CN115906411A - Electric heating comprehensive energy system optimal energy flow modeling method and system considering full dynamic - Google Patents
Electric heating comprehensive energy system optimal energy flow modeling method and system considering full dynamic Download PDFInfo
- Publication number
- CN115906411A CN115906411A CN202211302252.0A CN202211302252A CN115906411A CN 115906411 A CN115906411 A CN 115906411A CN 202211302252 A CN202211302252 A CN 202211302252A CN 115906411 A CN115906411 A CN 115906411A
- Authority
- CN
- China
- Prior art keywords
- node
- model
- power
- flow
- electric heating
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000005485 electric heating Methods 0.000 title claims abstract description 51
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000009826 distribution Methods 0.000 claims abstract description 12
- 238000010248 power generation Methods 0.000 claims abstract description 12
- 238000002485 combustion reaction Methods 0.000 claims description 29
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 27
- 239000000446 fuel Substances 0.000 claims description 24
- 230000005540 biological transmission Effects 0.000 claims description 18
- 230000006870 function Effects 0.000 claims description 13
- 238000003860 storage Methods 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 12
- 238000004458 analytical method Methods 0.000 claims description 8
- 239000011159 matrix material Substances 0.000 claims description 8
- 230000008859 change Effects 0.000 claims description 7
- 150000001875 compounds Chemical class 0.000 claims description 7
- 238000004519 manufacturing process Methods 0.000 claims description 7
- 239000003245 coal Substances 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 6
- 230000005611 electricity Effects 0.000 claims description 6
- 238000005338 heat storage Methods 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 4
- PMGQWSIVQFOFOQ-YKVZVUFRSA-N clemastine fumarate Chemical compound OC(=O)\C=C\C(O)=O.CN1CCC[C@@H]1CCO[C@@](C)(C=1C=CC(Cl)=CC=1)C1=CC=CC=C1 PMGQWSIVQFOFOQ-YKVZVUFRSA-N 0.000 claims description 4
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 3
- 239000003546 flue gas Substances 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 230000005251 gamma ray Effects 0.000 claims description 2
- 239000000203 mixture Substances 0.000 claims description 2
- 230000002123 temporal effect Effects 0.000 claims description 2
- 238000012216 screening Methods 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 12
- 238000012545 processing Methods 0.000 description 7
- 238000010438 heat treatment Methods 0.000 description 4
- 238000005457 optimization Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000013507 mapping Methods 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 238000011217 control strategy Methods 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000001902 propagating effect Effects 0.000 description 2
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000002918 waste heat Substances 0.000 description 1
Images
Landscapes
- Feedback Control In General (AREA)
Abstract
The invention discloses an optimal energy flow modeling method of an electric heating comprehensive energy system considering full dynamics, which comprises the following steps: step 1, collecting data of an electric heating comprehensive energy system, wherein the data comprises pipeline length, thermal resistance, a user temperature comfort interval and electric load time sequence distribution; step 2, establishing an electric heating comprehensive energy system model considering full dynamics by combining the data of the electric heating comprehensive energy system; step 3, establishing a simplified equation of nonlinear alternating current power flow, a discrete equation of a dynamic cogeneration unit and a thermodynamic system, and simplifying the alternating current power flow equation, the discrete cogeneration unit and the thermodynamic system equation; and 4, establishing an operation safety constraint condition of the electric heating comprehensive energy system based on the simplified alternating current power flow equation, the discrete combined heat and power generation unit and the thermodynamic system equation in the step 2 by taking the minimized system operation cost as a target, and constructing an optimal energy flow model.
Description
Technical Field
The invention belongs to the field of energy system modeling and operation analysis, and particularly relates to an optimal energy flow modeling method of an electric heating comprehensive energy system considering full dynamics.
Background
With the increasing negative effects of climate change and environmental pollution, countries around the world are exploring new forms of clean, efficient, and sustainable energy utilization. As a typical form of the integrated energy system, the electric heat integrated energy system couples two power supply subsystems and two heat supply subsystems to each other through a cogeneration unit or the like. Different from the traditional discrete energy system, the cogeneration system can fully utilize the waste heat recovered by power generation to supply part of industrial or civil heat loads, thereby improving the comprehensive energy efficiency of the system; meanwhile, the thermal inertia of the heat supply load can also provide flexible resources for the power supply system to consume more renewable energy sources.
The optimal energy flow of the electric heating comprehensive system is the state distribution when a certain performance index (such as economy, environmental protection, energy efficiency and the like) of the system reaches an optimal value by optimizing certain state quantity in the system under a given boundary condition. The optimal energy flow is a model described by nonlinear partial differential-ordinary differential-algebra, but the existing research generally ignores the nonlinear and dynamic modeling in the system, simplifies the complex characteristics of each state quantity of the electrothermal comprehensive energy system, and the obtained result has larger difference with the real situation. Therefore, a proper and accurate optimal energy flow model is established, and is the key for the operation optimization of the electric heating comprehensive energy system.
Prior art document 1 (CN 113111555A) discloses a method for quickly calculating energy flow of a mass-regulation thermodynamic system based on a superposition decoupling method, including: 10 Taking the ambient temperature as a reference temperature, constructing a thermodynamic system dynamic model, and simplifying according to quality regulation characteristics; 20 Based on the simplified branch heat conduction equation, constructing a thermodynamic system temperature dynamic mapping equation and a weight matrix, and determining a temperature mapping direction in the thermodynamic system according to values in the weight matrix; 30 According to the temperature mapping direction, decoupling the original thermodynamic system into a plurality of radial thermodynamic systems supplied with heat by a single heat source; 40 Energy flow distribution in each decoupling system is calculated respectively, the energy flow distribution of an original thermodynamic system is linear superposition of a plurality of decoupling systems, and the defects of the prior art document 1 are that the technology mainly evaluates the energy flow distribution of the thermodynamic system, but ignores the coupling relation between an electric power system and the thermodynamic system, and lacks the influence analysis of the dynamic characteristics of the thermodynamic system on the operation optimization of the electric heating comprehensive energy system.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method specifically models the nonlinear and dynamic characteristics in the electric heating comprehensive energy system, properly simplifies different nonlinear terms and dynamic terms to facilitate operation, and finally comprehensively considers the operation safety constraints of various types of state quantities in the system to establish an optimal energy flow model so as to realize the accuracy of operation optimization.
In order to solve the technical problem, the technical scheme adopts a full-dynamic electric heating comprehensive energy system optimal energy flow modeling method, which comprises the following steps:
step 3, establishing a simplified equation of nonlinear alternating current power flow, a discrete equation of a dynamic cogeneration unit and a thermodynamic system, and simplifying the alternating current power flow equation, the discrete cogeneration unit and the thermodynamic system equation;
and 4, establishing an operation safety constraint condition of the electric heating comprehensive energy system based on the simplified alternating current power flow equation, the discrete combined heat and power generation unit and the thermodynamic system equation in the step 2 by taking the minimized system operation cost as a target, and constructing an optimal energy flow model.
The step 2 specifically comprises the following steps: step 201, establishing an alternating current power flow model;
step 202, establishing a dynamic model of the cogeneration unit, wherein the dynamic model comprises a compressor model, a combustion chamber model, a turbine model and a heat exchanger model;
step 203, establishing a thermodynamic system dynamic model comprising a hydraulic part model and a thermodynamic part model; wherein the hydraulic part model is as follows:
Am＝d
BΔp＝0
Δp＝Km 2
in the formula, A and B are respectively a node-branch incidence matrix and a loop-branch incidence matrix of a thermodynamic system, m is a mass flow vector of a hot water pipeline, d is a mass flow vector injected by a node, delta p is a pipeline pressure drop vector, and K is a pipeline friction coefficient;
the thermal part model is:
where x and T are space and time variables, v is water flow velocity, T p Indicating the temperature of the pipe, T i n Denotes the temperature, m, of node i b Mass flow rate, T, of pipe b k p,o Is the outlet temperature, T, of the pipe k k p,i Is the inlet temperature of the pipeline k, λ is the thermal resistivity of the pipeline, T a Is at the temperature of the surroundings and is,
and &>
Respectively representing that the node i is taken as a head node and a tail node pipeline set.
The compressor model is as follows:
p 2,t ＝CPR 1 ×p 1,t
P c,t ＝C a m a,t (T 2,t -T 1,t )
wherein t is a time stamp, p 1,t And p 2,t Respectively representing the inlet and outlet pressure of the compressor at time T, T 1,t And T 2,t Representing the inlet and outlet temperatures of the compressor, CPR, respectively, at time t 1 Indicating the pressure ratio, beta, of the compressor 1 Expressing the adiabatic coefficient of air, eta 1 Indicating compressor efficiency, P c,t Represents the compressor power consumption at time t, C a Represents the specific heat capacity of air, m a,t Representing the mass flow of incoming air.
The combustion chamber model is:
in the formula, T 3,t Respectively representing the combustion chamber temperature, beta, at time t 2 Representing the heat storage coefficient of the combustion chamber, H g Indicating combustionRoom heating value, LHV represents the lower heating value of the fuel, m f,t Representing the mass flow of the inflowing fuel, C s Is the specific heat capacity of the mixed flue gas.
The turbine model is:
p 3,t ＝CPR 2 p 2,t
P b,t ＝C s (m a,t +m f,t )(T 4,t -T 3,t )
in the formula, T 4,t Respectively representing the turbine outlet temperature, beta, at time t 3 Denotes the adiabatic efficiency, P, of the combustion chamber b,t For the total power of the turbine production at time t, CPR 2 Indicating the pressure ratio of the turbine.
The power for generating electricity and supplying heat produced by the cogeneration unit is respectively as follows:
P g,t ＝η 2 η 3 (P b,t -P c,t )
φ g,t ＝η 2 (1-η 3 )(P b,t -P c,t )
in the formula, P g,t And phi g,t Power, eta, for electricity generation and heat supply, respectively, for cogeneration units 2 Indicating mechanical efficiency, eta, of the steam turbine 3 Indicating the thermoelectric ratio.
The heat exchanger model is as follows:
in the formula, beta 4 Denotes the heat storage coefficient of the heat exchanger, C w Represents the specific heat capacity of water, m w,t Is the mass flow, T, of the water flow in the heat exchanger 5,t And T 6,t Respectively representing the outlet and inlet temperatures of the heat exchanger.
Preferably, step 3 comprises:
step 301, in the transmission network system, simplifying the nonlinear alternating current power flow model by making the phase angle difference of the node voltage be 0 and making the amplitude of the node voltage be 1 to obtain a power system power flow model:
wherein i and j represent node numbers, respectively,
representing the voltage amplitude, P, of node i G,i And P L,i Generator active power and load active power, Q, representing node i G,i And Q L,i Generator reactive power and load reactive power, G, representing node i ij And B ij Representing the conductance and susceptance, θ, between node i and node j ij Representing the phase angle difference, P, between node i and node j ij And Q ij Active power and reactive power representing transmissions between node i and node j;
step 202, discretizing the thermodynamic part model in the thermodynamic system model by adopting a discrete time and space step length solution to obtain a discretized thermodynamic part model:
in the formula, J 1 ，J 2 ，J 3 And J 4 Respectively, the transmission coefficients;
represents the duct temperature at time j +1, is greater than>
Represents the duct temperature at instant i +1 j +1, is present>
Represents the duct temperature at time j, i->
Represents the pipe temperature at time i of j + 1; Δ x and Δ t are discrete space and time steps, respectively, L and Γ are the pipe length and time interval, respectively, N x And N t Number of spatial and temporal steps, respectively>
Step 203, discretizing a combustion chamber model and a heat exchanger model by adopting a backward Euler format;
wherein the discrete combustion chamber model is:
the discrete heat exchanger model is as follows:
step 4 comprises the following steps:
step 401, establishing power system operation safety constraints, including:
node voltage amplitude constraint:
phase angle constraint:
Branch transmission power constraint:
the active power of the generator is restricted:
and (3) generator reactive power constraint:
in the formula (I), the compound is shown in the specification,
represents an apparent power ceiling, greater or lesser, of a transmission between node i and node j>
And &>
Generating a lower and an upper limit of active power for generator i in conjunction with the control unit>
And &>
A lower limit and an upper limit for generating reactive power for the generator i;
step 402, establishing thermodynamic system operation safety constraints, including:
and (3) node pressure constraint:
capacity constraints of mass flow of pipes and nodes:
and (3) restricting the change rate of the mass flow of the pipeline: gamma ray min m i,t-1 ≤m i,t ≤γ max m i,t-1 ,γ min d i,t-1 ≤d i,t ≤γ max d i,t-1 ；
And (3) node water supply temperature constraint:
and (3) node return water temperature constraint:
in the formula (I), the compound is shown in the specification,
and &>
A lower limit and an upper limit, respectively, for the water pressure in node i>
And &>
Lower and upper mass flow limits,. And `, respectively, of the conduit i>
And &>
Lower and upper limits, respectively, of the mass flow of node i min And gamma max Lower and upper limit, respectively, for the rate of change of the mass flow>
And &>
Lower limit and upper limit of water supply temperature for a node i at the time t respectively>
And &>
Respectively is the lower limit and the upper limit of the backwater temperature of the node i at the time t;
step 403, establishing the operation safety constraint of the cogeneration unit, including:
compressor inlet temperature and pressure constraints: t is a unit of 1 min ≤T 1,t ≤T 1 max ，
Mass flow constraint of combustor input fuel:
steam turbine outlet pressure constraint:
fuel and air mixture ratio constraints: alpha (alpha) ("alpha") min m f,t ≤m a,t ≤α max m f,t ；
Temperature constraint of the combustion chamber: t is 3 min ≤T 3,t ≤T 3 max
And (3) restricting the temperature of the outlet of the steam turbine:
thermoelectric ratio constraint:
in the formula, T 1 min And T 1 max Respectively a lower limit and an upper limit for the compressor inlet temperature,
and &>
Lower and upper limit, respectively, of the compressor inlet pressure>
And &>
Lower and upper limits of fuel mass flow, respectivelyLimit,. Or>
And &>
Lower and upper limits, respectively, of the turbine outlet pressure, alpha min And alpha max Distribution is lower and upper limits of air-fuel ratio, T 3 min And T 3 max Lower and upper limits of combustion chamber temperature, and T 4 max Lower limit and upper limit of the turbine outlet temperature, respectively>
And &>
Lower and upper limits of the thermoelectric ratio, respectively;
step 404, aiming at minimizing the system operation cost, establishing an objective function of the optimal energy flow model:
wherein F represents the total operation cost of the electric heating integrated energy system, minF represents the minimum total operation cost of the electric heating integrated energy system, and c 1 f i,t The total operation cost of the ith combined heat and power generation unit in the electric-heat integrated energy system in the time period t is represented; c. C 2 g j,t The total operation cost of the jth common generator in the electric heating comprehensive energy system in the time period t is represented; c. C 1 And c 2 The fuel unit price of the cogeneration unit and the coal unit price of the common generator are respectively,
for a common generator set>
For a combined heat and power plant set, f i,t And g j,t Respectively as cost functions of the cogeneration unit i and the common generator j in the time period t;N t the optimal sampling time set for a given calculation period is respectively expressed as:
in the formula, mu 11 ，μ 12 And mu 13 Respectively, the conversion factor between the generated power and the fuel flow of the cogeneration unit 21 ，μ 22 And mu 23 Respectively are conversion coefficients between the generating power of the common generator and the coal burning quantity; p is G,j Representing the generator active power at node j; p G,i Representing the generator active power at node i.
The electric heating comprehensive energy system optimal energy flow modeling system considering full dynamic comprises the following steps of:
the system comprises a data acquisition module, an energy management analysis module, a logic calculation module and a heat supply model module;
the data acquisition module is used for acquiring data of the electric heating comprehensive energy system;
the energy management analysis module is used for establishing a simplified equation of the nonlinear alternating current power flow and a discrete equation of the dynamic combined heat and power generation unit and the thermodynamic system;
the logic calculation module is used for establishing an operation safety constraint condition of the electric heating comprehensive energy system based on a simplified alternating current power flow equation, a discrete combined heat and power generation unit and a thermodynamic system equation by taking the minimized system operation cost as a target;
the optimal energy flow modeling module is used for establishing an optimal energy flow model considering full dynamics according to the operation safety constraint conditions and the electric heating comprehensive energy system model.
The invention has the beneficial effects that: the method comprehensively reduces modeling for the full dynamic and nonlinear characteristics of the electric heating comprehensive energy system, and is beneficial to accurately depicting the system state, thereby accurately formulating the operation control strategy of the electric heating comprehensive energy system and improving the economy and safety of the system.
Drawings
FIG. 1 is a schematic flow chart of an optimal energy flow modeling method of an electric heating comprehensive energy system considering full dynamic state in the embodiment of the invention;
FIG. 2 is a block diagram of a cogeneration unit in an embodiment of the invention;
FIG. 3 is a block diagram of a thermodynamic system employed in an embodiment of the present invention;
fig. 4 is a time sequence distribution of heating power of the node 1 in the thermal system according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments obtained by a person skilled in the art without any inventive step based on the spirit of the present invention are within the scope of the present invention.
Example 1: an optimal energy flow modeling method of an electric heating comprehensive energy system considering full dynamic is shown in figure 1. The method comprises the following steps:
step 101, establishing an alternating current power flow model, including node power conservation and branch power conservation equations:
wherein i and j represent node numbers respectively,
representing the magnitude of the voltage at node i, P G,i And P L,i Generator active power and load active power, Q, representing node i G,i And Q L,i Generator reactive power and load reactive power, G, representing node i ij And B ij Representing the conductance and susceptance, θ, between node i and node j ij Representing the phase angle difference, P, between node i and node j ij And Q ij Representing the real and reactive power transferred between node i and node j.
Step 102, establishing a dynamic model of the cogeneration unit, wherein the dynamic model comprises a compressor model, a combustion chamber model, a steam turbine model and a heat exchanger model, and the topology and the distribution of various state quantities of the cogeneration unit are shown in fig. 2. The compressor model describes the inlet and outlet temperature, pressure relationship and electric power consumption calculation formula, which are respectively expressed as:
p 2,t ＝CPR 1 ×p 1,t (5)
P c,t ＝C a m a,t (T 2,t -T 1,t ) (7)
wherein t is a time stamp, p 1,t And p 2,t Respectively representing the inlet and outlet pressures of the compressor at time T, T 1,t And T 2,t Representing the inlet and outlet temperatures of the compressor, CPR, respectively, at time t 1 Indicating the pressure ratio, beta, of the compressor 1 Expressing the adiabatic coefficient of air, eta 1 Indicating compressor efficiency, P c,t Representing compressor power consumption at time t, C a Indicates nullSpecific heat capacity of gas, m a,t Representing the mass flow of incoming air.
The combustion chamber model describes the conservation of energy within the combustion chamber, which can be expressed as:
in the formula, T 3,t Respectively representing the combustion chamber temperature, beta, at time t 2 Represents the heat storage coefficient of the combustion chamber, H g Representing the heat value of the combustion chamber, LHV representing the lower heating value of the fuel, m f,t Representing the mass flow of the inflowing fuel, C s Is the specific heat capacity of the mixed flue gas.
The turbine model includes calculation formulas of inlet and outlet temperature, pressure and generated power, which can be respectively expressed as:
p 3,t ＝CPR 2 p 2,t (9)
P b,t ＝C s (m a,t +m f,t )(T 4,t -T 3,t ) (11)
in the formula, T 4,t Respectively representing the turbine outlet temperature, beta, at time t 3 Denotes the adiabatic efficiency, P, of the combustion chamber b,t For the total power of the turbine production at time t, CPR 2 Indicating the pressure ratio of the turbine.
The power produced by the cogeneration unit for generating electricity and supplying heat may be expressed as:
P g,t ＝η 2 η 3 (P b,t -P c,t ) (12)
φ g,t ＝η 2 (1-η 3 )(P b,t -P c,t ) (13)
in the formula, P g,t And phi g,t Power, eta, for electricity generation and heat supply, respectively, for cogeneration units 2 Indicating mechanical efficiency, eta, of the steam turbine 3 Indicating the thermoelectric ratio.
The heat exchanger model describes the conservation of energy inside the heat exchanger and can be expressed as:
in the formula, beta 4 Representing the heat storage coefficient of the heat exchanger, C w Represents the specific heat capacity of water, m w,t Is the mass flow, T, of the water flow in the heat exchanger 5,t And T 6,t Respectively representing the outlet and inlet temperatures of the heat exchanger.
And 103, establishing a thermodynamic system dynamic model comprising a hydraulic part and a thermodynamic part. The hydraulic part comprises:
Am＝d (15)
BΔp＝0 (16)
Δp＝Km 2 (17)
in the formula, A and B are respectively a node-branch incidence matrix and a loop-branch incidence matrix of the thermodynamic system, m is a mass flow vector of a hot water pipeline, d is a mass flow vector injected by a node, delta p is a pipeline pressure drop vector, and K is a pipeline friction coefficient. Where equation (15) describes the conservation of mass at the node, equation (16) describes the pressure drop balance of the circuit, and equation (17) describes the relationship between the pipe pressure drop and the pipe mass flow.
The thermal part comprises:
where x and T are space and time variables, v is water flow velocity, T p Indicating the temperature, T, of the pipe i n Denotes the temperature, m, of node i b Is a pipeMass flow rate of b, T k p,o Is the outlet temperature, T, of the pipe k k p,i Is the inlet temperature of the pipeline k, λ is the thermal resistivity of the pipeline, T a Is at the temperature of the surroundings and is,
and &>
Respectively representing that the node i is taken as a first node and a last node pipeline set.
the step 2 comprises the following steps:
step 201, simplifying a nonlinear alternating current power flow model. In the grid-level system, the phase angle difference of the node voltage approaches 0, and the following can be obtained:
in addition, the node voltage amplitude in the grid-level system approaches to 1, and the following can be obtained:
substituting equations (21) and (22) into equations (1) to (4), the power system flow model may be rewritten as:
step 202, discretizing a partial differential equation in the thermodynamic system model by adopting a central implicit format. Firstly, discretizing a researched time interval to obtain discrete time and space step lengths, wherein the discrete time and space step lengths are respectively as follows:
where Δ x and Δ t are discrete space and time steps, respectively, L and Γ are the pipe length and time interval, respectively, N x And N t Space and time step numbers, respectively. Each partial derivative term in equation (18) can be expressed as:
in the formula, J 1 ，J 2 ，J 3 And J 4 Respectively are the transmission coefficients;
represents the duct temperature at time j + 1, <' > in>
Represents the duct temperature at time i +1 j +1>
Represents the duct temperature at time j, i->
Representing the pipe at time i of j +1(ii) temperature; . By substituting equations (28) to (30) into equation (18), the following can be obtained:
in the formula, J 1 ，J 2 ，J 3 And J 4 Respectively the transmission coefficient.
And step 203, discretizing ordinary differential equations in the cogeneration unit model by adopting a backward Euler format. The ordinary differential in equations (8) and (14) can be discretized as:
by substituting formula (33) and formula (34) for formula (8) and formula (14), respectively, it is possible to obtain:
and 3, establishing operation safety constraint of the electric heating comprehensive energy system by combining a simplified alternating current power flow equation, a discrete combined heat and power generation unit and a thermodynamic system equation, and constructing an optimal energy flow model.
The step 3 comprises the following steps:
step 301, establishing operation safety constraints of the power system, including node voltage amplitude constraint, phase angle constraint, branch transmission power constraint, generator active power constraint and reactive power constraint, which are respectively expressed as formulas (37) to (41):
in the formula (I), the compound is shown in the specification,
represents an apparent upper power limit, based on a comparison of the apparent power level of the transmission between node i and node j>
And &>
The lower and upper limits of active power production for generator i, device for selecting or keeping>
And &>
Lower and upper limits of reactive power production for generator i.
Step 302, establishing thermodynamic system operation safety constraints including node pressure constraints, capacity constraints of mass flows of pipelines and nodes, pipeline mass flow rate change constraints, node water supply temperature constraints and node return water temperature constraints, wherein the node water supply temperature constraints and the node return water temperature constraints correspond to user comfortable temperature intervals as shown in formulas (42) to (46):
γ min m i,t-1 ≤m i,t ≤γ max m i,t-1 ,γ min d i,t-1 ≤d i,t ≤γ max d i,t-1 (44)
in the formula (I), the compound is shown in the specification,
and &>
A lower limit and an upper limit, respectively, for the water pressure in node i>
And &>
Lower and upper mass flow limits and/or upper mass flow limits in duct i, respectively>
And &>
Lower and upper limits, respectively, of the mass flow of node i min And gamma max Respectively, a lower limit and an upper limit of the rate of change of the mass flow>
And &>
Lower limit and upper limit of water supply temperature for a node i at the time t respectively>
And &>
Respectively is the lower limit and the upper limit of the return water temperature of the node i at the time t.
Step 303, establishing the operation safety constraints of the cogeneration unit, including the constraints of the inlet temperature and the pressure of the compressor, the constraints of the mass flow of the fuel input into the combustion chamber, the constraints of the outlet pressure of the turbine, the constraints of the mixing ratio of the fuel and the air, the constraints of the temperature of the combustion chamber, the constraints of the outlet temperature of the turbine and the constraints of the thermoelectric ratio, which are respectively expressed as the formula (47)
To formula (54).
T 1 min ≤T 1,t ≤T 1 max (47)
α min m f,t ≤m a,t ≤α max m f,t (51)
T 3 min ≤T 3,t ≤T 3 max (52)
In the formula, T 1 min And T 1 max Respectively a lower limit and an upper limit for the compressor inlet temperature,
and &>
Lower and upper limit, respectively, of the compressor inlet pressure>
And &>
Lower and upper limits, respectively, of the fuel mass flow, ->
And &>
Lower and upper limits, respectively, of the turbine outlet pressure, alpha min And alpha max Distribution is lower and upper limits of air-fuel ratio, T 3 min And T 3 max Lower and upper limits of combustion chamber temperature, and T 4 max Respectively a lower limit and an upper limit of the outlet temperature of the steam turbine>
And &>
Respectively, the lower limit and the upper limit of the thermoelectric ratio.
Step 304, aiming at minimizing the system operation cost, an objective function of the optimal power flow model is established, which can be expressed as:
in the formula, c 1 And c 2 Respectively the fuel unit price of the cogeneration unit and the coal burning unit price of the common generator,
is a common generator set and is used for>
For a combined heat and power generating unit, f i,t And g j,t Respectively as cost functions of the cogeneration unit i and the common generator j in the time period t; n is a radical of t The optimal sampling time set in the set calculation period is respectively expressed as:
in the formula (I), the compound is shown in the specification,
μ 11 ，μ 12 and mu 13 Respectively the conversion coefficient between the generating power and the fuel flow of the cogeneration unit,
μ 21 ，μ 22 and mu 23 Respectively are conversion coefficients between the generating power of the common generator and the coal burning quantity. Based on this, the optimal energy flow model of the electric-thermal comprehensive energy system considering the full dynamic state can be expressed as follows:
s.t. formulae (5) - (7), (9) - (13), (31), (37) - (41), (47) - (54)
Formulae (15) - (17), (19) - (20), (35) - (36), (42) - (46) (58)
Formulas (23) - (26), (37) - (41), (56) - (57)
Taking the system shown in fig. 3 as an example, the calculation period is 24 hours, the optimized time interval is 20 minutes, the space step length is 250 meters, and the output heat power of the cogeneration unit obtained by optimization is shown in fig. 4.
The electric heating comprehensive energy system optimal energy flow modeling system considering full dynamic comprises the following steps of:
the system comprises a data acquisition module, an energy management analysis module, a logic calculation module and a heat supply model module;
the data acquisition module is used for acquiring data of the electric heating comprehensive energy system;
the energy management analysis module is used for establishing a simplified equation of the nonlinear alternating current power flow and a discrete equation of the dynamic cogeneration unit and the thermodynamic system;
the logic calculation module is used for establishing an operation safety constraint condition of the electric heating comprehensive energy system based on a simplified alternating current power flow equation, a discrete combined heat and power generation unit and a thermodynamic system equation by taking the minimized system operation cost as a target;
the optimal energy flow modeling module is used for establishing an optimal energy flow model considering full dynamics according to the operation safety constraint conditions and the electric heating comprehensive energy system model.
Compared with the prior art, the method has the advantages that the method comprehensively reduces modeling for the full-dynamic and nonlinear characteristics of the electric heating comprehensive energy system, and is beneficial to accurately depicting the system state, so that the operation control strategy of the electric heating comprehensive energy system is accurately formulated, and the economy and the safety of the system are improved.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer-readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
Computer program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (12)
1. The optimal energy flow modeling method of the electric heating comprehensive energy system considering full dynamic is characterized by comprising the following steps of:
step 1, collecting data of an electric heating comprehensive energy system, wherein the data comprises pipeline length, thermal resistance, a user temperature comfort interval and electric load time sequence distribution;
step 2, establishing an electric heating comprehensive energy system model considering full dynamics by combining the data of the electric heating comprehensive energy system;
step 3, establishing a simplified equation of nonlinear alternating current power flow, a discrete equation of a dynamic cogeneration unit and a thermodynamic system, and simplifying the alternating current power flow equation, the discrete cogeneration unit and the thermodynamic system equation;
and 4, establishing an operation safety constraint condition of the electric heating comprehensive energy system based on the simplified alternating current power flow equation, the discrete combined heat and power generation unit and the thermodynamic system equation in the step 2 by taking the minimized system operation cost as a target, and constructing an optimal energy flow model.
2. The method for modeling optimal power flow for an electrothermal integrated energy system considering full dynamics according to claim 1,
the step 2 specifically comprises the following steps: step 201, establishing an alternating current power flow model:
step 202, establishing a dynamic model of the cogeneration unit, wherein the dynamic model comprises a compressor model, a combustion chamber model, a turbine model and a heat exchanger model;
step 203, establishing a thermodynamic system dynamic model comprising a hydraulic part model and a thermodynamic part model; wherein the hydraulic part model is as follows:
Am＝d
BΔp＝0
Δp＝Km 2
in the formula, A and B are respectively a node-branch incidence matrix and a loop-branch incidence matrix of a thermodynamic system, m is a mass flow vector of a hot water pipeline, d is a mass flow vector injected by a node, delta p is a pipeline pressure drop vector, and K is a pipeline friction coefficient;
the thermal part model is:
where x and T are space and time variables, respectively, v is the water flow velocity, T p Indicating the temperature, T, of the pipe i n Denotes the temperature, m, of node i b Is the mass flow rate of the pipe b,
is the outlet temperature of the duct k, < >>
Is the inlet temperature of the pipeline k, λ is the thermal resistivity of the pipeline, T a Is ambient temperature, is based on>
And &>
Respectively representing that the node i is taken as a head node and a tail node pipeline set.
3. The method for modeling optimal power flow for an electrothermal integrated energy system considering full dynamics according to claim 2,
the compressor model is as follows:
p 2,t ＝CPR 1 ×p 1,t
P c,t ＝C a m a,t (T 2,t -T 1,t )
wherein t is a time stamp, p 1,t And p 2,t Respectively representing the inlet and outlet pressures of the compressor at time T, T 1,t And T 2,t Respectively representing the inlet and outlet temperatures, CPR, of the compressor at time t 1 Indicating the pressure ratio, beta, of the compressor 1 Expressing the adiabatic coefficient of air, eta 1 To representCompressor efficiency, P c,t Representing compressor power consumption at time t, C a Represents the specific heat capacity of air, m a,t Representing the mass flow of incoming air.
4. The method for modeling optimal power flow for an electrothermal integrated energy system considering full dynamics according to claim 2,
the combustion chamber model is:
in the formula, T 3,t Respectively representing the combustion chamber temperature, beta, at time t 2 Representing the heat storage coefficient of the combustion chamber, H g Represents a combustion chamber calorific value, LHV represents a lower calorific value of fuel, m f,t Representing the mass flow of the inflowing fuel, C s Is the specific heat capacity of the mixed flue gas.
5. The method for modeling optimal power flow for an electrothermal integrated energy system considering full dynamics according to claim 2,
the turbine model is:
p 3,t ＝CPR 2 p 2,t
P b,t ＝C s (m a,t +m f,t )(T 4,t -T 3,t )
in the formula, T 4,t Respectively representing the turbine outlet temperature, beta, at time t 3 Denotes the adiabatic efficiency, P, of the combustion chamber b,t For the total power of the turbine production at time t, CPR 2 Indicating the pressure ratio of the turbine.
6. The method for modeling optimal power flow for an electrothermal integrated energy system considering full dynamics according to claim 2,
the power for generating electricity and supplying heat produced by the cogeneration unit is respectively as follows:
P g,t ＝η 2 η 3 (P b,t -P c,t )
φ g,t ＝η 2 (1-η 3 )(P b,t -P c,t )
in the formula, P g,t And phi g,t Power, eta, for electricity generation and heat supply, respectively, for cogeneration units 2 Indicating mechanical efficiency, eta, of the steam turbine 3 Indicating the thermoelectric ratio.
7. The optimal energy flow modeling method considering full dynamics of the electric-thermal integrated energy system according to claim 2, characterized in that the heat exchanger model is:
in the formula, beta 4 Representing the heat storage coefficient of the heat exchanger, C w Represents the specific heat capacity of water, m w,t Is the mass flow, T, of the water flow in the heat exchanger 5,t And T 6,t Respectively representing the outlet and inlet temperatures of the heat exchanger.
8. The electric heat integrated energy system optimal power flow modeling method considering full dynamics as claimed in claim 1, wherein the step 3 comprises:
step 301, in the transmission network system, simplifying the nonlinear alternating current power flow model by making the phase angle difference of the node voltage be 0 and making the amplitude of the node voltage be 1 to obtain a power system power flow model:
wherein i and j represent node numbers, respectively,
representing the voltage amplitude, P, of node i G,i And P L,i Representing the generator active power and the load active power of node i, Q G,i And Q L,i Generator reactive power and load reactive power, G, representing node i ij And B ij Representing the conductance and susceptance, θ, between node i and node j ij Representing the phase angle difference, P, between node i and node j ij And Q ij Active power and reactive power representing transmissions between node i and node j;
step 202, discretizing a thermodynamic part model in the thermodynamic system model by adopting a discrete time and space step length solution to obtain a discretized thermodynamic part model:
in the formula, J 1 ，J 2 ，J 3 And J 4 Respectively are the transmission coefficients;
represents the duct temperature at time j +1, is greater than>
Represents the duct temperature at instant i +1 j +1, is present>
Represents the duct temperature at time j, i->
Represents the pipe temperature at time i of j + 1; Δ x and Δ t are discrete space and time steps, respectively, L and Γ are the pipe length and time interval, respectively, N x And N t Number of spatial and temporal steps, respectively>
Step 203, discretizing a combustion chamber model and a heat exchanger model by adopting a backward Euler format;
wherein the discrete combustion chamber model is:
the discrete heat exchanger model is as follows:
9. the method of modeling optimal power flow for an electrothermal integrated energy system considering full dynamics according to claim 8,
step 4 comprises the following steps:
step 401, establishing a power system operation safety constraint, including:
node voltage amplitude constraint:
phase angle constraint:
Branch transmission power constraint:
the active power of the generator is restricted:
and (3) generator reactive power constraint:
in the formula (I), the compound is shown in the specification,
represents an apparent upper power limit, based on a comparison of the apparent power level of the transmission between node i and node j>
And &>
The lower and upper limits of active power production for generator i, device for combining or screening>
And &>
A lower limit and an upper limit for producing reactive power for the generator i;
step 402, establishing thermodynamic system operation safety constraints, including:
and (3) node pressure constraint:
capacity constraints of mass flow of pipes and nodes:
and (3) restricting the change rate of the mass flow of the pipeline: gamma ray min m i,t-1 ≤m i,t ≤γ max m i,t-1 ,γ min d i,t-1 ≤d i,t ≤γ max d i,t-1 ；
And (3) node water supply temperature constraint:
and (3) node return water temperature constraint:
in the formula (I), the compound is shown in the specification,
and &>
Lower and upper limits, respectively, of the water pressure at node i>
And &>
Lower and upper mass flow limits,. And `, respectively, of the conduit i>
And &>
Lower and upper limits, respectively, of the mass flow of node i min And gamma max Are respectively massLower and upper limits for the rate of change of flow>
And &>
Lower limit and upper limit of water supply temperature for a node i at the time t respectively>
And
respectively is the lower limit and the upper limit of the backwater temperature of the node i at the time t;
step 403, establishing the operation safety constraint of the cogeneration unit, including:
compressor inlet temperature and pressure constraints: t is a unit of 1 min ≤T 1,t ≤T 1 max ，
Mass flow constraints of combustor input fuel:
steam turbine outlet pressure constraint:
fuel and air mixture ratio constraints: alpha is alpha min m f,t ≤m a,t ≤α max m f,t ；
Temperature constraint of the combustion chamber:
steam turbine outlet temperature constraint:
thermoelectric ratio constraint:
in the formula, T 1 min And T 1 max Respectively a lower limit and an upper limit for the compressor inlet temperature,
and &>
Lower and upper limit of the compressor inlet pressure, respectively>
And &>
Lower limit and upper limit, respectively, of the fuel mass flow>
And &>
Lower and upper limits, respectively, of the turbine outlet pressure, alpha min And alpha max Distribution is lower and upper limits of air-fuel ratio, T 3 min And T 3 max Lower and upper limits of combustion chamber temperature, and T 4 max Respectively a lower limit and an upper limit of the outlet temperature of the steam turbine>
And &>
Lower and upper limits of the thermoelectric ratio, respectively;
step 404, establishing an objective function of the optimal energy flow model with the aim of minimizing the system operation cost:
wherein F represents the total operating cost of the electric heating integrated energy system, minF represents the minimum total operating cost of the electric heating integrated energy system, and c 1 f i,t The total operation cost of the ith combined heat and power generation unit in the electric-heat integrated energy system in the time period t is represented; c. C 2 g j,t The total operation cost of the jth common generator in the electric heating comprehensive energy system in the time period t is represented; c. C 1 And c 2 The fuel unit price of the cogeneration unit and the coal unit price of the common generator are respectively,
is a common generator set and is used for>
For a combined heat and power generating unit, f i,t And g j,t Respectively as cost functions of the cogeneration unit i and the common generator j in the time period t; n is a radical of t The optimal sampling time set in the set calculation period is respectively expressed as:
in the formula, mu 11 ，μ 12 And mu 13 Respectively, the conversion factor between the generated power and the fuel flow of the cogeneration unit 21 ，μ 22 And mu 23 Respectively are conversion coefficients between the generating power of the common generator and the coal burning quantity; p G,j Representing the generator active power at node j; p G,i Representing the generator active power at node i.
10. The electric heating comprehensive energy system optimal energy flow modeling system considering full dynamics is based on the electric heating comprehensive energy system optimal energy flow modeling method considering full dynamics in any one of claims 1 to 9, and is characterized in that the system comprises:
the system comprises a data acquisition module, an energy management analysis module, a logic calculation module and a heat supply model module;
the data acquisition module is used for acquiring data of the electric heating comprehensive energy system;
the energy management analysis module is used for establishing a simplified equation of the nonlinear alternating current power flow and a discrete equation of the dynamic cogeneration unit and the thermodynamic system;
the logic calculation module is used for establishing an operation safety constraint condition of the electric heating comprehensive energy system based on a simplified alternating current power flow equation, a discrete combined heat and power generation unit and a thermodynamic system equation by taking the minimized system operation cost as a target;
and the optimal energy flow modeling module is used for establishing an optimal energy flow model considering full dynamics according to the operation safety constraint conditions and the electric heating comprehensive energy system model.
11. A terminal comprising a processor and a storage medium; the method is characterized in that:
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 9.
12. 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 according to any one of claims 1 to 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211302252.0A CN115906411B (en) | 2022-10-24 | 2022-10-24 | Optimal energy flow modeling method and system for electric heating comprehensive energy system considering full dynamics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211302252.0A CN115906411B (en) | 2022-10-24 | 2022-10-24 | Optimal energy flow modeling method and system for electric heating comprehensive energy system considering full dynamics |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115906411A true CN115906411A (en) | 2023-04-04 |
| CN115906411B CN115906411B (en) | 2024-06-04 |
Family
ID=86480336
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211302252.0A Active CN115906411B (en) | 2022-10-24 | 2022-10-24 | Optimal energy flow modeling method and system for electric heating comprehensive energy system considering full dynamics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115906411B (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150032278A1 (en) * | 2013-07-25 | 2015-01-29 | International Business Machines Corporation | Managing devices within micro-grids |
| CN110752605A (en) * | 2019-10-29 | 2020-02-04 | 国家电网有限公司 | Optimal power flow calculation method for electric-thermal coupling comprehensive energy system |
| CN110866627A (en) * | 2019-08-16 | 2020-03-06 | 东南大学 | Multi-region electricity-gas coupling comprehensive energy system optimal scheduling method considering step gas price |
| WO2020093296A1 (en) * | 2018-11-06 | 2020-05-14 | 国电南瑞科技股份有限公司 | Interval power flow calculation method for power-heat integrated energy system |
| CN111815042A (en) * | 2020-06-30 | 2020-10-23 | 天津大学 | Electric heating comprehensive energy system optimization scheduling method considering refinement heat supply network model |
| CN112989612A (en) * | 2021-03-18 | 2021-06-18 | 贵州电网有限责任公司 | Linear modeling method for electric heating comprehensive energy system based on Mackek envelope |
| CN113111555A (en) * | 2021-04-14 | 2021-07-13 | 东南大学 | Mass-adjustment thermodynamic system energy flow rapid calculation method based on superposition decoupling method |
| CN113725915A (en) * | 2021-08-26 | 2021-11-30 | 国网浙江省电力有限公司经济技术研究院 | Rural electric heating comprehensive energy system operation optimization method considering renewable energy uncertainty and thermal inertia |
| CN113837589A (en) * | 2021-09-18 | 2021-12-24 | 清华大学 | N-1 scheduling method and device for comprehensive energy system |
| CN114077934A (en) * | 2022-01-19 | 2022-02-22 | 国网湖北省电力有限公司经济技术研究院 | Comprehensive energy microgrid interconnection system and scheduling method thereof |
| CN114971147A (en) * | 2022-03-23 | 2022-08-30 | 南京工程学院 | Electric-heat-gas comprehensive energy system distribution robust optimization scheduling method based on Hausdorff distance |
-
2022
- 2022-10-24 CN CN202211302252.0A patent/CN115906411B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150032278A1 (en) * | 2013-07-25 | 2015-01-29 | International Business Machines Corporation | Managing devices within micro-grids |
| WO2020093296A1 (en) * | 2018-11-06 | 2020-05-14 | 国电南瑞科技股份有限公司 | Interval power flow calculation method for power-heat integrated energy system |
| CN110866627A (en) * | 2019-08-16 | 2020-03-06 | 东南大学 | Multi-region electricity-gas coupling comprehensive energy system optimal scheduling method considering step gas price |
| CN110752605A (en) * | 2019-10-29 | 2020-02-04 | 国家电网有限公司 | Optimal power flow calculation method for electric-thermal coupling comprehensive energy system |
| CN111815042A (en) * | 2020-06-30 | 2020-10-23 | 天津大学 | Electric heating comprehensive energy system optimization scheduling method considering refinement heat supply network model |
| CN112989612A (en) * | 2021-03-18 | 2021-06-18 | 贵州电网有限责任公司 | Linear modeling method for electric heating comprehensive energy system based on Mackek envelope |
| CN113111555A (en) * | 2021-04-14 | 2021-07-13 | 东南大学 | Mass-adjustment thermodynamic system energy flow rapid calculation method based on superposition decoupling method |
| CN113725915A (en) * | 2021-08-26 | 2021-11-30 | 国网浙江省电力有限公司经济技术研究院 | Rural electric heating comprehensive energy system operation optimization method considering renewable energy uncertainty and thermal inertia |
| CN113837589A (en) * | 2021-09-18 | 2021-12-24 | 清华大学 | N-1 scheduling method and device for comprehensive energy system |
| CN114077934A (en) * | 2022-01-19 | 2022-02-22 | 国网湖北省电力有限公司经济技术研究院 | Comprehensive energy microgrid interconnection system and scheduling method thereof |
| CN114971147A (en) * | 2022-03-23 | 2022-08-30 | 南京工程学院 | Electric-heat-gas comprehensive energy system distribution robust optimization scheduling method based on Hausdorff distance |
Non-Patent Citations (4)
| Title |
|---|
| BO ZHAO等: "Optimal Scheduling Method for Electrical-Thermal Integrated Energy System Considering Heat Storage Characteristics of Heating Network", IEEE * |
| LIU, L.等: "Regionmodel and application of regional integrated energy system security analysis", APPL. ENERGY 260, 114268 * |
| 李杭;王晗;侯恺;: "考虑多种能源耦合运行方式的综合能源潮流计算方法", 分布式能源, no. 04 * |
| 黎静华;黄玉金;张鹏;: "综合能源系统多能流潮流计算模型与方法综述", 电力建设, no. 03 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115906411B (en) | 2024-06-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Tian et al. | System modeling and optimal dispatching of multi-energy microgrid with energy storage | |
| Luo et al. | Optimization of a solar-based integrated energy system considering interaction between generation, network, and demand side | |
| Tan et al. | A wind power accommodation capability assessment method for multi-energy microgrids | |
| Xu et al. | Quantification of flexibility of a district heating system for the power grid | |
| CN110175311B (en) | Optimized power flow calculation method based on multi-energy coupling model | |
| Daneshgar et al. | Optimization of power and heat dual generation cycle of gas microturbines through economic, exergy and environmental analysis by bee algorithm | |
| JP2008083971A (en) | Method for simulating system having solar generator/wind generator/cogenerator | |
| Rigo-Mariani et al. | A combined cycle gas turbine model for heat and power dispatch subject to grid constraints | |
| CN111428351B (en) | Electric-thermal comprehensive energy system tide calculation method based on forward-push back substitution method | |
| Zhang et al. | Energy management of ultra-short-term optimal scheduling of integrated energy system considering the characteristics of heating network | |
| Li et al. | Gradient descent iterative method for energy flow of integrated energy system considering multiple modes of compressors | |
| Tahir et al. | Exergy hub based modelling and performance evaluation of integrated energy system | |
| CN113177185B (en) | Comprehensive thermoelectric system scheduling method based on tightening McCormick method | |
| CN115392035A (en) | Improvement method for optimizing and scheduling electric-gas interconnection comprehensive energy system | |
| Yanan et al. | Intelligent scheduling optimization of seasonal CCHP system using rolling horizon hybrid optimization algorithm and matrix model framework | |
| Dai et al. | Active and passive thermal energy storage in combined heat and power plants to promote wind power accommodation | |
| CN111783309A (en) | Dynamic simulation method of steam heating network based on internal conservation | |
| CN112926835A (en) | Comprehensive energy system optimization scheduling method considering dynamic characteristics of heat supply network | |
| Li et al. | Classification and location scheme selection of coupling components in integrated electrical and heating systems with renewable energy | |
| Qin et al. | Increasing flexibility of combined heat and power systems through optimal dispatch with variable mass flow | |
| CN110020506A (en) | Difference scheme selection method based on electroheating type integrated energy system running optimizatin | |
| Jin et al. | Evaluating the impact of virtual energy storage under air conditioning and building coupling on the performance of a grid-connected distributed energy system | |
| CN116611706A (en) | Dynamic carbon emission factor measuring and calculating method based on multi-energy main body | |
| CN115906411B (en) | Optimal energy flow modeling method and system for electric heating comprehensive energy system considering full dynamics | |
| Deng et al. | Optimal operation of energy hubs in an integrated energy network considering multiple energy carriers |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |