CN115940213A - Power distribution network source and network load and storage collaborative optimization planning method and device considering carbon emission - Google Patents
Power distribution network source and network load and storage collaborative optimization planning method and device considering carbon emission Download PDFInfo
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Abstract
The invention relates to a power distribution network source network load storage collaborative optimization planning method considering carbon emission, which comprises the following steps: the method comprises the steps of considering minimization of generating cost, investment cost, operation and maintenance cost and carbon emission cost of a unit, obtaining a distributed power distribution network source-grid and storage collaborative planning model considering carbon emission, wherein constraint conditions of the model comprise flexible load constraint and carbon capture equipment constraint; solving the model based on a discrete binary particle swarm algorithm to obtain a power distribution network source network load storage planning scheme; according to the invention, a distributed power distribution network source network load storage collaborative optimization planning model considering carbon emission is constructed aiming at the problems of scene diversification, analysis dimension comprehensiveness and the like in the novel power distribution network construction process, and scientific auxiliary decisions are provided for the power system close-range and long-range annual planning and novel energy path planning schemes.
Description
Technical Field
The invention belongs to the technical field of optimal configuration of a power distribution network, and particularly relates to a power distribution network source-network load-storage collaborative optimization planning method and device considering carbon emission.
Background
With the access of distributed resources such as large-scale distributed renewable energy, electric vehicles, energy storage, flexible load and the like to a distribution network, the distribution network gradually presents the characteristics of source generation and activation, the regional distribution network can realize the possibility of time-interval supply and demand balance, the phenomenon of light load and even no load of power grid operation equipment is caused to frequently occur, the contradiction between the efficient utilization of equipment resources and reliable power supply is formed, and the traditional mode of determining a power grid planning scheme by the requirement of a load side is difficult to adapt to the new development. The power grid planning is urgently needed to adapt to new situation, the asset utilization efficiency and the power supply reliability are considered comprehensively, and research on a distributed power distribution network-oriented multiple distributed resource coordination planning method is developed.
Distributed power generation is used as an important component of future strategic energy layout, and is rapidly developed by the advantages of low carbon, environmental protection, flexible installation and the like. With the continuous increase of the proportion of new energy in an electric power system, the operation mode tends to be complex and diversified, the intermittent and fluctuating output of the new energy provides challenges for the operation of a power grid, the existing planning method based on a typical mode is difficult to adapt to the large-scale access of the new energy, and a collaborative optimization planning method for a distributed power distribution network considering carbon emission simultaneously needs to be designed urgently to adapt to the construction of a novel electric power system taking the new energy as a main body. The method has great significance for supporting power grid project planning work, realizing planning and accurate planning as required, improving the accuracy of power grid investment, strengthening the scientific rationality of distribution network storage projects, strengthening accurate investment, improving investment output benefits and improving the proportion of clean energy consumption.
Disclosure of Invention
The invention introduces carbon emission and carbon capture equipment, provides a distributed intelligent power distribution network source network and load storage collaborative optimization planning method considering 'energy flow-carbon flow', further deals with uncertainty of distributed power sources and loads, and guides novel power distribution network planning and low-carbon transformation.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
the power distribution network load storage collaborative optimization planning method considering carbon emission comprises the following steps:
the method comprises the steps of considering minimization of generating cost, investment cost, operation and maintenance cost and carbon emission cost of a unit, obtaining a distributed power distribution network source-grid and storage collaborative planning model considering carbon emission, wherein constraint conditions of the model comprise flexible load constraint and carbon capture equipment constraint;
and solving the model based on a discrete binary particle swarm algorithm to obtain a power distribution network source network load storage planning scheme.
Further, the distributed power distribution network source-network-load-storage collaborative planning model is as follows:
in the formula: f. of unit 、f in 、f om 、f co2 The group power generation cost, the investment cost, the operation and maintenance cost and the carbon emission cost are respectively set; t is a time interval set; g is a conventional unit set; j is a node set; p is a set of carbon capture equipment; a is a g 、b g 、c g The power generation cost coefficients of the conventional unit g are respectively; p g,t The output of the conventional unit g at the moment t; r and m are the investment discount rate and the age limit respectively; n is g And C g The number and unit cost of conventional units; n is w And C w The number and unit cost of the wind turbine generators are set; n is a radical of an alkyl radical v And C v The number of photovoltaic array groups and unit cost; n is e And C e The number of storage battery groups and unit cost;
and &>
The unit operation and maintenance costs of a conventional unit, wind power, photovoltaic and storage battery are respectively; />
Is the unit carbon emission cost; c p,t The amount of carbon emissions captured by the carbon capture device p during time t; c j,t Is the carbon emission of node j during time t.
Further, the flexible load constraints include reducible load constraints and transferable load constraints; the load can be reduced because the electricity price change can reduce the electricity demand in a certain period, but the electricity transfer behavior does not occur; the transferable load is to transfer the load from the high electricity price period to the low electricity price period as the electricity price fluctuates.
Further, the reducible load constraint is:
the reducible load only has a self-elastic coefficient, and the load at the time period i can be expressed as:
in the formula: d is a radical of i And
respectively representing the load demand and the initial load demand, p, at time period i i 、p 0i 、A i Respectively represent the actual electricity price, the initial electricity price and the incentive compensation electricity price at the time period i, E i Represents the coefficient of self-elasticity at time period i;
self-elastic response coefficient Rs at time interval i i Comprises the following steps:
the electrical demand that can reduce the load, described by the self-elastic response coefficient, is:
the transferable load constraints are: the transferable load transfers the electricity utilization time of the load from one time zone to other time zones, which is a demand-side management means with obvious effect, the sensitivity to the electricity price is high, and the load under the time period i can be expressed as:
in the formula: e ij The cross elasticity coefficient of the time interval i and the time interval j is shown; when i = j, E ij Less than or equal to 0; when i ≠ j, E ij ≥0；A j The power consumption peak period is positive, and the power consumption peak period is zero in other periods; definition of Rco i The cross-price elastic coefficient at time period i can be expressed as:
then the load demand at time period i is available to the Rco i Expressed as:
further, the carbon capture plant is constrained by:
C p,t ＝η p ε p P p,t Δt (16)
in the formula: c p,t Amount of carbon emission, η, captured for period t of the carbon capture device p p And ε p Carbon capture efficiency and carbon emission intensity, respectively; p is p,t Device output power for time period t;
carbon capture energy consumption for the carbon capture device p in a period t; mu.s p, t Carbon capture energy consumption of carbon dioxide is unit in t time period; Δ t is the time interval.
Further, the method for acquiring the carbon emission of each node of the power grid comprises the following steps:
s1, acquiring a node carbon potential:
step S101: determining a set of all injection nodes of the jth node, if the carbon potentials of all nodes in the set are known, calculating the carbon potential of the node j according to a formula (9), and turning to the step S102; if unknown carbon potential of the injection node exists in the set, jumping over the node, and turning to the step S103;
in the formula: e.g. of the type j Is the carbon potential of node j, P ij Active power for branch ij; p g Is the active power output of the generator g; e.g. of the type g Carbon emission intensity of the generator g;
step S102: judging whether the carbon potential of all nodes is calculated, if not, turning to the step S103; if all the node carbon potentials are solved, ending the carbon potential sequence calculation;
step S103: updating the node j = j +1, and returning to the step S101 to continue calculation;
s2, calculating the carbon emission of the node load according to the acquired node carbon potential by adopting the following formula:
C j,t ＝e j,t ·L j,t
in the formula: e.g. of the type j,t 、L j,t And C j,t Respectively, the carbon potential, the load and the carbon emission of the node j in the period t.
Further, when a distributed power distribution network source network load storage collaborative planning model considering carbon emission is obtained, original wind power, photovoltaic and load historical data are preprocessed, and typical day scenes of spring, summer, autumn and winter are generated by using a k-means clustering algorithm.
Consider distribution network source net load storage collaborative optimization planning device that carbon discharged includes:
the model acquisition module is used for acquiring a distributed power distribution network source-grid and storage-load collaborative planning model considering carbon emission in consideration of minimization of generating cost, investment cost, operation and maintenance cost and carbon emission cost of a unit, and constraint conditions of the model comprise flexible load constraint and carbon capture equipment constraint;
and the model solving module is used for solving the model based on a discrete binary particle swarm algorithm to obtain a power distribution network source network load storage planning scheme.
A computing device, comprising:
one or more processing units;
a storage unit for storing one or more programs,
wherein the one or more programs, when executed by the one or more processing units, cause the one or more processing units to execute a power distribution grid source-storage co-optimization planning method that takes into account carbon emissions.
A computer-readable storage medium having non-volatile program code executable by a processor, the computer program, when executed by the processor, implementing the steps of a method for grid-source load-storage co-optimization planning of a power distribution grid taking into account carbon emissions.
The invention has the advantages and positive effects that:
according to the invention, a distributed power distribution network source-storage and load-storage collaborative optimization planning model considering carbon emission is constructed aiming at the problems of scene diversification, analysis dimension comprehensiveness and the like in the novel power distribution network construction process, and scientific aid decision is provided for a power system close-range and long-range annual planning and novel energy path planning scheme.
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The technical solutions of the present invention will be described in further detail below with reference to the accompanying drawings and examples, but it should be understood that these drawings are designed for illustrative purposes only and thus do not limit the scope of the present invention. Furthermore, unless otherwise indicated, the drawings are intended to be illustrative of the structural configurations described herein and are not necessarily drawn to scale.
Fig. 1 is a schematic flow chart of a model solution method of a power distribution network source-grid-storage collaborative optimization planning method considering carbon emission according to an embodiment of the present invention.
Detailed Description
First, it should be noted that the specific structures, features, advantages, etc. of the present invention will be specifically described below by way of example, but all the descriptions are for illustrative purposes only and should not be construed as limiting the invention in any way. Furthermore, any single feature described or implicit in any embodiment or any single feature shown or implicit in any drawing may still be combined or subtracted between any of the features (or equivalents thereof) to obtain still further embodiments of the invention that may not be directly mentioned herein. In addition, for the sake of simplicity, the same or similar features may be indicated in only one place in the same drawing.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings or the orientations or positional relationships that the products of the present invention are conventionally placed in use, and are only used for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict.
Example 1
As shown in fig. 1, the method for collaborative optimization planning of source network and storage of a power distribution network considering carbon emission provided by this embodiment includes the following steps:
the method comprises the steps of considering minimization of generating cost, investment cost, operation and maintenance cost and carbon emission cost of a unit, obtaining a distributed power distribution network source-grid and storage collaborative planning model considering carbon emission, wherein constraint conditions of the model comprise flexible load constraint and carbon capture equipment constraint;
and solving the model based on a discrete binary particle swarm algorithm to obtain a power distribution network source network load storage planning scheme.
Specifically, the source network load storage collaborative planning model of the distributed power distribution network is as follows:
in the formula: f. of unit 、f in 、f om 、f co2 The group power generation cost, the investment cost, the operation and maintenance cost and the carbon emission cost are respectively set; t is a time interval set; g is a conventional unit set; j is a node set; p is a set of carbon capture equipment; a is g 、b g 、c g Respectively the power generation cost coefficient of the conventional unit g; p g,t The output of the conventional unit g at the moment t; r and m are the investment discount rate and the age limit respectively; n is a radical of an alkyl radical g And C g The number and unit cost of conventional units; n is w And C w The number and unit cost of the wind turbine generators are calculated; n is v And C v The number of photovoltaic array groups and unit cost; n is e And C e The number of storage battery groups and unit cost;
and &>
The unit operation and maintenance costs of a conventional unit, wind power, photovoltaic and storage battery are respectively; />
Is the unit carbon emission cost; c p,t The amount of carbon emissions captured by the carbon capture device p during time t; c j,t Is the carbon emission of node j during the period t.
The flexible load constraints include reducible load constraints and transferable load constraints; the load can be reduced because the electricity price change can reduce the electricity demand in a certain period, but the electricity transfer behavior does not occur; the transferable load is to transfer the load from the high electricity price period to the low electricity price period as the electricity price fluctuates.
The reducible load constraints are:
the reducible load only has a self-elastic coefficient, and the load at the time period i can be expressed as:
in the formula: d is a radical of i And
respectively representing the load demand and the initial load demand, p, at time period i i 、p 0i 、A i Respectively represent the actual electricity price, the initial electricity price and the incentive compensation electricity price at the time period i, E i Represents the coefficient of self-elasticity at time period i;
self-elastic response coefficient Rs under time interval i i Comprises the following steps:
the power demand described by the self-elastic response coefficient, which can reduce the load, is:
the transferable load can transfer the electricity utilization time of the load from one time zone to other time zones, is a demand side management means with obvious effect, and has high sensitivity to the electricity price; the transferable load constraints are: the load at time period i can be expressed as:
in the formula: e ij The cross elasticity coefficient of the time interval i and the time interval j is shown; when i = j, E ij Less than or equal to 0; when i ≠ j, E ij ≥0；A j The power consumption peak period is positive, and the power consumption peak period is zero in other periods; definition of Rco i The cross-price elastic coefficient at time period i can be expressed as:
then the load demand at time period i is available to the Rco i Expressed as:
the carbon capture plant constraints are:
C p,t ＝η p ε p P p,t Δt (25)
in the formula: c p,t Amount of carbon emission, η, captured for period t of the carbon capture plant p p And epsilon p Respectively carbon capture efficiency and carbon emissionReleasing strength; p is p,t Device output power for time period t;
carbon capture energy consumption for the carbon capture device p in a period t; mu.s p,t Carbon capture energy consumption of carbon dioxide is unit in t time period; Δ t is the time interval.
In the theory of carbon emission flow of the power system, the node carbon potential represents the carbon emission density of the node, and the carbon emission of each node of the power grid is obtained by calculating the node carbon potential.
Because no circulation exists in the radial distribution network, the node carbon potential is related to branch power flow and node injection carbon potential, the node carbon potential is consistent with the carbon current density of active power flowing out of the node, and if the active power flows from the node i to the node j, the node carbon potential calculation formula is as follows
In the formula: e.g. of the type j Is the carbon potential of node j, P ij Active power for branch ij; p g The active power output of the generator g is obtained; e.g. of the type g Is the carbon emission intensity of the generator g.
If the carbon potential of a certain node needs to be calculated, the carbon potential of an adjacent node only needs to be known. Therefore, the invention is based on a sequential calculation method, the calculation is started from the node j =1, the carbon potentials of other nodes are calculated in sequence based on the nodes with known carbon potentials, the carbon potentials of a plurality of nodes can be obtained by each calculation, and the carbon potentials of all the nodes cannot be calculated at one time, so that the carbon potentials of all the nodes can be obtained by a plurality of calculations. The detailed calculation steps are as follows:
step 1: determining a set of all injection nodes of the jth node, if the carbon potentials of all the nodes in the set are known, calculating the carbon potential of the node j according to a formula (9), and turning to the step 2; if unknown carbon potential of the injection node exists in the set, jumping over the node, and turning to the step 3;
and 2, step: judging whether the carbon potential of all nodes is calculated, if not, turning to the step 3; if all the node carbon potentials are solved, ending the carbon potential sequence calculation;
and step 3: and updating the j = j +1 node, and returning to the step 1 to continue the calculation.
And calculating the carbon emission of the node load according to the node carbon potential obtained in the step by adopting the following formula:
C j,t ＝e j,t ·L j,t
in the formula: e.g. of the type j,t 、L j,t And C j,t Respectively, the carbon potential, the load and the carbon emission of the node j in the period t.
When a distributed power distribution network source network load storage collaborative planning model considering carbon emission is obtained, original wind power, photovoltaic and load historical data are preprocessed, and typical day scenes of spring, summer, autumn and winter are generated by using a k-means clustering algorithm; the specific process is as follows:
step 1: initializing a clustering number n and a scene number k, and selecting one scene as a clustering center 1;
step 2: calculating the distances between the rest k-1 scenes and the clustering center 1, and selecting the scene with the largest distance as a clustering center 2;
and step 3: calculating the sum of the distances between the rest k-2 scenes and the clustering centers 1 and 2, and selecting the scene with the largest distance as a clustering center 3 until n clustering centers are selected;
and 4, step 4: classifying other scenes to the nearest clustering center, and re-solving various clustering centers;
and 5: calculating a clustering error, and stopping clustering if the convergence precision is met; if the error requirement is not met, continuing to step 4.
The constraint conditions of the distributed power distribution network source-network charge-storage collaborative planning model further comprise transformer substation capacity constraint, network topology constraint, distributed power supply output constraint, node voltage and branch current constraint and energy storage system constraint; specifically, the following:
1) Capacity constraints for transformer substation
In the formula:
and &>
Respectively, the scalable capacity and the upper limit of the ith substation.
2) Network topology constraints
In the formula: f. of di Is the virtual load of node i; f. of ij,t The virtual power flow of the branch ij is a time period t; c (i) is a set of nodes j connected with the node i; alpha (alpha) ("alpha") ij Decision variables for branch ij construction; n is a radical of b And N n The number of branches and the number of nodes, respectively.
3) Distributed power supply output constraint
In the formula: p w,t And P v,t Respectively outputting the wind power and the photovoltaic at the time t;
and &>
Respectively the upper limit and the lower limit of the wind power output; />
And &>
Respectively the upper and lower limits of the photovoltaic output.
4) Node voltage and branch current constraints
In the formula: v i,t Is the voltage at node i at time t; v i max And V i min The upper limit and the lower limit of the voltage of the node i are respectively; i is ij,t The current of branch ij at time t;
is the upper limit value of the branch current.
5) Energy storage system restraint
In the formula:
and &>
Respectively representing the charging and discharging states of the stored energy e at the moment t; />
And &>
Is the charging and discharging power of the stored energy e at the moment t respectively>
And &>
Respectively corresponding allowable upper limit values; />
And &>
Charge-discharge efficiency; e e,t The energy value of the stored energy e at the moment t is obtained; />
And &>
Allowing upper and lower limits for the energy of the stored energy e; e e,0 And E e,T The energy values of the first and last time periods are respectively.
The method for solving the model based on the discrete binary particle swarm algorithm comprises the following steps:
1. initializing parameters of a power distribution network, and inputting parameters such as source network load storage of the power distribution network;
2. setting initial values of parameters of a discrete binary particle swarm algorithm, generating an initial population, and screening out a non-inferior solution set from the initial population;
3. updating a network topological structure according to the position of the population individual, solving an objective function according to the model, and calculating a particle fitness value;
4. calculating a Pareto solution, calculating particle density information in the non-inferior solution set through a grid method, sorting according to the density information, removing particles with large density information, and obtaining a cut Pareto non-inferior solution set;
5. selecting an individual extreme value and a group extreme value, calculating dynamic weight, and updating the position and the speed of the particles;
6. and (4) judging whether the termination condition is met, if so, outputting a final planning result, and if not, returning to the step (2) to continue solving.
Example 2
The embodiment provides a distribution network source net storage collaborative optimization planning device who considers carbon emission, includes:
the model acquisition module is used for acquiring a distributed power distribution network source-grid and storage-load collaborative planning model considering carbon emission in consideration of minimization of generating cost, investment cost, operation and maintenance cost and carbon emission cost of a unit, and constraint conditions of the model comprise flexible load constraint and carbon capture equipment constraint;
and the model solving module is used for solving the model based on a discrete binary particle swarm algorithm to obtain a power distribution network source network load storage planning scheme.
A computing device, comprising:
one or more processing units;
a storage unit for storing one or more programs,
when the one or more programs are executed by the one or more processing units, the one or more processing units are enabled to execute the power distribution network source-grid-storage collaborative optimization planning method considering carbon emission in the embodiment; it is noted that the computing device may include, but is not limited to, a processing unit, a storage unit; those skilled in the art will appreciate that the computing device includes processing units, memory units, and not limitation of the computing device, and may include more components, or combine certain components, or different components, e.g., the computing device may also include input output devices, network access devices, buses, and the like.
A computer-readable storage medium having non-volatile program code executable by a processor, the computer program, when executed by the processor, implementing the steps of the method for collaborative optimization planning of source-grid-load-storage of a power distribution network considering carbon emissions in the present embodiment; it should be noted that the readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof; the program embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. For example, program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the C programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, or entirely on a remote computing device or server. In situations involving remote computing devices, the remote computing devices may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to external computing devices (e.g., through the internet using an internet service provider).
The present invention has been described in detail with reference to the above examples, but the description is only for the preferred examples of the present invention and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (10)
1. The power distribution network source-network load-storage collaborative optimization planning method considering carbon emission is characterized by comprising the following steps:
the method comprises the steps of considering minimization of generating cost, investment cost, operation and maintenance cost and carbon emission cost of a unit, obtaining a distributed power distribution network source-grid and storage collaborative planning model considering carbon emission, wherein constraint conditions of the model comprise flexible load constraint and carbon capture equipment constraint;
and solving the model based on a discrete binary particle swarm algorithm to obtain a power distribution network source network load storage planning scheme.
2. The power distribution network source-grid-storage collaborative optimization planning method considering carbon emission according to claim 1, characterized in that: the source network and storage collaborative planning model of the distributed power distribution network is as follows:
min f＝f unit +f in +f om +f co2
in the formula: f. of unit 、f in 、f om 、f co2 The group power generation cost, the investment cost, the operation and maintenance cost and the carbon emission cost are respectively set; t is a time interval set; g is a conventional unit set; j is a node set; p is a set of carbon capture equipment; a is a g 、b g 、c g The power generation cost coefficients of the conventional unit g are respectively; p is g,t The output of the conventional unit g is obtained at the moment t; r and m are the investment discount rate and the age limit respectively; n is g And C g The number and unit cost of conventional units; n is a radical of an alkyl radical w And C w The number and unit cost of the wind turbine generators are calculated; n is v And C v The number of photovoltaic array groups and unit cost; n is e And C e The number of storage battery groups and unit cost;
and &>
The unit operation and maintenance costs of a conventional unit, wind power, photovoltaic and storage battery are respectively calculated; />
Is the unit carbon emission cost; c p,t The amount of carbon emissions captured by the carbon capture device p during time t; c j,t Is the carbon emission of node j during the period t.
3. The power distribution network source-grid-storage collaborative optimization planning method considering carbon emission according to claim 1, characterized in that: the flexible load constraints include reducible load constraints and transferable load constraints;
the reducible load constraints are:
the reducible load only has a self-elastic coefficient, and the load at the time period i can be expressed as:
in the formula: d is a radical of i And
respectively representing the load demand and the initial load demand, p, at time period i i 、/>
A i Respectively represent the actual electricity price, the initial electricity price and the incentive compensation electricity price in the time period i, E i Represents the coefficient of self-elasticity at time period i;
self-elastic response coefficient Rs under time interval i i Comprises the following steps:
the power demand described by the self-elastic response coefficient, which can reduce the load, is:
the transferable load constraints are:
in the formula: e ij The cross elasticity coefficient of the time interval i and the time interval j is shown; when i = j, E ij Less than or equal to 0; when i ≠ j, E ij ≥0；A j Positive only during peak periods of electricity consumption, othersThe time interval is zero; definition of RCO i The cross price elastic coefficient at time period i can be expressed as:
then the load demand at time period i is available to the Rco i Expressed as:
4. the power distribution network source-grid-storage collaborative optimization planning method considering carbon emission according to claim 1, characterized in that: the carbon capture plant constraints are:
C p,t ＝η p ε p P p,t Δt (7)
in the formula: c p,t Amount of carbon emission, η, captured for period t of the carbon capture plant p p And epsilon p Carbon capture efficiency and carbon emission intensity, respectively; p p,t Device output power for time period t;
carbon capture energy consumption for the carbon capture device p in a period t; mu.s p,t Carbon capture energy consumption per unit of carbon dioxide in a time period t; Δ t is the time interval.
5. The power distribution network source-grid-storage collaborative optimization planning method considering carbon emission according to claim 1, characterized in that: the method for acquiring the carbon emission of each node of the power grid comprises the following steps:
s1, acquiring a node carbon potential:
s101: determining a set of all injection nodes of the jth node, if the carbon potentials of all the nodes in the set are known, calculating the carbon potential of the node j according to a formula (9), and turning to the step S102; if unknown carbon potential of the injection node exists in the set, jumping over the node, and turning to the step S103;
in the formula: e.g. of the type j Is the carbon potential of node j, P ij Active power for branch ij; p g Is the active power output of the generator g; e.g. of a cylinder g Is the carbon emission intensity of the generator g;
s102: judging whether the carbon potential of all nodes is calculated, if not, turning to the step S103; if all the node carbon potentials are solved, ending the carbon potential sequence calculation;
s103: updating the node j = j +1, and returning to the step S101 to continue calculation;
s2, calculating the carbon emission of the node load according to the acquired node carbon potential by adopting the following formula:
C j,t ＝e j,t ·L j,t
in the formula: e.g. of the type j,t 、L j,t And C j,t Respectively, the carbon potential, the load and the carbon emission of the node j in the period t.
6. The power distribution network source-grid-storage collaborative optimization planning method considering carbon emission according to claim 1, characterized in that: when a distributed power distribution network source network load storage collaborative planning model considering carbon emission is obtained, original wind power, photovoltaic and load historical data are preprocessed, and typical day scenes of spring, summer, autumn and winter are generated by using a k-means clustering algorithm.
7. The power distribution network source-grid-storage collaborative optimization planning method considering carbon emission according to claim 1, characterized in that: the constraint conditions of the distributed power distribution network source-network charge-storage collaborative planning model further comprise transformer substation capacity constraint, network topology constraint, distributed power supply output constraint, node voltage and branch current constraint and energy storage system constraint.
8. Consider distribution network source net load storage collaborative optimization planning device that carbon discharged, its characterized in that includes:
the model acquisition module is used for considering the minimization of the generating cost, the investment cost, the operation and maintenance cost and the carbon emission cost of the unit and acquiring a distributed power distribution network source-grid and storage collaborative planning model considering the carbon emission, and the constraint conditions of the model comprise flexible load constraint and carbon capture equipment constraint;
and the model solving module is used for solving the model based on a discrete binary particle swarm algorithm to obtain a power distribution network source network load storage planning scheme.
9. A computing device, characterized in that: the method comprises the following steps:
one or more processing units;
a storage unit for storing one or more programs,
wherein the one or more programs, when executed by the one or more processing units, cause the one or more processing units to perform the method of any of claims 1-7.
10. A computer-readable storage medium with non-volatile program code executable by a processor, characterized in that the computer program realizes the steps of the method according to any one of claims 1 to 7 when executed by the processor.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116663823A (en) * | 2023-05-25 | 2023-08-29 | 国网江苏省电力有限公司连云港供电分公司 | Power distribution energy grid carbon emission optimal planning method and system integrating accurate carbon data |
| CN117374974A (en) * | 2023-12-06 | 2024-01-09 | 国网浙江省电力有限公司 | Distribution network scheduling method, system, medium and equipment |
| CN117713176A (en) * | 2024-02-06 | 2024-03-15 | 内蒙古科技大学 | Source network charge storage low-carbon operation method and device, electronic equipment and storage medium |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116663823A (en) * | 2023-05-25 | 2023-08-29 | 国网江苏省电力有限公司连云港供电分公司 | Power distribution energy grid carbon emission optimal planning method and system integrating accurate carbon data |
| CN117374974A (en) * | 2023-12-06 | 2024-01-09 | 国网浙江省电力有限公司 | Distribution network scheduling method, system, medium and equipment |
| CN117713176A (en) * | 2024-02-06 | 2024-03-15 | 内蒙古科技大学 | Source network charge storage low-carbon operation method and device, electronic equipment and storage medium |
| CN117713176B (en) * | 2024-02-06 | 2024-05-03 | 内蒙古科技大学 | Source network charge storage low-carbon operation method and device, electronic equipment and storage medium |
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