CN115952990A - Carbon emission accounting method and system based on park demand response economic dispatching - Google Patents
Carbon emission accounting method and system based on park demand response economic dispatching Download PDFInfo
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Abstract
The invention provides a carbon emission accounting method and a system based on park demand response economic dispatch, which comprises the following steps: building each component model in the park to obtain a park demand response economic dispatching model and a flow result thereof; calculating the carbon emission amount of the input of the power distribution network by combining the carbon emission coefficient of the power grid in the area of the park; calculating the carbon emission of the combined heat and power based on the combined heat and power characteristics of the combined heat and power and combined with a combined heat and power unit model; based on the battery energy storage device model, combining the carbon storage and carbon transfer characteristics of the battery energy storage device to calculate the carbon emission of the battery energy storage device; and calculating net carbon intensity of each node in the park based on a trend result of a park demand response economic dispatching model, the input carbon emission of the power distribution network, the carbon emission of the cogeneration and the carbon emission of a battery energy storage device, and accounting for main carbon emission at a demand side. The method fully considers and calculates the carbon emission of each component in the park, and provides data support for promoting carbon reduction development of the park.
Description
Technical Field
The invention relates to the technical field of electric power regulation, in particular to a carbon emission accounting method and system based on park demand response economic dispatch, and also provides a corresponding terminal and medium.
Background
The multi-energy park is the visual expression form at the tail end of the energy internet, is coupled with various energy sources, coordinates and dispatches various energy supply systems, improves the energy utilization rate and reduces the economic cost of the energy system. With the increase of global energy crisis and greenhouse effect, the carbon emission of multi-energy parks is gradually becoming a research hotspot. With the gradual development of a multi-energy park, the access proportion of renewable energy sources such as wind power (WT) and photovoltaic (VP) is increased continuously, and the possibility of participating in carbon emission reduction in the park is provided. If the optimal scheduling capability of the park can be fully exerted, the huge potential of the park comprehensive energy system under the double-carbon target to realize carbon emission reduction is excavated, and support can be provided for the low-carbon development of the park.
Although carbon emission accounting methods are established preliminarily in China, the practical problems of incomplete working mechanism, relatively backward method system, large deviation of energy consumption and partial fossil energy carbon emission factor statistics bases, lack of annual continuity of carbon emission accounting results and the like still exist, and for different enterprises, the carbon accounting and evaluation analysis of the enterprises are different. At present, a plurality of park multi-energy systems are researched for low-carbon economic dispatching, general research is mostly developed on the power generation side, and carbon emission accounting research on the demand side is relatively less. With the development of low-carbon operation of a power system, demand side carbon calculation based on a carbon emission flow theory has been applied to a certain extent. The method has obvious advantages for the low-carbon economic dispatching of the park.
The invention discloses an optimized dispatching method and system for an electricity-cold-heat-gas multi-energy demand typical park in Chinese patent application with publication number CN114611823A, and discloses an optimized dispatching method for an objective function of an electricity-cold-heat-gas multi-energy system considering cost and carbon emission. The method optimizes the cost of electricity and gas, carbon emission and energy demand by adopting reinforcement learning, and realizes the real-time scheduling optimization of the multi-energy system. However, the method only reduces the carbon emission to the power generation side, cannot account for the carbon emission on the load side, and cannot mobilize the positivity of the load side to participate in emission reduction.
The invention discloses a Chinese patent application with publication number CN115241931A, and discloses a park comprehensive energy system scheduling method based on a time-varying power carbon factor curve, and a park comprehensive energy system low-carbon economic scheduling method based on time-varying power carbon factor modeling under the influence of a conventional unit. The method aims at modeling the unit carbon emission variation versus degree electric carbon emission factor, and realizes the form that the schedulable resources on the charge side participate in low-carbon economic coordination optimization. However, the method adopts a unified carbon emission factor in the park, and the influence of the micro-grid structure on the carbon emission factor is not considered.
The invention discloses a Chinese patent application with publication number CN115238597A, relating to a construction method of a campus level comprehensive energy system source grid carbon-loaded emission model, and discloses a reduction method for distributing grid loss to power consumption terminals by adopting empire state competition algorithm optimization. The method utilizes the DBN neural network to train and account the carbon emission of the coal-fired power plant, and utilizes the carbon emission flow theory to reduce the carbon emission accounting for the network loss to the power consumption terminal, thereby realizing the accounting of the source network carbon charge emission of the park-level comprehensive energy system. However, the method does not perform fine modeling on the components in the park, and cannot reflect the carbon emission characteristics of the components in the park.
Therefore, there is a strong need in the art to find methods for considering the microgrid structure, for fine modeling of the carbon emission model of the demand-side component, and for accounting for the carbon emission. At present, no explanation or report of the similar technology of the invention is found, and similar data at home and abroad are not collected.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a carbon emission accounting method and a system based on park demand response economic dispatch, and also provides a corresponding terminal and a corresponding medium.
According to an aspect of the present invention, there is provided a carbon emission accounting method based on a campus demand response economic dispatch, including:
building a park demand response economic dispatching model to obtain each component model in the park and a trend result thereof; wherein, each subassembly in the garden includes: the system comprises a cogeneration unit, a battery energy storage device, a wind turbine, a photovoltaic unit and a charging pile;
calculating the carbon emission amount of the input of the power distribution network by combining the carbon emission coefficient of the power grid in the area of the park;
calculating the carbon emission of the combined heat and power generation based on the combined heat and power generation power characteristics and combined with the combined heat and power generation unit model;
calculating the carbon emission of the battery energy storage device based on the battery energy storage device model and by combining the carbon storage and carbon transfer characteristics of the battery energy storage device;
and calculating net carbon intensity of each node of the park based on the trend result of the park demand response economic dispatching model, the input carbon emission of the power distribution network, the carbon emission of the cogeneration and the carbon emission of the battery energy storage device, and accounting for main carbon emission on the demand side.
Optionally, the constructing a park demand response economic dispatching model to obtain each component model and a trend result thereof in the park includes:
analyzing source network and load storage resources of park contact power distribution network, classifying nodes of the power distribution network, and defining S feeder For each feeder set in the park, S bus For a node set of the distribution network, S G For a set of garden power nodes, S D A load node set of the garden is obtained;
based on a Distflow model, a second-order cone relaxation method is adopted, and the park microgrid second-order cone current constraint in the t-th time period is established as follows:
wherein: the formulas (1) and (2) are respectively the balance constraints of active power and reactive power of the garden node, in the formula, P ki,t And Q ki,t Respectively representing the active power and reactive power variation of feeder line ki in the representation time period tAmount, P ij,t And Q ij,t Respectively represents the active power and reactive power variables r of the feeder ij in the time period t ij And x ij Respectively the resistance and reactance parameters of the feeder ij,
the squared current variable, P, of the feeder ij for a period t g,t And Q g,t Respectively an active power variable and a reactive power variable P of the unit at a t node i in a time period d,t And Q d,t Load active power and reactive power variables are respectively at a node at a time interval t; the formula (3) is the relation constraint among the node voltage, the active power, the reactive power and the current on the park feeder line, wherein, the relation constraint is that the voltage is greater than or equal to the preset value>
For a time period t node i a squared variable, <' >>
Is the square variable of the voltage at node j in the time period t; the formula (4) is the feeder capacity constraint after the second-order cone relaxation; the formulas (5) and (6) are respectively the upper and lower limits of the voltage square and the current square of the park, wherein V max And V min Respectively an upper and a lower voltage limit, I ij,max Is the current upper limit value;
the various components in the campus are modeled as follows:
the method for constructing the cogeneration unit model comprises the following steps:
wherein, the formula (7) is the restriction of the upper and lower output limits of the cogeneration unit in the time period t, in the formula,
is a variable from 0 to 1, and is,
the power variable is the time t of the cogeneration unit;
And &>
Respectively the minimum power and the maximum power of the cogeneration unit;
the method for constructing the battery energy storage device model comprises the following steps:
wherein, the equations (8) and (9) are the charge and discharge power constraints of the battery energy storage device in the time period t, wherein,
and
is a charging and discharging power variable, respectively>
Is a variable of 0-1, restricts the energy storage device of the battery not to be charged and discharged simultaneously,
and &>
Respectively charging upper and lower limits of power for the battery energy storage device>
And &>
Respectively representing the upper and lower limits of the discharge power of the battery energy storage device; equation (10) is a constraint on the energy change of the battery energy storage device over a time period t, in which case>
For the energy storage situation of the battery energy storage device at the time t, is>
For the energy storage situation of the battery energy storage device at the time t-1, ->
And
respectively the charging efficiency and the discharging efficiency of the battery energy storage device, and delta t is a discrete time step length; formula (11) is the energy upper and lower limit constraint of the battery energy storage device in time period t, wherein, the device is combined with the device>
And &>
The energy storage upper and lower limits; equation (12) is that the energy storage of the battery energy storage is equal at the end time and at the beginning time, in which case the combination is greater or less>
For initial battery energy storage meansEnergy storage condition->
The energy storage condition of the battery energy storage device at the ending moment;
the method comprises the following steps of (1) constructing a photovoltaic unit and a wind generating unit model:
wherein, the formulas (13) and (14) are respectively the upper and lower limit constraints of the output of the photovoltaic generator and the wind generator in the time period t,
and &>
Is the power variable of the photovoltaic unit and the wind generator unit in the time period t respectively>
And &>
The maximum power of the photovoltaic unit and the maximum power of the wind turbine unit are respectively;
the construction of the charging pile model comprises the following steps:
wherein, the formula (15) and the formula (16) are respectively the charging power constraint of the electric automobile in a quick charging pile which adopts direct current to realize the charging function and a slow charging pile which adopts alternating current to match with a vehicle-mounted charger to realize the charging function under the time interval t,
and &>
Is respectively the charging power variable of the electric automobile in the fast charging pile and the slow charging pile at the time t>
And &>
Is a 0-1 variable, is selected>
And &>
Respectively the minimum charging power and the maximum charging power of the electric automobile in the quick charging pile,
and &>
Respectively obtaining the minimum charging power and the maximum charging power of the electric automobile in the slow charging pile; the formula (17) restricts the electric automobile to be charged only in one pile at the same time period; the formulas (18) and (19) are respectively the simultaneous working quantity restriction of the quick-filling pile and the slow-filling pile, wherein N is f And N s The number of the fast-filling piles and the number of the slow-filling piles are respectively; equations (20) to (22) are constraints related to the charging state of the electric vehicle, wherein the charge status is greater than or equal to the charge status of the electric vehicle>
For the charging state of the electric vehicle at time t>
The state of charge is expected for the end of the electric vehicle,
for the charging state of the electric vehicle at the time t-1>
For the charging efficiency of the electric automobile in the quick charging pile, delta t is a discrete time step length, and the length of the delta t is greater than or equal to the length of the delta t>
For the charging efficiency of the electric automobile in the slow charging pile,
the scheduling time of the park is T, the minimization of the total power purchase cost of the park is taken as a target, and the target function for constructing the park demand response economic scheduling model is as follows:
in the formula (23), P D,t For the active power of the distribution network input park during the period t, a t The time-sharing electricity price is divided in the time period t of the park; taking the formula (23) as an objective function and the formulas (1) to (22) as constraints to obtain a park demand response economic dispatching model; and carrying out optimization objective solution on the park demand response economic dispatching model to obtain a park microgrid second-order cone power flow constraint condition and states of all components in the park, and obtaining a power flow result of the park demand response economic dispatching model.
Optionally, the calculating, in combination with the power grid carbon emission coefficient of the area where the park is located, an input carbon emission amount of the power distribution network includes:
analyzing the carbon emission level of the power distribution network corresponding to the target park, and obtaining the carbon emission coefficient c of the power distribution network in the park by using historical data 0 Taking this as the carbon emission level of the campus input power;
based on the carbon emission coefficient c of the power distribution network 0 And calculating the input carbon emission of the distribution network by combining the park electricity purchasing result as follows:
E 0,t ＝c 0 P D,t Δt(62)
wherein E is 0,t Carbon emissions, c, into the distribution network for a period of time t 0 Carbon emission coefficient, P, of distribution network in the area D,t And inputting the active power of the power distribution network input park in the period of t, wherein delta t is a discrete time step length.
Optionally, the calculating the co-generation carbon emission based on the co-generation electric heat power characteristics in combination with the co-generation plant model includes:
defining the electric heating coefficient of the cogeneration unit as the electric power equivalent thermal power coefficient
The carbon emission of the cogeneration heating power is calculated as follows:
wherein,
for the electric power carbon emission amount of the cogeneration unit>
A heat power carbon emission coefficient for the combined heat and power unit>
For the electric power equivalent thermal power coefficient of the cogeneration unit, is modulated>
The method comprises the following steps of (1) obtaining a power variable of a cogeneration unit at a time t, wherein delta t is a discrete time step; the formula (25) converts the electric power of the cogeneration unit into thermal power through electric-carbon conversion, and calculates the carbon emission according to the thermal power carbon emission coefficient;
according to the equivalent carbon emission coefficient of the electric heating power instead of the carbon emission coefficient of the loss part, calculating the carbon emission coefficient of the loss part of the cogeneration unit
Comprises the following steps:
wherein eta is h Is the heat efficiency of the cogeneration unit eta e The electric efficiency of the cogeneration unit is obtained; calculating to obtain an equivalent carbon emission coefficient of the total value of the electric power and the thermal power by the formula (26), and taking the equivalent carbon emission coefficient as the carbon emission coefficient of the external output power of the combined heat and power generation unit;
analyzing carbon emission of a loss part of the cogeneration unit, wherein the carbon emission of the loss part is carbon emission which the cogeneration unit should bear, and calculating the carbon emission of the cogeneration unit as follows:
wherein,
the carbon emission of the loss part of the cogeneration unit is reduced; the equation (27) calculates the carbon emission coefficient due to the loss part by using the equivalent carbon emission coefficient, and forms the carbon emission to be borne by the cogeneration itself.
Optionally, the calculating, based on the battery energy storage device model, carbon emission of the battery energy storage device by combining carbon storage and carbon transfer characteristics of the battery energy storage device includes:
according to the self carbon storage level of the battery energy storage device, calculating the carbon emission coefficient of the battery energy storage device in the discharge state as follows:
wherein,
carbon emission coefficient for the discharge state of a battery energy storage device>
For the carbon purge stored in the battery energy storage device at the preceding time t-1, is/are>
The energy storage condition of the battery energy storage device at the last moment t-1 is obtained; />
Analyzing the carbon emission of the battery energy storage device, and calculating the carbon emission according to two states of charging and discharging of the battery energy storage device as follows:
wherein,
storing a carbon emission value for the battery energy storage device itself at time t>
Storing the carbon emissions for the battery energy storage device itself at time t-1>
And &>
The charging efficiency and the discharging efficiency of the battery energy storage device are respectively,
and &>
A charging and discharging power variable for the time period t, respectively>
The discharge amount of the power carbon is charged and discharged for the battery energy storage device at the time t; equations (29) and (30) store and undertake carbon emissions calculations for the battery energy storage device, respectively, NCI i,t Representing the net carbon strength at the location of the battery energy storage device over time period t.
Optionally, the calculating net carbon intensity of each node in the park based on the trend result of the park demand response economic dispatch model, the input carbon emission of the power distribution network, the co-generation carbon emission of the heat and power, and the carbon emission of the battery energy storage device, and accounting for the main carbon emission at the demand side includes:
calculating net carbon intensity and network carbon loss of each node of the park in a certain time period according to a carbon emission flow analysis method based on a trend result of the park demand response economic dispatching model, the input carbon emission of the power distribution network, the carbon emission of the cogeneration and the carbon emission of the battery energy storage device;
performing carbon accounting on the electric automobile and the conventional load by combining the calculation result of the net carbon strength;
and completing accounting of the main carbon emission on the demand side based on the carbon accounting result.
Optionally, the calculating, based on the trend result of the park demand response economic dispatch model, the input carbon emission of the power distribution network, the co-generation carbon emission of the heat and power, and the carbon emission of the battery energy storage device, the net carbon intensity of each node of the park and the network carbon loss in a certain period according to a carbon emission flow analysis method includes:
taking the carbon emission input by the power distribution network, the carbon emission of the cogeneration and the carbon emission of the battery energy storage device as the carbon emission input by each node, and calculating the initial input power and the initial input carbon emission coefficient of each node as follows:
wherein, P Ni Injecting power, N, into each node unit of the park network g For the number of units at each node of the park, P i-n Power is injected for the nth set of nodes i,
carbon emissions, c, are input for the unit time node i n Is the carbon emission coefficient of the nth unit,
is the carbon emission per unit time of the node in which the ith energy storage input is located>
For the power of the node at which the ith energy storage input is located>
A carbon emission coefficient for the ith stored energy;
obtaining a load flow result of each node outflow node set gamma based on the park demand response economic dispatching model + (i) And an ingress node set Γ - (i) And a corresponding number d + (i) And d - (i)；
Search for satisfaction of d - (k) Node k of =0, and d thereof - (k) Is set to-1;
the node k net carbon strength is calculated as:
if d- (i) is-1 for any node i, finishing the calculation, otherwise, performing the next step;
for all outflow nodes j of the node i, transferring the carbon flow to the node j through a feeder ij, subtracting one from the number of inflow nodes of the node j, and simultaneously calculating the carbon loss of the feeder ij as follows:
re-executing the searching process until the net carbon strength and carbon loss of all the nodes are finished;
and performing carbon accounting on the electric automobile and the conventional load by combining the calculated result of the net carbon strength, wherein the carbon accounting comprises the following steps:
wherein,
and &>
Respectively the carbon emission of the electric automobile in the fast charging pile and the slow charging pile in the time period t,
and &>
Respectively charging power variable, NCI, of the electric automobile in the fast charging pile and the slow charging pile in the time period t i,t Representing the net carbon intensity at the location of the battery energy storage device over time period t, device for selecting or keeping>
Based on the carbon emissions for a conventional load at time t, <' >>
Power at time t for a conventional load; and (36) to (38) respectively account for the quick charge, the slow charge and the normal load carbon responsibility of the electric automobile.
According to another aspect of the present invention, there is provided a carbon emission accounting system based on a campus demand response economic dispatch, including:
the park demand response economic dispatching model building module is used for building each component model in a park to obtain a park demand response economic dispatching model and a trend result thereof; wherein, each subassembly in the garden includes: the system comprises a cogeneration unit, a battery energy storage device, a wind turbine, a photovoltaic unit and a charging pile;
the power distribution network input carbon emission calculation module is used for calculating the power distribution network input carbon emission by combining the power distribution network carbon emission coefficient of the region where the park is located;
the heat and power cogeneration carbon emission calculation module is used for calculating the heat and power cogeneration carbon emission by combining the heat and power cogeneration unit model based on the heat and power cogeneration power characteristic;
the battery energy storage device carbon emission calculation module is used for calculating the carbon emission of the battery energy storage device based on the battery energy storage device model and combining the carbon storage and carbon transfer characteristics of the battery energy storage device;
and the carbon emission accounting module is used for calculating net carbon intensity of each node of the park and accounting the main body carbon emission at the demand side based on the trend result of the park demand response economic dispatching model, the input carbon emission of the power distribution network, the combined heat and power carbon emission and the carbon emission of the battery energy storage device.
According to a third aspect of the present invention, there is provided a computer terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the program being operable to perform the method of any of the above, or to operate the system of any of the above.
According to a fourth aspect of the invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, is operable to perform a method, or to run a system, as described in any of the above.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following beneficial effects:
according to the carbon emission accounting method and system based on park demand response economic dispatching, provided by the invention, the provided calculation model of the carbon emission of the cogeneration unit carries out fine modeling on the electric power carbon emission and the self-loss carbon emission of the cogeneration unit, is beneficial to analyzing the carbon emission composition of the cogeneration unit, and determines the carbon emission responsibility to be born.
According to the carbon emission accounting method and system based on park demand response economic dispatch, provided by the invention, the carbon emission calculation model of the battery energy storage device is used for carrying out fine modeling on the carbon transfer characteristic of the battery energy storage device, and the carbon reduction effect on a longer time scale in a park is facilitated by means of the carbon transfer characteristic of the battery energy storage device.
The carbon emission accounting method and system based on park demand response economic dispatch can fully consider and calculate the carbon emission of each component in a park, are beneficial to clearing the carbon emission responsibility of each component, and provide data reference support for promoting carbon reduction development of the park.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
fig. 1 is a flowchart of a method for carbon emissions accounting based on park demand response economic dispatch in a preferred embodiment of the present invention.
Fig. 2 is a schematic diagram of a carbon counting process according to a preferred embodiment of the present invention.
Figure 3 is a block diagram of the components of a carbon emissions accounting system based on park demand response economic dispatch in a preferred embodiment of the present invention.
Detailed Description
The following examples illustrate the invention in detail: the embodiment is implemented on the premise of the technical scheme of the invention, and a detailed implementation mode and a specific operation process are given. 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.
An embodiment of the invention provides a carbon emission accounting method based on park demand response economic dispatching, which can account carbon emission of each component of a park while optimizing dispatching.
According to an aspect of the present invention, there is provided a carbon emission accounting method based on a park demand response economic dispatch, which may include:
s1, building each component model in a park to obtain a park demand response economic dispatching model and a tide result thereof; wherein, each subassembly in the garden includes: the system comprises a cogeneration unit, a battery energy storage device, a wind turbine, a photovoltaic unit and a charging pile;
s2, establishing a power distribution network input carbon emission calculation model by combining the power grid carbon emission coefficient of the region where the park is located, and calculating the power distribution network input carbon emission;
s3, establishing a combined heat and power carbon emission calculation model based on combined heat and power characteristics and combined with a combined heat and power unit model, and calculating combined heat and power carbon emission;
s4, establishing a carbon emission calculation model of the battery energy storage device based on the battery energy storage device model and combining the carbon storage and carbon transfer characteristics of the battery energy storage device, and calculating the carbon emission of the battery energy storage device;
and S5, calculating net carbon intensity of each node of the park based on a trend result of the park demand response economic dispatching model, the input carbon emission of the power distribution network, the carbon emission of cogeneration and the carbon emission of a battery energy storage device, and accounting for main body carbon emission on the demand side.
In a preferred embodiment of S1, constructing each component model in the campus to obtain a campus demand response economic dispatch model and its load flow result may include:
s11, analyzing the source network and the storage resources of the park contact power distribution network, classifying the nodes of the power distribution network, and defining S feeder For each feeder set in the park, S bus For a node set of the distribution network, S G For a set of park power nodes, S D A load node set of the garden is obtained;
s12, based on the existing Distflow model, a second-order cone relaxation method is adopted, and the park microgrid second-order cone power flow constraint in the t-th time period is established as follows:
wherein: the formulas (1) and (2) are respectively the balance constraints of active power and reactive power of the garden node, in the formula, P ki,t And Q ki,t Respectively representing the variables P representing the active and reactive power of the feeder ki over a period t ij,t And Q ij,t Respectively representing the active power and reactive power variables, r, of the feeder ij in the time interval t ij And x ij Respectively the resistance and reactance parameters of the feeder ij,
the squared current variable, P, of the feeder ij for a period t g,t And Q g,t Respectively an active power variable and a reactive power variable P of the unit at a t node i in a time period d,t And Q d,t Load active power and reactive power variables are respectively at a node at a time interval t; the formula (3) is the relation constraint among the node voltage, the active power, the reactive power and the current on the park feeder line, wherein, the relation constraint is that the voltage is greater than or equal to the preset value>
For a time period t node i a squared variable, <' >>
Is the square variable of the voltage at node j in the time period t; the formula (4) is the feeder capacity constraint after the second-order cone relaxation; the equations (5) and (6) are respectively the upper and lower limits of the voltage square and the current square of the park, in which V max And V min Respectively an upper and a lower voltage limit, I ij,max Is the current upper limit value;
s13, the park comprises a cogeneration unit, a battery energy storage device, a wind turbine generator, a photovoltaic unit and a charging pile, and each component in the park is modeled as follows:
s131, constructing a cogeneration unit model as follows:
wherein, the formula (7) is the restriction of the upper and lower output limits of the cogeneration unit in the time period t, in the formula,
is a variable from 0 to 1, and is,
the power variable of the cogeneration unit in the time period t is obtained;
And &>
Respectively the minimum power and the maximum power of the cogeneration unit;
s132, constructing a battery energy storage device model as follows:
wherein, the equations (8) and (9) are the charge and discharge power constraints of the battery energy storage device in the time period t, wherein,
and
is a charging and discharging power variable, respectively>
Is a variable of 0-1, restricts the energy storage device of the battery not to be charged and discharged simultaneously,
and &>
Respectively charging upper and lower limits of power for the battery energy storage device>
And &>
Respectively representing the upper and lower limits of the discharge power of the battery energy storage device; equation (10) is a constraint on the energy change of the battery energy storage device over a time period t, in which case>
For the energy storage situation of the battery energy storage device at the time t, is>
In order to store energy in the battery energy storage device at the time t-1>
And
respectively the charging efficiency and the discharging efficiency of the battery energy storage device, and delta t is a discrete time step length; the formula (11) is the energy upper and lower limit constraint of the battery energy storage device in the time period t, wherein, the device is used for judging whether the battery energy storage device is in a normal state or not>
And &>
The energy storage upper and lower limits; equation (12) is that the energy storage of the battery energy storage is equal at the end time and at the beginning time, in which case the combination is greater or less>
Based on the energy storage condition of the battery energy storage device at the initial moment>
The energy storage condition of the battery energy storage device at the ending moment;
s133, building a photovoltaic unit and a wind generating unit model as follows:
wherein, the formulas (13) and (14) are respectively the upper and lower limit constraints of the output of the photovoltaic generator and the wind generator in the time period t,
and &>
Respectively is the power variable of the photovoltaic unit and the wind generator unit in a time period t>
And &>
The maximum power of the photovoltaic unit and the maximum power of the wind turbine unit are respectively;
s134, constructing a charging pile model as follows:
wherein, the formula (15) and the formula (16) are respectively the charging power constraints of the electric automobile in a fast charging pile (a charging pile realizing fast charging by adopting direct current) and a slow charging pile (a charging pile realizing charging function by adopting alternating current to be matched with a vehicle-mounted charger) at the time t,
and &>
Respectively charging power variables of the electric automobile in a fast charging pile and a slow charging pile in a time period t,
and &>
Is a 0-1 variable, is selected>
And &>
Respectively is the minimum charging power and the maximum charging power of the electric automobile in the quick charging pile>
And &>
Respectively obtaining the minimum charging power and the maximum charging power of the electric automobile in the slow charging pile; the formula (17) restricts the electric automobile to be charged only in one pile at the same time period; the simultaneous working quantity of the fast filling pile and the slow filling pile is respectively restricted by the formula (18) and the formula (19), wherein N is f And N s The number of the fast-filling piles and the number of the slow-filling piles are respectively; equations (20) to (22) are constraints related to the charging state of the electric vehicle, in combination with>
For the charging state of the electric vehicle at the time t->
For an expected charging state at the end of the electric vehicle>
Is charged at the moment t-1 and is>
For the charging efficiency of the electric automobile in the quick charging pile, delta t is a discrete time step length, and the length of the delta t is greater than or equal to the length of the delta t>
For the charging efficiency of the electric automobile in the slow charging pile, the charging efficiency is greater than or equal to>
And &>
For the upper and lower limits of the charging state of the electric automobile>
The charging state is the charging state at the end time of the electric automobile;
s14, the dispatching time of the park is T, the minimization of the total power purchasing cost of the park is taken as a target, and the target function for constructing the park demand response economic dispatching model is as follows:
in the formula (23), P D,t For the active power of the distribution network input park at t time period, a t Time-sharing electricity price in the time period t of the park; taking the formula (23) as an objective function and the formulas (1) to (22) as constraints to obtain a park demand response economic dispatching model; and (3) carrying out optimization objective solution on the park demand response economic dispatching model to obtain a park microgrid second-order cone power flow constraint condition and the states of all components in the park (namely the calculation results of the formulas (7) to (22)), and obtaining the power flow result of the park demand response economic dispatching model.
In a preferred embodiment of S2, establishing a power distribution network input carbon emission calculation model in combination with a power grid carbon emission coefficient of a region where the park is located, and calculating the power distribution network input carbon emission may include:
s21, analyzing the carbon emission level of the power distribution network corresponding to the target park, and obtaining the carbon emission coefficient c of the power distribution network in the park by using historical data 0 Taking this as the carbon emission level of the campus input power;
s22, based on the carbon emission coefficient c of the power distribution network 0 And combining the park electricity purchasing result, constructing a power distribution network input carbon emission calculation model, and calculating the power distribution network input carbon emission as follows:
E 0,t ＝c 0 P 0,t Δt(100)
wherein E is 0,t Carbon emissions, c, into the distribution network for a period of time t 0 Carbon emission coefficient, P, of distribution network in the area D,t And inputting the active power of the power distribution network input park in the period of t, wherein delta t is a discrete time step length.
In a preferred embodiment of S3, establishing a cogeneration carbon emission calculation model based on the cogeneration power-heat power characteristics and in combination with the cogeneration unit model, and calculating the input carbon emission of the power distribution network may include:
s31, analyzing the carbon emission of the cogeneration unit, dividing the energy output by the cogeneration unit into electric energy and heat energy, and defining the electric heating coefficient of the cogeneration unit as the electric power equivalent thermal power coefficient
Then, constructing a combined heat and power (cogeneration) carbon emission calculation model, and calculating the combined heat and power (cogeneration) carbon emission as follows:
wherein,
for the carbon discharge amount of the loss part of the cogeneration unit, the steam or the liquid is used for the judgment of the steam or the liquid>
A carbon emission coefficient for the heat power of the cogeneration unit>
For the electric power equivalent thermal power coefficient of the combined heat and power unit, is combined>
The method comprises the following steps of (1) obtaining a power variable of a cogeneration unit at a time t, wherein delta t is a discrete time step; the formula (25) changes the electric power of the cogeneration unit into thermal power through electric-carbon conversion, and calculates carbon emission according to a thermal power carbon emission coefficient;
s32, calculating the carbon emission coefficient of the loss part of the cogeneration unit
According to electricityThe thermal power equivalent carbon emission coefficient replaces the loss part carbon emission coefficient and is calculated as follows:
wherein,
the carbon emission coefficient, eta, of the loss part of the cogeneration unit h Is the heat efficiency of the cogeneration unit eta e The electric efficiency of the cogeneration unit is obtained; calculating to obtain an equivalent carbon emission coefficient of the total value of the electric power and the thermal power by the formula (26), and taking the equivalent carbon emission coefficient as the carbon emission coefficient of the external output power of the combined heat and power generation unit;
analyzing the carbon emission of the loss part of the cogeneration unit, namely the carbon emission which should be borne by the cogeneration unit, constructing a cogeneration carbon emission calculation model, and calculating the cogeneration carbon emission as follows:
wherein,
the carbon emission of the loss part of the cogeneration unit is reduced; the equation (27) calculates the carbon emission coefficient due to the loss part by using the equivalent carbon emission coefficient, and forms the carbon emission to be borne by the cogeneration itself.
In a preferred embodiment of S4, constructing a battery energy storage device carbon emission calculation model based on the battery energy storage device model and combining the carbon storage and carbon transfer characteristics of the battery energy storage device, and calculating the carbon emission of the battery energy storage device may include:
s41, analyzing the carbon emission coefficient of the battery energy storage device in the discharge state, and calculating the carbon emission coefficient of the battery energy storage device in the discharge state according to the carbon storage level of the battery energy storage device:
wherein,
a carbon discharge factor for the discharge state of the battery energy storage device>
For the carbon purge stored in the battery energy storage device at the preceding time t-1, is/are>
The energy storage condition of the battery energy storage device at the last moment t-1 is obtained;
s42, analyzing the carbon emission of the battery energy storage device, constructing a carbon emission calculation model of the battery energy storage device in two states of charging and discharging according to the two states of charging and discharging of the battery energy storage device, and calculating the carbon emission as follows:
wherein,
for the moment t the battery energy storage device stores the carbon discharge amount by itself, and>
for the moment t-1 the battery energy storage device stores the carbon discharge amount by itself, and>
and &>
Charging efficiency and discharging efficiency for the battery energy storage device, respectively>
And &>
Charge and discharge power variables in or on the battery energy storage device, respectively, during a time period t>
The discharge amount of the power carbon is charged and discharged for the battery energy storage device at the time t; equations (29) and (30) respectively store and undertake carbon emissions calculations for the battery energy storage device, NCI i,t The net carbon intensity at the location of the battery energy storage device, representing time period t, is generated during the subsequent carbon accounting process.
In a preferred embodiment of S5, as shown in fig. 2, calculating net carbon strength of each node in the park based on the trend result of the park demand response economic dispatch model, the distribution network input carbon emission, the cogeneration carbon emission, and the battery energy storage device carbon emission, and accounting for the demand-side bulk carbon emission may include:
s51, calculating net carbon strength and network carbon loss of each node in the park in a certain time period according to a carbon emission flow analysis method based on a trend result of the park demand response economic dispatching model, the input carbon emission of the power distribution network, the carbon emission of cogeneration and the carbon emission of a battery energy storage device; the method specifically comprises the following steps:
s511, taking the carbon emission input by the power distribution network, the carbon emission of cogeneration and the carbon emission of the battery energy storage device as the carbon emission input by each node, and calculating the initial input power and initial input carbon emission coefficients of each node as follows:
wherein, P Ni Injecting power, N, into each node unit of the park network g Number of units for each node of the park, P i-n Power is injected for the nth set of nodes i,
inputting carbon emission, c, for node i per unit time n Is the carbon discharge coefficient of the nth unit>
Is the carbon emission per unit time of the node in which the ith energy storage input is located>
For the power of the node at which the ith energy storage input is located>
A carbon emission coefficient for the ith stored energy;
s512, responding to the load flow result of the economic dispatching model based on the garden demand, and obtaining an outflow node set gamma of each node + (i) And an ingress node set Γ - (i) And a corresponding number d + (i) And d - (i)；
S513, searching for satisfying d - (k) Node k of =0, and d thereof - (k) Set to-1;
s514, calculating the net carbon intensity of the node k as follows:
s515, if for any node i, d - (i) If the value is-1, the calculation is ended, otherwise, the step S516 is entered;
s516, for all outflow nodes j of the node i, transferring the carbon flow to the node j through the feeder ij, reducing the number of inflow nodes of the node j by one, and meanwhile calculating the carbon loss of the feeder ij as follows:
re-executing S513 until the net carbon strength and carbon loss of all the nodes are finished;
s52, combining the calculation result of the net carbon strength, and performing carbon accounting on the electric automobile and the conventional load; the accounting process may include:
wherein,
and &>
Respectively the carbon emission of the electric automobile in a fast charging pile and a slow charging pile in a time period t,
and &>
Respectively charging power variable, NCI, of the electric automobile in the fast charging pile and the slow charging pile in the time period t i,t Represents a time period t the net carbon intensity at the location of the battery energy storage means>
In the case of a conventional load with a period t, the carbon emissions are combined>
Power at time t for a conventional load; the formula (36) and the formula (38) respectively account for the quick charging, the slow charging and the conventional load carbon responsibility of the electric automobile.
According to the carbon emission accounting method based on park demand response economic dispatching provided by the embodiment of the invention, the demand response characteristic of an electric automobile as a translatable load is considered, a park microgrid second-order cone power flow constraint is formed by using a Distflow model, a demand response economic dispatching model is established, the dispatching model takes the minimum total system operation cost as a target, the model is analyzed and solved, and an optimal dispatching solution is obtained; establishing a distribution network input carbon emission model by combining the regional power grid carbon emission coefficient; considering electric power carbon emission, thermal power carbon emission and loss of the cogeneration unit, and establishing a cogeneration carbon emission model; calculating the carbon emission of the battery energy storage device by combining the carbon storage and carbon transfer characteristics of the battery energy storage device; according to the carbon emission flow analysis method, the net carbon intensity of each node in the park is formed according to the tidal current condition and the carbon emission amount of the battery energy storage device, the main body carbon emission accounting of the demand side is completed, and the carbon emission accounting of the demand side of the comprehensive energy system of the park is realized.
An embodiment of the invention provides a carbon emission accounting system based on park demand response economic dispatch.
As shown in fig. 3, the carbon emission accounting system based on the park demand response economic dispatch provided by the embodiment may include:
the park demand response economic dispatching model building module is used for building each component model in a park to obtain a park demand response economic dispatching model and a trend result thereof; wherein, each subassembly in the garden includes: the system comprises a cogeneration unit, a battery energy storage device, a wind turbine, a photovoltaic unit and a charging pile;
the power distribution network input carbon emission calculation module is used for establishing a power distribution network input carbon emission calculation model by combining the power distribution network carbon emission coefficient of the region where the park is located, and calculating the power distribution network input carbon emission;
the heat and power cogeneration carbon emission calculation module is used for establishing a heat and power cogeneration carbon emission calculation model and calculating the heat and power cogeneration carbon emission based on the heat and power cogeneration power characteristic and in combination with a heat and power cogeneration unit model;
the battery energy storage device carbon emission calculation module is used for establishing a battery energy storage device carbon emission calculation model and calculating the carbon emission of the battery energy storage device based on a battery energy storage device model by combining the carbon storage and carbon transfer characteristics of the battery energy storage device;
and the carbon emission accounting module is used for calculating net carbon intensity of each node in the park and accounting main body carbon emission on the demand side based on a trend result of the park demand response economic dispatching model, input carbon emission of the power distribution network, cogeneration carbon emission and carbon emission of a battery energy storage device.
It should be noted that, the steps in the method provided by the present invention may be implemented by using corresponding modules, devices, units, and the like in the system, and those skilled in the art may implement the composition of the system with reference to the technical solution of the method, that is, the embodiment in the method may be understood as a preferred embodiment of constructing the system, and details are not described herein.
An embodiment of the present invention provides a computer terminal, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor is configured to execute the method according to any one of the above embodiments of the present invention or execute the system according to any one of the above embodiments of the present invention when executing the computer program.
Optionally, a memory for storing a program; a Memory, which may include a volatile Memory (RAM), such as a Static Random Access Memory (SRAM), a Double Data Rate Synchronous Dynamic Random Access Memory (DDR SDRAM), and the like; the memory may also comprise a non-volatile memory, such as a flash memory. The memories are used to store computer programs (e.g., applications, functional modules, etc. that implement the above-described methods), computer instructions, etc., which may be stored in partition in the memory or memories. And the computer programs, computer instructions, data, etc. described above may be invoked by a processor.
The computer programs, computer instructions, etc. described above may be stored in partitions in one or more memories. And the computer programs, computer instructions, data, etc. described above may be invoked by a processor.
A processor for executing the computer program stored in the memory to implement the steps of the method or the modules of the system related to the above embodiments. Reference may be made in particular to the preceding method and system embodiments with respect to the description.
The processor and the memory may be separate structures or may be an integrated structure integrated together. When the processor and the memory are separate structures, the memory, the processor may be coupled by a bus.
According to a fourth aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, is operable to perform the method of any one of the above-described embodiments of the present invention, or to run the system of any one of the above-described embodiments of the present invention.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices provided by the present invention in purely computer readable program code means, the method steps can be fully programmed to implement the same functions by implementing the system and its various devices in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices thereof provided by the present invention can be regarded as a hardware component, and the devices included in the system and various devices thereof for realizing various functions can also be regarded as structures in the hardware component; means for performing the various functions may also be conceived of as structures within both software modules and hardware components of the illustrated method.
The above embodiments of the present invention are not exhaustive of the techniques known in the art.
The foregoing description has described specific embodiments of the present invention. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention.
Claims (10)
1. A method for carbon emission accounting based on park demand response economic dispatch, comprising:
building each component model in the park to obtain a park demand response economic dispatching model and a tide result thereof; wherein, each subassembly in the garden includes: the system comprises a cogeneration unit, a battery energy storage device, a wind turbine, a photovoltaic unit and a charging pile;
calculating the carbon emission amount of the input of the power distribution network by combining the carbon emission coefficient of the power grid in the area of the park;
calculating the carbon emission of the combined heat and power generation based on the combined heat and power generation power characteristics and combined with the combined heat and power generation unit model;
calculating the carbon emission of the battery energy storage device based on the battery energy storage device model and by combining the carbon storage and carbon transfer characteristics of the battery energy storage device;
and calculating net carbon intensity of each node of the park based on the trend result of the park demand response economic dispatching model, the input carbon emission of the power distribution network, the carbon emission of the cogeneration and the carbon emission of the battery energy storage device, and accounting for main carbon emission on the demand side.
2. The method for carbon emission accounting based on the park demand response economic dispatch of claim 1, wherein the building of each component model in the park to obtain the park demand response economic dispatch model and the trend result thereof comprises:
analyzing the source network and storage resources of the park contact power distribution network, classifying the nodes of the power distribution network, and definingS feeder For each feeder set in the park, S bus For a node set of the distribution network, S G For a set of garden power nodes, S D A load node set of the garden is obtained;
based on a Distflow model, a second-order cone relaxation method is adopted, and the park microgrid second-order cone current constraint in the t-th time period is established as follows:
wherein: the formulas (1) and (2) are respectively the balance constraints of active power and reactive power of the garden node, in the formula, P ki,t And Q ki,t Respectively representing the variables P representing the active and reactive power of the feeder ki over a period t ij,t And Q ij,t Respectively representing the active power and reactive power variables, r, of the feeder ij in the time interval t ij And x ij Respectively the resistance and reactance parameters of the feeder ij,
the squared current variable, P, of the feeder ij for a period t g,t And Q g,t Respectively an active power variable and a reactive power variable P of the unit at a t node i in a time period d,t And Q d,t Load active power and reactive power variables are respectively at a node at a time interval t; formula (3) is the relation constraint among node voltage, active power, reactive power and current on the park feeder, wherein>
For a time period t node i a squared variable, <' >>
Is the square variable of the voltage at node j in time period t; the formula (4) is the feeder capacity constraint after the second-order cone relaxation; the formulas (5) and (6) are respectively the upper and lower limits of the voltage square and the current square of the park, wherein V max And V min Respectively an upper and a lower voltage limit, I ij,max Is the current upper limit value; />
The various components in the campus are modeled as follows:
the method for constructing the cogeneration unit model comprises the following steps:
wherein, the formula (7) is the upper and lower limit constraints of the output of the cogeneration unit in the time period t, in the formula,
is a 0-1 variable, is selected>
The power variable of the cogeneration unit in the time period t is obtained;
And &>
Respectively the minimum power and the maximum power of the cogeneration unit;
the method for constructing the battery energy storage device model comprises the following steps:
wherein, the equations (8) and (9) are the charge and discharge power constraints of the battery energy storage device in the time period t, wherein,
and &>
Is a charging and discharging power variable, respectively>
Is a variable between 0 and 1, restricts the energy storage device of the battery not to be charged and discharged simultaneously, and>
and
respectively charging upper and lower limits of power for the battery energy storage device>
And &>
Respectively the upper limit and the lower limit of the discharge power of the battery energy storage device; equation (10) is a constraint on the energy change of the battery energy storage device over a time period t, in which case>
For the energy storage situation of the battery energy storage device at the time t, based on>
For the energy storage situation of the battery energy storage device at the time t-1, ->
And &>
Respectively the charging efficiency and the discharging efficiency of the battery energy storage device, and delta t is a discrete time step length; formula (11) is the energy upper and lower limit constraint of the battery energy storage device in time period t, wherein, the device is combined with the device>
And &>
The energy storage upper and lower limits; the energy storage of the battery energy storage device at the end time and at the initial time is equal in formula (12), wherein>
Based on the energy storage condition of the battery energy storage device at the initial moment>
The energy storage condition of the battery energy storage device at the ending moment;
the method comprises the following steps of (1) constructing a photovoltaic unit and a wind generating unit model:
wherein, the formulas (13) and (14) are respectively the upper and lower limit constraints of the output of the photovoltaic generator set and the wind generator set in the time period t, in the formulas,
and &>
Respectively is the power variable of the photovoltaic unit and the wind generator unit in a time period t>
And &>
The maximum power of the photovoltaic unit and the maximum power of the wind power unit are respectively;
the construction of the charging pile model comprises the following steps:
wherein, the formula (15) and the formula (16) are respectively the charging power constraint of the electric automobile in a quick charging pile which adopts direct current to realize the charging function and a slow charging pile which adopts alternating current to match with a vehicle-mounted charger to realize the charging function under the time interval t,
and
is respectively the charging power variable of the electric automobile in the fast charging pile and the slow charging pile at the time t>
And &>
Is a 0-1 variable, is selected>
And &>
Respectively is the minimum charging power and the maximum charging power of the electric automobile in the quick charging pile>
And
respectively obtaining the minimum charging power and the maximum charging power of the electric automobile in the slow charging pile; the formula (17) restricts the electric automobile to be charged only in one pile at the same time period; the formulas (18) and (19) are respectively the simultaneous working quantity restriction of the quick-filling pile and the slow-filling pile, wherein N is f And N s The number of the fast-filling piles and the number of the slow-filling piles are respectively; equations (20) to (22) are constraints related to the charging state of the electric vehicle, wherein the charge status is greater than or equal to the charge status of the electric vehicle>
For the charging state of the electric vehicle at time t>
Based on the desired charging state at the end of the electric vehicle>
Is charged at the moment t-1 and is>
For the charging efficiency of the electric automobile in the quick charging pile, delta t is discrete time step length,
for the charging efficiency of the electric automobile in the slow charging pile, the charging efficiency is greater than or equal to>
And &>
The upper limit and the lower limit of the charging state of the electric automobile,
the charging state is the charging state at the end time of the electric automobile;
the scheduling time of the park is T, the goal of minimizing the total cost of electricity purchased by the park is taken, and the objective function for constructing the park demand response economic scheduling model is as follows:
in the formula (23), P D,t For the active power of the distribution network input park at t time period, a t Time-sharing electricity price in the time period t of the park; taking the formula (23) as an objective function and the formulas (1) to (22) as constraints to obtain a park demand response economic dispatching model; and carrying out optimization objective solution on the park demand response economic dispatching model to obtain a park microgrid second-order cone power flow constraint condition and states of all components in the park, and obtaining a power flow result of the park demand response economic dispatching model.
3. The method of claim 1, wherein the calculating the input carbon emission of the distribution network according to the carbon emission coefficient of the power grid in the area of the park comprises:
analyzing and utilizing the carbon emission level of the corresponding power distribution network of the target parkObtaining the carbon emission coefficient c of the distribution network in the region through historical data 0 Taking this as the carbon emission level of the campus input power;
based on the carbon emission coefficient c of the power distribution network 0 And calculating the input carbon emission of the distribution network by combining the park electricity purchasing result:
E 0,t ＝c 0 P D,t Δ t (24) wherein E 0,t Carbon emissions, c, into the distribution network for a period of time t 0 Carbon emission coefficient, P, of distribution network in the area D,t And inputting the active power of the power distribution network input park in the period of t, wherein delta t is a discrete time step length.
4. The method of claim 1, wherein the calculating the co-generation carbon emission based on the cogeneration heat and power characteristics in combination with the model of the cogeneration unit comprises:
defining the electric heating coefficient of the combined heat and power generation unit as the electric power equivalent thermal power coefficient
Calculating the carbon emission of the cogeneration power as follows:
wherein,
for the electric power carbon emission of the cogeneration unit, the judgment result is based on the judgment result>
Is a thermal power carbon emission coefficient of a cogeneration unit,
for the electric power equivalent thermal power coefficient of the cogeneration unit, is modulated>
The power variable of the cogeneration unit in a time period t is shown, and delta t is a discrete time step; the formula (25) changes the electric power of the cogeneration unit into thermal power through electric-carbon conversion, and calculates carbon emission according to a thermal power carbon emission coefficient;
according to the equivalent carbon emission coefficient of the electric heating power instead of the carbon emission coefficient of the loss part, calculating the carbon emission coefficient of the loss part of the cogeneration unit
Comprises the following steps:
wherein eta h Is the heat efficiency of the cogeneration unit eta e The electric efficiency of the cogeneration unit is obtained; calculating to obtain an equivalent carbon emission coefficient of the total value of the electric power and the thermal power by the formula (26), and taking the equivalent carbon emission coefficient as the carbon emission coefficient of the external output power of the combined heat and power generation unit;
analyzing carbon emission of a loss part of the cogeneration unit, wherein the carbon emission of the loss part is carbon emission which the cogeneration unit should bear, and calculating the carbon emission of the cogeneration unit as follows:
wherein,
the carbon emission of the loss part of the cogeneration unit is reduced; the equation (27) calculates the carbon emission coefficient due to the loss part by using the equivalent carbon emission coefficient, and forms the carbon emission to be borne by the cogeneration itself.
5. The method of claim 1, wherein calculating battery energy storage device carbon emissions based on the battery energy storage device model in combination with the battery energy storage device carbon storage and carbon transfer characteristics comprises:
according to the self carbon storage level of the battery energy storage device, calculating the carbon emission coefficient of the battery energy storage device in the discharge state as follows:
wherein,
a carbon discharge factor for the discharge state of the battery energy storage device>
For the carbon purge stored in the battery energy storage device at the preceding time t-1, is/are>
The energy storage condition of the battery energy storage device at the last moment t-1 is obtained;
analyzing the carbon emission of the battery energy storage device, and calculating the carbon emission according to two states of charging and discharging of the battery energy storage device as follows:
wherein,
for the moment t the battery energy storage device stores the carbon discharge amount by itself, and>
for the moment t-1 the battery energy storage device stores the carbon discharge amount by itself, and>
and &>
Charging efficiency and discharging efficiency for the battery energy storage device, respectively>
And
a charging and discharging power variable for the time period t, respectively>
The discharge amount of the power carbon is charged and discharged for the battery energy storage device at the time t; equations (29) and (30) respectively store and undertake carbon emissions calculations for the battery energy storage device, NCI i,t Representing the net carbon strength at the location of the battery energy storage device over time period t.
6. The method according to claim 1, wherein the calculating of the net carbon intensity of each node in the park and the accounting of the main carbon emission on the demand side based on the trend result of the park demand response economic dispatch model, the input carbon emission of the distribution network, the carbon emission of the cogeneration and the carbon emission of the battery energy storage device comprises:
calculating net carbon intensity and network carbon loss of each node of the park in a certain time period according to a carbon emission flow analysis method based on a trend result of the park demand response economic dispatching model, the input carbon emission of the power distribution network, the carbon emission of the cogeneration and the carbon emission of the battery energy storage device;
performing carbon accounting on the electric automobile and the conventional load by combining the calculation result of the net carbon strength;
and completing accounting of the main carbon emission on the demand side based on the carbon accounting result.
7. The method of claim 6, wherein the calculating of net carbon intensity and network carbon loss of each node in the park according to the carbon emission flow analysis method based on the result of the power flow of the park demand response economic dispatch model, the input carbon emission of the distribution network, the cogeneration carbon emission, and the carbon emission of the battery energy storage device comprises:
taking the carbon emission input by the power distribution network, the carbon emission of the cogeneration and the carbon emission of the battery energy storage device as the carbon emission input by each node, and calculating the initial input power and the initial input carbon emission coefficient of each node as follows:
wherein, P Ni Injecting power, N, into each node unit of the park network g For the number of units at each node of the park, P i-n Power is injected to the nth set of nodes i,
carbon emissions, c, are input for the unit time node i n Is the carbon discharge coefficient of the nth unit>
Carbon emission, P, of the node at which the ith energy storage input is located per unit time i ESS,dis For the power at the node where the ith energy storage input is located,
a carbon emission coefficient for the ith stored energy;
obtaining a load flow result of each node outflow node set gamma based on the park demand response economic dispatching model + (i) And an ingress node set Γ - (i) And a corresponding number d + (i) And d - (i)；
Search for satisfaction of d _ (k) Node k of =0, and d thereof - (k) Set to-1;
calculating the net carbon strength of the node k as follows:
if for any node i, d - (i) If the value is-1, finishing the calculation, otherwise, performing the next step;
for all outflow nodes j of the node i, transferring the carbon flow to the node j through the feeder ij, reducing the number of inflow nodes of the node j by one, and simultaneously calculating the carbon loss of the feeder ij as follows:
re-executing the searching process until the net carbon strength and carbon loss of all the nodes are finished;
and combining the calculated result of the net carbon strength to carry out carbon accounting on the electric automobile and the conventional load, wherein the carbon accounting comprises the following steps:
wherein,
and &>
Respectively the carbon discharge amount of the electric automobile in the quick charging pile and the slow charging pile at the time t>
And
is the charging power variable, NCI, of the electric automobile in the fast charging pile and the slow charging pile at the time t i,t Representing the net carbon strength at the location of the battery energy storage device over a time period t, device for combining or screening>
Based on the carbon emissions for a conventional load at time t, <' >>
Power at time t for a conventional load; and (36) to (38) respectively account for the quick charge, the slow charge and the normal load carbon responsibility of the electric automobile.
8. A carbon emissions accounting system based on a campus demand response economic dispatch, comprising:
the park demand response economic dispatching model building module is used for building each component model in a park to obtain a park demand response economic dispatching model and a trend result thereof; wherein, each subassembly in the garden includes: the system comprises a cogeneration unit, a battery energy storage device, a wind turbine, a photovoltaic unit and a charging pile;
the power distribution network input carbon emission calculation module is used for calculating the power distribution network input carbon emission by combining the power distribution network carbon emission coefficient of the region where the park is located;
the heat and power cogeneration carbon emission calculation module is used for calculating the heat and power cogeneration carbon emission by combining the heat and power cogeneration unit model based on the heat and power cogeneration power characteristic;
the battery energy storage device carbon emission calculation module is used for calculating the carbon emission of the battery energy storage device based on the battery energy storage device model and combining the carbon storage and carbon transfer characteristics of the battery energy storage device;
and the carbon emission accounting module is used for calculating net carbon intensity of each node of the park and accounting the main body carbon emission at the demand side based on the trend result of the park demand response economic dispatching model, the input carbon emission of the power distribution network, the combined heat and power carbon emission and the carbon emission of the battery energy storage device.
9. A computer terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, is operable to perform the method of any of claims 1-7 or to execute the system of claim 8.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, is adapted to carry out the method of any one of claims 1 to 7 or to carry out the system of claim 8.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116231657A (en) * | 2023-05-09 | 2023-06-06 | 国网浙江省电力有限公司 | Global carbon flow distributed determination method and device for transmission and distribution network |
| CN117171949A (en) * | 2023-07-18 | 2023-12-05 | 南京电力设计研究院有限公司 | Method for deducting carbon emission situation of digital park |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116231657A (en) * | 2023-05-09 | 2023-06-06 | 国网浙江省电力有限公司 | Global carbon flow distributed determination method and device for transmission and distribution network |
| CN116231657B (en) * | 2023-05-09 | 2023-09-29 | 国网浙江省电力有限公司 | Global carbon flow distributed determination method and device for transmission and distribution network |
| CN117171949A (en) * | 2023-07-18 | 2023-12-05 | 南京电力设计研究院有限公司 | Method for deducting carbon emission situation of digital park |
| CN117171949B (en) * | 2023-07-18 | 2024-04-05 | 南京电力设计研究院有限公司 | Method for deducting carbon emission situation of digital park |
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