corresponding to each segment.
The active and reactive power balance equation of the power supply side in the energy station is linearized according to the processing method of the power supply network as follows:
Figure BDA0002165632020000141
the cooling/heat balance equation inside the energy station involves the product of a discrete variable and a continuous variable, e.g. the total cooling capacity of a flue gas type absorption chiller
Figure BDA0002165632020000142
And (3) converting the constraint condition into a mixed integer linear constraint by using a large M method, wherein the constraint condition is processed as follows: order to
Figure BDA0002165632020000143
Has an upper limit ofLower limit of 0, introduce
Figure BDA0002165632020000145
A binary variable
Figure BDA0002165632020000146
Order to
Figure BDA0002165632020000147
Then there are:
similarly, the total cooling capacity of the flue gas type absorption refrigerator is processed as shown in formula (34), so that
Figure BDA0002165632020000149
Has an upper limit ofLower limit of 0, introduce
Figure BDA00021656320200001411
A binary variable
Figure BDA00021656320200001412
Order to
Figure BDA00021656320200001413
Then there are:
Figure BDA00021656320200001414
the total heat supply of the heat exchanger unit is shown as a formula (35) to ensure thatHas an upper limit of
Figure BDA00021656320200001416
Lower limit of 0, introduce
Figure BDA00021656320200001417
A binary system variable
Figure BDA00021656320200001418
Order toThen there are:
Figure BDA00021656320200001420
in addition, the cooling/heat balance equation inside the energy station can be represented linearly as:
in one embodiment, the operating characteristic constraints of the energy storage device include: the system comprises an operating characteristic model of the cold storage tank, an operating characteristic model of the heat storage tank and an operating characteristic model of the storage battery.
In one embodiment, the operational characteristics of the heat-storage tank are modeled as follows:
Figure BDA0002165632020000151
in the formula (I), the compound is shown in the specification,
Figure BDA0002165632020000152
for the cold stored in the cold storage tanks, deltacIs the loss rate of cold amount of the cold storage tank etac.ch/ηc.disFor cold accumulation/discharge efficiency of the cold storage tank, Ec.min/Ec.maxIs the minimum/maximum cold storage capacity of the cold storage tank,
Figure BDA0002165632020000153
upper limit of cold accumulation/discharge power of the cold storage tank, Ec0/EcTThe cold storage capacity of the cold storage tank at the beginning/ending time of the dispatching cycle.
The operating characteristic model of the heat storage tank is as follows:
in the formula (I), the compound is shown in the specification,
Figure BDA0002165632020000155
heat stored for the heat storage tank, deltahIs the heat loss rate, eta, of the heat storage tankc.ch/ηc.disThe heat storage/release efficiency of the heat storage tank. Eh.min/Eh.maxIs the minimum/maximum heat storage capacity of the thermal storage tank,
Figure BDA0002165632020000156
upper limit of heat storage/release power of heat storage tank, Eh0/EhTThe heat storage quantity of the heat storage tank in the starting/ending period of the scheduling cycle.
The operation characteristic model of the storage battery is as follows:
in the formula (I), the compound is shown in the specification,
Figure BDA0002165632020000158
the quantity of electricity stored for the accumulator, deltaeIs the rate of loss of electric power of the battery, etae.ch/ηe.disThe charge/discharge efficiency of the storage battery. Ee.min/Ee.maxIs the minimum/maximum charge storage capacity of the battery,
Figure BDA0002165632020000159
upper limit of charging/discharging power for accumulator, Ee0/EeTThe storage capacity of the storage battery at the starting/ending time of the scheduling cycle.
In an embodiment, the process of linearizing the constraint condition of the operating characteristic of the energy storage device is specifically as follows: such as battery charge and discharge power constraints
Figure BDA0002165632020000161
Introducing charge-discharge state indicationCarry variable
Figure BDA0002165632020000162
Andwhen in use
Figure BDA0002165632020000164
Figure BDA0002165632020000165
The storage battery is in a charging state; when in useThe storage battery is in a discharging state; when in useThe battery is in neither a charged nor a discharged state. The processed battery operation characteristic model is shown as an equation (40). Similarly, a binary variable for indicating the heat storage and release state is introduced into the heat storage tank operation characteristic model
Figure BDA0002165632020000168
And
Figure BDA0002165632020000169
after processing as in equation 41); binary variable for introducing heat storage and release state indication into cold storage tank operation characteristic model
Figure BDA00021656320200001610
And
Figure BDA00021656320200001611
after processing as in equation (42).
Figure BDA00021656320200001612
Figure BDA00021656320200001613
Figure BDA00021656320200001614
Step 130: and acquiring upper and lower limit constraints of variables of the cold, hot and electric power supply system as design variables.
In one embodiment, the obtaining upper and lower limit constraints of the variables of the cold and hot electric power supply system as design variables includes: and acquiring the temperature of the cooling and heating network, the flow of the cooling and heating network, the pressure of the gas supply network, the power of the power supply network, the equipment variable of the energy station and the upper and lower limit constraints of a storage battery of the energy storage device as design variables.
Specifically, the upper and lower limit constraints of the variable of the cold and hot power supply system comprise: the upper and lower limits of the voltage and power of each node of the power supply network, the temperature of each node of the heat supply/cooling network and the flow of each pipeline, the upper and lower limits of the pressure of each pipeline of the natural gas network, and the upper and lower limits of the variable of each equipment of the energy station (including the active and reactive power of the gas generator, the input power of the absorption refrigerator and the heat exchanger unit, the input power of the electric refrigerator, the electric boiler and the gas boiler, the energy storage/discharge power of the energy storage equipment, etc.), namely:
xmin≤x≤xmaxformula (43)
Step 140: and constructing a planning model of the combined cooling heating and power system according to the objective function, the operating characteristic constraint condition and the design variable.
In one embodiment, after obtaining the operating characteristic constraints of the cold-hot electric power supply system, the method further includes: carrying out linearization processing on the operating characteristic constraint condition to obtain a linear constraint condition; the obtaining of the planning model of the combined cooling heating and power system according to the objective function, the operation characteristic constraint condition and the design variable includes: and constructing a planning model of the combined cooling heating and power system according to the target function, the linear constraint condition and the design variable.
Specifically, the planning model of the combined cooling heating and power system is as shown in formula (44):
Figure BDA0002165632020000171
wherein, the formula (44) includes the formula corresponding to the corresponding number.
Step 150: and calculating an optimized scheduling result of the combined cooling heating and power system according to the planning model.
In one embodiment, the calculating an optimized scheduling result of the combined cooling heating and power system according to the planning model includes: and calculating an optimal scheduling result of power supply and power storage, an optimal scheduling result of cold supply and cold storage and an optimal scheduling result of heat supply and heat storage of the combined cooling, heating and power supply system according to the planning model.
In an embodiment, the GAMS software version adopted for calculating the optimized scheduling result is GAMS win6424.5.3.
Step 160: and controlling the work allocation of the combined cooling heating and power system according to the optimized scheduling result.
In one embodiment, the controlling the work allocation of the combined cooling heating and power system according to the optimized scheduling result includes: and controlling the active power output distribution, the cooling supply distribution and the heating supply distribution of the combined cooling heating and power system according to the optimized scheduling result.
According to the method for calculating the optimized scheduling result of the combined cooling heating and power system according to the planning model, the high-nonlinearity constraint condition is subjected to linearization processing through processing methods such as piecewise linearization processing, first-order Taylor expansion, large M-method equivalent transformation and the like, so that the established planning model of the combined cooling and power system is converted into a mixed integer linear planning model which is easy to solve, the solving operation time is obviously reduced, and the calculation efficiency is improved. The calculation method obtains the global optimal solution, the operation cost of the obtained optimized scheduling scheme is less than the result of the mixed integer nonlinear programming model, and the economical efficiency is better.
In an embodiment, the simulation experiment verification process for calculating the optimal scheduling result of the combined cooling heating and power system according to the planning model includes:
the microgrid of a natural gas combined cooling heating and power supply park is taken as an example, and the wiring is shown in fig. 3. Wherein, the cooling network and the heating network both comprise 13 nodes and 12 sections of pipelines; the gas supply network comprises 9 nodes and 6 sections of pipelines; the power supply network comprises 54 nodes and 78 branches; the energy station is internally provided with a gas generator, a smoke type and hot water type absorption refrigerator, an electric boiler, a gas boiler, a heat exchange unit and a circulating water pump for supplying cold and heat. The gas purchase price is 3.5 yuan/m 3, and the electricity purchase price of the distribution network is 1.0228 yuan/kWh. Fig. 4 shows a total cooling, heating and power load curve of the microgrid in 96 periods of a day, wherein the uppermost curve is a power load curve, the middle curve is a cooling load curve, the lowermost curve is a heating load curve, and the total natural gas load curve is shown in fig. 5. The capacity of the photovoltaic power station connected to the park microgrid is 15MW, and the output prediction curve of the photovoltaic power station is shown in figure 6. The GAMS software version adopted by the optimization calculation is GAMS win6424.5.3. And solving a mixed integer linear planning model of the park micro-grid optimized scheduling to obtain an optimized scheduling scheme.
According to the result of the optimized scheduling of the park power supply and the power storage, the gas generator set in the microgrid preferentially generates power to supply power load, and the unit price (about 0.810 yuan/kWh) of the gas generator set of the energy station for generating power at rated power to consume natural gas is obviously lower than the unit price (1.0228 yuan/kWh) for purchasing power from a power distribution network. In the off-peak period of the power load at night, active power injected into the micro-grid by the power distribution network is maintained at the minimum value, the residual power supply load of the park is provided by all the gas generator sets, and meanwhile, the storage battery is used for storing electricity at night; in the daytime, the power load is obviously increased, and the rest power supply load is only bought from the power distribution network under the conditions that the gas generator outputs the maximum active power and the photovoltaic power generation is fully consumed; and in two periods of power load peak, the storage battery is used for discharging so as to reduce the electricity purchasing from the power distribution network and save the operating cost of the micro-grid.
According to the optimized scheduling results of cold supply and cold storage in the garden, as the flue gas type refrigerating machines and the hot water type refrigerating machines utilize the waste heat of power generation for refrigeration and have high energy utilization efficiency, the two absorption refrigerating machines are preferentially used for refrigeration, wherein the flue gas type refrigerating machines have higher efficiency, so that the highest priority is achieved and all the refrigerating machines are put into use. In the low valley period of the cold load used at night, the cold load is basically provided by two absorption refrigerators and the cold storage is carried out by using a cold storage tank; the cold load used in the daytime is obviously increased, and the rest cold load is borne by the electric refrigerator under the condition that the two absorption refrigerators provide the maximum cooling capacity; in two cold load peak periods, the cold storage tank is used for cold discharge to play a role in peak clipping, and particularly in the second cold load peak period, the photovoltaic power generation output is obviously reduced, and the cold discharge power of the cold storage tank is higher, so that the electric energy consumed by the electric refrigerator in refrigeration is reduced.
According to the result of the optimal scheduling of heat supply and heat storage of the park, as the heat exchange unit utilizes the waste heat of power generation to heat, the power consumption is lower during heating, the energy utilization efficiency is high, the heat exchange unit is preferentially used for heating, and the heat exchange unit is completely put into use; since the gas boiler consumes less gas (0.163 yuan/MJ) than the electric boiler when outputting the same heat (0.355 yuan/MJ), the gas boiler is used in preference to the electric boiler. In the low-ebb period of the heat load at night, the heat load is basically provided by the heat exchange unit and the gas boiler, and the heat storage is carried out by utilizing the heat storage tank; in the two heat load peak periods in the daytime, the heat storage tank is used for releasing heat under the condition that the heat exchange unit provides the maximum heat supply amount, so that the electric energy consumed by heating of the electric heating boiler and the natural gas consumed by heating of the gas-fired boiler are reduced. When the heat load is reduced at 51-54 noon, the photovoltaic power generation output is in the peak time, the photovoltaic power generation output is consumed by the micro-grid for heating the electric boiler, and the redundant heat is stored through the heat storage tank, so that the consumption effect of the heat storage device on the photovoltaic power generation output is reflected.
The optimal scheduling results of the micro-grid in the park before and after linearization are shown in table 1, an SBB solver is used for solving the mixed integer nonlinear programming model before linearization, and a GUROBI solver is used for solving the mixed integer linear programming model after linearization. It can be seen that the difference of the optimized scheduling objective functions of the micro-grid in the park before and after linearization is small, and the total operation cost of the optimized scheduling result of the micro-grid in the park after linearization is smaller than that before linearization, because the optimized scheduling model is converted into the mixed integer linear programming model after linearization, the global optimal solution of the model can be obtained. In the aspect of running time, the time consumed for solving before linearization is 261.5 seconds, the time consumed for solving after linearization is only 4.6 seconds, the solving time is greatly reduced, and the mixed integer linear programming algorithm for optimizing and scheduling the micro-grid energy in the natural gas combined cooling, heating and power supply park has great advantage in solving speed.
Figure BDA0002165632020000191
Table 1 comparison of optimized scheduling results of micro-grid in whole park before and after linearization
The percentage of maximum and minimum deviation of available thermal power at each end of each pipe under the cooling network before and after linearization is shown in Table 2 (using C)ijThe available heat power contained in the cold water flowing into/out of the node i from the pipeline ij) can be seen, the deviations of the cooling supply network after the linearization treatment are all very small, and the maximum deviation percentage is about 1%; the percentage of maximum and minimum deviation of available thermal power at each end of each pipe in the heating network is shown in Table 3 (by H)ijRepresenting the available heat power contained in the hot water flowing into/out of the node i through the pipeline ij), it can be seen that the deviations of the heat supply network after linearization treatment are all very small, and the maximum deviation percentage is all within 2%; the percentage of the maximum deviation and the minimum deviation of the pressure intensity of each node of the gas supply network is shown in table 4, and it can be seen that the deviation of the pressure intensity of each node after linearization processing is very small and is within 1%; therefore, the deviation of various variables before and after linearization is small, and the calculation accuracy is high.
Figure BDA0002165632020000192
Figure BDA0002165632020000201
TABLE 2 percentage deviation of available thermal power at both ends of each pipeline under cooling network
TABLE 3 percentage deviation of available thermal power at both ends of each pipeline under heating network
Figure BDA0002165632020000203
TABLE 4 deviation percentage of pressure intensity of each node of air supply network
In an embodiment, referring to fig. 7, the present application further provides a control device 60 of a combined cooling, heating and power system, where the control device 20 of the combined cooling, heating and power system includes: the first obtaining module 601 is configured to obtain a target function of the minimum operating cost of the combined cooling heating and power system in a preset time according to cost data of the combined cooling heating and power system in the preset time; a second obtaining module 602, configured to obtain an operating characteristic constraint condition of the cold, hot and electric power supply system; a third obtaining module 603, configured to obtain upper and lower limit constraints of the cold, heat, and power supply system as design variables; a planning model constructing module 604, configured to construct a planning model of the combined cooling heating and power system according to the objective function, the operating characteristic constraint condition, and the design variable; a calculating module 605, configured to calculate an optimized scheduling result of the combined cooling heating and power system according to the planning model; and the control module 606 is configured to control the work allocation of the combined cooling heating and power system according to the optimized scheduling result.
The control device of the combined cooling heating and power system obtains the objective function of the minimum running cost according to the cost data of the combined cooling, heating and power system in the preset time, obtains the running characteristic constraint condition of the combined cooling, heating and power system and obtains the upper and lower limit constraint of the combined cooling, heating and power system as the design variables, and can construct the planning model of the combined cooling, heating and power system according to the objective function, the running characteristic constraint condition and the design variables, calculate the optimal scheduling result of the combined cooling, heating and power system according to the planning model, and control the work distribution of the combined cooling, heating and power system according to the optimal scheduling result, so as to reduce the running cost of the combined cooling, heating and power system.
In an embodiment, the first obtaining module is configured to obtain natural gas purchase cost data, power distribution network purchase cost data, and energy storage device operation cost data of the combined cooling heating and power system in a preset time, and obtain a target function of a minimum operation cost of the combined cooling heating and power system in the preset time according to the natural gas purchase cost data, the power distribution network purchase cost data, and the energy storage device operation cost data.
In one embodiment, the natural gas combined cooling heating power system comprises a cooling and heating network, a gas supply network, a power supply network, an energy station and an energy storage device; the second obtaining module is used for obtaining the operating characteristic constraint conditions of the cooling and heating network, the gas supply network, the power supply network, the energy station and the energy storage device.
In an embodiment, the third obtaining module is configured to obtain, as design variables, a temperature of the cooling and heating network, a flow rate of the cooling and heating network, a pressure of the gas supply network, a power of the power supply network, equipment variables of the energy station, and upper and lower limit constraints of a storage battery of the energy storage device.
In an embodiment, the calculation module is configured to calculate an optimal scheduling result of power supply and power storage, an optimal scheduling result of cooling and cooling storage, and an optimal scheduling result of heating and heat storage of the combined cooling and heating and power supply system according to the planning model.
In an embodiment, the control module is configured to control active power distribution, cooling capacity distribution, and heating capacity distribution of the combined cooling, heating, and power system according to the optimized scheduling result.
In an embodiment, the combined cooling, heating and power system further includes a linearization processing module, where the linearization processing module is configured to linearize the operating characteristic constraint condition to obtain a linear constraint condition; and the planning model construction module is used for constructing a planning model of the combined cooling heating and power system according to the objective function, the linear constraint condition and the design variable.
In one embodiment, as shown in fig. 8, there is provided a computer, that is, a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: according to the cost data of the combined cooling heating and power system in the preset time, obtaining a target function of the minimum running cost of the combined cooling heating and power system in the preset time; acquiring an operating characteristic constraint condition of the cold and hot electric power supply system; acquiring upper and lower limit constraints of variables of the cold, hot and electric power supply system as design variables; constructing a planning model of the combined cooling heating and power system according to the objective function, the operating characteristic constraint condition and the design variable; calculating an optimized scheduling result of the combined cooling heating and power system according to the planning model; and controlling the work allocation of the combined cooling heating and power system according to the optimized scheduling result.
In one embodiment, the processor, when executing the computer program, performs the steps of: acquiring natural gas purchase cost data, power distribution network purchase cost data and energy storage device operation cost data of the combined cooling heating and power system within preset time; and obtaining an objective function of the minimum operating cost of the combined cooling heating and power system within the preset time according to the natural gas purchase cost data, the power distribution network purchase cost data and the energy storage device operating cost data.
In one embodiment, the natural gas combined cooling heating system comprises a cooling and heating network, a gas supply network, a power supply network, an energy station and an energy storage device, and when a processor executes a computer program, the following steps are realized: obtaining operating characteristic constraints of the cooling and heating network, the gas supply network, the power supply network, the energy station and the energy storage device.
In one embodiment, the processor, when executing the computer program, performs the steps of: and acquiring the temperature of the cooling and heating network, the flow of the cooling and heating network, the pressure of the gas supply network, the power of the power supply network, the equipment variable of the energy station and the upper and lower limit constraints of a storage battery of the energy storage device as design variables.
In one embodiment, the processor, when executing the computer program, performs the steps of: and calculating an optimized scheduling result of power supply and power storage, an optimized scheduling result of cold supply and cold storage and an optimized scheduling result of heat supply and heat storage of the combined cooling, heating and power supply system according to the planning model.
In one embodiment, the processor, when executing the computer program, performs the steps of: and controlling the active power output distribution, the cooling supply distribution and the heating supply distribution of the combined cooling heating and power system according to the optimized scheduling result.
In an embodiment, the processor when executing the computer program further performs the steps of: linearly processing the operating characteristic constraint condition to obtain a linear constraint condition; and constructing a planning model of the combined cooling heating and power system according to the objective function, the linear constraint condition and the design variable.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: according to the cost data of the combined cooling heating and power system in a preset time, obtaining a target function of the minimum operating cost of the combined cooling heating and power system in the preset time; acquiring operating characteristic constraint conditions of the cold-hot electric power supply system; acquiring upper and lower limit constraints of variables of the cold, hot and electric power supply system as design variables; constructing a planning model of the combined cooling heating and power system according to the objective function, the operating characteristic constraint condition and the design variable; calculating an optimized scheduling result of the combined cooling heating and power system according to the planning model; and controlling the work allocation of the combined cooling heating and power system according to the optimized scheduling result.
In an embodiment, the computer program when executed by the processor implements the steps of: acquiring natural gas purchase cost data, power distribution network purchase cost data and energy storage device operation cost data of the combined cooling heating and power system within preset time; and obtaining an objective function of the minimum operating cost of the combined cooling heating and power system within the preset time according to the natural gas purchase cost data, the power distribution network purchase cost data and the energy storage device operating cost data.
In one embodiment, the combined cooling, heating and power natural gas system comprises a cooling and heating network, a gas supply network, a power supply network, an energy station and an energy storage device, and the computer program when executed by the processor implements the steps of: obtaining operating characteristic constraints of the cooling and heating network, the gas supply network, the power supply network, the energy station and the energy storage device.
In an embodiment, the computer program when executed by the processor implements the steps of: and acquiring the temperature of the cooling and heating network, the flow of the cooling and heating network, the pressure of the gas supply network, the power of the power supply network, the equipment variable of the energy station and the upper and lower limit constraints of a storage battery of the energy storage device as design variables.
In an embodiment, the computer program when executed by the processor implements the steps of: and calculating an optimized scheduling result of power supply and power storage, an optimized scheduling result of cold supply and cold storage and an optimized scheduling result of heat supply and heat storage of the combined cooling, heating and power supply system according to the planning model.
In an embodiment, the computer program when executed by the processor implements the steps of: and controlling the active power output distribution, the cooling supply distribution and the heating supply distribution of the combined cooling heating and power system according to the optimized scheduling result.
In an embodiment, the computer program when executed by the processor implements the steps of: linearly processing the operating characteristic constraint condition to obtain a linear constraint condition; and constructing a planning model of the combined cooling heating and power system according to the objective function, the linear constraint condition and the design variable.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims (10)

1. A control method of a combined cooling heating and power system is characterized by comprising the following steps:
according to the cost data of the combined cooling heating and power system in the preset time, obtaining a target function of the minimum operating cost of the combined cooling and power system in the preset time;
acquiring operating characteristic constraint conditions of the cold-hot electric power supply system;
acquiring upper and lower limit constraints of variables of the cold, hot and electric power supply system as design variables;
constructing a planning model of the combined cooling heating and power system according to the objective function, the operating characteristic constraint condition and the design variable;
calculating an optimized scheduling result of the combined cooling heating and power system according to the planning model;
and controlling the work allocation of the combined cooling heating and power system according to the optimized scheduling result.
2. The method according to claim 1, wherein the obtaining an objective function of the minimum operating cost of the combined cooling heating and power system in a preset time according to the cost data of the combined cooling, heating and power system in the preset time comprises:
acquiring natural gas purchase cost data, power distribution network purchase cost data and energy storage device operation cost data of the combined cooling heating and power system within preset time;
and obtaining a target function of the minimum operating cost of the combined cooling heating and power system within the preset time according to the natural gas purchase cost data, the power distribution network purchase cost data and the energy storage device operating cost data.
3. The method according to claim 2, wherein the natural gas combined cooling heating power system comprises a cooling and heating network, a gas supply network, a power supply network, an energy station and an energy storage device;
the acquiring of the operating characteristic constraint condition of the cold-hot electric power supply system comprises the following steps: and acquiring the operating characteristic constraint conditions of the cooling and heating network, the gas supply network, the power supply network, the energy station and the energy storage device.
4. The method of claim 3, wherein the obtaining upper and lower limit constraints of variables of the cold and hot power supply system as design variables comprises:
and acquiring the temperature of the cooling and heating network, the flow of the cooling and heating network, the pressure of the gas supply network, the power of the power supply network, the equipment variable of the energy station and the upper and lower limit constraints of a storage battery of the energy storage device as design variables.
5. The method according to claim 4, wherein the calculating the optimal scheduling result of the combined cooling heating and power system according to the planning model comprises:
and calculating an optimized scheduling result of power supply and power storage, an optimized scheduling result of cold supply and cold storage and an optimized scheduling result of heat supply and heat storage of the combined cooling, heating and power supply system according to the planning model.
6. The method according to claim 5, wherein the controlling the work allocation of the combined cooling heating and power system according to the optimized scheduling result comprises:
and controlling the active power output distribution, the cooling supply distribution and the heating supply distribution of the combined cooling heating and power system according to the optimized scheduling result.
7. The method of claim 1, wherein the obtaining operational characteristic constraints of the hot and cold electrical power supply system further comprises:
carrying out linearization processing on the operating characteristic constraint condition to obtain a linear constraint condition;
the obtaining of the planning model of the combined cooling heating and power system according to the objective function, the operating characteristic constraint condition and the design variable includes:
and constructing a planning model of the combined cooling heating and power system according to the objective function, the linear constraint condition and the design variable.
8. A control device for a combined cooling heating and power system, the device comprising:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring a target function of the minimum running cost of the combined cooling heating and power system in a preset time according to the cost data of the combined cooling heating and power system in the preset time;
the second acquisition module is used for acquiring the operating characteristic constraint condition of the cold-hot electric power supply system;
the third acquisition module is used for acquiring upper and lower limit constraints of the cold, heat and electricity power supply system as design variables;
the planning model construction module is used for constructing a planning model of the combined cooling heating and power system according to the objective function, the operating characteristic constraint condition and the design variable;
the calculation module is used for calculating an optimized scheduling result of the combined cooling heating and power system according to the planning model;
and the control module is used for controlling the work allocation of the combined cooling heating and power system according to the optimized scheduling result.
9. A computer, a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 7 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
CN201910746147.8A 2019-08-13 2019-08-13 Control method and device of combined cooling heating and power system, computer and storage medium Pending CN110716429A (en)

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